id	sid	tid	token	lemma	pos
brj-24683	1	1	peer	peer	NOUN
brj-24683	1	2	-	-	PUNCT
brj-24683	1	3	review	review	NOUN
brj-24683	1	4	article	article	NOUN
brj-24683	1	5	peer	peer	NOUN
brj-24683	1	6	-	-	PUNCT
brj-24683	1	7	reviewed	review	VERB
brj-24683	1	8	article	article	NOUN
brj-24683	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	1	10	dou	dou	PROPN
brj-24683	1	11	&	&	CCONJ
brj-24683	1	12	you	you	PRON
brj-24683	1	13	(	(	PUNCT
brj-24683	1	14	2025	2025	NUM
brj-24683	1	15	)	)	PUNCT
brj-24683	1	16	.	.	PUNCT
brj-24683	2	1	“	"	PUNCT
brj-24683	2	2	wood	wood	NOUN
brj-24683	2	3	defect	defect	NOUN
brj-24683	2	4	identification	identification	NOUN
brj-24683	2	5	,	,	PUNCT
brj-24683	2	6	”	"	PUNCT
brj-24683	2	7	bioresources	bioresource	NOUN
brj-24683	2	8	20(3	20(3	NOUN
brj-24683	2	9	)	)	PUNCT
brj-24683	2	10	,	,	PUNCT
brj-24683	2	11	5709	5709	NUM
brj-24683	2	12	-	-	SYM
brj-24683	2	13	5730	5730	NUM
brj-24683	2	14	.	.	PUNCT
brj-24683	3	1	5709	5709	NUM
brj-24683	3	2	a	a	DET
brj-24683	3	3	novel	novel	ADJ
brj-24683	3	4	wood	wood	NOUN
brj-24683	3	5	surface	surface	NOUN
brj-24683	3	6	defect	defect	NOUN
brj-24683	3	7	detection	detection	NOUN
brj-24683	3	8	model	model	NOUN
brj-24683	3	9	based	base	VERB
brj-24683	3	10	on	on	ADP
brj-24683	3	11	improved	improved	ADJ
brj-24683	3	12	yolov8	yolov8	NOUN
brj-24683	3	13	wenmiao	wenmiao	PROPN
brj-24683	3	14	dou	dou	PROPN
brj-24683	3	15	and	and	CCONJ
brj-24683	3	16	jun	jun	PROPN
brj-24683	3	17	you	you	PRON
brj-24683	3	18	*	*	PUNCT
brj-24683	3	19	to	to	PART
brj-24683	3	20	address	address	VERB
brj-24683	3	21	the	the	DET
brj-24683	3	22	challenges	challenge	NOUN
brj-24683	3	23	posed	pose	VERB
brj-24683	3	24	by	by	ADP
brj-24683	3	25	complex	complex	ADJ
brj-24683	3	26	and	and	CCONJ
brj-24683	3	27	variable	variable	ADJ
brj-24683	3	28	backgrounds	background	NOUN
brj-24683	3	29	coupled	couple	VERB
brj-24683	3	30	with	with	ADP
brj-24683	3	31	the	the	DET
brj-24683	3	32	small	small	ADJ
brj-24683	3	33	-	-	PUNCT
brj-24683	3	34	target	target	NOUN
brj-24683	3	35	characteristics	characteristic	NOUN
brj-24683	3	36	of	of	ADP
brj-24683	3	37	wood	wood	NOUN
brj-24683	3	38	surface	surface	NOUN
brj-24683	3	39	defects	defect	NOUN
brj-24683	3	40	such	such	ADJ
brj-24683	3	41	as	as	ADP
brj-24683	3	42	knots	knot	NOUN
brj-24683	3	43	and	and	CCONJ
brj-24683	3	44	cracks	crack	NOUN
brj-24683	3	45	,	,	PUNCT
brj-24683	3	46	a	a	DET
brj-24683	3	47	novel	novel	ADJ
brj-24683	3	48	wood	wood	NOUN
brj-24683	3	49	surface	surface	NOUN
brj-24683	3	50	defect	defect	NOUN
brj-24683	3	51	detection	detection	NOUN
brj-24683	3	52	model	model	NOUN
brj-24683	3	53	based	base	VERB
brj-24683	3	54	on	on	ADP
brj-24683	3	55	improved	improve	VERB
brj-24683	3	56	you	you	PRON
brj-24683	3	57	only	only	ADV
brj-24683	3	58	look	look	VERB
brj-24683	3	59	once	once	ADV
brj-24683	3	60	version	version	NOUN
brj-24683	3	61	8	8	NUM
brj-24683	3	62	(	(	PUNCT
brj-24683	3	63	yolov8	yolov8	NOUN
brj-24683	3	64	)	)	PUNCT
brj-24683	3	65	is	be	AUX
brj-24683	3	66	proposed	propose	VERB
brj-24683	3	67	.	.	PUNCT
brj-24683	4	1	the	the	DET
brj-24683	4	2	model	model	NOUN
brj-24683	4	3	integrates	integrate	VERB
brj-24683	4	4	a	a	DET
brj-24683	4	5	multi	multi	ADJ
brj-24683	4	6	-	-	ADJ
brj-24683	4	7	head	head	ADJ
brj-24683	4	8	mixed	mixed	ADJ
brj-24683	4	9	self	self	NOUN
brj-24683	4	10	-	-	PUNCT
brj-24683	4	11	attention	attention	NOUN
brj-24683	4	12	mechanism	mechanism	NOUN
brj-24683	4	13	into	into	ADP
brj-24683	4	14	the	the	DET
brj-24683	4	15	backbone	backbone	NOUN
brj-24683	4	16	to	to	PART
brj-24683	4	17	improve	improve	VERB
brj-24683	4	18	the	the	DET
brj-24683	4	19	representation	representation	NOUN
brj-24683	4	20	of	of	ADP
brj-24683	4	21	fine	fine	ADV
brj-24683	4	22	-	-	PUNCT
brj-24683	4	23	grained	grain	VERB
brj-24683	4	24	defect	defect	NOUN
brj-24683	4	25	features	feature	NOUN
brj-24683	4	26	.	.	PUNCT
brj-24683	5	1	a	a	DET
brj-24683	5	2	learnable	learnable	ADJ
brj-24683	5	3	dynamic	dynamic	ADJ
brj-24683	5	4	upsampling	upsampling	NOUN
brj-24683	5	5	module	module	NOUN
brj-24683	5	6	replaces	replace	VERB
brj-24683	5	7	traditional	traditional	ADJ
brj-24683	5	8	nearestneighbor	nearestneighbor	NOUN
brj-24683	5	9	interpolation	interpolation	NOUN
brj-24683	5	10	to	to	PART
brj-24683	5	11	mitigate	mitigate	VERB
brj-24683	5	12	feature	feature	NOUN
brj-24683	5	13	loss	loss	NOUN
brj-24683	5	14	during	during	ADP
brj-24683	5	15	resolution	resolution	NOUN
brj-24683	5	16	recovery	recovery	NOUN
brj-24683	5	17	.	.	PUNCT
brj-24683	6	1	additionally	additionally	ADV
brj-24683	6	2	,	,	PUNCT
brj-24683	6	3	a	a	DET
brj-24683	6	4	structural	structural	ADJ
brj-24683	6	5	re	re	ADJ
brj-24683	6	6	-	-	ADJ
brj-24683	6	7	parameterizable	parameterizable	ADJ
brj-24683	6	8	block	block	NOUN
brj-24683	6	9	is	be	AUX
brj-24683	6	10	adopted	adopt	VERB
brj-24683	6	11	to	to	PART
brj-24683	6	12	enhance	enhance	VERB
brj-24683	6	13	feature	feature	NOUN
brj-24683	6	14	expressiveness	expressiveness	NOUN
brj-24683	6	15	during	during	ADP
brj-24683	6	16	inference	inference	NOUN
brj-24683	6	17	,	,	PUNCT
brj-24683	6	18	and	and	CCONJ
brj-24683	6	19	a	a	DET
brj-24683	6	20	small	small	ADJ
brj-24683	6	21	-	-	PUNCT
brj-24683	6	22	object	object	NOUN
brj-24683	6	23	detection	detection	NOUN
brj-24683	6	24	head	head	NOUN
brj-24683	6	25	is	be	AUX
brj-24683	6	26	added	add	VERB
brj-24683	6	27	to	to	PART
brj-24683	6	28	enhance	enhance	VERB
brj-24683	6	29	the	the	DET
brj-24683	6	30	detection	detection	NOUN
brj-24683	6	31	of	of	ADP
brj-24683	6	32	small	small	ADJ
brj-24683	6	33	defects	defect	NOUN
brj-24683	6	34	while	while	SCONJ
brj-24683	6	35	minimizing	minimize	VERB
brj-24683	6	36	both	both	CCONJ
brj-24683	6	37	missed	miss	VERB
brj-24683	6	38	and	and	CCONJ
brj-24683	6	39	incorrect	incorrect	ADJ
brj-24683	6	40	detections	detection	NOUN
brj-24683	6	41	.	.	PUNCT
brj-24683	7	1	the	the	DET
brj-24683	7	2	experimental	experimental	ADJ
brj-24683	7	3	results	result	NOUN
brj-24683	7	4	demonstrate	demonstrate	VERB
brj-24683	7	5	that	that	SCONJ
brj-24683	7	6	the	the	DET
brj-24683	7	7	proposed	propose	VERB
brj-24683	7	8	model	model	NOUN
brj-24683	7	9	effectively	effectively	ADV
brj-24683	7	10	enhances	enhance	VERB
brj-24683	7	11	detection	detection	NOUN
brj-24683	7	12	performance	performance	NOUN
brj-24683	7	13	,	,	PUNCT
brj-24683	7	14	increasing	increase	VERB
brj-24683	7	15	the	the	DET
brj-24683	7	16	map	map	NOUN
brj-24683	7	17	of	of	ADP
brj-24683	7	18	the	the	DET
brj-24683	7	19	baseline	baseline	NOUN
brj-24683	7	20	model	model	NOUN
brj-24683	7	21	from	from	ADP
brj-24683	7	22	72.9	72.9	NUM
brj-24683	7	23	%	%	NOUN
brj-24683	7	24	to	to	ADP
brj-24683	7	25	79.5	79.5	NUM
brj-24683	7	26	%	%	NOUN
brj-24683	7	27	.	.	PUNCT
brj-24683	8	1	furthermore	furthermore	ADV
brj-24683	8	2	,	,	PUNCT
brj-24683	8	3	the	the	DET
brj-24683	8	4	proposed	propose	VERB
brj-24683	8	5	model	model	NOUN
brj-24683	8	6	surpasses	surpass	VERB
brj-24683	8	7	other	other	ADJ
brj-24683	8	8	yolo	yolo	ADJ
brj-24683	8	9	variants	variant	NOUN
brj-24683	8	10	in	in	ADP
brj-24683	8	11	map	map	NOUN
brj-24683	8	12	across	across	ADP
brj-24683	8	13	all	all	DET
brj-24683	8	14	defect	defect	ADJ
brj-24683	8	15	categories	category	NOUN
brj-24683	8	16	.	.	PUNCT
brj-24683	9	1	this	this	DET
brj-24683	9	2	improvement	improvement	NOUN
brj-24683	9	3	better	well	ADV
brj-24683	9	4	meets	meet	VERB
brj-24683	9	5	the	the	DET
brj-24683	9	6	quality	quality	NOUN
brj-24683	9	7	control	control	NOUN
brj-24683	9	8	requirements	requirement	NOUN
brj-24683	9	9	of	of	ADP
brj-24683	9	10	wood	wood	NOUN
brj-24683	9	11	processing	processing	NOUN
brj-24683	9	12	and	and	CCONJ
brj-24683	9	13	manufacturing	manufacturing	NOUN
brj-24683	9	14	,	,	PUNCT
brj-24683	9	15	ensuring	ensure	VERB
brj-24683	9	16	the	the	DET
brj-24683	9	17	quality	quality	NOUN
brj-24683	9	18	of	of	ADP
brj-24683	9	19	wood	wood	NOUN
brj-24683	9	20	products	product	NOUN
brj-24683	9	21	.	.	PUNCT
brj-24683	10	1	doi	doi	NOUN
brj-24683	10	2	:	:	PUNCT
brj-24683	10	3	10.15376	10.15376	NUM
brj-24683	10	4	/	/	SYM
brj-24683	10	5	biores.20.3.5709	biores.20.3.5709	PROPN
brj-24683	10	6	-	-	PUNCT
brj-24683	10	7	5730	5730	NUM
brj-24683	10	8	keywords	keyword	NOUN
brj-24683	10	9	:	:	PUNCT
brj-24683	10	10	wood	wood	NOUN
brj-24683	10	11	surface	surface	NOUN
brj-24683	10	12	defect	defect	NOUN
brj-24683	10	13	detection	detection	NOUN
brj-24683	10	14	;	;	PUNCT
brj-24683	10	15	you	you	PRON
brj-24683	10	16	only	only	ADV
brj-24683	10	17	look	look	VERB
brj-24683	10	18	once	once	ADV
brj-24683	10	19	;	;	PUNCT
brj-24683	10	20	multi	multi	ADJ
brj-24683	10	21	-	-	ADJ
brj-24683	10	22	head	head	ADJ
brj-24683	10	23	mixed	mixed	ADJ
brj-24683	10	24	self	self	NOUN
brj-24683	10	25	-	-	PUNCT
brj-24683	10	26	attention	attention	NOUN
brj-24683	10	27	;	;	PUNCT
brj-24683	10	28	dynamic	dynamic	ADJ
brj-24683	10	29	upsampling	upsampling	NOUN
brj-24683	10	30	;	;	PUNCT
brj-24683	10	31	structural	structural	ADJ
brj-24683	10	32	re	re	ADJ
brj-24683	10	33	-	-	ADJ
brj-24683	10	34	parameterizable	parameterizable	ADJ
brj-24683	10	35	block	block	NOUN
brj-24683	10	36	contact	contact	NOUN
brj-24683	10	37	information	information	NOUN
brj-24683	10	38	:	:	PUNCT
brj-24683	10	39	school	school	NOUN
brj-24683	10	40	of	of	ADP
brj-24683	10	41	electronic	electronic	ADJ
brj-24683	10	42	engineering	engineering	NOUN
brj-24683	10	43	,	,	PUNCT
brj-24683	10	44	guilin	guilin	PROPN
brj-24683	10	45	institute	institute	PROPN
brj-24683	10	46	of	of	ADP
brj-24683	10	47	information	information	NOUN
brj-24683	10	48	technology	technology	NOUN
brj-24683	10	49	,	,	PUNCT
brj-24683	10	50	541101	541101	NUM
brj-24683	10	51	,	,	PUNCT
brj-24683	10	52	guilin	guilin	PROPN
brj-24683	10	53	,	,	PUNCT
brj-24683	10	54	china	china	PROPN
brj-24683	10	55	;	;	PUNCT
brj-24683	10	56	*	*	PUNCT
brj-24683	10	57	corresponding	correspond	VERB
brj-24683	10	58	author	author	NOUN
brj-24683	10	59	:	:	PUNCT
brj-24683	10	60	giit_youjun@126.com	giit_youjun@126.com	X
brj-24683	10	61	introduction	introduction	NOUN
brj-24683	10	62	wood	wood	NOUN
brj-24683	10	63	surface	surface	NOUN
brj-24683	10	64	defects	defect	NOUN
brj-24683	10	65	significantly	significantly	ADV
brj-24683	10	66	affect	affect	VERB
brj-24683	10	67	the	the	DET
brj-24683	10	68	quality	quality	NOUN
brj-24683	10	69	and	and	CCONJ
brj-24683	10	70	efficiency	efficiency	NOUN
brj-24683	10	71	of	of	ADP
brj-24683	10	72	wood	wood	NOUN
brj-24683	10	73	processing	processing	NOUN
brj-24683	10	74	.	.	PUNCT
brj-24683	11	1	these	these	DET
brj-24683	11	2	defects	defect	NOUN
brj-24683	11	3	not	not	PART
brj-24683	11	4	only	only	ADV
brj-24683	11	5	reduce	reduce	VERB
brj-24683	11	6	the	the	DET
brj-24683	11	7	utilization	utilization	NOUN
brj-24683	11	8	of	of	ADP
brj-24683	11	9	wood	wood	NOUN
brj-24683	11	10	,	,	PUNCT
brj-24683	11	11	causing	cause	VERB
brj-24683	11	12	resource	resource	NOUN
brj-24683	11	13	waste	waste	NOUN
brj-24683	11	14	,	,	PUNCT
brj-24683	11	15	but	but	CCONJ
brj-24683	11	16	they	they	PRON
brj-24683	11	17	also	also	ADV
brj-24683	11	18	have	have	VERB
brj-24683	11	19	a	a	DET
brj-24683	11	20	considerable	considerable	ADJ
brj-24683	11	21	negative	negative	ADJ
brj-24683	11	22	impact	impact	NOUN
brj-24683	11	23	on	on	ADP
brj-24683	11	24	the	the	DET
brj-24683	11	25	mechanical	mechanical	ADJ
brj-24683	11	26	properties	property	NOUN
brj-24683	11	27	and	and	CCONJ
brj-24683	11	28	functional	functional	ADJ
brj-24683	11	29	value	value	NOUN
brj-24683	11	30	of	of	ADP
brj-24683	11	31	wood	wood	NOUN
brj-24683	11	32	products	product	NOUN
brj-24683	11	33	(	(	PUNCT
brj-24683	11	34	chen	chen	PROPN
brj-24683	11	35	et	et	PROPN
brj-24683	11	36	al	al	PROPN
brj-24683	11	37	.	.	PROPN
brj-24683	11	38	2023a	2023a	NUM
brj-24683	11	39	;	;	PUNCT
brj-24683	11	40	li	li	PROPN
brj-24683	11	41	et	et	PROPN
brj-24683	11	42	al	al	PROPN
brj-24683	11	43	.	.	PROPN
brj-24683	11	44	2024	2024	NUM
brj-24683	11	45	)	)	PUNCT
brj-24683	11	46	.	.	PUNCT
brj-24683	12	1	therefore	therefore	ADV
brj-24683	12	2	,	,	PUNCT
brj-24683	12	3	surface	surface	NOUN
brj-24683	12	4	defect	defect	NOUN
brj-24683	12	5	detection	detection	NOUN
brj-24683	12	6	is	be	AUX
brj-24683	12	7	a	a	DET
brj-24683	12	8	critical	critical	ADJ
brj-24683	12	9	step	step	NOUN
brj-24683	12	10	in	in	ADP
brj-24683	12	11	wood	wood	NOUN
brj-24683	12	12	production	production	NOUN
brj-24683	12	13	and	and	CCONJ
brj-24683	12	14	classification	classification	NOUN
brj-24683	12	15	.	.	PUNCT
brj-24683	13	1	timely	timely	ADJ
brj-24683	13	2	detection	detection	NOUN
brj-24683	13	3	and	and	CCONJ
brj-24683	13	4	removal	removal	NOUN
brj-24683	13	5	of	of	ADP
brj-24683	13	6	defective	defective	ADJ
brj-24683	13	7	wood	wood	NOUN
brj-24683	13	8	can	can	AUX
brj-24683	13	9	effectively	effectively	ADV
brj-24683	13	10	improve	improve	VERB
brj-24683	13	11	the	the	DET
brj-24683	13	12	quality	quality	NOUN
brj-24683	13	13	of	of	ADP
brj-24683	13	14	wood	wood	NOUN
brj-24683	13	15	products	product	NOUN
brj-24683	13	16	,	,	PUNCT
brj-24683	13	17	maximize	maximize	VERB
brj-24683	13	18	the	the	DET
brj-24683	13	19	utilization	utilization	NOUN
brj-24683	13	20	of	of	ADP
brj-24683	13	21	wood	wood	NOUN
brj-24683	13	22	resources	resource	NOUN
brj-24683	13	23	,	,	PUNCT
brj-24683	13	24	and	and	CCONJ
brj-24683	13	25	thus	thus	ADV
brj-24683	13	26	promote	promote	VERB
brj-24683	13	27	the	the	DET
brj-24683	13	28	sustainable	sustainable	ADJ
brj-24683	13	29	development	development	NOUN
brj-24683	13	30	of	of	ADP
brj-24683	13	31	the	the	DET
brj-24683	13	32	wood	wood	NOUN
brj-24683	13	33	industry	industry	NOUN
brj-24683	13	34	(	(	PUNCT
brj-24683	13	35	yi	yi	X
brj-24683	13	36	et	et	PROPN
brj-24683	13	37	al	al	PROPN
brj-24683	13	38	.	.	PROPN
brj-24683	13	39	2024	2024	NUM
brj-24683	13	40	)	)	PUNCT
brj-24683	13	41	.	.	PUNCT
brj-24683	14	1	in	in	ADP
brj-24683	14	2	recent	recent	ADJ
brj-24683	14	3	years	year	NOUN
brj-24683	14	4	,	,	PUNCT
brj-24683	14	5	wood	wood	NOUN
brj-24683	14	6	processing	processing	NOUN
brj-24683	14	7	has	have	AUX
brj-24683	14	8	gradually	gradually	ADV
brj-24683	14	9	shifted	shift	VERB
brj-24683	14	10	from	from	ADP
brj-24683	14	11	manual	manual	ADJ
brj-24683	14	12	labor	labor	NOUN
brj-24683	14	13	to	to	ADP
brj-24683	14	14	automation	automation	NOUN
brj-24683	14	15	,	,	PUNCT
brj-24683	14	16	mechanization	mechanization	NOUN
brj-24683	14	17	,	,	PUNCT
brj-24683	14	18	and	and	CCONJ
brj-24683	14	19	intelligence	intelligence	NOUN
brj-24683	14	20	.	.	PUNCT
brj-24683	15	1	in	in	ADP
brj-24683	15	2	this	this	DET
brj-24683	15	3	process	process	NOUN
brj-24683	15	4	,	,	PUNCT
brj-24683	15	5	various	various	ADJ
brj-24683	15	6	non	non	ADJ
brj-24683	15	7	-	-	ADJ
brj-24683	15	8	destructive	destructive	ADJ
brj-24683	15	9	testing	testing	NOUN
brj-24683	15	10	(	(	PUNCT
brj-24683	15	11	ndt	ndt	NOUN
brj-24683	15	12	)	)	PUNCT
brj-24683	15	13	technologies	technology	NOUN
brj-24683	15	14	have	have	AUX
brj-24683	15	15	been	be	AUX
brj-24683	15	16	widely	widely	ADV
brj-24683	15	17	applied	apply	VERB
brj-24683	15	18	in	in	ADP
brj-24683	15	19	wood	wood	NOUN
brj-24683	15	20	surface	surface	NOUN
brj-24683	15	21	defect	defect	NOUN
brj-24683	15	22	detection	detection	NOUN
brj-24683	15	23	,	,	PUNCT
brj-24683	15	24	such	such	ADJ
brj-24683	15	25	as	as	ADP
brj-24683	15	26	ultrasonic	ultrasonic	ADJ
brj-24683	15	27	testing	testing	NOUN
brj-24683	15	28	(	(	PUNCT
brj-24683	15	29	jiang	jiang	PROPN
brj-24683	15	30	et	et	PROPN
brj-24683	15	31	al	al	PROPN
brj-24683	15	32	.	.	PROPN
brj-24683	15	33	2024	2024	NUM
brj-24683	15	34	;	;	PUNCT
brj-24683	15	35	wang	wang	PROPN
brj-24683	15	36	et	et	PROPN
brj-24683	15	37	al	al	PROPN
brj-24683	15	38	.	.	PROPN
brj-24683	15	39	2024	2024	NUM
brj-24683	15	40	;	;	PUNCT
brj-24683	15	41	)	)	PUNCT
brj-24683	15	42	,	,	PUNCT
brj-24683	15	43	x	x	X
brj-24683	15	44	-	-	NOUN
brj-24683	15	45	ray	ray	NOUN
brj-24683	15	46	inspection	inspection	NOUN
brj-24683	15	47	(	(	PUNCT
brj-24683	15	48	stängle	stängle	VERB
brj-24683	15	49	et	et	PROPN
brj-24683	15	50	al	al	PROPN
brj-24683	15	51	.	.	PROPN
brj-24683	15	52	2015	2015	NUM
brj-24683	15	53	;	;	PUNCT
brj-24683	15	54	zhang	zhang	PROPN
brj-24683	15	55	2017	2017	NUM
brj-24683	15	56	)	)	PUNCT
brj-24683	15	57	,	,	PUNCT
brj-24683	15	58	infrared	infrared	ADJ
brj-24683	15	59	detection	detection	NOUN
brj-24683	15	60	(	(	PUNCT
brj-24683	15	61	lópez	lópez	NOUN
brj-24683	15	62	et	et	PROPN
brj-24683	15	63	al	al	PROPN
brj-24683	15	64	.	.	PROPN
brj-24683	15	65	2014	2014	NUM
brj-24683	15	66	;	;	PUNCT
brj-24683	15	67	yu	yu	PROPN
brj-24683	15	68	et	et	PROPN
brj-24683	15	69	al	al	PROPN
brj-24683	15	70	.	.	PROPN
brj-24683	15	71	2019	2019	NUM
brj-24683	15	72	)	)	PUNCT
brj-24683	15	73	,	,	PUNCT
brj-24683	15	74	and	and	CCONJ
brj-24683	15	75	machine	machine	NOUN
brj-24683	15	76	vision	vision	NOUN
brj-24683	15	77	techniques	technique	NOUN
brj-24683	15	78	(	(	PUNCT
brj-24683	15	79	hittawe	hittawe	PROPN
brj-24683	15	80	et	et	PROPN
brj-24683	15	81	al	al	PROPN
brj-24683	15	82	.	.	PROPN
brj-24683	15	83	2017	2017	NUM
brj-24683	15	84	;	;	PUNCT
brj-24683	15	85	ji	ji	PROPN
brj-24683	15	86	et	et	PROPN
brj-24683	15	87	al	al	PROPN
brj-24683	15	88	.	.	PROPN
brj-24683	15	89	2024a	2024a	NUM
brj-24683	15	90	)	)	PUNCT
brj-24683	15	91	.	.	PUNCT
brj-24683	16	1	these	these	DET
brj-24683	16	2	technologies	technology	NOUN
brj-24683	16	3	provide	provide	VERB
brj-24683	16	4	practical	practical	ADJ
brj-24683	16	5	solutions	solution	NOUN
brj-24683	16	6	for	for	ADP
brj-24683	16	7	wood	wood	NOUN
brj-24683	16	8	defect	defect	NOUN
brj-24683	16	9	detection	detection	NOUN
brj-24683	16	10	,	,	PUNCT
brj-24683	16	11	successfully	successfully	ADV
brj-24683	16	12	facilitating	facilitate	VERB
brj-24683	16	13	the	the	DET
brj-24683	16	14	transition	transition	NOUN
brj-24683	16	15	peer	peer	NOUN
brj-24683	16	16	-	-	PUNCT
brj-24683	16	17	reviewed	review	VERB
brj-24683	16	18	article	article	NOUN
brj-24683	16	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	16	20	dou	dou	PROPN
brj-24683	16	21	&	&	CCONJ
brj-24683	16	22	you	you	PRON
brj-24683	16	23	(	(	PUNCT
brj-24683	16	24	2025	2025	NUM
brj-24683	16	25	)	)	PUNCT
brj-24683	16	26	.	.	PUNCT
brj-24683	17	1	“	"	PUNCT
brj-24683	17	2	wood	wood	NOUN
brj-24683	17	3	defect	defect	NOUN
brj-24683	17	4	identification	identification	NOUN
brj-24683	17	5	,	,	PUNCT
brj-24683	17	6	”	"	PUNCT
brj-24683	17	7	bioresources	bioresource	NOUN
brj-24683	17	8	20(3	20(3	NOUN
brj-24683	17	9	)	)	PUNCT
brj-24683	17	10	,	,	PUNCT
brj-24683	17	11	5709	5709	NUM
brj-24683	17	12	-	-	SYM
brj-24683	17	13	5730	5730	NUM
brj-24683	17	14	.	.	PUNCT
brj-24683	18	1	5710	5710	NUM
brj-24683	18	2	from	from	ADP
brj-24683	18	3	manual	manual	NOUN
brj-24683	18	4	to	to	ADP
brj-24683	18	5	automated	automate	VERB
brj-24683	18	6	and	and	CCONJ
brj-24683	18	7	mechanized	mechanized	ADJ
brj-24683	18	8	processes	process	NOUN
brj-24683	18	9	.	.	PUNCT
brj-24683	19	1	for	for	ADP
brj-24683	19	2	instance	instance	NOUN
brj-24683	19	3	,	,	PUNCT
brj-24683	19	4	conners	conners	PROPN
brj-24683	19	5	et	et	PROPN
brj-24683	19	6	al	al	PROPN
brj-24683	19	7	.	.	PROPN
brj-24683	20	1	(	(	PUNCT
brj-24683	20	2	1983	1983	NUM
brj-24683	20	3	)	)	PUNCT
brj-24683	20	4	designed	design	VERB
brj-24683	20	5	an	an	DET
brj-24683	20	6	automated	automate	VERB
brj-24683	20	7	wood	wood	NOUN
brj-24683	20	8	processing	processing	NOUN
brj-24683	20	9	system	system	NOUN
brj-24683	20	10	based	base	VERB
brj-24683	20	11	on	on	ADP
brj-24683	20	12	computed	compute	VERB
brj-24683	20	13	tomography	tomography	NOUN
brj-24683	20	14	and	and	CCONJ
brj-24683	20	15	optical	optical	ADJ
brj-24683	20	16	scanning	scanning	NOUN
brj-24683	20	17	,	,	PUNCT
brj-24683	20	18	capable	capable	ADJ
brj-24683	20	19	of	of	ADP
brj-24683	20	20	identifying	identify	VERB
brj-24683	20	21	and	and	CCONJ
brj-24683	20	22	classifying	classify	VERB
brj-24683	20	23	eight	eight	NUM
brj-24683	20	24	common	common	ADJ
brj-24683	20	25	wood	wood	NOUN
brj-24683	20	26	defects	defect	NOUN
brj-24683	20	27	.	.	PUNCT
brj-24683	21	1	wyckhuyse	wyckhuyse	NOUN
brj-24683	21	2	and	and	CCONJ
brj-24683	21	3	maldague	maldague	NOUN
brj-24683	21	4	(	(	PUNCT
brj-24683	21	5	2001	2001	NUM
brj-24683	21	6	)	)	PUNCT
brj-24683	21	7	experimentally	experimentally	ADV
brj-24683	21	8	validated	validate	VERB
brj-24683	21	9	the	the	DET
brj-24683	21	10	feasibility	feasibility	NOUN
brj-24683	21	11	of	of	ADP
brj-24683	21	12	infrared	infrared	ADJ
brj-24683	21	13	thermography	thermography	NOUN
brj-24683	21	14	for	for	ADP
brj-24683	21	15	wood	wood	NOUN
brj-24683	21	16	surface	surface	NOUN
brj-24683	21	17	defect	defect	NOUN
brj-24683	21	18	detection	detection	NOUN
brj-24683	21	19	.	.	PUNCT
brj-24683	22	1	sandak	sandak	NOUN
brj-24683	22	2	et	et	PROPN
brj-24683	22	3	al	al	PROPN
brj-24683	22	4	.	.	PROPN
brj-24683	22	5	(	(	PUNCT
brj-24683	22	6	2020	2020	NUM
brj-24683	22	7	)	)	PUNCT
brj-24683	22	8	developed	develop	VERB
brj-24683	22	9	a	a	DET
brj-24683	22	10	portable	portable	ADJ
brj-24683	22	11	spectrometer	spectrometer	NOUN
brj-24683	22	12	covering	cover	VERB
brj-24683	22	13	visible	visible	ADJ
brj-24683	22	14	and	and	CCONJ
brj-24683	22	15	near	near	ADV
brj-24683	22	16	-	-	PUNCT
brj-24683	22	17	infrared	infrared	ADJ
brj-24683	22	18	light	light	NOUN
brj-24683	22	19	,	,	PUNCT
brj-24683	22	20	which	which	PRON
brj-24683	22	21	can	can	AUX
brj-24683	22	22	directly	directly	ADV
brj-24683	22	23	detect	detect	VERB
brj-24683	22	24	defects	defect	NOUN
brj-24683	22	25	such	such	ADJ
brj-24683	22	26	as	as	ADP
brj-24683	22	27	knots	knot	NOUN
brj-24683	22	28	,	,	PUNCT
brj-24683	22	29	decay	decay	NOUN
brj-24683	22	30	,	,	PUNCT
brj-24683	22	31	and	and	CCONJ
brj-24683	22	32	resin	resin	NOUN
brj-24683	22	33	on	on	ADP
brj-24683	22	34	the	the	DET
brj-24683	22	35	surface	surface	NOUN
brj-24683	22	36	of	of	ADP
brj-24683	22	37	logs	log	NOUN
brj-24683	22	38	in	in	ADP
brj-24683	22	39	the	the	DET
brj-24683	22	40	forest	forest	NOUN
brj-24683	22	41	.	.	PUNCT
brj-24683	23	1	although	although	SCONJ
brj-24683	23	2	these	these	DET
brj-24683	23	3	ndt	ndt	PROPN
brj-24683	23	4	technologies	technology	NOUN
brj-24683	23	5	offer	offer	VERB
brj-24683	23	6	high	high	ADJ
brj-24683	23	7	precision	precision	NOUN
brj-24683	23	8	and	and	CCONJ
brj-24683	23	9	efficiency	efficiency	NOUN
brj-24683	23	10	,	,	PUNCT
brj-24683	23	11	their	their	PRON
brj-24683	23	12	adaptability	adaptability	NOUN
brj-24683	23	13	and	and	CCONJ
brj-24683	23	14	real	real	ADJ
brj-24683	23	15	-	-	PUNCT
brj-24683	23	16	time	time	NOUN
brj-24683	23	17	capabilities	capability	NOUN
brj-24683	23	18	in	in	ADP
brj-24683	23	19	complex	complex	ADJ
brj-24683	23	20	environments	environment	NOUN
brj-24683	23	21	still	still	ADV
brj-24683	23	22	face	face	VERB
brj-24683	23	23	significant	significant	ADJ
brj-24683	23	24	limitations	limitation	NOUN
brj-24683	23	25	.	.	PUNCT
brj-24683	24	1	with	with	ADP
brj-24683	24	2	the	the	DET
brj-24683	24	3	rapid	rapid	ADJ
brj-24683	24	4	development	development	NOUN
brj-24683	24	5	of	of	ADP
brj-24683	24	6	computer	computer	NOUN
brj-24683	24	7	and	and	CCONJ
brj-24683	24	8	artificial	artificial	ADJ
brj-24683	24	9	intelligence	intelligence	NOUN
brj-24683	24	10	technologies	technology	NOUN
brj-24683	24	11	,	,	PUNCT
brj-24683	24	12	wood	wood	NOUN
brj-24683	24	13	surface	surface	NOUN
brj-24683	24	14	defect	defect	NOUN
brj-24683	24	15	detection	detection	NOUN
brj-24683	24	16	technology	technology	NOUN
brj-24683	24	17	has	have	AUX
brj-24683	24	18	evolved	evolve	VERB
brj-24683	24	19	from	from	ADP
brj-24683	24	20	traditional	traditional	ADJ
brj-24683	24	21	rule	rule	NOUN
brj-24683	24	22	-	-	PUNCT
brj-24683	24	23	based	base	VERB
brj-24683	24	24	methods	method	NOUN
brj-24683	24	25	to	to	ADP
brj-24683	24	26	data	data	NOUN
brj-24683	24	27	-	-	PUNCT
brj-24683	24	28	driven	drive	VERB
brj-24683	24	29	deep	deep	ADJ
brj-24683	24	30	learning	learning	NOUN
brj-24683	24	31	approaches	approach	NOUN
brj-24683	24	32	.	.	PUNCT
brj-24683	25	1	deep	deep	ADJ
brj-24683	25	2	learning	learning	NOUN
brj-24683	25	3	-	-	PUNCT
brj-24683	25	4	based	base	VERB
brj-24683	25	5	detection	detection	NOUN
brj-24683	25	6	techniques	technique	NOUN
brj-24683	25	7	,	,	PUNCT
brj-24683	25	8	due	due	ADP
brj-24683	25	9	to	to	ADP
brj-24683	25	10	their	their	PRON
brj-24683	25	11	ability	ability	NOUN
brj-24683	25	12	to	to	PART
brj-24683	25	13	accurately	accurately	ADV
brj-24683	25	14	identify	identify	VERB
brj-24683	25	15	a	a	DET
brj-24683	25	16	variety	variety	NOUN
brj-24683	25	17	of	of	ADP
brj-24683	25	18	wood	wood	NOUN
brj-24683	25	19	surface	surface	NOUN
brj-24683	25	20	defect	defect	NOUN
brj-24683	25	21	types	type	NOUN
brj-24683	25	22	and	and	CCONJ
brj-24683	25	23	adapt	adapt	VERB
brj-24683	25	24	to	to	ADP
brj-24683	25	25	different	different	ADJ
brj-24683	25	26	lighting	lighting	NOUN
brj-24683	25	27	conditions	condition	NOUN
brj-24683	25	28	and	and	CCONJ
brj-24683	25	29	wood	wood	NOUN
brj-24683	25	30	species	specie	NOUN
brj-24683	25	31	,	,	PUNCT
brj-24683	25	32	have	have	AUX
brj-24683	25	33	become	become	VERB
brj-24683	25	34	a	a	DET
brj-24683	25	35	current	current	ADJ
brj-24683	25	36	research	research	NOUN
brj-24683	25	37	hotspot	hotspot	NOUN
brj-24683	25	38	.	.	PUNCT
brj-24683	26	1	for	for	ADP
brj-24683	26	2	instance	instance	NOUN
brj-24683	26	3	,	,	PUNCT
brj-24683	26	4	sun	sun	PROPN
brj-24683	26	5	et	et	PROPN
brj-24683	26	6	al	al	PROPN
brj-24683	26	7	.	.	PROPN
brj-24683	27	1	(	(	PUNCT
brj-24683	27	2	2022	2022	NUM
brj-24683	27	3	)	)	PUNCT
brj-24683	27	4	proposed	propose	VERB
brj-24683	27	5	a	a	DET
brj-24683	27	6	multicriteria	multicriteria	PROPN
brj-24683	27	7	framework	framework	NOUN
brj-24683	27	8	that	that	PRON
brj-24683	27	9	integrates	integrate	VERB
brj-24683	27	10	deep	deep	ADJ
brj-24683	27	11	learning	learning	NOUN
brj-24683	27	12	for	for	ADP
brj-24683	27	13	comprehensive	comprehensive	ADJ
brj-24683	27	14	wood	wood	NOUN
brj-24683	27	15	quality	quality	NOUN
brj-24683	27	16	assessment	assessment	NOUN
brj-24683	27	17	,	,	PUNCT
brj-24683	27	18	whereas	whereas	SCONJ
brj-24683	27	19	ji	ji	PROPN
brj-24683	27	20	et	et	PROPN
brj-24683	27	21	al	al	PROPN
brj-24683	27	22	.	.	PROPN
brj-24683	28	1	(	(	PUNCT
brj-24683	28	2	2024b	2024b	NUM
brj-24683	28	3	)	)	PUNCT
brj-24683	28	4	concentrated	concentrate	VERB
brj-24683	28	5	on	on	ADP
brj-24683	28	6	knot	knot	ADJ
brj-24683	28	7	detection	detection	NOUN
brj-24683	28	8	in	in	ADP
brj-24683	28	9	chinese	chinese	ADJ
brj-24683	28	10	fir	fir	NOUN
brj-24683	28	11	lumber	lumber	NOUN
brj-24683	28	12	using	use	VERB
brj-24683	28	13	traditional	traditional	ADJ
brj-24683	28	14	vision	vision	NOUN
brj-24683	28	15	methods	method	NOUN
brj-24683	28	16	.	.	PUNCT
brj-24683	29	1	özcan	özcan	PROPN
brj-24683	29	2	et	et	PROPN
brj-24683	29	3	al	al	PROPN
brj-24683	29	4	.	.	PROPN
brj-24683	30	1	(	(	PUNCT
brj-24683	30	2	2024	2024	NUM
brj-24683	30	3	)	)	PUNCT
brj-24683	30	4	applied	apply	VERB
brj-24683	30	5	deep	deep	ADJ
brj-24683	30	6	learning	learning	NOUN
brj-24683	30	7	for	for	ADP
brj-24683	30	8	general	general	ADJ
brj-24683	30	9	anomaly	anomaly	NOUN
brj-24683	30	10	detection	detection	NOUN
brj-24683	30	11	on	on	ADP
brj-24683	30	12	wood	wood	NOUN
brj-24683	30	13	surfaces	surface	NOUN
brj-24683	30	14	but	but	CCONJ
brj-24683	30	15	without	without	ADP
brj-24683	30	16	structural	structural	ADJ
brj-24683	30	17	enhancements	enhancement	NOUN
brj-24683	30	18	for	for	ADP
brj-24683	30	19	small	small	ADJ
brj-24683	30	20	defect	defect	NOUN
brj-24683	30	21	sensitivity	sensitivity	NOUN
brj-24683	30	22	.	.	PUNCT
brj-24683	31	1	zhu	zhu	PROPN
brj-24683	31	2	et	et	PROPN
brj-24683	31	3	al	al	PROPN
brj-24683	31	4	.	.	PROPN
brj-24683	31	5	(	(	PUNCT
brj-24683	31	6	2024	2024	NUM
brj-24683	31	7	)	)	PUNCT
brj-24683	31	8	introduced	introduce	VERB
brj-24683	31	9	a	a	DET
brj-24683	31	10	multi	multi	ADJ
brj-24683	31	11	-	-	ADJ
brj-24683	31	12	source	source	ADJ
brj-24683	31	13	data	data	NOUN
brj-24683	31	14	fusion	fusion	NOUN
brj-24683	31	15	network	network	NOUN
brj-24683	31	16	targeting	target	VERB
brj-24683	31	17	fine	fine	ADV
brj-24683	31	18	-	-	PUNCT
brj-24683	31	19	grained	grain	VERB
brj-24683	31	20	defect	defect	NOUN
brj-24683	31	21	segmentation	segmentation	NOUN
brj-24683	31	22	.	.	PUNCT
brj-24683	32	1	currently	currently	ADV
brj-24683	32	2	,	,	PUNCT
brj-24683	32	3	deep	deep	ADJ
brj-24683	32	4	learning	learning	NOUN
brj-24683	32	5	-	-	PUNCT
brj-24683	32	6	based	base	VERB
brj-24683	32	7	defect	defect	NOUN
brj-24683	32	8	detection	detection	NOUN
brj-24683	32	9	methods	method	NOUN
brj-24683	32	10	are	be	AUX
brj-24683	32	11	mainly	mainly	ADV
brj-24683	32	12	divided	divide	VERB
brj-24683	32	13	into	into	ADP
brj-24683	32	14	two	two	NUM
brj-24683	32	15	-	-	PUNCT
brj-24683	32	16	stage	stage	NOUN
brj-24683	32	17	and	and	CCONJ
brj-24683	32	18	single	single	ADJ
brj-24683	32	19	-	-	PUNCT
brj-24683	32	20	stage	stage	NOUN
brj-24683	32	21	detection	detection	NOUN
brj-24683	32	22	algorithms	algorithm	NOUN
brj-24683	32	23	.	.	PUNCT
brj-24683	33	1	two	two	NUM
brj-24683	33	2	-	-	PUNCT
brj-24683	33	3	stage	stage	NOUN
brj-24683	33	4	algorithms	algorithm	NOUN
brj-24683	33	5	,	,	PUNCT
brj-24683	33	6	such	such	ADJ
brj-24683	33	7	as	as	ADP
brj-24683	33	8	region	region	NOUN
brj-24683	33	9	-	-	PUNCT
brj-24683	33	10	based	base	VERB
brj-24683	33	11	convolutional	convolutional	ADJ
brj-24683	33	12	neural	neural	ADJ
brj-24683	33	13	network	network	NOUN
brj-24683	33	14	(	(	PUNCT
brj-24683	33	15	r	r	NOUN
brj-24683	33	16	-	-	PUNCT
brj-24683	33	17	cnn	cnn	NOUN
brj-24683	33	18	)	)	PUNCT
brj-24683	33	19	(	(	PUNCT
brj-24683	33	20	girshick	girshick	NOUN
brj-24683	33	21	et	et	PROPN
brj-24683	33	22	al	al	PROPN
brj-24683	33	23	.	.	PROPN
brj-24683	33	24	2014	2014	NUM
brj-24683	33	25	)	)	PUNCT
brj-24683	33	26	,	,	PUNCT
brj-24683	33	27	faster	fast	ADV
brj-24683	33	28	r	r	X
brj-24683	33	29	-	-	PUNCT
brj-24683	33	30	cnn	cnn	PROPN
brj-24683	33	31	(	(	PUNCT
brj-24683	33	32	ren	ren	NOUN
brj-24683	33	33	et	et	PROPN
brj-24683	33	34	al	al	PROPN
brj-24683	33	35	.	.	PROPN
brj-24683	33	36	2017	2017	NUM
brj-24683	33	37	)	)	PUNCT
brj-24683	33	38	,	,	PUNCT
brj-24683	33	39	and	and	CCONJ
brj-24683	33	40	mask	mask	VERB
brj-24683	33	41	r	r	NOUN
brj-24683	33	42	-	-	PUNCT
brj-24683	33	43	cnn	cnn	PROPN
brj-24683	33	44	(	(	PUNCT
brj-24683	33	45	he	he	PRON
brj-24683	33	46	et	et	PROPN
brj-24683	33	47	al	al	PROPN
brj-24683	33	48	.	.	PROPN
brj-24683	33	49	2017	2017	NUM
brj-24683	33	50	)	)	PUNCT
brj-24683	33	51	,	,	PUNCT
brj-24683	33	52	perform	perform	VERB
brj-24683	33	53	excellently	excellently	ADV
brj-24683	33	54	in	in	ADP
brj-24683	33	55	detection	detection	NOUN
brj-24683	33	56	accuracy	accuracy	NOUN
brj-24683	33	57	and	and	CCONJ
brj-24683	33	58	are	be	AUX
brj-24683	33	59	widely	widely	ADV
brj-24683	33	60	used	use	VERB
brj-24683	33	61	in	in	ADP
brj-24683	33	62	wood	wood	NOUN
brj-24683	33	63	surface	surface	NOUN
brj-24683	33	64	defect	defect	NOUN
brj-24683	33	65	detection	detection	NOUN
brj-24683	33	66	tasks	task	NOUN
brj-24683	33	67	(	(	PUNCT
brj-24683	33	68	gao	gao	PROPN
brj-24683	33	69	et	et	PROPN
brj-24683	33	70	al	al	PROPN
brj-24683	33	71	.	.	PROPN
brj-24683	33	72	2021	2021	NUM
brj-24683	33	73	;	;	PUNCT
brj-24683	33	74	li	li	PROPN
brj-24683	33	75	et	et	PROPN
brj-24683	33	76	al	al	PROPN
brj-24683	33	77	.	.	PROPN
brj-24683	33	78	2021	2021	NUM
brj-24683	33	79	;	;	PUNCT
brj-24683	33	80	chen	chen	PROPN
brj-24683	33	81	et	et	PROPN
brj-24683	33	82	al	al	PROPN
brj-24683	33	83	.	.	PROPN
brj-24683	33	84	2023b	2023b	NUM
brj-24683	33	85	;	;	PUNCT
brj-24683	33	86	zou	zou	PROPN
brj-24683	33	87	et	et	PROPN
brj-24683	33	88	al	al	PROPN
brj-24683	33	89	.	.	PROPN
brj-24683	33	90	2024	2024	NUM
brj-24683	33	91	)	)	PUNCT
brj-24683	33	92	.	.	PUNCT
brj-24683	34	1	fan	fan	PROPN
brj-24683	34	2	et	et	PROPN
brj-24683	34	3	al	al	PROPN
brj-24683	34	4	.	.	PROPN
brj-24683	35	1	(	(	PUNCT
brj-24683	35	2	2019	2019	NUM
brj-24683	35	3	)	)	PUNCT
brj-24683	35	4	were	be	AUX
brj-24683	35	5	the	the	DET
brj-24683	35	6	first	first	ADJ
brj-24683	35	7	to	to	PART
brj-24683	35	8	apply	apply	VERB
brj-24683	35	9	faster	fast	ADJ
brj-24683	35	10	r	r	NOUN
brj-24683	35	11	-	-	PUNCT
brj-24683	35	12	cnn	cnn	NOUN
brj-24683	35	13	to	to	ADP
brj-24683	35	14	wood	wood	NOUN
brj-24683	35	15	defect	defect	NOUN
brj-24683	35	16	detection	detection	NOUN
brj-24683	35	17	,	,	PUNCT
brj-24683	35	18	constructing	construct	VERB
brj-24683	35	19	a	a	DET
brj-24683	35	20	real	real	ADJ
brj-24683	35	21	-	-	PUNCT
brj-24683	35	22	time	time	NOUN
brj-24683	35	23	defect	defect	NOUN
brj-24683	35	24	detection	detection	NOUN
brj-24683	35	25	system	system	NOUN
brj-24683	35	26	for	for	ADP
brj-24683	35	27	solid	solid	ADJ
brj-24683	35	28	wood	wood	NOUN
brj-24683	35	29	flooring	flooring	NOUN
brj-24683	35	30	that	that	PRON
brj-24683	35	31	meets	meet	VERB
brj-24683	35	32	industrial	industrial	ADJ
brj-24683	35	33	production	production	NOUN
brj-24683	35	34	requirements	requirement	NOUN
brj-24683	35	35	.	.	PUNCT
brj-24683	36	1	they	they	PRON
brj-24683	36	2	validated	validate	VERB
brj-24683	36	3	the	the	DET
brj-24683	36	4	practicality	practicality	NOUN
brj-24683	36	5	of	of	ADP
brj-24683	36	6	the	the	DET
brj-24683	36	7	multi	multi	ADJ
brj-24683	36	8	-	-	NOUN
brj-24683	36	9	stage	stage	NOUN
brj-24683	36	10	faster	fast	ADJ
brj-24683	36	11	r	r	NOUN
brj-24683	36	12	-	-	PUNCT
brj-24683	36	13	cnn	cnn	PROPN
brj-24683	36	14	object	object	NOUN
brj-24683	36	15	detection	detection	NOUN
brj-24683	36	16	algorithm	algorithm	NOUN
brj-24683	36	17	under	under	ADP
brj-24683	36	18	deep	deep	ADJ
brj-24683	36	19	learning	learning	NOUN
brj-24683	36	20	for	for	ADP
brj-24683	36	21	solid	solid	ADJ
brj-24683	36	22	wood	wood	NOUN
brj-24683	36	23	board	board	NOUN
brj-24683	36	24	defect	defect	NOUN
brj-24683	36	25	detection	detection	NOUN
brj-24683	36	26	.	.	PUNCT
brj-24683	37	1	xia	xia	PROPN
brj-24683	37	2	et	et	PROPN
brj-24683	37	3	al	al	PROPN
brj-24683	37	4	.	.	PROPN
brj-24683	37	5	(	(	PUNCT
brj-24683	37	6	2022	2022	NUM
brj-24683	37	7	)	)	PUNCT
brj-24683	37	8	noticed	notice	VERB
brj-24683	37	9	that	that	SCONJ
brj-24683	37	10	the	the	DET
brj-24683	37	11	texture	texture	NOUN
brj-24683	37	12	features	feature	NOUN
brj-24683	37	13	of	of	ADP
brj-24683	37	14	wood	wood	NOUN
brj-24683	37	15	often	often	ADV
brj-24683	37	16	accompany	accompany	VERB
brj-24683	37	17	wood	wood	NOUN
brj-24683	37	18	defects	defect	NOUN
brj-24683	37	19	and	and	CCONJ
brj-24683	37	20	can	can	AUX
brj-24683	37	21	interfere	interfere	VERB
brj-24683	37	22	with	with	ADP
brj-24683	37	23	the	the	DET
brj-24683	37	24	final	final	ADJ
brj-24683	37	25	recognition	recognition	NOUN
brj-24683	37	26	results	result	NOUN
brj-24683	37	27	.	.	PUNCT
brj-24683	38	1	they	they	PRON
brj-24683	38	2	proposed	propose	VERB
brj-24683	38	3	a	a	DET
brj-24683	38	4	faster	fast	ADJ
brj-24683	38	5	r	r	NOUN
brj-24683	38	6	-	-	PUNCT
brj-24683	38	7	cnn	cnn	PROPN
brj-24683	38	8	surface	surface	NOUN
brj-24683	38	9	defect	defect	NOUN
brj-24683	38	10	detection	detection	NOUN
brj-24683	38	11	algorithm	algorithm	NOUN
brj-24683	38	12	that	that	PRON
brj-24683	38	13	improves	improve	VERB
brj-24683	38	14	image	image	NOUN
brj-24683	38	15	texture	texture	NOUN
brj-24683	38	16	background	background	NOUN
brj-24683	38	17	using	use	VERB
brj-24683	38	18	bilateral	bilateral	ADJ
brj-24683	38	19	filtering	filtering	NOUN
brj-24683	38	20	,	,	PUNCT
brj-24683	38	21	enhancing	enhance	VERB
brj-24683	38	22	the	the	DET
brj-24683	38	23	network	network	NOUN
brj-24683	38	24	’s	’s	PART
brj-24683	38	25	ability	ability	NOUN
brj-24683	38	26	to	to	PART
brj-24683	38	27	process	process	VERB
brj-24683	38	28	multi	multi	ADJ
brj-24683	38	29	-	-	ADJ
brj-24683	38	30	scale	scale	ADJ
brj-24683	38	31	defect	defect	NOUN
brj-24683	38	32	features	feature	NOUN
brj-24683	38	33	and	and	CCONJ
brj-24683	38	34	achieving	achieve	VERB
brj-24683	38	35	outstanding	outstanding	ADJ
brj-24683	38	36	performance	performance	NOUN
brj-24683	38	37	in	in	ADP
brj-24683	38	38	detecting	detect	VERB
brj-24683	38	39	small	small	ADJ
brj-24683	38	40	defects	defect	NOUN
brj-24683	38	41	.	.	PUNCT
brj-24683	39	1	hu	hu	PROPN
brj-24683	39	2	et	et	PROPN
brj-24683	39	3	al	al	PROPN
brj-24683	39	4	.	.	PROPN
brj-24683	39	5	(	(	PUNCT
brj-24683	39	6	2020	2020	NUM
brj-24683	39	7	)	)	PUNCT
brj-24683	39	8	combined	combine	VERB
brj-24683	39	9	progressive	progressive	ADJ
brj-24683	39	10	growing	grow	VERB
brj-24683	39	11	of	of	ADP
brj-24683	39	12	generative	generative	ADJ
brj-24683	39	13	adversarial	adversarial	ADJ
brj-24683	39	14	networks	network	NOUN
brj-24683	39	15	(	(	PUNCT
brj-24683	39	16	pggan	pggan	PROPN
brj-24683	39	17	)	)	PUNCT
brj-24683	39	18	with	with	ADP
brj-24683	39	19	the	the	DET
brj-24683	39	20	mask	mask	NOUN
brj-24683	39	21	r	r	NOUN
brj-24683	39	22	-	-	PUNCT
brj-24683	39	23	cnn	cnn	PROPN
brj-24683	39	24	model	model	NOUN
brj-24683	39	25	and	and	CCONJ
brj-24683	39	26	introduced	introduce	VERB
brj-24683	39	27	transfer	transfer	NOUN
brj-24683	39	28	learning	learn	VERB
brj-24683	39	29	to	to	PART
brj-24683	39	30	identify	identify	VERB
brj-24683	39	31	and	and	CCONJ
brj-24683	39	32	classify	classify	VERB
brj-24683	39	33	defects	defect	NOUN
brj-24683	39	34	in	in	ADP
brj-24683	39	35	poplar	poplar	ADJ
brj-24683	39	36	veneer	veneer	NOUN
brj-24683	39	37	,	,	PUNCT
brj-24683	39	38	compensating	compensate	VERB
brj-24683	39	39	for	for	ADP
brj-24683	39	40	the	the	DET
brj-24683	39	41	traditional	traditional	ADJ
brj-24683	39	42	sample	sample	NOUN
brj-24683	39	43	augmentation	augmentation	NOUN
brj-24683	39	44	methods	method	NOUN
brj-24683	39	45	that	that	PRON
brj-24683	39	46	lack	lack	VERB
brj-24683	39	47	fine	fine	ADJ
brj-24683	39	48	defect	defect	NOUN
brj-24683	39	49	details	detail	NOUN
brj-24683	39	50	,	,	PUNCT
brj-24683	39	51	poor	poor	ADJ
brj-24683	39	52	defect	defect	NOUN
brj-24683	39	53	image	image	NOUN
brj-24683	39	54	diversity	diversity	NOUN
brj-24683	39	55	,	,	PUNCT
brj-24683	39	56	and	and	CCONJ
brj-24683	39	57	limited	limited	ADJ
brj-24683	39	58	sample	sample	NOUN
brj-24683	39	59	distribution	distribution	NOUN
brj-24683	39	60	.	.	PUNCT
brj-24683	40	1	however	however	ADV
brj-24683	40	2	,	,	PUNCT
brj-24683	40	3	two	two	NUM
brj-24683	40	4	-	-	PUNCT
brj-24683	40	5	stage	stage	NOUN
brj-24683	40	6	algorithms	algorithm	NOUN
brj-24683	40	7	typically	typically	ADV
brj-24683	40	8	have	have	VERB
brj-24683	40	9	high	high	ADJ
brj-24683	40	10	computational	computational	ADJ
brj-24683	40	11	complexity	complexity	NOUN
brj-24683	40	12	,	,	PUNCT
brj-24683	40	13	making	make	VERB
brj-24683	40	14	it	it	PRON
brj-24683	40	15	difficult	difficult	ADJ
brj-24683	40	16	to	to	PART
brj-24683	40	17	fully	fully	ADV
brj-24683	40	18	meet	meet	VERB
brj-24683	40	19	the	the	DET
brj-24683	40	20	real	real	ADJ
brj-24683	40	21	-	-	PUNCT
brj-24683	40	22	time	time	NOUN
brj-24683	40	23	requirements	requirement	NOUN
brj-24683	40	24	of	of	ADP
brj-24683	40	25	wood	wood	NOUN
brj-24683	40	26	processing	processing	NOUN
brj-24683	40	27	.	.	PUNCT
brj-24683	41	1	in	in	ADP
brj-24683	41	2	contrast	contrast	NOUN
brj-24683	41	3	,	,	PUNCT
brj-24683	41	4	single	single	ADJ
brj-24683	41	5	-	-	PUNCT
brj-24683	41	6	stage	stage	NOUN
brj-24683	41	7	detection	detection	NOUN
brj-24683	41	8	algorithms	algorithm	NOUN
brj-24683	41	9	,	,	PUNCT
brj-24683	41	10	such	such	ADJ
brj-24683	41	11	as	as	SCONJ
brj-24683	41	12	you	you	PRON
brj-24683	41	13	only	only	ADV
brj-24683	41	14	look	look	VERB
brj-24683	41	15	once	once	ADV
brj-24683	41	16	(	(	PUNCT
brj-24683	41	17	yolo	yolo	PROPN
brj-24683	41	18	)	)	PUNCT
brj-24683	41	19	(	(	PUNCT
brj-24683	41	20	redmon	redmon	PROPN
brj-24683	41	21	et	et	PROPN
brj-24683	41	22	al	al	PROPN
brj-24683	41	23	.	.	PROPN
brj-24683	41	24	2016	2016	NUM
brj-24683	41	25	)	)	PUNCT
brj-24683	41	26	and	and	CCONJ
brj-24683	41	27	its	its	PRON
brj-24683	41	28	series	series	NOUN
brj-24683	41	29	of	of	ADP
brj-24683	41	30	versions	version	NOUN
brj-24683	41	31	,	,	PUNCT
brj-24683	41	32	as	as	ADV
brj-24683	41	33	well	well	ADV
brj-24683	41	34	as	as	ADP
brj-24683	41	35	single	single	ADJ
brj-24683	41	36	shot	shot	NOUN
brj-24683	41	37	multibox	multibox	NOUN
brj-24683	41	38	detector	detector	NOUN
brj-24683	41	39	(	(	PUNCT
brj-24683	41	40	ssd	ssd	NOUN
brj-24683	41	41	)	)	PUNCT
brj-24683	41	42	(	(	PUNCT
brj-24683	41	43	liu	liu	PROPN
brj-24683	41	44	et	et	PROPN
brj-24683	41	45	al	al	PROPN
brj-24683	41	46	.	.	PROPN
brj-24683	41	47	2016	2016	NUM
brj-24683	41	48	)	)	PUNCT
brj-24683	41	49	,	,	PUNCT
brj-24683	41	50	convert	convert	VERB
brj-24683	41	51	the	the	DET
brj-24683	41	52	object	object	NOUN
brj-24683	41	53	detection	detection	NOUN
brj-24683	41	54	problem	problem	NOUN
brj-24683	41	55	into	into	ADP
brj-24683	41	56	an	an	DET
brj-24683	41	57	end	end	NOUN
brj-24683	41	58	-	-	PUNCT
brj-24683	41	59	to	to	ADP
brj-24683	41	60	-	-	PUNCT
brj-24683	41	61	end	end	NOUN
brj-24683	41	62	prediction	prediction	NOUN
brj-24683	41	63	task	task	NOUN
brj-24683	41	64	.	.	PUNCT
brj-24683	42	1	this	this	PRON
brj-24683	42	2	eliminates	eliminate	VERB
brj-24683	42	3	the	the	DET
brj-24683	42	4	cumbersome	cumbersome	ADJ
brj-24683	42	5	process	process	NOUN
brj-24683	42	6	of	of	ADP
brj-24683	42	7	generating	generate	VERB
brj-24683	42	8	candidate	candidate	NOUN
brj-24683	42	9	regions	region	NOUN
brj-24683	42	10	,	,	PUNCT
brj-24683	42	11	offering	offer	VERB
brj-24683	42	12	advantages	advantage	NOUN
brj-24683	42	13	in	in	ADP
brj-24683	42	14	real	real	ADJ
brj-24683	42	15	-	-	PUNCT
brj-24683	42	16	time	time	NOUN
brj-24683	42	17	performance	performance	NOUN
brj-24683	42	18	,	,	PUNCT
brj-24683	42	19	simple	simple	ADJ
brj-24683	42	20	architecture	architecture	NOUN
brj-24683	42	21	,	,	PUNCT
brj-24683	42	22	and	and	CCONJ
brj-24683	42	23	computational	computational	ADJ
brj-24683	42	24	efficiency	efficiency	NOUN
brj-24683	42	25	.	.	PUNCT
brj-24683	43	1	wang	wang	PROPN
brj-24683	43	2	et	et	PROPN
brj-24683	43	3	al	al	PROPN
brj-24683	43	4	.	.	PROPN
brj-24683	43	5	(	(	PUNCT
brj-24683	43	6	2023	2023	NUM
brj-24683	43	7	)	)	PUNCT
brj-24683	43	8	introduced	introduce	VERB
brj-24683	43	9	a	a	DET
brj-24683	43	10	wood	wood	NOUN
brj-24683	43	11	surface	surface	NOUN
brj-24683	43	12	defect	defect	NOUN
brj-24683	43	13	detection	detection	NOUN
brj-24683	43	14	method	method	NOUN
brj-24683	43	15	called	call	VERB
brj-24683	43	16	omni	omni	ADJ
brj-24683	43	17	-	-	PUNCT
brj-24683	43	18	dynamic	dynamic	ADJ
brj-24683	43	19	convolution	convolution	NOUN
brj-24683	43	20	coordinate	coordinate	NOUN
brj-24683	43	21	attention	attention	NOUN
brj-24683	43	22	-	-	PUNCT
brj-24683	43	23	based	base	VERB
brj-24683	43	24	yolo	yolo	NOUN
brj-24683	43	25	(	(	PUNCT
brj-24683	43	26	odca	odca	PROPN
brj-24683	43	27	-	-	PUNCT
brj-24683	43	28	yolo	yolo	NOUN
brj-24683	43	29	)	)	PUNCT
brj-24683	43	30	,	,	PUNCT
brj-24683	43	31	which	which	PRON
brj-24683	43	32	incorporates	incorporate	VERB
brj-24683	43	33	an	an	DET
brj-24683	43	34	omni	omni	ADJ
brj-24683	43	35	-	-	PUNCT
brj-24683	43	36	dimensional	dimensional	ADJ
brj-24683	43	37	dynamic	dynamic	ADJ
brj-24683	43	38	convolution	convolution	NOUN
brj-24683	43	39	-	-	PUNCT
brj-24683	43	40	based	base	VERB
brj-24683	43	41	coordinate	coordinate	NOUN
brj-24683	43	42	attention	attention	NOUN
brj-24683	43	43	peer	peer	NOUN
brj-24683	43	44	-	-	PUNCT
brj-24683	43	45	reviewed	review	VERB
brj-24683	43	46	article	article	NOUN
brj-24683	43	47	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	43	48	dou	dou	PROPN
brj-24683	43	49	&	&	CCONJ
brj-24683	43	50	you	you	PRON
brj-24683	43	51	(	(	PUNCT
brj-24683	43	52	2025	2025	NUM
brj-24683	43	53	)	)	PUNCT
brj-24683	43	54	.	.	PUNCT
brj-24683	44	1	“	"	PUNCT
brj-24683	44	2	wood	wood	NOUN
brj-24683	44	3	defect	defect	NOUN
brj-24683	44	4	identification	identification	NOUN
brj-24683	44	5	,	,	PUNCT
brj-24683	44	6	”	"	PUNCT
brj-24683	44	7	bioresources	bioresource	NOUN
brj-24683	44	8	20(3	20(3	NOUN
brj-24683	44	9	)	)	PUNCT
brj-24683	44	10	,	,	PUNCT
brj-24683	44	11	5709	5709	NUM
brj-24683	44	12	-	-	SYM
brj-24683	44	13	5730	5730	NUM
brj-24683	44	14	.	.	PUNCT
brj-24683	45	1	5711	5711	NUM
brj-24683	45	2	(	(	PUNCT
brj-24683	45	3	odca	odca	ADJ
brj-24683	45	4	)	)	PUNCT
brj-24683	45	5	mechanism	mechanism	NOUN
brj-24683	45	6	.	.	PUNCT
brj-24683	46	1	this	this	DET
brj-24683	46	2	method	method	NOUN
brj-24683	46	3	effectively	effectively	ADV
brj-24683	46	4	enhances	enhance	VERB
brj-24683	46	5	the	the	DET
brj-24683	46	6	detection	detection	NOUN
brj-24683	46	7	capability	capability	NOUN
brj-24683	46	8	for	for	ADP
brj-24683	46	9	small	small	ADJ
brj-24683	46	10	defect	defect	NOUN
brj-24683	46	11	targets	target	NOUN
brj-24683	46	12	and	and	CCONJ
brj-24683	46	13	was	be	AUX
brj-24683	46	14	experimentally	experimentally	ADV
brj-24683	46	15	validated	validate	VERB
brj-24683	46	16	using	use	VERB
brj-24683	46	17	an	an	DET
brj-24683	46	18	optimized	optimize	VERB
brj-24683	46	19	wood	wood	NOUN
brj-24683	46	20	surface	surface	NOUN
brj-24683	46	21	defect	defect	NOUN
brj-24683	46	22	dataset	dataset	NOUN
brj-24683	46	23	,	,	PUNCT
brj-24683	46	24	fulfilling	fulfil	VERB
brj-24683	46	25	the	the	DET
brj-24683	46	26	practical	practical	ADJ
brj-24683	46	27	requirements	requirement	NOUN
brj-24683	46	28	for	for	ADP
brj-24683	46	29	accurate	accurate	ADJ
brj-24683	46	30	wood	wood	NOUN
brj-24683	46	31	surface	surface	NOUN
brj-24683	46	32	defect	defect	NOUN
brj-24683	46	33	detection	detection	NOUN
brj-24683	46	34	.	.	PUNCT
brj-24683	47	1	meng	meng	PROPN
brj-24683	47	2	and	and	CCONJ
brj-24683	47	3	yuan	yuan	PROPN
brj-24683	47	4	(	(	PUNCT
brj-24683	47	5	2023	2023	NUM
brj-24683	47	6	)	)	PUNCT
brj-24683	47	7	proposed	propose	VERB
brj-24683	47	8	a	a	DET
brj-24683	47	9	yolov5	yolov5	NOUN
brj-24683	47	10	model	model	NOUN
brj-24683	47	11	based	base	VERB
brj-24683	47	12	on	on	ADP
brj-24683	47	13	a	a	DET
brj-24683	47	14	semi	semi	ADJ
brj-24683	47	15	-	-	ADJ
brj-24683	47	16	global	global	ADJ
brj-24683	47	17	network	network	NOUN
brj-24683	47	18	(	(	PUNCT
brj-24683	47	19	sgn	sgn	NOUN
brj-24683	47	20	)	)	PUNCT
brj-24683	47	21	for	for	ADP
brj-24683	47	22	wood	wood	NOUN
brj-24683	47	23	defect	defect	NOUN
brj-24683	47	24	detection	detection	NOUN
brj-24683	47	25	.	.	PUNCT
brj-24683	48	1	by	by	ADP
brj-24683	48	2	integrating	integrate	VERB
brj-24683	48	3	a	a	DET
brj-24683	48	4	lightweight	lightweight	ADJ
brj-24683	48	5	sgn	sgn	NOUN
brj-24683	48	6	into	into	ADP
brj-24683	48	7	the	the	DET
brj-24683	48	8	backbone	backbone	NOUN
brj-24683	48	9	network	network	NOUN
brj-24683	48	10	to	to	PART
brj-24683	48	11	model	model	VERB
brj-24683	48	12	global	global	ADJ
brj-24683	48	13	context	context	NOUN
brj-24683	48	14	,	,	PUNCT
brj-24683	48	15	the	the	DET
brj-24683	48	16	method	method	NOUN
brj-24683	48	17	improves	improve	VERB
brj-24683	48	18	detection	detection	NOUN
brj-24683	48	19	accuracy	accuracy	NOUN
brj-24683	48	20	while	while	SCONJ
brj-24683	48	21	reducing	reduce	VERB
brj-24683	48	22	model	model	NOUN
brj-24683	48	23	complexity	complexity	NOUN
brj-24683	48	24	.	.	PUNCT
brj-24683	49	1	effectiveness	effectiveness	NOUN
brj-24683	49	2	was	be	AUX
brj-24683	49	3	validated	validate	VERB
brj-24683	49	4	on	on	ADP
brj-24683	49	5	a	a	DET
brj-24683	49	6	public	public	ADJ
brj-24683	49	7	wood	wood	NOUN
brj-24683	49	8	defect	defect	NOUN
brj-24683	49	9	dataset	dataset	NOUN
brj-24683	49	10	,	,	PUNCT
brj-24683	49	11	significantly	significantly	ADV
brj-24683	49	12	enhancing	enhance	VERB
brj-24683	49	13	detection	detection	NOUN
brj-24683	49	14	performance	performance	NOUN
brj-24683	49	15	across	across	ADP
brj-24683	49	16	various	various	ADJ
brj-24683	49	17	types	type	NOUN
brj-24683	49	18	of	of	ADP
brj-24683	49	19	defects	defect	NOUN
brj-24683	49	20	.	.	PUNCT
brj-24683	50	1	ding	de	VERB
brj-24683	50	2	et	et	PROPN
brj-24683	50	3	al	al	PROPN
brj-24683	50	4	.	.	PROPN
brj-24683	51	1	(	(	PUNCT
brj-24683	51	2	2020	2020	NUM
brj-24683	51	3	)	)	PUNCT
brj-24683	51	4	utilized	utilize	VERB
brj-24683	51	5	machine	machine	NOUN
brj-24683	51	6	vision	vision	NOUN
brj-24683	51	7	and	and	CCONJ
brj-24683	51	8	deep	deep	ADJ
brj-24683	51	9	learning	learning	NOUN
brj-24683	51	10	techniques	technique	NOUN
brj-24683	51	11	to	to	PART
brj-24683	51	12	detect	detect	VERB
brj-24683	51	13	three	three	NUM
brj-24683	51	14	types	type	NOUN
brj-24683	51	15	of	of	ADP
brj-24683	51	16	wood	wood	NOUN
brj-24683	51	17	surface	surface	NOUN
brj-24683	51	18	defects	defect	NOUN
brj-24683	51	19	:	:	PUNCT
brj-24683	51	20	live	live	ADJ
brj-24683	51	21	knots	knot	NOUN
brj-24683	51	22	,	,	PUNCT
brj-24683	51	23	dead	dead	ADJ
brj-24683	51	24	knots	knot	NOUN
brj-24683	51	25	,	,	PUNCT
brj-24683	51	26	and	and	CCONJ
brj-24683	51	27	cracks	crack	NOUN
brj-24683	51	28	.	.	PUNCT
brj-24683	52	1	they	they	PRON
brj-24683	52	2	applied	apply	VERB
brj-24683	52	3	transfer	transfer	NOUN
brj-24683	52	4	learning	learn	VERB
brj-24683	52	5	to	to	ADP
brj-24683	52	6	the	the	DET
brj-24683	52	7	ssd	ssd	ADJ
brj-24683	52	8	object	object	NOUN
brj-24683	52	9	detection	detection	NOUN
brj-24683	52	10	algorithm	algorithm	NOUN
brj-24683	52	11	and	and	CCONJ
brj-24683	52	12	improved	improve	VERB
brj-24683	52	13	it	it	PRON
brj-24683	52	14	by	by	ADP
brj-24683	52	15	incorporating	incorporate	VERB
brj-24683	52	16	a	a	DET
brj-24683	52	17	densenet	densenet	NOUN
brj-24683	52	18	network	network	NOUN
brj-24683	52	19	,	,	PUNCT
brj-24683	52	20	addressing	address	VERB
brj-24683	52	21	the	the	DET
brj-24683	52	22	issues	issue	NOUN
brj-24683	52	23	of	of	ADP
brj-24683	52	24	high	high	ADJ
brj-24683	52	25	labor	labor	NOUN
brj-24683	52	26	costs	cost	NOUN
brj-24683	52	27	and	and	CCONJ
brj-24683	52	28	low	low	ADJ
brj-24683	52	29	efficiency	efficiency	NOUN
brj-24683	52	30	in	in	ADP
brj-24683	52	31	wood	wood	NOUN
brj-24683	52	32	defect	defect	NOUN
brj-24683	52	33	detection	detection	NOUN
brj-24683	52	34	.	.	PUNCT
brj-24683	53	1	furthermore	furthermore	ADV
brj-24683	53	2	,	,	PUNCT
brj-24683	53	3	yolo	yolo	PROPN
brj-24683	53	4	-	-	PUNCT
brj-24683	53	5	based	base	VERB
brj-24683	53	6	algorithms	algorithm	NOUN
brj-24683	53	7	have	have	AUX
brj-24683	53	8	demonstrated	demonstrate	VERB
brj-24683	53	9	considerable	considerable	ADJ
brj-24683	53	10	potential	potential	NOUN
brj-24683	53	11	in	in	ADP
brj-24683	53	12	various	various	ADJ
brj-24683	53	13	fields	field	NOUN
brj-24683	53	14	.	.	PUNCT
brj-24683	54	1	for	for	ADP
brj-24683	54	2	example	example	NOUN
brj-24683	54	3	,	,	PUNCT
brj-24683	54	4	karimi	karimi	PROPN
brj-24683	54	5	et	et	PROPN
brj-24683	54	6	al	al	PROPN
brj-24683	54	7	.	.	PROPN
brj-24683	55	1	(	(	PUNCT
brj-24683	55	2	2024	2024	NUM
brj-24683	55	3	)	)	PUNCT
brj-24683	55	4	developed	develop	VERB
brj-24683	55	5	an	an	DET
brj-24683	55	6	automated	automate	VERB
brj-24683	55	7	defect	defect	NOUN
brj-24683	55	8	detection	detection	NOUN
brj-24683	55	9	system	system	NOUN
brj-24683	55	10	for	for	ADP
brj-24683	55	11	portuguese	portuguese	ADJ
brj-24683	55	12	cultural	cultural	ADJ
brj-24683	55	13	heritage	heritage	NOUN
brj-24683	55	14	buildings	building	NOUN
brj-24683	55	15	,	,	PUNCT
brj-24683	55	16	specifically	specifically	ADV
brj-24683	55	17	targeting	target	VERB
brj-24683	55	18	tile	tile	NOUN
brj-24683	55	19	defects	defect	NOUN
brj-24683	55	20	using	use	VERB
brj-24683	55	21	yolo	yolo	PRON
brj-24683	55	22	.	.	PUNCT
brj-24683	56	1	additionally	additionally	ADV
brj-24683	56	2	,	,	PUNCT
brj-24683	56	3	mishra	mishra	PROPN
brj-24683	56	4	and	and	CCONJ
brj-24683	56	5	lourenço	lourenço	PROPN
brj-24683	56	6	(	(	PUNCT
brj-24683	56	7	2024	2024	NUM
brj-24683	56	8	)	)	PUNCT
brj-24683	56	9	offered	offer	VERB
brj-24683	56	10	a	a	DET
brj-24683	56	11	comprehensive	comprehensive	ADJ
brj-24683	56	12	review	review	NOUN
brj-24683	56	13	of	of	ADP
brj-24683	56	14	artificial	artificial	ADJ
brj-24683	56	15	intelligence	intelligence	NOUN
brj-24683	56	16	-	-	PUNCT
brj-24683	56	17	assisted	assist	VERB
brj-24683	56	18	visual	visual	ADJ
brj-24683	56	19	inspection	inspection	NOUN
brj-24683	56	20	techniques	technique	NOUN
brj-24683	56	21	,	,	PUNCT
brj-24683	56	22	emphasizing	emphasize	VERB
brj-24683	56	23	their	their	PRON
brj-24683	56	24	application	application	NOUN
brj-24683	56	25	in	in	ADP
brj-24683	56	26	the	the	DET
brj-24683	56	27	monitoring	monitoring	NOUN
brj-24683	56	28	and	and	CCONJ
brj-24683	56	29	preservation	preservation	NOUN
brj-24683	56	30	of	of	ADP
brj-24683	56	31	cultural	cultural	ADJ
brj-24683	56	32	heritage	heritage	NOUN
brj-24683	56	33	(	(	PUNCT
brj-24683	56	34	ch	ch	NOUN
brj-24683	56	35	)	)	PUNCT
brj-24683	56	36	sites	site	NOUN
brj-24683	56	37	.	.	PUNCT
brj-24683	57	1	these	these	DET
brj-24683	57	2	studies	study	NOUN
brj-24683	57	3	highlight	highlight	VERB
brj-24683	57	4	the	the	DET
brj-24683	57	5	effectiveness	effectiveness	NOUN
brj-24683	57	6	of	of	ADP
brj-24683	57	7	yolo	yolo	NOUN
brj-24683	57	8	-	-	PUNCT
brj-24683	57	9	based	base	VERB
brj-24683	57	10	frameworks	framework	NOUN
brj-24683	57	11	in	in	ADP
brj-24683	57	12	detecting	detect	VERB
brj-24683	57	13	small	small	ADJ
brj-24683	57	14	-	-	PUNCT
brj-24683	57	15	scale	scale	NOUN
brj-24683	57	16	defects	defect	NOUN
brj-24683	57	17	,	,	PUNCT
brj-24683	57	18	thereby	thereby	ADV
brj-24683	57	19	reinforcing	reinforce	VERB
brj-24683	57	20	the	the	DET
brj-24683	57	21	relevance	relevance	NOUN
brj-24683	57	22	of	of	ADP
brj-24683	57	23	our	our	PRON
brj-24683	57	24	approach	approach	NOUN
brj-24683	57	25	in	in	ADP
brj-24683	57	26	optimizing	optimize	VERB
brj-24683	57	27	yolo	yolo	ADV
brj-24683	57	28	for	for	ADP
brj-24683	57	29	wood	wood	NOUN
brj-24683	57	30	defect	defect	NOUN
brj-24683	57	31	detection	detection	NOUN
brj-24683	57	32	tasks	task	NOUN
brj-24683	57	33	.	.	PUNCT
brj-24683	58	1	however	however	ADV
brj-24683	58	2	,	,	PUNCT
brj-24683	58	3	wood	wood	NOUN
brj-24683	58	4	,	,	PUNCT
brj-24683	58	5	being	be	AUX
brj-24683	58	6	a	a	DET
brj-24683	58	7	natural	natural	ADJ
brj-24683	58	8	material	material	NOUN
brj-24683	58	9	,	,	PUNCT
brj-24683	58	10	exhibits	exhibit	VERB
brj-24683	58	11	highly	highly	ADV
brj-24683	58	12	diverse	diverse	ADJ
brj-24683	58	13	grain	grain	NOUN
brj-24683	58	14	patterns	pattern	NOUN
brj-24683	58	15	and	and	CCONJ
brj-24683	58	16	structures	structure	NOUN
brj-24683	58	17	,	,	PUNCT
brj-24683	58	18	resulting	result	VERB
brj-24683	58	19	in	in	ADP
brj-24683	58	20	a	a	DET
brj-24683	58	21	complex	complex	ADJ
brj-24683	58	22	and	and	CCONJ
brj-24683	58	23	variable	variable	ADJ
brj-24683	58	24	background	background	NOUN
brj-24683	58	25	that	that	PRON
brj-24683	58	26	often	often	ADV
brj-24683	58	27	leads	lead	VERB
brj-24683	58	28	to	to	ADP
brj-24683	58	29	confusion	confusion	NOUN
brj-24683	58	30	between	between	ADP
brj-24683	58	31	defect	defect	ADJ
brj-24683	58	32	regions	region	NOUN
brj-24683	58	33	(	(	PUNCT
brj-24683	58	34	especially	especially	ADV
brj-24683	58	35	small	small	ADJ
brj-24683	58	36	defects	defect	NOUN
brj-24683	58	37	)	)	PUNCT
brj-24683	58	38	and	and	CCONJ
brj-24683	58	39	the	the	DET
brj-24683	58	40	inherent	inherent	ADJ
brj-24683	58	41	grain	grain	NOUN
brj-24683	58	42	patterns	pattern	NOUN
brj-24683	58	43	of	of	ADP
brj-24683	58	44	the	the	DET
brj-24683	58	45	wood	wood	NOUN
brj-24683	58	46	.	.	PUNCT
brj-24683	59	1	the	the	DET
brj-24683	59	2	variability	variability	NOUN
brj-24683	59	3	in	in	ADP
brj-24683	59	4	lighting	lighting	NOUN
brj-24683	59	5	conditions	condition	NOUN
brj-24683	59	6	and	and	CCONJ
brj-24683	59	7	the	the	DET
brj-24683	59	8	irregularities	irregularity	NOUN
brj-24683	59	9	of	of	ADP
brj-24683	59	10	the	the	DET
brj-24683	59	11	wood	wood	NOUN
brj-24683	59	12	surface	surface	NOUN
brj-24683	59	13	(	(	PUNCT
brj-24683	59	14	such	such	ADJ
brj-24683	59	15	as	as	ADP
brj-24683	59	16	knots	knot	NOUN
brj-24683	59	17	,	,	PUNCT
brj-24683	59	18	cracks	crack	NOUN
brj-24683	59	19	,	,	PUNCT
brj-24683	59	20	etc	etc	X
brj-24683	59	21	.	.	X
brj-24683	59	22	)	)	PUNCT
brj-24683	59	23	further	far	ADV
brj-24683	59	24	complicate	complicate	VERB
brj-24683	59	25	the	the	DET
brj-24683	59	26	background	background	NOUN
brj-24683	59	27	,	,	PUNCT
brj-24683	59	28	making	make	VERB
brj-24683	59	29	it	it	PRON
brj-24683	59	30	challenging	challenging	ADJ
brj-24683	59	31	to	to	PART
brj-24683	59	32	distinguish	distinguish	VERB
brj-24683	59	33	defect	defect	ADJ
brj-24683	59	34	regions	region	NOUN
brj-24683	59	35	from	from	ADP
brj-24683	59	36	the	the	DET
brj-24683	59	37	surrounding	surround	VERB
brj-24683	59	38	wood	wood	NOUN
brj-24683	59	39	.	.	PUNCT
brj-24683	60	1	moreover	moreover	ADV
brj-24683	60	2	,	,	PUNCT
brj-24683	60	3	wood	wood	NOUN
brj-24683	60	4	surface	surface	NOUN
brj-24683	60	5	defects	defect	NOUN
brj-24683	60	6	are	be	AUX
brj-24683	60	7	typically	typically	ADV
brj-24683	60	8	small	small	ADJ
brj-24683	60	9	in	in	ADP
brj-24683	60	10	size	size	NOUN
brj-24683	60	11	,	,	PUNCT
brj-24683	60	12	with	with	ADP
brj-24683	60	13	many	many	ADJ
brj-24683	60	14	defects	defect	NOUN
brj-24683	60	15	(	(	PUNCT
brj-24683	60	16	e.g.	e.g.	ADV
brj-24683	60	17	,	,	PUNCT
brj-24683	60	18	fine	fine	ADJ
brj-24683	60	19	cracks	crack	NOUN
brj-24683	60	20	,	,	PUNCT
brj-24683	60	21	stains	stain	NOUN
brj-24683	60	22	,	,	PUNCT
brj-24683	60	23	knots	knot	NOUN
brj-24683	60	24	)	)	PUNCT
brj-24683	60	25	measuring	measure	VERB
brj-24683	60	26	just	just	ADV
brj-24683	60	27	a	a	DET
brj-24683	60	28	few	few	ADJ
brj-24683	60	29	millimeters	millimeter	NOUN
brj-24683	60	30	or	or	CCONJ
brj-24683	60	31	even	even	ADV
brj-24683	60	32	smaller	small	ADJ
brj-24683	60	33	.	.	PUNCT
brj-24683	61	1	these	these	DET
brj-24683	61	2	defects	defect	NOUN
brj-24683	61	3	often	often	ADV
brj-24683	61	4	occupy	occupy	VERB
brj-24683	61	5	only	only	ADV
brj-24683	61	6	a	a	DET
brj-24683	61	7	small	small	ADJ
brj-24683	61	8	portion	portion	NOUN
brj-24683	61	9	of	of	ADP
brj-24683	61	10	the	the	DET
brj-24683	61	11	image	image	NOUN
brj-24683	61	12	and	and	CCONJ
brj-24683	61	13	fall	fall	VERB
brj-24683	61	14	within	within	ADP
brj-24683	61	15	the	the	DET
brj-24683	61	16	category	category	NOUN
brj-24683	61	17	of	of	ADP
brj-24683	61	18	small	small	ADJ
brj-24683	61	19	-	-	PUNCT
brj-24683	61	20	object	object	NOUN
brj-24683	61	21	detection	detection	NOUN
brj-24683	61	22	.	.	PUNCT
brj-24683	62	1	small	small	ADJ
brj-24683	62	2	defects	defect	NOUN
brj-24683	62	3	on	on	ADP
brj-24683	62	4	wood	wood	NOUN
brj-24683	62	5	surfaces	surface	NOUN
brj-24683	62	6	tend	tend	VERB
brj-24683	62	7	to	to	PART
brj-24683	62	8	have	have	VERB
brj-24683	62	9	low	low	ADJ
brj-24683	62	10	contrast	contrast	NOUN
brj-24683	62	11	,	,	PUNCT
brj-24683	62	12	particularly	particularly	ADV
brj-24683	62	13	in	in	ADP
brj-24683	62	14	regions	region	NOUN
brj-24683	62	15	with	with	ADP
brj-24683	62	16	dense	dense	ADJ
brj-24683	62	17	natural	natural	ADJ
brj-24683	62	18	grain	grain	NOUN
brj-24683	62	19	patterns	pattern	NOUN
brj-24683	62	20	,	,	PUNCT
brj-24683	62	21	where	where	SCONJ
brj-24683	62	22	they	they	PRON
brj-24683	62	23	may	may	AUX
brj-24683	62	24	lack	lack	VERB
brj-24683	62	25	distinct	distinct	ADJ
brj-24683	62	26	edges	edge	NOUN
brj-24683	62	27	or	or	CCONJ
brj-24683	62	28	shapes	shape	NOUN
brj-24683	62	29	.	.	PUNCT
brj-24683	63	1	this	this	PRON
brj-24683	63	2	makes	make	VERB
brj-24683	63	3	the	the	DET
brj-24683	63	4	task	task	NOUN
brj-24683	63	5	of	of	ADP
brj-24683	63	6	localization	localization	NOUN
brj-24683	63	7	and	and	CCONJ
brj-24683	63	8	classification	classification	NOUN
brj-24683	63	9	more	more	ADV
brj-24683	63	10	difficult	difficult	ADJ
brj-24683	63	11	,	,	PUNCT
brj-24683	63	12	leading	lead	VERB
brj-24683	63	13	to	to	ADP
brj-24683	63	14	a	a	DET
brj-24683	63	15	higher	high	ADJ
brj-24683	63	16	likelihood	likelihood	NOUN
brj-24683	63	17	of	of	ADP
brj-24683	63	18	false	false	ADJ
brj-24683	63	19	negatives	negative	NOUN
brj-24683	63	20	(	(	PUNCT
brj-24683	63	21	missed	miss	VERB
brj-24683	63	22	detections	detection	NOUN
brj-24683	63	23	)	)	PUNCT
brj-24683	63	24	and	and	CCONJ
brj-24683	63	25	false	false	ADJ
brj-24683	63	26	positives	positive	NOUN
brj-24683	63	27	(	(	PUNCT
brj-24683	63	28	incorrect	incorrect	ADJ
brj-24683	63	29	detections	detection	NOUN
brj-24683	63	30	)	)	PUNCT
brj-24683	63	31	.	.	PUNCT
brj-24683	64	1	these	these	DET
brj-24683	64	2	factors	factor	NOUN
brj-24683	64	3	collectively	collectively	ADV
brj-24683	64	4	render	render	VERB
brj-24683	64	5	the	the	DET
brj-24683	64	6	detection	detection	NOUN
brj-24683	64	7	of	of	ADP
brj-24683	64	8	wood	wood	NOUN
brj-24683	64	9	surface	surface	NOUN
brj-24683	64	10	defects	defect	NOUN
brj-24683	64	11	exceptionally	exceptionally	ADV
brj-24683	64	12	challenging	challenging	ADJ
brj-24683	64	13	.	.	PUNCT
brj-24683	65	1	to	to	PART
brj-24683	65	2	address	address	VERB
brj-24683	65	3	the	the	DET
brj-24683	65	4	aforementioned	aforementioned	ADJ
brj-24683	65	5	issues	issue	NOUN
brj-24683	65	6	,	,	PUNCT
brj-24683	65	7	this	this	DET
brj-24683	65	8	study	study	NOUN
brj-24683	65	9	proposes	propose	VERB
brj-24683	65	10	an	an	DET
brj-24683	65	11	improved	improved	ADJ
brj-24683	65	12	yolov8	yolov8	NOUN
brj-24683	65	13	-	-	PUNCT
brj-24683	65	14	based	base	VERB
brj-24683	65	15	method	method	NOUN
brj-24683	65	16	for	for	ADP
brj-24683	65	17	wood	wood	NOUN
brj-24683	65	18	surface	surface	NOUN
brj-24683	65	19	defect	defect	NOUN
brj-24683	65	20	detection	detection	NOUN
brj-24683	65	21	.	.	PUNCT
brj-24683	66	1	this	this	DET
brj-24683	66	2	approach	approach	NOUN
brj-24683	66	3	provides	provide	VERB
brj-24683	66	4	an	an	DET
brj-24683	66	5	accurate	accurate	ADJ
brj-24683	66	6	and	and	CCONJ
brj-24683	66	7	efficient	efficient	ADJ
brj-24683	66	8	solution	solution	NOUN
brj-24683	66	9	for	for	ADP
brj-24683	66	10	the	the	DET
brj-24683	66	11	application	application	NOUN
brj-24683	66	12	of	of	ADP
brj-24683	66	13	wood	wood	NOUN
brj-24683	66	14	surface	surface	NOUN
brj-24683	66	15	defect	defect	NOUN
brj-24683	66	16	detection	detection	NOUN
brj-24683	66	17	technology	technology	NOUN
brj-24683	66	18	in	in	ADP
brj-24683	66	19	the	the	DET
brj-24683	66	20	manufacturing	manufacturing	NOUN
brj-24683	66	21	industry	industry	NOUN
brj-24683	66	22	.	.	PUNCT
brj-24683	67	1	the	the	DET
brj-24683	67	2	main	main	ADJ
brj-24683	67	3	contributions	contribution	NOUN
brj-24683	67	4	of	of	ADP
brj-24683	67	5	this	this	DET
brj-24683	67	6	paper	paper	NOUN
brj-24683	67	7	are	be	AUX
brj-24683	67	8	as	as	SCONJ
brj-24683	67	9	follows	follow	VERB
brj-24683	67	10	:	:	PUNCT
brj-24683	67	11	1	1	X
brj-24683	67	12	.	.	X
brj-24683	67	13	a	a	DET
brj-24683	67	14	novel	novel	ADJ
brj-24683	67	15	lightweight	lightweight	ADJ
brj-24683	67	16	multi	multi	ADJ
brj-24683	67	17	-	-	ADJ
brj-24683	67	18	head	head	ADJ
brj-24683	67	19	mixed	mixed	ADJ
brj-24683	67	20	self	self	NOUN
brj-24683	67	21	-	-	PUNCT
brj-24683	67	22	attention	attention	NOUN
brj-24683	67	23	(	(	PUNCT
brj-24683	67	24	mmsa	mmsa	NOUN
brj-24683	67	25	)	)	PUNCT
brj-24683	67	26	module	module	NOUN
brj-24683	67	27	is	be	AUX
brj-24683	67	28	designed	design	VERB
brj-24683	67	29	and	and	CCONJ
brj-24683	67	30	seamlessly	seamlessly	ADV
brj-24683	67	31	integrated	integrate	VERB
brj-24683	67	32	into	into	ADP
brj-24683	67	33	the	the	DET
brj-24683	67	34	c2f	c2f	NOUN
brj-24683	67	35	module	module	NOUN
brj-24683	67	36	,	,	PUNCT
brj-24683	67	37	resulting	result	VERB
brj-24683	67	38	in	in	ADP
brj-24683	67	39	the	the	DET
brj-24683	67	40	c2f	c2f	NOUN
brj-24683	67	41	-	-	PUNCT
brj-24683	67	42	mmsa	mmsa	NOUN
brj-24683	67	43	module	module	NOUN
brj-24683	67	44	.	.	PUNCT
brj-24683	68	1	this	this	DET
brj-24683	68	2	integration	integration	NOUN
brj-24683	68	3	significantly	significantly	ADV
brj-24683	68	4	enhances	enhance	VERB
brj-24683	68	5	the	the	DET
brj-24683	68	6	model	model	NOUN
brj-24683	68	7	’s	’s	PART
brj-24683	68	8	capacity	capacity	NOUN
brj-24683	68	9	to	to	PART
brj-24683	68	10	capture	capture	VERB
brj-24683	68	11	contextual	contextual	ADJ
brj-24683	68	12	and	and	CCONJ
brj-24683	68	13	background	background	NOUN
brj-24683	68	14	information	information	NOUN
brj-24683	68	15	for	for	ADP
brj-24683	68	16	small	small	ADJ
brj-24683	68	17	targets	target	NOUN
brj-24683	68	18	,	,	PUNCT
brj-24683	68	19	effectively	effectively	ADV
brj-24683	68	20	overcoming	overcome	VERB
brj-24683	68	21	the	the	DET
brj-24683	68	22	limitations	limitation	NOUN
brj-24683	68	23	of	of	ADP
brj-24683	68	24	the	the	DET
brj-24683	68	25	original	original	ADJ
brj-24683	68	26	c2f	c2f	NOUN
brj-24683	68	27	module	module	NOUN
brj-24683	68	28	in	in	ADP
brj-24683	68	29	detecting	detect	VERB
brj-24683	68	30	small	small	ADJ
brj-24683	68	31	defects	defect	NOUN
brj-24683	68	32	within	within	ADP
brj-24683	68	33	complex	complex	ADJ
brj-24683	68	34	backgrounds	background	NOUN
brj-24683	68	35	.	.	PUNCT
brj-24683	69	1	2	2	X
brj-24683	69	2	.	.	X
brj-24683	69	3	a	a	DET
brj-24683	69	4	learnable	learnable	ADJ
brj-24683	69	5	dynamic	dynamic	ADJ
brj-24683	69	6	upsampling	upsampling	NOUN
brj-24683	69	7	module	module	NOUN
brj-24683	69	8	is	be	AUX
brj-24683	69	9	introduced	introduce	VERB
brj-24683	69	10	to	to	PART
brj-24683	69	11	replace	replace	VERB
brj-24683	69	12	the	the	DET
brj-24683	69	13	upsampling	upsampling	NOUN
brj-24683	69	14	module	module	NOUN
brj-24683	69	15	based	base	VERB
brj-24683	69	16	on	on	ADP
brj-24683	69	17	nearest	near	ADJ
brj-24683	69	18	-	-	PUNCT
brj-24683	69	19	neighbor	neighbor	NOUN
brj-24683	69	20	interpolation	interpolation	NOUN
brj-24683	69	21	,	,	PUNCT
brj-24683	69	22	alleviating	alleviate	VERB
brj-24683	69	23	the	the	DET
brj-24683	69	24	issue	issue	NOUN
brj-24683	69	25	of	of	ADP
brj-24683	69	26	feature	feature	NOUN
brj-24683	69	27	information	information	NOUN
brj-24683	69	28	loss	loss	NOUN
brj-24683	69	29	for	for	ADP
brj-24683	69	30	small	small	ADJ
brj-24683	69	31	-	-	PUNCT
brj-24683	69	32	scale	scale	NOUN
brj-24683	69	33	wood	wood	NOUN
brj-24683	69	34	surface	surface	NOUN
brj-24683	69	35	defects	defect	NOUN
brj-24683	69	36	during	during	ADP
brj-24683	69	37	upsampling	upsample	VERB
brj-24683	69	38	,	,	PUNCT
brj-24683	69	39	and	and	CCONJ
brj-24683	69	40	improving	improve	VERB
brj-24683	69	41	the	the	DET
brj-24683	69	42	model	model	NOUN
brj-24683	69	43	's	's	PART
brj-24683	69	44	feature	feature	NOUN
brj-24683	69	45	representation	representation	NOUN
brj-24683	69	46	capability	capability	NOUN
brj-24683	69	47	and	and	CCONJ
brj-24683	69	48	precision	precision	NOUN
brj-24683	69	49	.	.	PUNCT
brj-24683	70	1	peer	peer	NOUN
brj-24683	70	2	-	-	PUNCT
brj-24683	70	3	reviewed	review	VERB
brj-24683	70	4	article	article	NOUN
brj-24683	70	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	70	6	dou	dou	PROPN
brj-24683	70	7	&	&	CCONJ
brj-24683	70	8	you	you	PRON
brj-24683	70	9	(	(	PUNCT
brj-24683	70	10	2025	2025	NUM
brj-24683	70	11	)	)	PUNCT
brj-24683	70	12	.	.	PUNCT
brj-24683	71	1	“	"	PUNCT
brj-24683	71	2	wood	wood	NOUN
brj-24683	71	3	defect	defect	NOUN
brj-24683	71	4	identification	identification	NOUN
brj-24683	71	5	,	,	PUNCT
brj-24683	71	6	”	"	PUNCT
brj-24683	71	7	bioresources	bioresource	NOUN
brj-24683	71	8	20(3	20(3	NOUN
brj-24683	71	9	)	)	PUNCT
brj-24683	71	10	,	,	PUNCT
brj-24683	71	11	5709	5709	NUM
brj-24683	71	12	-	-	SYM
brj-24683	71	13	5730	5730	NUM
brj-24683	71	14	.	.	PUNCT
brj-24683	72	1	5712	5712	NUM
brj-24683	72	2	3	3	X
brj-24683	72	3	.	.	PUNCT
brj-24683	73	1	a	a	DET
brj-24683	73	2	re	re	ADJ
brj-24683	73	3	-	-	ADJ
brj-24683	73	4	parameterizable	parameterizable	ADJ
brj-24683	73	5	block	block	NOUN
brj-24683	73	6	is	be	AUX
brj-24683	73	7	designed	design	VERB
brj-24683	73	8	to	to	PART
brj-24683	73	9	accurately	accurately	ADV
brj-24683	73	10	capture	capture	VERB
brj-24683	73	11	small	small	ADJ
brj-24683	73	12	-	-	PUNCT
brj-24683	73	13	scale	scale	NOUN
brj-24683	73	14	and	and	CCONJ
brj-24683	73	15	multiscale	multiscale	ADJ
brj-24683	73	16	features	feature	NOUN
brj-24683	73	17	in	in	ADP
brj-24683	73	18	complex	complex	ADJ
brj-24683	73	19	backgrounds	background	NOUN
brj-24683	73	20	,	,	PUNCT
brj-24683	73	21	further	far	ADV
brj-24683	73	22	exploring	explore	VERB
brj-24683	73	23	the	the	DET
brj-24683	73	24	fine	fine	ADV
brj-24683	73	25	-	-	PUNCT
brj-24683	73	26	grained	grain	VERB
brj-24683	73	27	and	and	CCONJ
brj-24683	73	28	multiscale	multiscale	ADJ
brj-24683	73	29	characteristics	characteristic	NOUN
brj-24683	73	30	of	of	ADP
brj-24683	73	31	wood	wood	NOUN
brj-24683	73	32	surface	surface	NOUN
brj-24683	73	33	defects	defect	NOUN
brj-24683	73	34	.	.	PUNCT
brj-24683	74	1	experimental	experimental	ADJ
brj-24683	74	2	wood	wood	NOUN
brj-24683	74	3	surface	surface	NOUN
brj-24683	74	4	defects	defect	NOUN
brj-24683	74	5	dataset	dataset	VERB
brj-24683	74	6	the	the	DET
brj-24683	74	7	dataset	dataset	NOUN
brj-24683	74	8	used	use	VERB
brj-24683	74	9	in	in	ADP
brj-24683	74	10	this	this	DET
brj-24683	74	11	study	study	NOUN
brj-24683	74	12	consists	consist	VERB
brj-24683	74	13	of	of	ADP
brj-24683	74	14	wood	wood	NOUN
brj-24683	74	15	surface	surface	NOUN
brj-24683	74	16	defect	defect	NOUN
brj-24683	74	17	images	image	NOUN
brj-24683	74	18	collected	collect	VERB
brj-24683	74	19	from	from	ADP
brj-24683	74	20	real	real	ADJ
brj-24683	74	21	-	-	PUNCT
brj-24683	74	22	world	world	NOUN
brj-24683	74	23	industrial	industrial	ADJ
brj-24683	74	24	environments	environment	NOUN
brj-24683	74	25	.	.	PUNCT
brj-24683	75	1	a	a	DET
brj-24683	75	2	custom	custom	ADV
brj-24683	75	3	-	-	PUNCT
brj-24683	75	4	designed	design	VERB
brj-24683	75	5	imaging	imaging	NOUN
brj-24683	75	6	system	system	NOUN
brj-24683	75	7	was	be	AUX
brj-24683	75	8	employed	employ	VERB
brj-24683	75	9	for	for	ADP
brj-24683	75	10	data	data	NOUN
brj-24683	75	11	acquisition	acquisition	NOUN
brj-24683	75	12	(	(	PUNCT
brj-24683	75	13	the	the	DET
brj-24683	75	14	field	field	NOUN
brj-24683	75	15	of	of	ADP
brj-24683	75	16	view	view	NOUN
brj-24683	75	17	is	be	AUX
brj-24683	75	18	15	15	NUM
brj-24683	75	19	cm	cm	NUM
brj-24683	75	20	×	×	NOUN
brj-24683	75	21	500	500	NUM
brj-24683	75	22	cm	cm	NOUN
brj-24683	75	23	)	)	PUNCT
brj-24683	75	24	,	,	PUNCT
brj-24683	75	25	capturing	capture	VERB
brj-24683	75	26	10	10	NUM
brj-24683	75	27	categories	category	NOUN
brj-24683	75	28	of	of	ADP
brj-24683	75	29	wood	wood	NOUN
brj-24683	75	30	surface	surface	NOUN
brj-24683	75	31	defects	defect	NOUN
brj-24683	75	32	that	that	PRON
brj-24683	75	33	comprehensively	comprehensively	ADV
brj-24683	75	34	cover	cover	VERB
brj-24683	75	35	common	common	ADJ
brj-24683	75	36	defect	defect	NOUN
brj-24683	75	37	types	type	NOUN
brj-24683	75	38	encountered	encounter	VERB
brj-24683	75	39	in	in	ADP
brj-24683	75	40	industrial	industrial	ADJ
brj-24683	75	41	settings	setting	NOUN
brj-24683	75	42	(	(	PUNCT
brj-24683	75	43	kodytek	kodytek	X
brj-24683	75	44	et	et	PROPN
brj-24683	75	45	al	al	PROPN
brj-24683	75	46	.	.	PROPN
brj-24683	75	47	2021	2021	NUM
brj-24683	75	48	)	)	PUNCT
brj-24683	75	49	.	.	PUNCT
brj-24683	76	1	to	to	PART
brj-24683	76	2	mitigate	mitigate	VERB
brj-24683	76	3	potential	potential	ADJ
brj-24683	76	4	biases	bias	NOUN
brj-24683	76	5	arising	arise	VERB
brj-24683	76	6	from	from	ADP
brj-24683	76	7	class	class	NOUN
brj-24683	76	8	imbalance	imbalance	NOUN
brj-24683	76	9	and	and	CCONJ
brj-24683	76	10	limited	limited	ADJ
brj-24683	76	11	sample	sample	NOUN
brj-24683	76	12	sizes	size	NOUN
brj-24683	76	13	,	,	PUNCT
brj-24683	76	14	the	the	DET
brj-24683	76	15	dataset	dataset	NOUN
brj-24683	76	16	was	be	AUX
brj-24683	76	17	refined	refine	VERB
brj-24683	76	18	to	to	PART
brj-24683	76	19	include	include	VERB
brj-24683	76	20	seven	seven	NUM
brj-24683	76	21	predominant	predominant	ADJ
brj-24683	76	22	defect	defect	NOUN
brj-24683	76	23	categories	category	NOUN
brj-24683	76	24	:	:	PUNCT
brj-24683	76	25	live_knot	live_knot	ADJ
brj-24683	76	26	,	,	PUNCT
brj-24683	76	27	dead_knot	dead_knot	ADV
brj-24683	76	28	,	,	PUNCT
brj-24683	76	29	knot_with_crack	knot_with_crack	NOUN
brj-24683	76	30	,	,	PUNCT
brj-24683	76	31	knot_missing	knot_missing	NOUN
brj-24683	76	32	,	,	PUNCT
brj-24683	76	33	crack	crack	NOUN
brj-24683	76	34	,	,	PUNCT
brj-24683	76	35	marrow	marrow	NOUN
brj-24683	76	36	,	,	PUNCT
brj-24683	76	37	and	and	CCONJ
brj-24683	76	38	resin	resin	NOUN
brj-24683	76	39	,	,	PUNCT
brj-24683	76	40	comprising	comprise	VERB
brj-24683	76	41	a	a	DET
brj-24683	76	42	total	total	NOUN
brj-24683	76	43	of	of	ADP
brj-24683	76	44	4,500	4,500	NUM
brj-24683	76	45	annotated	annotate	VERB
brj-24683	76	46	images	image	NOUN
brj-24683	76	47	with	with	ADP
brj-24683	76	48	a	a	DET
brj-24683	76	49	resolution	resolution	NOUN
brj-24683	76	50	of	of	ADP
brj-24683	76	51	2800×1024	2800×1024	NUM
brj-24683	76	52	.	.	PUNCT
brj-24683	77	1	the	the	DET
brj-24683	77	2	dataset	dataset	NOUN
brj-24683	77	3	was	be	AUX
brj-24683	77	4	split	split	VERB
brj-24683	77	5	into	into	ADP
brj-24683	77	6	training	training	NOUN
brj-24683	77	7	and	and	CCONJ
brj-24683	77	8	test	test	NOUN
brj-24683	77	9	sets	set	NOUN
brj-24683	77	10	in	in	ADP
brj-24683	77	11	a	a	DET
brj-24683	77	12	9:1	9:1	NUM
brj-24683	77	13	ratio	ratio	NOUN
brj-24683	77	14	,	,	PUNCT
brj-24683	77	15	with	with	ADP
brj-24683	77	16	10	10	NUM
brj-24683	77	17	%	%	NOUN
brj-24683	77	18	of	of	ADP
brj-24683	77	19	the	the	DET
brj-24683	77	20	training	training	NOUN
brj-24683	77	21	set	set	NOUN
brj-24683	77	22	used	use	VERB
brj-24683	77	23	for	for	ADP
brj-24683	77	24	validation	validation	NOUN
brj-24683	77	25	.	.	PUNCT
brj-24683	78	1	the	the	DET
brj-24683	78	2	distribution	distribution	NOUN
brj-24683	78	3	of	of	ADP
brj-24683	78	4	each	each	DET
brj-24683	78	5	defect	defect	NOUN
brj-24683	78	6	type	type	NOUN
brj-24683	78	7	,	,	PUNCT
brj-24683	78	8	including	include	VERB
brj-24683	78	9	number	number	NOUN
brj-24683	78	10	of	of	ADP
brj-24683	78	11	images	image	NOUN
brj-24683	78	12	,	,	PUNCT
brj-24683	78	13	occurrence	occurrence	NOUN
brj-24683	78	14	frequencies	frequency	NOUN
brj-24683	78	15	,	,	PUNCT
brj-24683	78	16	and	and	CCONJ
brj-24683	78	17	proportional	proportional	ADJ
brj-24683	78	18	representation	representation	NOUN
brj-24683	78	19	within	within	ADP
brj-24683	78	20	the	the	DET
brj-24683	78	21	dataset	dataset	NOUN
brj-24683	78	22	,	,	PUNCT
brj-24683	78	23	is	be	AUX
brj-24683	78	24	systematically	systematically	ADV
brj-24683	78	25	summarized	summarize	VERB
brj-24683	78	26	in	in	ADP
brj-24683	78	27	table	table	NOUN
brj-24683	78	28	1	1	NUM
brj-24683	78	29	.	.	PUNCT
brj-24683	78	30	table	table	NOUN
brj-24683	78	31	1	1	NUM
brj-24683	78	32	.	.	PUNCT
brj-24683	79	1	distribution	distribution	NOUN
brj-24683	79	2	of	of	ADP
brj-24683	79	3	each	each	DET
brj-24683	79	4	defect	defect	ADJ
brj-24683	79	5	type	type	NOUN
brj-24683	79	6	in	in	ADP
brj-24683	79	7	dataset	dataset	NOUN
brj-24683	79	8	defect	defect	NOUN
brj-24683	79	9	type	type	NOUN
brj-24683	79	10	number	number	NOUN
brj-24683	79	11	of	of	ADP
brj-24683	79	12	images	image	NOUN
brj-24683	79	13	with	with	ADP
brj-24683	79	14	defect	defect	ADJ
brj-24683	79	15	number	number	NOUN
brj-24683	79	16	of	of	ADP
brj-24683	79	17	defect	defect	ADJ
brj-24683	79	18	occurrences	occurrence	NOUN
brj-24683	79	19	images	image	NOUN
brj-24683	79	20	in	in	ADP
brj-24683	79	21	dataset	dataset	NOUN
brj-24683	79	22	(	(	PUNCT
brj-24683	79	23	%	%	INTJ
brj-24683	79	24	)	)	PUNCT
brj-24683	79	25	live_knot	live_knot	ADV
brj-24683	79	26	2956	2956	NUM
brj-24683	79	27	5112	5112	NUM
brj-24683	79	28	65.69	65.69	NUM
brj-24683	79	29	dead_knot	dead_knot	NOUN
brj-24683	79	30	2075	2075	NUM
brj-24683	79	31	3574	3574	NUM
brj-24683	79	32	46.11	46.11	NUM
brj-24683	79	33	knot_with_crack	knot_with_crack	NOUN
brj-24683	79	34	327	327	NUM
brj-24683	79	35	574	574	NUM
brj-24683	79	36	7.27	7.27	NUM
brj-24683	79	37	knot_missing	knot_misse	VERB
brj-24683	79	38	152	152	NUM
brj-24683	79	39	208	208	NUM
brj-24683	79	40	3.38	3.38	NUM
brj-24683	79	41	crack	crack	NOUN
brj-24683	79	42	479	479	NUM
brj-24683	79	43	671	671	NUM
brj-24683	79	44	10.64	10.64	NUM
brj-24683	79	45	marrow	marrow	NOUN
brj-24683	79	46	281	281	NUM
brj-24683	79	47	316	316	NUM
brj-24683	79	48	6.24	6.24	NUM
brj-24683	79	49	resin	resin	NOUN
brj-24683	79	50	623	623	NUM
brj-24683	79	51	719	719	NUM
brj-24683	79	52	13.84	13.84	NUM
brj-24683	79	53	note	note	NOUN
brj-24683	79	54	:	:	PUNCT
brj-24683	79	55	the	the	DET
brj-24683	79	56	images	image	NOUN
brj-24683	79	57	in	in	ADP
brj-24683	79	58	dataset	dataset	NOUN
brj-24683	79	59	(	(	PUNCT
brj-24683	79	60	%	%	INTJ
brj-24683	79	61	)	)	PUNCT
brj-24683	79	62	means	mean	VERB
brj-24683	79	63	the	the	DET
brj-24683	79	64	proportion	proportion	NOUN
brj-24683	79	65	of	of	ADP
brj-24683	79	66	images	image	NOUN
brj-24683	79	67	with	with	ADP
brj-24683	79	68	such	such	ADJ
brj-24683	79	69	defects	defect	NOUN
brj-24683	79	70	in	in	ADP
brj-24683	79	71	the	the	DET
brj-24683	79	72	dataset	dataset	NOUN
brj-24683	79	73	.	.	PUNCT
brj-24683	80	1	yolov8	yolov8	NOUN
brj-24683	80	2	improvement	improvement	NOUN
brj-24683	80	3	to	to	PART
brj-24683	80	4	address	address	VERB
brj-24683	80	5	the	the	DET
brj-24683	80	6	aforementioned	aforementioned	ADJ
brj-24683	80	7	challenges	challenge	NOUN
brj-24683	80	8	,	,	PUNCT
brj-24683	80	9	this	this	DET
brj-24683	80	10	study	study	NOUN
brj-24683	80	11	adopted	adopt	VERB
brj-24683	80	12	yolov8	yolov8	NOUN
brj-24683	80	13	as	as	ADP
brj-24683	80	14	the	the	DET
brj-24683	80	15	baseline	baseline	PROPN
brj-24683	80	16	model	model	NOUN
brj-24683	80	17	.	.	PUNCT
brj-24683	81	1	a	a	DET
brj-24683	81	2	novel	novel	ADJ
brj-24683	81	3	version	version	NOUN
brj-24683	81	4	of	of	ADP
brj-24683	81	5	the	the	DET
brj-24683	81	6	model	model	NOUN
brj-24683	81	7	was	be	AUX
brj-24683	81	8	developed	develop	VERB
brj-24683	81	9	with	with	ADP
brj-24683	81	10	a	a	DET
brj-24683	81	11	particular	particular	ADJ
brj-24683	81	12	emphasis	emphasis	NOUN
brj-24683	81	13	on	on	ADP
brj-24683	81	14	enhancing	enhance	VERB
brj-24683	81	15	small	small	ADJ
brj-24683	81	16	object	object	NOUN
brj-24683	81	17	detection	detection	NOUN
brj-24683	81	18	performance	performance	NOUN
brj-24683	81	19	.	.	PUNCT
brj-24683	82	1	this	this	PRON
brj-24683	82	2	involved	involve	VERB
brj-24683	82	3	tuning	tune	VERB
brj-24683	82	4	the	the	DET
brj-24683	82	5	network	network	NOUN
brj-24683	82	6	’s	’s	PART
brj-24683	82	7	multiscale	multiscale	ADJ
brj-24683	82	8	feature	feature	NOUN
brj-24683	82	9	representation	representation	NOUN
brj-24683	82	10	and	and	CCONJ
brj-24683	82	11	adjusting	adjust	VERB
brj-24683	82	12	detection	detection	NOUN
brj-24683	82	13	heads	head	NOUN
brj-24683	82	14	to	to	PART
brj-24683	82	15	better	well	ADV
brj-24683	82	16	capture	capture	VERB
brj-24683	82	17	finegrained	finegraine	VERB
brj-24683	82	18	details	detail	NOUN
brj-24683	82	19	,	,	PUNCT
brj-24683	82	20	ensuring	ensure	VERB
brj-24683	82	21	that	that	SCONJ
brj-24683	82	22	small	small	ADJ
brj-24683	82	23	defects	defect	NOUN
brj-24683	82	24	receive	receive	VERB
brj-24683	82	25	sufficient	sufficient	ADJ
brj-24683	82	26	attention	attention	NOUN
brj-24683	82	27	during	during	ADP
brj-24683	82	28	inference	inference	NOUN
brj-24683	82	29	.	.	PUNCT
brj-24683	83	1	details	detail	NOUN
brj-24683	83	2	are	be	AUX
brj-24683	83	3	as	as	SCONJ
brj-24683	83	4	follows	follow	VERB
brj-24683	83	5	:	:	PUNCT
brj-24683	83	6	in	in	ADP
brj-24683	83	7	the	the	DET
brj-24683	83	8	backbone	backbone	NOUN
brj-24683	83	9	,	,	PUNCT
brj-24683	83	10	a	a	DET
brj-24683	83	11	novel	novel	ADJ
brj-24683	83	12	multi	multi	ADJ
brj-24683	83	13	-	-	ADJ
brj-24683	83	14	head	head	ADJ
brj-24683	83	15	mixed	mixed	ADJ
brj-24683	83	16	self	self	NOUN
brj-24683	83	17	-	-	PUNCT
brj-24683	83	18	attention	attention	NOUN
brj-24683	83	19	(	(	PUNCT
brj-24683	83	20	mmsa	mmsa	NOUN
brj-24683	83	21	)	)	PUNCT
brj-24683	83	22	mechanism	mechanism	NOUN
brj-24683	83	23	was	be	AUX
brj-24683	83	24	designed	design	VERB
brj-24683	83	25	to	to	PART
brj-24683	83	26	effectively	effectively	ADV
brj-24683	83	27	integrate	integrate	VERB
brj-24683	83	28	channel	channel	NOUN
brj-24683	83	29	attention	attention	NOUN
brj-24683	83	30	and	and	CCONJ
brj-24683	83	31	spatial	spatial	ADJ
brj-24683	83	32	information	information	NOUN
brj-24683	83	33	,	,	PUNCT
brj-24683	83	34	thereby	thereby	ADV
brj-24683	83	35	enhancing	enhance	VERB
brj-24683	83	36	the	the	DET
brj-24683	83	37	representation	representation	NOUN
brj-24683	83	38	of	of	ADP
brj-24683	83	39	wood	wood	NOUN
brj-24683	83	40	surface	surface	NOUN
brj-24683	83	41	defect	defect	NOUN
brj-24683	83	42	features	feature	NOUN
brj-24683	83	43	under	under	ADP
brj-24683	83	44	complex	complex	ADJ
brj-24683	83	45	backgrounds	background	NOUN
brj-24683	83	46	.	.	PUNCT
brj-24683	84	1	the	the	DET
brj-24683	84	2	mmsa	mmsa	NOUN
brj-24683	84	3	module	module	NOUN
brj-24683	84	4	was	be	AUX
brj-24683	84	5	incorporated	incorporate	VERB
brj-24683	84	6	into	into	ADP
brj-24683	84	7	the	the	DET
brj-24683	84	8	c2f	c2f	NOUN
brj-24683	84	9	module	module	NOUN
brj-24683	84	10	to	to	PART
brj-24683	84	11	improve	improve	VERB
brj-24683	84	12	the	the	DET
brj-24683	84	13	model	model	NOUN
brj-24683	84	14	’s	’s	PART
brj-24683	84	15	ability	ability	NOUN
brj-24683	84	16	to	to	PART
brj-24683	84	17	capture	capture	VERB
brj-24683	84	18	contextual	contextual	ADJ
brj-24683	84	19	and	and	CCONJ
brj-24683	84	20	background	background	NOUN
brj-24683	84	21	information	information	NOUN
brj-24683	84	22	for	for	ADP
brj-24683	84	23	small	small	ADJ
brj-24683	84	24	targets	target	NOUN
brj-24683	84	25	,	,	PUNCT
brj-24683	84	26	effectively	effectively	ADV
brj-24683	84	27	addressing	address	VERB
brj-24683	84	28	the	the	DET
brj-24683	84	29	limitations	limitation	NOUN
brj-24683	84	30	of	of	ADP
brj-24683	84	31	the	the	DET
brj-24683	84	32	original	original	ADJ
brj-24683	84	33	c2f	c2f	NOUN
brj-24683	84	34	module	module	NOUN
brj-24683	84	35	in	in	ADP
brj-24683	84	36	detecting	detect	VERB
brj-24683	84	37	small	small	ADJ
brj-24683	84	38	defects	defect	NOUN
brj-24683	84	39	under	under	ADP
brj-24683	84	40	complex	complex	ADJ
brj-24683	84	41	backgrounds	background	NOUN
brj-24683	84	42	.	.	PUNCT
brj-24683	85	1	peer	peer	NOUN
brj-24683	85	2	-	-	PUNCT
brj-24683	85	3	reviewed	review	VERB
brj-24683	85	4	article	article	NOUN
brj-24683	85	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	85	6	dou	dou	PROPN
brj-24683	85	7	&	&	CCONJ
brj-24683	85	8	you	you	PRON
brj-24683	85	9	(	(	PUNCT
brj-24683	85	10	2025	2025	NUM
brj-24683	85	11	)	)	PUNCT
brj-24683	85	12	.	.	PUNCT
brj-24683	86	1	“	"	PUNCT
brj-24683	86	2	wood	wood	NOUN
brj-24683	86	3	defect	defect	NOUN
brj-24683	86	4	identification	identification	NOUN
brj-24683	86	5	,	,	PUNCT
brj-24683	86	6	”	"	PUNCT
brj-24683	86	7	bioresources	bioresource	NOUN
brj-24683	86	8	20(3	20(3	NOUN
brj-24683	86	9	)	)	PUNCT
brj-24683	86	10	,	,	PUNCT
brj-24683	86	11	5709	5709	NUM
brj-24683	86	12	-	-	SYM
brj-24683	86	13	5730	5730	NUM
brj-24683	86	14	.	.	PUNCT
brj-24683	87	1	5713	5713	NUM
brj-24683	87	2	in	in	ADP
brj-24683	87	3	the	the	DET
brj-24683	87	4	neck	neck	NOUN
brj-24683	87	5	,	,	PUNCT
brj-24683	87	6	to	to	PART
brj-24683	87	7	mitigate	mitigate	VERB
brj-24683	87	8	the	the	DET
brj-24683	87	9	loss	loss	NOUN
brj-24683	87	10	of	of	ADP
brj-24683	87	11	fine	fine	ADV
brj-24683	87	12	-	-	PUNCT
brj-24683	87	13	grained	grain	VERB
brj-24683	87	14	details	detail	NOUN
brj-24683	87	15	during	during	ADP
brj-24683	87	16	feature	feature	NOUN
brj-24683	87	17	fusion	fusion	NOUN
brj-24683	87	18	and	and	CCONJ
brj-24683	87	19	reduce	reduce	VERB
brj-24683	87	20	the	the	DET
brj-24683	87	21	degradation	degradation	NOUN
brj-24683	87	22	of	of	ADP
brj-24683	87	23	small	small	ADJ
brj-24683	87	24	-	-	PUNCT
brj-24683	87	25	scale	scale	NOUN
brj-24683	87	26	defect	defect	NOUN
brj-24683	87	27	features	feature	NOUN
brj-24683	87	28	,	,	PUNCT
brj-24683	87	29	an	an	DET
brj-24683	87	30	ultra	ultra	ADJ
brj-24683	87	31	-	-	ADJ
brj-24683	87	32	lightweight	lightweight	ADJ
brj-24683	87	33	learnable	learnable	ADJ
brj-24683	87	34	dynamic	dynamic	ADJ
brj-24683	87	35	upsampling	upsampling	NOUN
brj-24683	87	36	module	module	NOUN
brj-24683	87	37	(	(	PUNCT
brj-24683	87	38	dysample	dysample	PROPN
brj-24683	87	39	)	)	PUNCT
brj-24683	87	40	was	be	AUX
brj-24683	87	41	introduced	introduce	VERB
brj-24683	87	42	.	.	PUNCT
brj-24683	88	1	this	this	DET
brj-24683	88	2	module	module	NOUN
brj-24683	88	3	enhances	enhance	VERB
brj-24683	88	4	feature	feature	NOUN
brj-24683	88	5	representation	representation	NOUN
brj-24683	88	6	capability	capability	NOUN
brj-24683	88	7	and	and	CCONJ
brj-24683	88	8	localization	localization	NOUN
brj-24683	88	9	accuracy	accuracy	NOUN
brj-24683	88	10	by	by	ADP
brj-24683	88	11	adaptively	adaptively	ADV
brj-24683	88	12	preserving	preserve	VERB
brj-24683	88	13	critical	critical	ADJ
brj-24683	88	14	spatial	spatial	ADJ
brj-24683	88	15	information	information	NOUN
brj-24683	88	16	.	.	PUNCT
brj-24683	89	1	furthermore	furthermore	ADV
brj-24683	89	2	,	,	PUNCT
brj-24683	89	3	a	a	DET
brj-24683	89	4	structural	structural	ADJ
brj-24683	89	5	re	re	ADJ
brj-24683	89	6	-	-	ADJ
brj-24683	89	7	parameterizable	parameterizable	ADJ
brj-24683	89	8	block	block	NOUN
brj-24683	89	9	(	(	PUNCT
brj-24683	89	10	repblock	repblock	NOUN
brj-24683	89	11	)	)	PUNCT
brj-24683	89	12	was	be	AUX
brj-24683	89	13	integrated	integrate	VERB
brj-24683	89	14	to	to	PART
brj-24683	89	15	precisely	precisely	ADV
brj-24683	89	16	capture	capture	VERB
brj-24683	89	17	multi	multi	ADJ
brj-24683	89	18	-	-	ADJ
brj-24683	89	19	scale	scale	ADJ
brj-24683	89	20	defect	defect	NOUN
brj-24683	89	21	features	feature	NOUN
brj-24683	89	22	,	,	PUNCT
brj-24683	89	23	enabling	enable	VERB
brj-24683	89	24	the	the	DET
brj-24683	89	25	model	model	NOUN
brj-24683	89	26	to	to	PART
brj-24683	89	27	exploit	exploit	VERB
brj-24683	89	28	latent	latent	NOUN
brj-24683	89	29	fine	fine	ADV
brj-24683	89	30	-	-	PUNCT
brj-24683	89	31	grained	grain	VERB
brj-24683	89	32	and	and	CCONJ
brj-24683	89	33	multi	multi	ADJ
brj-24683	89	34	-	-	ADJ
brj-24683	89	35	scale	scale	ADJ
brj-24683	89	36	characteristics	characteristic	NOUN
brj-24683	89	37	of	of	ADP
brj-24683	89	38	wood	wood	NOUN
brj-24683	89	39	surface	surface	NOUN
brj-24683	89	40	defects	defect	NOUN
brj-24683	89	41	.	.	PUNCT
brj-24683	90	1	for	for	ADP
brj-24683	90	2	the	the	DET
brj-24683	90	3	detection	detection	NOUN
brj-24683	90	4	head	head	NOUN
brj-24683	90	5	,	,	PUNCT
brj-24683	90	6	an	an	DET
brj-24683	90	7	additional	additional	ADJ
brj-24683	90	8	small	small	ADJ
brj-24683	90	9	-	-	PUNCT
brj-24683	90	10	object	object	NOUN
brj-24683	90	11	detection	detection	NOUN
brj-24683	90	12	head	head	NOUN
brj-24683	90	13	with	with	ADP
brj-24683	90	14	a	a	DET
brj-24683	90	15	resolution	resolution	NOUN
brj-24683	90	16	of	of	ADP
brj-24683	90	17	160×160	160×160	NUM
brj-24683	90	18	was	be	AUX
brj-24683	90	19	introduced	introduce	VERB
brj-24683	90	20	.	.	PUNCT
brj-24683	91	1	this	this	DET
brj-24683	91	2	design	design	NOUN
brj-24683	91	3	ensures	ensure	VERB
brj-24683	91	4	that	that	SCONJ
brj-24683	91	5	fine	fine	ADV
brj-24683	91	6	-	-	PUNCT
brj-24683	91	7	grained	grain	VERB
brj-24683	91	8	features	feature	NOUN
brj-24683	91	9	of	of	ADP
brj-24683	91	10	small	small	ADJ
brj-24683	91	11	defects	defect	NOUN
brj-24683	91	12	propagate	propagate	VERB
brj-24683	91	13	through	through	ADP
brj-24683	91	14	the	the	DET
brj-24683	91	15	downsampling	downsample	VERB
brj-24683	91	16	pathway	pathway	NOUN
brj-24683	91	17	to	to	ADP
brj-24683	91	18	other	other	ADJ
brj-24683	91	19	feature	feature	NOUN
brj-24683	91	20	maps	map	NOUN
brj-24683	91	21	at	at	ADP
brj-24683	91	22	scales	scale	NOUN
brj-24683	91	23	of	of	ADP
brj-24683	91	24	20×20	20×20	NUM
brj-24683	91	25	,	,	PUNCT
brj-24683	91	26	40×40	40×40	NUM
brj-24683	91	27	,	,	PUNCT
brj-24683	91	28	and	and	CCONJ
brj-24683	91	29	80×80	80×80	NUM
brj-24683	91	30	,	,	PUNCT
brj-24683	91	31	thereby	thereby	ADV
brj-24683	91	32	improving	improve	VERB
brj-24683	91	33	detection	detection	NOUN
brj-24683	91	34	performance	performance	NOUN
brj-24683	91	35	for	for	ADP
brj-24683	91	36	small	small	ADJ
brj-24683	91	37	defects	defect	NOUN
brj-24683	91	38	under	under	ADP
brj-24683	91	39	complex	complex	ADJ
brj-24683	91	40	backgrounds	background	NOUN
brj-24683	91	41	and	and	CCONJ
brj-24683	91	42	reducing	reduce	VERB
brj-24683	91	43	false	false	ADJ
brj-24683	91	44	positives	positive	NOUN
brj-24683	91	45	and	and	CCONJ
brj-24683	91	46	missed	miss	VERB
brj-24683	91	47	detections	detection	NOUN
brj-24683	91	48	.	.	PUNCT
brj-24683	92	1	the	the	DET
brj-24683	92	2	architecture	architecture	NOUN
brj-24683	92	3	of	of	ADP
brj-24683	92	4	the	the	DET
brj-24683	92	5	proposed	propose	VERB
brj-24683	92	6	model	model	NOUN
brj-24683	92	7	is	be	AUX
brj-24683	92	8	illustrated	illustrate	VERB
brj-24683	92	9	in	in	ADP
brj-24683	92	10	fig	fig	NOUN
brj-24683	92	11	.	.	PUNCT
brj-24683	93	1	1	1	X
brj-24683	93	2	.	.	X
brj-24683	93	3	conv	conv	PROPN
brj-24683	93	4	conv	conv	PROPN
brj-24683	93	5	c2f	c2f	PROPN
brj-24683	93	6	-	-	PUNCT
brj-24683	93	7	mmsa	mmsa	PROPN
brj-24683	93	8	conv	conv	PROPN
brj-24683	93	9	c2f	c2f	PROPN
brj-24683	93	10	-	-	PUNCT
brj-24683	93	11	mmsa	mmsa	PROPN
brj-24683	93	12	conv	conv	PROPN
brj-24683	93	13	c2f	c2f	PROPN
brj-24683	93	14	-	-	PUNCT
brj-24683	93	15	mmsa	mmsa	PROPN
brj-24683	93	16	conv	conv	PROPN
brj-24683	93	17	c2f	c2f	PROPN
brj-24683	93	18	-	-	PUNCT
brj-24683	93	19	mmsa	mmsa	PROPN
brj-24683	93	20	sppf	sppf	PROPN
brj-24683	93	21	backbone	backbone	NOUN
brj-24683	93	22	head	head	NOUN
brj-24683	93	23	concat	concat	NOUN
brj-24683	93	24	repblock	repblock	NOUN
brj-24683	93	25	repblock	repblock	NOUN
brj-24683	93	26	dysample	dysample	PROPN
brj-24683	93	27	concat	concat	PROPN
brj-24683	93	28	dysample	dysample	PROPN
brj-24683	93	29	concat	concat	PROPN
brj-24683	93	30	conv	conv	PROPN
brj-24683	93	31	repblock	repblock	PROPN
brj-24683	93	32	conv	conv	PROPN
brj-24683	93	33	concat	concat	PROPN
brj-24683	93	34	repblock	repblock	NOUN
brj-24683	93	35	detect	detect	NOUN
brj-24683	93	36	detect	detect	NOUN
brj-24683	93	37	detect	detect	NOUN
brj-24683	93	38	neck	neck	NOUN
brj-24683	93	39	repblockconcat	repblockconcat	NOUN
brj-24683	93	40	dysample	dysample	PROPN
brj-24683	93	41	concat	concat	PROPN
brj-24683	93	42	conv	conv	PROPN
brj-24683	93	43	detect	detect	PROPN
brj-24683	93	44	repblock	repblock	NOUN
brj-24683	93	45	conv	conv	PROPN
brj-24683	93	46	conv	conv	PROPN
brj-24683	93	47	conv	conv	PROPN
brj-24683	93	48	conv	conv	PROPN
brj-24683	93	49	conv2d	conv2d	PROPN
brj-24683	93	50	conv2d	conv2d	PROPN
brj-24683	93	51	cls.loss	cls.loss	NOUN
brj-24683	93	52	bbox.loss	bbox.loss	NUM
brj-24683	93	53	detect	detect	NOUN
brj-24683	93	54	repconv	repconv	INTJ
brj-24683	93	55	repconv	repconv	VERB
brj-24683	93	56	1×1conv2d	1×1conv2d	NUM
brj-24683	93	57	3×3conv2d	3×3conv2d	NUM
brj-24683	93	58	bn	bn	NOUN
brj-24683	93	59	bnbn	bnbn	NOUN
brj-24683	93	60	silu	silu	NOUN
brj-24683	93	61	3×3conv2d	3×3conv2d	NUM
brj-24683	93	62	silu	silu	NOUN
brj-24683	93	63	train	train	NOUN
brj-24683	93	64	val	val	NOUN
brj-24683	93	65	repconv	repconv	VERB
brj-24683	94	1	1×1conv	1×1conv	NUM
brj-24683	94	2	1×1conv	1×1conv	NUM
brj-24683	94	3	repconv	repconv	NOUN
brj-24683	94	4	1×1conv	1×1conv	NUM
brj-24683	94	5	n×	n×	PRON
brj-24683	94	6	conv2d	conv2d	PROPN
brj-24683	94	7	batchnorm2d	batchnorm2d	ADJ
brj-24683	94	8	silu	silu	ADJ
brj-24683	94	9	conv	conv	PROPN
brj-24683	94	10	c×h×w	c×h×w	PROPN
brj-24683	94	11	input	input	NOUN
brj-24683	94	12	output	output	NOUN
brj-24683	94	13	lmp	lmp	NOUN
brj-24683	94	14	c×ks×ks	c×ks×ks	ADJ
brj-24683	94	15	gmp	gmp	PROPN
brj-24683	94	16	1×1×c	1×1×c	NUM
brj-24683	94	17	1×1×c	1×1×c	NUM
brj-24683	94	18	conv1d	conv1d	NOUN
brj-24683	94	19	k	k	PROPN
brj-24683	94	20	unap	unap	PROPN
brj-24683	94	21	c×ks×ks	c×ks×ks	PROPN
brj-24683	94	22	c	c	PROPN
brj-24683	94	23	×	×	NOUN
brj-24683	94	24	k	k	X
brj-24683	94	25	s×	s×	PROPN
brj-24683	94	26	k	k	X
brj-24683	94	27	s	s	X
brj-24683	94	28	c×ks×ks	c×ks×ks	ADJ
brj-24683	94	29	1×1×(c×ks×ks	1×1×(c×ks×ks	NUM
brj-24683	94	30	)	)	PUNCT
brj-24683	94	31	..	..	PUNCT
brj-24683	94	32	.	.	PUNCT
brj-24683	95	1	conv1d	conv1d	NOUN
brj-24683	96	1	k	k	NOUN
brj-24683	96	2	r	r	NOUN
brj-24683	96	3	e	e	NOUN
brj-24683	96	4	sh	sh	PROPN
brj-24683	96	5	a	a	DET
brj-24683	96	6	p	p	X
brj-24683	96	7	e	e	NOUN
brj-24683	96	8	reshape	reshape	NOUN
brj-24683	96	9	..	..	PUNCT
brj-24683	96	10	.	.	PUNCT
brj-24683	97	1	num	num	PROPN
brj-24683	98	1	=	=	NOUN
brj-24683	98	2	ks×ks	ks×ks	ADJ
brj-24683	98	3	num	num	ADJ
brj-24683	98	4	num	num	PROPN
brj-24683	98	5	u	u	PROPN
brj-24683	98	6	n	n	DET
brj-24683	98	7	a	a	DET
brj-24683	98	8	p	p	NOUN
brj-24683	98	9	c×h×w	c×h×w	NOUN
brj-24683	98	10	where	where	SCONJ
brj-24683	98	11	ks=5	ks=5	PROPN
brj-24683	98	12	r	r	NOUN
brj-24683	98	13	e	e	NOUN
brj-24683	98	14	sh	sh	PROPN
brj-24683	98	15	a	a	DET
brj-24683	98	16	p	p	X
brj-24683	98	17	e	e	NOUN
brj-24683	98	18	r	r	NOUN
brj-24683	98	19	e	e	NOUN
brj-24683	98	20	sh	sh	PROPN
brj-24683	98	21	a	a	DET
brj-24683	98	22	p	p	X
brj-24683	98	23	e	e	X
brj-24683	98	24	c×h×w	c×h×w	NOUN
brj-24683	98	25	multi	multi	ADJ
brj-24683	98	26	-	-	ADJ
brj-24683	98	27	head	head	ADJ
brj-24683	98	28	attention	attention	NOUN
brj-24683	98	29	w	w	PROPN
brj-24683	98	30	/	/	SYM
brj-24683	98	31	ks	ks	PROPN
brj-24683	98	32	h	h	PROPN
brj-24683	98	33	/	/	SYM
brj-24683	98	34	ks	ks	PROPN
brj-24683	98	35	residual	residual	ADJ
brj-24683	98	36	structure	structure	NOUN
brj-24683	98	37	+	+	ADP
brj-24683	98	38	×	×	PROPN
brj-24683	98	39	conv	conv	ADJ
brj-24683	98	40	split	split	PROPN
brj-24683	98	41	mmsa	mmsa	PROPN
brj-24683	98	42	concat	concat	PROPN
brj-24683	98	43	convmmsa	convmmsa	PROPN
brj-24683	98	44	c2f	c2f	PROPN
brj-24683	98	45	-	-	PUNCT
brj-24683	98	46	smma	smma	PROPN
brj-24683	98	47	g	g	PROPN
brj-24683	98	48	o	o	NOUN
brj-24683	98	49	sf	sf	INTJ
brj-24683	98	50	0.5*sigmoid	0.5*sigmoid	PROPN
brj-24683	98	51	·	·	PUNCT
brj-24683	98	52	c×h×w	c×h×w	NUM
brj-24683	98	53	linear	linear	ADJ
brj-24683	98	54	2gs	2gs	NOUN
brj-24683	98	55	2	2	NUM
brj-24683	98	56	×h×w	×h×w	ADP
brj-24683	98	57	2gs	2gs	ADJ
brj-24683	98	58	2	2	NUM
brj-24683	98	59	×h×w	×h×w	ADP
brj-24683	98	60	2gs	2gs	NUM
brj-24683	98	61	2	2	NUM
brj-24683	98	62	×h×w	×h×w	ADP
brj-24683	98	63	2g×sh×sw	2g×sh×sw	NUM
brj-24683	98	64	2g×sh×sw	2g×sh×sw	NUM
brj-24683	98	65	2g×sh×sw	2g×sh×sw	NOUN
brj-24683	98	66	pixel	pixel	VERB
brj-24683	98	67	shuffle	shuffle	PROPN
brj-24683	98	68	dysample	dysample	PROPN
brj-24683	98	69	mmsa	mmsa	PROPN
brj-24683	98	70	repblock	repblock	PROPN
brj-24683	98	71	fig	fig	NOUN
brj-24683	98	72	.	.	PUNCT
brj-24683	99	1	1	1	X
brj-24683	99	2	.	.	X
brj-24683	99	3	architecture	architecture	NOUN
brj-24683	99	4	of	of	ADP
brj-24683	99	5	the	the	DET
brj-24683	99	6	proposed	propose	VERB
brj-24683	99	7	model	model	NOUN
brj-24683	99	8	peer	peer	NOUN
brj-24683	99	9	-	-	PUNCT
brj-24683	99	10	reviewed	review	VERB
brj-24683	99	11	article	article	NOUN
brj-24683	99	12	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	99	13	dou	dou	PROPN
brj-24683	99	14	&	&	CCONJ
brj-24683	99	15	you	you	PRON
brj-24683	99	16	(	(	PUNCT
brj-24683	99	17	2025	2025	NUM
brj-24683	99	18	)	)	PUNCT
brj-24683	99	19	.	.	PUNCT
brj-24683	100	1	“	"	PUNCT
brj-24683	100	2	wood	wood	NOUN
brj-24683	100	3	defect	defect	NOUN
brj-24683	100	4	identification	identification	NOUN
brj-24683	100	5	,	,	PUNCT
brj-24683	100	6	”	"	PUNCT
brj-24683	100	7	bioresources	bioresource	NOUN
brj-24683	100	8	20(3	20(3	NOUN
brj-24683	100	9	)	)	PUNCT
brj-24683	100	10	,	,	PUNCT
brj-24683	100	11	5709	5709	NUM
brj-24683	100	12	-	-	SYM
brj-24683	100	13	5730	5730	NUM
brj-24683	100	14	.	.	PUNCT
brj-24683	101	1	5714	5714	NUM
brj-24683	101	2	c2f	c2f	NOUN
brj-24683	101	3	-	-	PUNCT
brj-24683	101	4	mmsa	mmsa	NOUN
brj-24683	101	5	the	the	DET
brj-24683	101	6	c2f	c2f	NOUN
brj-24683	101	7	is	be	AUX
brj-24683	101	8	a	a	DET
brj-24683	101	9	key	key	ADJ
brj-24683	101	10	feature	feature	NOUN
brj-24683	101	11	capture	capture	NOUN
brj-24683	101	12	and	and	CCONJ
brj-24683	101	13	fusion	fusion	NOUN
brj-24683	101	14	module	module	NOUN
brj-24683	101	15	in	in	ADP
brj-24683	101	16	yolov8	yolov8	PROPN
brj-24683	101	17	,	,	PUNCT
brj-24683	101	18	employing	employ	VERB
brj-24683	101	19	the	the	DET
brj-24683	101	20	cross	cross	ADJ
brj-24683	101	21	stage	stage	NOUN
brj-24683	101	22	partial	partial	ADJ
brj-24683	101	23	networks	network	NOUN
brj-24683	101	24	(	(	PUNCT
brj-24683	101	25	cspnet	cspnet	NOUN
brj-24683	101	26	)	)	PUNCT
brj-24683	101	27	design	design	NOUN
brj-24683	101	28	to	to	PART
brj-24683	101	29	enhance	enhance	VERB
brj-24683	101	30	feature	feature	NOUN
brj-24683	101	31	propagation	propagation	NOUN
brj-24683	101	32	and	and	CCONJ
brj-24683	101	33	fusion	fusion	NOUN
brj-24683	101	34	(	(	PUNCT
brj-24683	101	35	varghese	varghese	NOUN
brj-24683	101	36	and	and	CCONJ
brj-24683	101	37	sambath	sambath	NOUN
brj-24683	101	38	2024	2024	NUM
brj-24683	101	39	)	)	PUNCT
brj-24683	101	40	.	.	PUNCT
brj-24683	102	1	however	however	ADV
brj-24683	102	2	,	,	PUNCT
brj-24683	102	3	when	when	SCONJ
brj-24683	102	4	applied	apply	VERB
brj-24683	102	5	to	to	ADP
brj-24683	102	6	small	small	ADJ
brj-24683	102	7	object	object	NOUN
brj-24683	102	8	detection	detection	NOUN
brj-24683	102	9	tasks	task	NOUN
brj-24683	102	10	,	,	PUNCT
brj-24683	102	11	particularly	particularly	ADV
brj-24683	102	12	in	in	ADP
brj-24683	102	13	the	the	DET
brj-24683	102	14	context	context	NOUN
brj-24683	102	15	of	of	ADP
brj-24683	102	16	wood	wood	NOUN
brj-24683	102	17	surface	surface	NOUN
brj-24683	102	18	defects	defect	NOUN
brj-24683	102	19	with	with	ADP
brj-24683	102	20	complex	complex	ADJ
brj-24683	102	21	backgrounds	background	NOUN
brj-24683	102	22	,	,	PUNCT
brj-24683	102	23	the	the	DET
brj-24683	102	24	c2f	c2f	NOUN
brj-24683	102	25	structure	structure	NOUN
brj-24683	102	26	presents	present	VERB
brj-24683	102	27	several	several	ADJ
brj-24683	102	28	challenges	challenge	NOUN
brj-24683	102	29	:	:	PUNCT
brj-24683	102	30	•	•	NUM
brj-24683	102	31	insufficient	insufficient	ADJ
brj-24683	102	32	fusion	fusion	NOUN
brj-24683	102	33	of	of	ADP
brj-24683	102	34	local	local	ADJ
brj-24683	102	35	and	and	CCONJ
brj-24683	102	36	global	global	ADJ
brj-24683	102	37	information	information	NOUN
brj-24683	102	38	:	:	PUNCT
brj-24683	102	39	the	the	DET
brj-24683	102	40	c2f	c2f	NOUN
brj-24683	102	41	structure	structure	NOUN
brj-24683	102	42	merges	merge	VERB
brj-24683	102	43	features	feature	NOUN
brj-24683	102	44	from	from	ADP
brj-24683	102	45	different	different	ADJ
brj-24683	102	46	layers	layer	NOUN
brj-24683	102	47	through	through	ADP
brj-24683	102	48	cross	cross	ADJ
brj-24683	102	49	-	-	ADJ
brj-24683	102	50	stage	stage	ADJ
brj-24683	102	51	fusion	fusion	NOUN
brj-24683	102	52	,	,	PUNCT
brj-24683	102	53	with	with	ADP
brj-24683	102	54	a	a	DET
brj-24683	102	55	primary	primary	ADJ
brj-24683	102	56	focus	focus	NOUN
brj-24683	102	57	on	on	ADP
brj-24683	102	58	the	the	DET
brj-24683	102	59	propagation	propagation	NOUN
brj-24683	102	60	of	of	ADP
brj-24683	102	61	local	local	ADJ
brj-24683	102	62	features	feature	NOUN
brj-24683	102	63	.	.	PUNCT
brj-24683	103	1	however	however	ADV
brj-24683	103	2	,	,	PUNCT
brj-24683	103	3	it	it	PRON
brj-24683	103	4	does	do	AUX
brj-24683	103	5	not	not	PART
brj-24683	103	6	fully	fully	ADV
brj-24683	103	7	consider	consider	VERB
brj-24683	103	8	global	global	ADJ
brj-24683	103	9	context	context	NOUN
brj-24683	103	10	information	information	NOUN
brj-24683	103	11	,	,	PUNCT
brj-24683	103	12	which	which	PRON
brj-24683	103	13	is	be	AUX
brj-24683	103	14	crucial	crucial	ADJ
brj-24683	103	15	for	for	ADP
brj-24683	103	16	accurate	accurate	ADJ
brj-24683	103	17	localization	localization	NOUN
brj-24683	103	18	of	of	ADP
brj-24683	103	19	wood	wood	NOUN
brj-24683	103	20	surface	surface	NOUN
brj-24683	103	21	defects	defect	NOUN
brj-24683	103	22	,	,	PUNCT
brj-24683	103	23	often	often	ADV
brj-24683	103	24	embedded	embed	VERB
brj-24683	103	25	within	within	ADP
brj-24683	103	26	complex	complex	ADJ
brj-24683	103	27	backgrounds	background	NOUN
brj-24683	103	28	.	.	PUNCT
brj-24683	104	1	these	these	DET
brj-24683	104	2	defects	defect	NOUN
brj-24683	104	3	typically	typically	ADV
brj-24683	104	4	require	require	VERB
brj-24683	104	5	global	global	ADJ
brj-24683	104	6	information	information	NOUN
brj-24683	104	7	from	from	ADP
brj-24683	104	8	surrounding	surround	VERB
brj-24683	104	9	areas	area	NOUN
brj-24683	104	10	for	for	ADP
brj-24683	104	11	precise	precise	ADJ
brj-24683	104	12	detection	detection	NOUN
brj-24683	104	13	.	.	PUNCT
brj-24683	105	1	•	•	NUM
brj-24683	105	2	inadequate	inadequate	ADJ
brj-24683	105	3	focus	focus	NOUN
brj-24683	105	4	on	on	ADP
brj-24683	105	5	small	small	ADJ
brj-24683	105	6	targets	target	NOUN
brj-24683	105	7	:	:	PUNCT
brj-24683	105	8	c2f	c2f	NOUN
brj-24683	105	9	is	be	AUX
brj-24683	105	10	designed	design	VERB
brj-24683	105	11	to	to	PART
brj-24683	105	12	process	process	VERB
brj-24683	105	13	larger	large	ADJ
brj-24683	105	14	or	or	CCONJ
brj-24683	105	15	more	more	ADV
brj-24683	105	16	conventional	conventional	ADJ
brj-24683	105	17	objects	object	NOUN
brj-24683	105	18	through	through	ADP
brj-24683	105	19	hierarchical	hierarchical	ADJ
brj-24683	105	20	feature	feature	NOUN
brj-24683	105	21	fusion	fusion	NOUN
brj-24683	105	22	.	.	PUNCT
brj-24683	106	1	however	however	ADV
brj-24683	106	2	,	,	PUNCT
brj-24683	106	3	for	for	ADP
brj-24683	106	4	small	small	ADJ
brj-24683	106	5	targets	target	NOUN
brj-24683	106	6	,	,	PUNCT
brj-24683	106	7	particularly	particularly	ADV
brj-24683	106	8	those	those	PRON
brj-24683	106	9	occupying	occupy	VERB
brj-24683	106	10	only	only	ADV
brj-24683	106	11	a	a	DET
brj-24683	106	12	few	few	ADJ
brj-24683	106	13	pixels	pixel	NOUN
brj-24683	106	14	,	,	PUNCT
brj-24683	106	15	the	the	DET
brj-24683	106	16	c2f	c2f	NOUN
brj-24683	106	17	structure	structure	NOUN
brj-24683	106	18	may	may	AUX
brj-24683	106	19	fail	fail	VERB
brj-24683	106	20	to	to	PART
brj-24683	106	21	capture	capture	VERB
brj-24683	106	22	fine	fine	ADV
brj-24683	106	23	-	-	PUNCT
brj-24683	106	24	grained	grain	VERB
brj-24683	106	25	features	feature	NOUN
brj-24683	106	26	adequately	adequately	ADV
brj-24683	106	27	.	.	PUNCT
brj-24683	107	1	in	in	ADP
brj-24683	107	2	particular	particular	ADJ
brj-24683	107	3	,	,	PUNCT
brj-24683	107	4	when	when	SCONJ
brj-24683	107	5	feature	feature	NOUN
brj-24683	107	6	map	map	NOUN
brj-24683	107	7	sizes	size	NOUN
brj-24683	107	8	are	be	AUX
brj-24683	107	9	reduced	reduce	VERB
brj-24683	107	10	or	or	CCONJ
brj-24683	107	11	when	when	SCONJ
brj-24683	107	12	information	information	NOUN
brj-24683	107	13	is	be	AUX
brj-24683	107	14	sparsely	sparsely	ADV
brj-24683	107	15	distributed	distribute	VERB
brj-24683	107	16	,	,	PUNCT
brj-24683	107	17	small	small	ADJ
brj-24683	107	18	target	target	NOUN
brj-24683	107	19	information	information	NOUN
brj-24683	107	20	may	may	AUX
brj-24683	107	21	be	be	AUX
brj-24683	107	22	lost	lose	VERB
brj-24683	107	23	or	or	CCONJ
brj-24683	107	24	incorrectly	incorrectly	ADV
brj-24683	107	25	localized	localize	VERB
brj-24683	107	26	.	.	PUNCT
brj-24683	108	1	•	•	NUM
brj-24683	108	2	separation	separation	NOUN
brj-24683	108	3	of	of	ADP
brj-24683	108	4	channel	channel	NOUN
brj-24683	108	5	and	and	CCONJ
brj-24683	108	6	spatial	spatial	ADJ
brj-24683	108	7	information	information	NOUN
brj-24683	108	8	:	:	PUNCT
brj-24683	108	9	while	while	SCONJ
brj-24683	108	10	c2f	c2f	NOUN
brj-24683	108	11	strengthens	strengthen	VERB
brj-24683	108	12	cross	cross	ADJ
brj-24683	108	13	-	-	ADJ
brj-24683	108	14	layer	layer	ADJ
brj-24683	108	15	feature	feature	NOUN
brj-24683	108	16	fusion	fusion	NOUN
brj-24683	108	17	,	,	PUNCT
brj-24683	108	18	it	it	PRON
brj-24683	108	19	does	do	AUX
brj-24683	108	20	not	not	PART
brj-24683	108	21	specifically	specifically	ADV
brj-24683	108	22	address	address	VERB
brj-24683	108	23	the	the	DET
brj-24683	108	24	relationship	relationship	NOUN
brj-24683	108	25	between	between	ADP
brj-24683	108	26	channel	channel	NOUN
brj-24683	108	27	and	and	CCONJ
brj-24683	108	28	spatial	spatial	ADJ
brj-24683	108	29	information	information	NOUN
brj-24683	108	30	.	.	PUNCT
brj-24683	109	1	in	in	ADP
brj-24683	109	2	small	small	ADJ
brj-24683	109	3	object	object	NOUN
brj-24683	109	4	detection	detection	NOUN
brj-24683	109	5	tasks	task	NOUN
brj-24683	109	6	,	,	PUNCT
brj-24683	109	7	the	the	DET
brj-24683	109	8	interaction	interaction	NOUN
brj-24683	109	9	between	between	ADP
brj-24683	109	10	channel	channel	NOUN
brj-24683	109	11	information	information	NOUN
brj-24683	109	12	and	and	CCONJ
brj-24683	109	13	spatial	spatial	ADJ
brj-24683	109	14	information	information	NOUN
brj-24683	109	15	is	be	AUX
brj-24683	109	16	crucial	crucial	ADJ
brj-24683	109	17	.	.	PUNCT
brj-24683	110	1	however	however	ADV
brj-24683	110	2	,	,	PUNCT
brj-24683	110	3	c2f	c2f	PROPN
brj-24683	110	4	lacks	lack	VERB
brj-24683	110	5	the	the	DET
brj-24683	110	6	mechanisms	mechanism	NOUN
brj-24683	110	7	to	to	PART
brj-24683	110	8	dynamically	dynamically	ADV
brj-24683	110	9	capture	capture	VERB
brj-24683	110	10	these	these	DET
brj-24683	110	11	interactions	interaction	NOUN
brj-24683	110	12	effectively	effectively	ADV
brj-24683	110	13	.	.	PUNCT
brj-24683	111	1	to	to	PART
brj-24683	111	2	address	address	VERB
brj-24683	111	3	these	these	DET
brj-24683	111	4	issues	issue	NOUN
brj-24683	111	5	,	,	PUNCT
brj-24683	111	6	this	this	DET
brj-24683	111	7	paper	paper	NOUN
brj-24683	111	8	introduces	introduce	VERB
brj-24683	111	9	a	a	DET
brj-24683	111	10	lightweight	lightweight	ADJ
brj-24683	111	11	mmsa	mmsa	NOUN
brj-24683	111	12	(	(	PUNCT
brj-24683	111	13	su	su	PROPN
brj-24683	111	14	et	et	PROPN
brj-24683	111	15	al	al	PROPN
brj-24683	111	16	.	.	PROPN
brj-24683	111	17	2025	2025	NUM
brj-24683	111	18	)	)	PUNCT
brj-24683	111	19	mechanism	mechanism	NOUN
brj-24683	111	20	,	,	PUNCT
brj-24683	111	21	combining	combine	VERB
brj-24683	111	22	the	the	DET
brj-24683	111	23	concepts	concept	NOUN
brj-24683	111	24	of	of	ADP
brj-24683	111	25	multi	multi	ADJ
brj-24683	111	26	-	-	ADJ
brj-24683	111	27	head	head	ADJ
brj-24683	111	28	attention	attention	NOUN
brj-24683	111	29	and	and	CCONJ
brj-24683	111	30	multi	multi	ADJ
brj-24683	111	31	-	-	ADJ
brj-24683	111	32	scale	scale	ADJ
brj-24683	111	33	attention	attention	NOUN
brj-24683	111	34	,	,	PUNCT
brj-24683	111	35	inspired	inspire	VERB
brj-24683	111	36	by	by	ADP
brj-24683	111	37	the	the	DET
brj-24683	111	38	efficient	efficient	ADJ
brj-24683	111	39	channel	channel	NOUN
brj-24683	111	40	attention	attention	NOUN
brj-24683	111	41	(	(	PUNCT
brj-24683	111	42	eca	eca	NOUN
brj-24683	111	43	)	)	PUNCT
brj-24683	111	44	design	design	NOUN
brj-24683	111	45	pattern	pattern	NOUN
brj-24683	111	46	.	.	PUNCT
brj-24683	112	1	this	this	DET
brj-24683	112	2	mechanism	mechanism	NOUN
brj-24683	112	3	effectively	effectively	ADV
brj-24683	112	4	integrates	integrate	VERB
brj-24683	112	5	local	local	ADJ
brj-24683	112	6	and	and	CCONJ
brj-24683	112	7	global	global	ADJ
brj-24683	112	8	information	information	NOUN
brj-24683	112	9	within	within	ADP
brj-24683	112	10	the	the	DET
brj-24683	112	11	image	image	NOUN
brj-24683	112	12	,	,	PUNCT
brj-24683	112	13	as	as	ADV
brj-24683	112	14	well	well	ADV
brj-24683	112	15	as	as	ADP
brj-24683	112	16	channel	channel	NOUN
brj-24683	112	17	and	and	CCONJ
brj-24683	112	18	spatial	spatial	ADJ
brj-24683	112	19	information	information	NOUN
brj-24683	112	20	,	,	PUNCT
brj-24683	112	21	capturing	capture	VERB
brj-24683	112	22	global	global	ADJ
brj-24683	112	23	contextual	contextual	ADJ
brj-24683	112	24	information	information	NOUN
brj-24683	112	25	across	across	ADP
brj-24683	112	26	both	both	CCONJ
brj-24683	112	27	spatial	spatial	ADJ
brj-24683	112	28	and	and	CCONJ
brj-24683	112	29	channel	channel	NOUN
brj-24683	112	30	dimensions	dimension	NOUN
brj-24683	112	31	.	.	PUNCT
brj-24683	113	1	with	with	ADP
brj-24683	113	2	this	this	DET
brj-24683	113	3	mechanism	mechanism	NOUN
brj-24683	113	4	,	,	PUNCT
brj-24683	113	5	the	the	DET
brj-24683	113	6	model	model	NOUN
brj-24683	113	7	is	be	AUX
brj-24683	113	8	better	well	ADV
brj-24683	113	9	equipped	equip	VERB
brj-24683	113	10	to	to	PART
brj-24683	113	11	understand	understand	VERB
brj-24683	113	12	the	the	DET
brj-24683	113	13	context	context	NOUN
brj-24683	113	14	and	and	CCONJ
brj-24683	113	15	background	background	NOUN
brj-24683	113	16	of	of	ADP
brj-24683	113	17	small	small	ADJ
brj-24683	113	18	targets	target	NOUN
brj-24683	113	19	in	in	ADP
brj-24683	113	20	the	the	DET
brj-24683	113	21	image	image	NOUN
brj-24683	113	22	,	,	PUNCT
brj-24683	113	23	thereby	thereby	ADV
brj-24683	113	24	overcoming	overcome	VERB
brj-24683	113	25	the	the	DET
brj-24683	113	26	challenges	challenge	NOUN
brj-24683	113	27	encountered	encounter	VERB
brj-24683	113	28	by	by	ADP
brj-24683	113	29	the	the	DET
brj-24683	113	30	c2f	c2f	NOUN
brj-24683	113	31	in	in	ADP
brj-24683	113	32	wood	wood	NOUN
brj-24683	113	33	surface	surface	NOUN
brj-24683	113	34	defect	defect	NOUN
brj-24683	113	35	detection	detection	NOUN
brj-24683	113	36	in	in	ADP
brj-24683	113	37	complex	complex	ADJ
brj-24683	113	38	backgrounds	background	NOUN
brj-24683	113	39	.	.	PUNCT
brj-24683	114	1	additionally	additionally	ADV
brj-24683	114	2	,	,	PUNCT
brj-24683	114	3	the	the	DET
brj-24683	114	4	incorporation	incorporation	NOUN
brj-24683	114	5	of	of	ADP
brj-24683	114	6	the	the	DET
brj-24683	114	7	multi	multi	ADJ
brj-24683	114	8	-	-	ADJ
brj-24683	114	9	head	head	ADJ
brj-24683	114	10	attention	attention	NOUN
brj-24683	114	11	mechanism	mechanism	NOUN
brj-24683	114	12	allows	allow	VERB
brj-24683	114	13	the	the	DET
brj-24683	114	14	model	model	NOUN
brj-24683	114	15	to	to	PART
brj-24683	114	16	dynamically	dynamically	ADV
brj-24683	114	17	adjust	adjust	VERB
brj-24683	114	18	the	the	DET
brj-24683	114	19	attention	attention	NOUN
brj-24683	114	20	allocated	allocate	VERB
brj-24683	114	21	to	to	ADP
brj-24683	114	22	different	different	ADJ
brj-24683	114	23	features	feature	NOUN
brj-24683	114	24	,	,	PUNCT
brj-24683	114	25	enabling	enable	VERB
brj-24683	114	26	adaptive	adaptive	ADJ
brj-24683	114	27	attention	attention	NOUN
brj-24683	114	28	distribution	distribution	NOUN
brj-24683	114	29	across	across	ADP
brj-24683	114	30	different	different	ADJ
brj-24683	114	31	scales	scale	NOUN
brj-24683	114	32	and	and	CCONJ
brj-24683	114	33	regions	region	NOUN
brj-24683	114	34	.	.	PUNCT
brj-24683	115	1	this	this	PRON
brj-24683	115	2	enhances	enhance	VERB
brj-24683	115	3	the	the	DET
brj-24683	115	4	model	model	NOUN
brj-24683	115	5	’s	’s	PART
brj-24683	115	6	ability	ability	NOUN
brj-24683	115	7	to	to	PART
brj-24683	115	8	focus	focus	VERB
brj-24683	115	9	on	on	ADP
brj-24683	115	10	small	small	ADJ
brj-24683	115	11	defect	defect	NOUN
brj-24683	115	12	regions	region	NOUN
brj-24683	115	13	and	and	CCONJ
brj-24683	115	14	facilitates	facilitate	VERB
brj-24683	115	15	feature	feature	NOUN
brj-24683	115	16	extraction	extraction	NOUN
brj-24683	115	17	across	across	ADP
brj-24683	115	18	multiple	multiple	ADJ
brj-24683	115	19	scales	scale	NOUN
brj-24683	115	20	.	.	PUNCT
brj-24683	116	1	the	the	DET
brj-24683	116	2	principle	principle	NOUN
brj-24683	116	3	of	of	ADP
brj-24683	116	4	the	the	DET
brj-24683	116	5	mmsa	mmsa	NOUN
brj-24683	116	6	is	be	AUX
brj-24683	116	7	illustrated	illustrate	VERB
brj-24683	116	8	in	in	ADP
brj-24683	116	9	fig	fig	NOUN
brj-24683	116	10	.	.	PUNCT
brj-24683	117	1	2	2	X
brj-24683	117	2	.	.	X
brj-24683	117	3	first	first	ADV
brj-24683	117	4	,	,	PUNCT
brj-24683	117	5	the	the	DET
brj-24683	117	6	input	input	NOUN
brj-24683	117	7	image	image	NOUN
brj-24683	117	8	feature	feature	NOUN
brj-24683	117	9	vectors	vector	NOUN
brj-24683	117	10	undergo	undergo	VERB
brj-24683	117	11	local	local	ADJ
brj-24683	117	12	max	max	PROPN
brj-24683	117	13	pooling	pooling	NOUN
brj-24683	117	14	(	(	PUNCT
brj-24683	117	15	lmp	lmp	PROPN
brj-24683	117	16	)	)	PUNCT
brj-24683	117	17	to	to	PART
brj-24683	117	18	extract	extract	VERB
brj-24683	117	19	local	local	ADJ
brj-24683	117	20	spatial	spatial	ADJ
brj-24683	117	21	information	information	NOUN
brj-24683	117	22	,	,	PUNCT
brj-24683	117	23	which	which	PRON
brj-24683	117	24	is	be	AUX
brj-24683	117	25	then	then	ADV
brj-24683	117	26	transformed	transform	VERB
brj-24683	117	27	into	into	ADP
brj-24683	117	28	a	a	DET
brj-24683	117	29	1×c×ks×ks	1×c×ks×ks	NUM
brj-24683	117	30	vector	vector	NOUN
brj-24683	117	31	.	.	PUNCT
brj-24683	118	1	the	the	DET
brj-24683	118	2	structure	structure	NOUN
brj-24683	118	3	comprises	comprise	VERB
brj-24683	118	4	two	two	NUM
brj-24683	118	5	branches	branch	NOUN
brj-24683	118	6	:	:	PUNCT
brj-24683	118	7	one	one	NUM
brj-24683	118	8	captures	capture	VERB
brj-24683	118	9	global	global	ADJ
brj-24683	118	10	information	information	NOUN
brj-24683	118	11	,	,	PUNCT
brj-24683	118	12	while	while	SCONJ
brj-24683	118	13	the	the	DET
brj-24683	118	14	other	other	ADJ
brj-24683	118	15	focuses	focus	VERB
brj-24683	118	16	on	on	ADP
brj-24683	118	17	local	local	ADJ
brj-24683	118	18	spatial	spatial	ADJ
brj-24683	118	19	information	information	NOUN
brj-24683	118	20	.	.	PUNCT
brj-24683	119	1	the	the	DET
brj-24683	119	2	information	information	NOUN
brj-24683	119	3	extracted	extract	VERB
brj-24683	119	4	by	by	ADP
brj-24683	119	5	lmp	lmp	PROPN
brj-24683	119	6	is	be	AUX
brj-24683	119	7	transformed	transform	VERB
brj-24683	119	8	into	into	ADP
brj-24683	119	9	a	a	DET
brj-24683	119	10	1×c×ks×ks	1×c×ks×ks	NUM
brj-24683	119	11	vector	vector	NOUN
brj-24683	119	12	,	,	PUNCT
brj-24683	119	13	which	which	PRON
brj-24683	119	14	is	be	AUX
brj-24683	119	15	then	then	ADV
brj-24683	119	16	processed	process	VERB
brj-24683	119	17	by	by	ADP
brj-24683	119	18	a	a	DET
brj-24683	119	19	1d	1d	NUM
brj-24683	119	20	convolution	convolution	NOUN
brj-24683	119	21	(	(	PUNCT
brj-24683	119	22	conv1d	conv1d	NOUN
brj-24683	119	23	)	)	PUNCT
brj-24683	119	24	.	.	PUNCT
brj-24683	120	1	afterward	afterward	ADV
brj-24683	120	2	,	,	PUNCT
brj-24683	120	3	de	de	ADJ
brj-24683	120	4	-	-	ADJ
brj-24683	120	5	pooling	pooling	NOUN
brj-24683	120	6	is	be	AUX
brj-24683	120	7	applied	apply	VERB
brj-24683	120	8	to	to	PART
brj-24683	120	9	recover	recover	VERB
brj-24683	120	10	the	the	DET
brj-24683	120	11	original	original	ADJ
brj-24683	120	12	resolution	resolution	NOUN
brj-24683	120	13	of	of	ADP
brj-24683	120	14	both	both	DET
brj-24683	120	15	vectors	vector	NOUN
brj-24683	120	16	,	,	PUNCT
brj-24683	120	17	followed	follow	VERB
brj-24683	120	18	by	by	ADP
brj-24683	120	19	the	the	DET
brj-24683	120	20	fusion	fusion	NOUN
brj-24683	120	21	of	of	ADP
brj-24683	120	22	attention	attention	NOUN
brj-24683	120	23	information	information	NOUN
brj-24683	120	24	from	from	ADP
brj-24683	120	25	the	the	DET
brj-24683	120	26	two	two	NUM
brj-24683	120	27	branches	branch	NOUN
brj-24683	120	28	.	.	PUNCT
brj-24683	121	1	peer	peer	NOUN
brj-24683	121	2	-	-	PUNCT
brj-24683	121	3	reviewed	review	VERB
brj-24683	121	4	article	article	NOUN
brj-24683	121	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	121	6	dou	dou	PROPN
brj-24683	121	7	&	&	CCONJ
brj-24683	121	8	you	you	PRON
brj-24683	121	9	(	(	PUNCT
brj-24683	121	10	2025	2025	NUM
brj-24683	121	11	)	)	PUNCT
brj-24683	121	12	.	.	PUNCT
brj-24683	122	1	“	"	PUNCT
brj-24683	122	2	wood	wood	NOUN
brj-24683	122	3	defect	defect	NOUN
brj-24683	122	4	identification	identification	NOUN
brj-24683	122	5	,	,	PUNCT
brj-24683	122	6	”	"	PUNCT
brj-24683	122	7	bioresources	bioresource	NOUN
brj-24683	122	8	20(3	20(3	NOUN
brj-24683	122	9	)	)	PUNCT
brj-24683	122	10	,	,	PUNCT
brj-24683	122	11	5709	5709	NUM
brj-24683	122	12	-	-	SYM
brj-24683	122	13	5730	5730	NUM
brj-24683	122	14	.	.	PUNCT
brj-24683	123	1	5715	5715	NUM
brj-24683	123	2	c×h×w	c×h×w	NOUN
brj-24683	123	3	input	input	NOUN
brj-24683	123	4	output	output	NOUN
brj-24683	123	5	lmp	lmp	NOUN
brj-24683	123	6	c×ks×ks	c×ks×ks	ADJ
brj-24683	123	7	gmp	gmp	PROPN
brj-24683	123	8	1×1×c	1×1×c	NUM
brj-24683	123	9	1×1×c	1×1×c	NUM
brj-24683	123	10	conv1d	conv1d	NOUN
brj-24683	123	11	k	k	PROPN
brj-24683	123	12	unap	unap	PROPN
brj-24683	123	13	c×ks×ks	c×ks×ks	PROPN
brj-24683	123	14	c	c	PROPN
brj-24683	123	15	×	×	NOUN
brj-24683	123	16	k	k	X
brj-24683	123	17	s×	s×	PROPN
brj-24683	123	18	k	k	X
brj-24683	123	19	s	s	X
brj-24683	123	20	c×ks×ks	c×ks×ks	ADJ
brj-24683	123	21	1×1×(c×ks×ks	1×1×(c×ks×ks	NUM
brj-24683	123	22	)	)	PUNCT
brj-24683	123	23	..	..	PUNCT
brj-24683	123	24	.	.	PUNCT
brj-24683	124	1	conv1d	conv1d	NOUN
brj-24683	125	1	k	k	NOUN
brj-24683	125	2	r	r	NOUN
brj-24683	125	3	e	e	NOUN
brj-24683	125	4	sh	sh	PROPN
brj-24683	125	5	a	a	DET
brj-24683	125	6	p	p	X
brj-24683	125	7	e	e	NOUN
brj-24683	125	8	reshape	reshape	NOUN
brj-24683	125	9	..	..	PUNCT
brj-24683	125	10	.	.	PUNCT
brj-24683	126	1	num	num	PROPN
brj-24683	127	1	=	=	NOUN
brj-24683	127	2	ks×ks	ks×ks	ADJ
brj-24683	127	3	num	num	ADJ
brj-24683	127	4	num	num	PROPN
brj-24683	127	5	u	u	PROPN
brj-24683	127	6	n	n	DET
brj-24683	127	7	a	a	DET
brj-24683	127	8	p	p	NOUN
brj-24683	127	9	c×h×w	c×h×w	NOUN
brj-24683	127	10	where	where	SCONJ
brj-24683	127	11	ks=5	ks=5	PROPN
brj-24683	127	12	r	r	NOUN
brj-24683	127	13	e	e	NOUN
brj-24683	127	14	sh	sh	PROPN
brj-24683	127	15	a	a	DET
brj-24683	127	16	p	p	X
brj-24683	127	17	e	e	NOUN
brj-24683	127	18	r	r	NOUN
brj-24683	127	19	e	e	NOUN
brj-24683	127	20	sh	sh	PROPN
brj-24683	127	21	a	a	DET
brj-24683	127	22	p	p	X
brj-24683	127	23	e	e	X
brj-24683	127	24	c×h×w	c×h×w	NOUN
brj-24683	127	25	multi	multi	ADJ
brj-24683	127	26	-	-	ADJ
brj-24683	127	27	head	head	ADJ
brj-24683	127	28	attention	attention	NOUN
brj-24683	127	29	w	w	PROPN
brj-24683	127	30	/	/	SYM
brj-24683	127	31	ks	ks	PROPN
brj-24683	127	32	h	h	PROPN
brj-24683	127	33	/	/	SYM
brj-24683	127	34	ks	ks	PROPN
brj-24683	127	35	residual	residual	ADJ
brj-24683	127	36	structure	structure	NOUN
brj-24683	128	1	+	+	NOUN
brj-24683	128	2	×	×	NOUN
brj-24683	128	3	feature	feature	NOUN
brj-24683	128	4	vectors	vector	NOUN
brj-24683	128	5	c×h×w	c×h×w	NOUN
brj-24683	128	6	feature	feature	NOUN
brj-24683	128	7	vectors	vector	NOUN
brj-24683	128	8	after	after	ADP
brj-24683	128	9	lmp	lmp	ADJ
brj-24683	128	10	feature	feature	NOUN
brj-24683	128	11	vectors	vector	NOUN
brj-24683	128	12	after	after	ADP
brj-24683	128	13	gmp	gmp	PROPN
brj-24683	128	14	in	in	ADP
brj-24683	128	15	global	global	ADJ
brj-24683	128	16	branch	branch	NOUN
brj-24683	128	17	feature	feature	NOUN
brj-24683	128	18	vectors	vector	NOUN
brj-24683	128	19	after	after	ADP
brj-24683	128	20	conv1d	conv1d	NOUN
brj-24683	128	21	in	in	ADP
brj-24683	128	22	global	global	ADJ
brj-24683	128	23	branch	branch	NOUN
brj-24683	128	24	feature	feature	NOUN
brj-24683	128	25	vectors	vector	NOUN
brj-24683	128	26	after	after	ADP
brj-24683	128	27	unap	unap	ADJ
brj-24683	128	28	multi	multi	ADJ
brj-24683	128	29	-	-	ADJ
brj-24683	128	30	head	head	ADJ
brj-24683	128	31	attention	attention	NOUN
brj-24683	128	32	feature	feature	NOUN
brj-24683	128	33	vectors	vector	NOUN
brj-24683	128	34	after	after	ADP
brj-24683	128	35	reshape	reshape	NOUN
brj-24683	128	36	in	in	ADP
brj-24683	128	37	local	local	ADJ
brj-24683	128	38	branch	branch	NOUN
brj-24683	128	39	feature	feature	NOUN
brj-24683	128	40	vectors	vector	NOUN
brj-24683	128	41	after	after	ADP
brj-24683	128	42	conv1d	conv1d	NOUN
brj-24683	128	43	in	in	ADP
brj-24683	128	44	local	local	ADJ
brj-24683	128	45	branch	branch	NOUN
brj-24683	128	46	fig	fig	NOUN
brj-24683	128	47	.	.	PUNCT
brj-24683	129	1	2	2	X
brj-24683	129	2	.	.	X
brj-24683	129	3	block	block	NOUN
brj-24683	129	4	diagram	diagram	NOUN
brj-24683	129	5	of	of	ADP
brj-24683	129	6	mmsa	mmsa	NOUN
brj-24683	129	7	for	for	ADP
brj-24683	129	8	multi	multi	ADJ
brj-24683	129	9	-	-	ADJ
brj-24683	129	10	head	head	ADJ
brj-24683	129	11	attention	attention	NOUN
brj-24683	129	12	,	,	PUNCT
brj-24683	129	13	the	the	DET
brj-24683	129	14	input	input	NOUN
brj-24683	129	15	data	data	NOUN
brj-24683	129	16	is	be	AUX
brj-24683	129	17	reshaped	reshape	VERB
brj-24683	129	18	(	(	PUNCT
brj-24683	129	19	feature	feature	NOUN
brj-24683	129	20	vector	vector	NOUN
brj-24683	129	21	transformation	transformation	NOUN
brj-24683	129	22	)	)	PUNCT
brj-24683	129	23	,	,	PUNCT
brj-24683	129	24	and	and	CCONJ
brj-24683	129	25	multi	multi	ADJ
brj-24683	129	26	-	-	ADJ
brj-24683	129	27	head	head	ADJ
brj-24683	129	28	attention	attention	NOUN
brj-24683	129	29	weights	weight	NOUN
brj-24683	129	30	are	be	AUX
brj-24683	129	31	computed	compute	VERB
brj-24683	129	32	to	to	PART
brj-24683	129	33	select	select	VERB
brj-24683	129	34	feature	feature	NOUN
brj-24683	129	35	vectors	vector	NOUN
brj-24683	129	36	that	that	PRON
brj-24683	129	37	meet	meet	VERB
brj-24683	129	38	the	the	DET
brj-24683	129	39	weight	weight	NOUN
brj-24683	129	40	requirements	requirement	NOUN
brj-24683	129	41	.	.	PUNCT
brj-24683	130	1	the	the	DET
brj-24683	130	2	feature	feature	NOUN
brj-24683	130	3	vectors	vector	NOUN
brj-24683	130	4	are	be	AUX
brj-24683	130	5	then	then	ADV
brj-24683	130	6	reshaped	reshape	VERB
brj-24683	130	7	once	once	ADV
brj-24683	130	8	again	again	ADV
brj-24683	130	9	.	.	PUNCT
brj-24683	131	1	finally	finally	ADV
brj-24683	131	2	,	,	PUNCT
brj-24683	131	3	the	the	DET
brj-24683	131	4	attention	attention	NOUN
brj-24683	131	5	outputs	output	NOUN
brj-24683	131	6	from	from	ADP
brj-24683	131	7	the	the	DET
brj-24683	131	8	three	three	NUM
brj-24683	131	9	components	component	NOUN
brj-24683	131	10	are	be	AUX
brj-24683	131	11	weighted	weight	VERB
brj-24683	131	12	and	and	CCONJ
brj-24683	131	13	fused	fuse	VERB
brj-24683	131	14	.	.	PUNCT
brj-24683	132	1	the	the	DET
brj-24683	132	2	mmsa	mmsa	NOUN
brj-24683	132	3	mechanism	mechanism	NOUN
brj-24683	132	4	combines	combine	VERB
brj-24683	132	5	global	global	ADJ
brj-24683	132	6	channel	channel	NOUN
brj-24683	132	7	attention	attention	NOUN
brj-24683	132	8	,	,	PUNCT
brj-24683	132	9	localized	localize	VERB
brj-24683	132	10	channel	channel	NOUN
brj-24683	132	11	attention	attention	NOUN
brj-24683	132	12	that	that	PRON
brj-24683	132	13	refines	refine	VERB
brj-24683	132	14	spatial	spatial	ADJ
brj-24683	132	15	information	information	NOUN
brj-24683	132	16	,	,	PUNCT
brj-24683	132	17	and	and	CCONJ
brj-24683	132	18	multi	multi	ADJ
brj-24683	132	19	-	-	ADJ
brj-24683	132	20	head	head	ADJ
brj-24683	132	21	attention	attention	NOUN
brj-24683	132	22	results	result	NOUN
brj-24683	132	23	.	.	PUNCT
brj-24683	133	1	to	to	PART
brj-24683	133	2	achieve	achieve	VERB
brj-24683	133	3	the	the	DET
brj-24683	133	4	fusion	fusion	NOUN
brj-24683	133	5	of	of	ADP
brj-24683	133	6	spatial	spatial	ADJ
brj-24683	133	7	and	and	CCONJ
brj-24683	133	8	channel	channel	NOUN
brj-24683	133	9	attention	attention	NOUN
brj-24683	133	10	,	,	PUNCT
brj-24683	133	11	1d	1d	NUM
brj-24683	133	12	convolution	convolution	NOUN
brj-24683	133	13	(	(	PUNCT
brj-24683	133	14	conv1d	conv1d	NOUN
brj-24683	133	15	)	)	PUNCT
brj-24683	133	16	as	as	SCONJ
brj-24683	133	17	shown	show	VERB
brj-24683	133	18	in	in	ADP
brj-24683	133	19	fig	fig	NOUN
brj-24683	133	20	.	.	PUNCT
brj-24683	134	1	2	2	NUM
brj-24683	134	2	is	be	AUX
brj-24683	134	3	employed	employ	VERB
brj-24683	134	4	.	.	PUNCT
brj-24683	135	1	the	the	DET
brj-24683	135	2	size	size	NOUN
brj-24683	135	3	of	of	ADP
brj-24683	135	4	the	the	DET
brj-24683	135	5	1d	1d	NUM
brj-24683	135	6	convolution	convolution	NOUN
brj-24683	135	7	kernel	kernel	PROPN
brj-24683	135	8	k	k	PROPN
brj-24683	135	9	is	be	AUX
brj-24683	135	10	proportional	proportional	ADJ
brj-24683	135	11	to	to	ADP
brj-24683	135	12	the	the	DET
brj-24683	135	13	number	number	NOUN
brj-24683	135	14	of	of	ADP
brj-24683	135	15	channels	channel	NOUN
brj-24683	135	16	c.	c.	NOUN
brj-24683	135	17	when	when	SCONJ
brj-24683	135	18	capturing	capture	VERB
brj-24683	135	19	local	local	ADJ
brj-24683	135	20	cross	cross	ADJ
brj-24683	135	21	-	-	ADJ
brj-24683	135	22	channel	channel	ADJ
brj-24683	135	23	interactions	interaction	NOUN
brj-24683	135	24	,	,	PUNCT
brj-24683	135	25	only	only	ADV
brj-24683	135	26	the	the	DET
brj-24683	135	27	relationship	relationship	NOUN
brj-24683	135	28	between	between	ADP
brj-24683	135	29	each	each	DET
brj-24683	135	30	channel	channel	NOUN
brj-24683	135	31	and	and	CCONJ
brj-24683	135	32	its	its	PRON
brj-24683	135	33	k	k	ADJ
brj-24683	135	34	neighboring	neighboring	NOUN
brj-24683	135	35	channels	channel	NOUN
brj-24683	135	36	is	be	AUX
brj-24683	135	37	considered	consider	VERB
brj-24683	135	38	.	.	PUNCT
brj-24683	136	1	the	the	DET
brj-24683	136	2	selection	selection	NOUN
brj-24683	136	3	of	of	ADP
brj-24683	136	4	k	k	PROPN
brj-24683	136	5	follows	follow	VERB
brj-24683	136	6	the	the	DET
brj-24683	136	7	approach	approach	NOUN
brj-24683	136	8	used	use	VERB
brj-24683	136	9	in	in	ADP
brj-24683	136	10	eca	eca	PROPN
brj-24683	136	11	(	(	PUNCT
brj-24683	136	12	wang	wang	PROPN
brj-24683	136	13	et	et	PROPN
brj-24683	136	14	al	al	PROPN
brj-24683	136	15	.	.	PROPN
brj-24683	136	16	2020	2020	NUM
brj-24683	136	17	)	)	PUNCT
brj-24683	136	18	.	.	PUNCT
brj-24683	137	1	figure	figure	NOUN
brj-24683	137	2	3	3	NUM
brj-24683	137	3	illustrates	illustrate	VERB
brj-24683	137	4	the	the	DET
brj-24683	137	5	relationships	relationship	NOUN
brj-24683	137	6	between	between	ADP
brj-24683	137	7	global	global	ADJ
brj-24683	137	8	max	max	PROPN
brj-24683	137	9	pooling	pooling	PROPN
brj-24683	137	10	(	(	PUNCT
brj-24683	137	11	gmp	gmp	PROPN
brj-24683	137	12	)	)	PUNCT
brj-24683	137	13	,	,	PUNCT
brj-24683	137	14	lmp	lmp	NOUN
brj-24683	137	15	,	,	PUNCT
brj-24683	137	16	and	and	CCONJ
brj-24683	137	17	unpooling	unpooling	ADJ
brj-24683	137	18	average	average	ADJ
brj-24683	137	19	pooling	pooling	NOUN
brj-24683	137	20	(	(	PUNCT
brj-24683	137	21	unap	unap	NOUN
brj-24683	137	22	)	)	PUNCT
brj-24683	137	23	within	within	ADP
brj-24683	137	24	the	the	DET
brj-24683	137	25	mmsa	mmsa	NOUN
brj-24683	137	26	structure	structure	NOUN
brj-24683	137	27	.	.	PUNCT
brj-24683	138	1	the	the	DET
brj-24683	138	2	gmp	gmp	PROPN
brj-24683	138	3	extracts	extract	VERB
brj-24683	138	4	the	the	DET
brj-24683	138	5	global	global	ADJ
brj-24683	138	6	maximum	maximum	ADJ
brj-24683	138	7	feature	feature	NOUN
brj-24683	138	8	,	,	PUNCT
brj-24683	138	9	producing	produce	VERB
brj-24683	138	10	a	a	DET
brj-24683	138	11	feature	feature	NOUN
brj-24683	138	12	map	map	NOUN
brj-24683	138	13	of	of	ADP
brj-24683	138	14	size	size	NOUN
brj-24683	138	15	1×1	1×1	NOUN
brj-24683	138	16	.	.	PUNCT
brj-24683	139	1	the	the	DET
brj-24683	139	2	lmp	lmp	NOUN
brj-24683	139	3	divides	divide	VERB
brj-24683	139	4	the	the	DET
brj-24683	139	5	entire	entire	ADJ
brj-24683	139	6	feature	feature	NOUN
brj-24683	139	7	map	map	NOUN
brj-24683	139	8	into	into	ADP
brj-24683	139	9	k×k	k×k	PROPN
brj-24683	139	10	small	small	ADJ
brj-24683	139	11	regions	region	NOUN
brj-24683	139	12	and	and	CCONJ
brj-24683	139	13	performs	perform	VERB
brj-24683	139	14	k×k	k×k	PROPN
brj-24683	139	15	max	max	PROPN
brj-24683	139	16	pooling	pooling	NOUN
brj-24683	139	17	within	within	ADP
brj-24683	139	18	each	each	DET
brj-24683	139	19	region	region	NOUN
brj-24683	139	20	.	.	PUNCT
brj-24683	140	1	the	the	DET
brj-24683	140	2	unap	unap	NOUN
brj-24683	140	3	,	,	PUNCT
brj-24683	140	4	also	also	ADV
brj-24683	140	5	known	know	VERB
brj-24683	140	6	as	as	ADP
brj-24683	140	7	reverse	reverse	ADJ
brj-24683	140	8	pooling	pooling	NOUN
brj-24683	140	9	,	,	PUNCT
brj-24683	140	10	focuses	focus	VERB
brj-24683	140	11	on	on	ADP
brj-24683	140	12	preserving	preserve	VERB
brj-24683	140	13	the	the	DET
brj-24683	140	14	attributes	attribute	NOUN
brj-24683	140	15	of	of	ADP
brj-24683	140	16	the	the	DET
brj-24683	140	17	pooled	pool	VERB
brj-24683	140	18	features	feature	NOUN
brj-24683	140	19	while	while	SCONJ
brj-24683	140	20	expanding	expand	VERB
brj-24683	140	21	them	they	PRON
brj-24683	140	22	to	to	ADP
brj-24683	140	23	the	the	DET
brj-24683	140	24	desired	desire	VERB
brj-24683	140	25	size	size	NOUN
brj-24683	140	26	.	.	PUNCT
brj-24683	141	1	the	the	DET
brj-24683	141	2	unap	unap	NOUN
brj-24683	141	3	can	can	AUX
brj-24683	141	4	be	be	AUX
brj-24683	141	5	implemented	implement	VERB
brj-24683	141	6	using	use	VERB
brj-24683	141	7	adaptive	adaptive	ADJ
brj-24683	141	8	pooling	pooling	NOUN
brj-24683	141	9	to	to	PART
brj-24683	141	10	ensure	ensure	VERB
brj-24683	141	11	that	that	SCONJ
brj-24683	141	12	the	the	DET
brj-24683	141	13	output	output	NOUN
brj-24683	141	14	size	size	NOUN
brj-24683	141	15	matches	match	VERB
brj-24683	141	16	the	the	DET
brj-24683	141	17	size	size	NOUN
brj-24683	141	18	of	of	ADP
brj-24683	141	19	the	the	DET
brj-24683	141	20	original	original	ADJ
brj-24683	141	21	feature	feature	NOUN
brj-24683	141	22	map	map	NOUN
brj-24683	141	23	.	.	PUNCT
brj-24683	142	1	when	when	SCONJ
brj-24683	142	2	extending	extend	VERB
brj-24683	142	3	lmp	lmp	NOUN
brj-24683	142	4	,	,	PUNCT
brj-24683	142	5	if	if	SCONJ
brj-24683	142	6	the	the	DET
brj-24683	142	7	size	size	NOUN
brj-24683	142	8	of	of	ADP
brj-24683	142	9	the	the	DET
brj-24683	142	10	pooled	pool	VERB
brj-24683	142	11	features	feature	NOUN
brj-24683	142	12	is	be	AUX
brj-24683	142	13	not	not	PART
brj-24683	142	14	1×1	1×1	NUM
brj-24683	142	15	,	,	PUNCT
brj-24683	142	16	a	a	DET
brj-24683	142	17	direct	direct	ADJ
brj-24683	142	18	expansion	expansion	NOUN
brj-24683	142	19	operation	operation	NOUN
brj-24683	142	20	is	be	AUX
brj-24683	142	21	not	not	PART
brj-24683	142	22	feasible	feasible	ADJ
brj-24683	142	23	.	.	PUNCT
brj-24683	143	1	instead	instead	ADV
brj-24683	143	2	,	,	PUNCT
brj-24683	143	3	the	the	DET
brj-24683	143	4	unap	unap	ADJ
brj-24683	143	5	process	process	NOUN
brj-24683	143	6	must	must	AUX
brj-24683	143	7	be	be	AUX
brj-24683	143	8	employed	employ	VERB
brj-24683	143	9	to	to	PART
brj-24683	143	10	restore	restore	VERB
brj-24683	143	11	the	the	DET
brj-24683	143	12	feature	feature	NOUN
brj-24683	143	13	map	map	NOUN
brj-24683	143	14	to	to	ADP
brj-24683	143	15	its	its	PRON
brj-24683	143	16	original	original	ADJ
brj-24683	143	17	size	size	NOUN
brj-24683	143	18	.	.	PUNCT
brj-24683	144	1	as	as	SCONJ
brj-24683	144	2	shown	show	VERB
brj-24683	144	3	in	in	ADP
brj-24683	144	4	fig	fig	NOUN
brj-24683	144	5	.	.	PUNCT
brj-24683	145	1	3	3	NUM
brj-24683	145	2	,	,	PUNCT
brj-24683	145	3	unap	unap	NOUN
brj-24683	145	4	restores	restore	VERB
brj-24683	145	5	the	the	DET
brj-24683	145	6	resolution	resolution	NOUN
brj-24683	145	7	of	of	ADP
brj-24683	145	8	the	the	DET
brj-24683	145	9	original	original	ADJ
brj-24683	145	10	feature	feature	NOUN
brj-24683	145	11	map	map	NOUN
brj-24683	145	12	by	by	ADP
brj-24683	145	13	applying	apply	VERB
brj-24683	145	14	parameters	parameter	NOUN
brj-24683	145	15	derived	derive	VERB
brj-24683	145	16	from	from	ADP
brj-24683	145	17	the	the	DET
brj-24683	145	18	unpooling	unpooling	ADJ
brj-24683	145	19	operation	operation	NOUN
brj-24683	145	20	,	,	PUNCT
brj-24683	145	21	filling	fill	VERB
brj-24683	145	22	the	the	DET
brj-24683	145	23	corresponding	correspond	VERB
brj-24683	145	24	positions	position	NOUN
brj-24683	145	25	with	with	ADP
brj-24683	145	26	the	the	DET
brj-24683	145	27	pooled	pool	VERB
brj-24683	145	28	results	result	NOUN
brj-24683	145	29	.	.	PUNCT
brj-24683	146	1	peer	peer	NOUN
brj-24683	146	2	-	-	PUNCT
brj-24683	146	3	reviewed	review	VERB
brj-24683	146	4	article	article	NOUN
brj-24683	146	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	146	6	dou	dou	PROPN
brj-24683	146	7	&	&	CCONJ
brj-24683	146	8	you	you	PRON
brj-24683	146	9	(	(	PUNCT
brj-24683	146	10	2025	2025	NUM
brj-24683	146	11	)	)	PUNCT
brj-24683	146	12	.	.	PUNCT
brj-24683	147	1	“	"	PUNCT
brj-24683	147	2	wood	wood	NOUN
brj-24683	147	3	defect	defect	NOUN
brj-24683	147	4	identification	identification	NOUN
brj-24683	147	5	,	,	PUNCT
brj-24683	147	6	”	"	PUNCT
brj-24683	147	7	bioresources	bioresource	NOUN
brj-24683	147	8	20(3	20(3	NOUN
brj-24683	147	9	)	)	PUNCT
brj-24683	147	10	,	,	PUNCT
brj-24683	147	11	5709	5709	NUM
brj-24683	147	12	-	-	SYM
brj-24683	147	13	5730	5730	NUM
brj-24683	147	14	.	.	PUNCT
brj-24683	148	1	5716	5716	NUM
brj-24683	148	2	fig	fig	NOUN
brj-24683	148	3	.	.	PUNCT
brj-24683	149	1	3	3	X
brj-24683	149	2	.	.	X
brj-24683	149	3	relationships	relationship	NOUN
brj-24683	149	4	between	between	ADP
brj-24683	149	5	gmp	gmp	PROPN
brj-24683	149	6	,	,	PUNCT
brj-24683	149	7	lmp	lmp	PROPN
brj-24683	149	8	,	,	PUNCT
brj-24683	149	9	and	and	CCONJ
brj-24683	149	10	unap	unap	NOUN
brj-24683	149	11	within	within	ADP
brj-24683	149	12	the	the	DET
brj-24683	149	13	mmsa	mmsa	NOUN
brj-24683	149	14	the	the	DET
brj-24683	149	15	structural	structural	ADJ
brj-24683	149	16	relationships	relationship	NOUN
brj-24683	149	17	among	among	ADP
brj-24683	149	18	lmp	lmp	PROPN
brj-24683	149	19	,	,	PUNCT
brj-24683	149	20	gmp	gmp	PROPN
brj-24683	149	21	,	,	PUNCT
brj-24683	149	22	and	and	CCONJ
brj-24683	149	23	unap	unap	NOUN
brj-24683	149	24	in	in	ADP
brj-24683	149	25	mmsa	mmsa	NOUN
brj-24683	149	26	are	be	AUX
brj-24683	149	27	illustrated	illustrate	VERB
brj-24683	149	28	in	in	ADP
brj-24683	149	29	fig	fig	NOUN
brj-24683	149	30	.	.	PUNCT
brj-24683	150	1	2	2	NUM
brj-24683	150	2	and	and	CCONJ
brj-24683	150	3	fig	fig	NOUN
brj-24683	150	4	.	.	PUNCT
brj-24683	151	1	3	3	NUM
brj-24683	152	1	and	and	CCONJ
brj-24683	152	2	can	can	AUX
brj-24683	152	3	be	be	AUX
brj-24683	152	4	summarized	summarize	VERB
brj-24683	152	5	as	as	SCONJ
brj-24683	152	6	follows	follow	VERB
brj-24683	152	7	:	:	PUNCT
brj-24683	152	8	lmp	lmp	PROPN
brj-24683	152	9	⟶	⟶	NOUN
brj-24683	152	10	(	(	PUNCT
brj-24683	152	11	c	c	NOUN
brj-24683	152	12	,	,	PUNCT
brj-24683	152	13	ks	ks	NOUN
brj-24683	152	14	,	,	PUNCT
brj-24683	152	15	ks	ks	NOUN
brj-24683	152	16	)	)	PUNCT
brj-24683	152	17	⟶	⟶	NOUN
brj-24683	152	18	gmp	gmp	PROPN
brj-24683	152	19	⟶	⟶	PROPN
brj-24683	152	20	(	(	PUNCT
brj-24683	152	21	1	1	NUM
brj-24683	152	22	,	,	PUNCT
brj-24683	152	23	1	1	NUM
brj-24683	152	24	,	,	PUNCT
brj-24683	152	25	c	c	NOUN
brj-24683	152	26	)	)	PUNCT
brj-24683	152	27	⟶	⟶	NOUN
brj-24683	152	28	conv1d	conv1d	NOUN
brj-24683	152	29	⟶	⟶	NOUN
brj-24683	152	30	(	(	PUNCT
brj-24683	152	31	1	1	NUM
brj-24683	152	32	,	,	PUNCT
brj-24683	152	33	1	1	NUM
brj-24683	152	34	,	,	PUNCT
brj-24683	152	35	c	c	NOUN
brj-24683	152	36	)	)	PUNCT
brj-24683	152	37	⟶	⟶	NOUN
brj-24683	152	38	unap	unap	NOUN
brj-24683	152	39	.	.	PUNCT
brj-24683	153	1	when	when	SCONJ
brj-24683	153	2	expanding	expand	VERB
brj-24683	153	3	lmp	lmp	NOUN
brj-24683	153	4	,	,	PUNCT
brj-24683	153	5	direct	direct	ADJ
brj-24683	153	6	expansion	expansion	NOUN
brj-24683	153	7	is	be	AUX
brj-24683	153	8	not	not	PART
brj-24683	153	9	feasible	feasible	ADJ
brj-24683	153	10	if	if	SCONJ
brj-24683	153	11	the	the	DET
brj-24683	153	12	feature	feature	NOUN
brj-24683	153	13	size	size	NOUN
brj-24683	153	14	is	be	AUX
brj-24683	153	15	not	not	PART
brj-24683	153	16	1×1	1×1	ADJ
brj-24683	153	17	.	.	PUNCT
brj-24683	154	1	to	to	PART
brj-24683	154	2	address	address	VERB
brj-24683	154	3	this	this	DET
brj-24683	154	4	limitation	limitation	NOUN
brj-24683	154	5	,	,	PUNCT
brj-24683	154	6	the	the	DET
brj-24683	154	7	unap	unap	ADJ
brj-24683	154	8	process	process	NOUN
brj-24683	154	9	is	be	AUX
brj-24683	154	10	employed	employ	VERB
brj-24683	154	11	to	to	PART
brj-24683	154	12	restore	restore	VERB
brj-24683	154	13	the	the	DET
brj-24683	154	14	feature	feature	NOUN
brj-24683	154	15	map	map	NOUN
brj-24683	154	16	to	to	ADP
brj-24683	154	17	its	its	PRON
brj-24683	154	18	original	original	ADJ
brj-24683	154	19	resolution	resolution	NOUN
brj-24683	154	20	.	.	PUNCT
brj-24683	155	1	specifically	specifically	ADV
brj-24683	155	2	,	,	PUNCT
brj-24683	155	3	during	during	ADP
brj-24683	155	4	the	the	DET
brj-24683	155	5	lmp	lmp	PROPN
brj-24683	155	6	⟶	⟶	NOUN
brj-24683	155	7	(	(	PUNCT
brj-24683	155	8	c	c	NOUN
brj-24683	155	9	,	,	PUNCT
brj-24683	155	10	ks	ks	NOUN
brj-24683	155	11	,	,	PUNCT
brj-24683	155	12	ks	ks	NOUN
brj-24683	155	13	)	)	PUNCT
brj-24683	155	14	⟶	⟶	NOUN
brj-24683	155	15	gmp	gmp	PROPN
brj-24683	155	16	⟶	⟶	PROPN
brj-24683	155	17	(	(	PUNCT
brj-24683	155	18	1	1	NUM
brj-24683	155	19	,	,	PUNCT
brj-24683	155	20	1	1	NUM
brj-24683	155	21	,	,	PUNCT
brj-24683	155	22	c	c	NOUN
brj-24683	155	23	)	)	PUNCT
brj-24683	155	24	⟶	⟶	NOUN
brj-24683	155	25	conv1d	conv1d	NOUN
brj-24683	155	26	⟶	⟶	NOUN
brj-24683	155	27	(	(	PUNCT
brj-24683	155	28	1	1	NUM
brj-24683	155	29	,	,	PUNCT
brj-24683	155	30	1	1	NUM
brj-24683	155	31	,	,	PUNCT
brj-24683	155	32	c	c	NOUN
brj-24683	155	33	)	)	PUNCT
brj-24683	155	34	⟶	⟶	NOUN
brj-24683	155	35	unap	unap	ADJ
brj-24683	155	36	sequence	sequence	NOUN
brj-24683	155	37	,	,	PUNCT
brj-24683	155	38	the	the	DET
brj-24683	155	39	unap	unap	NOUN
brj-24683	155	40	utilizes	utilize	VERB
brj-24683	155	41	the	the	DET
brj-24683	155	42	parameters	parameter	NOUN
brj-24683	155	43	from	from	ADP
brj-24683	155	44	the	the	DET
brj-24683	155	45	pooling	pool	VERB
brj-24683	155	46	operation	operation	NOUN
brj-24683	155	47	to	to	PART
brj-24683	155	48	recover	recover	VERB
brj-24683	155	49	the	the	DET
brj-24683	155	50	resolution	resolution	NOUN
brj-24683	155	51	of	of	ADP
brj-24683	155	52	the	the	DET
brj-24683	155	53	original	original	ADJ
brj-24683	155	54	feature	feature	NOUN
brj-24683	155	55	map	map	NOUN
brj-24683	155	56	.	.	PUNCT
brj-24683	156	1	subsequently	subsequently	ADV
brj-24683	156	2	,	,	PUNCT
brj-24683	156	3	the	the	DET
brj-24683	156	4	pooled	pool	VERB
brj-24683	156	5	results	result	NOUN
brj-24683	156	6	are	be	AUX
brj-24683	156	7	placed	place	VERB
brj-24683	156	8	at	at	ADP
brj-24683	156	9	their	their	PRON
brj-24683	156	10	corresponding	correspond	VERB
brj-24683	156	11	locations	location	NOUN
brj-24683	156	12	.	.	PUNCT
brj-24683	157	1	in	in	ADP
brj-24683	157	2	this	this	DET
brj-24683	157	3	process	process	NOUN
brj-24683	157	4	,	,	PUNCT
brj-24683	157	5	a	a	DET
brj-24683	157	6	reshape	reshape	NOUN
brj-24683	157	7	operation	operation	NOUN
brj-24683	157	8	is	be	AUX
brj-24683	157	9	introduced	introduce	VERB
brj-24683	157	10	within	within	ADP
brj-24683	157	11	the	the	DET
brj-24683	157	12	lmp	lmp	PROPN
brj-24683	157	13	⟶	⟶	NOUN
brj-24683	157	14	gmp	gmp	PROPN
brj-24683	157	15	⟶	⟶	PROPN
brj-24683	157	16	unap	unap	ADJ
brj-24683	157	17	pathway	pathway	NOUN
brj-24683	157	18	to	to	PART
brj-24683	157	19	facilitate	facilitate	VERB
brj-24683	157	20	proper	proper	ADJ
brj-24683	157	21	feature	feature	NOUN
brj-24683	157	22	alignment	alignment	NOUN
brj-24683	157	23	.	.	PUNCT
brj-24683	158	1	as	as	SCONJ
brj-24683	158	2	depicted	depict	VERB
brj-24683	158	3	in	in	ADP
brj-24683	158	4	fig	fig	NOUN
brj-24683	158	5	.	.	PUNCT
brj-24683	159	1	2	2	NUM
brj-24683	159	2	,	,	PUNCT
brj-24683	159	3	after	after	ADP
brj-24683	159	4	extracting	extract	VERB
brj-24683	159	5	global	global	ADJ
brj-24683	159	6	attention	attention	NOUN
brj-24683	159	7	and	and	CCONJ
brj-24683	159	8	local	local	ADJ
brj-24683	159	9	attention	attention	NOUN
brj-24683	159	10	within	within	ADP
brj-24683	159	11	the	the	DET
brj-24683	159	12	mmsa	mmsa	NOUN
brj-24683	159	13	module	module	NOUN
brj-24683	159	14	,	,	PUNCT
brj-24683	159	15	the	the	DET
brj-24683	159	16	two	two	NUM
brj-24683	159	17	attention	attention	NOUN
brj-24683	159	18	mechanisms	mechanism	NOUN
brj-24683	159	19	are	be	AUX
brj-24683	159	20	adaptively	adaptively	ADV
brj-24683	159	21	fused	fuse	VERB
brj-24683	159	22	using	use	VERB
brj-24683	159	23	weighted	weight	VERB
brj-24683	159	24	summation	summation	NOUN
brj-24683	159	25	.	.	PUNCT
brj-24683	160	1	the	the	DET
brj-24683	160	2	resulting	result	VERB
brj-24683	160	3	fused	fuse	VERB
brj-24683	160	4	features	feature	NOUN
brj-24683	160	5	are	be	AUX
brj-24683	160	6	combined	combine	VERB
brj-24683	160	7	with	with	ADP
brj-24683	160	8	the	the	DET
brj-24683	160	9	initial	initial	ADJ
brj-24683	160	10	input	input	NOUN
brj-24683	160	11	via	via	ADP
brj-24683	160	12	a	a	DET
brj-24683	160	13	residual	residual	ADJ
brj-24683	160	14	connection	connection	NOUN
brj-24683	160	15	,	,	PUNCT
brj-24683	160	16	followed	follow	VERB
brj-24683	160	17	by	by	ADP
brj-24683	160	18	integration	integration	NOUN
brj-24683	160	19	with	with	ADP
brj-24683	160	20	the	the	DET
brj-24683	160	21	multi	multi	ADJ
brj-24683	160	22	-	-	ADJ
brj-24683	160	23	head	head	ADJ
brj-24683	160	24	attention	attention	NOUN
brj-24683	160	25	output	output	NOUN
brj-24683	160	26	.	.	PUNCT
brj-24683	161	1	this	this	DET
brj-24683	161	2	final	final	ADJ
brj-24683	161	3	step	step	NOUN
brj-24683	161	4	produces	produce	VERB
brj-24683	161	5	the	the	DET
brj-24683	161	6	output	output	NOUN
brj-24683	161	7	of	of	ADP
brj-24683	161	8	the	the	DET
brj-24683	161	9	mmsa	mmsa	NOUN
brj-24683	161	10	.	.	PUNCT
brj-24683	162	1	additionally	additionally	ADV
brj-24683	162	2	,	,	PUNCT
brj-24683	162	3	the	the	DET
brj-24683	162	4	mmsa	mmsa	NOUN
brj-24683	162	5	adopts	adopt	VERB
brj-24683	162	6	the	the	DET
brj-24683	162	7	hard	hard	ADJ
brj-24683	162	8	-	-	PUNCT
brj-24683	162	9	sigmoid	sigmoid	NOUN
brj-24683	162	10	function	function	NOUN
brj-24683	162	11	as	as	ADP
brj-24683	162	12	the	the	DET
brj-24683	162	13	normalization	normalization	NOUN
brj-24683	162	14	mechanism	mechanism	NOUN
brj-24683	162	15	,	,	PUNCT
brj-24683	162	16	effectively	effectively	ADV
brj-24683	162	17	mitigating	mitigate	VERB
brj-24683	162	18	gradient	gradient	ADJ
brj-24683	162	19	vanishing	vanish	VERB
brj-24683	162	20	issues	issue	NOUN
brj-24683	162	21	during	during	ADP
brj-24683	162	22	backpropagation	backpropagation	NOUN
brj-24683	162	23	.	.	PUNCT
brj-24683	163	1	fig	fig	NOUN
brj-24683	163	2	.	.	PUNCT
brj-24683	164	1	4	4	X
brj-24683	164	2	.	.	X
brj-24683	164	3	processing	processing	NOUN
brj-24683	164	4	flow	flow	NOUN
brj-24683	164	5	of	of	ADP
brj-24683	164	6	the	the	DET
brj-24683	164	7	mmsa	mmsa	NOUN
brj-24683	164	8	the	the	DET
brj-24683	164	9	detailed	detailed	ADJ
brj-24683	164	10	processing	processing	NOUN
brj-24683	164	11	flow	flow	NOUN
brj-24683	164	12	of	of	ADP
brj-24683	164	13	the	the	DET
brj-24683	164	14	multi	multi	ADJ
brj-24683	164	15	-	-	ADJ
brj-24683	164	16	head	head	ADJ
brj-24683	164	17	mixed	mixed	ADJ
brj-24683	164	18	self	self	NOUN
brj-24683	164	19	-	-	PUNCT
brj-24683	164	20	attention	attention	NOUN
brj-24683	164	21	(	(	PUNCT
brj-24683	164	22	mmsa	mmsa	NOUN
brj-24683	164	23	)	)	PUNCT
brj-24683	164	24	mechanism	mechanism	NOUN
brj-24683	164	25	is	be	AUX
brj-24683	164	26	illustrated	illustrate	VERB
brj-24683	164	27	in	in	ADP
brj-24683	164	28	fig	fig	NOUN
brj-24683	164	29	.	.	PUNCT
brj-24683	165	1	4	4	X
brj-24683	165	2	.	.	X
brj-24683	165	3	it	it	PRON
brj-24683	165	4	involves	involve	VERB
brj-24683	165	5	the	the	DET
brj-24683	165	6	following	follow	VERB
brj-24683	165	7	steps	step	NOUN
brj-24683	165	8	:	:	PUNCT
brj-24683	165	9	first	first	ADV
brj-24683	165	10	,	,	PUNCT
brj-24683	165	11	the	the	DET
brj-24683	165	12	feature	feature	NOUN
brj-24683	165	13	map	map	NOUN
brj-24683	165	14	undergoes	undergo	VERB
brj-24683	165	15	both	both	DET
brj-24683	165	16	local	local	ADJ
brj-24683	165	17	max	max	PROPN
brj-24683	165	18	pooling	pooling	NOUN
brj-24683	165	19	(	(	PUNCT
brj-24683	165	20	lmp	lmp	PROPN
brj-24683	165	21	)	)	PUNCT
brj-24683	165	22	and	and	CCONJ
brj-24683	165	23	global	global	ADJ
brj-24683	165	24	max	max	PROPN
brj-24683	165	25	pooling	pooling	PROPN
brj-24683	165	26	(	(	PUNCT
brj-24683	165	27	gmp	gmp	PROPN
brj-24683	165	28	)	)	PUNCT
brj-24683	165	29	to	to	PART
brj-24683	165	30	extract	extract	VERB
brj-24683	165	31	local	local	ADJ
brj-24683	165	32	and	and	CCONJ
brj-24683	165	33	global	global	ADJ
brj-24683	165	34	contextual	contextual	ADJ
brj-24683	165	35	information	information	NOUN
brj-24683	165	36	.	.	PUNCT
brj-24683	166	1	the	the	DET
brj-24683	166	2	pooled	pool	VERB
brj-24683	166	3	feature	feature	NOUN
brj-24683	166	4	map	map	NOUN
brj-24683	166	5	6	6	NUM
brj-24683	166	6	6	6	NUM
brj-24683	166	7	6	6	NUM
brj-24683	166	8	6	6	NUM
brj-24683	166	9	8	8	NUM
brj-24683	166	10	8	8	NUM
brj-24683	166	11	8	8	NUM
brj-24683	166	12	8	8	NUM
brj-24683	166	13	3	3	NUM
brj-24683	166	14	3	3	NUM
brj-24683	166	15	3	3	NUM
brj-24683	166	16	3	3	NUM
brj-24683	166	17	3	3	NUM
brj-24683	166	18	3	3	NUM
brj-24683	166	19	3	3	NUM
brj-24683	166	20	3	3	NUM
brj-24683	166	21	3	3	NUM
brj-24683	166	22	6	6	NUM
brj-24683	166	23	1	1	NUM
brj-24683	166	24	6	6	NUM
brj-24683	166	25	2	2	NUM
brj-24683	166	26	6	6	NUM
brj-24683	166	27	4	4	NUM
brj-24683	166	28	8	8	NUM
brj-24683	166	29	3	3	NUM
brj-24683	166	30	1	1	NUM
brj-24683	166	31	3	3	NUM
brj-24683	166	32	2	2	NUM
brj-24683	166	33	1	1	NUM
brj-24683	166	34	1	1	NUM
brj-24683	166	35	0	0	NUM
brj-24683	166	36	0	0	NUM
brj-24683	166	37	6	6	NUM
brj-24683	166	38	8	8	NUM
brj-24683	166	39	3	3	NUM
brj-24683	166	40	3	3	NUM
brj-24683	166	41	8	8	NUM
brj-24683	166	42	8	8	NUM
brj-24683	166	43	8	8	NUM
brj-24683	166	44	8	8	NUM
brj-24683	166	45	8	8	NUM
brj-24683	166	46	8	8	NUM
brj-24683	166	47	8	8	NUM
brj-24683	166	48	8	8	NUM
brj-24683	166	49	8	8	NUM
brj-24683	166	50	8	8	NUM
brj-24683	166	51	8	8	NUM
brj-24683	166	52	8	8	NUM
brj-24683	166	53	8	8	NUM
brj-24683	166	54	8	8	NUM
brj-24683	166	55	8	8	NUM
brj-24683	166	56	8	8	NUM
brj-24683	166	57	3	3	NUM
brj-24683	166	58	6	6	NUM
brj-24683	166	59	1	1	NUM
brj-24683	166	60	6	6	NUM
brj-24683	166	61	2	2	NUM
brj-24683	166	62	6	6	NUM
brj-24683	166	63	4	4	NUM
brj-24683	166	64	8	8	NUM
brj-24683	166	65	3	3	NUM
brj-24683	166	66	1	1	NUM
brj-24683	166	67	3	3	NUM
brj-24683	166	68	2	2	NUM
brj-24683	166	69	1	1	NUM
brj-24683	166	70	1	1	NUM
brj-24683	166	71	0	0	NUM
brj-24683	166	72	0	0	NUM
brj-24683	166	73	8	8	NUM
brj-24683	166	74	max(3,1,6,6)=6	max(3,1,6,6)=6	NOUN
brj-24683	166	75	lmp	lmp	PROPN
brj-24683	166	76	unap	unap	PROPN
brj-24683	166	77	unavgpool	unavgpool	ADJ
brj-24683	166	78	gmp	gmp	PROPN
brj-24683	166	79	unap	unap	NOUN
brj-24683	166	80	or	or	CCONJ
brj-24683	166	81	expand	expand	VERB
brj-24683	166	82	input	input	NOUN
brj-24683	166	83	lmp	lmp	PROPN
brj-24683	166	84	gmp	gmp	PROPN
brj-24683	166	85	reshape	reshape	NOUN
brj-24683	166	86	conv1d	conv1d	PROPN
brj-24683	166	87	unap	unap	PROPN
brj-24683	166	88	reshape	reshape	NOUN
brj-24683	166	89	conv1d	conv1d	PROPN
brj-24683	166	90	reshape	reshape	NOUN
brj-24683	166	91	unap	unap	NOUN
brj-24683	166	92	reshape	reshape	NOUN
brj-24683	166	93	multi	multi	ADJ
brj-24683	166	94	-	-	ADJ
brj-24683	166	95	head	head	ADJ
brj-24683	166	96	attention	attention	NOUN
brj-24683	166	97	reshape	reshape	NOUN
brj-24683	166	98	output	output	NOUN
brj-24683	166	99	r	r	NOUN
brj-24683	166	100	e	e	NOUN
brj-24683	166	101	si	si	PROPN
brj-24683	166	102	d	d	X
brj-24683	166	103	u	u	PROPN
brj-24683	166	104	a	a	DET
brj-24683	166	105	l	l	NOUN
brj-24683	166	106	s	s	PART
brj-24683	166	107	tr	tr	NOUN
brj-24683	166	108	u	u	NOUN
brj-24683	166	109	c	c	NOUN
brj-24683	166	110	tu	tu	X
brj-24683	166	111	r	r	NOUN
brj-24683	166	112	e	e	PROPN
brj-24683	166	113	+	+	CCONJ
brj-24683	166	114	×	×	PROPN
brj-24683	166	115	weight	weight	NOUN
brj-24683	166	116	weight	weight	NOUN
brj-24683	166	117	peer	peer	NOUN
brj-24683	166	118	-	-	PUNCT
brj-24683	166	119	reviewed	review	VERB
brj-24683	166	120	article	article	NOUN
brj-24683	166	121	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	166	122	dou	dou	PROPN
brj-24683	166	123	&	&	CCONJ
brj-24683	166	124	you	you	PRON
brj-24683	166	125	(	(	PUNCT
brj-24683	166	126	2025	2025	NUM
brj-24683	166	127	)	)	PUNCT
brj-24683	166	128	.	.	PUNCT
brj-24683	167	1	“	"	PUNCT
brj-24683	167	2	wood	wood	NOUN
brj-24683	167	3	defect	defect	NOUN
brj-24683	167	4	identification	identification	NOUN
brj-24683	167	5	,	,	PUNCT
brj-24683	167	6	”	"	PUNCT
brj-24683	167	7	bioresources	bioresource	NOUN
brj-24683	167	8	20(3	20(3	NOUN
brj-24683	167	9	)	)	PUNCT
brj-24683	167	10	,	,	PUNCT
brj-24683	167	11	5709	5709	NUM
brj-24683	167	12	-	-	SYM
brj-24683	167	13	5730	5730	NUM
brj-24683	167	14	.	.	PUNCT
brj-24683	168	1	5717	5717	NUM
brj-24683	168	2	is	be	AUX
brj-24683	168	3	then	then	ADV
brj-24683	168	4	reshaped	reshape	VERB
brj-24683	168	5	into	into	ADP
brj-24683	168	6	a	a	DET
brj-24683	168	7	format	format	NOUN
brj-24683	168	8	suitable	suitable	ADJ
brj-24683	168	9	for	for	ADP
brj-24683	168	10	multi	multi	ADJ
brj-24683	168	11	-	-	ADJ
brj-24683	168	12	head	head	ADJ
brj-24683	168	13	attention	attention	NOUN
brj-24683	168	14	computation	computation	NOUN
brj-24683	168	15	.	.	PUNCT
brj-24683	169	1	selfattention	selfattention	NOUN
brj-24683	169	2	is	be	AUX
brj-24683	169	3	computed	compute	VERB
brj-24683	169	4	based	base	VERB
brj-24683	169	5	on	on	ADP
brj-24683	169	6	this	this	DET
brj-24683	169	7	reshaped	reshape	VERB
brj-24683	169	8	feature	feature	NOUN
brj-24683	169	9	representation	representation	NOUN
brj-24683	169	10	,	,	PUNCT
brj-24683	169	11	capturing	capture	VERB
brj-24683	169	12	dependencies	dependency	NOUN
brj-24683	169	13	across	across	ADP
brj-24683	169	14	different	different	ADJ
brj-24683	169	15	spatial	spatial	ADJ
brj-24683	169	16	regions	region	NOUN
brj-24683	169	17	.	.	PUNCT
brj-24683	170	1	the	the	DET
brj-24683	170	2	self	self	NOUN
brj-24683	170	3	-	-	PUNCT
brj-24683	170	4	attention	attention	NOUN
brj-24683	170	5	results	result	NOUN
brj-24683	170	6	are	be	AUX
brj-24683	170	7	subsequently	subsequently	ADV
brj-24683	170	8	transformed	transform	VERB
brj-24683	170	9	back	back	ADV
brj-24683	170	10	to	to	PART
brj-24683	170	11	match	match	VERB
brj-24683	170	12	the	the	DET
brj-24683	170	13	original	original	ADJ
brj-24683	170	14	feature	feature	NOUN
brj-24683	170	15	map	map	NOUN
brj-24683	170	16	dimensions	dimension	NOUN
brj-24683	170	17	.	.	PUNCT
brj-24683	171	1	local	local	ADJ
brj-24683	171	2	and	and	CCONJ
brj-24683	171	3	global	global	ADJ
brj-24683	171	4	attention	attention	NOUN
brj-24683	171	5	weights	weight	NOUN
brj-24683	171	6	are	be	AUX
brj-24683	171	7	derived	derive	VERB
brj-24683	171	8	from	from	ADP
brj-24683	171	9	the	the	DET
brj-24683	171	10	self	self	NOUN
brj-24683	171	11	-	-	PUNCT
brj-24683	171	12	attention	attention	NOUN
brj-24683	171	13	results	result	NOUN
brj-24683	171	14	,	,	PUNCT
brj-24683	171	15	followed	follow	VERB
brj-24683	171	16	by	by	ADP
brj-24683	171	17	the	the	DET
brj-24683	171	18	application	application	NOUN
brj-24683	171	19	of	of	ADP
brj-24683	171	20	a	a	DET
brj-24683	171	21	hard	hard	ADJ
brj-24683	171	22	sigmoid	sigmoid	NOUN
brj-24683	171	23	function	function	NOUN
brj-24683	171	24	to	to	PART
brj-24683	171	25	constrain	constrain	VERB
brj-24683	171	26	these	these	DET
brj-24683	171	27	weights	weight	NOUN
brj-24683	171	28	within	within	ADP
brj-24683	171	29	the	the	DET
brj-24683	171	30	range	range	NOUN
brj-24683	171	31	of	of	ADP
brj-24683	171	32	0	0	NUM
brj-24683	171	33	to	to	PART
brj-24683	171	34	1	1	NUM
brj-24683	171	35	.	.	PUNCT
brj-24683	171	36	to	to	PART
brj-24683	171	37	ensure	ensure	VERB
brj-24683	171	38	scale	scale	NOUN
brj-24683	171	39	consistency	consistency	NOUN
brj-24683	171	40	,	,	PUNCT
brj-24683	171	41	the	the	DET
brj-24683	171	42	attention	attention	NOUN
brj-24683	171	43	weights	weight	NOUN
brj-24683	171	44	undergo	undergo	VERB
brj-24683	171	45	adaptive	adaptive	ADJ
brj-24683	171	46	average	average	ADJ
brj-24683	171	47	pooling	pooling	NOUN
brj-24683	171	48	.	.	PUNCT
brj-24683	172	1	next	next	ADV
brj-24683	172	2	,	,	PUNCT
brj-24683	172	3	the	the	DET
brj-24683	172	4	local	local	ADJ
brj-24683	172	5	and	and	CCONJ
brj-24683	172	6	global	global	ADJ
brj-24683	172	7	attention	attention	NOUN
brj-24683	172	8	weights	weight	NOUN
brj-24683	172	9	are	be	AUX
brj-24683	172	10	fused	fuse	VERB
brj-24683	172	11	to	to	PART
brj-24683	172	12	construct	construct	VERB
brj-24683	172	13	a	a	DET
brj-24683	172	14	comprehensive	comprehensive	ADJ
brj-24683	172	15	attention	attention	NOUN
brj-24683	172	16	map	map	NOUN
brj-24683	172	17	,	,	PUNCT
brj-24683	172	18	which	which	PRON
brj-24683	172	19	is	be	AUX
brj-24683	172	20	then	then	ADV
brj-24683	172	21	applied	apply	VERB
brj-24683	172	22	to	to	ADP
brj-24683	172	23	the	the	DET
brj-24683	172	24	original	original	ADJ
brj-24683	172	25	feature	feature	NOUN
brj-24683	172	26	map	map	NOUN
brj-24683	172	27	to	to	PART
brj-24683	172	28	emphasize	emphasize	VERB
brj-24683	172	29	critical	critical	ADJ
brj-24683	172	30	features	feature	NOUN
brj-24683	172	31	.	.	PUNCT
brj-24683	173	1	finally	finally	ADV
brj-24683	173	2	,	,	PUNCT
brj-24683	173	3	the	the	DET
brj-24683	173	4	selfattention	selfattention	NOUN
brj-24683	173	5	result	result	NOUN
brj-24683	173	6	is	be	AUX
brj-24683	173	7	added	add	VERB
brj-24683	173	8	back	back	ADV
brj-24683	173	9	to	to	ADP
brj-24683	173	10	the	the	DET
brj-24683	173	11	original	original	ADJ
brj-24683	173	12	feature	feature	NOUN
brj-24683	173	13	map	map	NOUN
brj-24683	173	14	,	,	PUNCT
brj-24683	173	15	producing	produce	VERB
brj-24683	173	16	the	the	DET
brj-24683	173	17	refined	refined	ADJ
brj-24683	173	18	output	output	NOUN
brj-24683	173	19	feature	feature	NOUN
brj-24683	173	20	map	map	NOUN
brj-24683	173	21	with	with	ADP
brj-24683	173	22	enhanced	enhanced	ADJ
brj-24683	173	23	feature	feature	NOUN
brj-24683	173	24	representation	representation	NOUN
brj-24683	173	25	.	.	PUNCT
brj-24683	174	1	the	the	DET
brj-24683	174	2	mmsa	mmsa	NOUN
brj-24683	174	3	mechanism	mechanism	NOUN
brj-24683	174	4	is	be	AUX
brj-24683	174	5	integrated	integrate	VERB
brj-24683	174	6	into	into	ADP
brj-24683	174	7	the	the	DET
brj-24683	174	8	c2f	c2f	NOUN
brj-24683	174	9	,	,	PUNCT
brj-24683	174	10	as	as	SCONJ
brj-24683	174	11	shown	show	VERB
brj-24683	174	12	in	in	ADP
brj-24683	174	13	fig	fig	NOUN
brj-24683	174	14	.	.	PUNCT
brj-24683	175	1	5	5	NUM
brj-24683	175	2	,	,	PUNCT
brj-24683	175	3	and	and	CCONJ
brj-24683	175	4	subsequently	subsequently	ADV
brj-24683	175	5	embedded	embed	VERB
brj-24683	175	6	into	into	ADP
brj-24683	175	7	the	the	DET
brj-24683	175	8	backbone	backbone	NOUN
brj-24683	175	9	of	of	ADP
brj-24683	175	10	yolov8	yolov8	NOUN
brj-24683	175	11	to	to	PART
brj-24683	175	12	enhance	enhance	VERB
brj-24683	175	13	the	the	DET
brj-24683	175	14	model	model	NOUN
brj-24683	175	15	’s	’s	PART
brj-24683	175	16	feature	feature	NOUN
brj-24683	175	17	extraction	extraction	NOUN
brj-24683	175	18	and	and	CCONJ
brj-24683	175	19	fusion	fusion	NOUN
brj-24683	175	20	capabilities	capability	NOUN
brj-24683	175	21	.	.	PUNCT
brj-24683	176	1	fig	fig	NOUN
brj-24683	176	2	.	.	PUNCT
brj-24683	177	1	5	5	X
brj-24683	177	2	.	.	X
brj-24683	177	3	structure	structure	NOUN
brj-24683	177	4	of	of	ADP
brj-24683	177	5	c2f	c2f	NOUN
brj-24683	177	6	-	-	PUNCT
brj-24683	177	7	smma	smma	NOUN
brj-24683	177	8	dynamic	dynamic	ADJ
brj-24683	177	9	upsampling	upsampling	NOUN
brj-24683	177	10	the	the	DET
brj-24683	177	11	yolov8	yolov8	PROPN
brj-24683	177	12	model	model	PROPN
brj-24683	177	13	employs	employ	VERB
brj-24683	177	14	nearest	near	ADJ
brj-24683	177	15	neighbor	neighbor	NOUN
brj-24683	177	16	interpolation	interpolation	NOUN
brj-24683	177	17	for	for	ADP
brj-24683	177	18	feature	feature	NOUN
brj-24683	177	19	map	map	NOUN
brj-24683	177	20	upsampling	upsample	VERB
brj-24683	177	21	,	,	PUNCT
brj-24683	177	22	facilitating	facilitate	VERB
brj-24683	177	23	feature	feature	NOUN
brj-24683	177	24	fusion	fusion	NOUN
brj-24683	177	25	across	across	ADP
brj-24683	177	26	different	different	ADJ
brj-24683	177	27	layers	layer	NOUN
brj-24683	177	28	.	.	PUNCT
brj-24683	178	1	while	while	SCONJ
brj-24683	178	2	this	this	DET
brj-24683	178	3	method	method	NOUN
brj-24683	178	4	is	be	AUX
brj-24683	178	5	computationally	computationally	ADV
brj-24683	178	6	efficient	efficient	ADJ
brj-24683	178	7	and	and	CCONJ
brj-24683	178	8	fast	fast	ADJ
brj-24683	178	9	,	,	PUNCT
brj-24683	178	10	the	the	DET
brj-24683	178	11	simplicity	simplicity	NOUN
brj-24683	178	12	of	of	ADP
brj-24683	178	13	nearest	near	ADJ
brj-24683	178	14	neighbor	neighbor	NOUN
brj-24683	178	15	interpolation	interpolation	NOUN
brj-24683	178	16	(	(	PUNCT
brj-24683	178	17	wang	wang	PROPN
brj-24683	178	18	et	et	PROPN
brj-24683	178	19	al	al	PROPN
brj-24683	178	20	.	.	PROPN
brj-24683	178	21	2019	2019	NUM
brj-24683	178	22	)	)	PUNCT
brj-24683	178	23	leads	lead	VERB
brj-24683	178	24	to	to	ADP
brj-24683	178	25	the	the	DET
brj-24683	178	26	loss	loss	NOUN
brj-24683	178	27	of	of	ADP
brj-24683	178	28	fine	fine	ADJ
brj-24683	178	29	details	detail	NOUN
brj-24683	178	30	,	,	PUNCT
brj-24683	178	31	particularly	particularly	ADV
brj-24683	178	32	affecting	affect	VERB
brj-24683	178	33	the	the	DET
brj-24683	178	34	features	feature	NOUN
brj-24683	178	35	of	of	ADP
brj-24683	178	36	small	small	ADJ
brj-24683	178	37	-	-	PUNCT
brj-24683	178	38	scale	scale	NOUN
brj-24683	178	39	spatial	spatial	ADJ
brj-24683	178	40	targets	target	NOUN
brj-24683	178	41	.	.	PUNCT
brj-24683	179	1	this	this	DET
brj-24683	179	2	results	result	NOUN
brj-24683	179	3	in	in	ADP
brj-24683	179	4	inadequate	inadequate	ADJ
brj-24683	179	5	feature	feature	NOUN
brj-24683	179	6	representation	representation	NOUN
brj-24683	179	7	,	,	PUNCT
brj-24683	179	8	which	which	PRON
brj-24683	179	9	,	,	PUNCT
brj-24683	179	10	as	as	ADP
brj-24683	179	11	the	the	DET
brj-24683	179	12	network	network	NOUN
brj-24683	179	13	depth	depth	NOUN
brj-24683	179	14	increases	increase	NOUN
brj-24683	179	15	,	,	PUNCT
brj-24683	179	16	severely	severely	ADV
brj-24683	179	17	impacts	impact	VERB
brj-24683	179	18	the	the	DET
brj-24683	179	19	detection	detection	NOUN
brj-24683	179	20	accuracy	accuracy	NOUN
brj-24683	179	21	of	of	ADP
brj-24683	179	22	small	small	ADJ
brj-24683	179	23	targets	target	NOUN
brj-24683	179	24	.	.	PUNCT
brj-24683	180	1	to	to	PART
brj-24683	180	2	address	address	VERB
brj-24683	180	3	this	this	DET
brj-24683	180	4	issue	issue	NOUN
brj-24683	180	5	,	,	PUNCT
brj-24683	180	6	this	this	DET
brj-24683	180	7	paper	paper	NOUN
brj-24683	180	8	introduces	introduce	VERB
brj-24683	180	9	a	a	DET
brj-24683	180	10	super	super	NOUN
brj-24683	180	11	-	-	ADJ
brj-24683	180	12	lightweight	lightweight	ADJ
brj-24683	180	13	,	,	PUNCT
brj-24683	180	14	learnable	learnable	ADJ
brj-24683	180	15	dynamic	dynamic	ADJ
brj-24683	180	16	upsampling	upsampling	NOUN
brj-24683	180	17	(	(	PUNCT
brj-24683	180	18	dysample	dysample	ADJ
brj-24683	180	19	)	)	PUNCT
brj-24683	180	20	method	method	NOUN
brj-24683	180	21	(	(	PUNCT
brj-24683	180	22	liu	liu	PROPN
brj-24683	180	23	et	et	PROPN
brj-24683	180	24	al	al	PROPN
brj-24683	180	25	.	.	PROPN
brj-24683	180	26	2023	2023	NUM
brj-24683	180	27	)	)	PUNCT
brj-24683	180	28	,	,	PUNCT
brj-24683	180	29	designed	design	VERB
brj-24683	180	30	to	to	PART
brj-24683	180	31	mitigate	mitigate	VERB
brj-24683	180	32	feature	feature	NOUN
brj-24683	180	33	loss	loss	NOUN
brj-24683	180	34	and	and	CCONJ
brj-24683	180	35	enhance	enhance	VERB
brj-24683	180	36	both	both	DET
brj-24683	180	37	feature	feature	NOUN
brj-24683	180	38	representation	representation	NOUN
brj-24683	180	39	and	and	CCONJ
brj-24683	180	40	detection	detection	NOUN
brj-24683	180	41	accuracy	accuracy	NOUN
brj-24683	180	42	.	.	PUNCT
brj-24683	181	1	the	the	DET
brj-24683	181	2	specific	specific	ADJ
brj-24683	181	3	structure	structure	NOUN
brj-24683	181	4	is	be	AUX
brj-24683	181	5	shown	show	VERB
brj-24683	181	6	in	in	ADP
brj-24683	181	7	fig	fig	NOUN
brj-24683	181	8	.	.	PUNCT
brj-24683	182	1	6	6	NUM
brj-24683	182	2	.	.	X
brj-24683	182	3	fig	fig	NOUN
brj-24683	182	4	.	.	PUNCT
brj-24683	183	1	6	6	X
brj-24683	183	2	.	.	X
brj-24683	183	3	structure	structure	NOUN
brj-24683	183	4	of	of	ADP
brj-24683	183	5	dysample	dysample	PROPN
brj-24683	183	6	given	give	VERB
brj-24683	183	7	the	the	DET
brj-24683	183	8	upsampling	upsampling	ADJ
brj-24683	183	9	scale	scale	NOUN
brj-24683	183	10	factor	factor	NOUN
brj-24683	183	11	s	s	PART
brj-24683	183	12	and	and	CCONJ
brj-24683	183	13	a	a	DET
brj-24683	183	14	feature	feature	NOUN
brj-24683	183	15	map	map	NOUN
brj-24683	183	16	f	f	PROPN
brj-24683	183	17	of	of	ADP
brj-24683	183	18	size	size	NOUN
brj-24683	183	19	c×h×w	c×h×w	PROPN
brj-24683	183	20	,	,	PUNCT
brj-24683	183	21	the	the	DET
brj-24683	183	22	feature	feature	NOUN
brj-24683	183	23	map	map	NOUN
brj-24683	183	24	is	be	AUX
brj-24683	183	25	first	first	ADV
brj-24683	183	26	divided	divide	VERB
brj-24683	183	27	along	along	ADP
brj-24683	183	28	the	the	DET
brj-24683	183	29	channel	channel	NOUN
brj-24683	183	30	dimension	dimension	NOUN
brj-24683	183	31	into	into	ADP
brj-24683	183	32	g	g	NOUN
brj-24683	183	33	groups	group	NOUN
brj-24683	183	34	(	(	PUNCT
brj-24683	183	35	g=4	g=4	CCONJ
brj-24683	183	36	)	)	PUNCT
brj-24683	183	37	,	,	PUNCT
brj-24683	183	38	which	which	PRON
brj-24683	183	39	helps	help	VERB
brj-24683	183	40	further	far	ADV
brj-24683	183	41	reduce	reduce	VERB
brj-24683	183	42	computational	computational	ADJ
brj-24683	183	43	complexity	complexity	NOUN
brj-24683	183	44	.	.	PUNCT
brj-24683	184	1	a	a	DET
brj-24683	184	2	linear	linear	ADJ
brj-24683	184	3	layer	layer	NOUN
brj-24683	184	4	is	be	AUX
brj-24683	184	5	then	then	ADV
brj-24683	184	6	applied	apply	VERB
brj-24683	184	7	to	to	PART
brj-24683	184	8	generate	generate	VERB
brj-24683	184	9	offsets	offset	NOUN
brj-24683	184	10	conv	conv	PROPN
brj-24683	184	11	split	split	PROPN
brj-24683	184	12	mmsa	mmsa	PROPN
brj-24683	184	13	concat	concat	PROPN
brj-24683	184	14	convmmsa	convmmsa	PROPN
brj-24683	184	15	conv	conv	PROPN
brj-24683	184	16	split	split	PROPN
brj-24683	184	17	bottleneck	bottleneck	NOUN
brj-24683	184	18	concat	concat	PROPN
brj-24683	184	19	convbottleneckn	convbottleneckn	PROPN
brj-24683	184	20	c2f	c2f	PROPN
brj-24683	184	21	c2f	c2f	PROPN
brj-24683	184	22	-	-	PUNCT
brj-24683	184	23	smma	smma	PROPN
brj-24683	184	24	g	g	PROPN
brj-24683	184	25	o	o	NOUN
brj-24683	184	26	sf	sf	INTJ
brj-24683	184	27	0.5*sigmoid	0.5*sigmoid	PROPN
brj-24683	184	28	·	·	PUNCT
brj-24683	185	1	c×h×w	c×h×w	NUM
brj-24683	185	2	linear	linear	ADJ
brj-24683	185	3	2gs	2gs	NOUN
brj-24683	185	4	2	2	NUM
brj-24683	185	5	×h×w	×h×w	ADP
brj-24683	185	6	2gs	2gs	ADJ
brj-24683	185	7	2	2	NUM
brj-24683	185	8	×h×w	×h×w	ADP
brj-24683	185	9	2gs	2gs	NUM
brj-24683	185	10	2	2	NUM
brj-24683	185	11	×h×w	×h×w	ADP
brj-24683	185	12	2g×sh×sw	2g×sh×sw	NUM
brj-24683	185	13	2g×sh×sw	2g×sh×sw	NUM
brj-24683	185	14	2g×sh×sw	2g×sh×sw	NOUN
brj-24683	185	15	pixel	pixel	VERB
brj-24683	185	16	shuffle	shuffle	ADJ
brj-24683	185	17	peer	peer	NOUN
brj-24683	185	18	-	-	PUNCT
brj-24683	185	19	reviewed	review	VERB
brj-24683	185	20	article	article	NOUN
brj-24683	185	21	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	185	22	dou	dou	PROPN
brj-24683	185	23	&	&	CCONJ
brj-24683	185	24	you	you	PRON
brj-24683	185	25	(	(	PUNCT
brj-24683	185	26	2025	2025	NUM
brj-24683	185	27	)	)	PUNCT
brj-24683	185	28	.	.	PUNCT
brj-24683	186	1	“	"	PUNCT
brj-24683	186	2	wood	wood	NOUN
brj-24683	186	3	defect	defect	NOUN
brj-24683	186	4	identification	identification	NOUN
brj-24683	186	5	,	,	PUNCT
brj-24683	186	6	”	"	PUNCT
brj-24683	186	7	bioresources	bioresource	NOUN
brj-24683	186	8	20(3	20(3	NOUN
brj-24683	186	9	)	)	PUNCT
brj-24683	186	10	,	,	PUNCT
brj-24683	186	11	5709	5709	NUM
brj-24683	186	12	-	-	SYM
brj-24683	186	13	5730	5730	NUM
brj-24683	186	14	.	.	PUNCT
brj-24683	187	1	5718	5718	NUM
brj-24683	187	2	of	of	ADP
brj-24683	187	3	size	size	NOUN
brj-24683	187	4	2gs2×h×w	2gs2×h×w	NUM
brj-24683	187	5	.	.	PUNCT
brj-24683	187	6	to	to	PART
brj-24683	187	7	increase	increase	VERB
brj-24683	187	8	the	the	DET
brj-24683	187	9	flexibility	flexibility	NOUN
brj-24683	187	10	of	of	ADP
brj-24683	187	11	the	the	DET
brj-24683	187	12	offsets	offset	NOUN
brj-24683	187	13	,	,	PUNCT
brj-24683	187	14	a	a	DET
brj-24683	187	15	sigmoid	sigmoid	NOUN
brj-24683	187	16	function	function	NOUN
brj-24683	187	17	along	along	ADP
brj-24683	187	18	with	with	ADP
brj-24683	187	19	a	a	DET
brj-24683	187	20	static	static	ADJ
brj-24683	187	21	factor	factor	NOUN
brj-24683	187	22	of	of	ADP
brj-24683	187	23	0.5	0.5	NUM
brj-24683	187	24	is	be	AUX
brj-24683	187	25	used	use	VERB
brj-24683	187	26	to	to	PART
brj-24683	187	27	produce	produce	VERB
brj-24683	187	28	a	a	DET
brj-24683	187	29	per	per	ADP
brj-24683	187	30	-	-	PUNCT
brj-24683	187	31	point	point	NOUN
brj-24683	187	32	“	"	PUNCT
brj-24683	187	33	dynamic	dynamic	ADJ
brj-24683	187	34	range	range	NOUN
brj-24683	187	35	factor	factor	NOUN
brj-24683	187	36	,	,	PUNCT
brj-24683	187	37	”	"	PUNCT
brj-24683	187	38	with	with	ADP
brj-24683	187	39	the	the	DET
brj-24683	187	40	dynamic	dynamic	ADJ
brj-24683	187	41	range	range	NOUN
brj-24683	187	42	taking	take	VERB
brj-24683	187	43	values	value	NOUN
brj-24683	187	44	within	within	ADP
brj-24683	187	45	the	the	DET
brj-24683	187	46	range	range	NOUN
brj-24683	187	47	of	of	ADP
brj-24683	187	48	[	[	X
brj-24683	187	49	0	0	NUM
brj-24683	187	50	,	,	PUNCT
brj-24683	187	51	0.5	0.5	NUM
brj-24683	187	52	]	]	PUNCT
brj-24683	187	53	.	.	PUNCT
brj-24683	188	1	finally	finally	ADV
brj-24683	188	2	,	,	PUNCT
brj-24683	188	3	pixel	pixel	VERB
brj-24683	188	4	shuffle	shuffle	NOUN
brj-24683	188	5	(	(	PUNCT
brj-24683	188	6	ps	ps	NOUN
brj-24683	188	7	)	)	PUNCT
brj-24683	188	8	is	be	AUX
brj-24683	188	9	applied	apply	VERB
brj-24683	188	10	to	to	PART
brj-24683	188	11	reshape	reshape	VERB
brj-24683	188	12	the	the	DET
brj-24683	188	13	offset	offset	NOUN
brj-24683	188	14	o	o	NOUN
brj-24683	188	15	into	into	ADP
brj-24683	188	16	a	a	DET
brj-24683	188	17	size	size	NOUN
brj-24683	188	18	of	of	ADP
brj-24683	188	19	2	2	NUM
brj-24683	188	20	g	g	NOUN
brj-24683	188	21	×	×	NOUN
brj-24683	188	22	sh	sh	INTJ
brj-24683	188	23	×	×	PROPN
brj-24683	188	24	sw	sw	PROPN
brj-24683	188	25	.	.	PUNCT
brj-24683	189	1	the	the	DET
brj-24683	189	2	mathematical	mathematical	ADJ
brj-24683	189	3	expression	expression	NOUN
brj-24683	189	4	for	for	ADP
brj-24683	189	5	this	this	DET
brj-24683	189	6	process	process	NOUN
brj-24683	189	7	is	be	AUX
brj-24683	189	8	as	as	SCONJ
brj-24683	189	9	follows	follow	VERB
brj-24683	189	10	:	:	PUNCT
brj-24683	190	1	𝑶	𝑶	X
brj-24683	190	2	=	=	PUNCT
brj-24683	190	3	𝑃𝑠	𝑃𝑠	PROPN
brj-24683	190	4	(	(	PUNCT
brj-24683	190	5	0.5	0.5	NUM
brj-24683	190	6	×	×	NOUN
brj-24683	190	7	sigmoid(linear(𝑭)⊙	sigmoid(linear(𝑭)⊙	ADJ
brj-24683	190	8	linear(𝑭	linear(𝑭	PROPN
brj-24683	190	9	)	)	PUNCT
brj-24683	190	10	)	)	PUNCT
brj-24683	190	11	)	)	PUNCT
brj-24683	191	1	the	the	DET
brj-24683	191	2	sampling	sample	VERB
brj-24683	191	3	set	set	NOUN
brj-24683	191	4	s	s	VERB
brj-24683	191	5	is	be	AUX
brj-24683	191	6	the	the	DET
brj-24683	191	7	sum	sum	NOUN
brj-24683	191	8	of	of	ADP
brj-24683	191	9	the	the	DET
brj-24683	191	10	offset	offset	NOUN
brj-24683	191	11	o	o	NOUN
brj-24683	191	12	and	and	CCONJ
brj-24683	191	13	the	the	DET
brj-24683	191	14	original	original	ADJ
brj-24683	191	15	sampling	sampling	NOUN
brj-24683	191	16	grid	grid	NOUN
brj-24683	191	17	g	g	NOUN
brj-24683	191	18	,	,	PUNCT
brj-24683	191	19	i.e.	i.e.	X
brj-24683	191	20	,	,	PUNCT
brj-24683	191	21	s	s	NOUN
brj-24683	191	22	=	=	SYM
brj-24683	191	23	g	g	PROPN
brj-24683	191	24	+	+	NOUN
brj-24683	191	25	o	o	NOUN
brj-24683	191	26	re	re	ADJ
brj-24683	191	27	-	-	ADJ
brj-24683	191	28	parameterizable	parameterizable	ADJ
brj-24683	191	29	block	block	NOUN
brj-24683	191	30	in	in	ADP
brj-24683	191	31	the	the	DET
brj-24683	191	32	neck	neck	NOUN
brj-24683	191	33	section	section	NOUN
brj-24683	191	34	,	,	PUNCT
brj-24683	191	35	although	although	SCONJ
brj-24683	191	36	the	the	DET
brj-24683	191	37	c2f	c2f	NOUN
brj-24683	191	38	partially	partially	ADV
brj-24683	191	39	facilitates	facilitate	VERB
brj-24683	191	40	feature	feature	NOUN
brj-24683	191	41	extraction	extraction	NOUN
brj-24683	191	42	for	for	ADP
brj-24683	191	43	wood	wood	NOUN
brj-24683	191	44	surface	surface	NOUN
brj-24683	191	45	defects	defect	NOUN
brj-24683	191	46	,	,	PUNCT
brj-24683	191	47	it	it	PRON
brj-24683	191	48	struggles	struggle	VERB
brj-24683	191	49	to	to	PART
brj-24683	191	50	accurately	accurately	ADV
brj-24683	191	51	capture	capture	VERB
brj-24683	191	52	features	feature	NOUN
brj-24683	191	53	of	of	ADP
brj-24683	191	54	small	small	ADJ
brj-24683	191	55	-	-	PUNCT
brj-24683	191	56	scale	scale	NOUN
brj-24683	191	57	and	and	CCONJ
brj-24683	191	58	multiscale	multiscale	ADJ
brj-24683	191	59	wood	wood	NOUN
brj-24683	191	60	surface	surface	NOUN
brj-24683	191	61	defects	defect	NOUN
brj-24683	191	62	under	under	ADP
brj-24683	191	63	complex	complex	ADJ
brj-24683	191	64	backgrounds	background	NOUN
brj-24683	191	65	.	.	PUNCT
brj-24683	192	1	this	this	DET
brj-24683	192	2	limitation	limitation	NOUN
brj-24683	192	3	hinders	hinder	VERB
brj-24683	192	4	the	the	DET
brj-24683	192	5	effective	effective	ADJ
brj-24683	192	6	exploration	exploration	NOUN
brj-24683	192	7	of	of	ADP
brj-24683	192	8	fine	fine	ADV
brj-24683	192	9	-	-	PUNCT
brj-24683	192	10	grained	grain	VERB
brj-24683	192	11	and	and	CCONJ
brj-24683	192	12	multi	multi	ADJ
brj-24683	192	13	-	-	ADJ
brj-24683	192	14	scale	scale	ADJ
brj-24683	192	15	features	feature	NOUN
brj-24683	192	16	.	.	PUNCT
brj-24683	193	1	to	to	PART
brj-24683	193	2	address	address	VERB
brj-24683	193	3	these	these	DET
brj-24683	193	4	challenges	challenge	NOUN
brj-24683	193	5	,	,	PUNCT
brj-24683	193	6	the	the	DET
brj-24683	193	7	structural	structural	ADJ
brj-24683	193	8	re	re	ADJ
brj-24683	193	9	-	-	ADJ
brj-24683	193	10	parameterizable	parameterizable	ADJ
brj-24683	193	11	module	module	NOUN
brj-24683	193	12	(	(	PUNCT
brj-24683	193	13	repblock	repblock	NOUN
brj-24683	193	14	)	)	PUNCT
brj-24683	193	15	is	be	AUX
brj-24683	193	16	introduced	introduce	VERB
brj-24683	193	17	to	to	PART
brj-24683	193	18	alleviate	alleviate	VERB
brj-24683	193	19	these	these	DET
brj-24683	193	20	issues	issue	NOUN
brj-24683	193	21	.	.	PUNCT
brj-24683	194	1	repblock	repblock	NOUN
brj-24683	194	2	leverages	leverage	NOUN
brj-24683	194	3	a	a	DET
brj-24683	194	4	multi	multi	ADJ
brj-24683	194	5	-	-	ADJ
brj-24683	194	6	branch	branch	ADJ
brj-24683	194	7	structure	structure	NOUN
brj-24683	194	8	during	during	ADP
brj-24683	194	9	the	the	DET
brj-24683	194	10	training	training	NOUN
brj-24683	194	11	phase	phase	NOUN
brj-24683	194	12	to	to	PART
brj-24683	194	13	enrich	enrich	VERB
brj-24683	194	14	feature	feature	NOUN
brj-24683	194	15	representation	representation	NOUN
brj-24683	194	16	,	,	PUNCT
brj-24683	194	17	capturing	capture	VERB
brj-24683	194	18	wood	wood	NOUN
brj-24683	194	19	surface	surface	NOUN
brj-24683	194	20	defect	defect	NOUN
brj-24683	194	21	features	feature	NOUN
brj-24683	194	22	from	from	ADP
brj-24683	194	23	global	global	ADJ
brj-24683	194	24	to	to	ADP
brj-24683	194	25	local	local	ADJ
brj-24683	194	26	scales	scale	NOUN
brj-24683	194	27	and	and	CCONJ
brj-24683	194	28	from	from	ADP
brj-24683	194	29	small	small	ADJ
brj-24683	194	30	to	to	ADP
brj-24683	194	31	large	large	ADJ
brj-24683	194	32	scales	scale	NOUN
brj-24683	194	33	,	,	PUNCT
brj-24683	194	34	thereby	thereby	ADV
brj-24683	194	35	effectively	effectively	ADV
brj-24683	194	36	improving	improve	VERB
brj-24683	194	37	detection	detection	NOUN
brj-24683	194	38	accuracy	accuracy	NOUN
brj-24683	194	39	.	.	PUNCT
brj-24683	195	1	during	during	ADP
brj-24683	195	2	the	the	DET
brj-24683	195	3	inference	inference	NOUN
brj-24683	195	4	phase	phase	NOUN
brj-24683	195	5	,	,	PUNCT
brj-24683	195	6	the	the	DET
brj-24683	195	7	reparameterization	reparameterization	NOUN
brj-24683	195	8	technique	technique	NOUN
brj-24683	195	9	transforms	transform	VERB
brj-24683	195	10	the	the	DET
brj-24683	195	11	multibranch	multibranch	ADJ
brj-24683	195	12	structure	structure	NOUN
brj-24683	195	13	into	into	ADP
brj-24683	195	14	a	a	DET
brj-24683	195	15	more	more	ADV
brj-24683	195	16	compact	compact	ADJ
brj-24683	195	17	single	single	ADJ
brj-24683	195	18	-	-	PUNCT
brj-24683	195	19	branch	branch	NOUN
brj-24683	195	20	form	form	NOUN
brj-24683	195	21	,	,	PUNCT
brj-24683	195	22	effectively	effectively	ADV
brj-24683	195	23	accelerating	accelerate	VERB
brj-24683	195	24	model	model	NOUN
brj-24683	195	25	inference	inference	NOUN
brj-24683	195	26	speed	speed	NOUN
brj-24683	195	27	without	without	ADP
brj-24683	195	28	compromising	compromise	VERB
brj-24683	195	29	detection	detection	NOUN
brj-24683	195	30	performance	performance	NOUN
brj-24683	195	31	,	,	PUNCT
brj-24683	195	32	thus	thus	ADV
brj-24683	195	33	meeting	meet	VERB
brj-24683	195	34	real	real	ADJ
brj-24683	195	35	-	-	PUNCT
brj-24683	195	36	time	time	NOUN
brj-24683	195	37	requirements	requirement	NOUN
brj-24683	195	38	.	.	PUNCT
brj-24683	196	1	the	the	DET
brj-24683	196	2	structure	structure	NOUN
brj-24683	196	3	of	of	ADP
brj-24683	196	4	re	re	ADJ
brj-24683	196	5	-	-	ADJ
brj-24683	196	6	parameterizable	parameterizable	ADJ
brj-24683	196	7	convolution	convolution	NOUN
brj-24683	196	8	(	(	PUNCT
brj-24683	196	9	repconv	repconv	NOUN
brj-24683	196	10	)	)	PUNCT
brj-24683	196	11	during	during	ADP
brj-24683	196	12	both	both	DET
brj-24683	196	13	training	training	NOUN
brj-24683	196	14	and	and	CCONJ
brj-24683	196	15	inference	inference	NOUN
brj-24683	196	16	phases	phase	NOUN
brj-24683	196	17	is	be	AUX
brj-24683	196	18	illustrated	illustrate	VERB
brj-24683	196	19	in	in	ADP
brj-24683	196	20	fig	fig	NOUN
brj-24683	196	21	.	.	PUNCT
brj-24683	197	1	7	7	X
brj-24683	197	2	.	.	NOUN
brj-24683	197	3	during	during	ADP
brj-24683	197	4	training	training	NOUN
brj-24683	197	5	,	,	PUNCT
brj-24683	197	6	the	the	DET
brj-24683	197	7	multi	multi	ADJ
brj-24683	197	8	-	-	ADJ
brj-24683	197	9	branch	branch	ADJ
brj-24683	197	10	structure	structure	NOUN
brj-24683	197	11	consists	consist	VERB
brj-24683	197	12	of	of	ADP
brj-24683	197	13	a	a	DET
brj-24683	197	14	3×3	3×3	NUM
brj-24683	197	15	convolution	convolution	NOUN
brj-24683	197	16	,	,	PUNCT
brj-24683	197	17	a	a	DET
brj-24683	197	18	1×1	1×1	NUM
brj-24683	197	19	convolution	convolution	NOUN
brj-24683	197	20	,	,	PUNCT
brj-24683	197	21	a	a	DET
brj-24683	197	22	residual	residual	ADJ
brj-24683	197	23	structure	structure	NOUN
brj-24683	197	24	,	,	PUNCT
brj-24683	197	25	and	and	CCONJ
brj-24683	197	26	batch	batch	VERB
brj-24683	197	27	normalization	normalization	NOUN
brj-24683	197	28	(	(	PUNCT
brj-24683	197	29	bn	bn	NOUN
brj-24683	197	30	)	)	PUNCT
brj-24683	197	31	layers	layer	NOUN
brj-24683	197	32	.	.	PUNCT
brj-24683	198	1	after	after	ADP
brj-24683	198	2	reparameterization	reparameterization	NOUN
brj-24683	198	3	,	,	PUNCT
brj-24683	198	4	it	it	PRON
brj-24683	198	5	is	be	AUX
brj-24683	198	6	converted	convert	VERB
brj-24683	198	7	into	into	ADP
brj-24683	198	8	a	a	DET
brj-24683	198	9	single	single	ADJ
brj-24683	198	10	-	-	PUNCT
brj-24683	198	11	branch	branch	NOUN
brj-24683	198	12	3×3	3×3	NUM
brj-24683	198	13	convolution	convolution	NOUN
brj-24683	198	14	for	for	ADP
brj-24683	198	15	inference	inference	NOUN
brj-24683	198	16	.	.	PUNCT
brj-24683	199	1	fig	fig	NOUN
brj-24683	199	2	.	.	PUNCT
brj-24683	200	1	7	7	X
brj-24683	200	2	.	.	X
brj-24683	200	3	structure	structure	NOUN
brj-24683	200	4	of	of	ADP
brj-24683	200	5	repconv	repconv	NOUN
brj-24683	200	6	during	during	ADP
brj-24683	200	7	both	both	DET
brj-24683	200	8	training	training	NOUN
brj-24683	200	9	and	and	CCONJ
brj-24683	200	10	inference	inference	NOUN
brj-24683	200	11	phases	phase	NOUN
brj-24683	200	12	the	the	DET
brj-24683	200	13	structural	structural	ADJ
brj-24683	200	14	reparameterization	reparameterization	NOUN
brj-24683	200	15	of	of	ADP
brj-24683	200	16	repconv	repconv	PROPN
brj-24683	200	17	is	be	AUX
brj-24683	200	18	illustrated	illustrate	VERB
brj-24683	200	19	in	in	ADP
brj-24683	200	20	fig	fig	NOUN
brj-24683	200	21	.	.	PUNCT
brj-24683	201	1	8	8	X
brj-24683	201	2	.	.	PUNCT
brj-24683	202	1	the	the	DET
brj-24683	202	2	specific	specific	ADJ
brj-24683	202	3	steps	step	NOUN
brj-24683	202	4	are	be	AUX
brj-24683	202	5	as	as	SCONJ
brj-24683	202	6	follows	follow	VERB
brj-24683	202	7	:	:	PUNCT
brj-24683	202	8	(	(	PUNCT
brj-24683	202	9	1	1	X
brj-24683	202	10	)	)	PUNCT
brj-24683	202	11	convert	convert	VERB
brj-24683	202	12	the	the	DET
brj-24683	202	13	1×1	1×1	NUM
brj-24683	202	14	convolution	convolution	NOUN
brj-24683	202	15	and	and	CCONJ
brj-24683	202	16	residual	residual	ADJ
brj-24683	202	17	structure	structure	NOUN
brj-24683	202	18	into	into	ADP
brj-24683	202	19	a	a	DET
brj-24683	202	20	3×3	3×3	NUM
brj-24683	202	21	convolution	convolution	NOUN
brj-24683	202	22	.	.	PUNCT
brj-24683	203	1	in	in	ADP
brj-24683	203	2	the	the	DET
brj-24683	203	3	convolution	convolution	NOUN
brj-24683	203	4	conversion	conversion	NOUN
brj-24683	203	5	step	step	NOUN
brj-24683	203	6	,	,	PUNCT
brj-24683	203	7	the	the	DET
brj-24683	203	8	original	original	ADJ
brj-24683	203	9	3×3	3×3	NUM
brj-24683	203	10	convolution	convolution	NOUN
brj-24683	203	11	remains	remain	VERB
brj-24683	203	12	unchanged	unchanged	ADJ
brj-24683	203	13	,	,	PUNCT
brj-24683	203	14	while	while	SCONJ
brj-24683	203	15	the	the	DET
brj-24683	203	16	1×1	1×1	NUM
brj-24683	203	17	convolution	convolution	NOUN
brj-24683	203	18	is	be	AUX
brj-24683	203	19	transformed	transform	VERB
brj-24683	203	20	into	into	ADP
brj-24683	203	21	a	a	DET
brj-24683	203	22	3×3	3×3	NUM
brj-24683	203	23	convolution	convolution	NOUN
brj-24683	203	24	by	by	ADP
brj-24683	203	25	padding	pad	VERB
brj-24683	203	26	zeros	zero	NOUN
brj-24683	203	27	around	around	ADP
brj-24683	203	28	it	it	PRON
brj-24683	203	29	.	.	PUNCT
brj-24683	204	1	the	the	DET
brj-24683	204	2	residual	residual	ADJ
brj-24683	204	3	structure	structure	NOUN
brj-24683	204	4	can	can	AUX
brj-24683	204	5	be	be	AUX
brj-24683	204	6	constructed	construct	VERB
brj-24683	204	7	with	with	ADP
brj-24683	204	8	four	four	NUM
brj-24683	204	9	convolution	convolution	NOUN
brj-24683	204	10	kernels	kernel	NOUN
brj-24683	204	11	,	,	PUNCT
brj-24683	204	12	two	two	NUM
brj-24683	204	13	of	of	ADP
brj-24683	204	14	which	which	PRON
brj-24683	204	15	have	have	VERB
brj-24683	204	16	center	center	ADJ
brj-24683	204	17	values	value	NOUN
brj-24683	204	18	of	of	ADP
brj-24683	204	19	1	1	NUM
brj-24683	204	20	,	,	PUNCT
brj-24683	204	21	and	and	CCONJ
brj-24683	204	22	the	the	DET
brj-24683	204	23	remaining	remain	VERB
brj-24683	204	24	two	two	NUM
brj-24683	204	25	are	be	AUX
brj-24683	204	26	set	set	VERB
brj-24683	204	27	to	to	ADP
brj-24683	204	28	0	0	NUM
brj-24683	204	29	.	.	PUNCT
brj-24683	205	1	the	the	DET
brj-24683	205	2	output	output	NOUN
brj-24683	205	3	of	of	ADP
brj-24683	205	4	the	the	DET
brj-24683	205	5	input	input	NOUN
brj-24683	205	6	feature	feature	NOUN
brj-24683	205	7	matrix	matrix	NOUN
brj-24683	205	8	,	,	PUNCT
brj-24683	205	9	processed	process	VERB
brj-24683	205	10	through	through	ADP
brj-24683	205	11	these	these	DET
brj-24683	205	12	four	four	NUM
brj-24683	205	13	convolution	convolution	NOUN
brj-24683	205	14	kernels	kernel	NOUN
brj-24683	205	15	,	,	PUNCT
brj-24683	205	16	is	be	AUX
brj-24683	205	17	identical	identical	ADJ
brj-24683	205	18	to	to	ADP
brj-24683	205	19	the	the	DET
brj-24683	205	20	input	input	NOUN
brj-24683	205	21	.	.	PUNCT
brj-24683	206	1	(	(	PUNCT
brj-24683	206	2	2	2	X
brj-24683	206	3	)	)	PUNCT
brj-24683	206	4	fusion	fusion	NOUN
brj-24683	206	5	of	of	ADP
brj-24683	206	6	the	the	DET
brj-24683	206	7	bn	bn	NOUN
brj-24683	206	8	layer	layer	NOUN
brj-24683	206	9	with	with	ADP
brj-24683	206	10	the	the	DET
brj-24683	206	11	convolution	convolution	NOUN
brj-24683	206	12	layer	layer	NOUN
brj-24683	206	13	:	:	PUNCT
brj-24683	206	14	the	the	DET
brj-24683	206	15	structure	structure	NOUN
brj-24683	206	16	is	be	AUX
brj-24683	206	17	transformed	transform	VERB
brj-24683	206	18	repconv	repconv	PROPN
brj-24683	206	19	repconv	repconv	VERB
brj-24683	206	20	1×1conv2d	1×1conv2d	NUM
brj-24683	207	1	3×3conv2d	3×3conv2d	NUM
brj-24683	207	2	bn	bn	NOUN
brj-24683	207	3	bnbn	bnbn	NOUN
brj-24683	207	4	silu	silu	NOUN
brj-24683	207	5	3×3conv2d	3×3conv2d	NUM
brj-24683	207	6	silu	silu	NOUN
brj-24683	207	7	train	train	NOUN
brj-24683	207	8	val	val	ADJ
brj-24683	207	9	peer	peer	NOUN
brj-24683	207	10	-	-	PUNCT
brj-24683	207	11	reviewed	review	VERB
brj-24683	207	12	article	article	NOUN
brj-24683	207	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	207	14	dou	dou	PROPN
brj-24683	207	15	&	&	CCONJ
brj-24683	207	16	you	you	PRON
brj-24683	207	17	(	(	PUNCT
brj-24683	207	18	2025	2025	NUM
brj-24683	207	19	)	)	PUNCT
brj-24683	207	20	.	.	PUNCT
brj-24683	208	1	“	"	PUNCT
brj-24683	208	2	wood	wood	NOUN
brj-24683	208	3	defect	defect	NOUN
brj-24683	208	4	identification	identification	NOUN
brj-24683	208	5	,	,	PUNCT
brj-24683	208	6	”	"	PUNCT
brj-24683	208	7	bioresources	bioresource	NOUN
brj-24683	208	8	20(3	20(3	NOUN
brj-24683	208	9	)	)	PUNCT
brj-24683	208	10	,	,	PUNCT
brj-24683	208	11	5709	5709	NUM
brj-24683	208	12	-	-	SYM
brj-24683	208	13	5730	5730	NUM
brj-24683	208	14	.	.	PUNCT
brj-24683	209	1	5719	5719	NUM
brj-24683	209	2	from	from	ADP
brj-24683	209	3	the	the	DET
brj-24683	209	4	convolution	convolution	NOUN
brj-24683	209	5	plus	plus	CCONJ
brj-24683	209	6	bn	bn	NOUN
brj-24683	209	7	layer	layer	NOUN
brj-24683	209	8	into	into	ADP
brj-24683	209	9	a	a	DET
brj-24683	209	10	convolution	convolution	NOUN
brj-24683	209	11	structure	structure	NOUN
brj-24683	209	12	with	with	ADP
brj-24683	209	13	bias	bias	NOUN
brj-24683	209	14	.	.	PUNCT
brj-24683	210	1	let	let	VERB
brj-24683	210	2	x∈rh×w×c	x∈rh×w×c	PROPN
brj-24683	210	3	be	be	AUX
brj-24683	210	4	the	the	DET
brj-24683	210	5	input	input	NOUN
brj-24683	210	6	tensor	tensor	NOUN
brj-24683	210	7	.	.	PUNCT
brj-24683	211	1	the	the	DET
brj-24683	211	2	computation	computation	NOUN
brj-24683	211	3	for	for	ADP
brj-24683	211	4	the	the	DET
brj-24683	211	5	bn	bn	NOUN
brj-24683	211	6	layer	layer	NOUN
brj-24683	211	7	is	be	AUX
brj-24683	211	8	given	give	VERB
brj-24683	211	9	by	by	ADP
brj-24683	211	10	,	,	PUNCT
brj-24683	211	11	𝐵𝑁(𝑋	𝐵𝑁(𝑋	ADJ
brj-24683	211	12	)	)	PUNCT
brj-24683	211	13	=	=	SYM
brj-24683	211	14	𝛾	𝛾	PROPN
brj-24683	211	15	𝑋−𝜇	𝑋−𝜇	X
brj-24683	211	16	𝜎	𝜎	PROPN
brj-24683	211	17	+	+	CCONJ
brj-24683	211	18	𝛽	𝛽	PROPN
brj-24683	211	19	(	(	PUNCT
brj-24683	211	20	1	1	NUM
brj-24683	211	21	)	)	PUNCT
brj-24683	211	22	where	where	SCONJ
brj-24683	211	23	μ	μ	PROPN
brj-24683	211	24	represents	represent	VERB
brj-24683	211	25	the	the	DET
brj-24683	211	26	mean	mean	NOUN
brj-24683	211	27	of	of	ADP
brj-24683	211	28	the	the	DET
brj-24683	211	29	samples	sample	NOUN
brj-24683	211	30	,	,	PUNCT
brj-24683	211	31	and	and	CCONJ
brj-24683	211	32	σ	σ	NOUN
brj-24683	211	33	represents	represent	VERB
brj-24683	211	34	the	the	DET
brj-24683	211	35	variance	variance	NOUN
brj-24683	211	36	of	of	ADP
brj-24683	211	37	the	the	DET
brj-24683	211	38	samples	sample	NOUN
brj-24683	211	39	.	.	PUNCT
brj-24683	212	1	γ	γ	NOUN
brj-24683	212	2	and	and	CCONJ
brj-24683	212	3	β	β	X
brj-24683	212	4	are	be	AUX
brj-24683	212	5	learnable	learnable	ADJ
brj-24683	212	6	parameters	parameter	NOUN
brj-24683	212	7	,	,	PUNCT
brj-24683	212	8	corresponding	correspond	VERB
brj-24683	212	9	to	to	ADP
brj-24683	212	10	the	the	DET
brj-24683	212	11	scaling	scaling	NOUN
brj-24683	212	12	and	and	CCONJ
brj-24683	212	13	shifting	shift	VERB
brj-24683	212	14	factors	factor	NOUN
brj-24683	212	15	,	,	PUNCT
brj-24683	212	16	respectively	respectively	ADV
brj-24683	212	17	.	.	PUNCT
brj-24683	213	1	the	the	DET
brj-24683	213	2	computation	computation	NOUN
brj-24683	213	3	for	for	ADP
brj-24683	213	4	the	the	DET
brj-24683	213	5	convolution	convolution	NOUN
brj-24683	213	6	without	without	ADP
brj-24683	213	7	bias	bias	NOUN
brj-24683	213	8	is	be	AUX
brj-24683	213	9	given	give	VERB
brj-24683	213	10	by	by	ADP
brj-24683	213	11	,	,	PUNCT
brj-24683	213	12	𝐶𝑜𝑛𝑣(𝑋	𝐶𝑜𝑛𝑣(𝑋	NOUN
brj-24683	213	13	)	)	PUNCT
brj-24683	214	1	=	=	SYM
brj-24683	214	2	𝑋	𝑋	PROPN
brj-24683	214	3	∗	∗	NOUN
brj-24683	214	4	𝑊	𝑊	PROPN
brj-24683	214	5	(	(	PUNCT
brj-24683	214	6	2	2	NUM
brj-24683	214	7	)	)	PUNCT
brj-24683	214	8	where	where	SCONJ
brj-24683	214	9	w	w	NOUN
brj-24683	214	10	is	be	AUX
brj-24683	214	11	the	the	DET
brj-24683	214	12	weight	weight	NOUN
brj-24683	214	13	matrix	matrix	NOUN
brj-24683	214	14	,	,	PUNCT
brj-24683	214	15	which	which	PRON
brj-24683	214	16	is	be	AUX
brj-24683	214	17	used	use	VERB
brj-24683	214	18	to	to	PART
brj-24683	214	19	perform	perform	VERB
brj-24683	214	20	a	a	DET
brj-24683	214	21	weighted	weighted	ADJ
brj-24683	214	22	sum	sum	NOUN
brj-24683	214	23	on	on	ADP
brj-24683	214	24	the	the	DET
brj-24683	214	25	input	input	NOUN
brj-24683	214	26	signals	signal	NOUN
brj-24683	214	27	.	.	PUNCT
brj-24683	215	1	convolutional	convolutional	ADJ
brj-24683	215	2	kernel	kernel	NOUN
brj-24683	215	3	parameters	parameter	NOUN
brj-24683	215	4	number	number	NOUN
brj-24683	215	5	of	of	ADP
brj-24683	215	6	bn	bn	NOUN
brj-24683	215	7	layers	layer	NOUN
brj-24683	215	8	β	β	PROPN
brj-24683	215	9	σ	σ	PROPN
brj-24683	215	10	μ	μ	PROPN
brj-24683	215	11	ᵞ	ᵞ	PROPN
brj-24683	215	12	b	b	X
brj-24683	215	13	fig	fig	NOUN
brj-24683	215	14	.	.	PUNCT
brj-24683	216	1	8	8	NUM
brj-24683	216	2	.	.	PUNCT
brj-24683	216	3	structural	structural	ADJ
brj-24683	216	4	reparameterization	reparameterization	NOUN
brj-24683	216	5	of	of	ADP
brj-24683	216	6	repconv	repconv	VERB
brj-24683	216	7	the	the	DET
brj-24683	216	8	input	input	NOUN
brj-24683	216	9	tensor	tensor	NOUN
brj-24683	216	10	x	x	NOUN
brj-24683	216	11	,	,	PUNCT
brj-24683	216	12	after	after	ADP
brj-24683	216	13	passing	pass	VERB
brj-24683	216	14	through	through	ADP
brj-24683	216	15	the	the	DET
brj-24683	216	16	convolutional	convolutional	ADJ
brj-24683	216	17	layer	layer	NOUN
brj-24683	216	18	and	and	CCONJ
brj-24683	216	19	bn	bn	NOUN
brj-24683	216	20	layer	layer	NOUN
brj-24683	216	21	,	,	PUNCT
brj-24683	216	22	can	can	AUX
brj-24683	216	23	be	be	AUX
brj-24683	216	24	expressed	express	VERB
brj-24683	216	25	as	as	ADP
brj-24683	216	26	:	:	PUNCT
brj-24683	216	27	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	NUM
brj-24683	216	28	)	)	PUNCT
brj-24683	216	29	)	)	PUNCT
brj-24683	217	1	=	=	PUNCT
brj-24683	217	2	𝛾	𝛾	ADP
brj-24683	217	3	𝑋∗𝑊−𝜇	𝑋∗𝑊−𝜇	ADJ
brj-24683	217	4	𝜎	𝜎	NOUN
brj-24683	217	5	+	+	CCONJ
brj-24683	217	6	𝛽	𝛽	PROPN
brj-24683	217	7	(	(	PUNCT
brj-24683	217	8	3	3	NUM
brj-24683	217	9	)	)	PUNCT
brj-24683	217	10	that	that	PRON
brj-24683	217	11	is	be	AUX
brj-24683	217	12	,	,	PUNCT
brj-24683	217	13	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	NOUN
brj-24683	217	14	)	)	PUNCT
brj-24683	217	15	)	)	PUNCT
brj-24683	218	1	=	=	SYM
brj-24683	218	2	𝑋	𝑋	NOUN
brj-24683	218	3	∗	∗	NOUN
brj-24683	218	4	(	(	PUNCT
brj-24683	218	5	𝛾	𝛾	NOUN
brj-24683	218	6	𝜎	𝜎	PRON
brj-24683	218	7	𝑊)𝛾	𝑊)𝛾	NOUN
brj-24683	218	8	−	−	PROPN
brj-24683	218	9	𝛾𝜇	𝛾𝜇	ADP
brj-24683	218	10	𝜎	𝜎	PROPN
brj-24683	218	11	+	+	CCONJ
brj-24683	218	12	𝛽	𝛽	PROPN
brj-24683	218	13	(	(	PUNCT
brj-24683	218	14	4	4	X
brj-24683	218	15	)	)	PUNCT
brj-24683	218	16	let	let	VERB
brj-24683	218	17	𝑊𝑓𝑢𝑠𝑒𝑑	𝑊𝑓𝑢𝑠𝑒𝑑	PROPN
brj-24683	218	18	=	=	PROPN
brj-24683	218	19	𝛾	𝛾	PROPN
brj-24683	218	20	𝜎	𝜎	X
brj-24683	218	21	𝑊	𝑊	NOUN
brj-24683	218	22	,	,	PUNCT
brj-24683	218	23	𝑏𝑓𝑢𝑠𝑒𝑑	𝑏𝑓𝑢𝑠𝑒𝑑	NOUN
brj-24683	218	24	=	=	PUNCT
brj-24683	218	25	−	−	PROPN
brj-24683	218	26	𝛾𝜇	𝛾𝜇	ADP
brj-24683	218	27	𝜎	𝜎	PROPN
brj-24683	218	28	,	,	PUNCT
brj-24683	218	29	wfused	wfuse	VERB
brj-24683	218	30	and	and	CCONJ
brj-24683	218	31	bfused	bfuse	VERB
brj-24683	218	32	represent	represent	VERB
brj-24683	218	33	the	the	DET
brj-24683	218	34	fused	fuse	VERB
brj-24683	218	35	convolution	convolution	NOUN
brj-24683	218	36	kernel	kernel	NOUN
brj-24683	218	37	weights	weight	NOUN
brj-24683	218	38	and	and	CCONJ
brj-24683	218	39	bias	bias	NOUN
brj-24683	218	40	terms	term	NOUN
brj-24683	218	41	,	,	PUNCT
brj-24683	218	42	respectively	respectively	ADV
brj-24683	218	43	.	.	PUNCT
brj-24683	219	1	the	the	DET
brj-24683	219	2	final	final	ADJ
brj-24683	219	3	result	result	NOUN
brj-24683	219	4	of	of	ADP
brj-24683	219	5	the	the	DET
brj-24683	219	6	convolution	convolution	NOUN
brj-24683	219	7	and	and	CCONJ
brj-24683	219	8	bn	bn	NOUN
brj-24683	219	9	layer	layer	NOUN
brj-24683	219	10	fusion	fusion	NOUN
brj-24683	219	11	is	be	AUX
brj-24683	219	12	expressed	express	VERB
brj-24683	219	13	as	as	ADP
brj-24683	219	14	:	:	PUNCT
brj-24683	219	15	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	𝐵𝑁(𝐶𝑜𝑛𝑣(𝑋	NUM
brj-24683	219	16	)	)	PUNCT
brj-24683	219	17	)	)	PUNCT
brj-24683	220	1	=	=	PUNCT
brj-24683	220	2	𝑋	𝑋	NOUN
brj-24683	220	3	∗	∗	NOUN
brj-24683	220	4	𝑊𝑓𝑢𝑠𝑒𝑑	𝑊𝑓𝑢𝑠𝑒𝑑	PROPN
brj-24683	220	5	+	+	CCONJ
brj-24683	220	6	𝑏𝑓𝑢𝑠𝑒𝑑	𝑏𝑓𝑢𝑠𝑒𝑑	NOUN
brj-24683	220	7	(	(	PUNCT
brj-24683	220	8	5	5	NUM
brj-24683	220	9	)	)	PUNCT
brj-24683	220	10	peer	peer	NOUN
brj-24683	220	11	-	-	PUNCT
brj-24683	220	12	reviewed	review	VERB
brj-24683	220	13	article	article	NOUN
brj-24683	220	14	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	220	15	dou	dou	PROPN
brj-24683	220	16	&	&	CCONJ
brj-24683	220	17	you	you	PRON
brj-24683	220	18	(	(	PUNCT
brj-24683	220	19	2025	2025	NUM
brj-24683	220	20	)	)	PUNCT
brj-24683	220	21	.	.	PUNCT
brj-24683	221	1	“	"	PUNCT
brj-24683	221	2	wood	wood	NOUN
brj-24683	221	3	defect	defect	NOUN
brj-24683	221	4	identification	identification	NOUN
brj-24683	221	5	,	,	PUNCT
brj-24683	221	6	”	"	PUNCT
brj-24683	221	7	bioresources	bioresource	NOUN
brj-24683	221	8	20(3	20(3	NOUN
brj-24683	221	9	)	)	PUNCT
brj-24683	221	10	,	,	PUNCT
brj-24683	221	11	5709	5709	NUM
brj-24683	221	12	-	-	SYM
brj-24683	221	13	5730	5730	NUM
brj-24683	221	14	.	.	PUNCT
brj-24683	222	1	5720	5720	NUM
brj-24683	222	2	by	by	ADP
brj-24683	222	3	the	the	DET
brj-24683	222	4	fusion	fusion	NOUN
brj-24683	222	5	method	method	NOUN
brj-24683	222	6	described	describe	VERB
brj-24683	222	7	above	above	ADV
brj-24683	222	8	,	,	PUNCT
brj-24683	222	9	the	the	DET
brj-24683	222	10	3×3	3×3	NUM
brj-24683	222	11	convolutional	convolutional	ADJ
brj-24683	222	12	layers	layer	NOUN
brj-24683	222	13	and	and	CCONJ
brj-24683	222	14	bn	bn	NOUN
brj-24683	222	15	layers	layer	NOUN
brj-24683	222	16	in	in	ADP
brj-24683	222	17	step	step	NOUN
brj-24683	222	18	(	(	PUNCT
brj-24683	222	19	1	1	X
brj-24683	222	20	)	)	PUNCT
brj-24683	222	21	can	can	AUX
brj-24683	222	22	be	be	AUX
brj-24683	222	23	merged	merge	VERB
brj-24683	222	24	,	,	PUNCT
brj-24683	222	25	reducing	reduce	VERB
brj-24683	222	26	the	the	DET
brj-24683	222	27	number	number	NOUN
brj-24683	222	28	of	of	ADP
brj-24683	222	29	parameters	parameter	NOUN
brj-24683	222	30	in	in	ADP
brj-24683	222	31	the	the	DET
brj-24683	222	32	network	network	NOUN
brj-24683	222	33	.	.	PUNCT
brj-24683	223	1	(	(	PUNCT
brj-24683	223	2	3	3	X
brj-24683	223	3	)	)	PUNCT
brj-24683	223	4	fusion	fusion	NOUN
brj-24683	223	5	of	of	ADP
brj-24683	223	6	the	the	DET
brj-24683	223	7	convolution	convolution	NOUN
brj-24683	223	8	layers	layer	NOUN
brj-24683	223	9	with	with	ADP
brj-24683	223	10	their	their	PRON
brj-24683	223	11	respective	respective	ADJ
brj-24683	223	12	biases	bias	NOUN
brj-24683	223	13	:	:	PUNCT
brj-24683	223	14	the	the	DET
brj-24683	223	15	three	three	NUM
brj-24683	223	16	sets	set	NOUN
brj-24683	223	17	of	of	ADP
brj-24683	223	18	3×3	3×3	NUM
brj-24683	223	19	convolutional	convolutional	ADJ
brj-24683	223	20	kernels	kernel	NOUN
brj-24683	223	21	and	and	CCONJ
brj-24683	223	22	their	their	PRON
brj-24683	223	23	corresponding	correspond	VERB
brj-24683	223	24	biases	bias	NOUN
brj-24683	223	25	are	be	AUX
brj-24683	223	26	stacked	stack	VERB
brj-24683	223	27	together	together	ADV
brj-24683	223	28	,	,	PUNCT
brj-24683	223	29	resulting	result	VERB
brj-24683	223	30	in	in	ADP
brj-24683	223	31	a	a	DET
brj-24683	223	32	single	single	ADJ
brj-24683	223	33	3×3	3×3	NUM
brj-24683	223	34	convolutional	convolutional	ADJ
brj-24683	223	35	kernel	kernel	NOUN
brj-24683	223	36	and	and	CCONJ
brj-24683	223	37	bias	bias	NOUN
brj-24683	223	38	.	.	PUNCT
brj-24683	224	1	let	let	VERB
brj-24683	224	2	the	the	DET
brj-24683	224	3	convolution	convolution	NOUN
brj-24683	224	4	kernel	kernel	NOUN
brj-24683	224	5	parameters	parameter	NOUN
brj-24683	224	6	and	and	CCONJ
brj-24683	224	7	biases	bias	NOUN
brj-24683	224	8	of	of	ADP
brj-24683	224	9	the	the	DET
brj-24683	224	10	three	three	NUM
brj-24683	224	11	sets	set	NOUN
brj-24683	224	12	of	of	ADP
brj-24683	224	13	3×3	3×3	NUM
brj-24683	224	14	convolutions	convolution	NOUN
brj-24683	224	15	be	be	AUX
brj-24683	224	16	w1	w1	NOUN
brj-24683	224	17	,	,	PUNCT
brj-24683	224	18	w2	w2	NOUN
brj-24683	224	19	,	,	PUNCT
brj-24683	224	20	w3	w3	PROPN
brj-24683	224	21	and	and	CCONJ
brj-24683	224	22	b1	b1	PROPN
brj-24683	224	23	,	,	PUNCT
brj-24683	224	24	b2	b2	NOUN
brj-24683	224	25	,	,	PUNCT
brj-24683	224	26	b3	b3	PROPN
brj-24683	224	27	,	,	PUNCT
brj-24683	224	28	respectively	respectively	ADV
brj-24683	224	29	.	.	PUNCT
brj-24683	225	1	the	the	DET
brj-24683	225	2	output	output	NOUN
brj-24683	225	3	tensor	tensor	NOUN
brj-24683	225	4	y∈rh×w×c	y∈rh×w×c	PROPN
brj-24683	225	5	after	after	ADP
brj-24683	225	6	processing	process	VERB
brj-24683	225	7	the	the	DET
brj-24683	225	8	input	input	NOUN
brj-24683	225	9	tensor	tensor	NOUN
brj-24683	225	10	x∈rh×w×c	x∈rh×w×c	PROPN
brj-24683	225	11	through	through	ADP
brj-24683	225	12	the	the	DET
brj-24683	225	13	three	three	NUM
brj-24683	225	14	sets	set	NOUN
brj-24683	225	15	of	of	ADP
brj-24683	225	16	3×3	3×3	NUM
brj-24683	225	17	convolutions	convolution	NOUN
brj-24683	225	18	and	and	CCONJ
brj-24683	225	19	their	their	PRON
brj-24683	225	20	corresponding	correspond	VERB
brj-24683	225	21	biases	bias	NOUN
brj-24683	225	22	can	can	AUX
brj-24683	225	23	be	be	AUX
brj-24683	225	24	expressed	express	VERB
brj-24683	225	25	as	as	ADP
brj-24683	225	26	:	:	PUNCT
brj-24683	225	27	𝑌	𝑌	PROPN
brj-24683	225	28	=	=	SYM
brj-24683	225	29	(	(	PUNCT
brj-24683	225	30	𝑋	𝑋	PROPN
brj-24683	225	31	∗𝑊1	∗𝑊1	ADP
brj-24683	225	32	+	+	CCONJ
brj-24683	225	33	𝑏1	𝑏1	ADJ
brj-24683	225	34	)	)	PUNCT
brj-24683	226	1	+	+	CCONJ
brj-24683	226	2	(	(	PUNCT
brj-24683	226	3	𝑋	𝑋	PROPN
brj-24683	226	4	∗𝑊2	∗𝑊2	PROPN
brj-24683	226	5	+	+	CCONJ
brj-24683	226	6	𝑏2	𝑏2	PROPN
brj-24683	226	7	)	)	PUNCT
brj-24683	227	1	+	+	CCONJ
brj-24683	227	2	(	(	PUNCT
brj-24683	227	3	𝑋	𝑋	NOUN
brj-24683	227	4	∗𝑊3	∗𝑊3	ADJ
brj-24683	227	5	+	+	CCONJ
brj-24683	227	6	𝑏3	𝑏3	NOUN
brj-24683	227	7	)	)	PUNCT
brj-24683	227	8	(	(	PUNCT
brj-24683	227	9	6	6	NUM
brj-24683	227	10	)	)	PUNCT
brj-24683	227	11	that	that	PRON
brj-24683	227	12	is	be	AUX
brj-24683	227	13	，	，	PUNCT
brj-24683	227	14	𝑌	𝑌	PROPN
brj-24683	227	15	=	=	SYM
brj-24683	227	16	𝑋	𝑋	PROPN
brj-24683	227	17	∗	∗	NOUN
brj-24683	227	18	(	(	PUNCT
brj-24683	227	19	𝑊1	𝑊1	PROPN
brj-24683	227	20	+	+	PROPN
brj-24683	227	21	𝑊2+𝑊3	𝑊2+𝑊3	NOUN
brj-24683	227	22	)	)	PUNCT
brj-24683	228	1	+	+	CCONJ
brj-24683	228	2	(	(	PUNCT
brj-24683	228	3	𝑏1	𝑏1	ADJ
brj-24683	228	4	+	+	CCONJ
brj-24683	228	5	𝑏2	𝑏2	NOUN
brj-24683	228	6	+	+	CCONJ
brj-24683	228	7	𝑏3	𝑏3	NOUN
brj-24683	228	8	)	)	PUNCT
brj-24683	228	9	(	(	PUNCT
brj-24683	228	10	7	7	X
brj-24683	228	11	)	)	PUNCT
brj-24683	228	12	based	base	VERB
brj-24683	228	13	on	on	ADP
brj-24683	228	14	the	the	DET
brj-24683	228	15	above	above	ADJ
brj-24683	228	16	calculations	calculation	NOUN
brj-24683	228	17	,	,	PUNCT
brj-24683	228	18	the	the	DET
brj-24683	228	19	repconv	repconv	ADJ
brj-24683	228	20	multi	multi	ADJ
brj-24683	228	21	-	-	ADJ
brj-24683	228	22	branch	branch	ADJ
brj-24683	228	23	structure	structure	NOUN
brj-24683	228	24	from	from	ADP
brj-24683	228	25	the	the	DET
brj-24683	228	26	training	training	NOUN
brj-24683	228	27	phase	phase	NOUN
brj-24683	228	28	is	be	AUX
brj-24683	228	29	transformed	transform	VERB
brj-24683	228	30	into	into	ADP
brj-24683	228	31	a	a	DET
brj-24683	228	32	single	single	ADJ
brj-24683	228	33	-	-	PUNCT
brj-24683	228	34	branch	branch	NOUN
brj-24683	228	35	convolutional	convolutional	ADJ
brj-24683	228	36	structure	structure	NOUN
brj-24683	228	37	through	through	ADP
brj-24683	228	38	structural	structural	ADJ
brj-24683	228	39	reparameterization	reparameterization	NOUN
brj-24683	228	40	.	.	PUNCT
brj-24683	229	1	subsequently	subsequently	ADV
brj-24683	229	2	,	,	PUNCT
brj-24683	229	3	repconv	repconv	PROPN
brj-24683	229	4	is	be	AUX
brj-24683	229	5	incorporated	incorporate	VERB
brj-24683	229	6	into	into	ADP
brj-24683	229	7	the	the	DET
brj-24683	229	8	repblock	repblock	NOUN
brj-24683	229	9	module	module	NOUN
brj-24683	229	10	,	,	PUNCT
brj-24683	229	11	as	as	SCONJ
brj-24683	229	12	shown	show	VERB
brj-24683	229	13	in	in	ADP
brj-24683	229	14	fig	fig	NOUN
brj-24683	229	15	.	.	PUNCT
brj-24683	230	1	9	9	X
brj-24683	230	2	.	.	X
brj-24683	230	3	fig	fig	NOUN
brj-24683	230	4	.	.	PUNCT
brj-24683	231	1	9	9	X
brj-24683	231	2	.	.	X
brj-24683	231	3	structure	structure	NOUN
brj-24683	231	4	of	of	ADP
brj-24683	231	5	repblock	repblock	NOUN
brj-24683	231	6	the	the	DET
brj-24683	231	7	repblock	repblock	NOUN
brj-24683	231	8	module	module	NOUN
brj-24683	231	9	combines	combine	VERB
brj-24683	231	10	repconv	repconv	NOUN
brj-24683	231	11	with	with	ADP
brj-24683	231	12	a	a	DET
brj-24683	231	13	dual	dual	ADJ
brj-24683	231	14	-	-	PUNCT
brj-24683	231	15	path	path	NOUN
brj-24683	231	16	architecture	architecture	NOUN
brj-24683	231	17	.	.	PUNCT
brj-24683	232	1	it	it	PRON
brj-24683	232	2	consists	consist	VERB
brj-24683	232	3	of	of	ADP
brj-24683	232	4	two	two	NUM
brj-24683	232	5	parallel	parallel	ADJ
brj-24683	232	6	1×1	1×1	NUM
brj-24683	232	7	convolution	convolution	NOUN
brj-24683	232	8	layers	layer	NOUN
brj-24683	232	9	:	:	PUNCT
brj-24683	232	10	one	one	NUM
brj-24683	232	11	directly	directly	ADV
brj-24683	232	12	passes	pass	VERB
brj-24683	232	13	the	the	DET
brj-24683	232	14	original	original	ADJ
brj-24683	232	15	information	information	NOUN
brj-24683	232	16	,	,	PUNCT
brj-24683	232	17	while	while	SCONJ
brj-24683	232	18	the	the	DET
brj-24683	232	19	other	other	ADJ
brj-24683	232	20	adjusts	adjust	VERB
brj-24683	232	21	the	the	DET
brj-24683	232	22	channel	channel	NOUN
brj-24683	232	23	size	size	NOUN
brj-24683	232	24	.	.	PUNCT
brj-24683	233	1	this	this	PRON
brj-24683	233	2	is	be	AUX
brj-24683	233	3	followed	follow	VERB
brj-24683	233	4	by	by	ADP
brj-24683	233	5	a	a	DET
brj-24683	233	6	module	module	NOUN
brj-24683	233	7	composed	compose	VERB
brj-24683	233	8	of	of	ADP
brj-24683	233	9	multiple	multiple	ADJ
brj-24683	233	10	repconv	repconv	ADJ
brj-24683	233	11	layers	layer	NOUN
brj-24683	233	12	for	for	ADP
brj-24683	233	13	in	in	ADP
brj-24683	233	14	-	-	PUNCT
brj-24683	233	15	depth	depth	NOUN
brj-24683	233	16	feature	feature	NOUN
brj-24683	233	17	extraction	extraction	NOUN
brj-24683	233	18	.	.	PUNCT
brj-24683	234	1	through	through	ADP
brj-24683	234	2	the	the	DET
brj-24683	234	3	repconv	repconv	PROPN
brj-24683	234	4	layers	layer	NOUN
brj-24683	234	5	,	,	PUNCT
brj-24683	234	6	repblock	repblock	NOUN
brj-24683	234	7	is	be	AUX
brj-24683	234	8	capable	capable	ADJ
brj-24683	234	9	of	of	ADP
brj-24683	234	10	capturing	capture	VERB
brj-24683	234	11	complex	complex	ADJ
brj-24683	234	12	features	feature	NOUN
brj-24683	234	13	within	within	ADP
brj-24683	234	14	the	the	DET
brj-24683	234	15	image	image	NOUN
brj-24683	234	16	with	with	ADP
brj-24683	234	17	greater	great	ADJ
brj-24683	234	18	detail	detail	NOUN
brj-24683	234	19	,	,	PUNCT
brj-24683	234	20	which	which	PRON
brj-24683	234	21	is	be	AUX
brj-24683	234	22	crucial	crucial	ADJ
brj-24683	234	23	for	for	ADP
brj-24683	234	24	improving	improve	VERB
brj-24683	234	25	detection	detection	NOUN
brj-24683	234	26	accuracy	accuracy	NOUN
brj-24683	234	27	.	.	PUNCT
brj-24683	235	1	additionally	additionally	ADV
brj-24683	235	2	,	,	PUNCT
brj-24683	235	3	the	the	DET
brj-24683	235	4	parallel	parallel	ADJ
brj-24683	235	5	residual	residual	ADJ
brj-24683	235	6	connections	connection	NOUN
brj-24683	235	7	help	help	AUX
brj-24683	235	8	retain	retain	VERB
brj-24683	235	9	the	the	DET
brj-24683	235	10	original	original	ADJ
brj-24683	235	11	features	feature	NOUN
brj-24683	235	12	,	,	PUNCT
brj-24683	235	13	mitigating	mitigate	VERB
brj-24683	235	14	the	the	DET
brj-24683	235	15	vanishing	vanish	VERB
brj-24683	235	16	gradient	gradient	NOUN
brj-24683	235	17	problem	problem	NOUN
brj-24683	235	18	,	,	PUNCT
brj-24683	235	19	and	and	CCONJ
brj-24683	235	20	enhancing	enhance	VERB
brj-24683	235	21	the	the	DET
brj-24683	235	22	stability	stability	NOUN
brj-24683	235	23	and	and	CCONJ
brj-24683	235	24	reliability	reliability	NOUN
brj-24683	235	25	of	of	ADP
brj-24683	235	26	the	the	DET
brj-24683	235	27	model	model	NOUN
brj-24683	235	28	.	.	PUNCT
brj-24683	236	1	addition	addition	NOUN
brj-24683	236	2	of	of	ADP
brj-24683	236	3	small	small	ADJ
brj-24683	236	4	-	-	PUNCT
brj-24683	236	5	object	object	NOUN
brj-24683	236	6	detection	detection	NOUN
brj-24683	236	7	head	head	NOUN
brj-24683	236	8	in	in	ADP
brj-24683	236	9	the	the	DET
brj-24683	236	10	dataset	dataset	NOUN
brj-24683	236	11	used	use	VERB
brj-24683	236	12	in	in	ADP
brj-24683	236	13	this	this	DET
brj-24683	236	14	study	study	NOUN
brj-24683	236	15	,	,	PUNCT
brj-24683	236	16	small	small	ADJ
brj-24683	236	17	-	-	PUNCT
brj-24683	236	18	object	object	NOUN
brj-24683	236	19	targets	target	NOUN
brj-24683	236	20	occupy	occupy	VERB
brj-24683	236	21	a	a	DET
brj-24683	236	22	very	very	ADV
brj-24683	236	23	small	small	ADJ
brj-24683	236	24	portion	portion	NOUN
brj-24683	236	25	of	of	ADP
brj-24683	236	26	the	the	DET
brj-24683	236	27	image	image	NOUN
brj-24683	236	28	.	.	PUNCT
brj-24683	237	1	after	after	ADP
brj-24683	237	2	setting	set	VERB
brj-24683	237	3	the	the	DET
brj-24683	237	4	image	image	NOUN
brj-24683	237	5	size	size	NOUN
brj-24683	237	6	to	to	ADP
brj-24683	237	7	640×640	640×640	NUM
brj-24683	237	8	,	,	PUNCT
brj-24683	237	9	many	many	ADJ
brj-24683	237	10	targets	target	NOUN
brj-24683	237	11	are	be	AUX
brj-24683	237	12	smaller	small	ADJ
brj-24683	237	13	than	than	ADP
brj-24683	237	14	3×3	3×3	NUM
brj-24683	237	15	pixels	pixel	NOUN
brj-24683	237	16	.	.	PUNCT
brj-24683	238	1	after	after	ADP
brj-24683	238	2	multiple	multiple	ADJ
brj-24683	238	3	downsampling	downsample	VERB
brj-24683	238	4	pooling	pool	VERB
brj-24683	238	5	operations	operation	NOUN
brj-24683	238	6	,	,	PUNCT
brj-24683	238	7	most	most	ADJ
brj-24683	238	8	of	of	ADP
brj-24683	238	9	the	the	DET
brj-24683	238	10	features	feature	NOUN
brj-24683	238	11	are	be	AUX
brj-24683	238	12	lost	lose	VERB
brj-24683	238	13	,	,	PUNCT
brj-24683	238	14	resulting	result	VERB
brj-24683	238	15	in	in	ADP
brj-24683	238	16	a	a	DET
brj-24683	238	17	high	high	ADJ
brj-24683	238	18	likelihood	likelihood	NOUN
brj-24683	238	19	of	of	ADP
brj-24683	238	20	false	false	ADJ
brj-24683	238	21	negatives	negative	NOUN
brj-24683	238	22	.	.	PUNCT
brj-24683	239	1	the	the	DET
brj-24683	239	2	detection	detection	NOUN
brj-24683	239	3	heads	head	VERB
brj-24683	239	4	in	in	ADP
brj-24683	239	5	the	the	DET
brj-24683	239	6	baseline	baseline	NOUN
brj-24683	239	7	model	model	NOUN
brj-24683	239	8	have	have	VERB
brj-24683	239	9	sizes	size	NOUN
brj-24683	239	10	of	of	ADP
brj-24683	239	11	20×20	20×20	NUM
brj-24683	239	12	,	,	PUNCT
brj-24683	239	13	40×40	40×40	NUM
brj-24683	239	14	,	,	PUNCT
brj-24683	239	15	and	and	CCONJ
brj-24683	239	16	80×80	80×80	NUM
brj-24683	239	17	,	,	PUNCT
brj-24683	239	18	and	and	CCONJ
brj-24683	239	19	when	when	SCONJ
brj-24683	239	20	using	use	VERB
brj-24683	239	21	the	the	DET
brj-24683	239	22	smallest	small	ADJ
brj-24683	239	23	detection	detection	NOUN
brj-24683	239	24	head	head	NOUN
brj-24683	239	25	(	(	PUNCT
brj-24683	239	26	80×80	80×80	NUM
brj-24683	239	27	)	)	PUNCT
brj-24683	239	28	to	to	PART
brj-24683	239	29	detect	detect	VERB
brj-24683	239	30	each	each	DET
brj-24683	239	31	grid	grid	NOUN
brj-24683	239	32	in	in	ADP
brj-24683	239	33	the	the	DET
brj-24683	239	34	image	image	NOUN
brj-24683	239	35	,	,	PUNCT
brj-24683	239	36	the	the	DET
brj-24683	239	37	receptive	receptive	ADJ
brj-24683	239	38	field	field	NOUN
brj-24683	239	39	is	be	AUX
brj-24683	239	40	only	only	ADV
brj-24683	239	41	8×8	8×8	NUM
brj-24683	239	42	.	.	PUNCT
brj-24683	240	1	this	this	PRON
brj-24683	240	2	limits	limit	VERB
brj-24683	240	3	the	the	DET
brj-24683	240	4	model	model	NOUN
brj-24683	240	5	’s	’s	PART
brj-24683	240	6	ability	ability	NOUN
brj-24683	240	7	to	to	PART
brj-24683	240	8	recognize	recognize	VERB
brj-24683	240	9	small	small	ADJ
brj-24683	240	10	targets	target	NOUN
brj-24683	240	11	.	.	PUNCT
brj-24683	241	1	to	to	PART
brj-24683	241	2	address	address	VERB
brj-24683	241	3	this	this	PRON
brj-24683	241	4	,	,	PUNCT
brj-24683	241	5	a	a	DET
brj-24683	241	6	small	small	ADJ
brj-24683	241	7	-	-	PUNCT
brj-24683	241	8	object	object	NOUN
brj-24683	241	9	detection	detection	NOUN
brj-24683	241	10	head	head	NOUN
brj-24683	241	11	with	with	ADP
brj-24683	241	12	a	a	DET
brj-24683	241	13	size	size	NOUN
brj-24683	241	14	of	of	ADP
brj-24683	241	15	160×160	160×160	NUM
brj-24683	241	16	is	be	AUX
brj-24683	241	17	added	add	VERB
brj-24683	241	18	to	to	ADP
brj-24683	241	19	the	the	DET
brj-24683	241	20	head	head	NOUN
brj-24683	241	21	layer	layer	NOUN
brj-24683	241	22	of	of	ADP
brj-24683	241	23	the	the	DET
brj-24683	241	24	baseline	baseline	NOUN
brj-24683	241	25	model	model	NOUN
brj-24683	241	26	,	,	PUNCT
brj-24683	241	27	improving	improve	VERB
brj-24683	241	28	the	the	DET
brj-24683	241	29	model	model	NOUN
brj-24683	241	30	’s	’s	PART
brj-24683	241	31	detection	detection	NOUN
brj-24683	241	32	capability	capability	NOUN
brj-24683	241	33	for	for	ADP
brj-24683	241	34	small	small	ADJ
brj-24683	241	35	targets	target	NOUN
brj-24683	241	36	.	.	PUNCT
brj-24683	242	1	the	the	DET
brj-24683	242	2	structure	structure	NOUN
brj-24683	242	3	of	of	ADP
brj-24683	242	4	the	the	DET
brj-24683	242	5	new	new	ADJ
brj-24683	242	6	detection	detection	NOUN
brj-24683	242	7	head	head	NOUN
brj-24683	242	8	is	be	AUX
brj-24683	242	9	shown	show	VERB
brj-24683	242	10	in	in	ADP
brj-24683	242	11	fig	fig	NOUN
brj-24683	242	12	.	.	PUNCT
brj-24683	243	1	2	2	X
brj-24683	243	2	.	.	X
brj-24683	243	3	first	first	ADV
brj-24683	243	4	,	,	PUNCT
brj-24683	243	5	the	the	DET
brj-24683	243	6	80×80	80×80	NUM
brj-24683	243	7	feature	feature	NOUN
brj-24683	243	8	map	map	NOUN
brj-24683	243	9	from	from	ADP
brj-24683	243	10	the	the	DET
brj-24683	243	11	second	second	ADJ
brj-24683	243	12	layer	layer	NOUN
brj-24683	243	13	of	of	ADP
brj-24683	243	14	the	the	DET
brj-24683	243	15	backbone	backbone	NOUN
brj-24683	243	16	network	network	NOUN
brj-24683	243	17	is	be	AUX
brj-24683	243	18	stacked	stack	VERB
brj-24683	243	19	with	with	ADP
brj-24683	243	20	the	the	DET
brj-24683	243	21	upsampled	upsample	VERB
brj-24683	243	22	feature	feature	NOUN
brj-24683	243	23	maps	map	NOUN
brj-24683	243	24	from	from	ADP
brj-24683	243	25	the	the	DET
brj-24683	243	26	neck	neck	NOUN
brj-24683	243	27	layer	layer	NOUN
brj-24683	243	28	.	.	PUNCT
brj-24683	244	1	after	after	ADP
brj-24683	244	2	passing	pass	VERB
brj-24683	244	3	through	through	ADP
brj-24683	244	4	repblock	repblock	NOUN
brj-24683	244	5	and	and	CCONJ
brj-24683	244	6	dysample	dysample	ADJ
brj-24683	244	7	processing	processing	NOUN
brj-24683	244	8	,	,	PUNCT
brj-24683	244	9	additional	additional	ADJ
brj-24683	244	10	feature	feature	NOUN
brj-24683	244	11	layers	layer	NOUN
brj-24683	244	12	with	with	ADP
brj-24683	244	13	small	small	ADJ
brj-24683	244	14	-	-	PUNCT
brj-24683	244	15	object	object	NOUN
brj-24683	244	16	characteristics	characteristic	NOUN
brj-24683	244	17	are	be	AUX
brj-24683	244	18	obtained	obtain	VERB
brj-24683	244	19	.	.	PUNCT
brj-24683	245	1	these	these	PRON
brj-24683	245	2	are	be	AUX
brj-24683	245	3	then	then	ADV
brj-24683	245	4	1×1conv	1×1conv	NUM
brj-24683	245	5	1×1conv	1×1conv	NUM
brj-24683	245	6	repconv	repconv	VERB
brj-24683	245	7	1×1conv	1×1conv	NUM
brj-24683	245	8	n×	n×	NOUN
brj-24683	245	9	peer	peer	NOUN
brj-24683	245	10	-	-	PUNCT
brj-24683	245	11	reviewed	review	VERB
brj-24683	245	12	article	article	NOUN
brj-24683	245	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	245	14	dou	dou	PROPN
brj-24683	245	15	&	&	CCONJ
brj-24683	245	16	you	you	PRON
brj-24683	245	17	(	(	PUNCT
brj-24683	245	18	2025	2025	NUM
brj-24683	245	19	)	)	PUNCT
brj-24683	245	20	.	.	PUNCT
brj-24683	246	1	“	"	PUNCT
brj-24683	246	2	wood	wood	NOUN
brj-24683	246	3	defect	defect	NOUN
brj-24683	246	4	identification	identification	NOUN
brj-24683	246	5	,	,	PUNCT
brj-24683	246	6	”	"	PUNCT
brj-24683	246	7	bioresources	bioresource	NOUN
brj-24683	246	8	20(3	20(3	NOUN
brj-24683	246	9	)	)	PUNCT
brj-24683	246	10	,	,	PUNCT
brj-24683	246	11	5709	5709	NUM
brj-24683	246	12	-	-	SYM
brj-24683	246	13	5730	5730	NUM
brj-24683	246	14	.	.	PUNCT
brj-24683	247	1	5721	5721	NUM
brj-24683	247	2	concatenated	concatenate	VERB
brj-24683	247	3	with	with	ADP
brj-24683	247	4	the	the	DET
brj-24683	247	5	160×160	160×160	NUM
brj-24683	247	6	feature	feature	NOUN
brj-24683	247	7	map	map	NOUN
brj-24683	247	8	output	output	NOUN
brj-24683	247	9	from	from	ADP
brj-24683	247	10	the	the	DET
brj-24683	247	11	second	second	ADJ
brj-24683	247	12	layer	layer	NOUN
brj-24683	247	13	of	of	ADP
brj-24683	247	14	the	the	DET
brj-24683	247	15	backbone	backbone	NOUN
brj-24683	247	16	network	network	NOUN
brj-24683	247	17	,	,	PUNCT
brj-24683	247	18	enhancing	enhance	VERB
brj-24683	247	19	the	the	DET
brj-24683	247	20	160×160	160×160	NUM
brj-24683	247	21	scale	scale	NOUN
brj-24683	247	22	feature	feature	NOUN
brj-24683	247	23	layer	layer	NOUN
brj-24683	247	24	’s	’s	PART
brj-24683	247	25	ability	ability	NOUN
brj-24683	247	26	to	to	PART
brj-24683	247	27	represent	represent	VERB
brj-24683	247	28	small	small	ADJ
brj-24683	247	29	targets	target	NOUN
brj-24683	247	30	related	relate	VERB
brj-24683	247	31	to	to	ADP
brj-24683	247	32	wood	wood	NOUN
brj-24683	247	33	surface	surface	NOUN
brj-24683	247	34	defects	defect	NOUN
brj-24683	247	35	.	.	PUNCT
brj-24683	248	1	the	the	DET
brj-24683	248	2	added	add	VERB
brj-24683	248	3	detection	detection	NOUN
brj-24683	248	4	head	head	NOUN
brj-24683	248	5	allows	allow	VERB
brj-24683	248	6	small	small	ADJ
brj-24683	248	7	-	-	PUNCT
brj-24683	248	8	object	object	NOUN
brj-24683	248	9	feature	feature	NOUN
brj-24683	248	10	information	information	NOUN
brj-24683	248	11	to	to	PART
brj-24683	248	12	be	be	AUX
brj-24683	248	13	propagated	propagate	VERB
brj-24683	248	14	through	through	ADP
brj-24683	248	15	the	the	DET
brj-24683	248	16	detection	detection	NOUN
brj-24683	248	17	layers	layer	NOUN
brj-24683	248	18	along	along	ADP
brj-24683	248	19	the	the	DET
brj-24683	248	20	downsampling	downsample	VERB
brj-24683	248	21	path	path	NOUN
brj-24683	248	22	to	to	ADP
brj-24683	248	23	other	other	ADJ
brj-24683	248	24	feature	feature	NOUN
brj-24683	248	25	layers	layer	NOUN
brj-24683	248	26	at	at	ADP
brj-24683	248	27	different	different	ADJ
brj-24683	248	28	scales	scale	NOUN
brj-24683	248	29	.	.	PUNCT
brj-24683	249	1	this	this	PRON
brj-24683	249	2	enables	enable	VERB
brj-24683	249	3	small	small	ADJ
brj-24683	249	4	-	-	PUNCT
brj-24683	249	5	object	object	NOUN
brj-24683	249	6	features	feature	NOUN
brj-24683	249	7	to	to	PART
brj-24683	249	8	be	be	AUX
brj-24683	249	9	extracted	extract	VERB
brj-24683	249	10	at	at	ADP
brj-24683	249	11	deeper	deep	ADJ
brj-24683	249	12	network	network	NOUN
brj-24683	249	13	layers	layer	NOUN
brj-24683	249	14	,	,	PUNCT
brj-24683	249	15	enhancing	enhance	VERB
brj-24683	249	16	the	the	DET
brj-24683	249	17	detection	detection	NOUN
brj-24683	249	18	of	of	ADP
brj-24683	249	19	wood	wood	NOUN
brj-24683	249	20	surface	surface	NOUN
brj-24683	249	21	defects	defect	NOUN
brj-24683	249	22	in	in	ADP
brj-24683	249	23	complex	complex	ADJ
brj-24683	249	24	backgrounds	background	NOUN
brj-24683	249	25	and	and	CCONJ
brj-24683	249	26	effectively	effectively	ADV
brj-24683	249	27	reducing	reduce	VERB
brj-24683	249	28	both	both	DET
brj-24683	249	29	false	false	ADJ
brj-24683	249	30	positives	positive	NOUN
brj-24683	249	31	and	and	CCONJ
brj-24683	249	32	false	false	ADJ
brj-24683	249	33	negatives	negative	NOUN
brj-24683	249	34	at	at	ADP
brj-24683	249	35	different	different	ADJ
brj-24683	249	36	scales	scale	NOUN
brj-24683	249	37	.	.	PUNCT
brj-24683	250	1	results	result	NOUN
brj-24683	250	2	and	and	CCONJ
brj-24683	250	3	discussion	discussion	NOUN
brj-24683	250	4	ablation	ablation	NOUN
brj-24683	250	5	experiments	experiment	VERB
brj-24683	250	6	the	the	DET
brj-24683	250	7	software	software	NOUN
brj-24683	250	8	and	and	CCONJ
brj-24683	250	9	hardware	hardware	NOUN
brj-24683	250	10	configuration	configuration	NOUN
brj-24683	250	11	used	use	VERB
brj-24683	250	12	in	in	ADP
brj-24683	250	13	the	the	DET
brj-24683	250	14	experiment	experiment	NOUN
brj-24683	250	15	is	be	AUX
brj-24683	250	16	detailed	detail	VERB
brj-24683	250	17	in	in	ADP
brj-24683	250	18	table	table	NOUN
brj-24683	250	19	2	2	NUM
brj-24683	250	20	.	.	PUNCT
brj-24683	251	1	the	the	DET
brj-24683	251	2	specific	specific	ADJ
brj-24683	251	3	training	training	NOUN
brj-24683	251	4	hyperparameters	hyperparameter	NOUN
brj-24683	251	5	are	be	AUX
brj-24683	251	6	as	as	SCONJ
brj-24683	251	7	follows	follow	VERB
brj-24683	251	8	:	:	PUNCT
brj-24683	251	9	(	(	PUNCT
brj-24683	251	10	1	1	X
brj-24683	251	11	)	)	PUNCT
brj-24683	251	12	input	input	NOUN
brj-24683	251	13	image	image	NOUN
brj-24683	251	14	size	size	NOUN
brj-24683	251	15	:	:	PUNCT
brj-24683	251	16	640	640	NUM
brj-24683	251	17	pixels	pixel	NOUN
brj-24683	251	18	.	.	PUNCT
brj-24683	252	1	(	(	PUNCT
brj-24683	252	2	2	2	X
brj-24683	252	3	)	)	PUNCT
brj-24683	252	4	number	number	NOUN
brj-24683	252	5	of	of	ADP
brj-24683	252	6	iterations	iteration	NOUN
brj-24683	252	7	:	:	PUNCT
brj-24683	252	8	200	200	NUM
brj-24683	252	9	.	.	PUNCT
brj-24683	253	1	(	(	PUNCT
brj-24683	253	2	3	3	X
brj-24683	253	3	)	)	PUNCT
brj-24683	253	4	batch	batch	NOUN
brj-24683	253	5	size	size	NOUN
brj-24683	253	6	:	:	PUNCT
brj-24683	253	7	8	8	NUM
brj-24683	253	8	.	.	PUNCT
brj-24683	254	1	(	(	PUNCT
brj-24683	254	2	4	4	X
brj-24683	254	3	)	)	PUNCT
brj-24683	254	4	initial	initial	ADJ
brj-24683	254	5	learning	learning	NOUN
brj-24683	254	6	rate	rate	NOUN
brj-24683	254	7	:	:	PUNCT
brj-24683	254	8	0.01	0.01	NUM
brj-24683	254	9	.	.	PUNCT
brj-24683	255	1	(	(	PUNCT
brj-24683	255	2	5	5	X
brj-24683	255	3	)	)	PUNCT
brj-24683	255	4	weight	weight	NOUN
brj-24683	255	5	decay	decay	NOUN
brj-24683	255	6	coefficient	coefficient	NOUN
brj-24683	255	7	:	:	PUNCT
brj-24683	255	8	0.0005	0.0005	NUM
brj-24683	255	9	.	.	PUNCT
brj-24683	256	1	(	(	PUNCT
brj-24683	256	2	6	6	NUM
brj-24683	256	3	)	)	PUNCT
brj-24683	256	4	momentum	momentum	NOUN
brj-24683	256	5	:	:	PUNCT
brj-24683	256	6	0.937	0.937	NUM
brj-24683	256	7	.	.	PUNCT
brj-24683	257	1	to	to	PART
brj-24683	257	2	assess	assess	VERB
brj-24683	257	3	the	the	DET
brj-24683	257	4	accuracy	accuracy	NOUN
brj-24683	257	5	and	and	CCONJ
brj-24683	257	6	effectiveness	effectiveness	NOUN
brj-24683	257	7	of	of	ADP
brj-24683	257	8	our	our	PRON
brj-24683	257	9	method	method	NOUN
brj-24683	257	10	,	,	PUNCT
brj-24683	257	11	two	two	NUM
brj-24683	257	12	performance	performance	NOUN
brj-24683	257	13	evaluation	evaluation	NOUN
brj-24683	257	14	metrics	metric	NOUN
brj-24683	257	15	were	be	AUX
brj-24683	257	16	employed	employ	VERB
brj-24683	257	17	:	:	PUNCT
brj-24683	257	18	average	average	ADJ
brj-24683	257	19	precision	precision	NOUN
brj-24683	257	20	(	(	PUNCT
brj-24683	257	21	ap	ap	PROPN
brj-24683	257	22	)	)	PUNCT
brj-24683	257	23	and	and	CCONJ
brj-24683	257	24	mean	mean	VERB
brj-24683	257	25	average	average	ADJ
brj-24683	257	26	precision	precision	NOUN
brj-24683	257	27	(	(	PUNCT
brj-24683	257	28	map	map	NOUN
brj-24683	257	29	)	)	PUNCT
brj-24683	257	30	.	.	PUNCT
brj-24683	258	1	table	table	NOUN
brj-24683	258	2	2	2	NUM
brj-24683	258	3	.	.	PUNCT
brj-24683	258	4	configuration	configuration	NOUN
brj-24683	258	5	of	of	ADP
brj-24683	258	6	software	software	NOUN
brj-24683	258	7	and	and	CCONJ
brj-24683	258	8	hardware	hardware	NOUN
brj-24683	258	9	used	use	VERB
brj-24683	258	10	in	in	ADP
brj-24683	258	11	the	the	DET
brj-24683	258	12	experiment	experiment	NOUN
brj-24683	258	13	device	device	NOUN
brj-24683	258	14	name	name	NOUN
brj-24683	258	15	parameter	parameter	NOUN
brj-24683	258	16	gpu	gpu	PROPN
brj-24683	258	17	nvidia	nvidia	PROPN
brj-24683	258	18	geforce	geforce	NOUN
brj-24683	258	19	rtx	rtx	PROPN
brj-24683	258	20	4060	4060	NUM
brj-24683	258	21	laptop	laptop	NOUN
brj-24683	258	22	8	8	NUM
brj-24683	258	23	g	g	NOUN
brj-24683	258	24	cpu	cpu	NOUN
brj-24683	258	25	13th	13th	NOUN
brj-24683	258	26	gen	gen	PROPN
brj-24683	258	27	intel(r	intel(r	PROPN
brj-24683	258	28	)	)	PUNCT
brj-24683	258	29	core(tm	core(tm	NOUN
brj-24683	258	30	)	)	PUNCT
brj-24683	258	31	i9	i9	NOUN
brj-24683	258	32	-	-	PUNCT
brj-24683	258	33	13900hx	13900hx	VERB
brj-24683	258	34	2.2ghz	2.2ghz	NUM
brj-24683	258	35	computer	computer	NOUN
brj-24683	258	36	operating	operating	NOUN
brj-24683	258	37	system	system	NOUN
brj-24683	258	38	windows	window	VERB
brj-24683	258	39	11	11	NUM
brj-24683	258	40	development	development	NOUN
brj-24683	258	41	environment	environment	NOUN
brj-24683	258	42	software	software	NOUN
brj-24683	258	43	pycharm	pycharm	VERB
brj-24683	258	44	2023.2.1	2023.2.1	NUM
brj-24683	258	45	programming	programming	NOUN
brj-24683	258	46	language	language	NOUN
brj-24683	258	47	python	python	NOUN
brj-24683	258	48	3.8	3.8	NUM
brj-24683	258	49	deep	deep	ADJ
brj-24683	258	50	learning	learning	NOUN
brj-24683	258	51	framework	framework	NOUN
brj-24683	258	52	pytorch	pytorch	NOUN
brj-24683	258	53	2.1.0	2.1.0	NUM
brj-24683	258	54	computational	computational	ADJ
brj-24683	258	55	acceleration	acceleration	NOUN
brj-24683	258	56	cuda11.0	cuda11.0	PROPN
brj-24683	258	57	to	to	PART
brj-24683	258	58	evaluate	evaluate	VERB
brj-24683	258	59	the	the	DET
brj-24683	258	60	impact	impact	NOUN
brj-24683	258	61	of	of	ADP
brj-24683	258	62	each	each	DET
brj-24683	258	63	module	module	NOUN
brj-24683	258	64	in	in	ADP
brj-24683	258	65	the	the	DET
brj-24683	258	66	improved	improved	ADJ
brj-24683	258	67	model	model	NOUN
brj-24683	258	68	,	,	PUNCT
brj-24683	258	69	an	an	DET
brj-24683	258	70	ablation	ablation	NOUN
brj-24683	258	71	study	study	NOUN
brj-24683	258	72	was	be	AUX
brj-24683	258	73	conducted	conduct	VERB
brj-24683	258	74	with	with	ADP
brj-24683	258	75	yolov8	yolov8	NOUN
brj-24683	258	76	as	as	ADP
brj-24683	258	77	the	the	DET
brj-24683	258	78	baseline	baseline	PROPN
brj-24683	258	79	model	model	NOUN
brj-24683	258	80	.	.	PUNCT
brj-24683	259	1	this	this	DET
brj-24683	259	2	study	study	NOUN
brj-24683	259	3	aimed	aim	VERB
brj-24683	259	4	to	to	PART
brj-24683	259	5	validate	validate	VERB
brj-24683	259	6	the	the	DET
brj-24683	259	7	effectiveness	effectiveness	NOUN
brj-24683	259	8	of	of	ADP
brj-24683	259	9	the	the	DET
brj-24683	259	10	proposed	propose	VERB
brj-24683	259	11	enhancements	enhancement	NOUN
brj-24683	259	12	.	.	PUNCT
brj-24683	260	1	the	the	DET
brj-24683	260	2	results	result	NOUN
brj-24683	260	3	of	of	ADP
brj-24683	260	4	the	the	DET
brj-24683	260	5	ablation	ablation	NOUN
brj-24683	260	6	experiments	experiment	NOUN
brj-24683	260	7	are	be	AUX
brj-24683	260	8	presented	present	VERB
brj-24683	260	9	in	in	ADP
brj-24683	260	10	table	table	NOUN
brj-24683	260	11	3	3	NUM
brj-24683	260	12	.	.	PUNCT
brj-24683	261	1	the	the	DET
brj-24683	261	2	table	table	NOUN
brj-24683	261	3	presents	present	VERB
brj-24683	261	4	the	the	DET
brj-24683	261	5	results	result	NOUN
brj-24683	261	6	of	of	ADP
brj-24683	261	7	different	different	ADJ
brj-24683	261	8	configurations	configuration	NOUN
brj-24683	261	9	,	,	PUNCT
brj-24683	261	10	where	where	SCONJ
brj-24683	261	11	each	each	DET
brj-24683	261	12	variant	variant	NOUN
brj-24683	261	13	isolates	isolate	VERB
brj-24683	261	14	the	the	DET
brj-24683	261	15	effect	effect	NOUN
brj-24683	261	16	of	of	ADP
brj-24683	261	17	an	an	DET
brj-24683	261	18	individual	individual	ADJ
brj-24683	261	19	module	module	NOUN
brj-24683	261	20	to	to	PART
brj-24683	261	21	evaluate	evaluate	VERB
brj-24683	261	22	its	its	PRON
brj-24683	261	23	contribution	contribution	NOUN
brj-24683	261	24	to	to	ADP
brj-24683	261	25	overall	overall	ADJ
brj-24683	261	26	performance	performance	NOUN
brj-24683	261	27	.	.	PUNCT
brj-24683	262	1	yolov8+c2f	yolov8+c2f	PROPN
brj-24683	262	2	-	-	PUNCT
brj-24683	262	3	mmsa	mmsa	NOUN
brj-24683	262	4	indicates	indicate	VERB
brj-24683	262	5	that	that	SCONJ
brj-24683	262	6	the	the	DET
brj-24683	262	7	mmsa	mmsa	NOUN
brj-24683	262	8	module	module	NOUN
brj-24683	262	9	is	be	AUX
brj-24683	262	10	seamlessly	seamlessly	ADV
brj-24683	262	11	integrated	integrate	VERB
brj-24683	262	12	into	into	ADP
brj-24683	262	13	the	the	DET
brj-24683	262	14	c2f	c2f	NOUN
brj-24683	262	15	module	module	NOUN
brj-24683	262	16	,	,	PUNCT
brj-24683	262	17	replacing	replace	VERB
brj-24683	262	18	the	the	DET
brj-24683	262	19	c2f	c2f	NOUN
brj-24683	262	20	module	module	NOUN
brj-24683	262	21	in	in	ADP
brj-24683	262	22	the	the	DET
brj-24683	262	23	backbone	backbone	NOUN
brj-24683	262	24	of	of	ADP
brj-24683	262	25	yolov8	yolov8	PROPN
brj-24683	262	26	.	.	PUNCT
brj-24683	263	1	yolov8+dysample	yolov8+dysample	PROPN
brj-24683	263	2	indicates	indicate	VERB
brj-24683	263	3	the	the	DET
brj-24683	263	4	incorporation	incorporation	NOUN
brj-24683	263	5	of	of	ADP
brj-24683	263	6	dysample	dysample	NOUN
brj-24683	263	7	into	into	ADP
brj-24683	263	8	the	the	DET
brj-24683	263	9	neck	neck	NOUN
brj-24683	263	10	section	section	NOUN
brj-24683	263	11	for	for	ADP
brj-24683	263	12	upsampling	upsample	VERB
brj-24683	263	13	.	.	PUNCT
brj-24683	264	1	yolov8+repblock	yolov8+repblock	PROPN
brj-24683	264	2	denotes	denote	VERB
brj-24683	264	3	the	the	DET
brj-24683	264	4	integration	integration	NOUN
brj-24683	264	5	of	of	ADP
brj-24683	264	6	the	the	DET
brj-24683	264	7	repblock	repblock	NOUN
brj-24683	264	8	module	module	NOUN
brj-24683	264	9	into	into	ADP
brj-24683	264	10	the	the	DET
brj-24683	264	11	neck	neck	NOUN
brj-24683	264	12	section	section	NOUN
brj-24683	264	13	.	.	PUNCT
brj-24683	265	1	yolov8+p2	yolov8+p2	PROPN
brj-24683	265	2	denotes	denote	VERB
brj-24683	265	3	the	the	DET
brj-24683	265	4	incorporation	incorporation	NOUN
brj-24683	265	5	of	of	ADP
brj-24683	265	6	the	the	DET
brj-24683	265	7	small	small	ADJ
brj-24683	265	8	-	-	PUNCT
brj-24683	265	9	object	object	NOUN
brj-24683	265	10	detection	detection	NOUN
brj-24683	265	11	head	head	NOUN
brj-24683	265	12	module	module	NOUN
brj-24683	265	13	into	into	ADP
brj-24683	265	14	the	the	DET
brj-24683	265	15	head	head	NOUN
brj-24683	265	16	section	section	NOUN
brj-24683	265	17	.	.	PUNCT
brj-24683	266	1	“	"	PUNCT
brj-24683	266	2	this	this	DET
brj-24683	266	3	work	work	NOUN
brj-24683	266	4	”	"	PUNCT
brj-24683	266	5	denotes	denote	VERB
brj-24683	266	6	the	the	DET
brj-24683	266	7	current	current	ADJ
brj-24683	266	8	proposed	propose	VERB
brj-24683	266	9	improved	improved	ADJ
brj-24683	266	10	model	model	NOUN
brj-24683	266	11	.	.	PUNCT
brj-24683	267	1	peer	peer	NOUN
brj-24683	267	2	-	-	PUNCT
brj-24683	267	3	reviewed	review	VERB
brj-24683	267	4	article	article	NOUN
brj-24683	267	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	267	6	dou	dou	PROPN
brj-24683	267	7	&	&	CCONJ
brj-24683	267	8	you	you	PRON
brj-24683	267	9	(	(	PUNCT
brj-24683	267	10	2025	2025	NUM
brj-24683	267	11	)	)	PUNCT
brj-24683	267	12	.	.	PUNCT
brj-24683	268	1	“	"	PUNCT
brj-24683	268	2	wood	wood	NOUN
brj-24683	268	3	defect	defect	NOUN
brj-24683	268	4	identification	identification	NOUN
brj-24683	268	5	,	,	PUNCT
brj-24683	268	6	”	"	PUNCT
brj-24683	268	7	bioresources	bioresource	NOUN
brj-24683	268	8	20(3	20(3	NOUN
brj-24683	268	9	)	)	PUNCT
brj-24683	268	10	,	,	PUNCT
brj-24683	268	11	5709	5709	NUM
brj-24683	268	12	-	-	SYM
brj-24683	268	13	5730	5730	NUM
brj-24683	268	14	.	.	PUNCT
brj-24683	269	1	5722	5722	NUM
brj-24683	269	2	table	table	NOUN
brj-24683	269	3	3	3	NUM
brj-24683	269	4	.	.	PUNCT
brj-24683	269	5	results	result	NOUN
brj-24683	269	6	of	of	ADP
brj-24683	269	7	ablation	ablation	NOUN
brj-24683	269	8	experiment	experiment	NOUN
brj-24683	269	9	methods	method	NOUN
brj-24683	269	10	map	map	VERB
brj-24683	269	11	ap	ap	PROPN
brj-24683	269	12	live_knot	live_knot	ADV
brj-24683	269	13	marrow	marrow	NOUN
brj-24683	270	1	resin	resin	NOUN
brj-24683	270	2	dead_knot	dead_knot	NOUN
brj-24683	270	3	knot_with_crack	knot_with_crack	NOUN
brj-24683	271	1	knot_missing	knot_misse	VERB
brj-24683	271	2	crack	crack	NOUN
brj-24683	271	3	yolov8	yolov8	NOUN
brj-24683	271	4	0.729	0.729	NUM
brj-24683	271	5	0.787	0.787	NUM
brj-24683	271	6	0.823	0.823	NUM
brj-24683	271	7	0.667	0.667	NUM
brj-24683	271	8	0.877	0.877	NUM
brj-24683	271	9	0.422	0.422	NUM
brj-24683	271	10	0.804	0.804	NUM
brj-24683	271	11	0.725	0.725	NUM
brj-24683	271	12	yolov8+c2f	yolov8+c2f	PROPN
brj-24683	271	13	-	-	PUNCT
brj-24683	271	14	mmsa	mmsa	NOUN
brj-24683	271	15	0.750	0.750	NUM
brj-24683	271	16	0.759	0.759	NUM
brj-24683	271	17	0.858	0.858	NUM
brj-24683	271	18	0.768	0.768	NUM
brj-24683	271	19	0.875	0.875	NUM
brj-24683	271	20	0.464	0.464	NUM
brj-24683	271	21	0.803	0.803	NUM
brj-24683	271	22	0.723	0.723	NUM
brj-24683	271	23	yolov8+dysample	yolov8+dysample	NOUN
brj-24683	271	24	0.747	0.747	NUM
brj-24683	271	25	0.783	0.783	NUM
brj-24683	271	26	0.778	0.778	NUM
brj-24683	271	27	0.720	0.720	NUM
brj-24683	271	28	0.870	0.870	NUM
brj-24683	271	29	0.523	0.523	NUM
brj-24683	271	30	0.776	0.776	NUM
brj-24683	271	31	0.776	0.776	NUM
brj-24683	271	32	yolov8+repblock	yolov8+repblock	NOUN
brj-24683	271	33	0.754	0.754	NUM
brj-24683	271	34	0.784	0.784	NUM
brj-24683	271	35	0.829	0.829	NUM
brj-24683	271	36	0.762	0.762	NUM
brj-24683	271	37	0.862	0.862	NUM
brj-24683	271	38	0.492	0.492	NUM
brj-24683	271	39	0.809	0.809	NUM
brj-24683	271	40	0.739	0.739	NUM
brj-24683	271	41	yolov8+p2	yolov8+p2	NUM
brj-24683	271	42	0.735	0.735	NUM
brj-24683	271	43	0.786	0.786	NUM
brj-24683	271	44	0.842	0.842	NUM
brj-24683	271	45	0.700	0.700	NUM
brj-24683	271	46	0.868	0.868	NUM
brj-24683	271	47	0.507	0.507	NUM
brj-24683	271	48	0.706	0.706	NUM
brj-24683	271	49	0.733	0.733	NUM
brj-24683	271	50	this	this	DET
brj-24683	271	51	work	work	NOUN
brj-24683	271	52	0.795	0.795	NUM
brj-24683	271	53	0.887	0.887	NUM
brj-24683	271	54	0.891	0.891	NUM
brj-24683	271	55	0.790	0.790	NUM
brj-24683	271	56	0.893	0.893	NUM
brj-24683	271	57	0.610	0.610	NUM
brj-24683	271	58	0.750	0.750	NUM
brj-24683	271	59	0.743	0.743	NUM
brj-24683	271	60	table	table	NOUN
brj-24683	271	61	4	4	NUM
brj-24683	271	62	.	.	PUNCT
brj-24683	271	63	comparison	comparison	NOUN
brj-24683	271	64	of	of	ADP
brj-24683	271	65	various	various	ADJ
brj-24683	271	66	detection	detection	NOUN
brj-24683	271	67	models	model	NOUN
brj-24683	271	68	methods	method	NOUN
brj-24683	271	69	map	map	VERB
brj-24683	271	70	ap	ap	PROPN
brj-24683	271	71	live_knot	live_knot	ADV
brj-24683	271	72	marrow	marrow	NOUN
brj-24683	271	73	resin	resin	NOUN
brj-24683	271	74	dead_knot	dead_knot	NOUN
brj-24683	271	75	knot_with_crack	knot_with_crack	NOUN
brj-24683	271	76	knot_missing	knot_misse	VERB
brj-24683	271	77	crack	crack	NOUN
brj-24683	271	78	yolov5	yolov5	NOUN
brj-24683	271	79	0.746	0.746	NUM
brj-24683	271	80	0.764	0.764	NUM
brj-24683	271	81	0.786	0.786	NUM
brj-24683	271	82	0.774	0.774	NUM
brj-24683	271	83	0.868	0.868	NUM
brj-24683	271	84	0.476	0.476	NUM
brj-24683	271	85	0.837	0.837	NUM
brj-24683	271	86	0.716	0.716	NUM
brj-24683	271	87	yolov7	yolov7	NOUN
brj-24683	271	88	0.734	0.734	NUM
brj-24683	271	89	0.767	0.767	NUM
brj-24683	271	90	0.814	0.814	NUM
brj-24683	271	91	0.724	0.724	NUM
brj-24683	271	92	0.858	0.858	NUM
brj-24683	271	93	0.529	0.529	NUM
brj-24683	271	94	0.782	0.782	NUM
brj-24683	271	95	0.665	0.665	NUM
brj-24683	271	96	yolov8	yolov8	NOUN
brj-24683	271	97	0.729	0.729	NUM
brj-24683	271	98	0.787	0.787	NUM
brj-24683	271	99	0.823	0.823	NUM
brj-24683	271	100	0.667	0.667	NUM
brj-24683	271	101	0.877	0.877	NUM
brj-24683	271	102	0.422	0.422	NUM
brj-24683	271	103	0.804	0.804	NUM
brj-24683	271	104	0.725	0.725	NUM
brj-24683	271	105	yolov9	yolov9	NOUN
brj-24683	271	106	0.757	0.757	NUM
brj-24683	271	107	0.789	0.789	NUM
brj-24683	271	108	0.805	0.805	NUM
brj-24683	271	109	0.786	0.786	NUM
brj-24683	271	110	0.875	0.875	NUM
brj-24683	271	111	0.515	0.515	NUM
brj-24683	271	112	0.759	0.759	NUM
brj-24683	271	113	0.769	0.769	NUM
brj-24683	271	114	yolov10	yolov10	NOUN
brj-24683	271	115	0.672	0.672	NUM
brj-24683	271	116	0.725	0.725	NUM
brj-24683	271	117	0.799	0.799	NUM
brj-24683	271	118	0.671	0.671	NUM
brj-24683	271	119	0.851	0.851	NUM
brj-24683	271	120	0.378	0.378	NUM
brj-24683	271	121	0.690	0.690	NUM
brj-24683	271	122	0.590	0.590	NUM
brj-24683	271	123	yolo11	yolo11	NOUN
brj-24683	271	124	0.745	0.745	NUM
brj-24683	271	125	0.768	0.768	NUM
brj-24683	271	126	0.805	0.805	NUM
brj-24683	271	127	0.768	0.768	NUM
brj-24683	271	128	0.879	0.879	NUM
brj-24683	271	129	0.488	0.488	NUM
brj-24683	271	130	0.752	0.752	NUM
brj-24683	271	131	0.757	0.757	NUM
brj-24683	271	132	yolov12	yolov12	VERB
brj-24683	271	133	0.746	0.746	NUM
brj-24683	271	134	0.773	0.773	NUM
brj-24683	271	135	0.835	0.835	NUM
brj-24683	271	136	0.760	0.760	NUM
brj-24683	271	137	0.882	0.882	NUM
brj-24683	271	138	0.493	0.493	NUM
brj-24683	271	139	0.814	0.814	NUM
brj-24683	271	140	0.666	0.666	NUM
brj-24683	271	141	wang	wang	PROPN
brj-24683	271	142	et	et	PROPN
brj-24683	271	143	al	al	PROPN
brj-24683	271	144	.	.	PROPN
brj-24683	272	1	(	(	PUNCT
brj-24683	272	2	2024	2024	NUM
brj-24683	272	3	)	)	PUNCT
brj-24683	272	4	0.777	0.777	NUM
brj-24683	272	5	0.814	0.814	NUM
brj-24683	272	6	0.958	0.958	NUM
brj-24683	272	7	0.852	0.852	NUM
brj-24683	272	8	0.868	0.868	NUM
brj-24683	272	9	0.481	0.481	NUM
brj-24683	272	10	0.825	0.825	NUM
brj-24683	272	11	0.642	0.642	NUM
brj-24683	272	12	xi	xi	ADP
brj-24683	272	13	et	et	PROPN
brj-24683	272	14	al	al	PROPN
brj-24683	272	15	.	.	PROPN
brj-24683	273	1	(	(	PUNCT
brj-24683	273	2	2024	2024	NUM
brj-24683	273	3	)	)	PUNCT
brj-24683	273	4	0.784	0.784	NUM
brj-24683	273	5	0.854	0.854	NUM
brj-24683	273	6	0.871	0.871	NUM
brj-24683	273	7	0.779	0.779	NUM
brj-24683	273	8	0.831	0.831	NUM
brj-24683	273	9	0.595	0.595	NUM
brj-24683	273	10	0.866	0.866	NUM
brj-24683	273	11	0.693	0.693	NUM
brj-24683	273	12	this	this	DET
brj-24683	273	13	work	work	NOUN
brj-24683	273	14	0.795	0.795	NUM
brj-24683	273	15	0.887	0.887	NUM
brj-24683	273	16	0.891	0.891	NUM
brj-24683	273	17	0.790	0.790	NUM
brj-24683	273	18	0.893	0.893	NUM
brj-24683	273	19	0.610	0.610	NUM
brj-24683	273	20	0.750	0.750	NUM
brj-24683	273	21	0.743	0.743	NUM
brj-24683	273	22	peer	peer	NOUN
brj-24683	273	23	-	-	PUNCT
brj-24683	273	24	reviewed	review	VERB
brj-24683	273	25	article	article	NOUN
brj-24683	273	26	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	273	27	dou	dou	PROPN
brj-24683	273	28	&	&	CCONJ
brj-24683	273	29	you	you	PRON
brj-24683	273	30	(	(	PUNCT
brj-24683	273	31	2025	2025	NUM
brj-24683	273	32	)	)	PUNCT
brj-24683	273	33	.	.	PUNCT
brj-24683	274	1	“	"	PUNCT
brj-24683	274	2	wood	wood	NOUN
brj-24683	274	3	defect	defect	NOUN
brj-24683	274	4	identification	identification	NOUN
brj-24683	274	5	,	,	PUNCT
brj-24683	274	6	”	"	PUNCT
brj-24683	274	7	bioresources	bioresource	NOUN
brj-24683	274	8	20(3	20(3	NOUN
brj-24683	274	9	)	)	PUNCT
brj-24683	274	10	,	,	PUNCT
brj-24683	274	11	5709	5709	NUM
brj-24683	274	12	-	-	SYM
brj-24683	274	13	5730	5730	NUM
brj-24683	274	14	.	.	PUNCT
brj-24683	275	1	5723	5723	NUM
brj-24683	275	2	integrating	integrate	VERB
brj-24683	275	3	the	the	DET
brj-24683	275	4	mmsa	mmsa	NOUN
brj-24683	275	5	module	module	NOUN
brj-24683	275	6	into	into	ADP
brj-24683	275	7	the	the	DET
brj-24683	275	8	c2f	c2f	NOUN
brj-24683	275	9	structure	structure	NOUN
brj-24683	275	10	(	(	PUNCT
brj-24683	275	11	yolov8+c2f	yolov8+c2f	PROPN
brj-24683	275	12	-	-	PUNCT
brj-24683	275	13	mmsa	mmsa	NOUN
brj-24683	275	14	)	)	PUNCT
brj-24683	275	15	improved	improved	ADJ
brj-24683	275	16	map	map	NOUN
brj-24683	275	17	by	by	ADP
brj-24683	275	18	2.1	2.1	NUM
brj-24683	275	19	%	%	NOUN
brj-24683	275	20	compared	compare	VERB
brj-24683	275	21	to	to	ADP
brj-24683	275	22	the	the	DET
brj-24683	275	23	baseline	baseline	NOUN
brj-24683	275	24	,	,	PUNCT
brj-24683	275	25	demonstrating	demonstrate	VERB
brj-24683	275	26	its	its	PRON
brj-24683	275	27	effectiveness	effectiveness	NOUN
brj-24683	275	28	in	in	ADP
brj-24683	275	29	enhancing	enhance	VERB
brj-24683	275	30	feature	feature	NOUN
brj-24683	275	31	extraction	extraction	NOUN
brj-24683	275	32	for	for	ADP
brj-24683	275	33	small	small	ADJ
brj-24683	275	34	defects	defect	NOUN
brj-24683	275	35	.	.	PUNCT
brj-24683	276	1	similarly	similarly	ADV
brj-24683	276	2	,	,	PUNCT
brj-24683	276	3	the	the	DET
brj-24683	276	4	introduction	introduction	NOUN
brj-24683	276	5	of	of	ADP
brj-24683	276	6	dysample	dysample	PROPN
brj-24683	276	7	(	(	PUNCT
brj-24683	276	8	yolov8+dysample	yolov8+dysample	PROPN
brj-24683	276	9	)	)	PUNCT
brj-24683	276	10	led	lead	VERB
brj-24683	276	11	to	to	ADP
brj-24683	276	12	an	an	DET
brj-24683	276	13	1.8	1.8	NUM
brj-24683	276	14	%	%	NOUN
brj-24683	276	15	improvement	improvement	NOUN
brj-24683	276	16	,	,	PUNCT
brj-24683	276	17	indicating	indicate	VERB
brj-24683	276	18	that	that	SCONJ
brj-24683	276	19	the	the	DET
brj-24683	276	20	learnable	learnable	ADJ
brj-24683	276	21	dynamic	dynamic	ADJ
brj-24683	276	22	upsampling	upsampling	NOUN
brj-24683	276	23	strategy	strategy	NOUN
brj-24683	276	24	mitigates	mitigate	VERB
brj-24683	276	25	information	information	NOUN
brj-24683	276	26	loss	loss	NOUN
brj-24683	276	27	during	during	ADP
brj-24683	276	28	feature	feature	NOUN
brj-24683	276	29	scaling	scaling	NOUN
brj-24683	276	30	.	.	PUNCT
brj-24683	277	1	the	the	DET
brj-24683	277	2	addition	addition	NOUN
brj-24683	277	3	of	of	ADP
brj-24683	277	4	repblock	repblock	NOUN
brj-24683	277	5	(	(	PUNCT
brj-24683	277	6	yolov8+repblock	yolov8+repblock	PROPN
brj-24683	277	7	)	)	PUNCT
brj-24683	277	8	further	far	ADV
brj-24683	277	9	enhanced	enhance	VERB
brj-24683	277	10	performance	performance	NOUN
brj-24683	277	11	by	by	ADP
brj-24683	277	12	2.5	2.5	NUM
brj-24683	277	13	%	%	NOUN
brj-24683	277	14	,	,	PUNCT
brj-24683	277	15	suggesting	suggest	VERB
brj-24683	277	16	its	its	PRON
brj-24683	277	17	ability	ability	NOUN
brj-24683	277	18	to	to	PART
brj-24683	277	19	capture	capture	VERB
brj-24683	277	20	multi	multi	ADJ
brj-24683	277	21	-	-	ADJ
brj-24683	277	22	scale	scale	ADJ
brj-24683	277	23	defect	defect	NOUN
brj-24683	277	24	features	feature	NOUN
brj-24683	277	25	effectively	effectively	ADV
brj-24683	277	26	.	.	PUNCT
brj-24683	278	1	finally	finally	ADV
brj-24683	278	2	,	,	PUNCT
brj-24683	278	3	incorporating	incorporate	VERB
brj-24683	278	4	the	the	DET
brj-24683	278	5	smallobject	smallobject	NOUN
brj-24683	278	6	detection	detection	NOUN
brj-24683	278	7	head	head	NOUN
brj-24683	278	8	(	(	PUNCT
brj-24683	278	9	yolov8+p2	yolov8+p2	PROPN
brj-24683	278	10	)	)	PUNCT
brj-24683	278	11	provided	provide	VERB
brj-24683	278	12	an	an	DET
brj-24683	278	13	additional	additional	ADJ
brj-24683	278	14	boost	boost	NOUN
brj-24683	278	15	of	of	ADP
brj-24683	278	16	0.6	0.6	NUM
brj-24683	278	17	%	%	NOUN
brj-24683	278	18	,	,	PUNCT
brj-24683	278	19	validating	validate	VERB
brj-24683	278	20	its	its	PRON
brj-24683	278	21	role	role	NOUN
brj-24683	278	22	in	in	ADP
brj-24683	278	23	refining	refine	VERB
brj-24683	278	24	small	small	ADJ
brj-24683	278	25	defect	defect	NOUN
brj-24683	278	26	detection	detection	NOUN
brj-24683	278	27	.	.	PUNCT
brj-24683	279	1	by	by	ADP
brj-24683	279	2	integrating	integrate	VERB
brj-24683	279	3	all	all	DET
brj-24683	279	4	proposed	propose	VERB
brj-24683	279	5	enhancements	enhancement	NOUN
brj-24683	279	6	,	,	PUNCT
brj-24683	279	7	the	the	DET
brj-24683	279	8	final	final	ADJ
brj-24683	279	9	model	model	NOUN
brj-24683	279	10	(	(	PUNCT
brj-24683	279	11	ours	ours	PRON
brj-24683	279	12	)	)	PUNCT
brj-24683	279	13	achieved	achieve	VERB
brj-24683	279	14	the	the	DET
brj-24683	279	15	highest	high	ADJ
brj-24683	279	16	performance	performance	NOUN
brj-24683	279	17	,	,	PUNCT
brj-24683	279	18	surpassing	surpass	VERB
brj-24683	279	19	the	the	DET
brj-24683	279	20	baseline	baseline	NOUN
brj-24683	279	21	by	by	ADP
brj-24683	279	22	6.6	6.6	NUM
brj-24683	279	23	%	%	NOUN
brj-24683	279	24	in	in	ADP
brj-24683	279	25	map	map	NOUN
brj-24683	279	26	.	.	PUNCT
brj-24683	280	1	this	this	PRON
brj-24683	280	2	demonstrates	demonstrate	VERB
brj-24683	280	3	that	that	SCONJ
brj-24683	280	4	the	the	DET
brj-24683	280	5	combined	combine	VERB
brj-24683	280	6	improvements	improvement	NOUN
brj-24683	280	7	contribute	contribute	VERB
brj-24683	280	8	synergistically	synergistically	ADV
brj-24683	280	9	to	to	ADP
brj-24683	280	10	the	the	DET
brj-24683	280	11	accuracy	accuracy	NOUN
brj-24683	280	12	and	and	CCONJ
brj-24683	280	13	robustness	robustness	NOUN
brj-24683	280	14	of	of	ADP
brj-24683	280	15	wood	wood	NOUN
brj-24683	280	16	surface	surface	NOUN
brj-24683	280	17	defect	defect	NOUN
brj-24683	280	18	detection	detection	NOUN
brj-24683	280	19	.	.	PUNCT
brj-24683	281	1	comparison	comparison	NOUN
brj-24683	281	2	experiments	experiment	NOUN
brj-24683	281	3	with	with	ADP
brj-24683	281	4	benchmark	benchmark	NOUN
brj-24683	281	5	models	model	NOUN
brj-24683	281	6	to	to	PART
brj-24683	281	7	further	far	ADV
brj-24683	281	8	validate	validate	VERB
brj-24683	281	9	the	the	DET
brj-24683	281	10	effectiveness	effectiveness	NOUN
brj-24683	281	11	of	of	ADP
brj-24683	281	12	the	the	DET
brj-24683	281	13	proposed	propose	VERB
brj-24683	281	14	model	model	NOUN
brj-24683	281	15	,	,	PUNCT
brj-24683	281	16	a	a	DET
brj-24683	281	17	comparative	comparative	ADJ
brj-24683	281	18	experiment	experiment	NOUN
brj-24683	281	19	was	be	AUX
brj-24683	281	20	conducted	conduct	VERB
brj-24683	281	21	against	against	ADP
brj-24683	281	22	several	several	ADJ
brj-24683	281	23	state	state	NOUN
brj-24683	281	24	-	-	PUNCT
brj-24683	281	25	of	of	ADP
brj-24683	281	26	-	-	PUNCT
brj-24683	281	27	the	the	DET
brj-24683	281	28	-	-	PUNCT
brj-24683	281	29	art	art	NOUN
brj-24683	281	30	wood	wood	NOUN
brj-24683	281	31	surface	surface	NOUN
brj-24683	281	32	defect	defect	NOUN
brj-24683	281	33	detection	detection	NOUN
brj-24683	281	34	models	model	NOUN
brj-24683	281	35	.	.	PUNCT
brj-24683	282	1	the	the	DET
brj-24683	282	2	benchmark	benchmark	NOUN
brj-24683	282	3	models	model	NOUN
brj-24683	282	4	selected	select	VERB
brj-24683	282	5	for	for	ADP
brj-24683	282	6	comparison	comparison	NOUN
brj-24683	282	7	include	include	VERB
brj-24683	282	8	yolov5	yolov5	NOUN
brj-24683	282	9	,	,	PUNCT
brj-24683	282	10	yolov7	yolov7	NOUN
brj-24683	282	11	,	,	PUNCT
brj-24683	282	12	yolov9	yolov9	PROPN
brj-24683	282	13	,	,	PUNCT
brj-24683	282	14	yolov10	yolov10	NOUN
brj-24683	282	15	,	,	PUNCT
brj-24683	282	16	yolo11	yolo11	ADJ
brj-24683	282	17	,	,	PUNCT
brj-24683	282	18	yolov12	yolov12	ADJ
brj-24683	282	19	,	,	PUNCT
brj-24683	282	20	and	and	CCONJ
brj-24683	282	21	yolov8	yolov8	NOUN
brj-24683	282	22	(	(	PUNCT
brj-24683	282	23	baseline	baseline	PROPN
brj-24683	282	24	)	)	PUNCT
brj-24683	282	25	.	.	PUNCT
brj-24683	283	1	all	all	DET
brj-24683	283	2	models	model	NOUN
brj-24683	283	3	were	be	AUX
brj-24683	283	4	trained	train	VERB
brj-24683	283	5	and	and	CCONJ
brj-24683	283	6	evaluated	evaluate	VERB
brj-24683	283	7	under	under	ADP
brj-24683	283	8	identical	identical	ADJ
brj-24683	283	9	conditions	condition	NOUN
brj-24683	283	10	using	use	VERB
brj-24683	283	11	the	the	DET
brj-24683	283	12	dataset	dataset	NOUN
brj-24683	283	13	mentioned	mention	VERB
brj-24683	283	14	in	in	ADP
brj-24683	283	15	section	section	NOUN
brj-24683	283	16	2.1	2.1	NUM
brj-24683	283	17	to	to	PART
brj-24683	283	18	ensure	ensure	VERB
brj-24683	283	19	fairness	fairness	NOUN
brj-24683	283	20	.	.	PUNCT
brj-24683	284	1	the	the	DET
brj-24683	284	2	results	result	NOUN
brj-24683	284	3	,	,	PUNCT
brj-24683	284	4	as	as	SCONJ
brj-24683	284	5	shown	show	VERB
brj-24683	284	6	in	in	ADP
brj-24683	284	7	table	table	NOUN
brj-24683	284	8	4	4	NUM
brj-24683	284	9	,	,	PUNCT
brj-24683	284	10	indicate	indicate	VERB
brj-24683	284	11	that	that	SCONJ
brj-24683	284	12	the	the	DET
brj-24683	284	13	proposed	propose	VERB
brj-24683	284	14	model	model	NOUN
brj-24683	284	15	achieved	achieve	VERB
brj-24683	284	16	the	the	DET
brj-24683	284	17	highest	high	ADJ
brj-24683	284	18	map	map	NOUN
brj-24683	284	19	,	,	PUNCT
brj-24683	284	20	outperforming	outperform	VERB
brj-24683	284	21	other	other	ADJ
brj-24683	284	22	models	model	NOUN
brj-24683	284	23	in	in	ADP
brj-24683	284	24	detecting	detect	VERB
brj-24683	284	25	wood	wood	NOUN
brj-24683	284	26	surface	surface	NOUN
brj-24683	284	27	defects	defect	NOUN
brj-24683	284	28	.	.	PUNCT
brj-24683	285	1	specifically	specifically	ADV
brj-24683	285	2	,	,	PUNCT
brj-24683	285	3	the	the	DET
brj-24683	285	4	present	present	ADJ
brj-24683	285	5	method	method	NOUN
brj-24683	285	6	improved	improve	VERB
brj-24683	285	7	the	the	DET
brj-24683	285	8	map	map	NOUN
brj-24683	285	9	by	by	ADP
brj-24683	285	10	6.6	6.6	NUM
brj-24683	285	11	%	%	NOUN
brj-24683	285	12	over	over	ADP
brj-24683	285	13	the	the	DET
brj-24683	285	14	yolov8	yolov8	PROPN
brj-24683	285	15	baseline	baseline	PROPN
brj-24683	285	16	,	,	PUNCT
brj-24683	285	17	demonstrating	demonstrate	VERB
brj-24683	285	18	the	the	DET
brj-24683	285	19	effectiveness	effectiveness	NOUN
brj-24683	285	20	of	of	ADP
brj-24683	285	21	the	the	DET
brj-24683	285	22	introduced	introduce	VERB
brj-24683	285	23	c2f	c2f	NOUN
brj-24683	285	24	-	-	PUNCT
brj-24683	285	25	mmsa	mmsa	NOUN
brj-24683	285	26	module	module	NOUN
brj-24683	285	27	,	,	PUNCT
brj-24683	285	28	dysample	dysample	ADJ
brj-24683	285	29	upsampling	upsampling	NOUN
brj-24683	285	30	strategy	strategy	NOUN
brj-24683	285	31	,	,	PUNCT
brj-24683	285	32	repblock	repblock	NOUN
brj-24683	285	33	,	,	PUNCT
brj-24683	285	34	and	and	CCONJ
brj-24683	285	35	small	small	ADJ
brj-24683	285	36	-	-	PUNCT
brj-24683	285	37	object	object	NOUN
brj-24683	285	38	detection	detection	NOUN
brj-24683	285	39	head	head	NOUN
brj-24683	285	40	in	in	ADP
brj-24683	285	41	enhancing	enhance	VERB
brj-24683	285	42	small	small	ADJ
brj-24683	285	43	defect	defect	NOUN
brj-24683	285	44	recognition	recognition	NOUN
brj-24683	285	45	.	.	PUNCT
brj-24683	286	1	in	in	ADP
brj-24683	286	2	the	the	DET
brj-24683	286	3	ap	ap	PROPN
brj-24683	286	4	results	result	VERB
brj-24683	286	5	,	,	PUNCT
brj-24683	286	6	the	the	DET
brj-24683	286	7	model	model	NOUN
brj-24683	286	8	of	of	ADP
brj-24683	286	9	xi	xi	PROPN
brj-24683	286	10	et	et	PROPN
brj-24683	286	11	al	al	PROPN
brj-24683	286	12	.	.	PROPN
brj-24683	287	1	(	(	PUNCT
brj-24683	287	2	2024	2024	NUM
brj-24683	287	3	)	)	PUNCT
brj-24683	287	4	achieved	achieve	VERB
brj-24683	287	5	the	the	DET
brj-24683	287	6	highest	high	ADJ
brj-24683	287	7	ap	ap	NOUN
brj-24683	287	8	of	of	ADP
brj-24683	287	9	0.866	0.866	NUM
brj-24683	287	10	for	for	ADP
brj-24683	287	11	the	the	DET
brj-24683	287	12	knot_missing	knot_misse	VERB
brj-24683	287	13	defect	defect	NOUN
brj-24683	287	14	,	,	PUNCT
brj-24683	287	15	while	while	SCONJ
brj-24683	287	16	yolov9	yolov9	PROPN
brj-24683	287	17	attained	attain	VERB
brj-24683	287	18	the	the	DET
brj-24683	287	19	best	good	ADJ
brj-24683	287	20	ap	ap	NOUN
brj-24683	287	21	of	of	ADP
brj-24683	287	22	0.769	0.769	NUM
brj-24683	287	23	for	for	ADP
brj-24683	287	24	the	the	DET
brj-24683	287	25	crack	crack	NOUN
brj-24683	287	26	defect	defect	NOUN
brj-24683	287	27	.	.	PUNCT
brj-24683	288	1	for	for	ADP
brj-24683	288	2	the	the	DET
brj-24683	288	3	remaining	remain	VERB
brj-24683	288	4	defect	defect	NOUN
brj-24683	288	5	types	type	NOUN
brj-24683	288	6	,	,	PUNCT
brj-24683	288	7	including	include	VERB
brj-24683	288	8	live_knot	live_knot	ADV
brj-24683	288	9	,	,	PUNCT
brj-24683	288	10	marrow	marrow	NOUN
brj-24683	288	11	,	,	PUNCT
brj-24683	288	12	resin	resin	NOUN
brj-24683	288	13	,	,	PUNCT
brj-24683	288	14	dead_knot	dead_knot	NOUN
brj-24683	288	15	,	,	PUNCT
brj-24683	288	16	and	and	CCONJ
brj-24683	288	17	knot_with_crack	knot_with_crack	NOUN
brj-24683	288	18	,	,	PUNCT
brj-24683	288	19	the	the	DET
brj-24683	288	20	proposed	propose	VERB
brj-24683	288	21	model	model	NOUN
brj-24683	288	22	consistently	consistently	ADV
brj-24683	288	23	outperformed	outperform	VERB
brj-24683	288	24	the	the	DET
brj-24683	288	25	other	other	ADJ
brj-24683	288	26	benchmark	benchmark	NOUN
brj-24683	288	27	models	model	NOUN
brj-24683	288	28	,	,	PUNCT
brj-24683	288	29	achieving	achieve	VERB
brj-24683	288	30	the	the	DET
brj-24683	288	31	highest	high	ADJ
brj-24683	288	32	ap	ap	PROPN
brj-24683	288	33	values	value	NOUN
brj-24683	288	34	across	across	ADP
brj-24683	288	35	these	these	DET
brj-24683	288	36	categories	category	NOUN
brj-24683	288	37	.	.	PUNCT
brj-24683	289	1	these	these	DET
brj-24683	289	2	results	result	NOUN
brj-24683	289	3	demonstrate	demonstrate	VERB
brj-24683	289	4	the	the	DET
brj-24683	289	5	effectiveness	effectiveness	NOUN
brj-24683	289	6	of	of	ADP
brj-24683	289	7	the	the	DET
brj-24683	289	8	proposed	propose	VERB
brj-24683	289	9	improvements	improvement	NOUN
brj-24683	289	10	in	in	ADP
brj-24683	289	11	enhancing	enhance	VERB
brj-24683	289	12	the	the	DET
brj-24683	289	13	detection	detection	NOUN
brj-24683	289	14	performance	performance	NOUN
brj-24683	289	15	for	for	ADP
brj-24683	289	16	various	various	ADJ
brj-24683	289	17	wood	wood	NOUN
brj-24683	289	18	surface	surface	NOUN
brj-24683	289	19	defects	defect	NOUN
brj-24683	289	20	.	.	PUNCT
brj-24683	290	1	the	the	DET
brj-24683	290	2	precision	precision	NOUN
brj-24683	290	3	-	-	PUNCT
brj-24683	290	4	recall	recall	NOUN
brj-24683	290	5	(	(	PUNCT
brj-24683	290	6	p	p	NOUN
brj-24683	290	7	-	-	PUNCT
brj-24683	290	8	r	r	NOUN
brj-24683	290	9	)	)	PUNCT
brj-24683	290	10	curve	curve	NOUN
brj-24683	290	11	provides	provide	VERB
brj-24683	290	12	an	an	DET
brj-24683	290	13	intuitive	intuitive	ADJ
brj-24683	290	14	visualization	visualization	NOUN
brj-24683	290	15	of	of	ADP
brj-24683	290	16	the	the	DET
brj-24683	290	17	average	average	ADJ
brj-24683	290	18	precision	precision	NOUN
brj-24683	290	19	(	(	PUNCT
brj-24683	290	20	ap	ap	NOUN
brj-24683	290	21	)	)	PUNCT
brj-24683	290	22	values	value	NOUN
brj-24683	290	23	.	.	PUNCT
brj-24683	291	1	it	it	PRON
brj-24683	291	2	represents	represent	VERB
brj-24683	291	3	the	the	DET
brj-24683	291	4	trade	trade	NOUN
brj-24683	291	5	-	-	PUNCT
brj-24683	291	6	off	off	NOUN
brj-24683	291	7	between	between	ADP
brj-24683	291	8	precision	precision	NOUN
brj-24683	291	9	and	and	CCONJ
brj-24683	291	10	recall	recall	NOUN
brj-24683	291	11	,	,	PUNCT
brj-24683	291	12	with	with	ADP
brj-24683	291	13	a	a	DET
brj-24683	291	14	larger	large	ADJ
brj-24683	291	15	area	area	NOUN
brj-24683	291	16	under	under	ADP
brj-24683	291	17	the	the	DET
brj-24683	291	18	curve	curve	NOUN
brj-24683	291	19	indicating	indicate	VERB
brj-24683	291	20	superior	superior	ADJ
brj-24683	291	21	model	model	NOUN
brj-24683	291	22	performance	performance	NOUN
brj-24683	291	23	.	.	PUNCT
brj-24683	292	1	when	when	SCONJ
brj-24683	292	2	the	the	DET
brj-24683	292	3	area	area	NOUN
brj-24683	292	4	reaches	reach	VERB
brj-24683	292	5	1	1	NUM
brj-24683	292	6	,	,	PUNCT
brj-24683	292	7	it	it	PRON
brj-24683	292	8	signifies	signify	VERB
brj-24683	292	9	that	that	SCONJ
brj-24683	292	10	the	the	DET
brj-24683	292	11	model	model	NOUN
brj-24683	292	12	has	have	AUX
brj-24683	292	13	perfectly	perfectly	ADV
brj-24683	292	14	detected	detect	VERB
brj-24683	292	15	all	all	DET
brj-24683	292	16	targets	target	NOUN
brj-24683	292	17	.	.	PUNCT
brj-24683	293	1	figure	figure	VERB
brj-24683	293	2	10	10	NUM
brj-24683	293	3	illustrates	illustrate	VERB
brj-24683	293	4	the	the	DET
brj-24683	293	5	ap	ap	PROPN
brj-24683	293	6	values	value	NOUN
brj-24683	293	7	for	for	ADP
brj-24683	293	8	the	the	DET
brj-24683	293	9	seven	seven	NUM
brj-24683	293	10	types	type	NOUN
brj-24683	293	11	of	of	ADP
brj-24683	293	12	wood	wood	NOUN
brj-24683	293	13	defects	defect	NOUN
brj-24683	293	14	evaluated	evaluate	VERB
brj-24683	293	15	in	in	ADP
brj-24683	293	16	this	this	DET
brj-24683	293	17	study	study	NOUN
brj-24683	293	18	.	.	PUNCT
brj-24683	294	1	subfigures	subfigure	NOUN
brj-24683	294	2	(	(	PUNCT
brj-24683	294	3	a	a	NOUN
brj-24683	294	4	)	)	PUNCT
brj-24683	294	5	,	,	PUNCT
brj-24683	294	6	(	(	PUNCT
brj-24683	294	7	b	b	NOUN
brj-24683	294	8	)	)	PUNCT
brj-24683	294	9	,	,	PUNCT
brj-24683	294	10	(	(	PUNCT
brj-24683	294	11	c	c	NOUN
brj-24683	294	12	)	)	PUNCT
brj-24683	294	13	,	,	PUNCT
brj-24683	294	14	(	(	PUNCT
brj-24683	294	15	d	d	NOUN
brj-24683	294	16	)	)	PUNCT
brj-24683	294	17	,	,	PUNCT
brj-24683	294	18	(	(	PUNCT
brj-24683	294	19	e	e	NOUN
brj-24683	294	20	)	)	PUNCT
brj-24683	294	21	,	,	PUNCT
brj-24683	294	22	(	(	PUNCT
brj-24683	294	23	f	f	X
brj-24683	294	24	)	)	PUNCT
brj-24683	294	25	,	,	PUNCT
brj-24683	294	26	(	(	PUNCT
brj-24683	294	27	g	g	NOUN
brj-24683	294	28	)	)	PUNCT
brj-24683	294	29	and	and	CCONJ
brj-24683	294	30	(	(	PUNCT
brj-24683	294	31	h	h	NOUN
brj-24683	294	32	)	)	PUNCT
brj-24683	294	33	correspond	correspond	VERB
brj-24683	294	34	to	to	ADP
brj-24683	294	35	the	the	DET
brj-24683	294	36	ap	ap	PROPN
brj-24683	294	37	values	value	NOUN
brj-24683	294	38	obtained	obtain	VERB
brj-24683	294	39	by	by	ADP
brj-24683	294	40	yolov5	yolov5	NOUN
brj-24683	294	41	,	,	PUNCT
brj-24683	294	42	yolov7	yolov7	NOUN
brj-24683	294	43	,	,	PUNCT
brj-24683	294	44	yolov8	yolov8	PROPN
brj-24683	294	45	,	,	PUNCT
brj-24683	294	46	yolov9	yolov9	PROPN
brj-24683	294	47	,	,	PUNCT
brj-24683	294	48	yolov10	yolov10	NOUN
brj-24683	294	49	,	,	PUNCT
brj-24683	294	50	yolo11	yolo11	ADJ
brj-24683	294	51	,	,	PUNCT
brj-24683	294	52	yolov12	yolov12	ADJ
brj-24683	294	53	,	,	PUNCT
brj-24683	294	54	and	and	CCONJ
brj-24683	294	55	the	the	DET
brj-24683	294	56	proposed	propose	VERB
brj-24683	294	57	model	model	NOUN
brj-24683	294	58	,	,	PUNCT
brj-24683	294	59	respectively	respectively	ADV
brj-24683	294	60	.	.	PUNCT
brj-24683	295	1	in	in	ADP
brj-24683	295	2	addition	addition	NOUN
brj-24683	295	3	,	,	PUNCT
brj-24683	295	4	to	to	PART
brj-24683	295	5	validate	validate	VERB
brj-24683	295	6	the	the	DET
brj-24683	295	7	performance	performance	NOUN
brj-24683	295	8	of	of	ADP
brj-24683	295	9	the	the	DET
brj-24683	295	10	current	current	ADJ
brj-24683	295	11	model	model	NOUN
brj-24683	295	12	in	in	ADP
brj-24683	295	13	complex	complex	ADJ
brj-24683	295	14	backgrounds	background	NOUN
brj-24683	295	15	,	,	PUNCT
brj-24683	295	16	the	the	DET
brj-24683	295	17	present	present	ADJ
brj-24683	295	18	results	result	NOUN
brj-24683	295	19	were	be	AUX
brj-24683	295	20	compared	compare	VERB
brj-24683	295	21	with	with	ADP
brj-24683	295	22	other	other	ADJ
brj-24683	295	23	yolo	yolo	PROPN
brj-24683	295	24	-	-	PUNCT
brj-24683	295	25	based	base	VERB
brj-24683	295	26	studies	study	NOUN
brj-24683	295	27	addressing	address	VERB
brj-24683	295	28	similarly	similarly	ADV
brj-24683	295	29	challenging	challenging	ADJ
brj-24683	295	30	detection	detection	NOUN
brj-24683	295	31	environments	environment	NOUN
brj-24683	295	32	.	.	PUNCT
brj-24683	296	1	for	for	ADP
brj-24683	296	2	instance	instance	NOUN
brj-24683	296	3	,	,	PUNCT
brj-24683	296	4	tile	tile	NOUN
brj-24683	296	5	defect	defect	NOUN
brj-24683	296	6	detection	detection	NOUN
brj-24683	296	7	in	in	ADP
brj-24683	296	8	historical	historical	ADJ
brj-24683	296	9	buildings	building	NOUN
brj-24683	296	10	karimi	karimi	PROPN
brj-24683	296	11	et	et	PROPN
brj-24683	296	12	al	al	PROPN
brj-24683	296	13	.	.	PROPN
brj-24683	297	1	(	(	PUNCT
brj-24683	297	2	2024	2024	NUM
brj-24683	297	3	)	)	PUNCT
brj-24683	297	4	report	report	VERB
brj-24683	297	5	overall	overall	ADJ
brj-24683	297	6	accuracy	accuracy	NOUN
brj-24683	297	7	of	of	ADP
brj-24683	297	8	over	over	ADP
brj-24683	297	9	72	72	NUM
brj-24683	297	10	%	%	NOUN
brj-24683	297	11	.	.	PUNCT
brj-24683	298	1	for	for	ADP
brj-24683	298	2	wood	wood	NOUN
brj-24683	298	3	surface	surface	NOUN
brj-24683	298	4	defect	defect	NOUN
brj-24683	298	5	detection	detection	NOUN
brj-24683	298	6	(	(	PUNCT
brj-24683	298	7	wang	wang	PROPN
brj-24683	298	8	et	et	PROPN
brj-24683	298	9	al	al	PROPN
brj-24683	298	10	.	.	PROPN
brj-24683	298	11	2024	2024	NUM
brj-24683	298	12	;	;	PUNCT
brj-24683	298	13	xi	xi	NUM
brj-24683	298	14	et	et	PROPN
brj-24683	298	15	al	al	PROPN
brj-24683	298	16	.	.	PROPN
brj-24683	298	17	2024	2024	NUM
brj-24683	298	18	)	)	PUNCT
brj-24683	298	19	,	,	PUNCT
brj-24683	298	20	researchers	researcher	NOUN
brj-24683	298	21	report	report	VERB
brj-24683	298	22	map	map	VERB
brj-24683	298	23	values	value	NOUN
brj-24683	298	24	of	of	ADP
brj-24683	298	25	77.7	77.7	NUM
brj-24683	298	26	%	%	NOUN
brj-24683	298	27	and	and	CCONJ
brj-24683	298	28	78.4	78.4	NUM
brj-24683	298	29	%	%	NOUN
brj-24683	298	30	,	,	PUNCT
brj-24683	298	31	respectively	respectively	ADV
brj-24683	298	32	.	.	PUNCT
brj-24683	299	1	the	the	DET
brj-24683	299	2	present	present	ADJ
brj-24683	299	3	model	model	NOUN
brj-24683	299	4	achieved	achieve	VERB
brj-24683	299	5	a	a	DET
brj-24683	299	6	comparable	comparable	ADJ
brj-24683	299	7	or	or	CCONJ
brj-24683	299	8	higher	high	ADJ
brj-24683	299	9	detection	detection	NOUN
brj-24683	299	10	accuracy	accuracy	NOUN
brj-24683	299	11	(	(	PUNCT
brj-24683	299	12	79.5	79.5	NUM
brj-24683	299	13	%	%	NOUN
brj-24683	299	14	map	map	NOUN
brj-24683	299	15	)	)	PUNCT
brj-24683	299	16	,	,	PUNCT
brj-24683	299	17	particularly	particularly	ADV
brj-24683	299	18	under	under	ADP
brj-24683	299	19	diverse	diverse	ADJ
brj-24683	299	20	wood	wood	NOUN
brj-24683	299	21	textures	texture	NOUN
brj-24683	299	22	and	and	CCONJ
brj-24683	299	23	irregular	irregular	ADJ
brj-24683	299	24	defect	defect	NOUN
brj-24683	299	25	patterns	pattern	NOUN
brj-24683	299	26	.	.	PUNCT
brj-24683	300	1	these	these	DET
brj-24683	300	2	results	result	NOUN
brj-24683	300	3	highlight	highlight	VERB
brj-24683	300	4	the	the	DET
brj-24683	300	5	robustness	robustness	NOUN
brj-24683	300	6	and	and	CCONJ
brj-24683	300	7	generalization	generalization	NOUN
brj-24683	300	8	capacity	capacity	NOUN
brj-24683	300	9	of	of	ADP
brj-24683	300	10	the	the	DET
brj-24683	300	11	present	present	ADJ
brj-24683	300	12	approach	approach	NOUN
brj-24683	300	13	under	under	ADP
brj-24683	300	14	real	real	ADJ
brj-24683	300	15	-	-	PUNCT
brj-24683	300	16	world	world	NOUN
brj-24683	300	17	complexity	complexity	NOUN
brj-24683	300	18	.	.	PUNCT
brj-24683	301	1	peer	peer	NOUN
brj-24683	301	2	-	-	PUNCT
brj-24683	301	3	reviewed	review	VERB
brj-24683	301	4	article	article	NOUN
brj-24683	301	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	301	6	dou	dou	PROPN
brj-24683	301	7	&	&	CCONJ
brj-24683	301	8	you	you	PRON
brj-24683	301	9	(	(	PUNCT
brj-24683	301	10	2025	2025	NUM
brj-24683	301	11	)	)	PUNCT
brj-24683	301	12	.	.	PUNCT
brj-24683	302	1	“	"	PUNCT
brj-24683	302	2	wood	wood	NOUN
brj-24683	302	3	defect	defect	NOUN
brj-24683	302	4	identification	identification	NOUN
brj-24683	302	5	,	,	PUNCT
brj-24683	302	6	”	"	PUNCT
brj-24683	302	7	bioresources	bioresource	NOUN
brj-24683	302	8	20(3	20(3	NOUN
brj-24683	302	9	)	)	PUNCT
brj-24683	302	10	,	,	PUNCT
brj-24683	302	11	5709	5709	NUM
brj-24683	302	12	-	-	SYM
brj-24683	302	13	5730	5730	NUM
brj-24683	302	14	.	.	PUNCT
brj-24683	303	1	5724	5724	NUM
brj-24683	303	2	(	(	PUNCT
brj-24683	303	3	a	a	NOUN
brj-24683	303	4	)	)	PUNCT
brj-24683	303	5	(	(	PUNCT
brj-24683	303	6	b	b	X
brj-24683	303	7	)	)	PUNCT
brj-24683	303	8	(	(	PUNCT
brj-24683	303	9	c	c	X
brj-24683	303	10	)	)	PUNCT
brj-24683	303	11	(	(	PUNCT
brj-24683	303	12	d	d	X
brj-24683	303	13	)	)	PUNCT
brj-24683	303	14	(	(	PUNCT
brj-24683	303	15	e	e	NOUN
brj-24683	303	16	)	)	PUNCT
brj-24683	303	17	(	(	PUNCT
brj-24683	303	18	f	f	X
brj-24683	303	19	)	)	PUNCT
brj-24683	303	20	(	(	PUNCT
brj-24683	303	21	g	g	NOUN
brj-24683	303	22	)	)	PUNCT
brj-24683	303	23	(	(	PUNCT
brj-24683	303	24	h	h	NOUN
brj-24683	303	25	)	)	PUNCT
brj-24683	303	26	fig	fig	NOUN
brj-24683	303	27	.	.	PUNCT
brj-24683	304	1	10	10	NUM
brj-24683	304	2	.	.	X
brj-24683	304	3	precision	precision	NOUN
brj-24683	304	4	–	–	PUNCT
brj-24683	304	5	recall	recall	NOUN
brj-24683	304	6	(	(	PUNCT
brj-24683	304	7	p	p	NOUN
brj-24683	304	8	–	–	PUNCT
brj-24683	304	9	r	r	NOUN
brj-24683	304	10	)	)	PUNCT
brj-24683	304	11	curves	curve	VERB
brj-24683	304	12	the	the	DET
brj-24683	304	13	proposed	propose	VERB
brj-24683	304	14	model	model	NOUN
brj-24683	304	15	achieved	achieve	VERB
brj-24683	304	16	higher	high	ADJ
brj-24683	304	17	ap	ap	PROPN
brj-24683	304	18	values	value	NOUN
brj-24683	304	19	than	than	ADP
brj-24683	304	20	other	other	ADJ
brj-24683	304	21	benchmark	benchmark	NOUN
brj-24683	304	22	models	model	NOUN
brj-24683	304	23	for	for	ADP
brj-24683	304	24	almost	almost	ADV
brj-24683	304	25	all	all	PRON
brj-24683	304	26	seven	seven	NUM
brj-24683	304	27	types	type	NOUN
brj-24683	304	28	of	of	ADP
brj-24683	304	29	wood	wood	NOUN
brj-24683	304	30	defects	defect	NOUN
brj-24683	304	31	,	,	PUNCT
brj-24683	304	32	except	except	SCONJ
brj-24683	304	33	for	for	ADP
brj-24683	304	34	knot_missing	knot_missing	NOUN
brj-24683	304	35	and	and	CCONJ
brj-24683	304	36	crack	crack	VERB
brj-24683	304	37	.	.	PUNCT
brj-24683	305	1	despite	despite	SCONJ
brj-24683	305	2	the	the	DET
brj-24683	305	3	overall	overall	ADJ
brj-24683	305	4	improvements	improvement	NOUN
brj-24683	305	5	achieved	achieve	VERB
brj-24683	305	6	by	by	ADP
brj-24683	305	7	the	the	DET
brj-24683	305	8	proposed	propose	VERB
brj-24683	305	9	model	model	NOUN
brj-24683	305	10	,	,	PUNCT
brj-24683	305	11	the	the	DET
brj-24683	305	12	detection	detection	NOUN
brj-24683	305	13	performance	performance	NOUN
brj-24683	305	14	for	for	ADP
brj-24683	305	15	knot_missing	knot_missing	NOUN
brj-24683	305	16	and	and	CCONJ
brj-24683	305	17	crack	crack	VERB
brj-24683	305	18	defects	defect	NOUN
brj-24683	305	19	did	do	AUX
brj-24683	305	20	not	not	PART
brj-24683	305	21	surpass	surpass	VERB
brj-24683	305	22	that	that	PRON
brj-24683	305	23	of	of	ADP
brj-24683	305	24	yolov5	yolov5	NOUN
brj-24683	305	25	and	and	CCONJ
brj-24683	305	26	yolov9	yolov9	PROPN
brj-24683	305	27	,	,	PUNCT
brj-24683	305	28	respectively	respectively	ADV
brj-24683	305	29	.	.	PUNCT
brj-24683	306	1	this	this	PRON
brj-24683	306	2	can	can	AUX
brj-24683	306	3	be	be	AUX
brj-24683	306	4	attributed	attribute	VERB
brj-24683	306	5	to	to	ADP
brj-24683	306	6	the	the	DET
brj-24683	306	7	following	following	ADJ
brj-24683	306	8	factors	factor	NOUN
brj-24683	306	9	:	:	PUNCT
brj-24683	306	10	(	(	PUNCT
brj-24683	306	11	1	1	X
brj-24683	306	12	)	)	PUNCT
brj-24683	306	13	knot_missing	knot_misse	VERB
brj-24683	306	14	defects	defect	NOUN
brj-24683	306	15	typically	typically	ADV
brj-24683	306	16	exhibit	exhibit	VERB
brj-24683	306	17	clear	clear	ADJ
brj-24683	306	18	edges	edge	NOUN
brj-24683	306	19	and	and	CCONJ
brj-24683	306	20	relatively	relatively	ADV
brj-24683	306	21	large	large	ADJ
brj-24683	306	22	missing	missing	ADJ
brj-24683	306	23	regions	region	NOUN
brj-24683	306	24	,	,	PUNCT
brj-24683	306	25	making	make	VERB
brj-24683	306	26	them	they	PRON
brj-24683	306	27	more	more	ADV
brj-24683	306	28	distinguishable	distinguishable	ADJ
brj-24683	306	29	.	.	PUNCT
brj-24683	307	1	yolov5	yolov5	NOUN
brj-24683	307	2	,	,	PUNCT
brj-24683	307	3	as	as	ADP
brj-24683	307	4	a	a	DET
brj-24683	307	5	well	well	ADV
brj-24683	307	6	-	-	PUNCT
brj-24683	307	7	established	establish	VERB
brj-24683	307	8	model	model	NOUN
brj-24683	307	9	,	,	PUNCT
brj-24683	307	10	may	may	AUX
brj-24683	307	11	have	have	AUX
brj-24683	307	12	been	be	AUX
brj-24683	307	13	optimized	optimize	VERB
brj-24683	307	14	for	for	ADP
brj-24683	307	15	such	such	ADJ
brj-24683	307	16	easily	easily	ADV
brj-24683	307	17	identifiable	identifiable	ADJ
brj-24683	307	18	defects	defect	NOUN
brj-24683	307	19	,	,	PUNCT
brj-24683	307	20	resulting	result	VERB
brj-24683	307	21	in	in	ADP
brj-24683	307	22	superior	superior	ADJ
brj-24683	307	23	performance	performance	NOUN
brj-24683	307	24	.	.	PUNCT
brj-24683	308	1	(	(	PUNCT
brj-24683	308	2	2	2	X
brj-24683	308	3	)	)	PUNCT
brj-24683	308	4	crack	crack	VERB
brj-24683	308	5	defects	defect	NOUN
brj-24683	308	6	,	,	PUNCT
brj-24683	308	7	in	in	ADP
brj-24683	308	8	contrast	contrast	NOUN
brj-24683	308	9	,	,	PUNCT
brj-24683	308	10	are	be	AUX
brj-24683	308	11	characterized	characterize	VERB
brj-24683	308	12	by	by	ADP
brj-24683	308	13	irregular	irregular	ADJ
brj-24683	308	14	,	,	PUNCT
brj-24683	308	15	thin	thin	ADJ
brj-24683	308	16	,	,	PUNCT
brj-24683	308	17	and	and	CCONJ
brj-24683	308	18	elongated	elongate	VERB
brj-24683	308	19	structures	structure	NOUN
brj-24683	308	20	,	,	PUNCT
brj-24683	308	21	which	which	PRON
brj-24683	308	22	can	can	AUX
brj-24683	308	23	resemble	resemble	VERB
brj-24683	308	24	natural	natural	ADJ
brj-24683	308	25	wood	wood	NOUN
brj-24683	308	26	grain	grain	NOUN
brj-24683	308	27	patterns	pattern	NOUN
brj-24683	308	28	.	.	PUNCT
brj-24683	309	1	the	the	DET
brj-24683	309	2	strong	strong	ADJ
brj-24683	309	3	performance	performance	NOUN
brj-24683	309	4	of	of	ADP
brj-24683	309	5	yolov9	yolov9	NOUN
brj-24683	309	6	in	in	ADP
brj-24683	309	7	this	this	DET
brj-24683	309	8	category	category	NOUN
brj-24683	309	9	suggests	suggest	VERB
brj-24683	309	10	that	that	SCONJ
brj-24683	309	11	its	its	PRON
brj-24683	309	12	feature	feature	NOUN
brj-24683	309	13	extraction	extraction	NOUN
brj-24683	309	14	and	and	CCONJ
brj-24683	309	15	detection	detection	NOUN
brj-24683	309	16	heads	head	NOUN
brj-24683	309	17	are	be	AUX
brj-24683	309	18	more	more	ADV
brj-24683	309	19	suited	suited	ADJ
brj-24683	309	20	for	for	ADP
brj-24683	309	21	capturing	capture	VERB
brj-24683	309	22	fine	fine	ADV
brj-24683	309	23	-	-	PUNCT
brj-24683	309	24	grained	grain	VERB
brj-24683	309	25	and	and	CCONJ
brj-24683	309	26	linear	linear	NOUN
brj-24683	309	27	features	feature	NOUN
brj-24683	309	28	.	.	PUNCT
brj-24683	310	1	(	(	PUNCT
brj-24683	310	2	3	3	X
brj-24683	310	3	)	)	PUNCT
brj-24683	310	4	the	the	DET
brj-24683	310	5	distribution	distribution	NOUN
brj-24683	310	6	of	of	ADP
brj-24683	310	7	knot_missing	knot_missing	NOUN
brj-24683	310	8	and	and	CCONJ
brj-24683	310	9	crack	crack	VERB
brj-24683	310	10	samples	sample	NOUN
brj-24683	310	11	in	in	ADP
brj-24683	310	12	the	the	DET
brj-24683	310	13	training	training	NOUN
brj-24683	310	14	dataset	dataset	NOUN
brj-24683	310	15	may	may	AUX
brj-24683	310	16	impact	impact	VERB
brj-24683	310	17	the	the	DET
brj-24683	310	18	model	model	NOUN
brj-24683	310	19	’s	’s	PART
brj-24683	310	20	generalization	generalization	NOUN
brj-24683	310	21	capability	capability	NOUN
brj-24683	310	22	.	.	PUNCT
brj-24683	311	1	if	if	SCONJ
brj-24683	311	2	these	these	DET
brj-24683	311	3	defect	defect	ADJ
brj-24683	311	4	types	type	NOUN
brj-24683	311	5	are	be	AUX
brj-24683	311	6	underrepresented	underrepresented	ADJ
brj-24683	311	7	or	or	CCONJ
brj-24683	311	8	exhibit	exhibit	VERB
brj-24683	311	9	high	high	ADJ
brj-24683	311	10	variability	variability	NOUN
brj-24683	311	11	,	,	PUNCT
brj-24683	311	12	the	the	DET
brj-24683	311	13	model	model	NOUN
brj-24683	311	14	may	may	AUX
brj-24683	311	15	struggle	struggle	VERB
brj-24683	311	16	to	to	PART
brj-24683	311	17	learn	learn	VERB
brj-24683	311	18	a	a	DET
brj-24683	311	19	robust	robust	ADJ
brj-24683	311	20	representation	representation	NOUN
brj-24683	311	21	for	for	ADP
brj-24683	311	22	them	they	PRON
brj-24683	311	23	.	.	PUNCT
brj-24683	312	1	(	(	PUNCT
brj-24683	312	2	4	4	X
brj-24683	312	3	)	)	PUNCT
brj-24683	312	4	knot_missing	knot_misse	VERB
brj-24683	312	5	defects	defect	NOUN
brj-24683	312	6	,	,	PUNCT
brj-24683	312	7	being	be	AUX
brj-24683	312	8	relatively	relatively	ADV
brj-24683	312	9	large	large	ADJ
brj-24683	312	10	and	and	CCONJ
brj-24683	312	11	distinct	distinct	ADJ
brj-24683	312	12	,	,	PUNCT
brj-24683	312	13	may	may	AUX
brj-24683	312	14	not	not	PART
brj-24683	312	15	benefit	benefit	VERB
brj-24683	312	16	as	as	ADV
brj-24683	312	17	significantly	significantly	ADV
brj-24683	312	18	from	from	ADP
brj-24683	312	19	the	the	DET
brj-24683	312	20	added	add	VERB
brj-24683	312	21	feature	feature	NOUN
brj-24683	312	22	extraction	extraction	NOUN
brj-24683	312	23	enhancements	enhancement	NOUN
brj-24683	312	24	,	,	PUNCT
brj-24683	312	25	as	as	SCONJ
brj-24683	312	26	their	their	PRON
brj-24683	312	27	characteristics	characteristic	NOUN
brj-24683	312	28	are	be	AUX
brj-24683	312	29	already	already	ADV
brj-24683	312	30	well	well	ADV
brj-24683	312	31	captured	capture	VERB
brj-24683	312	32	by	by	ADP
brj-24683	312	33	standard	standard	ADJ
brj-24683	312	34	detection	detection	NOUN
brj-24683	312	35	modules	module	NOUN
brj-24683	312	36	.	.	PUNCT
brj-24683	313	1	the	the	DET
brj-24683	313	2	visual	visual	ADJ
brj-24683	313	3	comparison	comparison	NOUN
brj-24683	313	4	results	result	NOUN
brj-24683	313	5	are	be	AUX
brj-24683	313	6	illustrated	illustrate	VERB
brj-24683	313	7	in	in	ADP
brj-24683	313	8	fig	fig	NOUN
brj-24683	313	9	.	.	PUNCT
brj-24683	314	1	11	11	NUM
brj-24683	314	2	.	.	PUNCT
brj-24683	315	1	each	each	DET
brj-24683	315	2	detection	detection	NOUN
brj-24683	315	3	box	box	NOUN
brj-24683	315	4	is	be	AUX
brj-24683	315	5	associated	associate	VERB
brj-24683	315	6	with	with	ADP
brj-24683	315	7	a	a	DET
brj-24683	315	8	confidence	confidence	NOUN
brj-24683	315	9	score	score	NOUN
brj-24683	315	10	,	,	PUNCT
brj-24683	315	11	which	which	PRON
brj-24683	315	12	quantifies	quantify	VERB
brj-24683	315	13	the	the	DET
brj-24683	315	14	model	model	NOUN
brj-24683	315	15	's	's	PART
brj-24683	315	16	certainty	certainty	NOUN
brj-24683	315	17	regarding	regard	VERB
brj-24683	315	18	its	its	PRON
brj-24683	315	19	detection	detection	NOUN
brj-24683	315	20	outcome	outcome	NOUN
brj-24683	315	21	.	.	PUNCT
brj-24683	316	1	this	this	DET
brj-24683	316	2	score	score	NOUN
brj-24683	316	3	ranges	range	VERB
brj-24683	316	4	from	from	ADP
brj-24683	316	5	0	0	NUM
brj-24683	316	6	to	to	ADP
brj-24683	316	7	1	1	NUM
brj-24683	316	8	,	,	PUNCT
brj-24683	316	9	where	where	SCONJ
brj-24683	316	10	higher	high	ADJ
brj-24683	316	11	values	value	NOUN
brj-24683	316	12	indicate	indicate	VERB
brj-24683	316	13	greater	great	ADJ
brj-24683	316	14	confidence	confidence	NOUN
brj-24683	316	15	in	in	ADP
brj-24683	316	16	the	the	DET
brj-24683	316	17	detection	detection	NOUN
brj-24683	316	18	,	,	PUNCT
brj-24683	316	19	whereas	whereas	SCONJ
brj-24683	316	20	lower	low	ADJ
brj-24683	316	21	values	value	NOUN
brj-24683	316	22	suggest	suggest	VERB
brj-24683	316	23	increased	increase	VERB
brj-24683	316	24	uncertainty	uncertainty	NOUN
brj-24683	316	25	in	in	ADP
brj-24683	316	26	the	the	DET
brj-24683	316	27	model	model	NOUN
brj-24683	316	28	's	's	PART
brj-24683	316	29	predictions	prediction	NOUN
brj-24683	316	30	.	.	PUNCT
brj-24683	317	1	the	the	DET
brj-24683	317	2	experimental	experimental	ADJ
brj-24683	317	3	results	result	NOUN
brj-24683	317	4	highlight	highlight	VERB
brj-24683	317	5	the	the	DET
brj-24683	317	6	superior	superior	ADJ
brj-24683	317	7	performance	performance	NOUN
brj-24683	317	8	of	of	ADP
brj-24683	317	9	the	the	DET
brj-24683	317	10	proposed	propose	VERB
brj-24683	317	11	model	model	NOUN
brj-24683	317	12	in	in	ADP
brj-24683	317	13	wood	wood	NOUN
brj-24683	317	14	defect	defect	NOUN
brj-24683	317	15	detection	detection	NOUN
brj-24683	317	16	.	.	PUNCT
brj-24683	318	1	for	for	ADP
brj-24683	318	2	instance	instance	NOUN
brj-24683	318	3	,	,	PUNCT
brj-24683	318	4	in	in	ADP
brj-24683	318	5	detecting	detect	VERB
brj-24683	318	6	the	the	DET
brj-24683	318	7	marrow	marrow	NOUN
brj-24683	318	8	defect	defect	NOUN
brj-24683	318	9	,	,	PUNCT
brj-24683	318	10	the	the	DET
brj-24683	318	11	confidence	confidence	NOUN
brj-24683	318	12	scores	score	NOUN
brj-24683	318	13	achieved	achieve	VERB
brj-24683	318	14	by	by	ADP
brj-24683	318	15	the	the	DET
brj-24683	318	16	proposed	propose	VERB
brj-24683	318	17	model	model	NOUN
brj-24683	318	18	were	be	AUX
brj-24683	318	19	0.94	0.94	NUM
brj-24683	318	20	and	and	CCONJ
brj-24683	318	21	0.92	0.92	NUM
brj-24683	318	22	,	,	PUNCT
brj-24683	318	23	compared	compare	VERB
brj-24683	318	24	to	to	ADP
brj-24683	318	25	only	only	ADV
brj-24683	318	26	0.84	0.84	NUM
brj-24683	318	27	and	and	CCONJ
brj-24683	318	28	0.72	0.72	NUM
brj-24683	318	29	for	for	ADP
brj-24683	318	30	the	the	DET
brj-24683	318	31	yolov8	yolov8	NOUN
brj-24683	318	32	,	,	PUNCT
brj-24683	318	33	reflecting	reflect	VERB
brj-24683	318	34	improvements	improvement	NOUN
brj-24683	318	35	of	of	ADP
brj-24683	318	36	0.10	0.10	NUM
brj-24683	318	37	and	and	CCONJ
brj-24683	318	38	0.20	0.20	NUM
brj-24683	318	39	,	,	PUNCT
brj-24683	318	40	respectively	respectively	ADV
brj-24683	318	41	.	.	PUNCT
brj-24683	319	1	in	in	ADP
brj-24683	319	2	detecting	detect	VERB
brj-24683	319	3	the	the	DET
brj-24683	319	4	resin	resin	NOUN
brj-24683	319	5	defect	defect	NOUN
brj-24683	319	6	,	,	PUNCT
brj-24683	319	7	the	the	DET
brj-24683	319	8	confidence	confidence	NOUN
brj-24683	319	9	score	score	NOUN
brj-24683	319	10	is	be	AUX
brj-24683	319	11	the	the	DET
brj-24683	319	12	highest	high	ADJ
brj-24683	319	13	at	at	ADP
brj-24683	319	14	0.95	0.95	NUM
brj-24683	319	15	.	.	PUNCT
brj-24683	320	1	furthermore	furthermore	ADV
brj-24683	320	2	,	,	PUNCT
brj-24683	320	3	the	the	DET
brj-24683	320	4	proposed	propose	VERB
brj-24683	320	5	model	model	NOUN
brj-24683	320	6	exhibited	exhibit	VERB
brj-24683	320	7	no	no	DET
brj-24683	320	8	misclassifications	misclassification	NOUN
brj-24683	320	9	or	or	CCONJ
brj-24683	320	10	missed	miss	VERB
brj-24683	320	11	detections	detection	NOUN
brj-24683	320	12	across	across	ADP
brj-24683	320	13	all	all	DET
brj-24683	320	14	defect	defect	ADJ
brj-24683	320	15	types	type	NOUN
brj-24683	320	16	,	,	PUNCT
brj-24683	320	17	demonstrating	demonstrate	VERB
brj-24683	320	18	its	its	PRON
brj-24683	320	19	high	high	ADJ
brj-24683	320	20	reliability	reliability	NOUN
brj-24683	320	21	and	and	CCONJ
brj-24683	320	22	accuracy	accuracy	NOUN
brj-24683	320	23	in	in	ADP
brj-24683	320	24	wood	wood	NOUN
brj-24683	320	25	defect	defect	NOUN
brj-24683	320	26	detection	detection	NOUN
brj-24683	320	27	.	.	PUNCT
brj-24683	321	1	peer	peer	NOUN
brj-24683	321	2	-	-	PUNCT
brj-24683	321	3	reviewed	review	VERB
brj-24683	321	4	article	article	NOUN
brj-24683	321	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	321	6	dou	dou	PROPN
brj-24683	321	7	&	&	CCONJ
brj-24683	321	8	you	you	PRON
brj-24683	321	9	(	(	PUNCT
brj-24683	321	10	2025	2025	NUM
brj-24683	321	11	)	)	PUNCT
brj-24683	321	12	.	.	PUNCT
brj-24683	322	1	“	"	PUNCT
brj-24683	322	2	wood	wood	NOUN
brj-24683	322	3	defect	defect	NOUN
brj-24683	322	4	identification	identification	NOUN
brj-24683	322	5	,	,	PUNCT
brj-24683	322	6	”	"	PUNCT
brj-24683	322	7	bioresources	bioresource	NOUN
brj-24683	322	8	20(3	20(3	NOUN
brj-24683	322	9	)	)	PUNCT
brj-24683	322	10	,	,	PUNCT
brj-24683	322	11	5709	5709	NUM
brj-24683	322	12	-	-	SYM
brj-24683	322	13	5730	5730	NUM
brj-24683	322	14	.	.	PUNCT
brj-24683	323	1	5725	5725	NUM
brj-24683	323	2	fig	fig	NOUN
brj-24683	323	3	.	.	PUNCT
brj-24683	324	1	11	11	NUM
brj-24683	324	2	.	.	PUNCT
brj-24683	325	1	examples	example	NOUN
brj-24683	325	2	of	of	ADP
brj-24683	325	3	visual	visual	ADJ
brj-24683	325	4	comparison	comparison	NOUN
brj-24683	325	5	results	result	NOUN
brj-24683	325	6	figure	figure	VERB
brj-24683	325	7	12	12	NUM
brj-24683	325	8	presents	present	NOUN
brj-24683	325	9	the	the	DET
brj-24683	325	10	visual	visual	ADJ
brj-24683	325	11	results	result	NOUN
brj-24683	325	12	obtained	obtain	VERB
brj-24683	325	13	using	use	VERB
brj-24683	325	14	grad	grad	NOUN
brj-24683	325	15	-	-	PUNCT
brj-24683	325	16	cam	cam	NOUN
brj-24683	325	17	for	for	ADP
brj-24683	325	18	yolov8	yolov8	NOUN
brj-24683	325	19	and	and	CCONJ
brj-24683	325	20	the	the	DET
brj-24683	325	21	proposed	propose	VERB
brj-24683	325	22	model	model	NOUN
brj-24683	325	23	in	in	ADP
brj-24683	325	24	wood	wood	NOUN
brj-24683	325	25	defect	defect	NOUN
brj-24683	325	26	detection	detection	NOUN
brj-24683	325	27	tasks	task	NOUN
brj-24683	325	28	.	.	PUNCT
brj-24683	326	1	grad	grad	NOUN
brj-24683	326	2	-	-	PUNCT
brj-24683	326	3	cam	cam	PROPN
brj-24683	326	4	is	be	AUX
brj-24683	326	5	a	a	DET
brj-24683	326	6	visualization	visualization	NOUN
brj-24683	326	7	technique	technique	NOUN
brj-24683	326	8	designed	design	VERB
brj-24683	326	9	to	to	PART
brj-24683	326	10	enhance	enhance	VERB
brj-24683	326	11	model	model	NOUN
brj-24683	326	12	interpretability	interpretability	NOUN
brj-24683	326	13	by	by	ADP
brj-24683	326	14	leveraging	leverage	VERB
brj-24683	326	15	gradient	gradient	ADJ
brj-24683	326	16	information	information	NOUN
brj-24683	326	17	to	to	PART
brj-24683	326	18	generate	generate	VERB
brj-24683	326	19	heatmaps	heatmap	NOUN
brj-24683	326	20	that	that	PRON
brj-24683	326	21	highlight	highlight	VERB
brj-24683	326	22	the	the	DET
brj-24683	326	23	most	most	ADV
brj-24683	326	24	influential	influential	ADJ
brj-24683	326	25	regions	region	NOUN
brj-24683	326	26	of	of	ADP
brj-24683	326	27	the	the	DET
brj-24683	326	28	input	input	NOUN
brj-24683	326	29	image	image	NOUN
brj-24683	326	30	for	for	ADP
brj-24683	326	31	a	a	DET
brj-24683	326	32	given	give	VERB
brj-24683	326	33	peer	peer	NOUN
brj-24683	326	34	-	-	PUNCT
brj-24683	326	35	reviewed	review	VERB
brj-24683	326	36	article	article	NOUN
brj-24683	326	37	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	326	38	dou	dou	PROPN
brj-24683	326	39	&	&	CCONJ
brj-24683	326	40	you	you	PRON
brj-24683	326	41	(	(	PUNCT
brj-24683	326	42	2025	2025	NUM
brj-24683	326	43	)	)	PUNCT
brj-24683	326	44	.	.	PUNCT
brj-24683	327	1	“	"	PUNCT
brj-24683	327	2	wood	wood	NOUN
brj-24683	327	3	defect	defect	NOUN
brj-24683	327	4	identification	identification	NOUN
brj-24683	327	5	,	,	PUNCT
brj-24683	327	6	”	"	PUNCT
brj-24683	327	7	bioresources	bioresource	NOUN
brj-24683	327	8	20(3	20(3	NOUN
brj-24683	327	9	)	)	PUNCT
brj-24683	327	10	,	,	PUNCT
brj-24683	327	11	5709	5709	NUM
brj-24683	327	12	-	-	SYM
brj-24683	327	13	5730	5730	NUM
brj-24683	327	14	.	.	PUNCT
brj-24683	328	1	5726	5726	NUM
brj-24683	328	2	prediction	prediction	NOUN
brj-24683	328	3	.	.	PUNCT
brj-24683	329	1	this	this	DET
brj-24683	329	2	approach	approach	NOUN
brj-24683	329	3	provides	provide	VERB
brj-24683	329	4	an	an	DET
brj-24683	329	5	intuitive	intuitive	ADJ
brj-24683	329	6	way	way	NOUN
brj-24683	329	7	to	to	PART
brj-24683	329	8	illustrate	illustrate	VERB
brj-24683	329	9	the	the	DET
brj-24683	329	10	model	model	NOUN
brj-24683	329	11	’s	’s	PART
brj-24683	329	12	attention	attention	NOUN
brj-24683	329	13	distribution	distribution	NOUN
brj-24683	329	14	and	and	CCONJ
brj-24683	329	15	explain	explain	VERB
brj-24683	329	16	its	its	PRON
brj-24683	329	17	decision	decision	NOUN
brj-24683	329	18	-	-	PUNCT
brj-24683	329	19	making	make	VERB
brj-24683	329	20	process	process	NOUN
brj-24683	329	21	.	.	PUNCT
brj-24683	330	1	the	the	DET
brj-24683	330	2	proposed	propose	VERB
brj-24683	330	3	model	model	NOUN
brj-24683	330	4	exhibits	exhibit	VERB
brj-24683	330	5	a	a	DET
brj-24683	330	6	more	more	ADV
brj-24683	330	7	precise	precise	ADJ
brj-24683	330	8	focus	focus	NOUN
brj-24683	330	9	on	on	ADP
brj-24683	330	10	key	key	ADJ
brj-24683	330	11	defect	defect	NOUN
brj-24683	330	12	areas	area	NOUN
brj-24683	330	13	during	during	ADP
brj-24683	330	14	wood	wood	NOUN
brj-24683	330	15	defect	defect	NOUN
brj-24683	330	16	detection	detection	NOUN
brj-24683	330	17	.	.	PUNCT
brj-24683	331	1	the	the	DET
brj-24683	331	2	generated	generate	VERB
brj-24683	331	3	heatmaps	heatmap	NOUN
brj-24683	331	4	display	display	VERB
brj-24683	331	5	deeper	deep	ADJ
brj-24683	331	6	color	color	NOUN
brj-24683	331	7	intensities	intensity	NOUN
brj-24683	331	8	,	,	PUNCT
brj-24683	331	9	indicating	indicate	VERB
brj-24683	331	10	a	a	DET
brj-24683	331	11	stronger	strong	ADJ
brj-24683	331	12	response	response	NOUN
brj-24683	331	13	to	to	ADP
brj-24683	331	14	the	the	DET
brj-24683	331	15	target	target	NOUN
brj-24683	331	16	regions	region	NOUN
brj-24683	331	17	,	,	PUNCT
brj-24683	331	18	with	with	ADP
brj-24683	331	19	attention	attention	NOUN
brj-24683	331	20	concentrated	concentrate	VERB
brj-24683	331	21	on	on	ADP
brj-24683	331	22	the	the	DET
brj-24683	331	23	critical	critical	ADJ
brj-24683	331	24	defect	defect	NOUN
brj-24683	331	25	features	feature	NOUN
brj-24683	331	26	.	.	PUNCT
brj-24683	332	1	in	in	ADP
brj-24683	332	2	contrast	contrast	NOUN
brj-24683	332	3	,	,	PUNCT
brj-24683	332	4	yolov8	yolov8	PROPN
brj-24683	332	5	’s	’s	PART
brj-24683	332	6	heatmap	heatmap	PROPN
brj-24683	332	7	presents	present	VERB
brj-24683	332	8	a	a	DET
brj-24683	332	9	more	more	ADV
brj-24683	332	10	dispersed	disperse	VERB
brj-24683	332	11	color	color	NOUN
brj-24683	332	12	distribution	distribution	NOUN
brj-24683	332	13	,	,	PUNCT
brj-24683	332	14	suggesting	suggest	VERB
brj-24683	332	15	that	that	SCONJ
brj-24683	332	16	its	its	PRON
brj-24683	332	17	attention	attention	NOUN
brj-24683	332	18	is	be	AUX
brj-24683	332	19	spread	spread	VERB
brj-24683	332	20	across	across	ADP
brj-24683	332	21	a	a	DET
brj-24683	332	22	broader	broad	ADJ
brj-24683	332	23	area	area	NOUN
brj-24683	332	24	,	,	PUNCT
brj-24683	332	25	potentially	potentially	ADV
brj-24683	332	26	including	include	VERB
brj-24683	332	27	irrelevant	irrelevant	ADJ
brj-24683	332	28	regions	region	NOUN
brj-24683	332	29	.	.	PUNCT
brj-24683	333	1	this	this	DET
brj-24683	333	2	distinction	distinction	NOUN
brj-24683	333	3	highlights	highlight	VERB
brj-24683	333	4	the	the	DET
brj-24683	333	5	superior	superior	ADJ
brj-24683	333	6	feature	feature	NOUN
brj-24683	333	7	extraction	extraction	NOUN
brj-24683	333	8	and	and	CCONJ
brj-24683	333	9	defect	defect	VERB
brj-24683	333	10	localization	localization	NOUN
brj-24683	333	11	capabilities	capability	NOUN
brj-24683	333	12	of	of	ADP
brj-24683	333	13	the	the	DET
brj-24683	333	14	proposed	propose	VERB
brj-24683	333	15	model	model	NOUN
brj-24683	333	16	,	,	PUNCT
brj-24683	333	17	enabling	enable	VERB
brj-24683	333	18	more	more	ADV
brj-24683	333	19	accurate	accurate	ADJ
brj-24683	333	20	identification	identification	NOUN
brj-24683	333	21	of	of	ADP
brj-24683	333	22	wood	wood	NOUN
brj-24683	333	23	defects	defect	NOUN
brj-24683	333	24	and	and	CCONJ
brj-24683	333	25	ultimately	ultimately	ADV
brj-24683	333	26	enhancing	enhance	VERB
brj-24683	333	27	detection	detection	NOUN
brj-24683	333	28	accuracy	accuracy	NOUN
brj-24683	333	29	and	and	CCONJ
brj-24683	333	30	reliability	reliability	NOUN
brj-24683	333	31	.	.	PUNCT
brj-24683	334	1	fig	fig	NOUN
brj-24683	334	2	.	.	PUNCT
brj-24683	335	1	12	12	NUM
brj-24683	335	2	.	.	PUNCT
brj-24683	335	3	grad	grad	NOUN
brj-24683	335	4	-	-	PUNCT
brj-24683	335	5	cam	cam	PROPN
brj-24683	335	6	comparison	comparison	NOUN
brj-24683	335	7	of	of	ADP
brj-24683	335	8	wood	wood	NOUN
brj-24683	335	9	defects	defect	NOUN
brj-24683	335	10	conclusions	conclusion	NOUN
brj-24683	335	11	the	the	DET
brj-24683	335	12	detection	detection	NOUN
brj-24683	335	13	of	of	ADP
brj-24683	335	14	wood	wood	NOUN
brj-24683	335	15	surface	surface	NOUN
brj-24683	335	16	defects	defect	NOUN
brj-24683	335	17	is	be	AUX
brj-24683	335	18	crucial	crucial	ADJ
brj-24683	335	19	for	for	ADP
brj-24683	335	20	ensuring	ensure	VERB
brj-24683	335	21	the	the	DET
brj-24683	335	22	quality	quality	NOUN
brj-24683	335	23	and	and	CCONJ
brj-24683	335	24	performance	performance	NOUN
brj-24683	335	25	of	of	ADP
brj-24683	335	26	wood	wood	NOUN
brj-24683	335	27	products	product	NOUN
brj-24683	335	28	.	.	PUNCT
brj-24683	336	1	to	to	PART
brj-24683	336	2	address	address	VERB
brj-24683	336	3	the	the	DET
brj-24683	336	4	challenges	challenge	NOUN
brj-24683	336	5	posed	pose	VERB
brj-24683	336	6	by	by	ADP
brj-24683	336	7	complex	complex	ADJ
brj-24683	336	8	and	and	CCONJ
brj-24683	336	9	variable	variable	ADJ
brj-24683	336	10	backgrounds	background	NOUN
brj-24683	336	11	,	,	PUNCT
brj-24683	336	12	as	as	ADV
brj-24683	336	13	well	well	ADV
brj-24683	336	14	as	as	ADP
brj-24683	336	15	the	the	DET
brj-24683	336	16	small	small	ADJ
brj-24683	336	17	size	size	NOUN
brj-24683	336	18	of	of	ADP
brj-24683	336	19	certain	certain	ADJ
brj-24683	336	20	defects	defect	NOUN
brj-24683	336	21	,	,	PUNCT
brj-24683	336	22	this	this	DET
brj-24683	336	23	paper	paper	NOUN
brj-24683	336	24	proposes	propose	VERB
brj-24683	336	25	an	an	DET
brj-24683	336	26	improved	improved	ADJ
brj-24683	336	27	yolov8	yolov8	NOUN
brj-24683	336	28	-	-	PUNCT
brj-24683	336	29	based	base	VERB
brj-24683	336	30	detection	detection	NOUN
brj-24683	336	31	model	model	NOUN
brj-24683	336	32	that	that	PRON
brj-24683	336	33	enhances	enhance	VERB
brj-24683	336	34	accuracy	accuracy	NOUN
brj-24683	336	35	while	while	SCONJ
brj-24683	336	36	minimizing	minimize	VERB
brj-24683	336	37	false	false	ADJ
brj-24683	336	38	positives	positive	NOUN
brj-24683	336	39	and	and	CCONJ
brj-24683	336	40	missed	miss	VERB
brj-24683	336	41	detections	detection	NOUN
brj-24683	336	42	.	.	PUNCT
brj-24683	337	1	specifically	specifically	ADV
brj-24683	337	2	,	,	PUNCT
brj-24683	337	3	the	the	DET
brj-24683	337	4	mmsa	mmsa	NOUN
brj-24683	337	5	module	module	NOUN
brj-24683	337	6	is	be	AUX
brj-24683	337	7	integrated	integrate	VERB
brj-24683	337	8	into	into	ADP
brj-24683	337	9	the	the	DET
brj-24683	337	10	c2f	c2f	NOUN
brj-24683	337	11	structure	structure	NOUN
brj-24683	337	12	in	in	ADP
brj-24683	337	13	the	the	DET
brj-24683	337	14	backbone	backbone	NOUN
brj-24683	337	15	to	to	PART
brj-24683	337	16	improve	improve	VERB
brj-24683	337	17	the	the	DET
brj-24683	337	18	model	model	NOUN
brj-24683	337	19	’s	’s	PART
brj-24683	337	20	ability	ability	NOUN
brj-24683	337	21	to	to	PART
brj-24683	337	22	capture	capture	VERB
brj-24683	337	23	contextual	contextual	ADJ
brj-24683	337	24	and	and	CCONJ
brj-24683	337	25	background	background	NOUN
brj-24683	337	26	information	information	NOUN
brj-24683	337	27	for	for	ADP
brj-24683	337	28	small	small	ADJ
brj-24683	337	29	targets	target	NOUN
brj-24683	337	30	.	.	PUNCT
brj-24683	338	1	in	in	ADP
brj-24683	338	2	the	the	DET
brj-24683	338	3	neck	neck	NOUN
brj-24683	338	4	,	,	PUNCT
brj-24683	338	5	the	the	DET
brj-24683	338	6	dysample	dysample	ADJ
brj-24683	338	7	module	module	NOUN
brj-24683	338	8	mitigates	mitigate	VERB
brj-24683	338	9	fine	fine	ADV
brj-24683	338	10	-	-	PUNCT
brj-24683	338	11	grained	grain	VERB
brj-24683	338	12	detail	detail	NOUN
brj-24683	338	13	loss	loss	NOUN
brj-24683	338	14	during	during	ADP
brj-24683	338	15	feature	feature	NOUN
brj-24683	338	16	fusion	fusion	NOUN
brj-24683	338	17	,	,	PUNCT
brj-24683	338	18	and	and	CCONJ
brj-24683	338	19	the	the	DET
brj-24683	338	20	repblock	repblock	NOUN
brj-24683	338	21	module	module	NOUN
brj-24683	338	22	strengthens	strengthen	VERB
brj-24683	338	23	the	the	DET
brj-24683	338	24	extraction	extraction	NOUN
brj-24683	338	25	of	of	ADP
brj-24683	338	26	multi	multi	ADJ
brj-24683	338	27	-	-	ADJ
brj-24683	338	28	scale	scale	ADJ
brj-24683	338	29	defect	defect	NOUN
brj-24683	338	30	features	feature	NOUN
brj-24683	338	31	.	.	PUNCT
brj-24683	339	1	additionally	additionally	ADV
brj-24683	339	2	,	,	PUNCT
brj-24683	339	3	a	a	DET
brj-24683	339	4	small	small	ADJ
brj-24683	339	5	-	-	PUNCT
brj-24683	339	6	object	object	NOUN
brj-24683	339	7	detection	detection	NOUN
brj-24683	339	8	head	head	NOUN
brj-24683	339	9	improves	improve	VERB
brj-24683	339	10	detection	detection	NOUN
brj-24683	339	11	accuracy	accuracy	NOUN
brj-24683	339	12	for	for	ADP
brj-24683	339	13	small	small	ADJ
brj-24683	339	14	defects	defect	NOUN
brj-24683	339	15	.	.	PUNCT
brj-24683	340	1	1	1	X
brj-24683	340	2	.	.	X
brj-24683	340	3	ablation	ablation	NOUN
brj-24683	340	4	experiments	experiment	NOUN
brj-24683	340	5	demonstrate	demonstrate	VERB
brj-24683	340	6	that	that	SCONJ
brj-24683	340	7	the	the	DET
brj-24683	340	8	integration	integration	NOUN
brj-24683	340	9	of	of	ADP
brj-24683	340	10	different	different	ADJ
brj-24683	340	11	enhanced	enhance	VERB
brj-24683	340	12	modules	module	NOUN
brj-24683	340	13	leads	lead	VERB
brj-24683	340	14	to	to	ADP
brj-24683	340	15	statistically	statistically	ADV
brj-24683	340	16	significant	significant	ADJ
brj-24683	340	17	improvements	improvement	NOUN
brj-24683	340	18	in	in	ADP
brj-24683	340	19	the	the	DET
brj-24683	340	20	detection	detection	NOUN
brj-24683	340	21	accuracy	accuracy	NOUN
brj-24683	340	22	of	of	ADP
brj-24683	340	23	the	the	DET
brj-24683	340	24	baseline	baseline	NOUN
brj-24683	340	25	model	model	NOUN
brj-24683	340	26	,	,	PUNCT
brj-24683	340	27	with	with	ADP
brj-24683	340	28	varying	vary	VERB
brj-24683	340	29	degrees	degree	NOUN
brj-24683	340	30	of	of	ADP
brj-24683	340	31	enhancement	enhancement	NOUN
brj-24683	340	32	observed	observe	VERB
brj-24683	340	33	across	across	ADP
brj-24683	340	34	the	the	DET
brj-24683	340	35	modules	module	NOUN
brj-24683	340	36	(	(	PUNCT
brj-24683	340	37	e.g.	e.g.	ADV
brj-24683	340	38	,	,	PUNCT
brj-24683	340	39	+2.1	+2.1	ADJ
brj-24683	340	40	%	%	NOUN
brj-24683	340	41	map	map	NOUN
brj-24683	340	42	with	with	ADP
brj-24683	340	43	c2f	c2f	NOUN
brj-24683	340	44	-	-	PUNCT
brj-24683	340	45	mmsa	mmsa	NOUN
brj-24683	340	46	,	,	PUNCT
brj-24683	340	47	+1.8	+1.8	NUM
brj-24683	340	48	%	%	NOUN
brj-24683	340	49	map	map	NOUN
brj-24683	340	50	with	with	ADP
brj-24683	340	51	dysample	dysample	PROPN
brj-24683	340	52	,	,	PUNCT
brj-24683	340	53	+2.5	+2.5	ADJ
brj-24683	340	54	%	%	NOUN
brj-24683	340	55	map	map	NOUN
brj-24683	340	56	with	with	ADP
brj-24683	340	57	repblock	repblock	NOUN
brj-24683	340	58	)	)	PUNCT
brj-24683	340	59	.	.	PUNCT
brj-24683	341	1	2	2	X
brj-24683	341	2	.	.	X
brj-24683	341	3	comparison	comparison	NOUN
brj-24683	341	4	experiments	experiment	NOUN
brj-24683	341	5	further	far	ADV
brj-24683	341	6	reveal	reveal	VERB
brj-24683	341	7	that	that	SCONJ
brj-24683	341	8	,	,	PUNCT
brj-24683	341	9	compared	compare	VERB
brj-24683	341	10	to	to	ADP
brj-24683	341	11	the	the	DET
brj-24683	341	12	baseline	baseline	PROPN
brj-24683	341	13	model	model	NOUN
brj-24683	341	14	,	,	PUNCT
brj-24683	341	15	the	the	DET
brj-24683	341	16	proposed	propose	VERB
brj-24683	341	17	method	method	NOUN
brj-24683	341	18	achieves	achieve	VERB
brj-24683	341	19	improved	improve	VERB
brj-24683	341	20	ap	ap	PROPN
brj-24683	341	21	values	value	NOUN
brj-24683	341	22	across	across	ADP
brj-24683	341	23	all	all	DET
brj-24683	341	24	defect	defect	ADJ
brj-24683	341	25	categories	category	NOUN
brj-24683	341	26	except	except	SCONJ
brj-24683	341	27	peer	peer	NOUN
brj-24683	341	28	-	-	PUNCT
brj-24683	341	29	reviewed	review	VERB
brj-24683	341	30	article	article	NOUN
brj-24683	341	31	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	341	32	dou	dou	PROPN
brj-24683	341	33	&	&	CCONJ
brj-24683	341	34	you	you	PRON
brj-24683	341	35	(	(	PUNCT
brj-24683	341	36	2025	2025	NUM
brj-24683	341	37	)	)	PUNCT
brj-24683	341	38	.	.	PUNCT
brj-24683	342	1	“	"	PUNCT
brj-24683	342	2	wood	wood	NOUN
brj-24683	342	3	defect	defect	NOUN
brj-24683	342	4	identification	identification	NOUN
brj-24683	342	5	,	,	PUNCT
brj-24683	342	6	”	"	PUNCT
brj-24683	342	7	bioresources	bioresource	NOUN
brj-24683	342	8	20(3	20(3	NOUN
brj-24683	342	9	)	)	PUNCT
brj-24683	342	10	,	,	PUNCT
brj-24683	342	11	5709	5709	NUM
brj-24683	342	12	-	-	SYM
brj-24683	342	13	5730	5730	NUM
brj-24683	342	14	.	.	PUNCT
brj-24683	343	1	5727	5727	NUM
brj-24683	343	2	knot_missing	knot_misse	VERB
brj-24683	343	3	.	.	PUNCT
brj-24683	344	1	similarly	similarly	ADV
brj-24683	344	2	,	,	PUNCT
brj-24683	344	3	when	when	SCONJ
brj-24683	344	4	compared	compare	VERB
brj-24683	344	5	to	to	ADP
brj-24683	344	6	yolo	yolo	ADJ
brj-24683	344	7	variants	variant	NOUN
brj-24683	344	8	(	(	PUNCT
brj-24683	344	9	v5	v5	PROPN
brj-24683	344	10	,	,	PUNCT
brj-24683	344	11	v7	v7	NUM
brj-24683	344	12	,	,	PUNCT
brj-24683	344	13	v9	v9	NOUN
brj-24683	344	14	,	,	PUNCT
brj-24683	344	15	v10	v10	NOUN
brj-24683	344	16	,	,	PUNCT
brj-24683	344	17	11	11	NUM
brj-24683	344	18	,	,	PUNCT
brj-24683	344	19	and	and	CCONJ
brj-24683	344	20	v12	v12	VERB
brj-24683	344	21	)	)	PUNCT
brj-24683	344	22	,	,	PUNCT
brj-24683	344	23	the	the	DET
brj-24683	344	24	proposed	propose	VERB
brj-24683	344	25	model	model	NOUN
brj-24683	344	26	generally	generally	ADV
brj-24683	344	27	outperforms	outperform	VERB
brj-24683	344	28	them	they	PRON
brj-24683	344	29	in	in	ADP
brj-24683	344	30	most	most	ADJ
brj-24683	344	31	defect	defect	ADJ
brj-24683	344	32	categories	category	NOUN
brj-24683	344	33	.	.	PUNCT
brj-24683	345	1	however	however	ADV
brj-24683	345	2	,	,	PUNCT
brj-24683	345	3	for	for	ADP
brj-24683	345	4	crack	crack	NOUN
brj-24683	345	5	,	,	PUNCT
brj-24683	345	6	yolov9	yolov9	PROPN
brj-24683	345	7	attains	attain	VERB
brj-24683	345	8	the	the	DET
brj-24683	345	9	highest	high	ADJ
brj-24683	345	10	ap	ap	NOUN
brj-24683	345	11	value	value	NOUN
brj-24683	345	12	,	,	PUNCT
brj-24683	345	13	while	while	SCONJ
brj-24683	345	14	for	for	ADP
brj-24683	345	15	knot_missing	knot_missing	NOUN
brj-24683	345	16	,	,	PUNCT
brj-24683	345	17	yolov5	yolov5	NOUN
brj-24683	345	18	performs	perform	VERB
brj-24683	345	19	best	good	ADJ
brj-24683	345	20	.	.	PUNCT
brj-24683	346	1	3	3	X
brj-24683	346	2	.	.	X
brj-24683	346	3	visualization	visualization	NOUN
brj-24683	346	4	results	result	NOUN
brj-24683	346	5	further	far	ADV
brj-24683	346	6	confirm	confirm	VERB
brj-24683	346	7	that	that	SCONJ
brj-24683	346	8	the	the	DET
brj-24683	346	9	proposed	propose	VERB
brj-24683	346	10	method	method	NOUN
brj-24683	346	11	effectively	effectively	ADV
brj-24683	346	12	reduces	reduce	VERB
brj-24683	346	13	both	both	CCONJ
brj-24683	346	14	missed	miss	VERB
brj-24683	346	15	and	and	CCONJ
brj-24683	346	16	incorrect	incorrect	ADJ
brj-24683	346	17	detections	detection	NOUN
brj-24683	346	18	,	,	PUNCT
brj-24683	346	19	ensuring	ensure	VERB
brj-24683	346	20	more	more	ADV
brj-24683	346	21	accurate	accurate	ADJ
brj-24683	346	22	and	and	CCONJ
brj-24683	346	23	reliable	reliable	ADJ
brj-24683	346	24	defect	defect	NOUN
brj-24683	346	25	identification	identification	NOUN
brj-24683	346	26	under	under	ADP
brj-24683	346	27	complex	complex	ADJ
brj-24683	346	28	wood	wood	NOUN
brj-24683	346	29	surface	surface	NOUN
brj-24683	346	30	conditions	condition	NOUN
brj-24683	346	31	.	.	PUNCT
brj-24683	347	1	in	in	ADP
brj-24683	347	2	summary	summary	NOUN
brj-24683	347	3	,	,	PUNCT
brj-24683	347	4	the	the	DET
brj-24683	347	5	proposed	propose	VERB
brj-24683	347	6	method	method	NOUN
brj-24683	347	7	effectively	effectively	ADV
brj-24683	347	8	addresses	address	VERB
brj-24683	347	9	the	the	DET
brj-24683	347	10	challenges	challenge	NOUN
brj-24683	347	11	of	of	ADP
brj-24683	347	12	detecting	detect	VERB
brj-24683	347	13	small	small	ADJ
brj-24683	347	14	-	-	PUNCT
brj-24683	347	15	target	target	NOUN
brj-24683	347	16	features	feature	NOUN
brj-24683	347	17	in	in	ADP
brj-24683	347	18	complex	complex	ADJ
brj-24683	347	19	backgrounds	background	NOUN
brj-24683	347	20	for	for	ADP
brj-24683	347	21	wood	wood	NOUN
brj-24683	347	22	surface	surface	NOUN
brj-24683	347	23	defect	defect	NOUN
brj-24683	347	24	detection	detection	NOUN
brj-24683	347	25	.	.	PUNCT
brj-24683	348	1	it	it	PRON
brj-24683	348	2	enhances	enhance	VERB
brj-24683	348	3	detection	detection	NOUN
brj-24683	348	4	accuracy	accuracy	NOUN
brj-24683	348	5	and	and	CCONJ
brj-24683	348	6	reliability	reliability	NOUN
brj-24683	348	7	,	,	PUNCT
brj-24683	348	8	fulfilling	fulfil	VERB
brj-24683	348	9	the	the	DET
brj-24683	348	10	practical	practical	ADJ
brj-24683	348	11	requirements	requirement	NOUN
brj-24683	348	12	of	of	ADP
brj-24683	348	13	wood	wood	NOUN
brj-24683	348	14	surface	surface	NOUN
brj-24683	348	15	defect	defect	NOUN
brj-24683	348	16	detection	detection	NOUN
brj-24683	348	17	.	.	PUNCT
brj-24683	349	1	future	future	ADJ
brj-24683	349	2	work	work	NOUN
brj-24683	349	3	will	will	AUX
brj-24683	349	4	explore	explore	VERB
brj-24683	349	5	adaptive	adaptive	ADJ
brj-24683	349	6	training	training	NOUN
brj-24683	349	7	strategies	strategy	NOUN
brj-24683	349	8	and	and	CCONJ
brj-24683	349	9	lightweight	lightweight	ADJ
brj-24683	349	10	deployment	deployment	NOUN
brj-24683	349	11	frameworks	framework	NOUN
brj-24683	349	12	to	to	PART
brj-24683	349	13	further	far	ADV
brj-24683	349	14	improve	improve	VERB
brj-24683	349	15	performance	performance	NOUN
brj-24683	349	16	in	in	ADP
brj-24683	349	17	in	in	ADP
brj-24683	349	18	different	different	ADJ
brj-24683	349	19	scenarios	scenario	NOUN
brj-24683	349	20	of	of	ADP
brj-24683	349	21	wood	wood	NOUN
brj-24683	349	22	production	production	NOUN
brj-24683	349	23	.	.	PUNCT
brj-24683	350	1	acknowledgments	acknowledgment	NOUN
brj-24683	350	2	this	this	DET
brj-24683	350	3	research	research	NOUN
brj-24683	350	4	was	be	AUX
brj-24683	350	5	financially	financially	ADV
brj-24683	350	6	supported	support	VERB
brj-24683	350	7	by	by	ADP
brj-24683	350	8	the	the	DET
brj-24683	350	9	science	science	NOUN
brj-24683	350	10	and	and	CCONJ
brj-24683	350	11	technology	technology	NOUN
brj-24683	350	12	planning	planning	NOUN
brj-24683	350	13	project	project	NOUN
brj-24683	350	14	of	of	ADP
brj-24683	350	15	guangxi	guangxi	PROPN
brj-24683	350	16	province	province	PROPN
brj-24683	350	17	,	,	PUNCT
brj-24683	350	18	china	china	PROPN
brj-24683	350	19	(	(	PUNCT
brj-24683	350	20	no	no	INTJ
brj-24683	350	21	.	.	NOUN
brj-24683	350	22	2022ac21012	2022ac21012	NUM
brj-24683	350	23	)	)	PUNCT
brj-24683	350	24	and	and	CCONJ
brj-24683	350	25	the	the	DET
brj-24683	350	26	industry	industry	NOUN
brj-24683	350	27	-	-	PUNCT
brj-24683	350	28	universityresearch	universityresearch	NOUN
brj-24683	350	29	innovation	innovation	NOUN
brj-24683	350	30	fund	fund	NOUN
brj-24683	350	31	projects	project	NOUN
brj-24683	350	32	of	of	ADP
brj-24683	350	33	china	china	PROPN
brj-24683	350	34	university	university	PROPN
brj-24683	350	35	in	in	ADP
brj-24683	350	36	2021	2021	NUM
brj-24683	350	37	(	(	PUNCT
brj-24683	350	38	no	no	INTJ
brj-24683	350	39	.	.	PUNCT
brj-24683	351	1	2021ita10018	2021ita10018	NUM
brj-24683	351	2	)	)	PUNCT
brj-24683	351	3	.	.	PUNCT
brj-24683	352	1	author	author	NOUN
brj-24683	352	2	contributions	contribution	VERB
brj-24683	352	3	w.d	w.d	PROPN
brj-24683	352	4	.	.	PUNCT
brj-24683	352	5	:	:	PUNCT
brj-24683	352	6	writing—-original	writing—-original	ADJ
brj-24683	352	7	draft	draft	NOUN
brj-24683	352	8	,	,	PUNCT
brj-24683	352	9	investigation	investigation	NOUN
brj-24683	352	10	,	,	PUNCT
brj-24683	352	11	software	software	NOUN
brj-24683	352	12	,	,	PUNCT
brj-24683	352	13	methodology	methodology	NOUN
brj-24683	352	14	.	.	PUNCT
brj-24683	353	1	j	j	PROPN
brj-24683	353	2	y	y	NOUN
brj-24683	353	3	:	:	PUNCT
brj-24683	353	4	conceptualization	conceptualization	NOUN
brj-24683	353	5	,	,	PUNCT
brj-24683	353	6	writing	writing	NOUN
brj-24683	353	7	—	—	PUNCT
brj-24683	353	8	review	review	NOUN
brj-24683	353	9	and	and	CCONJ
brj-24683	353	10	&	&	CCONJ
brj-24683	353	11	editing	editing	NOUN
brj-24683	353	12	,	,	PUNCT
brj-24683	353	13	supervision	supervision	NOUN
brj-24683	353	14	,	,	PUNCT
brj-24683	353	15	data	data	NOUN
brj-24683	353	16	curation	curation	NOUN
brj-24683	353	17	.	.	PUNCT
brj-24683	354	1	all	all	DET
brj-24683	354	2	authors	author	NOUN
brj-24683	354	3	have	have	AUX
brj-24683	354	4	read	read	VERB
brj-24683	354	5	and	and	CCONJ
brj-24683	354	6	agreed	agree	VERB
brj-24683	354	7	to	to	ADP
brj-24683	354	8	the	the	DET
brj-24683	354	9	published	publish	VERB
brj-24683	354	10	version	version	NOUN
brj-24683	354	11	of	of	ADP
brj-24683	354	12	the	the	DET
brj-24683	354	13	manuscript	manuscript	NOUN
brj-24683	354	14	.	.	PUNCT
brj-24683	355	1	data	datum	NOUN
brj-24683	355	2	availability	availability	NOUN
brj-24683	355	3	statement	statement	NOUN
brj-24683	355	4	data	datum	NOUN
brj-24683	355	5	will	will	AUX
brj-24683	355	6	be	be	AUX
brj-24683	355	7	made	make	VERB
brj-24683	355	8	available	available	ADJ
brj-24683	355	9	on	on	ADP
brj-24683	355	10	request	request	NOUN
brj-24683	355	11	.	.	PUNCT
brj-24683	356	1	conflicts	conflict	NOUN
brj-24683	356	2	of	of	ADP
brj-24683	356	3	interest	interest	NOUN
brj-24683	356	4	the	the	DET
brj-24683	356	5	authors	author	NOUN
brj-24683	356	6	declare	declare	VERB
brj-24683	356	7	no	no	DET
brj-24683	356	8	conflicts	conflict	NOUN
brj-24683	356	9	of	of	ADP
brj-24683	356	10	interest	interest	NOUN
brj-24683	356	11	.	.	PUNCT
brj-24683	357	1	references	reference	NOUN
brj-24683	357	2	cited	cite	VERB
brj-24683	357	3	chen	chen	PROPN
brj-24683	357	4	,	,	PUNCT
brj-24683	357	5	w.	w.	PROPN
brj-24683	357	6	,	,	PUNCT
brj-24683	357	7	liu	liu	PROPN
brj-24683	357	8	,	,	PUNCT
brj-24683	357	9	j.	j.	PROPN
brj-24683	357	10	,	,	PUNCT
brj-24683	357	11	fang	fang	PROPN
brj-24683	357	12	,	,	PUNCT
brj-24683	357	13	y.	y.	NOUN
brj-24683	357	14	,	,	PUNCT
brj-24683	357	15	and	and	CCONJ
brj-24683	357	16	zhao	zhao	PROPN
brj-24683	357	17	,	,	PUNCT
brj-24683	357	18	j.	j.	PROPN
brj-24683	357	19	(	(	PUNCT
brj-24683	357	20	2023a	2023a	NUM
brj-24683	357	21	)	)	PUNCT
brj-24683	357	22	.	.	PUNCT
brj-24683	358	1	“	"	PUNCT
brj-24683	358	2	timber	timber	NOUN
brj-24683	358	3	knot	knot	NOUN
brj-24683	358	4	detector	detector	NOUN
brj-24683	358	5	with	with	ADP
brj-24683	358	6	low	low	ADJ
brj-24683	358	7	falsepositive	falsepositive	ADJ
brj-24683	358	8	results	result	NOUN
brj-24683	358	9	by	by	ADP
brj-24683	358	10	integrating	integrate	VERB
brj-24683	358	11	an	an	DET
brj-24683	358	12	overlapping	overlap	VERB
brj-24683	358	13	bounding	bounding	NOUN
brj-24683	358	14	box	box	NOUN
brj-24683	358	15	filter	filter	NOUN
brj-24683	358	16	with	with	ADP
brj-24683	358	17	faster	fast	ADJ
brj-24683	358	18	r	r	NOUN
brj-24683	358	19	-	-	PUNCT
brj-24683	358	20	cnn	cnn	PROPN
brj-24683	358	21	algorithm	algorithm	NOUN
brj-24683	358	22	,	,	PUNCT
brj-24683	358	23	”	"	PUNCT
brj-24683	358	24	bioresources	bioresource	NOUN
brj-24683	358	25	18(3	18(3	NUM
brj-24683	358	26	)	)	PUNCT
brj-24683	358	27	,	,	PUNCT
brj-24683	358	28	4964	4964	NUM
brj-24683	358	29	-	-	SYM
brj-24683	358	30	4976	4976	NUM
brj-24683	358	31	.	.	PUNCT
brj-24683	359	1	doi	doi	NOUN
brj-24683	359	2	:	:	PUNCT
brj-24683	359	3	10.15376	10.15376	NUM
brj-24683	359	4	/	/	SYM
brj-24683	359	5	biores.18.3.4964	biores.18.3.4964	PROPN
brj-24683	359	6	-	-	PUNCT
brj-24683	359	7	4976	4976	NUM
brj-24683	359	8	chen	chen	PROPN
brj-24683	359	9	,	,	PUNCT
brj-24683	359	10	y.	y.	PROPN
brj-24683	359	11	,	,	PUNCT
brj-24683	359	12	sun	sun	PROPN
brj-24683	359	13	,	,	PUNCT
brj-24683	359	14	c.	c.	PROPN
brj-24683	359	15	,	,	PUNCT
brj-24683	359	16	ren	ren	PROPN
brj-24683	359	17	,	,	PUNCT
brj-24683	359	18	z.	z.	PROPN
brj-24683	359	19	,	,	PUNCT
brj-24683	359	20	and	and	CCONJ
brj-24683	359	21	na	na	ADP
brj-24683	359	22	,	,	PUNCT
brj-24683	359	23	b.	b.	PROPN
brj-24683	359	24	(	(	PUNCT
brj-24683	359	25	2023b	2023b	NUM
brj-24683	359	26	)	)	PUNCT
brj-24683	359	27	.	.	PUNCT
brj-24683	360	1	“	"	PUNCT
brj-24683	360	2	review	review	NOUN
brj-24683	360	3	of	of	ADP
brj-24683	360	4	the	the	DET
brj-24683	360	5	current	current	ADJ
brj-24683	360	6	state	state	NOUN
brj-24683	360	7	of	of	ADP
brj-24683	360	8	application	application	NOUN
brj-24683	360	9	of	of	ADP
brj-24683	360	10	wood	wood	NOUN
brj-24683	360	11	defect	defect	NOUN
brj-24683	360	12	recognition	recognition	NOUN
brj-24683	360	13	technology	technology	NOUN
brj-24683	360	14	,	,	PUNCT
brj-24683	360	15	”	"	PUNCT
brj-24683	360	16	bioresources	bioresource	NOUN
brj-24683	360	17	18(1	18(1	NOUN
brj-24683	360	18	)	)	PUNCT
brj-24683	360	19	,	,	PUNCT
brj-24683	360	20	2288	2288	NUM
brj-24683	360	21	-	-	SYM
brj-24683	360	22	2302	2302	NUM
brj-24683	360	23	.	.	PUNCT
brj-24683	361	1	doi	doi	NOUN
brj-24683	361	2	:	:	PUNCT
brj-24683	361	3	10.15376	10.15376	NUM
brj-24683	361	4	/	/	SYM
brj-24683	361	5	biores.18.1.2288	biores.18.1.2288	PROPN
brj-24683	361	6	-	-	SYM
brj-24683	361	7	2302	2302	NUM
brj-24683	361	8	conners	conner	NOUN
brj-24683	361	9	,	,	PUNCT
brj-24683	361	10	r.	r.	PROPN
brj-24683	361	11	w.	w.	PROPN
brj-24683	361	12	,	,	PUNCT
brj-24683	361	13	mcmillin	mcmillin	PROPN
brj-24683	361	14	,	,	PUNCT
brj-24683	361	15	c.	c.	PROPN
brj-24683	361	16	w.	w.	PROPN
brj-24683	361	17	,	,	PUNCT
brj-24683	361	18	lin	lin	PROPN
brj-24683	361	19	,	,	PUNCT
brj-24683	361	20	k.	k.	PROPN
brj-24683	361	21	,	,	PUNCT
brj-24683	361	22	and	and	CCONJ
brj-24683	361	23	ramon	ramon	NOUN
brj-24683	361	24	,	,	PUNCT
brj-24683	361	25	e.	e.	PROPN
brj-24683	361	26	(	(	PUNCT
brj-24683	361	27	1983	1983	NUM
brj-24683	361	28	)	)	PUNCT
brj-24683	361	29	.	.	PUNCT
brj-24683	362	1	“	"	PUNCT
brj-24683	362	2	identifying	identify	VERB
brj-24683	362	3	and	and	CCONJ
brj-24683	362	4	locating	locate	VERB
brj-24683	362	5	surface	surface	NOUN
brj-24683	362	6	defects	defect	NOUN
brj-24683	362	7	in	in	ADP
brj-24683	362	8	wood	wood	NOUN
brj-24683	362	9	:	:	PUNCT
brj-24683	362	10	part	part	NOUN
brj-24683	362	11	of	of	ADP
brj-24683	362	12	an	an	DET
brj-24683	362	13	automated	automate	VERB
brj-24683	362	14	lumber	lumber	NOUN
brj-24683	362	15	processing	processing	NOUN
brj-24683	362	16	system	system	NOUN
brj-24683	362	17	,	,	PUNCT
brj-24683	362	18	”	"	PUNCT
brj-24683	362	19	ieee	ieee	NOUN
brj-24683	362	20	transactions	transaction	NOUN
brj-24683	362	21	on	on	ADP
brj-24683	362	22	pattern	pattern	NOUN
brj-24683	362	23	analysis	analysis	NOUN
brj-24683	362	24	and	and	CCONJ
brj-24683	362	25	machine	machine	NOUN
brj-24683	362	26	intelligence	intelligence	NOUN
brj-24683	362	27	6	6	NUM
brj-24683	362	28	,	,	PUNCT
brj-24683	362	29	573	573	NUM
brj-24683	362	30	-	-	SYM
brj-24683	362	31	583	583	NUM
brj-24683	362	32	.	.	PUNCT
brj-24683	363	1	doi	doi	NOUN
brj-24683	363	2	:	:	PUNCT
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brj-24683	363	7	,	,	PUNCT
brj-24683	363	8	f.	f.	PROPN
brj-24683	363	9	,	,	PUNCT
brj-24683	363	10	zhuang	zhuang	PROPN
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brj-24683	363	12	z.	z.	PROPN
brj-24683	363	13	,	,	PUNCT
brj-24683	363	14	liu	liu	PROPN
brj-24683	363	15	,	,	PUNCT
brj-24683	363	16	y.	y.	PROPN
brj-24683	363	17	,	,	PUNCT
brj-24683	363	18	jiang	jiang	PROPN
brj-24683	363	19	,	,	PUNCT
brj-24683	363	20	d.	d.	PROPN
brj-24683	363	21	,	,	PUNCT
brj-24683	363	22	yan	yan	PROPN
brj-24683	363	23	,	,	PUNCT
brj-24683	363	24	x.	x.	NOUN
brj-24683	363	25	,	,	PUNCT
brj-24683	363	26	and	and	CCONJ
brj-24683	363	27	wang	wang	PROPN
brj-24683	363	28	,	,	PUNCT
brj-24683	363	29	z.	z.	PROPN
brj-24683	363	30	(	(	PUNCT
brj-24683	363	31	2020	2020	NUM
brj-24683	363	32	)	)	PUNCT
brj-24683	363	33	.	.	PUNCT
brj-24683	364	1	“	"	PUNCT
brj-24683	364	2	detecting	detect	VERB
brj-24683	364	3	defects	defect	NOUN
brj-24683	364	4	on	on	ADP
brj-24683	364	5	solid	solid	ADJ
brj-24683	364	6	wood	wood	NOUN
brj-24683	364	7	panels	panel	NOUN
brj-24683	364	8	based	base	VERB
brj-24683	364	9	on	on	ADP
brj-24683	364	10	an	an	DET
brj-24683	364	11	improved	improved	ADJ
brj-24683	364	12	ssd	ssd	NOUN
brj-24683	364	13	algorithm	algorithm	NOUN
brj-24683	364	14	,	,	PUNCT
brj-24683	364	15	”	"	PUNCT
brj-24683	364	16	sensors	sensor	NOUN
brj-24683	364	17	20	20	NUM
brj-24683	364	18	,	,	PUNCT
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brj-24683	364	20	https://doi.org/10.15376/biores.18.1.2288-2302	https://doi.org/10.15376/biores.18.1.2288-2302	PROPN
brj-24683	364	21	https://doi.org/10.1109/tpami.1983.4767446	https://doi.org/10.1109/tpami.1983.4767446	PROPN
brj-24683	364	22	peer	peer	NOUN
brj-24683	364	23	-	-	PUNCT
brj-24683	364	24	reviewed	review	VERB
brj-24683	364	25	article	article	NOUN
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brj-24683	364	27	dou	dou	PROPN
brj-24683	364	28	&	&	CCONJ
brj-24683	364	29	you	you	PRON
brj-24683	364	30	(	(	PUNCT
brj-24683	364	31	2025	2025	NUM
brj-24683	364	32	)	)	PUNCT
brj-24683	364	33	.	.	PUNCT
brj-24683	365	1	“	"	PUNCT
brj-24683	365	2	wood	wood	NOUN
brj-24683	365	3	defect	defect	NOUN
brj-24683	365	4	identification	identification	NOUN
brj-24683	365	5	,	,	PUNCT
brj-24683	365	6	”	"	PUNCT
brj-24683	365	7	bioresources	bioresource	NOUN
brj-24683	365	8	20(3	20(3	NOUN
brj-24683	365	9	)	)	PUNCT
brj-24683	365	10	,	,	PUNCT
brj-24683	365	11	5709	5709	NUM
brj-24683	365	12	-	-	SYM
brj-24683	365	13	5730	5730	NUM
brj-24683	365	14	.	.	PUNCT
brj-24683	366	1	5728	5728	NUM
brj-24683	366	2	5315	5315	NUM
brj-24683	366	3	.	.	PUNCT
brj-24683	367	1	doi	doi	NOUN
brj-24683	367	2	:	:	PUNCT
brj-24683	367	3	10.3390	10.3390	NUM
brj-24683	367	4	/	/	SYM
brj-24683	367	5	s20185315	s20185315	PROPN
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brj-24683	367	7	j.	j.	PROPN
brj-24683	367	8	,	,	PUNCT
brj-24683	367	9	liu	liu	PROPN
brj-24683	367	10	,	,	PUNCT
brj-24683	367	11	y.	y.	PROPN
brj-24683	367	12	,	,	PUNCT
brj-24683	367	13	hu	hu	PROPN
brj-24683	367	14	z.	z.	PROPN
brj-24683	367	15	,	,	PUNCT
brj-24683	367	16	h	h	PROPN
brj-24683	367	17	,	,	PUNCT
brj-24683	367	18	zhao	zhao	PROPN
brj-24683	367	19	,	,	PUNCT
brj-24683	367	20	qian	qian	PROPN
brj-24683	367	21	.	.	PROPN
brj-24683	367	22	,	,	PUNCT
brj-24683	367	23	shen	shen	PROPN
brj-24683	367	24	,	,	PUNCT
brj-24683	367	25	l.	l.	PROPN
brj-24683	367	26	,	,	PUNCT
brj-24683	367	27	and	and	CCONJ
brj-24683	367	28	zhou	zhou	PROPN
brj-24683	367	29	,	,	PUNCT
brj-24683	367	30	x.	x.	NOUN
brj-24683	367	31	(	(	PUNCT
brj-24683	367	32	2019	2019	NUM
brj-24683	367	33	)	)	PUNCT
brj-24683	367	34	.	.	PUNCT
brj-24683	368	1	“	"	PUNCT
brj-24683	368	2	solid	solid	ADJ
brj-24683	368	3	wood	wood	NOUN
brj-24683	368	4	panel	panel	NOUN
brj-24683	368	5	defect	defect	NOUN
brj-24683	368	6	detection	detection	NOUN
brj-24683	368	7	and	and	CCONJ
brj-24683	368	8	recognition	recognition	NOUN
brj-24683	368	9	system	system	NOUN
brj-24683	368	10	based	base	VERB
brj-24683	368	11	on	on	ADP
brj-24683	368	12	faster	fast	ADJ
brj-24683	368	13	r	r	NOUN
brj-24683	368	14	-	-	PUNCT
brj-24683	368	15	cnn	cnn	PROPN
brj-24683	368	16	,	,	PUNCT
brj-24683	368	17	”	"	PUNCT
brj-24683	368	18	journal	journal	NOUN
brj-24683	368	19	of	of	ADP
brj-24683	368	20	forestry	forestry	NOUN
brj-24683	368	21	engineering	engineering	PROPN
brj-24683	368	22	4(03	4(03	NUM
brj-24683	368	23	)	)	PUNCT
brj-24683	368	24	,	,	PUNCT
brj-24683	368	25	112	112	NUM
brj-24683	368	26	-	-	SYM
brj-24683	368	27	117	117	NUM
brj-24683	368	28	.	.	PUNCT
brj-24683	368	29	doi	doi	NOUN
brj-24683	368	30	:	:	PUNCT
brj-24683	368	31	10.13360	10.13360	NUM
brj-24683	368	32	/	/	SYM
brj-24683	368	33	j.issn.2096	j.issn.2096	NOUN
brj-24683	368	34	-	-	PUNCT
brj-24683	368	35	1359.2019.03.017	1359.2019.03.017	NUM
brj-24683	368	36	gao	gao	PROPN
brj-24683	368	37	,	,	PUNCT
brj-24683	368	38	m.	m.	NOUN
brj-24683	368	39	,	,	PUNCT
brj-24683	368	40	qi	qi	PROPN
brj-24683	368	41	,	,	PUNCT
brj-24683	368	42	d.	d.	PROPN
brj-24683	368	43	,	,	PUNCT
brj-24683	368	44	mu	mu	PROPN
brj-24683	368	45	,	,	PUNCT
brj-24683	368	46	h.	h.	PROPN
brj-24683	368	47	,	,	PUNCT
brj-24683	368	48	and	and	CCONJ
brj-24683	368	49	chen	chen	PROPN
brj-24683	368	50	,	,	PUNCT
brj-24683	368	51	j.	j.	PROPN
brj-24683	368	52	(	(	PUNCT
brj-24683	368	53	2021	2021	NUM
brj-24683	368	54	)	)	PUNCT
brj-24683	368	55	.	.	PUNCT
brj-24683	369	1	“	"	PUNCT
brj-24683	369	2	a	a	DET
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brj-24683	369	4	residual	residual	ADJ
brj-24683	369	5	neural	neural	ADJ
brj-24683	369	6	network	network	NOUN
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brj-24683	369	8	on	on	ADP
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brj-24683	369	10	for	for	ADP
brj-24683	369	11	detection	detection	NOUN
brj-24683	369	12	of	of	ADP
brj-24683	369	13	wood	wood	NOUN
brj-24683	369	14	knot	knot	NOUN
brj-24683	369	15	defects	defect	NOUN
brj-24683	369	16	,	,	PUNCT
brj-24683	369	17	”	"	PUNCT
brj-24683	369	18	forests	forest	VERB
brj-24683	369	19	12(2	12(2	NUM
brj-24683	369	20	)	)	PUNCT
brj-24683	369	21	,	,	PUNCT
brj-24683	369	22	article	article	NOUN
brj-24683	369	23	212	212	NUM
brj-24683	369	24	.	.	PUNCT
brj-24683	370	1	doi	doi	NOUN
brj-24683	370	2	:	:	PUNCT
brj-24683	370	3	10.3390	10.3390	NUM
brj-24683	370	4	/	/	SYM
brj-24683	370	5	f12020212	f12020212	NOUN
brj-24683	370	6	girshick	girshick	NOUN
brj-24683	370	7	,	,	PUNCT
brj-24683	370	8	r.	r.	PROPN
brj-24683	370	9	,	,	PUNCT
brj-24683	370	10	donahue	donahue	PROPN
brj-24683	370	11	,	,	PUNCT
brj-24683	370	12	j.	j.	PROPN
brj-24683	370	13	,	,	PUNCT
brj-24683	370	14	darrell	darrell	PROPN
brj-24683	370	15	,	,	PUNCT
brj-24683	370	16	t.	t.	PROPN
brj-24683	370	17	,	,	PUNCT
brj-24683	370	18	and	and	CCONJ
brj-24683	370	19	malik	malik	PROPN
brj-24683	370	20	,	,	PUNCT
brj-24683	370	21	j.	j.	PROPN
brj-24683	370	22	(	(	PUNCT
brj-24683	370	23	2014	2014	NUM
brj-24683	370	24	)	)	PUNCT
brj-24683	370	25	.	.	PUNCT
brj-24683	371	1	“	"	PUNCT
brj-24683	371	2	rich	rich	ADJ
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brj-24683	371	4	hierarchies	hierarchy	NOUN
brj-24683	371	5	for	for	ADP
brj-24683	371	6	accurate	accurate	ADJ
brj-24683	371	7	object	object	NOUN
brj-24683	371	8	detection	detection	NOUN
brj-24683	371	9	and	and	CCONJ
brj-24683	371	10	semantic	semantic	ADJ
brj-24683	371	11	segmentation	segmentation	NOUN
brj-24683	371	12	,	,	PUNCT
brj-24683	371	13	”	"	PUNCT
brj-24683	371	14	in	in	ADP
brj-24683	371	15	:	:	PUNCT
brj-24683	371	16	2014	2014	NUM
brj-24683	371	17	ieee	ieee	NOUN
brj-24683	371	18	conference	conference	NOUN
brj-24683	371	19	on	on	ADP
brj-24683	371	20	computer	computer	NOUN
brj-24683	371	21	vision	vision	NOUN
brj-24683	371	22	and	and	CCONJ
brj-24683	371	23	pattern	pattern	NOUN
brj-24683	371	24	recognition	recognition	NOUN
brj-24683	371	25	,	,	PUNCT
brj-24683	371	26	columbus	columbus	PROPN
brj-24683	371	27	,	,	PUNCT
brj-24683	371	28	oh	oh	INTJ
brj-24683	371	29	,	,	PUNCT
brj-24683	371	30	usa	usa	PROPN
brj-24683	371	31	,	,	PUNCT
brj-24683	371	32	pp	pp	ADJ
brj-24683	371	33	.	.	PUNCT
brj-24683	372	1	580	580	NUM
brj-24683	372	2	-	-	SYM
brj-24683	372	3	587	587	NUM
brj-24683	372	4	.	.	PUNCT
brj-24683	373	1	doi	doi	NOUN
brj-24683	373	2	:	:	PUNCT
brj-24683	373	3	10.1109	10.1109	NUM
brj-24683	373	4	/	/	SYM
brj-24683	373	5	cvpr.2014.81	cvpr.2014.81	NOUN
brj-24683	373	6	he	he	PRON
brj-24683	373	7	,	,	PUNCT
brj-24683	373	8	k.	k.	PROPN
brj-24683	373	9	,	,	PUNCT
brj-24683	373	10	gkioxari	gkioxari	NOUN
brj-24683	373	11	,	,	PUNCT
brj-24683	373	12	g.	g.	NOUN
brj-24683	373	13	,	,	PUNCT
brj-24683	373	14	dollár	dollár	NOUN
brj-24683	373	15	,	,	PUNCT
brj-24683	373	16	p.	p.	NOUN
brj-24683	373	17	,	,	PUNCT
brj-24683	373	18	and	and	CCONJ
brj-24683	373	19	girshick	girshick	PROPN
brj-24683	373	20	,	,	PUNCT
brj-24683	373	21	r.	r.	PROPN
brj-24683	373	22	(	(	PUNCT
brj-24683	373	23	2017	2017	NUM
brj-24683	373	24	)	)	PUNCT
brj-24683	373	25	.	.	PUNCT
brj-24683	374	1	“	"	PUNCT
brj-24683	374	2	mask	mask	VERB
brj-24683	374	3	r	r	NOUN
brj-24683	374	4	-	-	PUNCT
brj-24683	374	5	cnn	cnn	PROPN
brj-24683	374	6	,	,	PUNCT
brj-24683	374	7	”	"	PUNCT
brj-24683	374	8	in	in	ADP
brj-24683	374	9	:	:	PUNCT
brj-24683	374	10	2017	2017	NUM
brj-24683	374	11	ieee	ieee	NOUN
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brj-24683	374	13	conference	conference	NOUN
brj-24683	374	14	on	on	ADP
brj-24683	374	15	computer	computer	NOUN
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brj-24683	374	17	(	(	PUNCT
brj-24683	374	18	iccv	iccv	PROPN
brj-24683	374	19	)	)	PUNCT
brj-24683	374	20	,	,	PUNCT
brj-24683	374	21	venice	venice	PROPN
brj-24683	374	22	,	,	PUNCT
brj-24683	374	23	italy	italy	PROPN
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brj-24683	374	25	pp	pp	ADP
brj-24683	374	26	.	.	PUNCT
brj-24683	375	1	2980	2980	NUM
brj-24683	375	2	-	-	SYM
brj-24683	375	3	2988	2988	NUM
brj-24683	375	4	.	.	PUNCT
brj-24683	376	1	doi	doi	NOUN
brj-24683	376	2	:	:	PUNCT
brj-24683	376	3	10.1109	10.1109	NUM
brj-24683	376	4	/	/	SYM
brj-24683	376	5	iccv.2017.322	iccv.2017.322	PROPN
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brj-24683	376	7	,	,	PUNCT
brj-24683	376	8	m.	m.	NOUN
brj-24683	376	9	m.	m.	NOUN
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brj-24683	376	12	,	,	PUNCT
brj-24683	376	13	d.	d.	PROPN
brj-24683	376	14	,	,	PUNCT
brj-24683	376	15	beya	beya	NOUN
brj-24683	376	16	,	,	PUNCT
brj-24683	376	17	o.	o.	NOUN
brj-24683	376	18	,	,	PUNCT
brj-24683	376	19	and	and	CCONJ
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brj-24683	376	22	f.	f.	PROPN
brj-24683	376	23	(	(	PUNCT
brj-24683	376	24	2017	2017	NUM
brj-24683	376	25	)	)	PUNCT
brj-24683	376	26	.	.	PUNCT
brj-24683	377	1	“	"	PUNCT
brj-24683	377	2	machine	machine	NOUN
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brj-24683	377	17	26(6	26(6	NOUN
brj-24683	377	18	)	)	PUNCT
brj-24683	377	19	,	,	PUNCT
brj-24683	377	20	article	article	NOUN
brj-24683	377	21	063015	063015	NUM
brj-24683	377	22	.	.	PUNCT
brj-24683	378	1	doi	doi	NOUN
brj-24683	378	2	:	:	PUNCT
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brj-24683	378	4	hu	hu	PROPN
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brj-24683	378	6	k.	k.	PROPN
brj-24683	378	7	,	,	PUNCT
brj-24683	378	8	wang	wang	PROPN
brj-24683	378	9	,	,	PUNCT
brj-24683	378	10	b.	b.	PROPN
brj-24683	378	11	,	,	PUNCT
brj-24683	378	12	shen	shen	PROPN
brj-24683	378	13	,	,	PUNCT
brj-24683	378	14	y.	y.	PROPN
brj-24683	378	15	,	,	PUNCT
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brj-24683	378	17	,	,	PUNCT
brj-24683	378	18	j.	j.	PROPN
brj-24683	378	19	,	,	PUNCT
brj-24683	378	20	and	and	CCONJ
brj-24683	378	21	cai	cai	X
brj-24683	378	22	,	,	PUNCT
brj-24683	378	23	y.	y.	PROPN
brj-24683	378	24	(	(	PUNCT
brj-24683	378	25	2020	2020	NUM
brj-24683	378	26	)	)	PUNCT
brj-24683	378	27	.	.	PUNCT
brj-24683	379	1	“	"	PUNCT
brj-24683	379	2	defect	defect	VERB
brj-24683	379	3	identification	identification	NOUN
brj-24683	379	4	method	method	NOUN
brj-24683	379	5	for	for	ADP
brj-24683	379	6	poplar	poplar	ADJ
brj-24683	379	7	veneer	veneer	NOUN
brj-24683	379	8	based	base	VERB
brj-24683	379	9	on	on	ADP
brj-24683	379	10	progressive	progressive	ADJ
brj-24683	379	11	growing	grow	VERB
brj-24683	379	12	generated	generate	VERB
brj-24683	379	13	adversarial	adversarial	ADJ
brj-24683	379	14	network	network	NOUN
brj-24683	379	15	and	and	CCONJ
brj-24683	379	16	mask	mask	NOUN
brj-24683	379	17	r	r	PROPN
brj-24683	379	18	-	-	PUNCT
brj-24683	379	19	cnn	cnn	PROPN
brj-24683	379	20	model	model	NOUN
brj-24683	379	21	,	,	PUNCT
brj-24683	379	22	”	"	PUNCT
brj-24683	379	23	bioresources	bioresource	VERB
brj-24683	379	24	15(2	15(2	NUM
brj-24683	379	25	)	)	PUNCT
brj-24683	379	26	,	,	PUNCT
brj-24683	379	27	3041	3041	NUM
brj-24683	379	28	-	-	SYM
brj-24683	379	29	3052	3052	NUM
brj-24683	379	30	.	.	PUNCT
brj-24683	380	1	doi	doi	NOUN
brj-24683	380	2	:	:	PUNCT
brj-24683	380	3	10.15376	10.15376	NUM
brj-24683	380	4	/	/	SYM
brj-24683	380	5	biores.15.2.3041	biores.15.2.3041	NOUN
brj-24683	380	6	-	-	PUNCT
brj-24683	380	7	3052	3052	NUM
brj-24683	380	8	ji	ji	PROPN
brj-24683	380	9	,	,	PUNCT
brj-24683	380	10	m.	m.	NOUN
brj-24683	380	11	,	,	PUNCT
brj-24683	380	12	zhang	zhang	PROPN
brj-24683	380	13	,	,	PUNCT
brj-24683	380	14	w.	w.	PROPN
brj-24683	380	15	,	,	PUNCT
brj-24683	380	16	han	han	PROPN
brj-24683	380	17	,	,	PUNCT
brj-24683	380	18	j.	j.	PROPN
brj-24683	380	19	,	,	PUNCT
brj-24683	380	20	miao	miao	PROPN
brj-24683	380	21	,	,	PUNCT
brj-24683	380	22	h.	h.	PROPN
brj-24683	380	23	,	,	PUNCT
brj-24683	380	24	diao	diao	PROPN
brj-24683	380	25	,	,	PUNCT
brj-24683	380	26	x.	x.	NOUN
brj-24683	380	27	,	,	PUNCT
brj-24683	380	28	and	and	CCONJ
brj-24683	380	29	wang	wang	PROPN
brj-24683	380	30	,	,	PUNCT
brj-24683	380	31	g.	g.	PROPN
brj-24683	380	32	(	(	PUNCT
brj-24683	380	33	2024a	2024a	NUM
brj-24683	380	34	)	)	PUNCT
brj-24683	380	35	.	.	PUNCT
brj-24683	381	1	“	"	PUNCT
brj-24683	381	2	a	a	DET
brj-24683	381	3	deep	deep	ADJ
brj-24683	381	4	learningbased	learningbase	VERB
brj-24683	381	5	algorithm	algorithm	NOUN
brj-24683	381	6	for	for	ADP
brj-24683	381	7	online	online	ADJ
brj-24683	381	8	detection	detection	NOUN
brj-24683	381	9	of	of	ADP
brj-24683	381	10	small	small	ADJ
brj-24683	381	11	target	target	NOUN
brj-24683	381	12	defects	defect	NOUN
brj-24683	381	13	in	in	ADP
brj-24683	381	14	large	large	ADJ
brj-24683	381	15	-	-	PUNCT
brj-24683	381	16	size	size	NOUN
brj-24683	381	17	sawn	sawn	NOUN
brj-24683	381	18	timber	timber	NOUN
brj-24683	381	19	,	,	PUNCT
brj-24683	381	20	”	"	PUNCT
brj-24683	381	21	industrial	industrial	ADJ
brj-24683	381	22	crops	crop	NOUN
brj-24683	381	23	and	and	CCONJ
brj-24683	381	24	products	product	NOUN
brj-24683	381	25	222	222	NUM
brj-24683	381	26	,	,	PUNCT
brj-24683	381	27	article	article	NOUN
brj-24683	381	28	119671	119671	NUM
brj-24683	381	29	.	.	PUNCT
brj-24683	382	1	doi	doi	NOUN
brj-24683	382	2	:	:	PUNCT
brj-24683	382	3	10.1016	10.1016	NUM
brj-24683	382	4	/	/	SYM
brj-24683	382	5	j.indcrop.2024.119671	j.indcrop.2024.119671	PROPN
brj-24683	382	6	ji	ji	PROPN
brj-24683	382	7	,	,	PUNCT
brj-24683	382	8	m	m	PROPN
brj-24683	382	9	,	,	PUNCT
brj-24683	382	10	zhang	zhang	PROPN
brj-24683	382	11	,	,	PUNCT
brj-24683	382	12	w.	w.	PROPN
brj-24683	382	13	,	,	PUNCT
brj-24683	382	14	wang	wang	PROPN
brj-24683	382	15	,	,	PUNCT
brj-24683	382	16	g.	g.	PROPN
brj-24683	382	17	f.	f.	PROPN
brj-24683	382	18	,	,	PUNCT
brj-24683	382	19	diao	diao	PROPN
brj-24683	382	20	,	,	PUNCT
brj-24683	382	21	x.	x.	PROPN
brj-24683	382	22	l.	l.	PROPN
brj-24683	382	23	,	,	PUNCT
brj-24683	382	24	miao	miao	PROPN
brj-24683	382	25	,	,	PUNCT
brj-24683	382	26	h.	h.	PROPN
brj-24683	382	27	,	,	PUNCT
brj-24683	382	28	and	and	CCONJ
brj-24683	382	29	gao	gao	PROPN
brj-24683	382	30	,	,	PUNCT
brj-24683	382	31	r.	r.	PROPN
brj-24683	382	32	(	(	PUNCT
brj-24683	382	33	2024b	2024b	NUM
brj-24683	382	34	)	)	PUNCT
brj-24683	382	35	.	.	PUNCT
brj-24683	383	1	“	"	PUNCT
brj-24683	383	2	machine	machine	NOUN
brj-24683	383	3	vision	vision	NOUN
brj-24683	383	4	for	for	ADP
brj-24683	383	5	knot	knot	ADJ
brj-24683	383	6	detection	detection	NOUN
brj-24683	383	7	and	and	CCONJ
brj-24683	383	8	location	location	NOUN
brj-24683	383	9	in	in	ADP
brj-24683	383	10	chinese	chinese	ADJ
brj-24683	383	11	fir	fir	NOUN
brj-24683	383	12	lumber	lumber	NOUN
brj-24683	383	13	,	,	PUNCT
brj-24683	383	14	”	"	PUNCT
brj-24683	383	15	forest	forest	NOUN
brj-24683	383	16	products	product	NOUN
brj-24683	383	17	journal	journal	PROPN
brj-24683	383	18	74(2	74(2	PROPN
brj-24683	383	19	)	)	PUNCT
brj-24683	383	20	,	,	PUNCT
brj-24683	383	21	185	185	NUM
brj-24683	383	22	-	-	SYM
brj-24683	383	23	202	202	NUM
brj-24683	383	24	.	.	PUNCT
brj-24683	384	1	doi	doi	NOUN
brj-24683	384	2	:	:	PUNCT
brj-24683	384	3	10.13073	10.13073	NUM
brj-24683	384	4	/	/	SYM
brj-24683	384	5	fpj	fpj	PROPN
brj-24683	384	6	-	-	PUNCT
brj-24683	384	7	d-23	d-23	NOUN
brj-24683	384	8	-	-	PUNCT
brj-24683	384	9	00050	00050	NUM
brj-24683	384	10	jiang	jiang	PROPN
brj-24683	384	11	,	,	PUNCT
brj-24683	384	12	x.	x.	PROPN
brj-24683	384	13	,	,	PUNCT
brj-24683	384	14	wang	wang	PROPN
brj-24683	384	15	,	,	PUNCT
brj-24683	384	16	j.	j.	PROPN
brj-24683	384	17	,	,	PUNCT
brj-24683	384	18	zhang	zhang	PROPN
brj-24683	384	19	,	,	PUNCT
brj-24683	384	20	y.	y.	PROPN
brj-24683	384	21	,	,	PUNCT
brj-24683	384	22	and	and	CCONJ
brj-24683	384	23	jiang	jiang	PROPN
brj-24683	384	24	,	,	PUNCT
brj-24683	384	25	s.	s.	PROPN
brj-24683	384	26	(	(	PUNCT
brj-24683	384	27	2024	2024	NUM
brj-24683	384	28	)	)	PUNCT
brj-24683	384	29	.	.	PUNCT
brj-24683	385	1	“	"	PUNCT
brj-24683	385	2	defect	defect	VERB
brj-24683	385	3	detection	detection	NOUN
brj-24683	385	4	in	in	ADP
brj-24683	385	5	solid	solid	ADJ
brj-24683	385	6	timber	timber	NOUN
brj-24683	385	7	panels	panel	NOUN
brj-24683	385	8	using	use	VERB
brj-24683	385	9	air	air	NOUN
brj-24683	385	10	-	-	PUNCT
brj-24683	385	11	coupled	couple	VERB
brj-24683	385	12	ultrasonic	ultrasonic	ADJ
brj-24683	385	13	imaging	imaging	NOUN
brj-24683	385	14	techniques	technique	NOUN
brj-24683	385	15	,	,	PUNCT
brj-24683	385	16	”	"	PUNCT
brj-24683	385	17	appl	appl	NOUN
brj-24683	385	18	.	.	PUNCT
brj-24683	386	1	sci	sci	PROPN
brj-24683	386	2	.	.	PROPN
brj-24683	386	3	14	14	NUM
brj-24683	386	4	,	,	PUNCT
brj-24683	386	5	434	434	NUM
brj-24683	386	6	.	.	PUNCT
brj-24683	387	1	doi	doi	NOUN
brj-24683	387	2	:	:	PUNCT
brj-24683	387	3	10.3390	10.3390	NUM
brj-24683	387	4	/	/	SYM
brj-24683	387	5	app14010434	app14010434	ADJ
brj-24683	387	6	karimi	karimi	PROPN
brj-24683	387	7	,	,	PUNCT
brj-24683	387	8	n.	n.	PROPN
brj-24683	387	9	,	,	PUNCT
brj-24683	387	10	mishra	mishra	PROPN
brj-24683	387	11	,	,	PUNCT
brj-24683	387	12	m.	m.	NOUN
brj-24683	387	13	,	,	PUNCT
brj-24683	387	14	and	and	CCONJ
brj-24683	387	15	lourenço	lourenço	ADJ
brj-24683	387	16	,	,	PUNCT
brj-24683	387	17	p.	p.	PROPN
brj-24683	387	18	b.	b.	PROPN
brj-24683	387	19	(	(	PUNCT
brj-24683	387	20	2024	2024	NUM
brj-24683	387	21	)	)	PUNCT
brj-24683	387	22	.	.	PUNCT
brj-24683	388	1	“	"	PUNCT
brj-24683	388	2	deep	deep	ADJ
brj-24683	388	3	learning	learning	NOUN
brj-24683	388	4	-	-	PUNCT
brj-24683	388	5	based	base	VERB
brj-24683	388	6	automated	automate	VERB
brj-24683	388	7	tile	tile	NOUN
brj-24683	388	8	defect	defect	NOUN
brj-24683	388	9	detection	detection	NOUN
brj-24683	388	10	system	system	NOUN
brj-24683	388	11	for	for	ADP
brj-24683	388	12	portuguese	portuguese	ADJ
brj-24683	388	13	cultural	cultural	ADJ
brj-24683	388	14	heritage	heritage	NOUN
brj-24683	388	15	buildings	building	NOUN
brj-24683	388	16	,	,	PUNCT
brj-24683	388	17	”	"	PUNCT
brj-24683	388	18	journal	journal	NOUN
brj-24683	388	19	of	of	ADP
brj-24683	388	20	cultural	cultural	ADJ
brj-24683	388	21	heritage	heritage	NOUN
brj-24683	388	22	,	,	PUNCT
brj-24683	388	23	68	68	NUM
brj-24683	388	24	,	,	PUNCT
brj-24683	388	25	86	86	NUM
brj-24683	388	26	-	-	SYM
brj-24683	388	27	98	98	NUM
brj-24683	388	28	.	.	PUNCT
brj-24683	389	1	doi	doi	NOUN
brj-24683	389	2	:	:	PUNCT
brj-24683	389	3	10.1016	10.1016	NUM
brj-24683	389	4	/	/	SYM
brj-24683	389	5	j.culher.2024.05.009	j.culher.2024.05.009	PROPN
brj-24683	389	6	kodytek	kodytek	PROPN
brj-24683	389	7	,	,	PUNCT
brj-24683	389	8	p.	p.	NOUN
brj-24683	389	9	,	,	PUNCT
brj-24683	389	10	bodzas	bodzas	NOUN
brj-24683	389	11	,	,	PUNCT
brj-24683	389	12	a.	a.	NOUN
brj-24683	389	13	,	,	PUNCT
brj-24683	389	14	and	and	CCONJ
brj-24683	389	15	bilik	bilik	NOUN
brj-24683	389	16	,	,	PUNCT
brj-24683	389	17	p.	p.	NOUN
brj-24683	389	18	(	(	PUNCT
brj-24683	389	19	2021	2021	NUM
brj-24683	389	20	)	)	PUNCT
brj-24683	389	21	.	.	PUNCT
brj-24683	390	1	“	"	PUNCT
brj-24683	390	2	a	a	DET
brj-24683	390	3	large	large	ADJ
brj-24683	390	4	-	-	PUNCT
brj-24683	390	5	scale	scale	NOUN
brj-24683	390	6	image	image	NOUN
brj-24683	390	7	dataset	dataset	NOUN
brj-24683	390	8	of	of	ADP
brj-24683	390	9	wood	wood	NOUN
brj-24683	390	10	surface	surface	NOUN
brj-24683	390	11	defects	defect	NOUN
brj-24683	390	12	for	for	ADP
brj-24683	390	13	automated	automate	VERB
brj-24683	390	14	vision	vision	NOUN
brj-24683	390	15	-	-	PUNCT
brj-24683	390	16	based	base	VERB
brj-24683	390	17	quality	quality	NOUN
brj-24683	390	18	control	control	NOUN
brj-24683	390	19	processes	process	NOUN
brj-24683	390	20	,	,	PUNCT
brj-24683	390	21	”	"	PUNCT
brj-24683	390	22	f1000research	f1000research	NOUN
brj-24683	390	23	10	10	NUM
brj-24683	390	24	,	,	PUNCT
brj-24683	390	25	article	article	NOUN
brj-24683	390	26	581	581	NUM
brj-24683	390	27	.	.	PUNCT
brj-24683	391	1	doi	doi	NOUN
brj-24683	391	2	:	:	PUNCT
brj-24683	391	3	10.12688	10.12688	NUM
brj-24683	391	4	/	/	SYM
brj-24683	391	5	f1000research.52903.2	f1000research.52903.2	NOUN
brj-24683	391	6	li	li	PROPN
brj-24683	391	7	,	,	PUNCT
brj-24683	391	8	d.	d.	PROPN
brj-24683	391	9	,	,	PUNCT
brj-24683	391	10	xie	xie	PROPN
brj-24683	391	11	,	,	PUNCT
brj-24683	391	12	w.	w.	PROPN
brj-24683	391	13	,	,	PUNCT
brj-24683	391	14	wang	wang	PROPN
brj-24683	391	15	,	,	PUNCT
brj-24683	391	16	b.	b.	PROPN
brj-24683	391	17	,	,	PUNCT
brj-24683	391	18	zhong	zhong	PROPN
brj-24683	391	19	,	,	PUNCT
brj-24683	391	20	w.	w.	PROPN
brj-24683	391	21	,	,	PUNCT
brj-24683	391	22	and	and	CCONJ
brj-24683	391	23	wang	wang	PROPN
brj-24683	391	24	,	,	PUNCT
brj-24683	391	25	h.	h.	PROPN
brj-24683	391	26	(	(	PUNCT
brj-24683	391	27	2021	2021	NUM
brj-24683	391	28	)	)	PUNCT
brj-24683	391	29	.	.	PUNCT
brj-24683	392	1	“	"	PUNCT
brj-24683	392	2	data	datum	NOUN
brj-24683	392	3	augmentation	augmentation	NOUN
brj-24683	392	4	and	and	CCONJ
brj-24683	392	5	layered	layer	VERB
brj-24683	392	6	deformable	deformable	ADJ
brj-24683	392	7	mask	mask	NOUN
brj-24683	392	8	r	r	NOUN
brj-24683	392	9	-	-	PUNCT
brj-24683	392	10	cnn	cnn	NOUN
brj-24683	392	11	-	-	PUNCT
brj-24683	392	12	based	base	VERB
brj-24683	392	13	detection	detection	NOUN
brj-24683	392	14	of	of	ADP
brj-24683	392	15	wood	wood	NOUN
brj-24683	392	16	defects	defect	NOUN
brj-24683	392	17	,	,	PUNCT
brj-24683	392	18	”	"	PUNCT
brj-24683	392	19	ieee	ieee	NOUN
brj-24683	392	20	access	access	NOUN
brj-24683	392	21	9	9	NUM
brj-24683	392	22	,	,	PUNCT
brj-24683	392	23	108162	108162	NUM
brj-24683	392	24	-	-	SYM
brj-24683	392	25	108174	108174	NUM
brj-24683	392	26	.	.	PUNCT
brj-24683	393	1	doi	doi	NOUN
brj-24683	393	2	:	:	PUNCT
brj-24683	393	3	10.1109	10.1109	NUM
brj-24683	393	4	/	/	SYM
brj-24683	393	5	access.2021.3101247	access.2021.3101247	VERB
brj-24683	393	6	li	li	PROPN
brj-24683	393	7	,	,	PUNCT
brj-24683	393	8	h.	h.	PROPN
brj-24683	393	9	,	,	PUNCT
brj-24683	393	10	zhang	zhang	PROPN
brj-24683	393	11	,	,	PUNCT
brj-24683	393	12	j.	j.	PROPN
brj-24683	393	13	,	,	PUNCT
brj-24683	393	14	xu	xu	PROPN
brj-24683	393	15	,	,	PUNCT
brj-24683	393	16	g.	g.	PROPN
brj-24683	393	17	,	,	PUNCT
brj-24683	393	18	and	and	CCONJ
brj-24683	393	19	li	li	PROPN
brj-24683	393	20	,	,	PUNCT
brj-24683	393	21	w.	w.	PROPN
brj-24683	393	22	(	(	PUNCT
brj-24683	393	23	2024	2024	NUM
brj-24683	393	24	)	)	PUNCT
brj-24683	393	25	.	.	PUNCT
brj-24683	394	1	“	"	PUNCT
brj-24683	394	2	compression	compression	NOUN
brj-24683	394	3	test	test	NOUN
brj-24683	394	4	and	and	CCONJ
brj-24683	394	5	size	size	NOUN
brj-24683	394	6	effect	effect	NOUN
brj-24683	394	7	study	study	NOUN
brj-24683	394	8	of	of	ADP
brj-24683	394	9	defective	defective	ADJ
brj-24683	394	10	wood	wood	NOUN
brj-24683	394	11	,	,	PUNCT
brj-24683	394	12	”	"	PUNCT
brj-24683	394	13	wood	wood	NOUN
brj-24683	394	14	material	material	NOUN
brj-24683	394	15	science	science	NOUN
brj-24683	394	16	&	&	CCONJ
brj-24683	394	17	engineering	engineering	PROPN
brj-24683	394	18	1	1	NUM
brj-24683	394	19	-	-	SYM
brj-24683	394	20	9	9	NUM
brj-24683	394	21	.	.	PUNCT
brj-24683	394	22	doi	doi	NOUN
brj-24683	394	23	:	:	PUNCT
brj-24683	394	24	10.1080/17480272.2024.2383747	10.1080/17480272.2024.2383747	NUM
brj-24683	394	25	liu	liu	PROPN
brj-24683	394	26	,	,	PUNCT
brj-24683	394	27	w.	w.	PROPN
brj-24683	394	28	,	,	PUNCT
brj-24683	394	29	anguelov	anguelov	PROPN
brj-24683	394	30	,	,	PUNCT
brj-24683	394	31	d.	d.	PROPN
brj-24683	394	32	,	,	PUNCT
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brj-24683	397	8	-	-	PUNCT
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brj-24683	398	24	)	)	PUNCT
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brj-24683	399	9	)	)	PUNCT
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brj-24683	400	9	-	-	SYM
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brj-24683	400	11	.	.	PUNCT
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brj-24683	402	24	-	-	SYM
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brj-24683	403	15	.	.	PUNCT
brj-24683	404	1	“	"	PUNCT
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brj-24683	405	1	doi	doi	NOUN
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brj-24683	407	1	“	"	PUNCT
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brj-24683	407	17	-	-	PUNCT
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brj-24683	408	1	doi	doi	NOUN
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brj-24683	408	25	)	)	PUNCT
brj-24683	408	26	.	.	PUNCT
brj-24683	409	1	“	"	PUNCT
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brj-24683	409	17	)	)	PUNCT
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brj-24683	409	20	-	-	SYM
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brj-24683	410	1	doi	doi	NOUN
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brj-24683	410	24	)	)	PUNCT
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brj-24683	411	1	“	"	PUNCT
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brj-24683	411	18	2016	2016	NUM
brj-24683	411	19	ieee	ieee	NOUN
brj-24683	411	20	conference	conference	NOUN
brj-24683	411	21	on	on	ADP
brj-24683	411	22	computer	computer	NOUN
brj-24683	411	23	vision	vision	NOUN
brj-24683	411	24	and	and	CCONJ
brj-24683	411	25	pattern	pattern	NOUN
brj-24683	411	26	recognition	recognition	NOUN
brj-24683	411	27	(	(	PUNCT
brj-24683	411	28	cvpr	cvpr	NOUN
brj-24683	411	29	)	)	PUNCT
brj-24683	411	30	,	,	PUNCT
brj-24683	411	31	las	las	PROPN
brj-24683	411	32	vegas	vegas	PROPN
brj-24683	411	33	,	,	PUNCT
brj-24683	411	34	usa	usa	PROPN
brj-24683	411	35	,	,	PUNCT
brj-24683	411	36	pp	pp	X
brj-24683	411	37	.	.	PUNCT
brj-24683	412	1	779	779	NUM
brj-24683	412	2	-	-	SYM
brj-24683	412	3	788	788	NUM
brj-24683	412	4	.	.	PUNCT
brj-24683	413	1	doi	doi	NOUN
brj-24683	413	2	:	:	PUNCT
brj-24683	413	3	10.1109	10.1109	NUM
brj-24683	413	4	/	/	SYM
brj-24683	413	5	cvpr.2016.91	cvpr.2016.91	PROPN
brj-24683	413	6	ren	ren	PROPN
brj-24683	413	7	,	,	PUNCT
brj-24683	413	8	s.	s.	PROPN
brj-24683	413	9	,	,	PUNCT
brj-24683	413	10	he	he	PRON
brj-24683	413	11	,	,	PUNCT
brj-24683	413	12	k.	k.	PROPN
brj-24683	413	13	,	,	PUNCT
brj-24683	413	14	girshick	girshick	PROPN
brj-24683	413	15	,	,	PUNCT
brj-24683	413	16	r.	r.	PROPN
brj-24683	413	17	,	,	PUNCT
brj-24683	413	18	and	and	CCONJ
brj-24683	413	19	sun	sun	NOUN
brj-24683	413	20	,	,	PUNCT
brj-24683	413	21	j.	j.	PROPN
brj-24683	413	22	(	(	PUNCT
brj-24683	413	23	2017	2017	NUM
brj-24683	413	24	)	)	PUNCT
brj-24683	413	25	.	.	PUNCT
brj-24683	414	1	“	"	PUNCT
brj-24683	414	2	faster	fast	ADJ
brj-24683	414	3	r	r	NOUN
brj-24683	414	4	-	-	PUNCT
brj-24683	414	5	cnn	cnn	NOUN
brj-24683	414	6	:	:	PUNCT
brj-24683	414	7	towards	towards	ADP
brj-24683	414	8	real	real	ADJ
brj-24683	414	9	-	-	PUNCT
brj-24683	414	10	time	time	NOUN
brj-24683	414	11	object	object	NOUN
brj-24683	414	12	detection	detection	NOUN
brj-24683	414	13	with	with	ADP
brj-24683	414	14	region	region	NOUN
brj-24683	414	15	proposal	proposal	NOUN
brj-24683	414	16	networks	network	NOUN
brj-24683	414	17	,	,	PUNCT
brj-24683	414	18	”	"	PUNCT
brj-24683	414	19	ieee	ieee	NOUN
brj-24683	414	20	transactions	transaction	NOUN
brj-24683	414	21	on	on	ADP
brj-24683	414	22	pattern	pattern	NOUN
brj-24683	414	23	analysis	analysis	NOUN
brj-24683	414	24	and	and	CCONJ
brj-24683	414	25	machine	machine	NOUN
brj-24683	414	26	intelligence	intelligence	NOUN
brj-24683	414	27	39(6	39(6	NUM
brj-24683	414	28	)	)	PUNCT
brj-24683	414	29	,	,	PUNCT
brj-24683	414	30	1137	1137	NUM
brj-24683	414	31	-	-	SYM
brj-24683	414	32	1149	1149	NUM
brj-24683	414	33	.	.	PUNCT
brj-24683	415	1	doi	doi	NOUN
brj-24683	415	2	:	:	PUNCT
brj-24683	415	3	10.1109	10.1109	NUM
brj-24683	415	4	/	/	SYM
brj-24683	415	5	tpami.2016.2577031	tpami.2016.2577031	NUM
brj-24683	415	6	sandak	sandak	NOUN
brj-24683	415	7	,	,	PUNCT
brj-24683	415	8	j.	j.	PROPN
brj-24683	415	9	,	,	PUNCT
brj-24683	415	10	sandak	sandak	PROPN
brj-24683	415	11	,	,	PUNCT
brj-24683	415	12	a.	a.	PROPN
brj-24683	415	13	,	,	PUNCT
brj-24683	415	14	zitek	zitek	PROPN
brj-24683	415	15	,	,	PUNCT
brj-24683	415	16	a.	a.	PROPN
brj-24683	415	17	,	,	PUNCT
brj-24683	415	18	hintestoisser	hintestoisser	NOUN
brj-24683	415	19	,	,	PUNCT
brj-24683	415	20	b.	b.	PROPN
brj-24683	415	21	,	,	PUNCT
brj-24683	415	22	and	and	CCONJ
brj-24683	415	23	picchi	picchi	NOUN
brj-24683	415	24	,	,	PUNCT
brj-24683	415	25	g.	g.	PROPN
brj-24683	415	26	(	(	PUNCT
brj-24683	415	27	2020	2020	NUM
brj-24683	415	28	)	)	PUNCT
brj-24683	415	29	.	.	PUNCT
brj-24683	416	1	“	"	PUNCT
brj-24683	416	2	development	development	NOUN
brj-24683	416	3	of	of	ADP
brj-24683	416	4	low	low	ADJ
brj-24683	416	5	-	-	PUNCT
brj-24683	416	6	cost	cost	NOUN
brj-24683	416	7	portable	portable	ADJ
brj-24683	416	8	spectrometers	spectrometer	NOUN
brj-24683	416	9	for	for	ADP
brj-24683	416	10	detection	detection	NOUN
brj-24683	416	11	of	of	ADP
brj-24683	416	12	wood	wood	NOUN
brj-24683	416	13	defects	defect	NOUN
brj-24683	416	14	,	,	PUNCT
brj-24683	416	15	”	"	PUNCT
brj-24683	416	16	sensors	sensor	NOUN
brj-24683	416	17	20(2	20(2	NUM
brj-24683	416	18	)	)	PUNCT
brj-24683	416	19	,	,	PUNCT
brj-24683	416	20	article	article	NOUN
brj-24683	416	21	545	545	NUM
brj-24683	416	22	.	.	PUNCT
brj-24683	417	1	doi	doi	NOUN
brj-24683	417	2	:	:	PUNCT
brj-24683	417	3	10.3390	10.3390	NUM
brj-24683	417	4	/	/	SYM
brj-24683	417	5	s20020545	s20020545	PROPN
brj-24683	417	6	stängle	stängle	NOUN
brj-24683	417	7	,	,	PUNCT
brj-24683	417	8	s.m	s.m	PROPN
brj-24683	417	9	.	.	PROPN
brj-24683	417	10	,	,	PUNCT
brj-24683	417	11	brüchert	brüchert	PROPN
brj-24683	417	12	,	,	PUNCT
brj-24683	417	13	f.	f.	PROPN
brj-24683	417	14	,	,	PUNCT
brj-24683	417	15	heikkila	heikkila	PROPN
brj-24683	417	16	,	,	PUNCT
brj-24683	417	17	a.	a.	NOUN
brj-24683	417	18	,	,	PUNCT
brj-24683	417	19	usenius	usenius	NOUN
brj-24683	417	20	,	,	PUNCT
brj-24683	417	21	t.	t.	PROPN
brj-24683	417	22	,	,	PUNCT
brj-24683	417	23	usenius	usenius	NOUN
brj-24683	417	24	,	,	PUNCT
brj-24683	417	25	a.	a.	NOUN
brj-24683	417	26	,	,	PUNCT
brj-24683	417	27	and	and	CCONJ
brj-24683	417	28	sauter	sauter	NOUN
brj-24683	417	29	,	,	PUNCT
brj-24683	417	30	u.	u.	PROPN
brj-24683	417	31	h.	h.	PROPN
brj-24683	417	32	(	(	PUNCT
brj-24683	417	33	2015	2015	NUM
brj-24683	417	34	)	)	PUNCT
brj-24683	417	35	.	.	PUNCT
brj-24683	418	1	“	"	PUNCT
brj-24683	418	2	potentially	potentially	ADV
brj-24683	418	3	increased	increase	VERB
brj-24683	418	4	sawmill	sawmill	NOUN
brj-24683	418	5	yield	yield	NOUN
brj-24683	418	6	from	from	ADP
brj-24683	418	7	hardwoods	hardwood	NOUN
brj-24683	418	8	using	use	VERB
brj-24683	418	9	x	x	NOUN
brj-24683	418	10	-	-	NOUN
brj-24683	418	11	ray	ray	NOUN
brj-24683	418	12	computed	compute	VERB
brj-24683	418	13	tomography	tomography	NOUN
brj-24683	418	14	for	for	ADP
brj-24683	418	15	knot	knot	ADJ
brj-24683	418	16	detection	detection	NOUN
brj-24683	418	17	,	,	PUNCT
brj-24683	418	18	”	"	PUNCT
brj-24683	418	19	annals	annals	NOUN
brj-24683	418	20	of	of	ADP
brj-24683	418	21	forest	forest	NOUN
brj-24683	418	22	science	science	NOUN
brj-24683	418	23	72	72	NUM
brj-24683	418	24	,	,	PUNCT
brj-24683	418	25	57	57	NUM
brj-24683	418	26	-	-	SYM
brj-24683	418	27	65	65	NUM
brj-24683	418	28	.	.	PUNCT
brj-24683	419	1	doi	doi	NOUN
brj-24683	419	2	:	:	PUNCT
brj-24683	419	3	10.1007	10.1007	NUM
brj-24683	419	4	/	/	SYM
brj-24683	419	5	s13595	s13595	PROPN
brj-24683	419	6	-	-	PUNCT
brj-24683	419	7	014	014	NUM
brj-24683	419	8	-	-	PUNCT
brj-24683	419	9	0385	0385	NUM
brj-24683	419	10	-	-	PUNCT
brj-24683	419	11	1	1	NUM
brj-24683	419	12	sun	sun	NOUN
brj-24683	419	13	,	,	PUNCT
brj-24683	419	14	p.	p.	NOUN
brj-24683	419	15	a.	a.	NOUN
brj-24683	420	1	(	(	PUNCT
brj-24683	420	2	2022	2022	NUM
brj-24683	420	3	)	)	PUNCT
brj-24683	420	4	.	.	PUNCT
brj-24683	421	1	“	"	PUNCT
brj-24683	421	2	wood	wood	NOUN
brj-24683	421	3	quality	quality	NOUN
brj-24683	421	4	defect	defect	NOUN
brj-24683	421	5	detection	detection	NOUN
brj-24683	421	6	based	base	VERB
brj-24683	421	7	on	on	ADP
brj-24683	421	8	deep	deep	ADJ
brj-24683	421	9	learning	learning	NOUN
brj-24683	421	10	and	and	CCONJ
brj-24683	421	11	multicriteria	multicriteria	PROPN
brj-24683	421	12	framework	framework	NOUN
brj-24683	421	13	,	,	PUNCT
brj-24683	421	14	”	"	PUNCT
brj-24683	421	15	mathematical	mathematical	ADJ
brj-24683	421	16	problems	problem	NOUN
brj-24683	421	17	in	in	ADP
brj-24683	421	18	engineering	engineer	VERB
brj-24683	421	19	1	1	NUM
brj-24683	421	20	-	-	SYM
brj-24683	421	21	9	9	NUM
brj-24683	421	22	.	.	PUNCT
brj-24683	421	23	doi	doi	NOUN
brj-24683	421	24	:	:	PUNCT
brj-24683	421	25	10.1155/2022/4878090	10.1155/2022/4878090	NUM
brj-24683	421	26	su	su	PROPN
brj-24683	421	27	,	,	PUNCT
brj-24683	421	28	q.	q.	PROPN
brj-24683	421	29	,	,	PUNCT
brj-24683	421	30	mu	mu	PROPN
brj-24683	421	31	,	,	PUNCT
brj-24683	421	32	j.	j.	PROPN
brj-24683	421	33	,	,	PUNCT
brj-24683	421	34	liang	liang	PROPN
brj-24683	421	35	,	,	PUNCT
brj-24683	421	36	w.	w.	PROPN
brj-24683	421	37	,	,	PUNCT
brj-24683	421	38	wang	wang	PROPN
brj-24683	421	39	,	,	PUNCT
brj-24683	421	40	x.	x.	PROPN
brj-24683	421	41	,	,	PUNCT
brj-24683	421	42	and	and	CCONJ
brj-24683	421	43	li	li	PROPN
brj-24683	421	44	,	,	PUNCT
brj-24683	421	45	j.	j.	PROPN
brj-24683	421	46	(	(	PUNCT
brj-24683	421	47	2025	2025	NUM
brj-24683	421	48	)	)	PUNCT
brj-24683	421	49	.	.	PUNCT
brj-24683	422	1	“	"	PUNCT
brj-24683	422	2	multi	multi	ADJ
brj-24683	422	3	-	-	ADJ
brj-24683	422	4	head	head	ADJ
brj-24683	422	5	hybrid	hybrid	ADJ
brj-24683	422	6	self	self	NOUN
brj-24683	422	7	-	-	PUNCT
brj-24683	422	8	attention	attention	NOUN
brj-24683	422	9	mechanism	mechanism	NOUN
brj-24683	422	10	for	for	ADP
brj-24683	422	11	object	object	NOUN
brj-24683	422	12	detection	detection	NOUN
brj-24683	422	13	,	,	PUNCT
brj-24683	422	14	”	"	PUNCT
brj-24683	422	15	laser	laser	NOUN
brj-24683	422	16	&	&	CCONJ
brj-24683	422	17	optoelectronics	optoelectronic	NOUN
brj-24683	422	18	progress	progress	VERB
brj-24683	422	19	62(06	62(06	NUM
brj-24683	422	20	)	)	PUNCT
brj-24683	422	21	,	,	PUNCT
brj-24683	422	22	0637006	0637006	NUM
brj-24683	422	23	.	.	PUNCT
brj-24683	423	1	doi	doi	NOUN
brj-24683	423	2	:	:	PUNCT
brj-24683	423	3	10.3788	10.3788	NUM
brj-24683	423	4	/	/	SYM
brj-24683	423	5	lop241509	lop241509	ADJ
brj-24683	423	6	varghese	varghese	PROPN
brj-24683	423	7	,	,	PUNCT
brj-24683	423	8	r.	r.	PROPN
brj-24683	423	9	,	,	PUNCT
brj-24683	423	10	and	and	CCONJ
brj-24683	423	11	sambath	sambath	NOUN
brj-24683	423	12	,	,	PUNCT
brj-24683	423	13	m.	m.	NOUN
brj-24683	423	14	(	(	PUNCT
brj-24683	423	15	2024	2024	NUM
brj-24683	423	16	)	)	PUNCT
brj-24683	423	17	.	.	PUNCT
brj-24683	424	1	“	"	PUNCT
brj-24683	424	2	yolov8	yolov8	NOUN
brj-24683	424	3	:	:	PUNCT
brj-24683	424	4	a	a	DET
brj-24683	424	5	novel	novel	ADJ
brj-24683	424	6	object	object	NOUN
brj-24683	424	7	detection	detection	NOUN
brj-24683	424	8	algorithm	algorithm	NOUN
brj-24683	424	9	with	with	ADP
brj-24683	424	10	enhanced	enhanced	ADJ
brj-24683	424	11	performance	performance	NOUN
brj-24683	424	12	and	and	CCONJ
brj-24683	424	13	robustness	robustness	NOUN
brj-24683	424	14	,	,	PUNCT
brj-24683	424	15	”	"	PUNCT
brj-24683	424	16	in	in	ADP
brj-24683	424	17	:	:	PUNCT
brj-24683	424	18	2024	2024	NUM
brj-24683	424	19	international	international	ADJ
brj-24683	424	20	conference	conference	NOUN
brj-24683	424	21	on	on	ADP
brj-24683	424	22	advances	advance	NOUN
brj-24683	424	23	in	in	ADP
brj-24683	424	24	data	datum	NOUN
brj-24683	424	25	engineering	engineering	NOUN
brj-24683	424	26	and	and	CCONJ
brj-24683	424	27	intelligent	intelligent	ADJ
brj-24683	424	28	computing	computing	NOUN
brj-24683	424	29	systems	system	NOUN
brj-24683	424	30	(	(	PUNCT
brj-24683	424	31	adics	adic	NOUN
brj-24683	424	32	)	)	PUNCT
brj-24683	424	33	.	.	PUNCT
brj-24683	425	1	1	1	NUM
brj-24683	425	2	-	-	SYM
brj-24683	425	3	6	6	NUM
brj-24683	425	4	.	.	PUNCT
brj-24683	425	5	doi	doi	NOUN
brj-24683	425	6	:	:	PUNCT
brj-24683	425	7	10.1109	10.1109	NUM
brj-24683	425	8	/	/	SYM
brj-24683	425	9	adics58448.2024.10533619	adics58448.2024.10533619	PROPN
brj-24683	425	10	wang	wang	PROPN
brj-24683	425	11	,	,	PUNCT
brj-24683	425	12	j.	j.	PROPN
brj-24683	425	13	,	,	PUNCT
brj-24683	425	14	chen	chen	PROPN
brj-24683	425	15	,	,	PUNCT
brj-24683	425	16	k.	k.	PROPN
brj-24683	425	17	,	,	PUNCT
brj-24683	425	18	xu	xu	PROPN
brj-24683	425	19	,	,	PUNCT
brj-24683	425	20	r.	r.	PROPN
brj-24683	425	21	,	,	PUNCT
brj-24683	425	22	liu	liu	PROPN
brj-24683	425	23	,	,	PUNCT
brj-24683	425	24	z.	z.	PROPN
brj-24683	425	25	,	,	PUNCT
brj-24683	425	26	loy	loy	PROPN
brj-24683	425	27	,	,	PUNCT
brj-24683	425	28	c.	c.	PROPN
brj-24683	425	29	c.	c.	PROPN
brj-24683	425	30	,	,	PUNCT
brj-24683	425	31	and	and	CCONJ
brj-24683	425	32	lin	lin	PROPN
brj-24683	425	33	,	,	PUNCT
brj-24683	425	34	d.	d.	PROPN
brj-24683	425	35	(	(	PUNCT
brj-24683	425	36	2019	2019	NUM
brj-24683	425	37	)	)	PUNCT
brj-24683	425	38	.	.	PUNCT
brj-24683	426	1	“	"	PUNCT
brj-24683	426	2	carafe	carafe	NOUN
brj-24683	426	3	:	:	PUNCT
brj-24683	426	4	contentaware	contentaware	VERB
brj-24683	426	5	reassembly	reassembly	ADV
brj-24683	426	6	of	of	ADP
brj-24683	426	7	features	feature	NOUN
brj-24683	426	8	,	,	PUNCT
brj-24683	426	9	”	"	PUNCT
brj-24683	426	10	in	in	ADP
brj-24683	426	11	:	:	PUNCT
brj-24683	426	12	2019	2019	NUM
brj-24683	426	13	ieee	ieee	NOUN
brj-24683	426	14	/	/	SYM
brj-24683	426	15	cvf	cvf	NOUN
brj-24683	426	16	international	international	ADJ
brj-24683	426	17	conference	conference	NOUN
brj-24683	426	18	on	on	ADP
brj-24683	426	19	computer	computer	NOUN
brj-24683	426	20	vision	vision	NOUN
brj-24683	426	21	(	(	PUNCT
brj-24683	426	22	iccv	iccv	PROPN
brj-24683	426	23	)	)	PUNCT
brj-24683	426	24	,	,	PUNCT
brj-24683	426	25	seoul	seoul	PROPN
brj-24683	426	26	,	,	PUNCT
brj-24683	426	27	korea	korea	PROPN
brj-24683	426	28	,	,	PUNCT
brj-24683	426	29	pp	pp	ADP
brj-24683	426	30	.	.	PUNCT
brj-24683	426	31	3007	3007	NUM
brj-24683	426	32	-	-	SYM
brj-24683	426	33	3016	3016	NUM
brj-24683	426	34	.	.	PUNCT
brj-24683	427	1	doi	doi	NOUN
brj-24683	427	2	:	:	PUNCT
brj-24683	427	3	10.1109	10.1109	NUM
brj-24683	427	4	/	/	SYM
brj-24683	427	5	iccv.2019.00310	iccv.2019.00310	PROPN
brj-24683	427	6	wang	wang	PROPN
brj-24683	427	7	,	,	PUNCT
brj-24683	427	8	q.	q.	PROPN
brj-24683	427	9	,	,	PUNCT
brj-24683	427	10	wu	wu	PROPN
brj-24683	427	11	,	,	PUNCT
brj-24683	427	12	b.	b.	PROPN
brj-24683	427	13	,	,	PUNCT
brj-24683	427	14	zhu	zhu	PROPN
brj-24683	427	15	,	,	PUNCT
brj-24683	427	16	p.	p.	PROPN
brj-24683	427	17	,	,	PUNCT
brj-24683	427	18	li	li	PROPN
brj-24683	427	19	,	,	PUNCT
brj-24683	427	20	p.	p.	PROPN
brj-24683	427	21	,	,	PUNCT
brj-24683	427	22	zuo	zuo	PROPN
brj-24683	427	23	,	,	PUNCT
brj-24683	427	24	w.	w.	PROPN
brj-24683	427	25	,	,	PUNCT
brj-24683	427	26	and	and	CCONJ
brj-24683	427	27	hu	hu	PROPN
brj-24683	427	28	,	,	PUNCT
brj-24683	427	29	q.	q.	PROPN
brj-24683	427	30	(	(	PUNCT
brj-24683	427	31	2020	2020	NUM
brj-24683	427	32	)	)	PUNCT
brj-24683	427	33	.	.	PUNCT
brj-24683	428	1	“	"	PUNCT
brj-24683	428	2	eca	eca	NOUN
brj-24683	428	3	-	-	PUNCT
brj-24683	428	4	net	net	NOUN
brj-24683	428	5	:	:	PUNCT
brj-24683	428	6	efficient	efficient	ADJ
brj-24683	428	7	channel	channel	NOUN
brj-24683	428	8	attention	attention	NOUN
brj-24683	428	9	for	for	ADP
brj-24683	428	10	deep	deep	ADJ
brj-24683	428	11	convolutional	convolutional	ADJ
brj-24683	428	12	neural	neural	ADJ
brj-24683	428	13	networks	network	NOUN
brj-24683	428	14	,	,	PUNCT
brj-24683	428	15	”	"	PUNCT
brj-24683	428	16	in	in	ADP
brj-24683	428	17	:	:	PUNCT
brj-24683	428	18	2020	2020	NUM
brj-24683	428	19	ieee	ieee	NOUN
brj-24683	428	20	/	/	SYM
brj-24683	428	21	cvf	cvf	NOUN
brj-24683	428	22	conference	conference	NOUN
brj-24683	428	23	on	on	ADP
brj-24683	428	24	computer	computer	NOUN
brj-24683	428	25	vision	vision	NOUN
brj-24683	428	26	and	and	CCONJ
brj-24683	428	27	pattern	pattern	NOUN
brj-24683	428	28	recognition	recognition	NOUN
brj-24683	428	29	(	(	PUNCT
brj-24683	428	30	cvpr	cvpr	NOUN
brj-24683	428	31	)	)	PUNCT
brj-24683	428	32	,	,	PUNCT
brj-24683	428	33	seattle	seattle	PROPN
brj-24683	428	34	,	,	PUNCT
brj-24683	428	35	wa	wa	PROPN
brj-24683	428	36	,	,	PUNCT
brj-24683	428	37	usa	usa	PROPN
brj-24683	428	38	,	,	PUNCT
brj-24683	428	39	pp	pp	X
brj-24683	428	40	.	.	PUNCT
brj-24683	429	1	11531	11531	NUM
brj-24683	429	2	-	-	SYM
brj-24683	429	3	11539	11539	NUM
brj-24683	429	4	.	.	PUNCT
brj-24683	430	1	doi	doi	NOUN
brj-24683	430	2	:	:	PUNCT
brj-24683	430	3	10.1109	10.1109	NUM
brj-24683	430	4	/	/	SYM
brj-24683	430	5	cvpr42600.2020.01155	cvpr42600.2020.01155	SYM
brj-24683	430	6	wang	wang	PROPN
brj-24683	430	7	,	,	PUNCT
brj-24683	430	8	r.	r.	PROPN
brj-24683	430	9	,	,	PUNCT
brj-24683	430	10	liang	liang	PROPN
brj-24683	430	11	,	,	PUNCT
brj-24683	430	12	f.	f.	PROPN
brj-24683	430	13	,	,	PUNCT
brj-24683	430	14	wang	wang	PROPN
brj-24683	430	15	,	,	PUNCT
brj-24683	430	16	b.	b.	PROPN
brj-24683	430	17	,	,	PUNCT
brj-24683	430	18	and	and	CCONJ
brj-24683	430	19	mou	mou	PROPN
brj-24683	430	20	,	,	PUNCT
brj-24683	430	21	x.	x.	NOUN
brj-24683	430	22	(	(	PUNCT
brj-24683	430	23	2023	2023	NUM
brj-24683	430	24	)	)	PUNCT
brj-24683	430	25	.	.	PUNCT
brj-24683	431	1	“	"	PUNCT
brj-24683	431	2	odca	odca	PROPN
brj-24683	431	3	-	-	PUNCT
brj-24683	431	4	yolo	yolo	NOUN
brj-24683	431	5	:	:	PUNCT
brj-24683	431	6	an	an	DET
brj-24683	431	7	omni	omni	ADJ
brj-24683	431	8	-	-	PUNCT
brj-24683	431	9	dynamic	dynamic	ADJ
brj-24683	431	10	https://doi.org/10.1109/iccv51070.2023.00554	https://doi.org/10.1109/iccv51070.2023.00554	PROPN
brj-24683	431	11	https://doi.org/10.1080/15583058.2012.702369	https://doi.org/10.1080/15583058.2012.702369	PUNCT
brj-24683	431	12	https://doi.org/10.1080/15583058.2012.702369	https://doi.org/10.1080/15583058.2012.702369	PROPN
brj-24683	432	1	https://doi.org/10.3390/s23218705	https://doi.org/10.3390/s23218705	NUM
brj-24683	432	2	https://doi.org/10.1016/j.culher.2024.01.005	https://doi.org/10.1016/j.culher.2024.01.005	NUM
brj-24683	432	3	https://doi.org/10.5552/drvind.2024.0114	https://doi.org/10.5552/drvind.2024.0114	PROPN
brj-24683	432	4	https://doi.org/10.1109/cvpr.2016.91	https://doi.org/10.1109/cvpr.2016.91	PROPN
brj-24683	433	1	https://doi.org/10.1109/tpami.2016.2577031	https://doi.org/10.1109/tpami.2016.2577031	PROPN
brj-24683	434	1	https://doi.org/10.3390/s20020545	https://doi.org/10.3390/s20020545	NUM
brj-24683	434	2	https://doi.org/10.1007/s13595-014-0385-1	https://doi.org/10.1007/s13595-014-0385-1	NUM
brj-24683	434	3	https://doi.org/10.1155/2022/4878090	https://doi.org/10.1155/2022/4878090	PROPN
brj-24683	434	4	https://doi.org/10.3788/lop241509	https://doi.org/10.3788/lop241509	PROPN
brj-24683	434	5	https://doi.org/10.1109/adics58448.2024.10533619	https://doi.org/10.1109/adics58448.2024.10533619	PROPN
brj-24683	435	1	https://doi.org/10.1109/iccv.2019.00310	https://doi.org/10.1109/iccv.2019.00310	PROPN
brj-24683	435	2	https://doi.org/10.1109/cvpr42600.2020.01155	https://doi.org/10.1109/cvpr42600.2020.01155	VERB
brj-24683	435	3	peer	peer	NOUN
brj-24683	435	4	-	-	PUNCT
brj-24683	435	5	reviewed	review	VERB
brj-24683	435	6	article	article	NOUN
brj-24683	435	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	PUNCT
brj-24683	435	8	dou	dou	PROPN
brj-24683	435	9	&	&	CCONJ
brj-24683	435	10	you	you	PRON
brj-24683	435	11	(	(	PUNCT
brj-24683	435	12	2025	2025	NUM
brj-24683	435	13	)	)	PUNCT
brj-24683	435	14	.	.	PUNCT
brj-24683	436	1	“	"	PUNCT
brj-24683	436	2	wood	wood	NOUN
brj-24683	436	3	defect	defect	NOUN
brj-24683	436	4	identification	identification	NOUN
brj-24683	436	5	,	,	PUNCT
brj-24683	436	6	”	"	PUNCT
brj-24683	436	7	bioresources	bioresource	NOUN
brj-24683	436	8	20(3	20(3	NOUN
brj-24683	436	9	)	)	PUNCT
brj-24683	436	10	,	,	PUNCT
brj-24683	436	11	5709	5709	NUM
brj-24683	436	12	-	-	SYM
brj-24683	436	13	5730	5730	NUM
brj-24683	436	14	.	.	PUNCT
brj-24683	437	1	5730	5730	NUM
brj-24683	437	2	convolution	convolution	NOUN
brj-24683	437	3	coordinate	coordinate	NOUN
brj-24683	437	4	attention	attention	NOUN
brj-24683	437	5	-	-	PUNCT
brj-24683	437	6	based	base	VERB
brj-24683	437	7	yolo	yolo	NOUN
brj-24683	437	8	for	for	ADP
brj-24683	437	9	wood	wood	NOUN
brj-24683	437	10	defect	defect	NOUN
brj-24683	437	11	detection	detection	NOUN
brj-24683	437	12	,	,	PUNCT
brj-24683	437	13	”	"	PUNCT
brj-24683	437	14	forests	forest	VERB
brj-24683	437	15	14	14	NUM
brj-24683	437	16	,	,	PUNCT
brj-24683	437	17	article	article	NOUN
brj-24683	437	18	1885	1885	NUM
brj-24683	437	19	.	.	PUNCT
brj-24683	438	1	doi	doi	NOUN
brj-24683	438	2	:	:	PUNCT
brj-24683	438	3	10.3390	10.3390	NUM
brj-24683	438	4	/	/	SYM
brj-24683	438	5	f14091885	f14091885	PROPN
brj-24683	438	6	wang	wang	PROPN
brj-24683	438	7	,	,	PUNCT
brj-24683	438	8	j.	j.	PROPN
brj-24683	438	9	,	,	PUNCT
brj-24683	438	10	zhang	zhang	PROPN
brj-24683	438	11	,	,	PUNCT
brj-24683	438	12	c.	c.	PROPN
brj-24683	438	13	,	,	PUNCT
brj-24683	438	14	zhao	zhao	PROPN
brj-24683	438	15	,	,	PUNCT
brj-24683	438	16	m.	m.	NOUN
brj-24683	438	17	,	,	PUNCT
brj-24683	438	18	zou	zou	PROPN
brj-24683	438	19	,	,	PUNCT
brj-24683	438	20	h.	h.	PROPN
brj-24683	438	21	,	,	PUNCT
brj-24683	438	22	qi	qi	PROPN
brj-24683	438	23	,	,	PUNCT
brj-24683	438	24	l.	l.	PROPN
brj-24683	438	25	,	,	PUNCT
brj-24683	438	26	and	and	CCONJ
brj-24683	438	27	wang	wang	PROPN
brj-24683	438	28	,	,	PUNCT
brj-24683	438	29	z.	z.	PROPN
brj-24683	438	30	(	(	PUNCT
brj-24683	438	31	2024	2024	NUM
brj-24683	438	32	)	)	PUNCT
brj-24683	438	33	.	.	PUNCT
brj-24683	439	1	“	"	PUNCT
brj-24683	439	2	a	a	DET
brj-24683	439	3	composite	composite	ADJ
brj-24683	439	4	pulse	pulse	NOUN
brj-24683	439	5	excitation	excitation	NOUN
brj-24683	439	6	technique	technique	NOUN
brj-24683	439	7	for	for	ADP
brj-24683	439	8	air	air	NOUN
brj-24683	439	9	-	-	PUNCT
brj-24683	439	10	coupled	couple	VERB
brj-24683	439	11	ultrasonic	ultrasonic	ADJ
brj-24683	439	12	detection	detection	NOUN
brj-24683	439	13	of	of	ADP
brj-24683	439	14	defects	defect	NOUN
brj-24683	439	15	in	in	ADP
brj-24683	439	16	wood	wood	NOUN
brj-24683	439	17	,	,	PUNCT
brj-24683	439	18	”	"	PUNCT
brj-24683	439	19	sensors	sensor	NOUN
brj-24683	439	20	24	24	NUM
brj-24683	439	21	,	,	PUNCT
brj-24683	439	22	article	article	NOUN
brj-24683	439	23	7550	7550	NUM
brj-24683	439	24	.	.	PUNCT
brj-24683	440	1	doi	doi	NOUN
brj-24683	440	2	:	:	PUNCT
brj-24683	440	3	10.3390	10.3390	NUM
brj-24683	440	4	/	/	SYM
brj-24683	440	5	s24237550	s24237550	PROPN
brj-24683	440	6	wang	wang	PROPN
brj-24683	440	7	,	,	PUNCT
brj-24683	440	8	r.	r.	PROPN
brj-24683	440	9	,	,	PUNCT
brj-24683	440	10	liang	liang	PROPN
brj-24683	440	11	,	,	PUNCT
brj-24683	440	12	f.	f.	PROPN
brj-24683	440	13	,	,	PUNCT
brj-24683	440	14	wang	wang	PROPN
brj-24683	440	15	,	,	PUNCT
brj-24683	440	16	b.	b.	PROPN
brj-24683	440	17	,	,	PUNCT
brj-24683	440	18	zhang	zhang	PROPN
brj-24683	440	19	,	,	PUNCT
brj-24683	440	20	g.	g.	PROPN
brj-24683	440	21	,	,	PUNCT
brj-24683	440	22	chen	chen	PROPN
brj-24683	440	23	,	,	PUNCT
brj-24683	440	24	y.	y.	PROPN
brj-24683	440	25	,	,	PUNCT
brj-24683	440	26	and	and	CCONJ
brj-24683	440	27	mou	mou	PROPN
brj-24683	440	28	,	,	PUNCT
brj-24683	440	29	x.	x.	NOUN
brj-24683	440	30	(	(	PUNCT
brj-24683	440	31	2024	2024	NUM
brj-24683	440	32	)	)	PUNCT
brj-24683	440	33	,	,	PUNCT
brj-24683	440	34	“	"	PUNCT
brj-24683	440	35	an	an	DET
brj-24683	440	36	efficient	efficient	ADJ
brj-24683	440	37	and	and	CCONJ
brj-24683	440	38	accurate	accurate	ADJ
brj-24683	440	39	surface	surface	NOUN
brj-24683	440	40	defect	defect	NOUN
brj-24683	440	41	detection	detection	NOUN
brj-24683	440	42	method	method	NOUN
brj-24683	440	43	for	for	ADP
brj-24683	440	44	wood	wood	NOUN
brj-24683	440	45	based	base	VERB
brj-24683	440	46	on	on	ADP
brj-24683	440	47	improved	improved	ADJ
brj-24683	440	48	yolov8	yolov8	NOUN
brj-24683	440	49	,	,	PUNCT
brj-24683	440	50	”	"	PUNCT
brj-24683	440	51	forests	forest	NOUN
brj-24683	440	52	15(7	15(7	NUM
brj-24683	440	53	)	)	PUNCT
brj-24683	440	54	,	,	PUNCT
brj-24683	440	55	article	article	NOUN
brj-24683	440	56	1176	1176	NUM
brj-24683	440	57	.	.	PUNCT
brj-24683	441	1	doi	doi	NOUN
brj-24683	441	2	:	:	PUNCT
brj-24683	441	3	10.3390	10.3390	NUM
brj-24683	441	4	/	/	SYM
brj-24683	441	5	f15071176	f15071176	PROPN
brj-24683	441	6	wyckhuyse	wyckhuyse	NOUN
brj-24683	441	7	,	,	PUNCT
brj-24683	441	8	a.	a.	NOUN
brj-24683	441	9	,	,	PUNCT
brj-24683	441	10	and	and	CCONJ
brj-24683	441	11	maldague	maldague	NOUN
brj-24683	441	12	,	,	PUNCT
brj-24683	441	13	x.	x.	NOUN
brj-24683	441	14	(	(	PUNCT
brj-24683	441	15	2001	2001	NUM
brj-24683	441	16	)	)	PUNCT
brj-24683	441	17	.	.	PUNCT
brj-24683	442	1	“	"	PUNCT
brj-24683	442	2	a	a	DET
brj-24683	442	3	study	study	NOUN
brj-24683	442	4	of	of	ADP
brj-24683	442	5	wood	wood	NOUN
brj-24683	442	6	inspection	inspection	NOUN
brj-24683	442	7	by	by	ADP
brj-24683	442	8	infrared	infrared	ADJ
brj-24683	442	9	thermography	thermography	NOUN
brj-24683	442	10	,	,	PUNCT
brj-24683	442	11	part	part	NOUN
brj-24683	442	12	i	i	PRON
brj-24683	442	13	:	:	PUNCT
brj-24683	442	14	wood	wood	NOUN
brj-24683	442	15	pole	pole	NOUN
brj-24683	442	16	inspection	inspection	NOUN
brj-24683	442	17	by	by	ADP
brj-24683	442	18	infrared	infrared	ADJ
brj-24683	442	19	thermography	thermography	NOUN
brj-24683	442	20	,	,	PUNCT
brj-24683	442	21	”	"	PUNCT
brj-24683	442	22	research	research	NOUN
brj-24683	442	23	in	in	ADP
brj-24683	442	24	nondestructive	nondestructive	ADJ
brj-24683	442	25	evaluation	evaluation	NOUN
brj-24683	442	26	13	13	NUM
brj-24683	442	27	,	,	PUNCT
brj-24683	442	28	1	1	NUM
brj-24683	442	29	-	-	SYM
brj-24683	442	30	12	12	NUM
brj-24683	442	31	.	.	PUNCT
brj-24683	443	1	doi	doi	NOUN
brj-24683	443	2	:	:	PUNCT
brj-24683	443	3	10.1007	10.1007	NUM
brj-24683	443	4	/	/	SYM
brj-24683	443	5	s00164	s00164	NOUN
brj-24683	443	6	-	-	PUNCT
brj-24683	443	7	001	001	NUM
brj-24683	443	8	-	-	PUNCT
brj-24683	443	9	0005	0005	NUM
brj-24683	443	10	-	-	PUNCT
brj-24683	443	11	y	y	PROPN
brj-24683	443	12	xia	xia	PROPN
brj-24683	443	13	,	,	PUNCT
brj-24683	443	14	b.	b.	PROPN
brj-24683	443	15	,	,	PUNCT
brj-24683	443	16	luo	luo	PROPN
brj-24683	443	17	,	,	PUNCT
brj-24683	443	18	h.	h.	PROPN
brj-24683	443	19	,	,	PUNCT
brj-24683	443	20	and	and	CCONJ
brj-24683	443	21	shi	shi	PROPN
brj-24683	443	22	,	,	PUNCT
brj-24683	443	23	s.	s.	PROPN
brj-24683	443	24	(	(	PUNCT
brj-24683	443	25	2022	2022	NUM
brj-24683	443	26	)	)	PUNCT
brj-24683	443	27	.	.	PUNCT
brj-24683	444	1	“	"	PUNCT
brj-24683	444	2	improved	improve	VERB
brj-24683	444	3	faster	fast	ADV
brj-24683	444	4	r	r	NOUN
brj-24683	444	5	-	-	PUNCT
brj-24683	444	6	cnn	cnn	PROPN
brj-24683	444	7	based	base	VERB
brj-24683	444	8	surface	surface	NOUN
brj-24683	444	9	defect	defect	NOUN
brj-24683	444	10	detection	detection	NOUN
brj-24683	444	11	algorithm	algorithm	NOUN
brj-24683	444	12	for	for	ADP
brj-24683	444	13	plates	plate	NOUN
brj-24683	444	14	,	,	PUNCT
brj-24683	444	15	”	"	PUNCT
brj-24683	444	16	computational	computational	ADJ
brj-24683	444	17	intelligence	intelligence	NOUN
brj-24683	444	18	and	and	CCONJ
brj-24683	444	19	neuroscience	neuroscience	NOUN
brj-24683	444	20	1	1	NUM
brj-24683	444	21	-	-	SYM
brj-24683	444	22	11	11	NUM
brj-24683	444	23	.	.	PUNCT
brj-24683	445	1	doi	doi	NOUN
brj-24683	445	2	:	:	PUNCT
brj-24683	445	3	10.1155/2022/3248722	10.1155/2022/3248722	NUM
brj-24683	445	4	xi	xi	PROPN
brj-24683	445	5	,	,	PUNCT
brj-24683	445	6	h.	h.	PROPN
brj-24683	445	7	,	,	PUNCT
brj-24683	445	8	wang	wang	PROPN
brj-24683	445	9	,	,	PUNCT
brj-24683	445	10	r.	r.	PROPN
brj-24683	445	11	,	,	PUNCT
brj-24683	445	12	liang	liang	PROPN
brj-24683	445	13	,	,	PUNCT
brj-24683	445	14	f.	f.	PROPN
brj-24683	445	15	,	,	PUNCT
brj-24683	445	16	chen	chen	PROPN
brj-24683	445	17	,	,	PUNCT
brj-24683	445	18	y.	y.	PROPN
brj-24683	445	19	,	,	PUNCT
brj-24683	445	20	zhang	zhang	PROPN
brj-24683	445	21	,	,	PUNCT
brj-24683	445	22	g.	g.	PROPN
brj-24683	445	23	,	,	PUNCT
brj-24683	445	24	and	and	CCONJ
brj-24683	445	25	wang	wang	PROPN
brj-24683	445	26	,	,	PUNCT
brj-24683	445	27	b.	b.	PROPN
brj-24683	445	28	(	(	PUNCT
brj-24683	445	29	2024	2024	NUM
brj-24683	445	30	)	)	PUNCT
brj-24683	445	31	.	.	PUNCT
brj-24683	446	1	“	"	PUNCT
brj-24683	446	2	sim	sim	NOUN
brj-24683	446	3	-	-	PUNCT
brj-24683	446	4	yolo	yolo	NOUN
brj-24683	446	5	:	:	PUNCT
brj-24683	446	6	a	a	DET
brj-24683	446	7	wood	wood	NOUN
brj-24683	446	8	surface	surface	NOUN
brj-24683	446	9	defect	defect	NOUN
brj-24683	446	10	detection	detection	NOUN
brj-24683	446	11	method	method	NOUN
brj-24683	446	12	based	base	VERB
brj-24683	446	13	on	on	ADP
brj-24683	446	14	the	the	DET
brj-24683	446	15	improved	improved	ADJ
brj-24683	446	16	yolov8	yolov8	NOUN
brj-24683	446	17	,	,	PUNCT
brj-24683	446	18	”	"	PUNCT
brj-24683	446	19	coatings	coating	NOUN
brj-24683	446	20	14(8	14(8	NOUN
brj-24683	446	21	)	)	PUNCT
brj-24683	446	22	,	,	PUNCT
brj-24683	446	23	article	article	NOUN
brj-24683	446	24	1001	1001	NUM
brj-24683	446	25	.	.	PUNCT
brj-24683	447	1	doi	doi	NOUN
brj-24683	447	2	:	:	PUNCT
brj-24683	447	3	10.3390	10.3390	NUM
brj-24683	447	4	/	/	SYM
brj-24683	447	5	coatings14081001	coatings14081001	PROPN
brj-24683	447	6	yi	yi	PROPN
brj-24683	447	7	,	,	PUNCT
brj-24683	447	8	l.	l.	PROPN
brj-24683	447	9	p.	p.	PROPN
brj-24683	447	10	,	,	PUNCT
brj-24683	447	11	akbar	akbar	NOUN
brj-24683	447	12	,	,	PUNCT
brj-24683	447	13	m.	m.	NOUN
brj-24683	447	14	f.	f.	PROPN
brj-24683	447	15	,	,	PUNCT
brj-24683	447	16	wahab	wahab	PROPN
brj-24683	447	17	,	,	PUNCT
brj-24683	447	18	m.	m.	PROPN
brj-24683	447	19	n.	n.	PROPN
brj-24683	447	20	a.	a.	PROPN
brj-24683	447	21	,	,	PUNCT
brj-24683	447	22	rosdi	rosdi	NOUN
brj-24683	447	23	,	,	PUNCT
brj-24683	447	24	b.	b.	PROPN
brj-24683	447	25	a.	a.	PROPN
brj-24683	447	26	,	,	PUNCT
brj-24683	447	27	fauthan	fauthan	PROPN
brj-24683	447	28	,	,	PUNCT
brj-24683	447	29	m.	m.	NOUN
brj-24683	447	30	a.	a.	NOUN
brj-24683	447	31	,	,	PUNCT
brj-24683	447	32	and	and	CCONJ
brj-24683	447	33	shrifan	shrifan	NOUN
brj-24683	447	34	,	,	PUNCT
brj-24683	447	35	n.	n.	PROPN
brj-24683	447	36	h.	h.	PROPN
brj-24683	447	37	m.	m.	PROPN
brj-24683	447	38	m.	m.	PROPN
brj-24683	447	39	(	(	PUNCT
brj-24683	447	40	2024	2024	NUM
brj-24683	447	41	)	)	PUNCT
brj-24683	447	42	.	.	PUNCT
brj-24683	448	1	“	"	PUNCT
brj-24683	448	2	the	the	DET
brj-24683	448	3	prospect	prospect	NOUN
brj-24683	448	4	of	of	ADP
brj-24683	448	5	artificial	artificial	ADJ
brj-24683	448	6	intelligence	intelligence	NOUN
brj-24683	448	7	-	-	PUNCT
brj-24683	448	8	based	base	VERB
brj-24683	448	9	wood	wood	NOUN
brj-24683	448	10	surface	surface	NOUN
brj-24683	448	11	inspection	inspection	NOUN
brj-24683	448	12	:	:	PUNCT
brj-24683	448	13	a	a	DET
brj-24683	448	14	review	review	NOUN
brj-24683	448	15	,	,	PUNCT
brj-24683	448	16	”	"	PUNCT
brj-24683	448	17	ieee	ieee	NOUN
brj-24683	448	18	access	access	NOUN
brj-24683	448	19	12	12	NUM
brj-24683	448	20	,	,	PUNCT
brj-24683	448	21	84706	84706	NUM
brj-24683	448	22	-	-	SYM
brj-24683	448	23	84725	84725	NUM
brj-24683	448	24	.	.	PUNCT
brj-24683	449	1	doi	doi	NOUN
brj-24683	449	2	:	:	PUNCT
brj-24683	449	3	10.1109	10.1109	NUM
brj-24683	449	4	/	/	SYM
brj-24683	449	5	access.2024.3412928	access.2024.3412928	PROPN
brj-24683	449	6	yu	yu	PROPN
brj-24683	449	7	,	,	PUNCT
brj-24683	449	8	h.	h.	PROPN
brj-24683	449	9	,	,	PUNCT
brj-24683	449	10	liang	liang	PROPN
brj-24683	449	11	,	,	PUNCT
brj-24683	449	12	y.	y.	PROPN
brj-24683	449	13	,	,	PUNCT
brj-24683	449	14	liang	liang	PROPN
brj-24683	449	15	,	,	PUNCT
brj-24683	449	16	h.	h.	PROPN
brj-24683	449	17	,	,	PUNCT
brj-24683	449	18	and	and	CCONJ
brj-24683	449	19	zhang	zhang	PROPN
brj-24683	449	20	,	,	PUNCT
brj-24683	449	21	y.	y.	PROPN
brj-24683	449	22	(	(	PUNCT
brj-24683	449	23	2019	2019	NUM
brj-24683	449	24	)	)	PUNCT
brj-24683	449	25	.	.	PUNCT
brj-24683	450	1	“	"	PUNCT
brj-24683	450	2	recognition	recognition	NOUN
brj-24683	450	3	of	of	ADP
brj-24683	450	4	wood	wood	NOUN
brj-24683	450	5	surface	surface	NOUN
brj-24683	450	6	defects	defect	NOUN
brj-24683	450	7	with	with	ADP
brj-24683	450	8	near	near	ADP
brj-24683	450	9	infrared	infrared	ADJ
brj-24683	450	10	spectroscopy	spectroscopy	NOUN
brj-24683	450	11	and	and	CCONJ
brj-24683	450	12	machine	machine	NOUN
brj-24683	450	13	vision	vision	NOUN
brj-24683	450	14	,	,	PUNCT
brj-24683	450	15	”	"	PUNCT
brj-24683	450	16	journal	journal	NOUN
brj-24683	450	17	of	of	ADP
brj-24683	450	18	forestry	forestry	NOUN
brj-24683	450	19	research	research	NOUN
brj-24683	450	20	30	30	NUM
brj-24683	450	21	,	,	PUNCT
brj-24683	450	22	2379	2379	NUM
brj-24683	450	23	-	-	SYM
brj-24683	450	24	2386	2386	NUM
brj-24683	450	25	.	.	PUNCT
brj-24683	451	1	doi	doi	NOUN
brj-24683	451	2	:	:	PUNCT
brj-24683	451	3	10.1007	10.1007	NUM
brj-24683	451	4	/	/	SYM
brj-24683	451	5	s11676	s11676	NUM
brj-24683	451	6	-	-	PUNCT
brj-24683	451	7	018	018	NUM
brj-24683	451	8	-	-	PUNCT
brj-24683	451	9	00874	00874	NUM
brj-24683	451	10	-	-	PUNCT
brj-24683	451	11	w	w	NOUN
brj-24683	451	12	zhang	zhang	PROPN
brj-24683	451	13	,	,	PUNCT
brj-24683	451	14	y.	y.	PROPN
brj-24683	451	15	g.	g.	PROPN
brj-24683	451	16	(	(	PUNCT
brj-24683	451	17	2017	2017	NUM
brj-24683	451	18	)	)	PUNCT
brj-24683	451	19	.	.	PUNCT
brj-24683	452	1	restoration	restoration	NOUN
brj-24683	452	2	and	and	CCONJ
brj-24683	452	3	segmentation	segmentation	NOUN
brj-24683	452	4	of	of	ADP
brj-24683	452	5	wood	wood	NOUN
brj-24683	452	6	x	x	NOUN
brj-24683	452	7	-	-	NOUN
brj-24683	452	8	ray	ray	NOUN
brj-24683	452	9	images	image	NOUN
brj-24683	452	10	based	base	VERB
brj-24683	452	11	on	on	ADP
brj-24683	452	12	optimization	optimization	NOUN
brj-24683	452	13	algorithm	algorithm	NOUN
brj-24683	452	14	,	,	PUNCT
brj-24683	452	15	master	master	NOUN
brj-24683	452	16	’s	’s	PART
brj-24683	452	17	thesis	thesis	NOUN
brj-24683	452	18	,	,	PUNCT
brj-24683	452	19	northeastern	northeastern	ADJ
brj-24683	452	20	university	university	PROPN
brj-24683	452	21	,	,	PUNCT
brj-24683	452	22	shenyang	shenyang	PROPN
brj-24683	452	23	,	,	PUNCT
brj-24683	452	24	china	china	PROPN
brj-24683	452	25	.	.	PUNCT
brj-24683	453	1	zhu	zhu	PROPN
brj-24683	453	2	,	,	PUNCT
brj-24683	453	3	y.	y.	PROPN
brj-24683	453	4	,	,	PUNCT
brj-24683	453	5	xu	xu	PROPN
brj-24683	453	6	,	,	PUNCT
brj-24683	453	7	z.	z.	PROPN
brj-24683	453	8	,	,	PUNCT
brj-24683	453	9	lin	lin	PROPN
brj-24683	453	10	,	,	PUNCT
brj-24683	453	11	y.	y.	PROPN
brj-24683	453	12	,	,	PUNCT
brj-24683	453	13	chen	chen	PROPN
brj-24683	453	14	,	,	PUNCT
brj-24683	453	15	d.	d.	PROPN
brj-24683	453	16	ai	ai	PROPN
brj-24683	453	17	,	,	PUNCT
brj-24683	453	18	z.	z.	PROPN
brj-24683	453	19	,	,	PUNCT
brj-24683	453	20	and	and	CCONJ
brj-24683	453	21	zhang	zhang	PROPN
brj-24683	453	22	,	,	PUNCT
brj-24683	453	23	h.	h.	PROPN
brj-24683	453	24	(	(	PUNCT
brj-24683	453	25	2024	2024	NUM
brj-24683	453	26	)	)	PUNCT
brj-24683	453	27	.	.	PUNCT
brj-24683	454	1	“	"	PUNCT
brj-24683	454	2	a	a	DET
brj-24683	454	3	multi	multi	ADJ
brj-24683	454	4	-	-	ADJ
brj-24683	454	5	source	source	ADJ
brj-24683	454	6	data	data	NOUN
brj-24683	454	7	fusion	fusion	NOUN
brj-24683	454	8	network	network	NOUN
brj-24683	454	9	for	for	ADP
brj-24683	454	10	wood	wood	NOUN
brj-24683	454	11	surface	surface	NOUN
brj-24683	454	12	broken	break	VERB
brj-24683	454	13	defect	defect	NOUN
brj-24683	454	14	segmentation	segmentation	NOUN
brj-24683	454	15	,	,	PUNCT
brj-24683	454	16	”	"	PUNCT
brj-24683	454	17	sensors	sensor	NOUN
brj-24683	454	18	24	24	NUM
brj-24683	454	19	,	,	PUNCT
brj-24683	454	20	article	article	NOUN
brj-24683	454	21	1635	1635	NUM
brj-24683	454	22	.	.	PUNCT
brj-24683	455	1	doi	doi	NOUN
brj-24683	455	2	:	:	PUNCT
brj-24683	455	3	10.3390	10.3390	NUM
brj-24683	455	4	/	/	SYM
brj-24683	455	5	s24051635	s24051635	PROPN
brj-24683	455	6	zou	zou	PROPN
brj-24683	455	7	,	,	PUNCT
brj-24683	455	8	x.	x.	PROPN
brj-24683	455	9	,	,	PUNCT
brj-24683	455	10	wu	wu	PROPN
brj-24683	455	11	,	,	PUNCT
brj-24683	455	12	c.	c.	PROPN
brj-24683	455	13	,	,	PUNCT
brj-24683	455	14	liu	liu	PROPN
brj-24683	455	15	,	,	PUNCT
brj-24683	455	16	h.	h.	PROPN
brj-24683	455	17	,	,	PUNCT
brj-24683	455	18	yu	yu	PROPN
brj-24683	455	19	,	,	PUNCT
brj-24683	455	20	z.	z.	PROPN
brj-24683	455	21	,	,	PUNCT
brj-24683	455	22	and	and	CCONJ
brj-24683	455	23	kuang	kuang	PROPN
brj-24683	455	24	,	,	PUNCT
brj-24683	455	25	x.	x.	NOUN
brj-24683	455	26	(	(	PUNCT
brj-24683	455	27	2024	2024	NUM
brj-24683	455	28	)	)	PUNCT
brj-24683	455	29	.	.	PUNCT
brj-24683	456	1	“	"	PUNCT
brj-24683	456	2	an	an	DET
brj-24683	456	3	accurate	accurate	ADJ
brj-24683	456	4	object	object	NOUN
brj-24683	456	5	detection	detection	NOUN
brj-24683	456	6	of	of	ADP
brj-24683	456	7	wood	wood	NOUN
brj-24683	456	8	defects	defect	NOUN
brj-24683	456	9	using	use	VERB
brj-24683	456	10	an	an	DET
brj-24683	456	11	improved	improve	VERB
brj-24683	456	12	faster	fast	ADV
brj-24683	456	13	r	r	NOUN
brj-24683	456	14	-	-	PUNCT
brj-24683	456	15	cnn	cnn	PROPN
brj-24683	456	16	model	model	NOUN
brj-24683	456	17	,	,	PUNCT
brj-24683	456	18	”	"	PUNCT
brj-24683	456	19	wood	wood	NOUN
brj-24683	456	20	material	material	NOUN
brj-24683	456	21	science	science	NOUN
brj-24683	456	22	&	&	CCONJ
brj-24683	456	23	engineering	engineering	NOUN
brj-24683	456	24	20(2	20(2	NUM
brj-24683	456	25	)	)	PUNCT
brj-24683	456	26	,	,	PUNCT
brj-24683	456	27	413	413	NUM
brj-24683	456	28	-	-	SYM
brj-24683	456	29	419	419	NUM
brj-24683	456	30	.	.	PUNCT
brj-24683	457	1	doi	doi	NOUN
brj-24683	457	2	:	:	PUNCT
brj-24683	457	3	10.1080/17480272.2024.2352605	10.1080/17480272.2024.2352605	NUM
brj-24683	457	4	article	article	NOUN
brj-24683	457	5	submitted	submit	VERB
brj-24683	457	6	:	:	PUNCT
brj-24683	457	7	april	april	PROPN
brj-24683	457	8	10	10	NUM
brj-24683	457	9	,	,	PUNCT
brj-24683	457	10	2025	2025	NUM
brj-24683	457	11	;	;	PUNCT
brj-24683	457	12	peer	peer	NOUN
brj-24683	457	13	review	review	NOUN
brj-24683	457	14	completed	complete	VERB
brj-24683	457	15	:	:	PUNCT
brj-24683	457	16	april	april	PROPN
brj-24683	457	17	27	27	NUM
brj-24683	457	18	,	,	PUNCT
brj-24683	457	19	2025	2025	NUM
brj-24683	457	20	;	;	PUNCT
brj-24683	457	21	revisions	revision	NOUN
brj-24683	457	22	accepted	accept	VERB
brj-24683	457	23	:	:	PUNCT
brj-24683	457	24	may	may	AUX
brj-24683	457	25	22	22	NUM
brj-24683	457	26	,	,	PUNCT
brj-24683	457	27	2025	2025	NUM
brj-24683	457	28	;	;	PUNCT
brj-24683	457	29	published	publish	VERB
brj-24683	457	30	:	:	PUNCT
brj-24683	457	31	may	may	AUX
brj-24683	457	32	23	23	NUM
brj-24683	457	33	,	,	PUNCT
brj-24683	457	34	2025	2025	NUM
brj-24683	457	35	.	.	PUNCT
brj-24683	458	1	doi	doi	NOUN
brj-24683	458	2	:	:	PUNCT
brj-24683	458	3	10.15376	10.15376	NUM
brj-24683	458	4	/	/	SYM
brj-24683	458	5	biores.20.3.5709	biores.20.3.5709	PROPN
brj-24683	458	6	-	-	PUNCT
brj-24683	458	7	5730	5730	NUM
brj-24683	458	8	https://doi.org/10.3390/f14091885	https://doi.org/10.3390/f14091885	NUM
brj-24683	458	9	https://doi.org/10.3390/s24237550	https://doi.org/10.3390/s24237550	NUM
brj-24683	458	10	https://doi.org/10.3390/f15071176	https://doi.org/10.3390/f15071176	PROPN
brj-24683	458	11	https://doi.org/10.1007/s00164-001-0005-y	https://doi.org/10.1007/s00164-001-0005-y	PROPN
brj-24683	458	12	https://doi.org/10.1155/2022/3248722	https://doi.org/10.1155/2022/3248722	PROPN
brj-24683	458	13	https://doi.org/10.3390/coatings14081001	https://doi.org/10.3390/coatings14081001	ADJ
brj-24683	458	14	https://doi.org/10.1109/access.2024.3412928	https://doi.org/10.1109/access.2024.3412928	PROPN
brj-24683	459	1	https://doi.org/10.1007/s11676-018-00874-w	https://doi.org/10.1007/s11676-018-00874-w	INTJ
brj-24683	460	1	https://doi.org/10.3390/s24051635	https://doi.org/10.3390/s24051635	NUM
brj-24683	460	2	https://doi.org/10.1080/17480272.2024.2352605	https://doi.org/10.1080/17480272.2024.2352605	PUNCT
brj-24683	460	3	https://doi.org/10.1080/17480272.2024.2352605	https://doi.org/10.1080/17480272.2024.2352605	PUNCT