id	sid	tid	token	lemma	pos
brj-24141	1	1	peer	peer	NOUN
brj-24141	1	2	-	-	PUNCT
brj-24141	1	3	review	review	NOUN
brj-24141	1	4	article	article	NOUN
brj-24141	1	5	peer	peer	NOUN
brj-24141	1	6	-	-	PUNCT
brj-24141	1	7	reviewed	review	VERB
brj-24141	1	8	article	article	NOUN
brj-24141	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	1	10	li	li	PROPN
brj-24141	1	11	et	et	PROPN
brj-24141	1	12	al	al	PROPN
brj-24141	1	13	.	.	PROPN
brj-24141	2	1	(	(	PUNCT
brj-24141	2	2	2025	2025	NUM
brj-24141	2	3	)	)	PUNCT
brj-24141	2	4	.	.	PUNCT
brj-24141	3	1	“	"	PUNCT
brj-24141	3	2	improved	improved	ADJ
brj-24141	3	3	panel	panel	NOUN
brj-24141	3	4	defect	defect	NOUN
brj-24141	3	5	detection	detection	NOUN
brj-24141	3	6	,	,	PUNCT
brj-24141	3	7	”	"	PUNCT
brj-24141	3	8	bioresources	bioresource	NOUN
brj-24141	3	9	20(2	20(2	NUM
brj-24141	3	10	)	)	PUNCT
brj-24141	3	11	,	,	PUNCT
brj-24141	3	12	2556	2556	NUM
brj-24141	3	13	-	-	SYM
brj-24141	3	14	2573	2573	NUM
brj-24141	3	15	.	.	PUNCT
brj-24141	4	1	2556	2556	NUM
brj-24141	4	2	wood	wood	NOUN
brj-24141	4	3	panel	panel	NOUN
brj-24141	4	4	defect	defect	NOUN
brj-24141	4	5	detection	detection	NOUN
brj-24141	4	6	based	base	VERB
brj-24141	4	7	on	on	ADP
brj-24141	4	8	improved	improved	ADJ
brj-24141	4	9	yolov8n	yolov8n	PROPN
brj-24141	4	10	rui	rui	PROPN
brj-24141	4	11	li	li	PROPN
brj-24141	4	12	,	,	PUNCT
brj-24141	4	13	shilu	shilu	PROPN
brj-24141	4	14	zhong	zhong	PROPN
brj-24141	4	15	,	,	PUNCT
brj-24141	4	16	and	and	CCONJ
brj-24141	4	17	xuemei	xuemei	PROPN
brj-24141	4	18	yang	yang	PROPN
brj-24141	4	19	wood	wood	PROPN
brj-24141	4	20	panel	panel	NOUN
brj-24141	4	21	surface	surface	NOUN
brj-24141	4	22	defect	defect	NOUN
brj-24141	4	23	detection	detection	NOUN
brj-24141	4	24	is	be	AUX
brj-24141	4	25	critical	critical	ADJ
brj-24141	4	26	to	to	ADP
brj-24141	4	27	product	product	NOUN
brj-24141	4	28	quality	quality	NOUN
brj-24141	4	29	.	.	PUNCT
brj-24141	5	1	traditional	traditional	ADJ
brj-24141	5	2	detection	detection	NOUN
brj-24141	5	3	methods	method	NOUN
brj-24141	5	4	are	be	AUX
brj-24141	5	5	time	time	NOUN
brj-24141	5	6	-	-	PUNCT
brj-24141	5	7	consuming	consume	VERB
brj-24141	5	8	and	and	CCONJ
brj-24141	5	9	subjective	subjective	ADJ
brj-24141	5	10	,	,	PUNCT
brj-24141	5	11	and	and	CCONJ
brj-24141	5	12	they	they	PRON
brj-24141	5	13	can	can	AUX
brj-24141	5	14	lead	lead	VERB
brj-24141	5	15	to	to	ADP
brj-24141	5	16	economic	economic	ADJ
brj-24141	5	17	waste	waste	NOUN
brj-24141	5	18	,	,	PUNCT
brj-24141	5	19	while	while	SCONJ
brj-24141	5	20	deep	deep	ADJ
brj-24141	5	21	learning	learn	VERB
brj-24141	5	22	image	image	NOUN
brj-24141	5	23	recognition	recognition	NOUN
brj-24141	5	24	techniques	technique	NOUN
brj-24141	5	25	offer	offer	VERB
brj-24141	5	26	a	a	DET
brj-24141	5	27	new	new	ADJ
brj-24141	5	28	approach	approach	NOUN
brj-24141	5	29	.	.	PUNCT
brj-24141	6	1	however	however	ADV
brj-24141	6	2	,	,	PUNCT
brj-24141	6	3	the	the	DET
brj-24141	6	4	accuracy	accuracy	NOUN
brj-24141	6	5	and	and	CCONJ
brj-24141	6	6	convergence	convergence	NOUN
brj-24141	6	7	speed	speed	NOUN
brj-24141	6	8	of	of	ADP
brj-24141	6	9	existing	exist	VERB
brj-24141	6	10	defect	defect	NOUN
brj-24141	6	11	detection	detection	NOUN
brj-24141	6	12	techniques	technique	NOUN
brj-24141	6	13	still	still	ADV
brj-24141	6	14	require	require	VERB
brj-24141	6	15	improvement	improvement	NOUN
brj-24141	6	16	.	.	PUNCT
brj-24141	7	1	in	in	ADP
brj-24141	7	2	this	this	DET
brj-24141	7	3	paper	paper	NOUN
brj-24141	7	4	,	,	PUNCT
brj-24141	7	5	an	an	DET
brj-24141	7	6	improved	improved	ADJ
brj-24141	7	7	algorithm	algorithm	NOUN
brj-24141	7	8	based	base	VERB
brj-24141	7	9	on	on	ADP
brj-24141	7	10	yolov8n	yolov8n	PROPN
brj-24141	7	11	was	be	AUX
brj-24141	7	12	designed	design	VERB
brj-24141	7	13	for	for	ADP
brj-24141	7	14	accurate	accurate	ADJ
brj-24141	7	15	detection	detection	NOUN
brj-24141	7	16	of	of	ADP
brj-24141	7	17	wood	wood	NOUN
brj-24141	7	18	panel	panel	NOUN
brj-24141	7	19	defects	defect	NOUN
brj-24141	7	20	.	.	PUNCT
brj-24141	8	1	the	the	DET
brj-24141	8	2	c	c	NOUN
brj-24141	8	3	-	-	PUNCT
brj-24141	8	4	adown	adown	VERB
brj-24141	8	5	method	method	NOUN
brj-24141	8	6	was	be	AUX
brj-24141	8	7	designed	design	VERB
brj-24141	8	8	to	to	PART
brj-24141	8	9	replace	replace	VERB
brj-24141	8	10	traditional	traditional	ADJ
brj-24141	8	11	downsampling	downsampling	NOUN
brj-24141	8	12	,	,	PUNCT
brj-24141	8	13	while	while	SCONJ
brj-24141	8	14	preserving	preserve	VERB
brj-24141	8	15	high	high	ADJ
brj-24141	8	16	-	-	PUNCT
brj-24141	8	17	frequency	frequency	NOUN
brj-24141	8	18	features	feature	NOUN
brj-24141	8	19	.	.	PUNCT
brj-24141	9	1	the	the	DET
brj-24141	9	2	combination	combination	NOUN
brj-24141	9	3	of	of	ADP
brj-24141	9	4	the	the	DET
brj-24141	9	5	dilation	dilation	NOUN
brj-24141	9	6	-	-	PUNCT
brj-24141	9	7	wise	wise	ADJ
brj-24141	9	8	residual	residual	ADJ
brj-24141	9	9	module	module	NOUN
brj-24141	9	10	and	and	CCONJ
brj-24141	9	11	multi	multi	ADJ
brj-24141	9	12	-	-	ADJ
brj-24141	9	13	scale	scale	ADJ
brj-24141	9	14	dilation	dilation	NOUN
brj-24141	9	15	attention	attention	NOUN
brj-24141	9	16	was	be	AUX
brj-24141	9	17	employed	employ	VERB
brj-24141	9	18	to	to	PART
brj-24141	9	19	enhance	enhance	VERB
brj-24141	9	20	the	the	DET
brj-24141	9	21	multiscale	multiscale	ADJ
brj-24141	9	22	robustness	robustness	NOUN
brj-24141	9	23	of	of	ADP
brj-24141	9	24	defect	defect	ADJ
brj-24141	9	25	detection	detection	NOUN
brj-24141	9	26	.	.	PUNCT
brj-24141	10	1	a	a	DET
brj-24141	10	2	hybrid	hybrid	NOUN
brj-24141	10	3	encoder	encoder	NOUN
brj-24141	10	4	was	be	AUX
brj-24141	10	5	added	add	VERB
brj-24141	10	6	to	to	PART
brj-24141	10	7	improve	improve	VERB
brj-24141	10	8	localization	localization	NOUN
brj-24141	10	9	accuracy	accuracy	NOUN
brj-24141	10	10	.	.	PUNCT
brj-24141	11	1	the	the	DET
brj-24141	11	2	loss	loss	NOUN
brj-24141	11	3	function	function	NOUN
brj-24141	11	4	was	be	AUX
brj-24141	11	5	optimized	optimize	VERB
brj-24141	11	6	to	to	PART
brj-24141	11	7	improve	improve	VERB
brj-24141	11	8	detection	detection	NOUN
brj-24141	11	9	accuracy	accuracy	NOUN
brj-24141	11	10	and	and	CCONJ
brj-24141	11	11	convergence	convergence	NOUN
brj-24141	11	12	speed	speed	NOUN
brj-24141	11	13	.	.	PUNCT
brj-24141	12	1	compared	compare	VERB
brj-24141	12	2	to	to	ADP
brj-24141	12	3	the	the	DET
brj-24141	12	4	base	base	NOUN
brj-24141	12	5	yolov8	yolov8	PROPN
brj-24141	12	6	version	version	PROPN
brj-24141	12	7	,	,	PUNCT
brj-24141	12	8	the	the	DET
brj-24141	12	9	improved	improved	ADJ
brj-24141	12	10	model	model	NOUN
brj-24141	12	11	achieved	achieve	VERB
brj-24141	12	12	a	a	DET
brj-24141	12	13	6.1	6.1	NUM
brj-24141	12	14	%	%	NOUN
brj-24141	12	15	increase	increase	NOUN
brj-24141	12	16	in	in	ADP
brj-24141	12	17	map	map	NOUN
brj-24141	12	18	,	,	PUNCT
brj-24141	12	19	an	an	DET
brj-24141	12	20	8	8	NUM
brj-24141	12	21	%	%	NOUN
brj-24141	12	22	increase	increase	NOUN
brj-24141	12	23	in	in	ADP
brj-24141	12	24	recall	recall	NOUN
brj-24141	12	25	,	,	PUNCT
brj-24141	12	26	and	and	CCONJ
brj-24141	12	27	a	a	DET
brj-24141	12	28	3.6	3.6	NUM
brj-24141	12	29	%	%	NOUN
brj-24141	12	30	increase	increase	NOUN
brj-24141	12	31	in	in	ADP
brj-24141	12	32	precision	precision	NOUN
brj-24141	12	33	,	,	PUNCT
brj-24141	12	34	significantly	significantly	ADV
brj-24141	12	35	enhancing	enhance	VERB
brj-24141	12	36	the	the	DET
brj-24141	12	37	model	model	NOUN
brj-24141	12	38	’s	’s	PART
brj-24141	12	39	detection	detection	NOUN
brj-24141	12	40	capabilities	capability	NOUN
brj-24141	12	41	.	.	PUNCT
brj-24141	13	1	the	the	DET
brj-24141	13	2	github	github	PROPN
brj-24141	13	3	link	link	NOUN
brj-24141	13	4	to	to	ADP
brj-24141	13	5	the	the	DET
brj-24141	13	6	improved	improve	VERB
brj-24141	13	7	algorithm	algorithm	NOUN
brj-24141	13	8	files	file	NOUN
brj-24141	13	9	is	be	AUX
brj-24141	13	10	as	as	SCONJ
brj-24141	13	11	follows	follow	VERB
brj-24141	13	12	:	:	PUNCT
brj-24141	13	13	(	(	PUNCT
brj-24141	13	14	https://github.com/humblefactos1/yolov8-cdc/tree/main	https://github.com/humblefactos1/yolov8-cdc/tree/main	PROPN
brj-24141	13	15	.	.	PUNCT
brj-24141	13	16	)	)	PUNCT
brj-24141	14	1	doi	doi	NOUN
brj-24141	14	2	:	:	PUNCT
brj-24141	14	3	10.15376	10.15376	NUM
brj-24141	14	4	/	/	SYM
brj-24141	14	5	biores.20.2.2556	biores.20.2.2556	NOUN
brj-24141	14	6	-	-	PUNCT
brj-24141	14	7	2573	2573	NUM
brj-24141	14	8	keywords	keyword	NOUN
brj-24141	14	9	:	:	PUNCT
brj-24141	14	10	wood	wood	NOUN
brj-24141	14	11	panel	panel	NOUN
brj-24141	14	12	;	;	PUNCT
brj-24141	14	13	deep	deep	ADJ
brj-24141	14	14	learning	learning	NOUN
brj-24141	14	15	;	;	PUNCT
brj-24141	14	16	yolov8n	yolov8n	NOUN
brj-24141	14	17	;	;	PUNCT
brj-24141	14	18	c	c	X
brj-24141	14	19	-	-	PUNCT
brj-24141	14	20	adown	adown	NOUN
brj-24141	14	21	;	;	PUNCT
brj-24141	14	22	dilation	dilation	NOUN
brj-24141	14	23	-	-	PUNCT
brj-24141	14	24	wise	wise	ADJ
brj-24141	14	25	residual	residual	ADJ
brj-24141	14	26	;	;	PUNCT
brj-24141	14	27	multi	multi	ADJ
brj-24141	14	28	-	-	ADJ
brj-24141	14	29	scale	scale	ADJ
brj-24141	14	30	dilation	dilation	NOUN
brj-24141	14	31	attention	attention	NOUN
brj-24141	14	32	;	;	PUNCT
brj-24141	14	33	loss	loss	NOUN
brj-24141	14	34	function	function	NOUN
brj-24141	14	35	contact	contact	NOUN
brj-24141	14	36	information	information	NOUN
brj-24141	14	37	:	:	PUNCT
brj-24141	14	38	college	college	NOUN
brj-24141	14	39	of	of	ADP
brj-24141	14	40	furnishings	furnishing	NOUN
brj-24141	14	41	and	and	CCONJ
brj-24141	14	42	industrial	industrial	ADJ
brj-24141	14	43	design	design	NOUN
brj-24141	14	44	,	,	PUNCT
brj-24141	14	45	nanjing	nanjing	PROPN
brj-24141	14	46	forestry	forestry	PROPN
brj-24141	14	47	university	university	PROPN
brj-24141	14	48	,	,	PUNCT
brj-24141	14	49	nanjing	nanjing	PROPN
brj-24141	14	50	210037	210037	NUM
brj-24141	14	51	,	,	PUNCT
brj-24141	14	52	china	china	PROPN
brj-24141	14	53	;	;	PUNCT
brj-24141	14	54	*	*	PUNCT
brj-24141	14	55	corresponding	correspond	VERB
brj-24141	14	56	author	author	NOUN
brj-24141	14	57	:	:	PUNCT
brj-24141	14	58	mgslirui0909@gmail.com	mgslirui0909@gmail.com	X
brj-24141	15	1	introduction	introduction	NOUN
brj-24141	15	2	the	the	DET
brj-24141	15	3	detection	detection	NOUN
brj-24141	15	4	of	of	ADP
brj-24141	15	5	surface	surface	NOUN
brj-24141	15	6	defects	defect	NOUN
brj-24141	15	7	in	in	ADP
brj-24141	15	8	wood	wood	NOUN
brj-24141	15	9	panels	panel	NOUN
brj-24141	15	10	has	have	AUX
brj-24141	15	11	always	always	ADV
brj-24141	15	12	been	be	AUX
brj-24141	15	13	an	an	DET
brj-24141	15	14	urgent	urgent	ADJ
brj-24141	15	15	problem	problem	NOUN
brj-24141	15	16	for	for	ADP
brj-24141	15	17	the	the	DET
brj-24141	15	18	wood	wood	NOUN
brj-24141	15	19	processing	processing	NOUN
brj-24141	15	20	industry	industry	NOUN
brj-24141	15	21	.	.	PUNCT
brj-24141	16	1	the	the	DET
brj-24141	16	2	traditional	traditional	ADJ
brj-24141	16	3	method	method	NOUN
brj-24141	16	4	of	of	ADP
brj-24141	16	5	relying	rely	VERB
brj-24141	16	6	on	on	ADP
brj-24141	16	7	manual	manual	ADJ
brj-24141	16	8	visual	visual	ADJ
brj-24141	16	9	inspection	inspection	NOUN
brj-24141	16	10	has	have	VERB
brj-24141	16	11	many	many	ADJ
brj-24141	16	12	drawbacks	drawback	NOUN
brj-24141	16	13	,	,	PUNCT
brj-24141	16	14	including	include	VERB
brj-24141	16	15	low	low	ADJ
brj-24141	16	16	efficiency	efficiency	NOUN
brj-24141	16	17	,	,	PUNCT
brj-24141	16	18	low	low	ADJ
brj-24141	16	19	accuracy	accuracy	NOUN
brj-24141	16	20	,	,	PUNCT
brj-24141	16	21	and	and	CCONJ
brj-24141	16	22	high	high	ADJ
brj-24141	16	23	subjectivity	subjectivity	NOUN
brj-24141	16	24	.	.	PUNCT
brj-24141	17	1	in	in	ADP
brj-24141	17	2	addition	addition	NOUN
brj-24141	17	3	,	,	PUNCT
brj-24141	17	4	due	due	ADP
brj-24141	17	5	to	to	ADP
brj-24141	17	6	the	the	DET
brj-24141	17	7	low	low	ADJ
brj-24141	17	8	degree	degree	NOUN
brj-24141	17	9	of	of	ADP
brj-24141	17	10	automation	automation	NOUN
brj-24141	17	11	of	of	ADP
brj-24141	17	12	the	the	DET
brj-24141	17	13	production	production	NOUN
brj-24141	17	14	line	line	NOUN
brj-24141	17	15	,	,	PUNCT
brj-24141	17	16	it	it	PRON
brj-24141	17	17	is	be	AUX
brj-24141	17	18	impossible	impossible	ADJ
brj-24141	17	19	to	to	PART
brj-24141	17	20	realize	realize	VERB
brj-24141	17	21	real	real	ADJ
brj-24141	17	22	-	-	PUNCT
brj-24141	17	23	time	time	NOUN
brj-24141	17	24	monitoring	monitoring	NOUN
brj-24141	17	25	and	and	CCONJ
brj-24141	17	26	feedback	feedback	NOUN
brj-24141	17	27	,	,	PUNCT
brj-24141	17	28	resulting	result	VERB
brj-24141	17	29	in	in	ADP
brj-24141	17	30	a	a	DET
brj-24141	17	31	high	high	ADJ
brj-24141	17	32	rate	rate	NOUN
brj-24141	17	33	of	of	ADP
brj-24141	17	34	defective	defective	ADJ
brj-24141	17	35	products	product	NOUN
brj-24141	17	36	,	,	PUNCT
brj-24141	17	37	which	which	PRON
brj-24141	17	38	brings	bring	VERB
brj-24141	17	39	huge	huge	ADJ
brj-24141	17	40	economic	economic	ADJ
brj-24141	17	41	losses	loss	NOUN
brj-24141	17	42	to	to	ADP
brj-24141	17	43	enterprises	enterprise	NOUN
brj-24141	17	44	.	.	PUNCT
brj-24141	18	1	studies	study	NOUN
brj-24141	18	2	have	have	AUX
brj-24141	18	3	shown	show	VERB
brj-24141	18	4	that	that	SCONJ
brj-24141	18	5	traditional	traditional	ADJ
brj-24141	18	6	manual	manual	ADJ
brj-24141	18	7	defect	defect	NOUN
brj-24141	18	8	detection	detection	NOUN
brj-24141	18	9	results	result	VERB
brj-24141	18	10	in	in	ADP
brj-24141	18	11	approximately	approximately	ADV
brj-24141	18	12	25	25	NUM
brj-24141	18	13	%	%	NOUN
brj-24141	18	14	of	of	ADP
brj-24141	18	15	wood	wood	NOUN
brj-24141	18	16	resources	resource	NOUN
brj-24141	18	17	being	be	AUX
brj-24141	18	18	wasted	waste	VERB
brj-24141	18	19	.	.	PUNCT
brj-24141	19	1	a	a	DET
brj-24141	19	2	1	1	NUM
brj-24141	19	3	%	%	NOUN
brj-24141	19	4	reduction	reduction	NOUN
brj-24141	19	5	in	in	ADP
brj-24141	19	6	raw	raw	ADJ
brj-24141	19	7	material	material	NOUN
brj-24141	19	8	waste	waste	NOUN
brj-24141	19	9	can	can	AUX
brj-24141	19	10	decrease	decrease	VERB
brj-24141	19	11	overall	overall	ADJ
brj-24141	19	12	production	production	NOUN
brj-24141	19	13	costs	cost	NOUN
brj-24141	19	14	by	by	ADP
brj-24141	19	15	about	about	ADV
brj-24141	19	16	2	2	NUM
brj-24141	19	17	%	%	NOUN
brj-24141	19	18	(	(	PUNCT
brj-24141	19	19	buehlmann	buehlmann	PROPN
brj-24141	19	20	and	and	CCONJ
brj-24141	19	21	thomas	thomas	PROPN
brj-24141	19	22	2002	2002	NUM
brj-24141	19	23	)	)	PUNCT
brj-24141	19	24	.	.	PUNCT
brj-24141	20	1	in	in	ADP
brj-24141	20	2	addition	addition	NOUN
brj-24141	20	3	,	,	PUNCT
brj-24141	20	4	the	the	DET
brj-24141	20	5	repetitive	repetitive	ADJ
brj-24141	20	6	labor	labor	NOUN
brj-24141	20	7	of	of	ADP
brj-24141	20	8	manual	manual	ADJ
brj-24141	20	9	inspection	inspection	NOUN
brj-24141	20	10	easily	easily	ADV
brj-24141	20	11	leads	lead	VERB
brj-24141	20	12	to	to	ADP
brj-24141	20	13	inspector	inspector	NOUN
brj-24141	20	14	fatigue	fatigue	NOUN
brj-24141	20	15	,	,	PUNCT
brj-24141	20	16	which	which	PRON
brj-24141	20	17	affects	affect	VERB
brj-24141	20	18	the	the	DET
brj-24141	20	19	quality	quality	NOUN
brj-24141	20	20	of	of	ADP
brj-24141	20	21	inspection	inspection	NOUN
brj-24141	20	22	and	and	CCONJ
brj-24141	20	23	reduces	reduce	VERB
brj-24141	20	24	the	the	DET
brj-24141	20	25	mechanical	mechanical	ADJ
brj-24141	20	26	properties	property	NOUN
brj-24141	20	27	,	,	PUNCT
brj-24141	20	28	appearance	appearance	NOUN
brj-24141	20	29	and	and	CCONJ
brj-24141	20	30	utilization	utilization	NOUN
brj-24141	20	31	of	of	ADP
brj-24141	20	32	wood	wood	NOUN
brj-24141	20	33	,	,	PUNCT
brj-24141	20	34	resulting	result	VERB
brj-24141	20	35	in	in	ADP
brj-24141	20	36	a	a	DET
brj-24141	20	37	serious	serious	ADJ
brj-24141	20	38	waste	waste	NOUN
brj-24141	20	39	of	of	ADP
brj-24141	20	40	wood	wood	NOUN
brj-24141	20	41	resources	resource	NOUN
brj-24141	20	42	(	(	PUNCT
brj-24141	20	43	cheng	cheng	PROPN
brj-24141	20	44	2020	2020	NUM
brj-24141	20	45	)	)	PUNCT
brj-24141	20	46	.	.	PUNCT
brj-24141	21	1	the	the	DET
brj-24141	21	2	quality	quality	NOUN
brj-24141	21	3	of	of	ADP
brj-24141	21	4	inspections	inspection	NOUN
brj-24141	21	5	is	be	AUX
brj-24141	21	6	affected	affect	VERB
brj-24141	21	7	by	by	ADP
brj-24141	21	8	inspector	inspector	NOUN
brj-24141	21	9	fatigue	fatigue	NOUN
brj-24141	21	10	.	.	PUNCT
brj-24141	22	1	rapid	rapid	ADJ
brj-24141	22	2	advances	advance	NOUN
brj-24141	22	3	in	in	ADP
brj-24141	22	4	computer	computer	NOUN
brj-24141	22	5	and	and	CCONJ
brj-24141	22	6	sensor	sensor	NOUN
brj-24141	22	7	technology	technology	NOUN
brj-24141	22	8	have	have	AUX
brj-24141	22	9	revolutionized	revolutionize	VERB
brj-24141	22	10	the	the	DET
brj-24141	22	11	wood	wood	NOUN
brj-24141	22	12	industry	industry	NOUN
brj-24141	22	13	,	,	PUNCT
brj-24141	22	14	and	and	CCONJ
brj-24141	22	15	non	non	ADJ
brj-24141	22	16	-	-	ADJ
brj-24141	22	17	destructive	destructive	ADJ
brj-24141	22	18	testing	testing	NOUN
brj-24141	22	19	techniques	technique	NOUN
brj-24141	22	20	have	have	AUX
brj-24141	22	21	emerged	emerge	VERB
brj-24141	22	22	.	.	PUNCT
brj-24141	23	1	among	among	ADP
brj-24141	23	2	them	they	PRON
brj-24141	23	3	,	,	PUNCT
brj-24141	23	4	acoustic	acoustic	ADJ
brj-24141	23	5	,	,	PUNCT
brj-24141	23	6	radiographic	radiographic	ADJ
brj-24141	23	7	,	,	PUNCT
brj-24141	23	8	and	and	CCONJ
brj-24141	23	9	optical	optical	ADJ
brj-24141	23	10	inspection	inspection	NOUN
brj-24141	23	11	methods	method	NOUN
brj-24141	23	12	were	be	AUX
brj-24141	23	13	once	once	ADV
brj-24141	23	14	the	the	DET
brj-24141	23	15	traditional	traditional	ADJ
brj-24141	23	16	mainstream	mainstream	NOUN
brj-24141	23	17	means	mean	VERB
brj-24141	23	18	https://github.com/humblefactos1/yolov8-cdc/tree/main	https://github.com/humblefactos1/yolov8-cdc/tree/main	PROPN
brj-24141	23	19	peer	peer	NOUN
brj-24141	23	20	-	-	PUNCT
brj-24141	23	21	reviewed	review	VERB
brj-24141	23	22	article	article	NOUN
brj-24141	23	23	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	23	24	li	li	PROPN
brj-24141	23	25	et	et	PROPN
brj-24141	23	26	al	al	PROPN
brj-24141	23	27	.	.	PROPN
brj-24141	24	1	(	(	PUNCT
brj-24141	24	2	2025	2025	NUM
brj-24141	24	3	)	)	PUNCT
brj-24141	24	4	.	.	PUNCT
brj-24141	25	1	“	"	PUNCT
brj-24141	25	2	improved	improved	ADJ
brj-24141	25	3	panel	panel	NOUN
brj-24141	25	4	defect	defect	NOUN
brj-24141	25	5	detection	detection	NOUN
brj-24141	25	6	,	,	PUNCT
brj-24141	25	7	”	"	PUNCT
brj-24141	25	8	bioresources	bioresource	NOUN
brj-24141	25	9	20(2	20(2	NUM
brj-24141	25	10	)	)	PUNCT
brj-24141	25	11	,	,	PUNCT
brj-24141	25	12	2556	2556	NUM
brj-24141	25	13	-	-	SYM
brj-24141	25	14	2573	2573	NUM
brj-24141	25	15	.	.	PUNCT
brj-24141	26	1	2557	2557	NUM
brj-24141	26	2	(	(	PUNCT
brj-24141	26	3	wang	wang	PROPN
brj-24141	26	4	et	et	PROPN
brj-24141	26	5	al	al	PROPN
brj-24141	26	6	.	.	PROPN
brj-24141	26	7	2013	2013	NUM
brj-24141	26	8	)	)	PUNCT
brj-24141	26	9	.	.	PUNCT
brj-24141	27	1	however	however	ADV
brj-24141	27	2	,	,	PUNCT
brj-24141	27	3	as	as	SCONJ
brj-24141	27	4	deep	deep	ADJ
brj-24141	27	5	learning	learning	NOUN
brj-24141	27	6	technology	technology	NOUN
brj-24141	27	7	has	have	AUX
brj-24141	27	8	become	become	VERB
brj-24141	27	9	increasingly	increasingly	ADV
brj-24141	27	10	sophisticated	sophisticated	ADJ
brj-24141	27	11	,	,	PUNCT
brj-24141	27	12	it	it	PRON
brj-24141	27	13	has	have	AUX
brj-24141	27	14	become	become	VERB
brj-24141	27	15	increasingly	increasingly	ADV
brj-24141	27	16	popular	popular	ADJ
brj-24141	27	17	.	.	PUNCT
brj-24141	28	1	with	with	ADP
brj-24141	28	2	the	the	DET
brj-24141	28	3	maturity	maturity	NOUN
brj-24141	28	4	of	of	ADP
brj-24141	28	5	deep	deep	ADJ
brj-24141	28	6	learning	learning	NOUN
brj-24141	28	7	technology	technology	NOUN
brj-24141	28	8	,	,	PUNCT
brj-24141	28	9	ndt	ndt	PROPN
brj-24141	28	10	methods	method	NOUN
brj-24141	28	11	based	base	VERB
brj-24141	28	12	on	on	ADP
brj-24141	28	13	image	image	NOUN
brj-24141	28	14	recognition	recognition	NOUN
brj-24141	28	15	have	have	AUX
brj-24141	28	16	gradually	gradually	ADV
brj-24141	28	17	become	become	VERB
brj-24141	28	18	a	a	DET
brj-24141	28	19	research	research	NOUN
brj-24141	28	20	hotspot	hotspot	NOUN
brj-24141	28	21	in	in	ADP
brj-24141	28	22	academia	academia	NOUN
brj-24141	28	23	and	and	CCONJ
brj-24141	28	24	the	the	DET
brj-24141	28	25	industry	industry	NOUN
brj-24141	28	26	due	due	ADP
brj-24141	28	27	to	to	ADP
brj-24141	28	28	its	its	PRON
brj-24141	28	29	high	high	ADJ
brj-24141	28	30	efficiency	efficiency	NOUN
brj-24141	28	31	and	and	CCONJ
brj-24141	28	32	accuracy	accuracy	NOUN
brj-24141	28	33	(	(	PUNCT
brj-24141	28	34	wang	wang	PROPN
brj-24141	28	35	et	et	PROPN
brj-24141	28	36	al	al	PROPN
brj-24141	28	37	.	.	PROPN
brj-24141	28	38	2024a	2024a	NUM
brj-24141	28	39	)	)	PUNCT
brj-24141	28	40	.	.	PUNCT
brj-24141	29	1	this	this	DET
brj-24141	29	2	method	method	NOUN
brj-24141	29	3	realizes	realize	VERB
brj-24141	29	4	automatic	automatic	ADJ
brj-24141	29	5	identification	identification	NOUN
brj-24141	29	6	and	and	CCONJ
brj-24141	29	7	classification	classification	NOUN
brj-24141	29	8	of	of	ADP
brj-24141	29	9	defects	defect	NOUN
brj-24141	29	10	through	through	ADP
brj-24141	29	11	deep	deep	ADJ
brj-24141	29	12	learning	learning	NOUN
brj-24141	29	13	of	of	ADP
brj-24141	29	14	wood	wood	NOUN
brj-24141	29	15	panel	panel	NOUN
brj-24141	29	16	images	image	NOUN
brj-24141	29	17	,	,	PUNCT
brj-24141	29	18	providing	provide	VERB
brj-24141	29	19	a	a	DET
brj-24141	29	20	new	new	ADJ
brj-24141	29	21	paradigm	paradigm	NOUN
brj-24141	29	22	for	for	ADP
brj-24141	29	23	wood	wood	NOUN
brj-24141	29	24	quality	quality	NOUN
brj-24141	29	25	inspection	inspection	NOUN
brj-24141	29	26	.	.	PUNCT
brj-24141	30	1	related	relate	VERB
brj-24141	30	2	studies	study	NOUN
brj-24141	30	3	have	have	AUX
brj-24141	30	4	shown	show	VERB
brj-24141	30	5	that	that	SCONJ
brj-24141	30	6	the	the	DET
brj-24141	30	7	deep	deep	ADJ
brj-24141	30	8	learning	learning	NOUN
brj-24141	30	9	method	method	NOUN
brj-24141	30	10	shows	show	VERB
brj-24141	30	11	great	great	ADJ
brj-24141	30	12	potential	potential	NOUN
brj-24141	30	13	in	in	ADP
brj-24141	30	14	wood	wood	NOUN
brj-24141	30	15	panel	panel	NOUN
brj-24141	30	16	defect	defect	NOUN
brj-24141	30	17	detection	detection	NOUN
brj-24141	30	18	,	,	PUNCT
brj-24141	30	19	which	which	PRON
brj-24141	30	20	is	be	AUX
brj-24141	30	21	expected	expect	VERB
brj-24141	30	22	to	to	PART
brj-24141	30	23	significantly	significantly	ADV
brj-24141	30	24	improve	improve	VERB
brj-24141	30	25	the	the	DET
brj-24141	30	26	productivity	productivity	NOUN
brj-24141	30	27	and	and	CCONJ
brj-24141	30	28	product	product	NOUN
brj-24141	30	29	quality	quality	NOUN
brj-24141	30	30	in	in	ADP
brj-24141	30	31	the	the	DET
brj-24141	30	32	wood	wood	NOUN
brj-24141	30	33	processing	processing	NOUN
brj-24141	30	34	industry	industry	NOUN
brj-24141	30	35	(	(	PUNCT
brj-24141	30	36	liu	liu	PROPN
brj-24141	30	37	et	et	PROPN
brj-24141	30	38	al	al	PROPN
brj-24141	30	39	.	.	PROPN
brj-24141	30	40	2023	2023	NUM
brj-24141	30	41	)	)	PUNCT
brj-24141	30	42	.	.	PUNCT
brj-24141	31	1	urbonas	urbonas	PROPN
brj-24141	31	2	et	et	PROPN
brj-24141	31	3	al	al	PROPN
brj-24141	31	4	.	.	PROPN
brj-24141	32	1	(	(	PUNCT
brj-24141	32	2	2019	2019	NUM
brj-24141	32	3	)	)	PUNCT
brj-24141	32	4	used	use	VERB
brj-24141	32	5	a	a	DET
brj-24141	32	6	faster	fast	ADJ
brj-24141	32	7	r	r	NOUN
brj-24141	32	8	-	-	PUNCT
brj-24141	32	9	cnn	cnn	NOUN
brj-24141	32	10	-	-	PUNCT
brj-24141	32	11	based	base	VERB
brj-24141	32	12	target	target	NOUN
brj-24141	32	13	detection	detection	NOUN
brj-24141	32	14	network	network	NOUN
brj-24141	32	15	to	to	PART
brj-24141	32	16	localize	localize	VERB
brj-24141	32	17	and	and	CCONJ
brj-24141	32	18	classify	classify	VERB
brj-24141	32	19	surface	surface	NOUN
brj-24141	32	20	defects	defect	NOUN
brj-24141	32	21	in	in	ADP
brj-24141	32	22	wood	wood	NOUN
brj-24141	32	23	veneer	veneer	NOUN
brj-24141	32	24	,	,	PUNCT
brj-24141	32	25	achieving	achieve	VERB
brj-24141	32	26	an	an	DET
brj-24141	32	27	average	average	ADJ
brj-24141	32	28	accuracy	accuracy	NOUN
brj-24141	32	29	of	of	ADP
brj-24141	32	30	80.6	80.6	NUM
brj-24141	32	31	%	%	NOUN
brj-24141	32	32	using	use	VERB
brj-24141	32	33	resnet152	resnet152	PROPN
brj-24141	32	34	as	as	ADP
brj-24141	32	35	a	a	DET
brj-24141	32	36	pre	pre	ADJ
brj-24141	32	37	-	-	ADJ
brj-24141	32	38	trained	train	VERB
brj-24141	32	39	model	model	NOUN
brj-24141	32	40	.	.	PUNCT
brj-24141	33	1	cheng	cheng	PROPN
brj-24141	33	2	(	(	PUNCT
brj-24141	33	3	2023	2023	NUM
brj-24141	33	4	)	)	PUNCT
brj-24141	33	5	proposed	propose	VERB
brj-24141	33	6	a	a	DET
brj-24141	33	7	copy	copy	NOUN
brj-24141	33	8	-	-	PUNCT
brj-24141	33	9	paste	paste	NOUN
brj-24141	33	10	-	-	PUNCT
brj-24141	33	11	based	base	VERB
brj-24141	33	12	class	class	NOUN
brj-24141	33	13	coverage	coverage	NOUN
brj-24141	33	14	method	method	NOUN
brj-24141	33	15	to	to	PART
brj-24141	33	16	address	address	VERB
brj-24141	33	17	imbalanced	imbalanced	ADJ
brj-24141	33	18	datasets	dataset	NOUN
brj-24141	33	19	.	.	PUNCT
brj-24141	34	1	to	to	PART
brj-24141	34	2	tackle	tackle	VERB
brj-24141	34	3	real	real	ADJ
brj-24141	34	4	-	-	PUNCT
brj-24141	34	5	time	time	NOUN
brj-24141	34	6	performance	performance	NOUN
brj-24141	34	7	and	and	CCONJ
brj-24141	34	8	detection	detection	NOUN
brj-24141	34	9	accuracy	accuracy	NOUN
brj-24141	34	10	,	,	PUNCT
brj-24141	34	11	a	a	DET
brj-24141	34	12	cbi2	cbi2	PROPN
brj-24141	34	13	-	-	PUNCT
brj-24141	34	14	yolo	yolo	PROPN
brj-24141	34	15	model	model	NOUN
brj-24141	34	16	was	be	AUX
brj-24141	34	17	developed	develop	VERB
brj-24141	34	18	for	for	ADP
brj-24141	34	19	wood	wood	NOUN
brj-24141	34	20	panel	panel	NOUN
brj-24141	34	21	defect	defect	NOUN
brj-24141	34	22	detection	detection	NOUN
brj-24141	34	23	.	.	PUNCT
brj-24141	35	1	to	to	PART
brj-24141	35	2	fulfill	fulfill	VERB
brj-24141	35	3	the	the	DET
brj-24141	35	4	requirement	requirement	NOUN
brj-24141	35	5	of	of	ADP
brj-24141	35	6	calculating	calculate	VERB
brj-24141	35	7	defect	defect	NOUN
brj-24141	35	8	areas	area	NOUN
brj-24141	35	9	,	,	PUNCT
brj-24141	35	10	jia	jia	PROPN
brj-24141	35	11	et	et	PROPN
brj-24141	35	12	al	al	PROPN
brj-24141	35	13	.	.	PROPN
brj-24141	36	1	(	(	PUNCT
brj-24141	36	2	2023	2023	NUM
brj-24141	36	3	)	)	PUNCT
brj-24141	36	4	proposed	propose	VERB
brj-24141	36	5	a	a	DET
brj-24141	36	6	quantitative	quantitative	ADJ
brj-24141	36	7	recognition	recognition	NOUN
brj-24141	36	8	method	method	NOUN
brj-24141	36	9	based	base	VERB
brj-24141	36	10	on	on	ADP
brj-24141	36	11	yolov5	yolov5	NOUN
brj-24141	36	12	,	,	PUNCT
brj-24141	36	13	incorporating	incorporate	VERB
brj-24141	36	14	a	a	DET
brj-24141	36	15	dual	dual	ADJ
brj-24141	36	16	-	-	PUNCT
brj-24141	36	17	channel	channel	NOUN
brj-24141	36	18	attention	attention	NOUN
brj-24141	36	19	module	module	NOUN
brj-24141	36	20	to	to	PART
brj-24141	36	21	improve	improve	VERB
brj-24141	36	22	the	the	DET
brj-24141	36	23	model	model	NOUN
brj-24141	36	24	's	's	PART
brj-24141	36	25	ability	ability	NOUN
brj-24141	36	26	to	to	PART
brj-24141	36	27	recognize	recognize	VERB
brj-24141	36	28	specific	specific	ADJ
brj-24141	36	29	wood	wood	NOUN
brj-24141	36	30	defects	defect	NOUN
brj-24141	36	31	.	.	PUNCT
brj-24141	37	1	additionally	additionally	ADV
brj-24141	37	2	,	,	PUNCT
brj-24141	37	3	a	a	DET
brj-24141	37	4	shallow	shallow	ADJ
brj-24141	37	5	weighted	weight	VERB
brj-24141	37	6	feature	feature	NOUN
brj-24141	37	7	fusion	fusion	NOUN
brj-24141	37	8	network	network	NOUN
brj-24141	37	9	was	be	AUX
brj-24141	37	10	introduced	introduce	VERB
brj-24141	37	11	to	to	PART
brj-24141	37	12	fuse	fuse	VERB
brj-24141	37	13	feature	feature	NOUN
brj-24141	37	14	information	information	NOUN
brj-24141	37	15	from	from	ADP
brj-24141	37	16	various	various	ADJ
brj-24141	37	17	layers	layer	NOUN
brj-24141	37	18	extracted	extract	VERB
brj-24141	37	19	by	by	ADP
brj-24141	37	20	the	the	DET
brj-24141	37	21	backbone	backbone	NOUN
brj-24141	37	22	network	network	NOUN
brj-24141	37	23	,	,	PUNCT
brj-24141	37	24	reducing	reduce	VERB
brj-24141	37	25	the	the	DET
brj-24141	37	26	loss	loss	NOUN
brj-24141	37	27	of	of	ADP
brj-24141	37	28	feature	feature	NOUN
brj-24141	37	29	information	information	NOUN
brj-24141	37	30	for	for	ADP
brj-24141	37	31	small	small	ADJ
brj-24141	37	32	wood	wood	NOUN
brj-24141	37	33	defects	defect	NOUN
brj-24141	37	34	.	.	PUNCT
brj-24141	38	1	jiang	jiang	PROPN
brj-24141	38	2	and	and	CCONJ
brj-24141	38	3	zhao	zhao	PROPN
brj-24141	38	4	(	(	PUNCT
brj-24141	38	5	2024	2024	NUM
brj-24141	38	6	)	)	PUNCT
brj-24141	38	7	proposed	propose	VERB
brj-24141	38	8	yolov7	yolov7	PROPN
brj-24141	38	9	-	-	PUNCT
brj-24141	38	10	ess	ess	NOUN
brj-24141	38	11	based	base	VERB
brj-24141	38	12	on	on	ADP
brj-24141	38	13	yolov7	yolov7	PROPN
brj-24141	38	14	,	,	PUNCT
brj-24141	38	15	which	which	PRON
brj-24141	38	16	embeds	embed	VERB
brj-24141	38	17	a	a	DET
brj-24141	38	18	dual	dual	ADJ
brj-24141	38	19	-	-	PUNCT
brj-24141	38	20	channel	channel	NOUN
brj-24141	38	21	attention	attention	NOUN
brj-24141	38	22	module	module	NOUN
brj-24141	38	23	to	to	PART
brj-24141	38	24	improve	improve	VERB
brj-24141	38	25	the	the	DET
brj-24141	38	26	model	model	NOUN
brj-24141	38	27	’s	’s	PART
brj-24141	38	28	ability	ability	NOUN
brj-24141	38	29	to	to	PART
brj-24141	38	30	recognize	recognize	VERB
brj-24141	38	31	special	special	ADJ
brj-24141	38	32	defects	defect	NOUN
brj-24141	38	33	in	in	ADP
brj-24141	38	34	wood	wood	NOUN
brj-24141	38	35	panels	panel	NOUN
brj-24141	38	36	.	.	PUNCT
brj-24141	39	1	a	a	DET
brj-24141	39	2	shallow	shallow	ADJ
brj-24141	39	3	weighted	weight	VERB
brj-24141	39	4	feature	feature	NOUN
brj-24141	39	5	fusion	fusion	NOUN
brj-24141	39	6	network	network	NOUN
brj-24141	39	7	is	be	AUX
brj-24141	39	8	introduced	introduce	VERB
brj-24141	39	9	to	to	PART
brj-24141	39	10	fuse	fuse	VERB
brj-24141	39	11	the	the	DET
brj-24141	39	12	feature	feature	NOUN
brj-24141	39	13	information	information	NOUN
brj-24141	39	14	of	of	ADP
brj-24141	39	15	each	each	DET
brj-24141	39	16	layer	layer	NOUN
brj-24141	39	17	extracted	extract	VERB
brj-24141	39	18	by	by	ADP
brj-24141	39	19	the	the	DET
brj-24141	39	20	backbone	backbone	NOUN
brj-24141	39	21	network	network	NOUN
brj-24141	39	22	to	to	PART
brj-24141	39	23	reduce	reduce	VERB
brj-24141	39	24	the	the	DET
brj-24141	39	25	loss	loss	NOUN
brj-24141	39	26	of	of	ADP
brj-24141	39	27	feature	feature	NOUN
brj-24141	39	28	information	information	NOUN
brj-24141	39	29	of	of	ADP
brj-24141	39	30	small	small	ADJ
brj-24141	39	31	defects	defect	NOUN
brj-24141	39	32	in	in	ADP
brj-24141	39	33	wood	wood	NOUN
brj-24141	39	34	panel	panel	NOUN
brj-24141	39	35	.	.	PUNCT
brj-24141	40	1	yang	yang	PROPN
brj-24141	40	2	et	et	PROPN
brj-24141	40	3	al	al	PROPN
brj-24141	40	4	.	.	PROPN
brj-24141	41	1	(	(	PUNCT
brj-24141	41	2	2023	2023	NUM
brj-24141	41	3	)	)	PUNCT
brj-24141	41	4	employed	employ	VERB
brj-24141	41	5	global	global	ADJ
brj-24141	41	6	and	and	CCONJ
brj-24141	41	7	local	local	ADJ
brj-24141	41	8	adaptive	adaptive	ADJ
brj-24141	41	9	thresholding	thresholde	VERB
brj-24141	41	10	algorithms	algorithm	NOUN
brj-24141	41	11	to	to	PART
brj-24141	41	12	segment	segment	VERB
brj-24141	41	13	surface	surface	NOUN
brj-24141	41	14	defects	defect	NOUN
brj-24141	41	15	and	and	CCONJ
brj-24141	41	16	extract	extract	VERB
brj-24141	41	17	image	image	NOUN
brj-24141	41	18	patches	patch	NOUN
brj-24141	41	19	.	.	PUNCT
brj-24141	42	1	by	by	ADP
brj-24141	42	2	replacing	replace	VERB
brj-24141	42	3	the	the	DET
brj-24141	42	4	relu	relu	NOUN
brj-24141	42	5	activation	activation	NOUN
brj-24141	42	6	function	function	VERB
brj-24141	42	7	with	with	ADP
brj-24141	42	8	relu6	relu6	PROPN
brj-24141	42	9	and	and	CCONJ
brj-24141	42	10	introducing	introduce	VERB
brj-24141	42	11	an	an	DET
brj-24141	42	12	inverted	inverted	ADJ
brj-24141	42	13	residual	residual	ADJ
brj-24141	42	14	structure	structure	NOUN
brj-24141	42	15	,	,	PUNCT
brj-24141	42	16	the	the	DET
brj-24141	42	17	mobilenetv2	mobilenetv2	PROPN
brj-24141	42	18	deep	deep	ADJ
brj-24141	42	19	learning	learning	NOUN
brj-24141	42	20	network	network	NOUN
brj-24141	42	21	was	be	AUX
brj-24141	42	22	optimized	optimize	VERB
brj-24141	42	23	for	for	ADP
brj-24141	42	24	defect	defect	NOUN
brj-24141	42	25	detection	detection	NOUN
brj-24141	42	26	and	and	CCONJ
brj-24141	42	27	classification	classification	NOUN
brj-24141	42	28	.	.	PUNCT
brj-24141	43	1	wang	wang	PROPN
brj-24141	43	2	et	et	PROPN
brj-24141	43	3	al	al	PROPN
brj-24141	43	4	.	.	PROPN
brj-24141	43	5	(	(	PUNCT
brj-24141	43	6	2024b	2024b	NUM
brj-24141	43	7	)	)	PUNCT
brj-24141	43	8	constructed	construct	VERB
brj-24141	43	9	a	a	DET
brj-24141	43	10	wood	wood	NOUN
brj-24141	43	11	-	-	PUNCT
brj-24141	43	12	net	net	NOUN
brj-24141	43	13	network	network	NOUN
brj-24141	43	14	,	,	PUNCT
brj-24141	43	15	which	which	PRON
brj-24141	43	16	realizes	realize	VERB
brj-24141	43	17	the	the	DET
brj-24141	43	18	defect	defect	NOUN
brj-24141	43	19	recognition	recognition	NOUN
brj-24141	43	20	in	in	ADP
brj-24141	43	21	the	the	DET
brj-24141	43	22	process	process	NOUN
brj-24141	43	23	of	of	ADP
brj-24141	43	24	wood	wood	NOUN
brj-24141	43	25	preference	preference	NOUN
brj-24141	43	26	with	with	ADP
brj-24141	43	27	high	high	ADJ
brj-24141	43	28	accuracy	accuracy	NOUN
brj-24141	43	29	.	.	PUNCT
brj-24141	44	1	wang	wang	PROPN
brj-24141	44	2	et	et	PROPN
brj-24141	44	3	al	al	PROPN
brj-24141	44	4	.	.	PROPN
brj-24141	44	5	(	(	PUNCT
brj-24141	44	6	2024c	2024c	NUM
brj-24141	44	7	)	)	PUNCT
brj-24141	44	8	introduced	introduce	VERB
brj-24141	44	9	a	a	DET
brj-24141	44	10	two	two	NUM
brj-24141	44	11	-	-	PUNCT
brj-24141	44	12	way	way	NOUN
brj-24141	44	13	feature	feature	NOUN
brj-24141	44	14	fusion	fusion	NOUN
brj-24141	44	15	network	network	NOUN
brj-24141	44	16	based	base	VERB
brj-24141	44	17	on	on	ADP
brj-24141	44	18	the	the	DET
brj-24141	44	19	yolo	yolo	ADJ
brj-24141	44	20	-	-	PUNCT
brj-24141	44	21	v8	v8	NOUN
brj-24141	44	22	algorithm	algorithm	NOUN
brj-24141	44	23	and	and	CCONJ
brj-24141	44	24	proposes	propose	VERB
brj-24141	44	25	a	a	DET
brj-24141	44	26	feature	feature	NOUN
brj-24141	44	27	fusion	fusion	NOUN
brj-24141	44	28	network	network	NOUN
brj-24141	44	29	model	model	NOUN
brj-24141	44	30	that	that	PRON
brj-24141	44	31	combines	combine	VERB
brj-24141	44	32	the	the	DET
brj-24141	44	33	attention	attention	NOUN
brj-24141	44	34	mechanism	mechanism	NOUN
brj-24141	44	35	and	and	CCONJ
brj-24141	44	36	loss	loss	NOUN
brj-24141	44	37	function	function	NOUN
brj-24141	44	38	optimization	optimization	NOUN
brj-24141	44	39	.	.	PUNCT
brj-24141	45	1	aiming	aim	VERB
brj-24141	45	2	at	at	ADP
brj-24141	45	3	the	the	DET
brj-24141	45	4	lack	lack	NOUN
brj-24141	45	5	of	of	ADP
brj-24141	45	6	detection	detection	NOUN
brj-24141	45	7	and	and	CCONJ
brj-24141	45	8	leakage	leakage	NOUN
brj-24141	45	9	caused	cause	VERB
brj-24141	45	10	by	by	ADP
brj-24141	45	11	the	the	DET
brj-24141	45	12	complexity	complexity	NOUN
brj-24141	45	13	of	of	ADP
brj-24141	45	14	defects	defect	NOUN
brj-24141	45	15	and	and	CCONJ
brj-24141	45	16	low	low	ADJ
brj-24141	45	17	recognition	recognition	NOUN
brj-24141	45	18	degree	degree	NOUN
brj-24141	45	19	in	in	ADP
brj-24141	45	20	the	the	DET
brj-24141	45	21	nondestructive	nondestructive	ADJ
brj-24141	45	22	testing	testing	NOUN
brj-24141	45	23	of	of	ADP
brj-24141	45	24	wood	wood	NOUN
brj-24141	45	25	panel	panel	NOUN
brj-24141	45	26	,	,	PUNCT
brj-24141	45	27	this	this	DET
brj-24141	45	28	paper	paper	NOUN
brj-24141	45	29	designs	design	VERB
brj-24141	45	30	an	an	DET
brj-24141	45	31	improved	improved	ADJ
brj-24141	45	32	detection	detection	NOUN
brj-24141	45	33	model	model	NOUN
brj-24141	45	34	based	base	VERB
brj-24141	45	35	on	on	ADP
brj-24141	45	36	yolov8	yolov8	PROPN
brj-24141	45	37	.	.	PUNCT
brj-24141	46	1	the	the	DET
brj-24141	46	2	main	main	ADJ
brj-24141	46	3	improvement	improvement	NOUN
brj-24141	46	4	points	point	NOUN
brj-24141	46	5	are	be	AUX
brj-24141	46	6	as	as	SCONJ
brj-24141	46	7	follows	follow	VERB
brj-24141	46	8	:	:	PUNCT
brj-24141	47	1	1	1	X
brj-24141	47	2	.	.	PUNCT
brj-24141	47	3	the	the	DET
brj-24141	47	4	design	design	NOUN
brj-24141	47	5	of	of	ADP
brj-24141	47	6	c	c	NOUN
brj-24141	47	7	-	-	PUNCT
brj-24141	47	8	adown	adown	NOUN
brj-24141	47	9	instead	instead	ADV
brj-24141	47	10	of	of	ADP
brj-24141	47	11	the	the	DET
brj-24141	47	12	traditional	traditional	ADJ
brj-24141	47	13	convolutional	convolutional	ADJ
brj-24141	47	14	downsampling	downsampling	NOUN
brj-24141	47	15	retains	retain	VERB
brj-24141	47	16	the	the	DET
brj-24141	47	17	main	main	ADJ
brj-24141	47	18	features	feature	NOUN
brj-24141	47	19	of	of	ADP
brj-24141	47	20	the	the	DET
brj-24141	47	21	wood	wood	NOUN
brj-24141	47	22	panel	panel	NOUN
brj-24141	47	23	defects	defect	NOUN
brj-24141	47	24	while	while	SCONJ
brj-24141	47	25	effectively	effectively	ADV
brj-24141	47	26	reducing	reduce	VERB
brj-24141	47	27	the	the	DET
brj-24141	47	28	size	size	NOUN
brj-24141	47	29	of	of	ADP
brj-24141	47	30	the	the	DET
brj-24141	47	31	feature	feature	NOUN
brj-24141	47	32	map	map	NOUN
brj-24141	47	33	and	and	CCONJ
brj-24141	47	34	enhancing	enhance	VERB
brj-24141	47	35	the	the	DET
brj-24141	47	36	model	model	NOUN
brj-24141	47	37	’s	’s	PART
brj-24141	47	38	ability	ability	NOUN
brj-24141	47	39	to	to	PART
brj-24141	47	40	perceive	perceive	VERB
brj-24141	47	41	the	the	DET
brj-24141	47	42	local	local	ADJ
brj-24141	47	43	features	feature	NOUN
brj-24141	47	44	.	.	PUNCT
brj-24141	48	1	2	2	X
brj-24141	48	2	.	.	X
brj-24141	48	3	a	a	DET
brj-24141	48	4	dynamic	dynamic	ADJ
brj-24141	48	5	weight	weight	NOUN
brj-24141	48	6	residual	residual	ADJ
brj-24141	48	7	(	(	PUNCT
brj-24141	48	8	dwr	dwr	NOUN
brj-24141	48	9	)	)	PUNCT
brj-24141	48	10	module	module	NOUN
brj-24141	48	11	combining	combine	VERB
brj-24141	48	12	the	the	DET
brj-24141	48	13	attention	attention	NOUN
brj-24141	48	14	mechanism	mechanism	NOUN
brj-24141	48	15	of	of	ADP
brj-24141	48	16	multi	multi	ADJ
brj-24141	48	17	-	-	ADJ
brj-24141	48	18	scale	scale	ADJ
brj-24141	48	19	feature	feature	NOUN
brj-24141	48	20	alignment	alignment	NOUN
brj-24141	48	21	(	(	PUNCT
brj-24141	48	22	msda	msda	NOUN
brj-24141	48	23	)	)	PUNCT
brj-24141	48	24	is	be	AUX
brj-24141	48	25	designed	design	VERB
brj-24141	48	26	to	to	PART
brj-24141	48	27	replace	replace	VERB
brj-24141	48	28	the	the	DET
brj-24141	48	29	c2f	c2f	NOUN
brj-24141	48	30	and	and	CCONJ
brj-24141	48	31	bottleneck	bottleneck	NOUN
brj-24141	48	32	modules	module	NOUN
brj-24141	48	33	in	in	ADP
brj-24141	48	34	the	the	DET
brj-24141	48	35	original	original	ADJ
brj-24141	48	36	yolov8	yolov8	NOUN
brj-24141	48	37	.	.	PUNCT
brj-24141	49	1	this	this	DET
brj-24141	49	2	module	module	NOUN
brj-24141	49	3	can	can	AUX
brj-24141	49	4	adaptively	adaptively	ADV
brj-24141	49	5	adjust	adjust	VERB
brj-24141	49	6	the	the	DET
brj-24141	49	7	weights	weight	NOUN
brj-24141	49	8	of	of	ADP
brj-24141	49	9	different	different	ADJ
brj-24141	49	10	scale	scale	NOUN
brj-24141	49	11	features	feature	VERB
brj-24141	49	12	to	to	PART
brj-24141	49	13	improve	improve	VERB
brj-24141	49	14	the	the	DET
brj-24141	49	15	detection	detection	NOUN
brj-24141	49	16	accuracy	accuracy	NOUN
brj-24141	49	17	of	of	ADP
brj-24141	49	18	the	the	DET
brj-24141	49	19	model	model	NOUN
brj-24141	49	20	for	for	ADP
brj-24141	49	21	multi	multi	ADJ
brj-24141	49	22	-	-	ADJ
brj-24141	49	23	scale	scale	ADJ
brj-24141	49	24	targets	target	NOUN
brj-24141	49	25	.	.	PUNCT
brj-24141	50	1	peer	peer	NOUN
brj-24141	50	2	-	-	PUNCT
brj-24141	50	3	reviewed	review	VERB
brj-24141	50	4	article	article	NOUN
brj-24141	50	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	50	6	li	li	PROPN
brj-24141	50	7	et	et	PROPN
brj-24141	50	8	al	al	PROPN
brj-24141	50	9	.	.	PROPN
brj-24141	51	1	(	(	PUNCT
brj-24141	51	2	2025	2025	NUM
brj-24141	51	3	)	)	PUNCT
brj-24141	51	4	.	.	PUNCT
brj-24141	52	1	“	"	PUNCT
brj-24141	52	2	improved	improved	ADJ
brj-24141	52	3	panel	panel	NOUN
brj-24141	52	4	defect	defect	NOUN
brj-24141	52	5	detection	detection	NOUN
brj-24141	52	6	,	,	PUNCT
brj-24141	52	7	”	"	PUNCT
brj-24141	52	8	bioresources	bioresource	NOUN
brj-24141	52	9	20(2	20(2	NUM
brj-24141	52	10	)	)	PUNCT
brj-24141	52	11	,	,	PUNCT
brj-24141	52	12	2556	2556	NUM
brj-24141	52	13	-	-	SYM
brj-24141	52	14	2573	2573	NUM
brj-24141	52	15	.	.	PUNCT
brj-24141	52	16	2558	2558	NUM
brj-24141	52	17	3	3	X
brj-24141	52	18	.	.	PUNCT
brj-24141	53	1	the	the	DET
brj-24141	53	2	neck	neck	NOUN
brj-24141	53	3	structure	structure	NOUN
brj-24141	53	4	is	be	AUX
brj-24141	53	5	improved	improve	VERB
brj-24141	53	6	by	by	ADP
brj-24141	53	7	utilizing	utilize	VERB
brj-24141	53	8	a	a	DET
brj-24141	53	9	hybrid	hybrid	ADJ
brj-24141	53	10	encoder	encoder	NOUN
brj-24141	53	11	to	to	PART
brj-24141	53	12	convert	convert	VERB
brj-24141	53	13	multi	multi	ADJ
brj-24141	53	14	-	-	ADJ
brj-24141	53	15	scale	scale	ADJ
brj-24141	53	16	features	feature	NOUN
brj-24141	53	17	into	into	ADP
brj-24141	53	18	a	a	DET
brj-24141	53	19	series	series	NOUN
brj-24141	53	20	of	of	ADP
brj-24141	53	21	image	image	NOUN
brj-24141	53	22	features	feature	VERB
brj-24141	53	23	through	through	ADP
brj-24141	53	24	intra	intra	ADJ
brj-24141	53	25	-	-	ADJ
brj-24141	53	26	scale	scale	ADJ
brj-24141	53	27	feature	feature	NOUN
brj-24141	53	28	interaction	interaction	NOUN
brj-24141	53	29	and	and	CCONJ
brj-24141	53	30	crossscale	crossscale	ADJ
brj-24141	53	31	feature	feature	NOUN
brj-24141	53	32	fusion	fusion	NOUN
brj-24141	53	33	.	.	PUNCT
brj-24141	54	1	4	4	X
brj-24141	54	2	.	.	X
brj-24141	54	3	the	the	DET
brj-24141	54	4	loss	loss	NOUN
brj-24141	54	5	function	function	NOUN
brj-24141	54	6	is	be	AUX
brj-24141	54	7	improved	improve	VERB
brj-24141	54	8	to	to	PART
brj-24141	54	9	speed	speed	VERB
brj-24141	54	10	up	up	ADP
brj-24141	54	11	the	the	DET
brj-24141	54	12	convergence	convergence	NOUN
brj-24141	54	13	of	of	ADP
brj-24141	54	14	the	the	DET
brj-24141	54	15	model	model	NOUN
brj-24141	54	16	and	and	CCONJ
brj-24141	54	17	improve	improve	VERB
brj-24141	54	18	the	the	DET
brj-24141	54	19	detection	detection	NOUN
brj-24141	54	20	accuracy	accuracy	NOUN
brj-24141	54	21	.	.	PUNCT
brj-24141	55	1	yolov8	yolov8	NOUN
brj-24141	55	2	detection	detection	NOUN
brj-24141	55	3	algorithm	algorithm	PROPN
brj-24141	55	4	yolov8	yolov8	PROPN
brj-24141	55	5	,	,	PUNCT
brj-24141	55	6	released	release	VERB
brj-24141	55	7	by	by	ADP
brj-24141	55	8	ultralytics	ultralytic	NOUN
brj-24141	55	9	in	in	ADP
brj-24141	55	10	2023	2023	NUM
brj-24141	55	11	,	,	PUNCT
brj-24141	55	12	is	be	AUX
brj-24141	55	13	the	the	DET
brj-24141	55	14	latest	late	ADJ
brj-24141	55	15	iteration	iteration	NOUN
brj-24141	55	16	of	of	ADP
brj-24141	55	17	the	the	DET
brj-24141	55	18	yolo	yolo	ADJ
brj-24141	55	19	series	series	NOUN
brj-24141	55	20	,	,	PUNCT
brj-24141	55	21	building	build	VERB
brj-24141	55	22	upon	upon	SCONJ
brj-24141	55	23	the	the	DET
brj-24141	55	24	significant	significant	ADJ
brj-24141	55	25	speed	speed	NOUN
brj-24141	55	26	and	and	CCONJ
brj-24141	55	27	accuracy	accuracy	NOUN
brj-24141	55	28	improvements	improvement	NOUN
brj-24141	55	29	achieved	achieve	VERB
brj-24141	55	30	by	by	ADP
brj-24141	55	31	yolov5	yolov5	NOUN
brj-24141	55	32	.	.	PUNCT
brj-24141	56	1	it	it	PRON
brj-24141	56	2	consistently	consistently	ADV
brj-24141	56	3	demonstrates	demonstrate	VERB
brj-24141	56	4	state	state	NOUN
brj-24141	56	5	-	-	PUNCT
brj-24141	56	6	of	of	ADP
brj-24141	56	7	-	-	PUNCT
brj-24141	56	8	the	the	DET
brj-24141	56	9	-	-	PUNCT
brj-24141	56	10	art	art	NOUN
brj-24141	56	11	performance	performance	NOUN
brj-24141	56	12	on	on	ADP
brj-24141	56	13	various	various	ADJ
brj-24141	56	14	publicly	publicly	ADV
brj-24141	56	15	available	available	ADJ
brj-24141	56	16	datasets	dataset	NOUN
brj-24141	56	17	and	and	CCONJ
brj-24141	56	18	is	be	AUX
brj-24141	56	19	considered	consider	VERB
brj-24141	56	20	an	an	DET
brj-24141	56	21	enhanced	enhanced	ADJ
brj-24141	56	22	version	version	NOUN
brj-24141	56	23	of	of	ADP
brj-24141	56	24	existing	exist	VERB
brj-24141	56	25	yolo	yolo	ADJ
brj-24141	56	26	variants	variant	NOUN
brj-24141	56	27	such	such	ADJ
brj-24141	56	28	as	as	ADP
brj-24141	56	29	yolov5	yolov5	NOUN
brj-24141	56	30	and	and	CCONJ
brj-24141	56	31	yolox	yolox	NOUN
brj-24141	56	32	(	(	PUNCT
brj-24141	56	33	varghese	varghese	NOUN
brj-24141	56	34	and	and	CCONJ
brj-24141	56	35	sambath	sambath	NOUN
brj-24141	56	36	2024	2024	NUM
brj-24141	56	37	)	)	PUNCT
brj-24141	56	38	.	.	PUNCT
brj-24141	57	1	however	however	ADV
brj-24141	57	2	,	,	PUNCT
brj-24141	57	3	the	the	DET
brj-24141	57	4	original	original	ADJ
brj-24141	57	5	yolov8	yolov8	NOUN
brj-24141	57	6	architecture	architecture	NOUN
brj-24141	57	7	exhibits	exhibit	VERB
brj-24141	57	8	limitations	limitation	NOUN
brj-24141	57	9	when	when	SCONJ
brj-24141	57	10	tasked	task	VERB
brj-24141	57	11	with	with	ADP
brj-24141	57	12	detecting	detect	VERB
brj-24141	57	13	objects	object	NOUN
brj-24141	57	14	such	such	ADJ
brj-24141	57	15	as	as	ADP
brj-24141	57	16	wood	wood	NOUN
brj-24141	57	17	,	,	PUNCT
brj-24141	57	18	which	which	PRON
brj-24141	57	19	contain	contain	VERB
brj-24141	57	20	numerous	numerous	ADJ
brj-24141	57	21	small	small	ADJ
brj-24141	57	22	defects	defect	NOUN
brj-24141	57	23	.	.	PUNCT
brj-24141	58	1	these	these	DET
brj-24141	58	2	defects	defect	NOUN
brj-24141	58	3	typically	typically	ADV
brj-24141	58	4	occupy	occupy	VERB
brj-24141	58	5	a	a	DET
brj-24141	58	6	small	small	ADJ
brj-24141	58	7	portion	portion	NOUN
brj-24141	58	8	of	of	ADP
brj-24141	58	9	the	the	DET
brj-24141	58	10	image	image	NOUN
brj-24141	58	11	pixels	pixel	NOUN
brj-24141	58	12	and	and	CCONJ
brj-24141	58	13	possess	possess	VERB
brj-24141	58	14	low	low	ADJ
brj-24141	58	15	feature	feature	NOUN
brj-24141	58	16	resolution	resolution	NOUN
brj-24141	58	17	,	,	PUNCT
brj-24141	58	18	hindering	hinder	VERB
brj-24141	58	19	the	the	DET
brj-24141	58	20	capture	capture	NOUN
brj-24141	58	21	of	of	ADP
brj-24141	58	22	fine	fine	ADJ
brj-24141	58	23	defect	defect	NOUN
brj-24141	58	24	details	detail	NOUN
brj-24141	58	25	in	in	ADP
brj-24141	58	26	the	the	DET
brj-24141	58	27	deeper	deep	ADJ
brj-24141	58	28	network	network	NOUN
brj-24141	58	29	layers	layer	NOUN
brj-24141	58	30	.	.	PUNCT
brj-24141	59	1	to	to	PART
brj-24141	59	2	address	address	VERB
brj-24141	59	3	these	these	DET
brj-24141	59	4	challenges	challenge	NOUN
brj-24141	59	5	,	,	PUNCT
brj-24141	59	6	this	this	DET
brj-24141	59	7	paper	paper	NOUN
brj-24141	59	8	conducts	conduct	VERB
brj-24141	59	9	a	a	DET
brj-24141	59	10	thorough	thorough	ADJ
brj-24141	59	11	investigation	investigation	NOUN
brj-24141	59	12	of	of	ADP
brj-24141	59	13	the	the	DET
brj-24141	59	14	yolov8n	yolov8n	PROPN
brj-24141	59	15	model	model	NOUN
brj-24141	59	16	and	and	CCONJ
brj-24141	59	17	proposes	propose	VERB
brj-24141	59	18	several	several	ADJ
brj-24141	59	19	enhancements	enhancement	NOUN
brj-24141	59	20	to	to	PART
brj-24141	59	21	bolster	bolster	VERB
brj-24141	59	22	its	its	PRON
brj-24141	59	23	performance	performance	NOUN
brj-24141	59	24	in	in	ADP
brj-24141	59	25	detecting	detect	VERB
brj-24141	59	26	wood	wood	NOUN
brj-24141	59	27	panel	panel	NOUN
brj-24141	59	28	surface	surface	NOUN
brj-24141	59	29	,	,	PUNCT
brj-24141	59	30	thereby	thereby	ADV
brj-24141	59	31	better	well	ADV
brj-24141	59	32	aligning	align	VERB
brj-24141	59	33	with	with	ADP
brj-24141	59	34	the	the	DET
brj-24141	59	35	practical	practical	ADJ
brj-24141	59	36	demands	demand	NOUN
brj-24141	59	37	of	of	ADP
brj-24141	59	38	wood	wood	NOUN
brj-24141	59	39	defect	defect	NOUN
brj-24141	59	40	detection	detection	NOUN
brj-24141	59	41	.	.	PUNCT
brj-24141	60	1	fig	fig	NOUN
brj-24141	60	2	.	.	PUNCT
brj-24141	61	1	1	1	X
brj-24141	61	2	.	.	X
brj-24141	61	3	yolov8	yolov8	NOUN
brj-24141	61	4	structure	structure	PROPN
brj-24141	61	5	peer	peer	NOUN
brj-24141	61	6	-	-	PUNCT
brj-24141	61	7	reviewed	review	VERB
brj-24141	61	8	article	article	NOUN
brj-24141	61	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	61	10	li	li	PROPN
brj-24141	61	11	et	et	PROPN
brj-24141	61	12	al	al	PROPN
brj-24141	61	13	.	.	PROPN
brj-24141	62	1	(	(	PUNCT
brj-24141	62	2	2025	2025	NUM
brj-24141	62	3	)	)	PUNCT
brj-24141	62	4	.	.	PUNCT
brj-24141	63	1	“	"	PUNCT
brj-24141	63	2	improved	improved	ADJ
brj-24141	63	3	panel	panel	NOUN
brj-24141	63	4	defect	defect	NOUN
brj-24141	63	5	detection	detection	NOUN
brj-24141	63	6	,	,	PUNCT
brj-24141	63	7	”	"	PUNCT
brj-24141	63	8	bioresources	bioresource	NOUN
brj-24141	63	9	20(2	20(2	NUM
brj-24141	63	10	)	)	PUNCT
brj-24141	63	11	,	,	PUNCT
brj-24141	63	12	2556	2556	NUM
brj-24141	63	13	-	-	SYM
brj-24141	63	14	2573	2573	NUM
brj-24141	63	15	.	.	PUNCT
brj-24141	64	1	2559	2559	NUM
brj-24141	64	2	experimental	experimental	ADJ
brj-24141	64	3	improved	improve	VERB
brj-24141	64	4	yolov8n	yolov8n	NOUN
brj-24141	64	5	algorithm	algorithm	NOUN
brj-24141	64	6	c	c	NOUN
brj-24141	64	7	-	-	PUNCT
brj-24141	64	8	adown	adown	NOUN
brj-24141	64	9	downsampling	downsampling	NOUN
brj-24141	64	10	module	module	NOUN
brj-24141	64	11	design	design	NOUN
brj-24141	64	12	downsampling	downsampling	NOUN
brj-24141	64	13	is	be	AUX
brj-24141	64	14	a	a	DET
brj-24141	64	15	technique	technique	NOUN
brj-24141	64	16	employed	employ	VERB
brj-24141	64	17	to	to	PART
brj-24141	64	18	expand	expand	VERB
brj-24141	64	19	the	the	DET
brj-24141	64	20	receptive	receptive	ADJ
brj-24141	64	21	field	field	NOUN
brj-24141	64	22	by	by	ADP
brj-24141	64	23	reducing	reduce	VERB
brj-24141	64	24	the	the	DET
brj-24141	64	25	feature	feature	NOUN
brj-24141	64	26	map	map	NOUN
brj-24141	64	27	size	size	NOUN
brj-24141	64	28	,	,	PUNCT
brj-24141	64	29	which	which	PRON
brj-24141	64	30	allows	allow	VERB
brj-24141	64	31	the	the	DET
brj-24141	64	32	model	model	NOUN
brj-24141	64	33	to	to	PART
brj-24141	64	34	capture	capture	VERB
brj-24141	64	35	a	a	DET
brj-24141	64	36	broader	broad	ADJ
brj-24141	64	37	range	range	NOUN
brj-24141	64	38	of	of	ADP
brj-24141	64	39	contextual	contextual	ADJ
brj-24141	64	40	information	information	NOUN
brj-24141	64	41	within	within	ADP
brj-24141	64	42	an	an	DET
brj-24141	64	43	image	image	NOUN
brj-24141	64	44	.	.	PUNCT
brj-24141	65	1	traditional	traditional	ADJ
brj-24141	65	2	downsampling	downsample	VERB
brj-24141	65	3	methods	method	NOUN
brj-24141	65	4	for	for	ADP
brj-24141	65	5	convolution	convolution	NOUN
brj-24141	65	6	operations	operation	NOUN
brj-24141	65	7	often	often	ADV
brj-24141	65	8	increase	increase	VERB
brj-24141	65	9	the	the	DET
brj-24141	65	10	number	number	NOUN
brj-24141	65	11	and	and	CCONJ
brj-24141	65	12	size	size	NOUN
brj-24141	65	13	of	of	ADP
brj-24141	65	14	convolution	convolution	NOUN
brj-24141	65	15	kernels	kernel	NOUN
brj-24141	65	16	,	,	PUNCT
brj-24141	65	17	which	which	PRON
brj-24141	65	18	results	result	VERB
brj-24141	65	19	in	in	ADP
brj-24141	65	20	a	a	DET
brj-24141	65	21	significant	significant	ADJ
brj-24141	65	22	rise	rise	NOUN
brj-24141	65	23	in	in	ADP
brj-24141	65	24	both	both	DET
brj-24141	65	25	model	model	NOUN
brj-24141	65	26	parameters	parameter	NOUN
brj-24141	65	27	and	and	CCONJ
brj-24141	65	28	computational	computational	ADJ
brj-24141	65	29	complexity	complexity	NOUN
brj-24141	65	30	(	(	PUNCT
brj-24141	65	31	varghese	varghese	NOUN
brj-24141	65	32	and	and	CCONJ
brj-24141	65	33	sambath	sambath	NOUN
brj-24141	65	34	2024	2024	NUM
brj-24141	65	35	)	)	PUNCT
brj-24141	65	36	.	.	PUNCT
brj-24141	66	1	these	these	DET
brj-24141	66	2	conventional	conventional	ADJ
brj-24141	66	3	approaches	approach	NOUN
brj-24141	66	4	,	,	PUNCT
brj-24141	66	5	while	while	SCONJ
brj-24141	66	6	effective	effective	ADJ
brj-24141	66	7	in	in	ADP
brj-24141	66	8	capturing	capture	VERB
brj-24141	66	9	hierarchical	hierarchical	ADJ
brj-24141	66	10	information	information	NOUN
brj-24141	66	11	,	,	PUNCT
brj-24141	66	12	tend	tend	VERB
brj-24141	66	13	to	to	PART
brj-24141	66	14	be	be	AUX
brj-24141	66	15	resource	resource	NOUN
brj-24141	66	16	-	-	PUNCT
brj-24141	66	17	intensive	intensive	ADJ
brj-24141	66	18	and	and	CCONJ
brj-24141	66	19	can	can	AUX
brj-24141	66	20	lead	lead	VERB
brj-24141	66	21	to	to	ADP
brj-24141	66	22	overfitting	overfitte	VERB
brj-24141	66	23	,	,	PUNCT
brj-24141	66	24	particularly	particularly	ADV
brj-24141	66	25	when	when	SCONJ
brj-24141	66	26	working	work	VERB
brj-24141	66	27	with	with	ADP
brj-24141	66	28	high	high	ADJ
brj-24141	66	29	-	-	PUNCT
brj-24141	66	30	resolution	resolution	NOUN
brj-24141	66	31	images	image	NOUN
brj-24141	66	32	.	.	PUNCT
brj-24141	67	1	adown	adown	PROPN
brj-24141	67	2	,	,	PUNCT
brj-24141	67	3	the	the	DET
brj-24141	67	4	downsampling	downsample	VERB
brj-24141	67	5	method	method	NOUN
brj-24141	67	6	employed	employ	VERB
brj-24141	67	7	in	in	ADP
brj-24141	67	8	yolov9	yolov9	PROPN
brj-24141	67	9	,	,	PUNCT
brj-24141	67	10	effectively	effectively	ADV
brj-24141	67	11	preserves	preserve	VERB
brj-24141	67	12	global	global	ADJ
brj-24141	67	13	image	image	NOUN
brj-24141	67	14	information	information	NOUN
brj-24141	67	15	through	through	ADP
brj-24141	67	16	average	average	ADJ
brj-24141	67	17	pooling	pooling	NOUN
brj-24141	67	18	,	,	PUNCT
brj-24141	67	19	aiding	aid	VERB
brj-24141	67	20	in	in	ADP
brj-24141	67	21	the	the	DET
brj-24141	67	22	understanding	understanding	NOUN
brj-24141	67	23	of	of	ADP
brj-24141	67	24	overall	overall	ADJ
brj-24141	67	25	image	image	NOUN
brj-24141	67	26	structure	structure	NOUN
brj-24141	67	27	and	and	CCONJ
brj-24141	67	28	texture	texture	NOUN
brj-24141	67	29	.	.	PUNCT
brj-24141	68	1	additionally	additionally	ADV
brj-24141	68	2	,	,	PUNCT
brj-24141	68	3	maximum	maximum	ADJ
brj-24141	68	4	pooling	pooling	NOUN
brj-24141	68	5	is	be	AUX
brj-24141	68	6	used	use	VERB
brj-24141	68	7	to	to	PART
brj-24141	68	8	capture	capture	VERB
brj-24141	68	9	local	local	ADJ
brj-24141	68	10	features	feature	NOUN
brj-24141	68	11	such	such	ADJ
brj-24141	68	12	as	as	ADP
brj-24141	68	13	edges	edge	NOUN
brj-24141	68	14	and	and	CCONJ
brj-24141	68	15	corner	corner	NOUN
brj-24141	68	16	points	point	NOUN
brj-24141	68	17	,	,	PUNCT
brj-24141	68	18	contributing	contribute	VERB
brj-24141	68	19	to	to	PART
brj-24141	68	20	target	target	VERB
brj-24141	68	21	localization	localization	NOUN
brj-24141	68	22	.	.	PUNCT
brj-24141	69	1	in	in	ADP
brj-24141	69	2	this	this	DET
brj-24141	69	3	paper	paper	NOUN
brj-24141	69	4	,	,	PUNCT
brj-24141	69	5	focus	focus	NOUN
brj-24141	69	6	slicing	slicing	NOUN
brj-24141	69	7	is	be	AUX
brj-24141	69	8	introduced	introduce	VERB
brj-24141	69	9	as	as	ADP
brj-24141	69	10	a	a	DET
brj-24141	69	11	replacement	replacement	NOUN
brj-24141	69	12	for	for	ADP
brj-24141	69	13	the	the	DET
brj-24141	69	14	parallel	parallel	ADJ
brj-24141	69	15	3x3	3x3	NUM
brj-24141	69	16	convolution	convolution	NOUN
brj-24141	69	17	module	module	NOUN
brj-24141	69	18	in	in	ADP
brj-24141	69	19	adown	adown	NOUN
brj-24141	69	20	downsampling	downsample	VERB
brj-24141	69	21	.	.	PUNCT
brj-24141	70	1	this	this	DET
brj-24141	70	2	module	module	NOUN
brj-24141	70	3	downsamples	downsample	VERB
brj-24141	70	4	the	the	DET
brj-24141	70	5	feature	feature	NOUN
brj-24141	70	6	map	map	NOUN
brj-24141	70	7	by	by	ADP
brj-24141	70	8	slicing	slice	VERB
brj-24141	70	9	the	the	DET
brj-24141	70	10	image	image	NOUN
brj-24141	70	11	at	at	ADP
brj-24141	70	12	the	the	DET
brj-24141	70	13	pixel	pixel	PROPN
brj-24141	70	14	level	level	NOUN
brj-24141	70	15	and	and	CCONJ
brj-24141	70	16	converting	convert	VERB
brj-24141	70	17	spatial	spatial	ADJ
brj-24141	70	18	information	information	NOUN
brj-24141	70	19	into	into	ADP
brj-24141	70	20	channel	channel	NOUN
brj-24141	70	21	information	information	NOUN
brj-24141	70	22	,	,	PUNCT
brj-24141	70	23	ensuring	ensure	VERB
brj-24141	70	24	that	that	SCONJ
brj-24141	70	25	original	original	ADJ
brj-24141	70	26	pixel	pixel	NOUN
brj-24141	70	27	information	information	NOUN
brj-24141	70	28	is	be	AUX
brj-24141	70	29	not	not	PART
brj-24141	70	30	lost	lose	VERB
brj-24141	70	31	.	.	PUNCT
brj-24141	71	1	by	by	ADP
brj-24141	71	2	expanding	expand	VERB
brj-24141	71	3	the	the	DET
brj-24141	71	4	number	number	NOUN
brj-24141	71	5	of	of	ADP
brj-24141	71	6	channels	channel	NOUN
brj-24141	71	7	by	by	ADP
brj-24141	71	8	a	a	DET
brj-24141	71	9	factor	factor	NOUN
brj-24141	71	10	of	of	ADP
brj-24141	71	11	four	four	NUM
brj-24141	71	12	,	,	PUNCT
brj-24141	71	13	the	the	DET
brj-24141	71	14	network	network	NOUN
brj-24141	71	15	can	can	AUX
brj-24141	71	16	analyze	analyze	VERB
brj-24141	71	17	the	the	DET
brj-24141	71	18	image	image	NOUN
brj-24141	71	19	from	from	ADP
brj-24141	71	20	multiple	multiple	ADJ
brj-24141	71	21	perspectives	perspective	NOUN
brj-24141	71	22	,	,	PUNCT
brj-24141	71	23	extracting	extract	VERB
brj-24141	71	24	richer	rich	ADJ
brj-24141	71	25	features	feature	NOUN
brj-24141	71	26	.	.	PUNCT
brj-24141	72	1	an	an	DET
brj-24141	72	2	increased	increase	VERB
brj-24141	72	3	number	number	NOUN
brj-24141	72	4	of	of	ADP
brj-24141	72	5	channels	channel	NOUN
brj-24141	72	6	enhances	enhance	VERB
brj-24141	72	7	the	the	DET
brj-24141	72	8	network	network	NOUN
brj-24141	72	9	’s	’s	PART
brj-24141	72	10	feature	feature	NOUN
brj-24141	72	11	representation	representation	NOUN
brj-24141	72	12	capabilities	capability	NOUN
brj-24141	72	13	,	,	PUNCT
brj-24141	72	14	allowing	allow	VERB
brj-24141	72	15	for	for	ADP
brj-24141	72	16	better	well	ADJ
brj-24141	72	17	differentiation	differentiation	NOUN
brj-24141	72	18	between	between	ADP
brj-24141	72	19	various	various	ADJ
brj-24141	72	20	target	target	NOUN
brj-24141	72	21	types	type	NOUN
brj-24141	72	22	(	(	PUNCT
brj-24141	72	23	wang	wang	PROPN
brj-24141	72	24	et	et	PROPN
brj-24141	72	25	al	al	PROPN
brj-24141	72	26	.	.	PROPN
brj-24141	72	27	2024d	2024d	NUM
brj-24141	72	28	)	)	PUNCT
brj-24141	72	29	.	.	PUNCT
brj-24141	73	1	fig	fig	NOUN
brj-24141	73	2	.	.	PUNCT
brj-24141	74	1	2	2	NUM
brj-24141	74	2	.	.	X
brj-24141	74	3	c	c	X
brj-24141	74	4	-	-	PUNCT
brj-24141	74	5	adown	adown	NOUN
brj-24141	74	6	downsampling	downsample	VERB
brj-24141	74	7	structure	structure	NOUN
brj-24141	74	8	peer	peer	NOUN
brj-24141	74	9	-	-	PUNCT
brj-24141	74	10	reviewed	review	VERB
brj-24141	74	11	article	article	NOUN
brj-24141	74	12	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	74	13	li	li	PROPN
brj-24141	74	14	et	et	PROPN
brj-24141	74	15	al	al	PROPN
brj-24141	74	16	.	.	PROPN
brj-24141	75	1	(	(	PUNCT
brj-24141	75	2	2025	2025	NUM
brj-24141	75	3	)	)	PUNCT
brj-24141	75	4	.	.	PUNCT
brj-24141	76	1	“	"	PUNCT
brj-24141	76	2	improved	improved	ADJ
brj-24141	76	3	panel	panel	NOUN
brj-24141	76	4	defect	defect	NOUN
brj-24141	76	5	detection	detection	NOUN
brj-24141	76	6	,	,	PUNCT
brj-24141	76	7	”	"	PUNCT
brj-24141	76	8	bioresources	bioresource	NOUN
brj-24141	76	9	20(2	20(2	NUM
brj-24141	76	10	)	)	PUNCT
brj-24141	76	11	,	,	PUNCT
brj-24141	76	12	2556	2556	NUM
brj-24141	76	13	-	-	SYM
brj-24141	76	14	2573	2573	NUM
brj-24141	76	15	.	.	PUNCT
brj-24141	76	16	2560	2560	NUM
brj-24141	76	17	to	to	PART
brj-24141	76	18	prevent	prevent	VERB
brj-24141	76	19	the	the	DET
brj-24141	76	20	network	network	NOUN
brj-24141	76	21	from	from	ADP
brj-24141	76	22	over	over	ADV
brj-24141	76	23	-	-	PUNCT
brj-24141	76	24	relying	rely	VERB
brj-24141	76	25	on	on	ADP
brj-24141	76	26	specific	specific	ADJ
brj-24141	76	27	channels	channel	NOUN
brj-24141	76	28	,	,	PUNCT
brj-24141	76	29	channel	channel	NOUN
brj-24141	76	30	shuffling	shuffling	NOUN
brj-24141	76	31	is	be	AUX
brj-24141	76	32	performed	perform	VERB
brj-24141	76	33	after	after	ADP
brj-24141	76	34	maximum	maximum	ADJ
brj-24141	76	35	pooling	pooling	NOUN
brj-24141	76	36	of	of	ADP
brj-24141	76	37	parallel	parallel	ADJ
brj-24141	76	38	data	datum	NOUN
brj-24141	76	39	.	.	PUNCT
brj-24141	77	1	this	this	DET
brj-24141	77	2	technique	technique	NOUN
brj-24141	77	3	disrupts	disrupt	VERB
brj-24141	77	4	the	the	DET
brj-24141	77	5	channel	channel	NOUN
brj-24141	77	6	order	order	NOUN
brj-24141	77	7	in	in	ADP
brj-24141	77	8	the	the	DET
brj-24141	77	9	feature	feature	NOUN
brj-24141	77	10	map	map	NOUN
brj-24141	77	11	,	,	PUNCT
brj-24141	77	12	forcing	force	VERB
brj-24141	77	13	the	the	DET
brj-24141	77	14	network	network	NOUN
brj-24141	77	15	to	to	PART
brj-24141	77	16	learn	learn	VERB
brj-24141	77	17	more	more	ADJ
brj-24141	77	18	complex	complex	ADJ
brj-24141	77	19	feature	feature	NOUN
brj-24141	77	20	representations	representation	NOUN
brj-24141	77	21	and	and	CCONJ
brj-24141	77	22	improving	improve	VERB
brj-24141	77	23	model	model	NOUN
brj-24141	77	24	generalization	generalization	NOUN
brj-24141	77	25	.	.	PUNCT
brj-24141	78	1	this	this	DET
brj-24141	78	2	enhancement	enhancement	NOUN
brj-24141	78	3	enables	enable	VERB
brj-24141	78	4	the	the	DET
brj-24141	78	5	model	model	NOUN
brj-24141	78	6	to	to	PART
brj-24141	78	7	adapt	adapt	VERB
brj-24141	78	8	to	to	ADP
brj-24141	78	9	different	different	ADJ
brj-24141	78	10	feature	feature	NOUN
brj-24141	78	11	types	type	NOUN
brj-24141	78	12	,	,	PUNCT
brj-24141	78	13	facilitating	facilitate	VERB
brj-24141	78	14	the	the	DET
brj-24141	78	15	extraction	extraction	NOUN
brj-24141	78	16	of	of	ADP
brj-24141	78	17	complex	complex	ADJ
brj-24141	78	18	features	feature	NOUN
brj-24141	78	19	like	like	ADP
brj-24141	78	20	wood	wood	NOUN
brj-24141	78	21	grain	grain	NOUN
brj-24141	78	22	and	and	CCONJ
brj-24141	78	23	color	color	NOUN
brj-24141	78	24	and	and	CCONJ
brj-24141	78	25	improving	improve	VERB
brj-24141	78	26	the	the	DET
brj-24141	78	27	perception	perception	NOUN
brj-24141	78	28	of	of	ADP
brj-24141	78	29	subtle	subtle	ADJ
brj-24141	78	30	defects	defect	NOUN
brj-24141	78	31	.	.	PUNCT
brj-24141	79	1	the	the	DET
brj-24141	79	2	c	c	NOUN
brj-24141	79	3	-	-	PUNCT
brj-24141	79	4	adown	adown	VERB
brj-24141	79	5	module	module	NOUN
brj-24141	79	6	,	,	PUNCT
brj-24141	79	7	therefore	therefore	ADV
brj-24141	79	8	,	,	PUNCT
brj-24141	79	9	not	not	PART
brj-24141	79	10	only	only	ADV
brj-24141	79	11	minimizes	minimize	VERB
brj-24141	79	12	information	information	NOUN
brj-24141	79	13	loss	loss	NOUN
brj-24141	79	14	but	but	CCONJ
brj-24141	79	15	also	also	ADV
brj-24141	79	16	facilitates	facilitate	VERB
brj-24141	79	17	a	a	DET
brj-24141	79	18	smoother	smooth	ADJ
brj-24141	79	19	transition	transition	NOUN
brj-24141	79	20	of	of	ADP
brj-24141	79	21	the	the	DET
brj-24141	79	22	feature	feature	NOUN
brj-24141	79	23	map	map	NOUN
brj-24141	79	24	across	across	ADP
brj-24141	79	25	different	different	ADJ
brj-24141	79	26	scales	scale	NOUN
brj-24141	79	27	.	.	PUNCT
brj-24141	80	1	its	its	PRON
brj-24141	80	2	efficiency	efficiency	NOUN
brj-24141	80	3	is	be	AUX
brj-24141	80	4	evident	evident	ADJ
brj-24141	80	5	in	in	ADP
brj-24141	80	6	the	the	DET
brj-24141	80	7	reduction	reduction	NOUN
brj-24141	80	8	of	of	ADP
brj-24141	80	9	computational	computational	ADJ
brj-24141	80	10	complexity	complexity	NOUN
brj-24141	80	11	compared	compare	VERB
brj-24141	80	12	to	to	ADP
brj-24141	80	13	traditional	traditional	ADJ
brj-24141	80	14	convolutionbased	convolutionbase	VERB
brj-24141	80	15	downsampling	downsample	VERB
brj-24141	80	16	methods	method	NOUN
brj-24141	80	17	,	,	PUNCT
brj-24141	80	18	as	as	SCONJ
brj-24141	80	19	it	it	PRON
brj-24141	80	20	eliminates	eliminate	VERB
brj-24141	80	21	the	the	DET
brj-24141	80	22	need	need	NOUN
brj-24141	80	23	for	for	ADP
brj-24141	80	24	large	large	ADJ
brj-24141	80	25	convolutional	convolutional	ADJ
brj-24141	80	26	kernels	kernel	NOUN
brj-24141	80	27	while	while	SCONJ
brj-24141	80	28	still	still	ADV
brj-24141	80	29	capturing	capture	VERB
brj-24141	80	30	fine	fine	ADJ
brj-24141	80	31	details	detail	NOUN
brj-24141	80	32	.	.	PUNCT
brj-24141	81	1	the	the	DET
brj-24141	81	2	enhanced	enhanced	ADJ
brj-24141	81	3	channel	channel	NOUN
brj-24141	81	4	manipulation	manipulation	NOUN
brj-24141	81	5	capabilities	capability	NOUN
brj-24141	81	6	allow	allow	VERB
brj-24141	81	7	the	the	DET
brj-24141	81	8	network	network	NOUN
brj-24141	81	9	to	to	PART
brj-24141	81	10	learn	learn	VERB
brj-24141	81	11	more	more	ADJ
brj-24141	81	12	intricate	intricate	ADJ
brj-24141	81	13	features	feature	NOUN
brj-24141	81	14	,	,	PUNCT
brj-24141	81	15	particularly	particularly	ADV
brj-24141	81	16	in	in	ADP
brj-24141	81	17	areas	area	NOUN
brj-24141	81	18	with	with	ADP
brj-24141	81	19	complex	complex	ADJ
brj-24141	81	20	textures	texture	NOUN
brj-24141	81	21	or	or	CCONJ
brj-24141	81	22	small	small	ADJ
brj-24141	81	23	defects	defect	NOUN
brj-24141	81	24	.	.	PUNCT
brj-24141	82	1	these	these	DET
brj-24141	82	2	improvements	improvement	NOUN
brj-24141	82	3	contribute	contribute	VERB
brj-24141	82	4	significantly	significantly	ADV
brj-24141	82	5	to	to	ADP
brj-24141	82	6	the	the	DET
brj-24141	82	7	model	model	NOUN
brj-24141	82	8	’s	’s	PART
brj-24141	82	9	ability	ability	NOUN
brj-24141	82	10	to	to	PART
brj-24141	82	11	accurately	accurately	ADV
brj-24141	82	12	detect	detect	VERB
brj-24141	82	13	wood	wood	NOUN
brj-24141	82	14	panel	panel	NOUN
brj-24141	82	15	defects	defect	NOUN
brj-24141	82	16	,	,	PUNCT
brj-24141	82	17	making	make	VERB
brj-24141	82	18	it	it	PRON
brj-24141	82	19	particularly	particularly	ADV
brj-24141	82	20	effective	effective	ADJ
brj-24141	82	21	for	for	ADP
brj-24141	82	22	fine	fine	ADV
brj-24141	82	23	-	-	PUNCT
brj-24141	82	24	grained	grain	VERB
brj-24141	82	25	defect	defect	NOUN
brj-24141	82	26	detection	detection	NOUN
brj-24141	82	27	and	and	CCONJ
brj-24141	82	28	regions	region	NOUN
brj-24141	82	29	with	with	ADP
brj-24141	82	30	detailed	detailed	ADJ
brj-24141	82	31	structural	structural	ADJ
brj-24141	82	32	patterns	pattern	NOUN
brj-24141	82	33	.	.	PUNCT
brj-24141	83	1	the	the	DET
brj-24141	83	2	structure	structure	NOUN
brj-24141	83	3	of	of	ADP
brj-24141	83	4	the	the	DET
brj-24141	83	5	cadown	cadown	NOUN
brj-24141	83	6	module	module	NOUN
brj-24141	83	7	is	be	AUX
brj-24141	83	8	illustrated	illustrate	VERB
brj-24141	83	9	in	in	ADP
brj-24141	83	10	fig	fig	NOUN
brj-24141	83	11	.	.	PUNCT
brj-24141	84	1	2	2	X
brj-24141	84	2	.	.	X
brj-24141	84	3	c2f_dwr	c2f_dwr	NOUN
brj-24141	84	4	(	(	PUNCT
brj-24141	84	5	msda	msda	NOUN
brj-24141	84	6	)	)	PUNCT
brj-24141	84	7	design	design	VERB
brj-24141	84	8	the	the	DET
brj-24141	84	9	c2f	c2f	NOUN
brj-24141	84	10	structure	structure	NOUN
brj-24141	84	11	,	,	PUNCT
brj-24141	84	12	a	a	DET
brj-24141	84	13	key	key	ADJ
brj-24141	84	14	architectural	architectural	ADJ
brj-24141	84	15	component	component	NOUN
brj-24141	84	16	introduced	introduce	VERB
brj-24141	84	17	in	in	ADP
brj-24141	84	18	yolov8	yolov8	PROPN
brj-24141	84	19	,	,	PUNCT
brj-24141	84	20	combines	combine	VERB
brj-24141	84	21	the	the	DET
brj-24141	84	22	strengths	strength	NOUN
brj-24141	84	23	of	of	ADP
brj-24141	84	24	the	the	DET
brj-24141	84	25	c3	c3	PROPN
brj-24141	84	26	and	and	CCONJ
brj-24141	84	27	elan	elan	PROPN
brj-24141	84	28	modules	module	NOUN
brj-24141	84	29	to	to	PART
brj-24141	84	30	enhance	enhance	VERB
brj-24141	84	31	feature	feature	NOUN
brj-24141	84	32	extraction	extraction	NOUN
brj-24141	84	33	,	,	PUNCT
brj-24141	84	34	thereby	thereby	ADV
brj-24141	84	35	improving	improve	VERB
brj-24141	84	36	model	model	NOUN
brj-24141	84	37	accuracy	accuracy	NOUN
brj-24141	84	38	and	and	CCONJ
brj-24141	84	39	robustness	robustness	NOUN
brj-24141	84	40	.	.	PUNCT
brj-24141	85	1	however	however	ADV
brj-24141	85	2	,	,	PUNCT
brj-24141	85	3	the	the	DET
brj-24141	85	4	c2f	c2f	NOUN
brj-24141	85	5	architecture	architecture	NOUN
brj-24141	85	6	primarily	primarily	ADV
brj-24141	85	7	focuses	focus	VERB
brj-24141	85	8	on	on	ADP
brj-24141	85	9	local	local	ADJ
brj-24141	85	10	feature	feature	NOUN
brj-24141	85	11	capture	capture	NOUN
brj-24141	85	12	and	and	CCONJ
brj-24141	85	13	may	may	AUX
brj-24141	85	14	be	be	AUX
brj-24141	85	15	less	less	ADV
brj-24141	85	16	effective	effective	ADJ
brj-24141	85	17	in	in	ADP
brj-24141	85	18	processing	process	VERB
brj-24141	85	19	global	global	ADJ
brj-24141	85	20	semantic	semantic	ADJ
brj-24141	85	21	information	information	NOUN
brj-24141	85	22	.	.	PUNCT
brj-24141	86	1	this	this	DET
brj-24141	86	2	limitation	limitation	NOUN
brj-24141	86	3	can	can	AUX
brj-24141	86	4	hinder	hinder	VERB
brj-24141	86	5	the	the	DET
brj-24141	86	6	model	model	NOUN
brj-24141	86	7	’s	’s	PART
brj-24141	86	8	ability	ability	NOUN
brj-24141	86	9	to	to	PART
brj-24141	86	10	differentiate	differentiate	VERB
brj-24141	86	11	between	between	ADP
brj-24141	86	12	subtle	subtle	ADJ
brj-24141	86	13	category	category	NOUN
brj-24141	86	14	variations	variation	NOUN
brj-24141	86	15	when	when	SCONJ
brj-24141	86	16	dealing	deal	VERB
brj-24141	86	17	with	with	ADP
brj-24141	86	18	complex	complex	ADJ
brj-24141	86	19	wood	wood	NOUN
brj-24141	86	20	defects	defect	NOUN
brj-24141	86	21	,	,	PUNCT
brj-24141	86	22	potentially	potentially	ADV
brj-24141	86	23	affecting	affect	VERB
brj-24141	86	24	classification	classification	NOUN
brj-24141	86	25	or	or	CCONJ
brj-24141	86	26	detection	detection	NOUN
brj-24141	86	27	accuracy	accuracy	NOUN
brj-24141	86	28	.	.	PUNCT
brj-24141	87	1	to	to	PART
brj-24141	87	2	address	address	VERB
brj-24141	87	3	this	this	PRON
brj-24141	87	4	,	,	PUNCT
brj-24141	87	5	a	a	DET
brj-24141	87	6	semantic	semantic	ADJ
brj-24141	87	7	segmentation	segmentation	NOUN
brj-24141	87	8	module	module	NOUN
brj-24141	87	9	was	be	AUX
brj-24141	87	10	introduced	introduce	VERB
brj-24141	87	11	.	.	PUNCT
brj-24141	88	1	this	this	DET
brj-24141	88	2	module	module	NOUN
brj-24141	88	3	classifies	classify	VERB
brj-24141	88	4	each	each	DET
brj-24141	88	5	pixel	pixel	NOUN
brj-24141	88	6	in	in	ADP
brj-24141	88	7	the	the	DET
brj-24141	88	8	image	image	NOUN
brj-24141	88	9	,	,	PUNCT
brj-24141	88	10	providing	provide	VERB
brj-24141	88	11	finer	fine	ADJ
brj-24141	88	12	semantic	semantic	ADJ
brj-24141	88	13	information	information	NOUN
brj-24141	88	14	that	that	PRON
brj-24141	88	15	aids	aid	VERB
brj-24141	88	16	in	in	ADP
brj-24141	88	17	a	a	DET
brj-24141	88	18	deeper	deep	ADJ
brj-24141	88	19	understanding	understanding	NOUN
brj-24141	88	20	of	of	ADP
brj-24141	88	21	image	image	NOUN
brj-24141	88	22	content	content	NOUN
brj-24141	88	23	and	and	CCONJ
brj-24141	88	24	improves	improve	VERB
brj-24141	88	25	the	the	DET
brj-24141	88	26	detection	detection	NOUN
brj-24141	88	27	of	of	ADP
brj-24141	88	28	tiny	tiny	ADJ
brj-24141	88	29	targets	target	NOUN
brj-24141	88	30	.	.	PUNCT
brj-24141	89	1	fusing	fuse	VERB
brj-24141	89	2	the	the	DET
brj-24141	89	3	output	output	NOUN
brj-24141	89	4	of	of	ADP
brj-24141	89	5	the	the	DET
brj-24141	89	6	semantic	semantic	ADJ
brj-24141	89	7	segmentation	segmentation	NOUN
brj-24141	89	8	module	module	NOUN
brj-24141	89	9	with	with	ADP
brj-24141	89	10	the	the	DET
brj-24141	89	11	c2f	c2f	NOUN
brj-24141	89	12	module	module	NOUN
brj-24141	89	13	’s	’s	PART
brj-24141	89	14	features	feature	NOUN
brj-24141	89	15	enhances	enhance	VERB
brj-24141	89	16	feature	feature	NOUN
brj-24141	89	17	representation	representation	NOUN
brj-24141	89	18	richness	richness	NOUN
brj-24141	89	19	and	and	CCONJ
brj-24141	89	20	enables	enable	VERB
brj-24141	89	21	the	the	DET
brj-24141	89	22	model	model	NOUN
brj-24141	89	23	to	to	PART
brj-24141	89	24	more	more	ADV
brj-24141	89	25	effectively	effectively	ADV
brj-24141	89	26	distinguish	distinguish	VERB
brj-24141	89	27	between	between	ADP
brj-24141	89	28	different	different	ADJ
brj-24141	89	29	categories	category	NOUN
brj-24141	89	30	and	and	CCONJ
brj-24141	89	31	instances	instance	NOUN
brj-24141	89	32	.	.	PUNCT
brj-24141	90	1	in	in	ADP
brj-24141	90	2	this	this	DET
brj-24141	90	3	study	study	NOUN
brj-24141	90	4	,	,	PUNCT
brj-24141	90	5	the	the	DET
brj-24141	90	6	bottleneck	bottleneck	NOUN
brj-24141	90	7	module	module	NOUN
brj-24141	90	8	within	within	ADP
brj-24141	90	9	the	the	DET
brj-24141	90	10	c2f	c2f	NOUN
brj-24141	90	11	structure	structure	NOUN
brj-24141	90	12	was	be	AUX
brj-24141	90	13	improved	improve	VERB
brj-24141	90	14	by	by	ADP
brj-24141	90	15	designing	design	VERB
brj-24141	90	16	the	the	DET
brj-24141	90	17	c2f_dwr	c2f_dwr	NOUN
brj-24141	90	18	structure	structure	NOUN
brj-24141	90	19	and	and	CCONJ
brj-24141	90	20	incorporating	incorporate	VERB
brj-24141	90	21	the	the	DET
brj-24141	90	22	msda	msda	NOUN
brj-24141	90	23	multiscale	multiscale	ADJ
brj-24141	90	24	null	null	ADJ
brj-24141	90	25	attention	attention	NOUN
brj-24141	90	26	mechanism	mechanism	NOUN
brj-24141	90	27	.	.	PUNCT
brj-24141	91	1	this	this	DET
brj-24141	91	2	structure	structure	NOUN
brj-24141	91	3	is	be	AUX
brj-24141	91	4	capable	capable	ADJ
brj-24141	91	5	of	of	ADP
brj-24141	91	6	extracting	extract	VERB
brj-24141	91	7	multi	multi	ADJ
brj-24141	91	8	-	-	ADJ
brj-24141	91	9	scale	scale	ADJ
brj-24141	91	10	features	feature	NOUN
brj-24141	91	11	,	,	PUNCT
brj-24141	91	12	which	which	PRON
brj-24141	91	13	are	be	AUX
brj-24141	91	14	essential	essential	ADJ
brj-24141	91	15	for	for	ADP
brj-24141	91	16	detecting	detect	VERB
brj-24141	91	17	targets	target	NOUN
brj-24141	91	18	of	of	ADP
brj-24141	91	19	varying	vary	VERB
brj-24141	91	20	sizes	size	NOUN
brj-24141	91	21	.	.	PUNCT
brj-24141	92	1	the	the	DET
brj-24141	92	2	combination	combination	NOUN
brj-24141	92	3	of	of	ADP
brj-24141	92	4	dwr	dwr	NOUN
brj-24141	92	5	residuals	residual	NOUN
brj-24141	92	6	and	and	CCONJ
brj-24141	92	7	the	the	DET
brj-24141	92	8	msda	msda	NOUN
brj-24141	92	9	attention	attention	NOUN
brj-24141	92	10	mechanism	mechanism	NOUN
brj-24141	92	11	allows	allow	VERB
brj-24141	92	12	the	the	DET
brj-24141	92	13	model	model	NOUN
brj-24141	92	14	to	to	PART
brj-24141	92	15	extract	extract	VERB
brj-24141	92	16	more	more	ADJ
brj-24141	92	17	discriminative	discriminative	NOUN
brj-24141	92	18	features	feature	NOUN
brj-24141	92	19	for	for	ADP
brj-24141	92	20	defect	defect	NOUN
brj-24141	92	21	detection	detection	NOUN
brj-24141	92	22	and	and	CCONJ
brj-24141	92	23	adaptively	adaptively	ADV
brj-24141	92	24	select	select	VERB
brj-24141	92	25	features	feature	NOUN
brj-24141	92	26	that	that	PRON
brj-24141	92	27	are	be	AUX
brj-24141	92	28	more	more	ADV
brj-24141	92	29	relevant	relevant	ADJ
brj-24141	92	30	for	for	ADP
brj-24141	92	31	defect	defect	ADJ
brj-24141	92	32	classification	classification	NOUN
brj-24141	92	33	,	,	PUNCT
brj-24141	92	34	thus	thus	ADV
brj-24141	92	35	improving	improve	VERB
brj-24141	92	36	classification	classification	NOUN
brj-24141	92	37	ability	ability	NOUN
brj-24141	92	38	and	and	CCONJ
brj-24141	92	39	robustness	robustness	NOUN
brj-24141	92	40	.	.	PUNCT
brj-24141	93	1	dwrseg	dwrseg	VERB
brj-24141	93	2	residual	residual	ADJ
brj-24141	93	3	linkage	linkage	NOUN
brj-24141	93	4	the	the	DET
brj-24141	93	5	dilation	dilation	NOUN
brj-24141	93	6	-	-	PUNCT
brj-24141	93	7	wise	wise	ADJ
brj-24141	93	8	residual	residual	ADJ
brj-24141	93	9	module	module	NOUN
brj-24141	93	10	(	(	PUNCT
brj-24141	93	11	dwr	dwr	NOUN
brj-24141	93	12	)	)	PUNCT
brj-24141	93	13	is	be	AUX
brj-24141	93	14	an	an	DET
brj-24141	93	15	efficient	efficient	ADJ
brj-24141	93	16	multi	multi	ADJ
brj-24141	93	17	-	-	ADJ
brj-24141	93	18	scale	scale	ADJ
brj-24141	93	19	context	context	NOUN
brj-24141	93	20	information	information	NOUN
brj-24141	93	21	extraction	extraction	NOUN
brj-24141	93	22	technique	technique	NOUN
brj-24141	93	23	primarily	primarily	ADV
brj-24141	93	24	used	use	VERB
brj-24141	93	25	in	in	ADP
brj-24141	93	26	the	the	DET
brj-24141	93	27	field	field	NOUN
brj-24141	93	28	of	of	ADP
brj-24141	93	29	real	real	ADJ
brj-24141	93	30	-	-	PUNCT
brj-24141	93	31	time	time	NOUN
brj-24141	93	32	semantic	semantic	ADJ
brj-24141	93	33	segmentation	segmentation	NOUN
brj-24141	93	34	.	.	PUNCT
brj-24141	94	1	the	the	DET
brj-24141	94	2	module	module	NOUN
brj-24141	94	3	’s	’s	PART
brj-24141	94	4	structure	structure	NOUN
brj-24141	94	5	,	,	PUNCT
brj-24141	94	6	as	as	SCONJ
brj-24141	94	7	illustrated	illustrate	VERB
brj-24141	94	8	in	in	ADP
brj-24141	94	9	figure	figure	NOUN
brj-24141	94	10	3	3	NUM
brj-24141	94	11	,	,	PUNCT
brj-24141	94	12	employs	employ	VERB
brj-24141	94	13	a	a	DET
brj-24141	94	14	residual	residual	ADJ
brj-24141	94	15	structure	structure	NOUN
brj-24141	94	16	to	to	PART
brj-24141	94	17	efficiently	efficiently	ADV
brj-24141	94	18	extract	extract	VERB
brj-24141	94	19	multi	multi	ADJ
brj-24141	94	20	-	-	ADJ
brj-24141	94	21	scale	scale	ADJ
brj-24141	94	22	contextual	contextual	ADJ
brj-24141	94	23	information	information	NOUN
brj-24141	94	24	through	through	ADP
brj-24141	94	25	a	a	DET
brj-24141	94	26	two	two	NUM
brj-24141	94	27	-	-	PUNCT
brj-24141	94	28	stage	stage	NOUN
brj-24141	94	29	approach	approach	NOUN
brj-24141	94	30	,	,	PUNCT
brj-24141	94	31	fusing	fuse	VERB
brj-24141	94	32	this	this	DET
brj-24141	94	33	information	information	NOUN
brj-24141	94	34	to	to	PART
brj-24141	94	35	generate	generate	VERB
brj-24141	94	36	a	a	DET
brj-24141	94	37	feature	feature	NOUN
brj-24141	94	38	map	map	NOUN
brj-24141	94	39	with	with	ADP
brj-24141	94	40	multi	multi	ADJ
brj-24141	94	41	-	-	ADJ
brj-24141	94	42	scale	scale	ADJ
brj-24141	94	43	receptive	receptive	ADJ
brj-24141	94	44	fields	field	NOUN
brj-24141	94	45	(	(	PUNCT
brj-24141	94	46	wei	wei	PROPN
brj-24141	94	47	et	et	PROPN
brj-24141	94	48	al	al	PROPN
brj-24141	94	49	.	.	PROPN
brj-24141	94	50	2022	2022	NUM
brj-24141	94	51	)	)	PUNCT
brj-24141	94	52	.	.	PUNCT
brj-24141	95	1	in	in	ADP
brj-24141	95	2	the	the	DET
brj-24141	95	3	first	first	ADJ
brj-24141	95	4	stage	stage	NOUN
brj-24141	95	5	,	,	PUNCT
brj-24141	95	6	streamlined	streamline	VERB
brj-24141	95	7	feature	feature	NOUN
brj-24141	95	8	maps	map	NOUN
brj-24141	95	9	of	of	ADP
brj-24141	95	10	varying	vary	VERB
brj-24141	95	11	sizes	size	NOUN
brj-24141	95	12	were	be	AUX
brj-24141	95	13	generated	generate	VERB
brj-24141	95	14	through	through	ADP
brj-24141	95	15	regional	regional	ADJ
brj-24141	95	16	residualization	residualization	NOUN
brj-24141	95	17	to	to	PART
brj-24141	95	18	establish	establish	VERB
brj-24141	95	19	a	a	DET
brj-24141	95	20	foundation	foundation	NOUN
brj-24141	95	21	for	for	ADP
brj-24141	95	22	semantic	semantic	ADJ
brj-24141	95	23	residualization	residualization	NOUN
brj-24141	95	24	in	in	ADP
brj-24141	95	25	the	the	DET
brj-24141	95	26	second	second	ADJ
brj-24141	95	27	peer	peer	NOUN
brj-24141	95	28	-	-	PUNCT
brj-24141	95	29	reviewed	review	VERB
brj-24141	95	30	article	article	NOUN
brj-24141	95	31	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	95	32	li	li	PROPN
brj-24141	95	33	et	et	PROPN
brj-24141	95	34	al	al	PROPN
brj-24141	95	35	.	.	PROPN
brj-24141	96	1	(	(	PUNCT
brj-24141	96	2	2025	2025	NUM
brj-24141	96	3	)	)	PUNCT
brj-24141	96	4	.	.	PUNCT
brj-24141	97	1	“	"	PUNCT
brj-24141	97	2	improved	improved	ADJ
brj-24141	97	3	panel	panel	NOUN
brj-24141	97	4	defect	defect	NOUN
brj-24141	97	5	detection	detection	NOUN
brj-24141	97	6	,	,	PUNCT
brj-24141	97	7	”	"	PUNCT
brj-24141	97	8	bioresources	bioresource	NOUN
brj-24141	97	9	20(2	20(2	NUM
brj-24141	97	10	)	)	PUNCT
brj-24141	97	11	,	,	PUNCT
brj-24141	97	12	2556	2556	NUM
brj-24141	97	13	-	-	SYM
brj-24141	97	14	2573	2573	NUM
brj-24141	97	15	.	.	PUNCT
brj-24141	98	1	2561	2561	NUM
brj-24141	98	2	stage	stage	NOUN
brj-24141	98	3	.	.	PUNCT
brj-24141	99	1	this	this	DET
brj-24141	99	2	process	process	NOUN
brj-24141	99	3	was	be	AUX
brj-24141	99	4	achieved	achieve	VERB
brj-24141	99	5	using	use	VERB
brj-24141	99	6	a	a	DET
brj-24141	99	7	standard	standard	ADJ
brj-24141	99	8	3x3	3x3	NUM
brj-24141	99	9	convolution	convolution	NOUN
brj-24141	99	10	operation	operation	NOUN
brj-24141	99	11	combined	combine	VERB
brj-24141	99	12	with	with	ADP
brj-24141	99	13	a	a	DET
brj-24141	99	14	batch	batch	NOUN
brj-24141	99	15	normalization	normalization	NOUN
brj-24141	99	16	(	(	PUNCT
brj-24141	99	17	bn	bn	NOUN
brj-24141	99	18	)	)	PUNCT
brj-24141	99	19	layer	layer	NOUN
brj-24141	99	20	and	and	CCONJ
brj-24141	99	21	a	a	DET
brj-24141	99	22	relu	relu	NOUN
brj-24141	99	23	activation	activation	NOUN
brj-24141	99	24	layer	layer	NOUN
brj-24141	99	25	.	.	PUNCT
brj-24141	100	1	the	the	DET
brj-24141	100	2	3x3	3x3	NUM
brj-24141	100	3	convolution	convolution	NOUN
brj-24141	100	4	operation	operation	NOUN
brj-24141	100	5	was	be	AUX
brj-24141	100	6	responsible	responsible	ADJ
brj-24141	100	7	for	for	ADP
brj-24141	100	8	initial	initial	ADJ
brj-24141	100	9	feature	feature	NOUN
brj-24141	100	10	extraction	extraction	NOUN
brj-24141	100	11	.	.	PUNCT
brj-24141	101	1	subsequently	subsequently	ADV
brj-24141	101	2	,	,	PUNCT
brj-24141	101	3	in	in	ADP
brj-24141	101	4	the	the	DET
brj-24141	101	5	second	second	ADJ
brj-24141	101	6	stage	stage	NOUN
brj-24141	101	7	,	,	PUNCT
brj-24141	101	8	semantic	semantic	ADJ
brj-24141	101	9	residualization	residualization	NOUN
brj-24141	101	10	,	,	PUNCT
brj-24141	101	11	multirate	multirate	ADJ
brj-24141	101	12	depthseparable	depthseparable	ADJ
brj-24141	101	13	convolution	convolution	NOUN
brj-24141	101	14	was	be	AUX
brj-24141	101	15	used	use	VERB
brj-24141	101	16	for	for	ADP
brj-24141	101	17	morphological	morphological	ADJ
brj-24141	101	18	filtering	filtering	NOUN
brj-24141	101	19	of	of	ADP
brj-24141	101	20	regional	regional	ADJ
brj-24141	101	21	features	feature	NOUN
brj-24141	101	22	,	,	PUNCT
brj-24141	101	23	ensuring	ensure	VERB
brj-24141	101	24	that	that	SCONJ
brj-24141	101	25	each	each	DET
brj-24141	101	26	channel	channel	NOUN
brj-24141	101	27	feature	feature	NOUN
brj-24141	101	28	utilized	utilize	VERB
brj-24141	101	29	only	only	ADV
brj-24141	101	30	one	one	NUM
brj-24141	101	31	appropriate	appropriate	ADJ
brj-24141	101	32	receptive	receptive	ADJ
brj-24141	101	33	field	field	NOUN
brj-24141	101	34	.	.	PUNCT
brj-24141	102	1	in	in	ADP
brj-24141	102	2	the	the	DET
brj-24141	102	3	first	first	ADJ
brj-24141	102	4	stage	stage	NOUN
brj-24141	102	5	,	,	PUNCT
brj-24141	102	6	depending	depend	VERB
brj-24141	102	7	on	on	ADP
brj-24141	102	8	the	the	DET
brj-24141	102	9	desired	desire	VERB
brj-24141	102	10	receptive	receptive	ADJ
brj-24141	102	11	field	field	NOUN
brj-24141	102	12	size	size	NOUN
brj-24141	102	13	,	,	PUNCT
brj-24141	102	14	the	the	DET
brj-24141	102	15	network	network	NOUN
brj-24141	102	16	selectively	selectively	ADV
brj-24141	102	17	learned	learn	VERB
brj-24141	102	18	the	the	DET
brj-24141	102	19	appropriate	appropriate	ADJ
brj-24141	102	20	streamlined	streamlined	ADJ
brj-24141	102	21	regional	regional	ADJ
brj-24141	102	22	feature	feature	NOUN
brj-24141	102	23	map	map	NOUN
brj-24141	102	24	for	for	ADP
brj-24141	102	25	efficient	efficient	ADJ
brj-24141	102	26	matching	matching	NOUN
brj-24141	102	27	.	.	PUNCT
brj-24141	103	1	to	to	PART
brj-24141	103	2	accomplish	accomplish	VERB
brj-24141	103	3	this	this	PRON
brj-24141	103	4	,	,	PUNCT
brj-24141	103	5	the	the	DET
brj-24141	103	6	regional	regional	ADJ
brj-24141	103	7	feature	feature	NOUN
brj-24141	103	8	maps	map	NOUN
brj-24141	103	9	were	be	AUX
brj-24141	103	10	first	first	ADV
brj-24141	103	11	divided	divide	VERB
brj-24141	103	12	into	into	ADP
brj-24141	103	13	different	different	ADJ
brj-24141	103	14	step	step	NOUN
brj-24141	103	15	groups	group	NOUN
brj-24141	103	16	,	,	PUNCT
brj-24141	103	17	followed	follow	VERB
brj-24141	103	18	by	by	ADP
brj-24141	103	19	the	the	DET
brj-24141	103	20	application	application	NOUN
brj-24141	103	21	of	of	ADP
brj-24141	103	22	dilation	dilation	NOUN
brj-24141	103	23	depth	depth	NOUN
brj-24141	103	24	convolution	convolution	NOUN
brj-24141	103	25	with	with	ADP
brj-24141	103	26	varying	vary	VERB
brj-24141	103	27	rates	rate	NOUN
brj-24141	103	28	to	to	ADP
brj-24141	103	29	these	these	DET
brj-24141	103	30	groups	group	NOUN
brj-24141	103	31	.	.	PUNCT
brj-24141	104	1	different	different	ADJ
brj-24141	104	2	expansion	expansion	NOUN
brj-24141	104	3	rates	rate	NOUN
brj-24141	104	4	and	and	CCONJ
brj-24141	104	5	convolution	convolution	NOUN
brj-24141	104	6	capacities	capacity	NOUN
brj-24141	104	7	were	be	AUX
brj-24141	104	8	designed	design	VERB
brj-24141	104	9	for	for	ADP
brj-24141	104	10	different	different	ADJ
brj-24141	104	11	network	network	NOUN
brj-24141	104	12	stages	stage	NOUN
brj-24141	104	13	to	to	PART
brj-24141	104	14	fully	fully	ADV
brj-24141	104	15	leverage	leverage	VERB
brj-24141	104	16	the	the	DET
brj-24141	104	17	varying	vary	VERB
brj-24141	104	18	feature	feature	NOUN
brj-24141	104	19	map	map	NOUN
brj-24141	104	20	sizes	size	NOUN
brj-24141	104	21	produced	produce	VERB
brj-24141	104	22	at	at	ADP
brj-24141	104	23	each	each	DET
brj-24141	104	24	stage	stage	NOUN
brj-24141	104	25	.	.	PUNCT
brj-24141	105	1	this	this	DET
brj-24141	105	2	design	design	NOUN
brj-24141	105	3	transformed	transform	VERB
brj-24141	105	4	the	the	DET
brj-24141	105	5	function	function	NOUN
brj-24141	105	6	of	of	ADP
brj-24141	105	7	multirate	multirate	ADJ
brj-24141	105	8	depth	depth	NOUN
brj-24141	105	9	-	-	PUNCT
brj-24141	105	10	separable	separable	NOUN
brj-24141	105	11	convolution	convolution	NOUN
brj-24141	105	12	from	from	ADP
brj-24141	105	13	complex	complex	ADJ
brj-24141	105	14	semantic	semantic	ADJ
brj-24141	105	15	information	information	NOUN
brj-24141	105	16	extraction	extraction	NOUN
brj-24141	105	17	to	to	ADP
brj-24141	105	18	simple	simple	ADJ
brj-24141	105	19	morphological	morphological	ADJ
brj-24141	105	20	filtering	filtering	NOUN
brj-24141	105	21	,	,	PUNCT
brj-24141	105	22	thereby	thereby	ADV
brj-24141	105	23	improving	improve	VERB
brj-24141	105	24	the	the	DET
brj-24141	105	25	efficiency	efficiency	NOUN
brj-24141	105	26	of	of	ADP
brj-24141	105	27	multi	multi	ADJ
brj-24141	105	28	-	-	ADJ
brj-24141	105	29	scale	scale	ADJ
brj-24141	105	30	contextual	contextual	ADJ
brj-24141	105	31	information	information	NOUN
brj-24141	105	32	extraction	extraction	NOUN
brj-24141	105	33	(	(	PUNCT
brj-24141	105	34	zhao	zhao	PROPN
brj-24141	105	35	et	et	PROPN
brj-24141	105	36	al	al	PROPN
brj-24141	105	37	.	.	PROPN
brj-24141	105	38	2024	2024	NUM
brj-24141	105	39	)	)	PUNCT
brj-24141	105	40	.	.	PUNCT
brj-24141	106	1	fig	fig	NOUN
brj-24141	106	2	.	.	PUNCT
brj-24141	107	1	3	3	X
brj-24141	107	2	.	.	X
brj-24141	107	3	dual	dual	ADJ
brj-24141	107	4	-	-	PUNCT
brj-24141	107	5	scale	scale	NOUN
brj-24141	107	6	wide	wide	ADJ
brj-24141	107	7	residual	residual	ADJ
brj-24141	107	8	architecture	architecture	NOUN
brj-24141	107	9	msda	msda	NOUN
brj-24141	107	10	multiscale	multiscale	ADJ
brj-24141	107	11	void	void	VERB
brj-24141	107	12	attention	attention	NOUN
brj-24141	107	13	in	in	ADP
brj-24141	107	14	the	the	DET
brj-24141	107	15	field	field	NOUN
brj-24141	107	16	of	of	ADP
brj-24141	107	17	wood	wood	NOUN
brj-24141	107	18	panel	panel	NOUN
brj-24141	107	19	defect	defect	NOUN
brj-24141	107	20	detection	detection	NOUN
brj-24141	107	21	,	,	PUNCT
brj-24141	107	22	some	some	DET
brj-24141	107	23	defects	defect	NOUN
brj-24141	107	24	are	be	AUX
brj-24141	107	25	often	often	ADV
brj-24141	107	26	obscured	obscure	VERB
brj-24141	107	27	by	by	ADP
brj-24141	107	28	the	the	DET
brj-24141	107	29	features	feature	NOUN
brj-24141	107	30	of	of	ADP
brj-24141	107	31	large	large	ADJ
brj-24141	107	32	targets	target	NOUN
brj-24141	107	33	due	due	ADP
brj-24141	107	34	to	to	ADP
brj-24141	107	35	their	their	PRON
brj-24141	107	36	small	small	ADJ
brj-24141	107	37	size	size	NOUN
brj-24141	107	38	and	and	CCONJ
brj-24141	107	39	inconspicuous	inconspicuous	ADJ
brj-24141	107	40	features	feature	NOUN
brj-24141	107	41	,	,	PUNCT
brj-24141	107	42	leading	lead	VERB
brj-24141	107	43	to	to	ADP
brj-24141	107	44	poor	poor	ADJ
brj-24141	107	45	performance	performance	NOUN
brj-24141	107	46	of	of	ADP
brj-24141	107	47	target	target	NOUN
brj-24141	107	48	detection	detection	NOUN
brj-24141	107	49	models	model	NOUN
brj-24141	107	50	such	such	ADJ
brj-24141	107	51	as	as	ADP
brj-24141	107	52	yolov8	yolov8	NOUN
brj-24141	107	53	in	in	ADP
brj-24141	107	54	recognizing	recognize	VERB
brj-24141	107	55	small	small	ADJ
brj-24141	107	56	targets	target	NOUN
brj-24141	107	57	.	.	PUNCT
brj-24141	108	1	to	to	PART
brj-24141	108	2	address	address	VERB
brj-24141	108	3	this	this	DET
brj-24141	108	4	problem	problem	NOUN
brj-24141	108	5	,	,	PUNCT
brj-24141	108	6	a	a	DET
brj-24141	108	7	multi	multi	ADJ
brj-24141	108	8	-	-	ADJ
brj-24141	108	9	scale	scale	ADJ
brj-24141	108	10	dilation	dilation	NOUN
brj-24141	108	11	attention	attention	NOUN
brj-24141	108	12	(	(	PUNCT
brj-24141	108	13	msda	msda	NOUN
brj-24141	108	14	)	)	PUNCT
brj-24141	108	15	mechanism	mechanism	NOUN
brj-24141	108	16	was	be	AUX
brj-24141	108	17	incorporated	incorporate	VERB
brj-24141	108	18	into	into	ADP
brj-24141	108	19	the	the	DET
brj-24141	108	20	c2f	c2f	NOUN
brj-24141	108	21	structure	structure	NOUN
brj-24141	108	22	to	to	PART
brj-24141	108	23	improve	improve	VERB
brj-24141	108	24	the	the	DET
brj-24141	108	25	accuracy	accuracy	NOUN
brj-24141	108	26	of	of	ADP
brj-24141	108	27	the	the	DET
brj-24141	108	28	yolov8	yolov8	NOUN
brj-24141	108	29	model	model	NOUN
brj-24141	108	30	in	in	ADP
brj-24141	108	31	detecting	detect	VERB
brj-24141	108	32	small	small	ADJ
brj-24141	108	33	targets	target	NOUN
brj-24141	108	34	of	of	ADP
brj-24141	108	35	wood	wood	NOUN
brj-24141	108	36	panel	panel	NOUN
brj-24141	108	37	defects	defect	NOUN
brj-24141	108	38	.	.	PUNCT
brj-24141	109	1	the	the	DET
brj-24141	109	2	msda	msda	NOUN
brj-24141	109	3	mechanism	mechanism	NOUN
brj-24141	109	4	was	be	AUX
brj-24141	109	5	designed	design	VERB
brj-24141	109	6	to	to	PART
brj-24141	109	7	capture	capture	VERB
brj-24141	109	8	multi	multi	ADJ
brj-24141	109	9	-	-	ADJ
brj-24141	109	10	scale	scale	ADJ
brj-24141	109	11	features	feature	NOUN
brj-24141	109	12	by	by	ADP
brj-24141	109	13	configuring	configure	VERB
brj-24141	109	14	different	different	ADJ
brj-24141	109	15	dilation	dilation	NOUN
brj-24141	109	16	rates	rate	NOUN
brj-24141	109	17	in	in	ADP
brj-24141	109	18	different	different	ADJ
brj-24141	109	19	detection	detection	NOUN
brj-24141	109	20	heads	head	NOUN
brj-24141	109	21	,	,	PUNCT
brj-24141	109	22	effectively	effectively	ADV
brj-24141	109	23	strengthening	strengthen	VERB
brj-24141	109	24	the	the	DET
brj-24141	109	25	model	model	NOUN
brj-24141	109	26	’s	’s	PART
brj-24141	109	27	ability	ability	NOUN
brj-24141	109	28	to	to	PART
brj-24141	109	29	recognize	recognize	VERB
brj-24141	109	30	the	the	DET
brj-24141	109	31	features	feature	NOUN
brj-24141	109	32	of	of	ADP
brj-24141	109	33	small	small	ADJ
brj-24141	109	34	targets	target	NOUN
brj-24141	109	35	and	and	CCONJ
brj-24141	109	36	enhancing	enhance	VERB
brj-24141	109	37	the	the	DET
brj-24141	109	38	accuracy	accuracy	NOUN
brj-24141	109	39	of	of	ADP
brj-24141	109	40	its	its	PRON
brj-24141	109	41	localization	localization	NOUN
brj-24141	109	42	.	.	PUNCT
brj-24141	110	1	the	the	DET
brj-24141	110	2	proposed	propose	VERB
brj-24141	110	3	msda	msda	NOUN
brj-24141	110	4	was	be	AUX
brj-24141	110	5	based	base	VERB
brj-24141	110	6	on	on	ADP
brj-24141	110	7	the	the	DET
brj-24141	110	8	sliding	slide	VERB
brj-24141	110	9	window	window	NOUN
brj-24141	110	10	expansion	expansion	NOUN
brj-24141	110	11	attention	attention	NOUN
brj-24141	110	12	(	(	PUNCT
brj-24141	110	13	swda	swda	NOUN
brj-24141	110	14	)	)	PUNCT
brj-24141	110	15	method	method	NOUN
brj-24141	110	16	.	.	PUNCT
brj-24141	111	1	in	in	ADP
brj-24141	111	2	this	this	DET
brj-24141	111	3	method	method	NOUN
brj-24141	111	4	,	,	PUNCT
brj-24141	111	5	representative	representative	ADJ
brj-24141	111	6	keys	key	NOUN
brj-24141	111	7	and	and	CCONJ
brj-24141	111	8	values	value	NOUN
brj-24141	111	9	were	be	AUX
brj-24141	111	10	selected	select	VERB
brj-24141	111	11	for	for	ADP
brj-24141	111	12	sparsity	sparsity	NOUN
brj-24141	111	13	within	within	ADP
brj-24141	111	14	a	a	DET
brj-24141	111	15	sliding	slide	VERB
brj-24141	111	16	window	window	NOUN
brj-24141	111	17	.	.	PUNCT
brj-24141	112	1	subsequently	subsequently	ADV
brj-24141	112	2	,	,	PUNCT
brj-24141	112	3	these	these	DET
brj-24141	112	4	selected	select	VERB
brj-24141	112	5	patches	patch	NOUN
brj-24141	112	6	were	be	AUX
brj-24141	112	7	weighted	weight	VERB
brj-24141	112	8	by	by	ADP
brj-24141	112	9	performing	perform	VERB
brj-24141	112	10	the	the	DET
brj-24141	112	11	self	self	NOUN
brj-24141	112	12	-	-	PUNCT
brj-24141	112	13	attention	attention	NOUN
brj-24141	112	14	mechanism	mechanism	NOUN
brj-24141	112	15	to	to	PART
brj-24141	112	16	obtain	obtain	VERB
brj-24141	112	17	attention	attention	NOUN
brj-24141	112	18	scores	score	NOUN
brj-24141	112	19	(	(	PUNCT
brj-24141	112	20	saito	saito	PROPN
brj-24141	112	21	et	et	PROPN
brj-24141	112	22	al	al	PROPN
brj-24141	112	23	.	.	PROPN
brj-24141	112	24	2019	2019	NUM
brj-24141	112	25	)	)	PUNCT
brj-24141	112	26	.	.	PUNCT
brj-24141	113	1	the	the	DET
brj-24141	113	2	formula	formula	NOUN
brj-24141	113	3	for	for	ADP
brj-24141	113	4	this	this	DET
brj-24141	113	5	attention	attention	NOUN
brj-24141	113	6	is	be	AUX
brj-24141	113	7	as	as	SCONJ
brj-24141	113	8	follows	follow	VERB
brj-24141	113	9	.	.	PUNCT
brj-24141	114	1	(	(	PUNCT
brj-24141	114	2	,	,	PUNCT
brj-24141	114	3	,	,	PUNCT
brj-24141	114	4	,	,	PUNCT
brj-24141	114	5	)	)	PUNCT
brj-24141	114	6	x	x	PUNCT
brj-24141	114	7	swda	swda	VERB
brj-24141	114	8	q	q	PROPN
brj-24141	114	9	k	k	X
brj-24141	114	10	v	v	INTJ
brj-24141	114	11	r=	r=	ADJ
brj-24141	114	12	(	(	PUNCT
brj-24141	114	13	1	1	X
brj-24141	114	14	)	)	PUNCT
brj-24141	114	15	peer	peer	NOUN
brj-24141	114	16	-	-	PUNCT
brj-24141	114	17	reviewed	review	VERB
brj-24141	114	18	article	article	NOUN
brj-24141	114	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	114	20	li	li	PROPN
brj-24141	114	21	et	et	PROPN
brj-24141	114	22	al	al	PROPN
brj-24141	114	23	.	.	PROPN
brj-24141	115	1	(	(	PUNCT
brj-24141	115	2	2025	2025	NUM
brj-24141	115	3	)	)	PUNCT
brj-24141	115	4	.	.	PUNCT
brj-24141	116	1	“	"	PUNCT
brj-24141	116	2	improved	improved	ADJ
brj-24141	116	3	panel	panel	NOUN
brj-24141	116	4	defect	defect	NOUN
brj-24141	116	5	detection	detection	NOUN
brj-24141	116	6	,	,	PUNCT
brj-24141	116	7	”	"	PUNCT
brj-24141	116	8	bioresources	bioresource	NOUN
brj-24141	116	9	20(2	20(2	NUM
brj-24141	116	10	)	)	PUNCT
brj-24141	116	11	,	,	PUNCT
brj-24141	116	12	2556	2556	NUM
brj-24141	116	13	-	-	SYM
brj-24141	116	14	2573	2573	NUM
brj-24141	116	15	.	.	PUNCT
brj-24141	117	1	2562	2562	NUM
brj-24141	117	2	attention	attention	NOUN
brj-24141	117	3	(	(	PUNCT
brj-24141	117	4	,	,	PUNCT
brj-24141	117	5	,	,	PUNCT
brj-24141	117	6	)	)	PUNCT
brj-24141	117	7	,	,	PUNCT
brj-24141	117	8	softmax	softmax	NOUN
brj-24141	117	9	(	(	PUNCT
brj-24141	117	10	)	)	PUNCT
brj-24141	117	11	,	,	PUNCT
brj-24141	117	12	1	1	NUM
brj-24141	117	13	,	,	PUNCT
brj-24141	117	14	1	1	NUM
brj-24141	117	15	,	,	PUNCT
brj-24141	117	16	ij	ij	INTJ
brj-24141	117	17	ij	ij	INTJ
brj-24141	117	18	r	r	NOUN
brj-24141	117	19	r	r	NOUN
brj-24141	117	20	t	t	NOUN
brj-24141	117	21	ij	ij	NOUN
brj-24141	117	22	r	r	NOUN
brj-24141	117	23	r	r	NOUN
brj-24141	117	24	k	k	NOUN
brj-24141	117	25	x	x	X
brj-24141	117	26	q	q	PROPN
brj-24141	117	27	k	k	X
brj-24141	117	28	v	v	INTJ
brj-24141	117	29	q	q	PROPN
brj-24141	117	30	k	k	NOUN
brj-24141	117	31	v	v	INTJ
brj-24141	117	32	i	i	PROPN
brj-24141	117	33	w	w	PROPN
brj-24141	117	34	j	j	PROPN
brj-24141	117	35	h	h	PROPN
brj-24141	118	1	d	d	NOUN
brj-24141	118	2	=	=	SYM
brj-24141	118	3	=	=	SYM
brj-24141	118	4			NOUN
brj-24141	118	5			NOUN
brj-24141	118	6			NOUN
brj-24141	118	7			NOUN
brj-24141	118	8	(	(	PUNCT
brj-24141	118	9	2	2	X
brj-24141	118	10	)	)	PUNCT
brj-24141	118	11	the	the	DET
brj-24141	118	12	proposed	propose	VERB
brj-24141	118	13	msda	msda	NOUN
brj-24141	118	14	was	be	AUX
brj-24141	118	15	built	build	VERB
brj-24141	118	16	upon	upon	SCONJ
brj-24141	118	17	swda	swda	NOUN
brj-24141	118	18	by	by	ADP
brj-24141	118	19	dividing	divide	VERB
brj-24141	118	20	the	the	DET
brj-24141	118	21	feature	feature	NOUN
brj-24141	118	22	map	map	NOUN
brj-24141	118	23	into	into	ADP
brj-24141	118	24	windows	window	NOUN
brj-24141	118	25	at	at	ADP
brj-24141	118	26	multiple	multiple	ADJ
brj-24141	118	27	scales	scale	NOUN
brj-24141	118	28	through	through	ADP
brj-24141	118	29	value	value	NOUN
brj-24141	118	30	inflation	inflation	NOUN
brj-24141	118	31	.	.	PUNCT
brj-24141	119	1	the	the	DET
brj-24141	119	2	sliding	slide	VERB
brj-24141	119	3	window	window	NOUN
brj-24141	119	4	expansion	expansion	NOUN
brj-24141	119	5	attention	attention	NOUN
brj-24141	119	6	operation	operation	NOUN
brj-24141	119	7	was	be	AUX
brj-24141	119	8	then	then	ADV
brj-24141	119	9	performed	perform	VERB
brj-24141	119	10	on	on	ADP
brj-24141	119	11	these	these	DET
brj-24141	119	12	windows	window	NOUN
brj-24141	119	13	.	.	PUNCT
brj-24141	120	1	finally	finally	ADV
brj-24141	120	2	,	,	PUNCT
brj-24141	120	3	the	the	DET
brj-24141	120	4	outputs	output	NOUN
brj-24141	120	5	from	from	ADP
brj-24141	120	6	different	different	ADJ
brj-24141	120	7	windows	window	NOUN
brj-24141	120	8	were	be	AUX
brj-24141	120	9	stitched	stitch	VERB
brj-24141	120	10	together	together	ADV
brj-24141	120	11	,	,	PUNCT
brj-24141	120	12	and	and	CCONJ
brj-24141	120	13	feature	feature	NOUN
brj-24141	120	14	aggregation	aggregation	NOUN
brj-24141	120	15	was	be	AUX
brj-24141	120	16	carried	carry	VERB
brj-24141	120	17	out	out	ADP
brj-24141	120	18	using	use	VERB
brj-24141	120	19	a	a	DET
brj-24141	120	20	linear	linear	ADJ
brj-24141	120	21	layer	layer	NOUN
brj-24141	120	22	,	,	PUNCT
brj-24141	120	23	as	as	SCONJ
brj-24141	120	24	follows	follow	VERB
brj-24141	120	25	.	.	PUNCT
brj-24141	121	1	(	(	PUNCT
brj-24141	121	2	,	,	PUNCT
brj-24141	121	3	,	,	PUNCT
brj-24141	121	4	,	,	PUNCT
brj-24141	121	5	)	)	PUNCT
brj-24141	121	6	,	,	PUNCT
brj-24141	121	7	1	1	NUM
brj-24141	121	8	,	,	PUNCT
brj-24141	121	9	linear(concat	linear(concat	PROPN
brj-24141	121	10	[	[	PUNCT
brj-24141	121	11	,	,	PUNCT
brj-24141	121	12	,	,	PUNCT
brj-24141	121	13	]	]	PUNCT
brj-24141	121	14	)	)	PUNCT
brj-24141	122	1	i	i	PRON
brj-24141	122	2	i	i	PRON
brj-24141	123	1	i	i	PRON
brj-24141	124	1	i	i	PRON
brj-24141	125	1	i	i	PRON
brj-24141	125	2	n	n	VERB
brj-24141	125	3	h	h	NOUN
brj-24141	125	4	swda	swda	VERB
brj-24141	125	5	q	q	PROPN
brj-24141	126	1	k	k	NOUN
brj-24141	126	2	v	v	NOUN
brj-24141	126	3	r	r	NOUN
brj-24141	126	4	i	i	PRON
brj-24141	126	5	n	n	NOUN
brj-24141	126	6	x	x	NOUN
brj-24141	126	7	h	h	NOUN
brj-24141	126	8	h	h	NOUN
brj-24141	126	9	=	=	NOUN
brj-24141	126	10			NOUN
brj-24141	126	11			NOUN
brj-24141	126	12	=	=	SYM
brj-24141	126	13	(	(	PUNCT
brj-24141	126	14	3	3	X
brj-24141	126	15	)	)	PUNCT
brj-24141	126	16	the	the	DET
brj-24141	126	17	features	feature	NOUN
brj-24141	126	18	were	be	AUX
brj-24141	126	19	passed	pass	VERB
brj-24141	126	20	to	to	ADP
brj-24141	126	21	a	a	DET
brj-24141	126	22	linear	linear	ADJ
brj-24141	126	23	layer	layer	NOUN
brj-24141	126	24	for	for	ADP
brj-24141	126	25	aggregation	aggregation	NOUN
brj-24141	126	26	.	.	PUNCT
brj-24141	127	1	different	different	ADJ
brj-24141	127	2	expansion	expansion	NOUN
brj-24141	127	3	rates	rate	NOUN
brj-24141	127	4	were	be	AUX
brj-24141	127	5	configured	configure	VERB
brj-24141	127	6	for	for	ADP
brj-24141	127	7	individual	individual	ADJ
brj-24141	127	8	heads	head	NOUN
brj-24141	127	9	.	.	PUNCT
brj-24141	128	1	this	this	DET
brj-24141	128	2	multi	multi	ADJ
brj-24141	128	3	-	-	ADJ
brj-24141	128	4	scale	scale	ADJ
brj-24141	128	5	feature	feature	NOUN
brj-24141	128	6	aggregation	aggregation	NOUN
brj-24141	128	7	effectively	effectively	ADV
brj-24141	128	8	integrated	integrate	VERB
brj-24141	128	9	semantic	semantic	ADJ
brj-24141	128	10	information	information	NOUN
brj-24141	128	11	at	at	ADP
brj-24141	128	12	different	different	ADJ
brj-24141	128	13	scales	scale	NOUN
brj-24141	128	14	within	within	ADP
brj-24141	128	15	the	the	DET
brj-24141	128	16	supervised	supervised	ADJ
brj-24141	128	17	region	region	NOUN
brj-24141	128	18	,	,	PUNCT
brj-24141	128	19	significantly	significantly	ADV
brj-24141	128	20	reducing	reduce	VERB
brj-24141	128	21	redundancy	redundancy	NOUN
brj-24141	128	22	in	in	ADP
brj-24141	128	23	the	the	DET
brj-24141	128	24	self	self	NOUN
brj-24141	128	25	-	-	PUNCT
brj-24141	128	26	attention	attention	NOUN
brj-24141	128	27	mechanism	mechanism	NOUN
brj-24141	128	28	,	,	PUNCT
brj-24141	128	29	while	while	SCONJ
brj-24141	128	30	avoiding	avoid	VERB
brj-24141	128	31	complex	complex	ADJ
brj-24141	128	32	operations	operation	NOUN
brj-24141	128	33	and	and	CCONJ
brj-24141	128	34	additional	additional	ADJ
brj-24141	128	35	computational	computational	ADJ
brj-24141	128	36	overhead	overhead	NOUN
brj-24141	128	37	(	(	PUNCT
brj-24141	128	38	jiao	jiao	PROPN
brj-24141	128	39	et	et	PROPN
brj-24141	128	40	al	al	PROPN
brj-24141	128	41	.	.	PROPN
brj-24141	128	42	2023	2023	NUM
brj-24141	128	43	)	)	PUNCT
brj-24141	128	44	.	.	PUNCT
brj-24141	129	1	adaptive	adaptive	PROPN
brj-24141	129	2	gating	gate	VERB
brj-24141	129	3	was	be	AUX
brj-24141	129	4	incorporated	incorporate	VERB
brj-24141	129	5	into	into	ADP
brj-24141	129	6	the	the	DET
brj-24141	129	7	msda	msda	NOUN
brj-24141	129	8	null	null	ADJ
brj-24141	129	9	attention	attention	NOUN
brj-24141	129	10	mechanism	mechanism	NOUN
brj-24141	129	11	.	.	PUNCT
brj-24141	130	1	the	the	DET
brj-24141	130	2	gating	gate	VERB
brj-24141	130	3	weights	weight	NOUN
brj-24141	130	4	were	be	AUX
brj-24141	130	5	calculated	calculate	VERB
brj-24141	130	6	based	base	VERB
brj-24141	130	7	on	on	ADP
brj-24141	130	8	the	the	DET
brj-24141	130	9	similarity	similarity	NOUN
brj-24141	130	10	between	between	ADP
brj-24141	130	11	features	feature	NOUN
brj-24141	130	12	.	.	PUNCT
brj-24141	131	1	additionally	additionally	ADV
brj-24141	131	2	,	,	PUNCT
brj-24141	131	3	an	an	DET
brj-24141	131	4	initial	initial	ADJ
brj-24141	131	5	weight	weight	NOUN
brj-24141	131	6	was	be	AUX
brj-24141	131	7	assigned	assign	VERB
brj-24141	131	8	to	to	ADP
brj-24141	131	9	each	each	DET
brj-24141	131	10	type	type	NOUN
brj-24141	131	11	of	of	ADP
brj-24141	131	12	defective	defective	ADJ
brj-24141	131	13	feature	feature	NOUN
brj-24141	131	14	by	by	ADP
brj-24141	131	15	learning	learn	VERB
brj-24141	131	16	the	the	DET
brj-24141	131	17	defective	defective	ADJ
brj-24141	131	18	label	label	NOUN
brj-24141	131	19	information	information	NOUN
brj-24141	131	20	in	in	ADP
brj-24141	131	21	the	the	DET
brj-24141	131	22	dataset	dataset	NOUN
brj-24141	131	23	.	.	PUNCT
brj-24141	132	1	this	this	DET
brj-24141	132	2	initial	initial	ADJ
brj-24141	132	3	weight	weight	NOUN
brj-24141	132	4	was	be	AUX
brj-24141	132	5	continuously	continuously	ADV
brj-24141	132	6	updated	update	VERB
brj-24141	132	7	during	during	ADP
brj-24141	132	8	the	the	DET
brj-24141	132	9	model	model	NOUN
brj-24141	132	10	training	training	NOUN
brj-24141	132	11	process	process	NOUN
brj-24141	132	12	,	,	PUNCT
brj-24141	132	13	resulting	result	VERB
brj-24141	132	14	in	in	ADP
brj-24141	132	15	an	an	DET
brj-24141	132	16	adaptive	adaptive	ADJ
brj-24141	132	17	weight	weight	NOUN
brj-24141	132	18	that	that	PRON
brj-24141	132	19	reflected	reflect	VERB
brj-24141	132	20	the	the	DET
brj-24141	132	21	importance	importance	NOUN
brj-24141	132	22	of	of	ADP
brj-24141	132	23	different	different	ADJ
brj-24141	132	24	defective	defective	ADJ
brj-24141	132	25	features	feature	NOUN
brj-24141	132	26	(	(	PUNCT
brj-24141	132	27	nie	nie	PROPN
brj-24141	132	28	et	et	PROPN
brj-24141	132	29	al	al	PROPN
brj-24141	132	30	.	.	PROPN
brj-24141	132	31	2024	2024	NUM
brj-24141	132	32	)	)	PUNCT
brj-24141	132	33	.	.	PUNCT
brj-24141	133	1	when	when	SCONJ
brj-24141	133	2	performing	perform	VERB
brj-24141	133	3	attention	attention	NOUN
brj-24141	133	4	calculations	calculation	NOUN
brj-24141	133	5	,	,	PUNCT
brj-24141	133	6	the	the	DET
brj-24141	133	7	original	original	ADJ
brj-24141	133	8	attention	attention	NOUN
brj-24141	133	9	weight	weight	NOUN
brj-24141	133	10	was	be	AUX
brj-24141	133	11	multiplied	multiply	VERB
brj-24141	133	12	by	by	ADP
brj-24141	133	13	the	the	DET
brj-24141	133	14	adaptive	adaptive	ADJ
brj-24141	133	15	weight	weight	NOUN
brj-24141	133	16	gi	gi	NOUN
brj-24141	133	17	to	to	PART
brj-24141	133	18	obtain	obtain	VERB
brj-24141	133	19	the	the	DET
brj-24141	133	20	final	final	ADJ
brj-24141	133	21	attention	attention	NOUN
brj-24141	133	22	weight	weight	NOUN
brj-24141	133	23	.	.	PUNCT
brj-24141	134	1	this	this	PRON
brj-24141	134	2	allowed	allow	VERB
brj-24141	134	3	the	the	DET
brj-24141	134	4	model	model	NOUN
brj-24141	134	5	to	to	PART
brj-24141	134	6	focus	focus	VERB
brj-24141	134	7	more	more	ADV
brj-24141	134	8	on	on	ADP
brj-24141	134	9	defect	defect	ADJ
brj-24141	134	10	features	feature	NOUN
brj-24141	134	11	with	with	ADP
brj-24141	134	12	higher	high	ADJ
brj-24141	134	13	importance	importance	NOUN
brj-24141	134	14	,	,	PUNCT
brj-24141	134	15	thereby	thereby	ADV
brj-24141	134	16	improving	improve	VERB
brj-24141	134	17	detection	detection	NOUN
brj-24141	134	18	accuracy	accuracy	NOUN
brj-24141	134	19	.	.	PUNCT
brj-24141	135	1	the	the	DET
brj-24141	135	2	formula	formula	NOUN
brj-24141	135	3	is	be	AUX
brj-24141	135	4	shown	show	VERB
brj-24141	135	5	below	below	ADP
brj-24141	135	6	.	.	PUNCT
brj-24141	136	1	attention	attention	NOUN
brj-24141	136	2	(	(	PUNCT
brj-24141	136	3	,	,	PUNCT
brj-24141	136	4	,	,	PUNCT
brj-24141	136	5	)	)	PUNCT
brj-24141	136	6	ij	ij	INTJ
brj-24141	137	1	ij	ij	INTJ
brj-24141	137	2	r	r	NOUN
brj-24141	137	3	r	r	NOUN
brj-24141	137	4	ix	ix	ADP
brj-24141	137	5	q	q	PROPN
brj-24141	138	1	k	k	PROPN
brj-24141	138	2	v	v	PROPN
brj-24141	138	3	g=	g=	NOUN
brj-24141	138	4	(	(	PUNCT
brj-24141	138	5	4	4	NUM
brj-24141	138	6	)	)	PUNCT
brj-24141	138	7	(	(	PUNCT
brj-24141	138	8	,	,	PUNCT
brj-24141	138	9	,	,	PUNCT
brj-24141	138	10	,	,	PUNCT
brj-24141	138	11	,	,	PUNCT
brj-24141	138	12	)	)	PUNCT
brj-24141	138	13	,	,	PUNCT
brj-24141	138	14	1	1	X
brj-24141	138	15	,	,	PUNCT
brj-24141	139	1	i	i	PRON
brj-24141	139	2	i	i	PRON
brj-24141	140	1	i	i	PRON
brj-24141	140	2	ih	ih	NOUN
brj-24141	140	3	swda	swda	VERB
brj-24141	140	4	q	q	PROPN
brj-24141	141	1	k	k	NOUN
brj-24141	141	2	v	v	NOUN
brj-24141	141	3	r	r	NOUN
brj-24141	141	4	g	g	PROPN
brj-24141	141	5	i	i	PROPN
brj-24141	141	6	n=	n=	ADJ
brj-24141	141	7			NOUN
brj-24141	141	8			NOUN
brj-24141	141	9	(	(	PUNCT
brj-24141	141	10	5	5	NUM
brj-24141	141	11	)	)	PUNCT
brj-24141	141	12	fig	fig	NOUN
brj-24141	141	13	.	.	PUNCT
brj-24141	142	1	4	4	X
brj-24141	142	2	.	.	X
brj-24141	142	3	multi	multi	ADJ
brj-24141	142	4	-	-	ADJ
brj-24141	142	5	scale	scale	ADJ
brj-24141	142	6	dilated	dilated	ADJ
brj-24141	142	7	attention	attention	NOUN
brj-24141	142	8	architecture	architecture	NOUN
brj-24141	142	9	peer	peer	NOUN
brj-24141	142	10	-	-	PUNCT
brj-24141	142	11	reviewed	review	VERB
brj-24141	142	12	article	article	NOUN
brj-24141	142	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	142	14	li	li	PROPN
brj-24141	142	15	et	et	PROPN
brj-24141	142	16	al	al	PROPN
brj-24141	142	17	.	.	PROPN
brj-24141	143	1	(	(	PUNCT
brj-24141	143	2	2025	2025	NUM
brj-24141	143	3	)	)	PUNCT
brj-24141	143	4	.	.	PUNCT
brj-24141	144	1	“	"	PUNCT
brj-24141	144	2	improved	improved	ADJ
brj-24141	144	3	panel	panel	NOUN
brj-24141	144	4	defect	defect	NOUN
brj-24141	144	5	detection	detection	NOUN
brj-24141	144	6	,	,	PUNCT
brj-24141	144	7	”	"	PUNCT
brj-24141	144	8	bioresources	bioresource	NOUN
brj-24141	144	9	20(2	20(2	NUM
brj-24141	144	10	)	)	PUNCT
brj-24141	144	11	,	,	PUNCT
brj-24141	144	12	2556	2556	NUM
brj-24141	144	13	-	-	SYM
brj-24141	144	14	2573	2573	NUM
brj-24141	144	15	.	.	PUNCT
brj-24141	145	1	2563	2563	NUM
brj-24141	145	2	improvement	improvement	NOUN
brj-24141	145	3	of	of	ADP
brj-24141	145	4	the	the	DET
brj-24141	145	5	neck	neck	NOUN
brj-24141	145	6	structure	structure	NOUN
brj-24141	145	7	multiple	multiple	ADJ
brj-24141	145	8	integration	integration	NOUN
brj-24141	145	9	modules	module	NOUN
brj-24141	145	10	consisting	consist	VERB
brj-24141	145	11	of	of	ADP
brj-24141	145	12	convolutional	convolutional	ADJ
brj-24141	145	13	layers	layer	NOUN
brj-24141	145	14	were	be	AUX
brj-24141	145	15	embedded	embed	VERB
brj-24141	145	16	into	into	ADP
brj-24141	145	17	integration	integration	NOUN
brj-24141	145	18	paths	path	NOUN
brj-24141	145	19	,	,	PUNCT
brj-24141	145	20	which	which	PRON
brj-24141	145	21	is	be	AUX
brj-24141	145	22	a	a	DET
brj-24141	145	23	common	common	ADJ
brj-24141	145	24	technique	technique	NOUN
brj-24141	145	25	in	in	ADP
brj-24141	145	26	deep	deep	ADJ
brj-24141	145	27	learning	learning	NOUN
brj-24141	145	28	models	model	NOUN
brj-24141	145	29	.	.	PUNCT
brj-24141	146	1	the	the	DET
brj-24141	146	2	primary	primary	ADJ
brj-24141	146	3	objective	objective	NOUN
brj-24141	146	4	of	of	ADP
brj-24141	146	5	this	this	DET
brj-24141	146	6	approach	approach	NOUN
brj-24141	146	7	was	be	AUX
brj-24141	146	8	to	to	PART
brj-24141	146	9	enhance	enhance	VERB
brj-24141	146	10	the	the	DET
brj-24141	146	11	model	model	NOUN
brj-24141	146	12	’s	’s	PART
brj-24141	146	13	representational	representational	ADJ
brj-24141	146	14	ability	ability	NOUN
brj-24141	146	15	and	and	CCONJ
brj-24141	146	16	overall	overall	ADJ
brj-24141	146	17	performance	performance	NOUN
brj-24141	146	18	by	by	ADP
brj-24141	146	19	combining	combine	VERB
brj-24141	146	20	different	different	ADJ
brj-24141	146	21	feature	feature	NOUN
brj-24141	146	22	maps	map	NOUN
brj-24141	146	23	.	.	PUNCT
brj-24141	147	1	the	the	DET
brj-24141	147	2	ccff	ccff	NOUN
brj-24141	147	3	(	(	PUNCT
brj-24141	147	4	cross	cross	ADJ
brj-24141	147	5	-	-	ADJ
brj-24141	147	6	scale	scale	ADJ
brj-24141	147	7	context	context	NOUN
brj-24141	147	8	fusion	fusion	NOUN
brj-24141	147	9	framework	framework	NOUN
brj-24141	147	10	)	)	PUNCT
brj-24141	147	11	architecture	architecture	NOUN
brj-24141	147	12	was	be	AUX
brj-24141	147	13	optimized	optimize	VERB
brj-24141	147	14	based	base	VERB
brj-24141	147	15	on	on	ADP
brj-24141	147	16	the	the	DET
brj-24141	147	17	cross	cross	ADJ
brj-24141	147	18	-	-	ADJ
brj-24141	147	19	scale	scale	ADJ
brj-24141	147	20	integration	integration	NOUN
brj-24141	147	21	module	module	NOUN
brj-24141	147	22	,	,	PUNCT
brj-24141	147	23	which	which	PRON
brj-24141	147	24	involved	involve	VERB
brj-24141	147	25	inserting	insert	VERB
brj-24141	147	26	multiple	multiple	ADJ
brj-24141	147	27	integration	integration	NOUN
brj-24141	147	28	modules	module	NOUN
brj-24141	147	29	composed	compose	VERB
brj-24141	147	30	of	of	ADP
brj-24141	147	31	convolutional	convolutional	ADJ
brj-24141	147	32	layers	layer	NOUN
brj-24141	147	33	into	into	ADP
brj-24141	147	34	the	the	DET
brj-24141	147	35	integration	integration	NOUN
brj-24141	147	36	path	path	NOUN
brj-24141	147	37	.	.	PUNCT
brj-24141	148	1	these	these	DET
brj-24141	148	2	integration	integration	NOUN
brj-24141	148	3	modules	module	NOUN
brj-24141	148	4	were	be	AUX
brj-24141	148	5	designed	design	VERB
brj-24141	148	6	to	to	PART
brj-24141	148	7	efficiently	efficiently	ADV
brj-24141	148	8	integrate	integrate	VERB
brj-24141	148	9	the	the	DET
brj-24141	148	10	feature	feature	NOUN
brj-24141	148	11	maps	map	NOUN
brj-24141	148	12	of	of	ADP
brj-24141	148	13	two	two	NUM
brj-24141	148	14	neighboring	neighboring	NOUN
brj-24141	148	15	scales	scale	NOUN
brj-24141	148	16	,	,	PUNCT
brj-24141	148	17	generating	generate	VERB
brj-24141	148	18	a	a	DET
brj-24141	148	19	new	new	ADJ
brj-24141	148	20	feature	feature	NOUN
brj-24141	148	21	map	map	NOUN
brj-24141	148	22	(	(	PUNCT
brj-24141	148	23	lv	lv	PROPN
brj-24141	148	24	et	et	PROPN
brj-24141	148	25	al	al	PROPN
brj-24141	148	26	.	.	PROPN
brj-24141	148	27	2024	2024	NUM
brj-24141	148	28	)	)	PUNCT
brj-24141	148	29	.	.	PUNCT
brj-24141	149	1	given	give	VERB
brj-24141	149	2	the	the	DET
brj-24141	149	3	varying	vary	VERB
brj-24141	149	4	receptive	receptive	ADJ
brj-24141	149	5	fields	field	NOUN
brj-24141	149	6	of	of	ADP
brj-24141	149	7	different	different	ADJ
brj-24141	149	8	convolutional	convolutional	ADJ
brj-24141	149	9	layers	layer	NOUN
brj-24141	149	10	,	,	PUNCT
brj-24141	149	11	they	they	PRON
brj-24141	149	12	were	be	AUX
brj-24141	149	13	capable	capable	ADJ
brj-24141	149	14	of	of	ADP
brj-24141	149	15	capturing	capture	VERB
brj-24141	149	16	feature	feature	NOUN
brj-24141	149	17	information	information	NOUN
brj-24141	149	18	at	at	ADP
brj-24141	149	19	different	different	ADJ
brj-24141	149	20	scales	scale	NOUN
brj-24141	149	21	.	.	PUNCT
brj-24141	150	1	by	by	ADP
brj-24141	150	2	strategically	strategically	ADV
brj-24141	150	3	designing	design	VERB
brj-24141	150	4	and	and	CCONJ
brj-24141	150	5	arranging	arrange	VERB
brj-24141	150	6	these	these	DET
brj-24141	150	7	integration	integration	NOUN
brj-24141	150	8	modules	module	NOUN
brj-24141	150	9	,	,	PUNCT
brj-24141	150	10	a	a	DET
brj-24141	150	11	more	more	ADV
brj-24141	150	12	refined	refined	ADJ
brj-24141	150	13	and	and	CCONJ
brj-24141	150	14	comprehensive	comprehensive	ADJ
brj-24141	150	15	multi	multi	ADJ
brj-24141	150	16	-	-	ADJ
brj-24141	150	17	scale	scale	ADJ
brj-24141	150	18	feature	feature	NOUN
brj-24141	150	19	integration	integration	NOUN
brj-24141	150	20	was	be	AUX
brj-24141	150	21	achieved	achieve	VERB
brj-24141	150	22	.	.	PUNCT
brj-24141	151	1	this	this	DET
brj-24141	151	2	fine	fine	ADV
brj-24141	151	3	-	-	PUNCT
brj-24141	151	4	grained	grain	VERB
brj-24141	151	5	multi	multi	ADJ
brj-24141	151	6	-	-	ADJ
brj-24141	151	7	scale	scale	ADJ
brj-24141	151	8	feature	feature	NOUN
brj-24141	151	9	integration	integration	NOUN
brj-24141	151	10	mechanism	mechanism	NOUN
brj-24141	151	11	enabled	enable	VERB
brj-24141	151	12	the	the	DET
brj-24141	151	13	model	model	NOUN
brj-24141	151	14	to	to	PART
brj-24141	151	15	more	more	ADV
brj-24141	151	16	accurately	accurately	ADV
brj-24141	151	17	capture	capture	VERB
brj-24141	151	18	the	the	DET
brj-24141	151	19	detailed	detailed	ADJ
brj-24141	151	20	and	and	CCONJ
brj-24141	151	21	contextual	contextual	ADJ
brj-24141	151	22	information	information	NOUN
brj-24141	151	23	of	of	ADP
brj-24141	151	24	the	the	DET
brj-24141	151	25	target	target	NOUN
brj-24141	151	26	object	object	NOUN
brj-24141	151	27	,	,	PUNCT
brj-24141	151	28	leading	lead	VERB
brj-24141	151	29	to	to	ADP
brj-24141	151	30	improved	improved	ADJ
brj-24141	151	31	performance	performance	NOUN
brj-24141	151	32	in	in	ADP
brj-24141	151	33	various	various	ADJ
brj-24141	151	34	visual	visual	ADJ
brj-24141	151	35	tasks	task	NOUN
brj-24141	151	36	.	.	PUNCT
brj-24141	152	1	its	its	PRON
brj-24141	152	2	structure	structure	NOUN
brj-24141	152	3	is	be	AUX
brj-24141	152	4	shown	show	VERB
brj-24141	152	5	in	in	ADP
brj-24141	152	6	fig	fig	NOUN
brj-24141	152	7	.	.	PUNCT
brj-24141	153	1	4	4	X
brj-24141	153	2	.	.	X
brj-24141	153	3	fig	fig	NOUN
brj-24141	153	4	.	.	PUNCT
brj-24141	154	1	5	5	X
brj-24141	154	2	.	.	X
brj-24141	154	3	cross	cross	ADJ
brj-24141	154	4	-	-	ADJ
brj-24141	154	5	scale	scale	ADJ
brj-24141	154	6	integration	integration	NOUN
brj-24141	154	7	module	module	NOUN
brj-24141	154	8	of	of	ADP
brj-24141	154	9	ccff	ccff	NOUN
brj-24141	154	10	improving	improve	VERB
brj-24141	154	11	the	the	DET
brj-24141	154	12	loss	loss	NOUN
brj-24141	154	13	function	function	NOUN
brj-24141	154	14	iou	iou	NOUN
brj-24141	154	15	(	(	PUNCT
brj-24141	154	16	intersection	intersection	NOUN
brj-24141	154	17	over	over	ADP
brj-24141	154	18	union	union	NOUN
brj-24141	154	19	)	)	PUNCT
brj-24141	154	20	was	be	AUX
brj-24141	154	21	used	use	VERB
brj-24141	154	22	as	as	ADP
brj-24141	154	23	a	a	DET
brj-24141	154	24	target	target	NOUN
brj-24141	154	25	detection	detection	NOUN
brj-24141	154	26	evaluation	evaluation	NOUN
brj-24141	154	27	metric	metric	NOUN
brj-24141	154	28	.	.	PUNCT
brj-24141	155	1	it	it	PRON
brj-24141	155	2	quantified	quantify	VERB
brj-24141	155	3	the	the	DET
brj-24141	155	4	overlap	overlap	NOUN
brj-24141	155	5	between	between	ADP
brj-24141	155	6	the	the	DET
brj-24141	155	7	predicted	predict	VERB
brj-24141	155	8	bounding	bounding	NOUN
brj-24141	155	9	box	box	NOUN
brj-24141	155	10	and	and	CCONJ
brj-24141	155	11	the	the	DET
brj-24141	155	12	ground	ground	NOUN
brj-24141	155	13	-	-	PUNCT
brj-24141	155	14	truth	truth	NOUN
brj-24141	155	15	box	box	NOUN
brj-24141	155	16	.	.	PUNCT
brj-24141	156	1	the	the	DET
brj-24141	156	2	iou	iou	PROPN
brj-24141	156	3	loss	loss	NOUN
brj-24141	156	4	function	function	NOUN
brj-24141	156	5	directly	directly	ADV
brj-24141	156	6	optimized	optimize	VERB
brj-24141	156	7	the	the	DET
brj-24141	156	8	model	model	NOUN
brj-24141	156	9	by	by	ADP
brj-24141	156	10	minimizing	minimize	VERB
brj-24141	156	11	the	the	DET
brj-24141	156	12	difference	difference	NOUN
brj-24141	156	13	between	between	ADP
brj-24141	156	14	iou	iou	NOUN
brj-24141	156	15	and	and	CCONJ
brj-24141	156	16	1	1	NUM
brj-24141	156	17	.	.	PUNCT
brj-24141	157	1	however	however	ADV
brj-24141	157	2	,	,	PUNCT
brj-24141	157	3	this	this	DET
brj-24141	157	4	loss	loss	NOUN
brj-24141	157	5	function	function	NOUN
brj-24141	157	6	suffered	suffer	VERB
brj-24141	157	7	from	from	ADP
brj-24141	157	8	a	a	DET
brj-24141	157	9	vanishing	vanish	VERB
brj-24141	157	10	gradient	gradient	NOUN
brj-24141	157	11	problem	problem	NOUN
brj-24141	157	12	when	when	SCONJ
brj-24141	157	13	iou	iou	PROPN
brj-24141	157	14	approached	approach	VERB
brj-24141	157	15	0	0	NUM
brj-24141	157	16	or	or	CCONJ
brj-24141	157	17	1	1	NUM
brj-24141	157	18	,	,	PUNCT
brj-24141	157	19	hindering	hinder	VERB
brj-24141	157	20	network	network	NOUN
brj-24141	157	21	convergence	convergence	NOUN
brj-24141	157	22	.	.	PUNCT
brj-24141	158	1	moreover	moreover	ADV
brj-24141	158	2	,	,	PUNCT
brj-24141	158	3	iou	iou	VERB
brj-24141	158	4	only	only	ADV
brj-24141	158	5	considered	consider	VERB
brj-24141	158	6	the	the	DET
brj-24141	158	7	overlapping	overlap	VERB
brj-24141	158	8	area	area	NOUN
brj-24141	158	9	,	,	PUNCT
brj-24141	158	10	neglecting	neglect	VERB
brj-24141	158	11	other	other	ADJ
brj-24141	158	12	geometric	geometric	ADJ
brj-24141	158	13	information	information	NOUN
brj-24141	158	14	such	such	ADJ
brj-24141	158	15	as	as	ADP
brj-24141	158	16	the	the	DET
brj-24141	158	17	center	center	NOUN
brj-24141	158	18	point	point	NOUN
brj-24141	158	19	distance	distance	NOUN
brj-24141	158	20	and	and	CCONJ
brj-24141	158	21	aspect	aspect	NOUN
brj-24141	158	22	ratio	ratio	NOUN
brj-24141	158	23	.	.	PUNCT
brj-24141	159	1	yolov8	yolov8	PROPN
brj-24141	159	2	employed	employ	VERB
brj-24141	159	3	ciou	ciou	NOUN
brj-24141	159	4	as	as	ADP
brj-24141	159	5	the	the	DET
brj-24141	159	6	loss	loss	NOUN
brj-24141	159	7	function	function	NOUN
brj-24141	159	8	for	for	ADP
brj-24141	159	9	bounding	bound	VERB
brj-24141	159	10	box	box	NOUN
brj-24141	159	11	regression	regression	NOUN
brj-24141	159	12	.	.	PUNCT
brj-24141	160	1	ciou	ciou	NOUN
brj-24141	160	2	not	not	PART
brj-24141	160	3	only	only	ADV
brj-24141	160	4	considered	consider	VERB
brj-24141	160	5	the	the	DET
brj-24141	160	6	overlap	overlap	NOUN
brj-24141	160	7	area	area	NOUN
brj-24141	160	8	but	but	CCONJ
brj-24141	160	9	also	also	ADV
brj-24141	160	10	incorporated	incorporate	VERB
brj-24141	160	11	the	the	DET
brj-24141	160	12	centroid	centroid	NOUN
brj-24141	160	13	distance	distance	NOUN
brj-24141	160	14	and	and	CCONJ
brj-24141	160	15	aspect	aspect	NOUN
brj-24141	160	16	ratio	ratio	NOUN
brj-24141	160	17	,	,	PUNCT
brj-24141	160	18	leading	lead	VERB
brj-24141	160	19	to	to	ADP
brj-24141	160	20	more	more	ADV
brj-24141	160	21	accurate	accurate	ADJ
brj-24141	160	22	and	and	CCONJ
brj-24141	160	23	efficient	efficient	ADJ
brj-24141	160	24	bounding	bounding	NOUN
brj-24141	160	25	box	box	NOUN
brj-24141	160	26	regression	regression	NOUN
brj-24141	160	27	(	(	PUNCT
brj-24141	160	28	zheng	zheng	PROPN
brj-24141	160	29	et	et	PROPN
brj-24141	160	30	al	al	PROPN
brj-24141	160	31	.	.	PROPN
brj-24141	160	32	2021	2021	NUM
brj-24141	160	33	)	)	PUNCT
brj-24141	160	34	.	.	PUNCT
brj-24141	161	1	by	by	ADP
brj-24141	161	2	addressing	address	VERB
brj-24141	161	3	the	the	DET
brj-24141	161	4	limitations	limitation	NOUN
brj-24141	161	5	of	of	ADP
brj-24141	161	6	iou	iou	NOUN
brj-24141	161	7	,	,	PUNCT
brj-24141	161	8	ciou	ciou	NOUN
brj-24141	161	9	significantly	significantly	ADV
brj-24141	161	10	improved	improve	VERB
brj-24141	161	11	the	the	DET
brj-24141	161	12	model	model	NOUN
brj-24141	161	13	’s	’s	PART
brj-24141	161	14	performance	performance	NOUN
brj-24141	161	15	:	:	PUNCT
brj-24141	161	16	2	2	NUM
brj-24141	161	17	2	2	NUM
brj-24141	161	18	(	(	PUNCT
brj-24141	161	19	)	)	PUNCT
brj-24141	161	20	p	p	NOUN
brj-24141	161	21	ciou	ciou	NOUN
brj-24141	161	22	iou	iou	NOUN
brj-24141	161	23	av	av	PROPN
brj-24141	161	24	c	c	PROPN
brj-24141	161	25	=	=	PUNCT
brj-24141	162	1	−	−	PROPN
brj-24141	162	2	−	−	PROPN
brj-24141	162	3	(	(	PUNCT
brj-24141	162	4	6	6	NUM
brj-24141	162	5	)	)	SYM
brj-24141	162	6	2	2	NUM
brj-24141	162	7	2	2	NUM
brj-24141	162	8	4	4	NUM
brj-24141	162	9	_	_	PRON
brj-24141	162	10	(	(	PUNCT
brj-24141	162	11	arctan	arctan	PROPN
brj-24141	162	12	(	(	PUNCT
brj-24141	162	13	)	)	PUNCT
brj-24141	162	14	arctan	arctan	PROPN
brj-24141	162	15	(	(	PUNCT
brj-24141	162	16	)	)	PUNCT
brj-24141	162	17	)	)	PUNCT
brj-24141	163	1	_	_	PUNCT
brj-24141	164	1	w	w	PUNCT
brj-24141	164	2	gt	gt	PROPN
brj-24141	164	3	w	w	PROPN
brj-24141	164	4	v	v	PROPN
brj-24141	164	5	h	h	NOUN
brj-24141	164	6	gt	gt	PROPN
brj-24141	164	7	h	h	PROPN
brj-24141	164	8	=	=	PUNCT
brj-24141	165	1	−	−	PROPN
brj-24141	165	2	(	(	PUNCT
brj-24141	165	3	7	7	NUM
brj-24141	165	4	)	)	PUNCT
brj-24141	165	5	peer	peer	NOUN
brj-24141	165	6	-	-	PUNCT
brj-24141	165	7	reviewed	review	VERB
brj-24141	165	8	article	article	NOUN
brj-24141	165	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	165	10	li	li	PROPN
brj-24141	165	11	et	et	PROPN
brj-24141	165	12	al	al	PROPN
brj-24141	165	13	.	.	PROPN
brj-24141	166	1	(	(	PUNCT
brj-24141	166	2	2025	2025	NUM
brj-24141	166	3	)	)	PUNCT
brj-24141	166	4	.	.	PUNCT
brj-24141	167	1	“	"	PUNCT
brj-24141	167	2	improved	improved	ADJ
brj-24141	167	3	panel	panel	NOUN
brj-24141	167	4	defect	defect	NOUN
brj-24141	167	5	detection	detection	NOUN
brj-24141	167	6	,	,	PUNCT
brj-24141	167	7	”	"	PUNCT
brj-24141	167	8	bioresources	bioresource	NOUN
brj-24141	167	9	20(2	20(2	NUM
brj-24141	167	10	)	)	PUNCT
brj-24141	167	11	,	,	PUNCT
brj-24141	167	12	2556	2556	NUM
brj-24141	167	13	-	-	SYM
brj-24141	167	14	2573	2573	NUM
brj-24141	167	15	.	.	PUNCT
brj-24141	168	1	2564	2564	NUM
brj-24141	168	2	1	1	NUM
brj-24141	168	3	v	v	ADP
brj-24141	168	4	a	a	DET
brj-24141	168	5	iou	iou	NOUN
brj-24141	168	6	v	v	NOUN
brj-24141	168	7	=	=	SYM
brj-24141	168	8	−	−	PROPN
brj-24141	169	1	+	+	CCONJ
brj-24141	169	2	(	(	PUNCT
brj-24141	169	3	8)	8)	NUM
brj-24141	169	4	in	in	ADP
brj-24141	169	5	eq	eq	ADP
brj-24141	169	6	.	.	PROPN
brj-24141	169	7	6	6	NUM
brj-24141	169	8	,	,	PUNCT
brj-24141	169	9	p2	p2	PROPN
brj-24141	169	10	is	be	AUX
brj-24141	169	11	the	the	DET
brj-24141	169	12	square	square	NOUN
brj-24141	169	13	of	of	ADP
brj-24141	169	14	the	the	DET
brj-24141	169	15	euclidean	euclidean	ADJ
brj-24141	169	16	distance	distance	NOUN
brj-24141	169	17	between	between	ADP
brj-24141	169	18	the	the	DET
brj-24141	169	19	centroid	centroid	NOUN
brj-24141	169	20	of	of	ADP
brj-24141	169	21	the	the	DET
brj-24141	169	22	predicted	predict	VERB
brj-24141	169	23	and	and	CCONJ
brj-24141	169	24	real	real	ADJ
brj-24141	169	25	frames	frame	NOUN
brj-24141	169	26	,	,	PUNCT
brj-24141	169	27	whereas	whereas	SCONJ
brj-24141	169	28	v	v	NOUN
brj-24141	169	29	is	be	AUX
brj-24141	169	30	a	a	DET
brj-24141	169	31	measure	measure	NOUN
brj-24141	169	32	of	of	ADP
brj-24141	169	33	the	the	DET
brj-24141	169	34	consistency	consistency	NOUN
brj-24141	169	35	of	of	ADP
brj-24141	169	36	the	the	DET
brj-24141	169	37	aspect	aspect	NOUN
brj-24141	169	38	ratio	ratio	NOUN
brj-24141	169	39	.	.	PUNCT
brj-24141	170	1	ciou	ciou	PROPN
brj-24141	170	2	assumed	assume	VERB
brj-24141	170	3	that	that	SCONJ
brj-24141	170	4	the	the	DET
brj-24141	170	5	aspect	aspect	NOUN
brj-24141	170	6	ratio	ratio	NOUN
brj-24141	170	7	of	of	ADP
brj-24141	170	8	the	the	DET
brj-24141	170	9	target	target	NOUN
brj-24141	170	10	bounding	bounding	NOUN
brj-24141	170	11	box	box	NOUN
brj-24141	170	12	was	be	AUX
brj-24141	170	13	a	a	DET
brj-24141	170	14	continuously	continuously	ADV
brj-24141	170	15	varying	vary	VERB
brj-24141	170	16	value	value	NOUN
brj-24141	170	17	and	and	CCONJ
brj-24141	170	18	used	use	VERB
brj-24141	170	19	a	a	DET
brj-24141	170	20	trigonometric	trigonometric	ADJ
brj-24141	170	21	function	function	NOUN
brj-24141	170	22	to	to	PART
brj-24141	170	23	calculate	calculate	VERB
brj-24141	170	24	its	its	PRON
brj-24141	170	25	consistency	consistency	NOUN
brj-24141	170	26	.	.	PUNCT
brj-24141	171	1	in	in	ADP
brj-24141	171	2	wood	wood	NOUN
brj-24141	171	3	defect	defect	NOUN
brj-24141	171	4	detection	detection	NOUN
brj-24141	171	5	,	,	PUNCT
brj-24141	171	6	defects	defect	NOUN
brj-24141	171	7	tended	tend	VERB
brj-24141	171	8	to	to	PART
brj-24141	171	9	occupy	occupy	VERB
brj-24141	171	10	a	a	DET
brj-24141	171	11	small	small	ADJ
brj-24141	171	12	percentage	percentage	NOUN
brj-24141	171	13	of	of	ADP
brj-24141	171	14	the	the	DET
brj-24141	171	15	area	area	NOUN
brj-24141	171	16	,	,	PUNCT
brj-24141	171	17	leading	lead	VERB
brj-24141	171	18	to	to	ADP
brj-24141	171	19	difficult	difficult	ADJ
brj-24141	171	20	samples	sample	NOUN
brj-24141	171	21	.	.	PUNCT
brj-24141	172	1	conversely	conversely	ADV
brj-24141	172	2	,	,	PUNCT
brj-24141	172	3	defect	defect	NOUN
brj-24141	172	4	-	-	PUNCT
brj-24141	172	5	free	free	ADJ
brj-24141	172	6	areas	area	NOUN
brj-24141	172	7	were	be	AUX
brj-24141	172	8	considered	consider	VERB
brj-24141	172	9	simple	simple	ADJ
brj-24141	172	10	samples	sample	NOUN
brj-24141	172	11	.	.	PUNCT
brj-24141	173	1	very	very	ADV
brj-24141	173	2	small	small	ADJ
brj-24141	173	3	defects	defect	NOUN
brj-24141	173	4	were	be	AUX
brj-24141	173	5	challenging	challenge	VERB
brj-24141	173	6	to	to	PART
brj-24141	173	7	accurately	accurately	ADV
brj-24141	173	8	locate	locate	VERB
brj-24141	173	9	due	due	ADP
brj-24141	173	10	to	to	ADP
brj-24141	173	11	their	their	PRON
brj-24141	173	12	size	size	NOUN
brj-24141	173	13	and	and	CCONJ
brj-24141	173	14	inconspicuous	inconspicuous	ADJ
brj-24141	173	15	features	feature	NOUN
brj-24141	173	16	.	.	PUNCT
brj-24141	174	1	the	the	DET
brj-24141	174	2	distribution	distribution	NOUN
brj-24141	174	3	of	of	ADP
brj-24141	174	4	aspect	aspect	NOUN
brj-24141	174	5	ratios	ratio	NOUN
brj-24141	174	6	in	in	ADP
brj-24141	174	7	the	the	DET
brj-24141	174	8	target	target	NOUN
brj-24141	174	9	bounding	bounding	NOUN
brj-24141	174	10	box	box	NOUN
brj-24141	174	11	might	might	AUX
brj-24141	174	12	be	be	AUX
brj-24141	174	13	more	more	ADV
brj-24141	174	14	complex	complex	ADJ
brj-24141	174	15	,	,	PUNCT
brj-24141	174	16	and	and	CCONJ
brj-24141	174	17	the	the	DET
brj-24141	174	18	ciou	ciou	NOUN
brj-24141	174	19	trigonometric	trigonometric	NOUN
brj-24141	174	20	function	function	NOUN
brj-24141	174	21	might	might	AUX
brj-24141	174	22	not	not	PART
brj-24141	174	23	accurately	accurately	ADV
brj-24141	174	24	capture	capture	VERB
brj-24141	174	25	this	this	DET
brj-24141	174	26	complexity	complexity	NOUN
brj-24141	174	27	.	.	PUNCT
brj-24141	175	1	therefore	therefore	ADV
brj-24141	175	2	,	,	PUNCT
brj-24141	175	3	attention	attention	NOUN
brj-24141	175	4	needed	need	VERB
brj-24141	175	5	to	to	PART
brj-24141	175	6	be	be	AUX
brj-24141	175	7	paid	pay	VERB
brj-24141	175	8	to	to	ADP
brj-24141	175	9	bounding	bound	VERB
brj-24141	175	10	box	box	NOUN
brj-24141	175	11	regression	regression	NOUN
brj-24141	175	12	for	for	ADP
brj-24141	175	13	difficult	difficult	ADJ
brj-24141	175	14	samples	sample	NOUN
brj-24141	175	15	.	.	PUNCT
brj-24141	176	1	the	the	DET
brj-24141	176	2	focaleriouintroduced	focaleriouintroduced	NOUN
brj-24141	176	3	in	in	ADP
brj-24141	176	4	this	this	DET
brj-24141	176	5	paper	paper	NOUN
brj-24141	176	6	reconstructed	reconstruct	VERB
brj-24141	176	7	the	the	DET
brj-24141	176	8	iou	iou	NOUN
brj-24141	176	9	loss	loss	NOUN
brj-24141	176	10	through	through	ADP
brj-24141	176	11	a	a	DET
brj-24141	176	12	linear	linear	ADJ
brj-24141	176	13	interval	interval	NOUN
brj-24141	176	14	mapping	mapping	NOUN
brj-24141	176	15	method	method	NOUN
brj-24141	176	16	,	,	PUNCT
brj-24141	176	17	which	which	PRON
brj-24141	176	18	could	could	AUX
brj-24141	176	19	focus	focus	VERB
brj-24141	176	20	more	more	ADV
brj-24141	176	21	on	on	ADP
brj-24141	176	22	different	different	ADJ
brj-24141	176	23	types	type	NOUN
brj-24141	176	24	of	of	ADP
brj-24141	176	25	samples	sample	NOUN
brj-24141	176	26	.	.	PUNCT
brj-24141	177	1	the	the	DET
brj-24141	177	2	weights	weight	NOUN
brj-24141	177	3	of	of	ADP
brj-24141	177	4	different	different	ADJ
brj-24141	177	5	samples	sample	NOUN
brj-24141	177	6	in	in	ADP
brj-24141	177	7	the	the	DET
brj-24141	177	8	loss	loss	NOUN
brj-24141	177	9	function	function	NOUN
brj-24141	177	10	were	be	AUX
brj-24141	177	11	adaptively	adaptively	ADV
brj-24141	177	12	adjusted	adjust	VERB
brj-24141	177	13	according	accord	VERB
brj-24141	177	14	to	to	ADP
brj-24141	177	15	the	the	DET
brj-24141	177	16	number	number	NOUN
brj-24141	177	17	of	of	ADP
brj-24141	177	18	different	different	ADJ
brj-24141	177	19	defect	defect	NOUN
brj-24141	177	20	types	type	NOUN
brj-24141	177	21	and	and	CCONJ
brj-24141	177	22	the	the	DET
brj-24141	177	23	difficulty	difficulty	NOUN
brj-24141	177	24	of	of	ADP
brj-24141	177	25	detection	detection	NOUN
brj-24141	177	26	(	(	PUNCT
brj-24141	177	27	zhang	zhang	PROPN
brj-24141	177	28	and	and	CCONJ
brj-24141	177	29	zhang	zhang	PROPN
brj-24141	177	30	2024	2024	NUM
brj-24141	177	31	)	)	PUNCT
brj-24141	177	32	.	.	PUNCT
brj-24141	178	1	this	this	DET
brj-24141	178	2	approach	approach	NOUN
brj-24141	178	3	effectively	effectively	ADV
brj-24141	178	4	addressed	address	VERB
brj-24141	178	5	the	the	DET
brj-24141	178	6	challenges	challenge	NOUN
brj-24141	178	7	posed	pose	VERB
brj-24141	178	8	by	by	ADP
brj-24141	178	9	difficult	difficult	ADJ
brj-24141	178	10	samples	sample	NOUN
brj-24141	178	11	in	in	ADP
brj-24141	178	12	wood	wood	NOUN
brj-24141	178	13	defect	defect	NOUN
brj-24141	178	14	detection	detection	NOUN
brj-24141	178	15	.	.	PUNCT
brj-24141	179	1	0	0	NUM
brj-24141	179	2	,	,	PUNCT
brj-24141	179	3	,	,	PUNCT
brj-24141	179	4	1	1	NUM
brj-24141	179	5	,	,	PUNCT
brj-24141	179	6	facaler	facaler	ADJ
brj-24141	179	7	iou	iou	PROPN
brj-24141	179	8	d	d	X
brj-24141	179	9	iou	iou	NOUN
brj-24141	179	10	d	d	X
brj-24141	179	11	iou	iou	NOUN
brj-24141	179	12	d	d	PROPN
brj-24141	179	13	iou	iou	PROPN
brj-24141	179	14	u	u	PROPN
brj-24141	179	15	u	u	NOUN
brj-24141	179	16	d	d	X
brj-24141	179	17	iou	iou	PROPN
brj-24141	179	18	d	d	X
brj-24141	179	19			NOUN
brj-24141	179	20			NUM
brj-24141	179	21	−	−	NOUN
brj-24141	179	22	=	=	PUNCT
brj-24141	179	23			NOUN
brj-24141	179	24	−	−	INTJ
brj-24141	179	25			PUNCT
brj-24141	179	26	(	(	PUNCT
brj-24141	179	27	9	9	NUM
brj-24141	179	28	)	)	PUNCT
brj-24141	179	29	where	where	SCONJ
brj-24141	179	30	ioufocaler	ioufocaler	NOUN
brj-24141	179	31	is	be	AUX
brj-24141	179	32	the	the	DET
brj-24141	179	33	reconstructed	reconstructed	ADJ
brj-24141	179	34	focaler	focaler	NOUN
brj-24141	179	35	-	-	PUNCT
brj-24141	179	36	iou	iou	NOUN
brj-24141	179	37	,	,	PUNCT
brj-24141	179	38	iou	iou	PROPN
brj-24141	179	39	is	be	AUX
brj-24141	179	40	the	the	DET
brj-24141	179	41	original	original	ADJ
brj-24141	179	42	iou	iou	NOUN
brj-24141	179	43	value	value	NOUN
brj-24141	179	44	,	,	PUNCT
brj-24141	179	45	and	and	CCONJ
brj-24141	179	46	[	[	X
brj-24141	179	47	d	d	X
brj-24141	179	48	,	,	PUNCT
brj-24141	179	49	u	u	NOUN
brj-24141	179	50	]	]	X
brj-24141	179	51	∈	∈	PROPN
brj-24141	180	1	[	[	X
brj-24141	180	2	0	0	NUM
brj-24141	180	3	,	,	PUNCT
brj-24141	180	4	1	1	NUM
brj-24141	180	5	]	]	PUNCT
brj-24141	180	6	.	.	PUNCT
brj-24141	181	1	by	by	ADP
brj-24141	181	2	adjusting	adjust	VERB
brj-24141	181	3	the	the	DET
brj-24141	181	4	values	value	NOUN
brj-24141	181	5	of	of	ADP
brj-24141	181	6	d	d	PROPN
brj-24141	181	7	and	and	CCONJ
brj-24141	181	8	u	u	NOUN
brj-24141	181	9	,	,	PUNCT
brj-24141	181	10	the	the	DET
brj-24141	181	11	ioufocaler	ioufocaler	NOUN
brj-24141	181	12	can	can	AUX
brj-24141	181	13	be	be	AUX
brj-24141	181	14	made	make	VERB
brj-24141	181	15	to	to	PART
brj-24141	181	16	focus	focus	VERB
brj-24141	181	17	on	on	ADP
brj-24141	181	18	different	different	ADJ
brj-24141	181	19	regression	regression	NOUN
brj-24141	181	20	samples	sample	NOUN
brj-24141	181	21	.	.	PUNCT
brj-24141	182	1	its	its	PRON
brj-24141	182	2	loss	loss	NOUN
brj-24141	182	3	is	be	AUX
brj-24141	182	4	defined	define	VERB
brj-24141	182	5	as	as	SCONJ
brj-24141	182	6	follows	follow	VERB
brj-24141	182	7	:	:	PUNCT
brj-24141	182	8	1	1	NUM
brj-24141	182	9	focaler	focaler	NOUN
brj-24141	182	10	focaler	focaler	NOUN
brj-24141	182	11	ioul	ioul	PROPN
brj-24141	182	12	iou−	iou−	PROPN
brj-24141	182	13	=	=	SYM
brj-24141	183	1	−	−	PROPN
brj-24141	183	2	(	(	PUNCT
brj-24141	183	3	10	10	NUM
brj-24141	183	4	)	)	PUNCT
brj-24141	183	5	peer	peer	NOUN
brj-24141	183	6	-	-	PUNCT
brj-24141	183	7	reviewed	review	VERB
brj-24141	183	8	article	article	NOUN
brj-24141	183	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	183	10	li	li	PROPN
brj-24141	183	11	et	et	PROPN
brj-24141	183	12	al	al	PROPN
brj-24141	183	13	.	.	PROPN
brj-24141	184	1	(	(	PUNCT
brj-24141	184	2	2025	2025	NUM
brj-24141	184	3	)	)	PUNCT
brj-24141	184	4	.	.	PUNCT
brj-24141	185	1	“	"	PUNCT
brj-24141	185	2	improved	improved	ADJ
brj-24141	185	3	panel	panel	NOUN
brj-24141	185	4	defect	defect	NOUN
brj-24141	185	5	detection	detection	NOUN
brj-24141	185	6	,	,	PUNCT
brj-24141	185	7	”	"	PUNCT
brj-24141	185	8	bioresources	bioresource	NOUN
brj-24141	185	9	20(2	20(2	NUM
brj-24141	185	10	)	)	PUNCT
brj-24141	185	11	,	,	PUNCT
brj-24141	185	12	2556	2556	NUM
brj-24141	185	13	-	-	SYM
brj-24141	185	14	2573	2573	NUM
brj-24141	185	15	.	.	PUNCT
brj-24141	185	16	2565	2565	NUM
brj-24141	185	17	fig	fig	NOUN
brj-24141	185	18	.	.	PUNCT
brj-24141	186	1	6	6	NUM
brj-24141	186	2	.	.	PUNCT
brj-24141	186	3	yolov8	yolov8	PROPN
brj-24141	186	4	-	-	PUNCT
brj-24141	186	5	cdc	cdc	PROPN
brj-24141	186	6	structure	structure	NOUN
brj-24141	186	7	diagram	diagram	VERB
brj-24141	186	8	the	the	DET
brj-24141	186	9	focaler	focaler	NOUN
brj-24141	186	10	-	-	PUNCT
brj-24141	186	11	iou	iou	NOUN
brj-24141	186	12	loss	loss	NOUN
brj-24141	186	13	was	be	AUX
brj-24141	186	14	combined	combine	VERB
brj-24141	186	15	with	with	ADP
brj-24141	186	16	the	the	DET
brj-24141	186	17	ciou	ciou	NOUN
brj-24141	186	18	loss	loss	NOUN
brj-24141	186	19	to	to	PART
brj-24141	186	20	form	form	VERB
brj-24141	186	21	the	the	DET
brj-24141	186	22	focaler	focaler	NOUN
brj-24141	186	23	-	-	PUNCT
brj-24141	186	24	ciou	ciou	NOUN
brj-24141	186	25	loss	loss	NOUN
brj-24141	186	26	.	.	PUNCT
brj-24141	187	1	this	this	DET
brj-24141	187	2	new	new	ADJ
brj-24141	187	3	loss	loss	NOUN
brj-24141	187	4	function	function	NOUN
brj-24141	187	5	incorporated	incorporate	VERB
brj-24141	187	6	a	a	DET
brj-24141	187	7	focusing	focus	VERB
brj-24141	187	8	mechanism	mechanism	NOUN
brj-24141	187	9	based	base	VERB
brj-24141	187	10	on	on	ADP
brj-24141	187	11	the	the	DET
brj-24141	187	12	ciou	ciou	NOUN
brj-24141	187	13	loss	loss	NOUN
brj-24141	187	14	,	,	PUNCT
brj-24141	187	15	assigning	assign	VERB
brj-24141	187	16	greater	great	ADJ
brj-24141	187	17	weight	weight	NOUN
brj-24141	187	18	to	to	ADP
brj-24141	187	19	hard	hard	ADJ
brj-24141	187	20	samples	sample	NOUN
brj-24141	187	21	.	.	PUNCT
brj-24141	188	1	by	by	ADP
brj-24141	188	2	doing	do	VERB
brj-24141	188	3	so	so	ADV
brj-24141	188	4	,	,	PUNCT
brj-24141	188	5	the	the	DET
brj-24141	188	6	model	model	NOUN
brj-24141	188	7	was	be	AUX
brj-24141	188	8	encouraged	encourage	VERB
brj-24141	188	9	to	to	PART
brj-24141	188	10	pay	pay	VERB
brj-24141	188	11	more	more	ADJ
brj-24141	188	12	attention	attention	NOUN
brj-24141	188	13	to	to	ADP
brj-24141	188	14	these	these	DET
brj-24141	188	15	difficult	difficult	ADJ
brj-24141	188	16	samples	sample	NOUN
brj-24141	188	17	,	,	PUNCT
brj-24141	188	18	thereby	thereby	ADV
brj-24141	188	19	improving	improve	VERB
brj-24141	188	20	the	the	DET
brj-24141	188	21	detection	detection	NOUN
brj-24141	188	22	accuracy	accuracy	NOUN
brj-24141	188	23	.	.	PUNCT
brj-24141	189	1	the	the	DET
brj-24141	189	2	focaler	focaler	NOUN
brj-24141	189	3	-	-	PUNCT
brj-24141	189	4	ciou	ciou	NOUN
brj-24141	189	5	loss	loss	NOUN
brj-24141	189	6	formula	formula	NOUN
brj-24141	189	7	is	be	AUX
brj-24141	189	8	as	as	SCONJ
brj-24141	189	9	follows	follow	VERB
brj-24141	189	10	:	:	PUNCT
brj-24141	189	11	(	(	PUNCT
brj-24141	189	12	11	11	NUM
brj-24141	189	13	)	)	PUNCT
brj-24141	189	14	in	in	ADP
brj-24141	189	15	the	the	DET
brj-24141	189	16	wood	wood	NOUN
brj-24141	189	17	surface	surface	NOUN
brj-24141	189	18	defect	defect	NOUN
brj-24141	189	19	detection	detection	NOUN
brj-24141	189	20	dataset	dataset	VERB
brj-24141	189	21	used	use	VERB
brj-24141	189	22	in	in	ADP
brj-24141	189	23	this	this	DET
brj-24141	189	24	paper	paper	NOUN
brj-24141	189	25	,	,	PUNCT
brj-24141	189	26	a	a	DET
brj-24141	189	27	portion	portion	NOUN
brj-24141	189	28	of	of	ADP
brj-24141	189	29	the	the	DET
brj-24141	189	30	defect	defect	NOUN
brj-24141	189	31	targets	target	NOUN
brj-24141	189	32	belong	belong	VERB
brj-24141	189	33	to	to	ADP
brj-24141	189	34	small	small	ADJ
brj-24141	189	35	-	-	PUNCT
brj-24141	189	36	sized	sized	ADJ
brj-24141	189	37	targets	target	NOUN
brj-24141	189	38	,	,	PUNCT
brj-24141	189	39	accounting	account	VERB
brj-24141	189	40	for	for	ADP
brj-24141	189	41	approximately	approximately	ADV
brj-24141	189	42	one	one	NUM
brj-24141	189	43	-	-	PUNCT
brj-24141	189	44	third	third	NOUN
brj-24141	189	45	of	of	ADP
brj-24141	189	46	the	the	DET
brj-24141	189	47	total	total	ADJ
brj-24141	189	48	targets	target	NOUN
brj-24141	189	49	in	in	ADP
brj-24141	189	50	the	the	DET
brj-24141	189	51	dataset	dataset	NOUN
brj-24141	189	52	.	.	PUNCT
brj-24141	190	1	therefore	therefore	ADV
brj-24141	190	2	,	,	PUNCT
brj-24141	190	3	considering	consider	VERB
brj-24141	190	4	the	the	DET
brj-24141	190	5	training	training	NOUN
brj-24141	190	6	perspective	perspective	NOUN
brj-24141	190	7	of	of	ADP
brj-24141	190	8	the	the	DET
brj-24141	190	9	dataset	dataset	NOUN
brj-24141	190	10	,	,	PUNCT
brj-24141	190	11	this	this	DET
brj-24141	190	12	paper	paper	NOUN
brj-24141	190	13	incorporated	incorporate	VERB
brj-24141	190	14	focaler	focaler	NOUN
brj-24141	190	15	-	-	PUNCT
brj-24141	190	16	ciou	ciou	NOUN
brj-24141	190	17	as	as	ADP
brj-24141	190	18	part	part	NOUN
brj-24141	190	19	of	of	ADP
brj-24141	190	20	the	the	DET
brj-24141	190	21	primary	primary	ADJ
brj-24141	190	22	loss	loss	NOUN
brj-24141	190	23	function	function	NOUN
brj-24141	190	24	to	to	PART
brj-24141	190	25	guide	guide	VERB
brj-24141	190	26	the	the	DET
brj-24141	190	27	model	model	NOUN
brj-24141	190	28	’s	’s	PART
brj-24141	190	29	bounding	bounding	NOUN
brj-24141	190	30	box	box	PROPN
brj-24141	190	31	regression	regression	NOUN
brj-24141	190	32	task	task	NOUN
brj-24141	190	33	.	.	PUNCT
brj-24141	191	1	during	during	ADP
brj-24141	191	2	the	the	DET
brj-24141	191	3	actual	actual	ADJ
brj-24141	191	4	training	training	NOUN
brj-24141	191	5	process	process	NOUN
brj-24141	191	6	,	,	PUNCT
brj-24141	191	7	the	the	DET
brj-24141	191	8	network	network	NOUN
brj-24141	191	9	calculates	calculate	VERB
brj-24141	191	10	the	the	DET
brj-24141	191	11	ciou	ciou	NOUN
brj-24141	191	12	loss	loss	NOUN
brj-24141	191	13	and	and	CCONJ
brj-24141	191	14	focaler	focaler	NOUN
brj-24141	191	15	-	-	PUNCT
brj-24141	191	16	ciou	ciou	NOUN
brj-24141	191	17	loss	loss	NOUN
brj-24141	191	18	for	for	ADP
brj-24141	191	19	each	each	DET
brj-24141	191	20	detection	detection	NOUN
brj-24141	191	21	box	box	NOUN
brj-24141	191	22	and	and	CCONJ
brj-24141	191	23	dynamically	dynamically	ADV
brj-24141	191	24	combines	combine	VERB
brj-24141	191	25	them	they	PRON
brj-24141	191	26	to	to	PART
brj-24141	191	27	form	form	VERB
brj-24141	191	28	the	the	DET
brj-24141	191	29	final	final	ADJ
brj-24141	191	30	focaler	focaler	NOUN
brj-24141	191	31	-	-	PUNCT
brj-24141	191	32	ciou	ciou	NOUN
brj-24141	191	33	loss	loss	NOUN
brj-24141	191	34	.	.	PUNCT
brj-24141	192	1	the	the	DET
brj-24141	192	2	improved	improved	ADJ
brj-24141	192	3	model	model	NOUN
brj-24141	192	4	is	be	AUX
brj-24141	192	5	named	name	VERB
brj-24141	192	6	yolov8n	yolov8n	PROPN
brj-24141	192	7	-	-	PUNCT
brj-24141	192	8	cdc	cdc	PROPN
brj-24141	192	9	,	,	PUNCT
brj-24141	192	10	and	and	CCONJ
brj-24141	192	11	its	its	PRON
brj-24141	192	12	structure	structure	NOUN
brj-24141	192	13	is	be	AUX
brj-24141	192	14	shown	show	VERB
brj-24141	192	15	in	in	ADP
brj-24141	192	16	fig	fig	NOUN
brj-24141	192	17	.	.	PUNCT
brj-24141	193	1	6	6	X
brj-24141	193	2	.	.	X
brj-24141	193	3	wood	wood	NOUN
brj-24141	193	4	panel	panel	NOUN
brj-24141	193	5	defects	defect	NOUN
brj-24141	193	6	database	database	NOUN
brj-24141	193	7	this	this	DET
brj-24141	193	8	experiment	experiment	NOUN
brj-24141	193	9	utilized	utilize	VERB
brj-24141	193	10	a	a	DET
brj-24141	193	11	wood	wood	NOUN
brj-24141	193	12	panel	panel	NOUN
brj-24141	193	13	dataset	dataset	VERB
brj-24141	193	14	uploaded	upload	VERB
brj-24141	193	15	by	by	ADP
brj-24141	193	16	individual	individual	ADJ
brj-24141	193	17	users	user	NOUN
brj-24141	193	18	of	of	ADP
brj-24141	193	19	roboflow	roboflow	ADJ
brj-24141	193	20	.	.	PUNCT
brj-24141	194	1	as	as	SCONJ
brj-24141	194	2	shown	show	VERB
brj-24141	194	3	in	in	ADP
brj-24141	194	4	fig	fig	NOUN
brj-24141	194	5	.	.	PUNCT
brj-24141	195	1	7	7	NUM
brj-24141	195	2	,	,	PUNCT
brj-24141	195	3	the	the	DET
brj-24141	195	4	dataset	dataset	NOUN
brj-24141	195	5	contained	contain	VERB
brj-24141	195	6	four	four	NUM
brj-24141	195	7	common	common	ADJ
brj-24141	195	8	types	type	NOUN
brj-24141	195	9	of	of	ADP
brj-24141	195	10	wood	wood	NOUN
brj-24141	195	11	surface	surface	NOUN
brj-24141	195	12	defects	defect	NOUN
brj-24141	195	13	:	:	PUNCT
brj-24141	195	14	dead	dead	ADJ
brj-24141	195	15	knot	knot	NOUN
brj-24141	195	16	,	,	PUNCT
brj-24141	195	17	live	live	ADJ
brj-24141	195	18	knot	knot	NOUN
brj-24141	195	19	,	,	PUNCT
brj-24141	195	20	crack	crack	NOUN
brj-24141	195	21	,	,	PUNCT
brj-24141	195	22	and	and	CCONJ
brj-24141	195	23	scar	scar	NOUN
brj-24141	195	24	.	.	PUNCT
brj-24141	196	1	a	a	DET
brj-24141	196	2	total	total	NOUN
brj-24141	196	3	of	of	ADP
brj-24141	196	4	950	950	NUM
brj-24141	196	5	defect	defect	NOUN
brj-24141	196	6	images	image	NOUN
brj-24141	196	7	were	be	AUX
brj-24141	196	8	included	include	VERB
brj-24141	196	9	.	.	PUNCT
brj-24141	197	1	to	to	PART
brj-24141	197	2	expand	expand	VERB
brj-24141	197	3	the	the	DET
brj-24141	197	4	dataset	dataset	NOUN
brj-24141	197	5	,	,	PUNCT
brj-24141	197	6	data	datum	NOUN
brj-24141	197	7	augmentation	augmentation	NOUN
brj-24141	197	8	techniques	technique	NOUN
brj-24141	197	9	such	such	ADJ
brj-24141	197	10	as	as	ADP
brj-24141	197	11	flipping	flip	VERB
brj-24141	197	12	,	,	PUNCT
brj-24141	197	13	scaling	scaling	NOUN
brj-24141	197	14	,	,	PUNCT
brj-24141	197	15	and	and	CCONJ
brj-24141	197	16	cropping	cropping	NOUN
brj-24141	197	17	were	be	AUX
brj-24141	197	18	applied	apply	VERB
brj-24141	197	19	.	.	PUNCT
brj-24141	198	1	the	the	DET
brj-24141	198	2	augmented	augment	VERB
brj-24141	198	3	dataset	dataset	NOUN
brj-24141	198	4	was	be	AUX
brj-24141	198	5	then	then	ADV
brj-24141	198	6	re	re	VERB
brj-24141	198	7	-	-	VERB
brj-24141	198	8	labeled	label	VERB
brj-24141	198	9	,	,	PUNCT
brj-24141	198	10	resulting	result	VERB
brj-24141	198	11	in	in	ADP
brj-24141	198	12	a	a	DET
brj-24141	198	13	training	training	NOUN
brj-24141	198	14	set	set	NOUN
brj-24141	198	15	of	of	ADP
brj-24141	198	16	2117	2117	NUM
brj-24141	198	17	images	image	NOUN
brj-24141	198	18	,	,	PUNCT
brj-24141	198	19	a	a	DET
brj-24141	198	20	validation	validation	NOUN
brj-24141	198	21	set	set	NOUN
brj-24141	198	22	of	of	ADP
brj-24141	198	23	248	248	NUM
brj-24141	198	24	images	image	NOUN
brj-24141	198	25	,	,	PUNCT
brj-24141	198	26	and	and	CCONJ
brj-24141	198	27	a	a	DET
brj-24141	198	28	test	test	NOUN
brj-24141	198	29	set	set	NOUN
brj-24141	198	30	of	of	ADP
brj-24141	198	31	248	248	NUM
brj-24141	198	32	images	image	NOUN
brj-24141	198	33	.	.	PUNCT
brj-24141	199	1	the	the	DET
brj-24141	199	2	content	content	NOUN
brj-24141	199	3	of	of	ADP
brj-24141	199	4	the	the	DET
brj-24141	199	5	dataset	dataset	NOUN
brj-24141	199	6	is	be	AUX
brj-24141	199	7	presented	present	VERB
brj-24141	199	8	in	in	ADP
brj-24141	199	9	fig.7	fig.7	ADJ
brj-24141	199	10	.	.	PUNCT
brj-24141	200	1	the	the	DET
brj-24141	200	2	number	number	NOUN
brj-24141	200	3	of	of	ADP
brj-24141	200	4	wood	wood	NOUN
brj-24141	200	5	panel	panel	NOUN
brj-24141	200	6	defects	defect	NOUN
brj-24141	200	7	is	be	AUX
brj-24141	200	8	summarized	summarize	VERB
brj-24141	200	9	in	in	ADP
brj-24141	200	10	table.1	table.1	PROPN
brj-24141	200	11	.	.	PUNCT
brj-24141	200	12	focaler	focaler	NOUN
brj-24141	200	13	focaler	focaler	PROPN
brj-24141	200	14	ciou	ciou	PROPN
brj-24141	200	15	cioul	cioul	PROPN
brj-24141	200	16	l	l	PROPN
brj-24141	200	17	iou	iou	PROPN
brj-24141	200	18	iou−	iou−	PROPN
brj-24141	200	19	=	=	PUNCT
brj-24141	201	1	+	+	CCONJ
brj-24141	201	2	−	−	NUM
brj-24141	201	3	peer	peer	NOUN
brj-24141	201	4	-	-	PUNCT
brj-24141	201	5	reviewed	review	VERB
brj-24141	201	6	article	article	NOUN
brj-24141	201	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	201	8	li	li	PROPN
brj-24141	201	9	et	et	PROPN
brj-24141	201	10	al	al	PROPN
brj-24141	201	11	.	.	PROPN
brj-24141	202	1	(	(	PUNCT
brj-24141	202	2	2025	2025	NUM
brj-24141	202	3	)	)	PUNCT
brj-24141	202	4	.	.	PUNCT
brj-24141	203	1	“	"	PUNCT
brj-24141	203	2	improved	improved	ADJ
brj-24141	203	3	panel	panel	NOUN
brj-24141	203	4	defect	defect	NOUN
brj-24141	203	5	detection	detection	NOUN
brj-24141	203	6	,	,	PUNCT
brj-24141	203	7	”	"	PUNCT
brj-24141	203	8	bioresources	bioresource	NOUN
brj-24141	203	9	20(2	20(2	NUM
brj-24141	203	10	)	)	PUNCT
brj-24141	203	11	,	,	PUNCT
brj-24141	203	12	2556	2556	NUM
brj-24141	203	13	-	-	SYM
brj-24141	203	14	2573	2573	NUM
brj-24141	203	15	.	.	PUNCT
brj-24141	204	1	2566	2566	NUM
brj-24141	204	2	fig	fig	NOUN
brj-24141	204	3	.	.	PUNCT
brj-24141	205	1	7	7	X
brj-24141	205	2	.	.	X
brj-24141	205	3	wood	wood	NOUN
brj-24141	205	4	panel	panel	NOUN
brj-24141	205	5	defects	defect	VERB
brj-24141	205	6	database	database	NOUN
brj-24141	205	7	table	table	NOUN
brj-24141	205	8	1	1	NUM
brj-24141	205	9	.	.	PUNCT
brj-24141	205	10	results	result	NOUN
brj-24141	205	11	of	of	ADP
brj-24141	205	12	ablation	ablation	NOUN
brj-24141	205	13	experiments	experiment	NOUN
brj-24141	205	14	defect	defect	VERB
brj-24141	205	15	category	category	NOUN
brj-24141	205	16	quantities	quantity	NOUN
brj-24141	205	17	dead	dead	ADJ
brj-24141	205	18	knot	knot	NOUN
brj-24141	205	19	4065	4065	NUM
brj-24141	205	20	live	live	ADJ
brj-24141	205	21	knot	knot	NOUN
brj-24141	205	22	3134	3134	NUM
brj-24141	205	23	scar	scar	NOUN
brj-24141	205	24	3495	3495	NUM
brj-24141	205	25	crack	crack	VERB
brj-24141	205	26	3278	3278	NUM
brj-24141	205	27	environment	environment	NOUN
brj-24141	205	28	configuration	configuration	NOUN
brj-24141	205	29	the	the	DET
brj-24141	205	30	experiments	experiment	NOUN
brj-24141	205	31	were	be	AUX
brj-24141	205	32	conducted	conduct	VERB
brj-24141	205	33	on	on	ADP
brj-24141	205	34	an	an	DET
brj-24141	205	35	nvidia	nvidia	PROPN
brj-24141	205	36	geforce	geforce	NOUN
brj-24141	205	37	rtx3070ti	rtx3070ti	NOUN
brj-24141	205	38	with	with	ADP
brj-24141	205	39	8	8	NUM
brj-24141	205	40	gb	gb	NOUN
brj-24141	205	41	of	of	ADP
brj-24141	205	42	video	video	NOUN
brj-24141	205	43	memory	memory	NOUN
brj-24141	205	44	,	,	PUNCT
brj-24141	205	45	using	use	VERB
brj-24141	205	46	the	the	DET
brj-24141	205	47	pytorch	pytorch	NOUN
brj-24141	205	48	framework	framework	NOUN
brj-24141	205	49	.	.	PUNCT
brj-24141	206	1	the	the	DET
brj-24141	206	2	experimental	experimental	ADJ
brj-24141	206	3	parameters	parameter	NOUN
brj-24141	206	4	included	include	VERB
brj-24141	206	5	300	300	NUM
brj-24141	206	6	epochs	epoch	NOUN
brj-24141	206	7	,	,	PUNCT
brj-24141	206	8	a	a	DET
brj-24141	206	9	batch	batch	NOUN
brj-24141	206	10	size	size	NOUN
brj-24141	206	11	of	of	ADP
brj-24141	206	12	16	16	NUM
brj-24141	206	13	,	,	PUNCT
brj-24141	206	14	and	and	CCONJ
brj-24141	206	15	an	an	DET
brj-24141	206	16	image	image	NOUN
brj-24141	206	17	size	size	NOUN
brj-24141	206	18	of	of	ADP
brj-24141	206	19	640	640	NUM
brj-24141	206	20	.	.	PUNCT
brj-24141	207	1	precision	precision	NOUN
brj-24141	207	2	,	,	PUNCT
brj-24141	207	3	recall	recall	NOUN
brj-24141	207	4	,	,	PUNCT
brj-24141	207	5	average	average	ADJ
brj-24141	207	6	precision	precision	NOUN
brj-24141	207	7	,	,	PUNCT
brj-24141	207	8	and	and	CCONJ
brj-24141	207	9	mean	mean	VERB
brj-24141	207	10	average	average	ADJ
brj-24141	207	11	precision	precision	NOUN
brj-24141	207	12	(	(	PUNCT
brj-24141	207	13	map	map	NOUN
brj-24141	207	14	)	)	PUNCT
brj-24141	207	15	were	be	AUX
brj-24141	207	16	used	use	VERB
brj-24141	207	17	as	as	ADP
brj-24141	207	18	evaluation	evaluation	NOUN
brj-24141	207	19	metrics	metric	NOUN
brj-24141	207	20	.	.	PUNCT
brj-24141	208	1	map	map	NOUN
brj-24141	208	2	,	,	PUNCT
brj-24141	208	3	which	which	PRON
brj-24141	208	4	measures	measure	VERB
brj-24141	208	5	the	the	DET
brj-24141	208	6	model	model	NOUN
brj-24141	208	7	's	's	PART
brj-24141	208	8	performance	performance	NOUN
brj-24141	208	9	across	across	ADP
brj-24141	208	10	various	various	ADJ
brj-24141	208	11	target	target	NOUN
brj-24141	208	12	categories	category	NOUN
brj-24141	208	13	and	and	CCONJ
brj-24141	208	14	confidence	confidence	NOUN
brj-24141	208	15	thresholds	threshold	NOUN
brj-24141	208	16	,	,	PUNCT
brj-24141	208	17	was	be	AUX
brj-24141	208	18	the	the	DET
brj-24141	208	19	primary	primary	ADJ
brj-24141	208	20	metric	metric	NOUN
brj-24141	208	21	.	.	PUNCT
brj-24141	209	1	precision	precision	NOUN
brj-24141	209	2	,	,	PUNCT
brj-24141	209	3	also	also	ADV
brj-24141	209	4	known	know	VERB
brj-24141	209	5	as	as	ADP
brj-24141	209	6	the	the	DET
brj-24141	209	7	detection	detection	NOUN
brj-24141	209	8	rate	rate	NOUN
brj-24141	209	9	,	,	PUNCT
brj-24141	209	10	evaluates	evaluate	VERB
brj-24141	209	11	a	a	DET
brj-24141	209	12	classification	classification	NOUN
brj-24141	209	13	model	model	NOUN
brj-24141	209	14	's	's	PART
brj-24141	209	15	performance	performance	NOUN
brj-24141	209	16	by	by	ADP
brj-24141	209	17	measuring	measure	VERB
brj-24141	209	18	the	the	DET
brj-24141	209	19	proportion	proportion	NOUN
brj-24141	209	20	of	of	ADP
brj-24141	209	21	true	true	ADJ
brj-24141	209	22	positives	positive	NOUN
brj-24141	209	23	among	among	ADP
brj-24141	209	24	predicted	predict	VERB
brj-24141	209	25	positives	positive	NOUN
brj-24141	209	26	.	.	PUNCT
brj-24141	210	1	recall	recall	NOUN
brj-24141	210	2	measures	measure	NOUN
brj-24141	210	3	the	the	DET
brj-24141	210	4	proportion	proportion	NOUN
brj-24141	210	5	of	of	ADP
brj-24141	210	6	correctly	correctly	ADV
brj-24141	210	7	detected	detect	VERB
brj-24141	210	8	targets	target	NOUN
brj-24141	210	9	to	to	ADP
brj-24141	210	10	all	all	DET
brj-24141	210	11	true	true	ADJ
brj-24141	210	12	targets	target	NOUN
brj-24141	210	13	[	[	X
brj-24141	210	14	10	10	NUM
brj-24141	210	15	]	]	PUNCT
brj-24141	210	16	.	.	PUNCT
brj-24141	211	1	the	the	DET
brj-24141	211	2	formula	formula	NOUN
brj-24141	211	3	for	for	ADP
brj-24141	211	4	the	the	DET
brj-24141	211	5	metric	metric	NOUN
brj-24141	211	6	is	be	AUX
brj-24141	211	7	as	as	SCONJ
brj-24141	211	8	follows	follow	VERB
brj-24141	211	9	,	,	PUNCT
brj-24141	211	10	where	where	SCONJ
brj-24141	211	11	tp	tp	NOUN
brj-24141	211	12	represents	represent	VERB
brj-24141	211	13	true	true	ADJ
brj-24141	211	14	positives	positive	NOUN
brj-24141	211	15	,	,	PUNCT
brj-24141	211	16	fp	fp	X
brj-24141	211	17	represents	represent	VERB
brj-24141	211	18	false	false	ADJ
brj-24141	211	19	positives	positive	NOUN
brj-24141	211	20	,	,	PUNCT
brj-24141	211	21	and	and	CCONJ
brj-24141	211	22	fn	fn	NOUN
brj-24141	211	23	represents	represent	VERB
brj-24141	211	24	false	false	ADJ
brj-24141	211	25	negatives	negative	NOUN
brj-24141	211	26	.	.	PUNCT
brj-24141	212	1	1	1	NUM
brj-24141	212	2	(	(	PUNCT
brj-24141	212	3	)	)	PUNCT
brj-24141	212	4	c	c	PROPN
brj-24141	212	5	d	d	X
brj-24141	212	6	ap	ap	PROPN
brj-24141	212	7	d	d	PROPN
brj-24141	212	8	map	map	NOUN
brj-24141	212	9	c	c	NOUN
brj-24141	213	1	=	=	NOUN
brj-24141	213	2	=	=	NOUN
brj-24141	213	3			X
brj-24141	213	4	(	(	PUNCT
brj-24141	213	5	12	12	NUM
brj-24141	213	6	)	)	PUNCT
brj-24141	213	7	tp	tp	PART
brj-24141	213	8	precision	precision	NOUN
brj-24141	213	9	tp	tp	ADP
brj-24141	213	10	fp	fp	PROPN
brj-24141	213	11	=	=	PUNCT
brj-24141	214	1	+	+	CCONJ
brj-24141	214	2	(	(	PUNCT
brj-24141	214	3	13	13	NUM
brj-24141	214	4	)	)	PUNCT
brj-24141	214	5	tp	tp	PART
brj-24141	214	6	recall	recall	VERB
brj-24141	214	7	tp	tp	ADP
brj-24141	214	8	fn	fn	NOUN
brj-24141	214	9	=	=	PUNCT
brj-24141	215	1	+	+	CCONJ
brj-24141	215	2	(	(	PUNCT
brj-24141	215	3	14	14	NUM
brj-24141	215	4	)	)	PUNCT
brj-24141	215	5	ablation	ablation	NOUN
brj-24141	215	6	experiment	experiment	NOUN
brj-24141	215	7	to	to	PART
brj-24141	215	8	assess	assess	VERB
brj-24141	215	9	the	the	DET
brj-24141	215	10	specific	specific	ADJ
brj-24141	215	11	impact	impact	NOUN
brj-24141	215	12	of	of	ADP
brj-24141	215	13	each	each	DET
brj-24141	215	14	module	module	NOUN
brj-24141	215	15	on	on	ADP
brj-24141	215	16	performance	performance	NOUN
brj-24141	215	17	,	,	PUNCT
brj-24141	215	18	the	the	DET
brj-24141	215	19	yolov8	yolov8	NOUN
brj-24141	215	20	base	base	PROPN
brj-24141	215	21	version	version	NOUN
brj-24141	215	22	was	be	AUX
brj-24141	215	23	used	use	VERB
brj-24141	215	24	as	as	ADP
brj-24141	215	25	the	the	DET
brj-24141	215	26	baseline	baseline	PROPN
brj-24141	215	27	model	model	NOUN
brj-24141	215	28	.	.	PUNCT
brj-24141	216	1	eight	eight	NUM
brj-24141	216	2	sets	set	NOUN
brj-24141	216	3	of	of	ADP
brj-24141	216	4	experiments	experiment	NOUN
brj-24141	216	5	were	be	AUX
brj-24141	216	6	designed	design	VERB
brj-24141	216	7	to	to	PART
brj-24141	216	8	conduct	conduct	VERB
brj-24141	216	9	ablation	ablation	NOUN
brj-24141	216	10	analysis	analysis	NOUN
brj-24141	216	11	of	of	ADP
brj-24141	216	12	the	the	DET
brj-24141	216	13	modules	module	NOUN
brj-24141	216	14	,	,	PUNCT
brj-24141	216	15	ensuring	ensure	VERB
brj-24141	216	16	consistent	consistent	ADJ
brj-24141	216	17	experimental	experimental	ADJ
brj-24141	216	18	conditions	condition	NOUN
brj-24141	216	19	.	.	PUNCT
brj-24141	217	1	the	the	DET
brj-24141	217	2	improved	improved	ADJ
brj-24141	217	3	modules	module	NOUN
brj-24141	217	4	were	be	AUX
brj-24141	217	5	gradually	gradually	ADV
brj-24141	217	6	introduced	introduce	VERB
brj-24141	217	7	to	to	PART
brj-24141	217	8	construct	construct	VERB
brj-24141	217	9	variant	variant	ADJ
brj-24141	217	10	models	model	NOUN
brj-24141	217	11	,	,	PUNCT
brj-24141	217	12	and	and	CCONJ
brj-24141	217	13	their	their	PRON
brj-24141	217	14	corresponding	correspond	VERB
brj-24141	217	15	performance	performance	NOUN
brj-24141	217	16	metrics	metric	NOUN
brj-24141	217	17	were	be	AUX
brj-24141	217	18	obtained	obtain	VERB
brj-24141	217	19	.	.	PUNCT
brj-24141	218	1	table	table	NOUN
brj-24141	218	2	2	2	NUM
brj-24141	218	3	compares	compare	VERB
brj-24141	218	4	the	the	DET
brj-24141	218	5	performance	performance	NOUN
brj-24141	218	6	data	datum	NOUN
brj-24141	218	7	of	of	ADP
brj-24141	218	8	these	these	DET
brj-24141	218	9	models	model	NOUN
brj-24141	218	10	,	,	PUNCT
brj-24141	218	11	analyzing	analyze	VERB
brj-24141	218	12	the	the	DET
brj-24141	218	13	specific	specific	ADJ
brj-24141	218	14	impact	impact	NOUN
brj-24141	218	15	of	of	ADP
brj-24141	218	16	removed	removed	ADJ
brj-24141	218	17	or	or	CCONJ
brj-24141	218	18	replaced	replace	VERB
brj-24141	218	19	modules	module	NOUN
brj-24141	218	20	on	on	ADP
brj-24141	218	21	overall	overall	ADJ
brj-24141	218	22	performance	performance	NOUN
brj-24141	218	23	.	.	PUNCT
brj-24141	219	1	peer	peer	NOUN
brj-24141	219	2	-	-	PUNCT
brj-24141	219	3	reviewed	review	VERB
brj-24141	219	4	article	article	NOUN
brj-24141	219	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	219	6	li	li	PROPN
brj-24141	219	7	et	et	PROPN
brj-24141	219	8	al	al	PROPN
brj-24141	219	9	.	.	PROPN
brj-24141	220	1	(	(	PUNCT
brj-24141	220	2	2025	2025	NUM
brj-24141	220	3	)	)	PUNCT
brj-24141	220	4	.	.	PUNCT
brj-24141	221	1	“	"	PUNCT
brj-24141	221	2	improved	improved	ADJ
brj-24141	221	3	panel	panel	NOUN
brj-24141	221	4	defect	defect	NOUN
brj-24141	221	5	detection	detection	NOUN
brj-24141	221	6	,	,	PUNCT
brj-24141	221	7	”	"	PUNCT
brj-24141	221	8	bioresources	bioresource	NOUN
brj-24141	221	9	20(2	20(2	NUM
brj-24141	221	10	)	)	PUNCT
brj-24141	221	11	,	,	PUNCT
brj-24141	221	12	2556	2556	NUM
brj-24141	221	13	-	-	SYM
brj-24141	221	14	2573	2573	NUM
brj-24141	221	15	.	.	PUNCT
brj-24141	222	1	2567	2567	NUM
brj-24141	222	2	table	table	NOUN
brj-24141	222	3	2	2	NUM
brj-24141	222	4	.	.	PUNCT
brj-24141	222	5	results	result	NOUN
brj-24141	222	6	of	of	ADP
brj-24141	222	7	ablation	ablation	NOUN
brj-24141	222	8	experiments	experiment	NOUN
brj-24141	222	9	cadown	cadown	VERB
brj-24141	222	10	mc2f	mc2f	PROPN
brj-24141	222	11	ccf	ccf	PROPN
brj-24141	222	12	f	f	PROPN
brj-24141	222	13	focaler	focaler	PROPN
brj-24141	222	14	-ciou	-ciou	PROPN
brj-24141	222	15	map50	map50	PROPN
brj-24141	222	16	(	(	PUNCT
brj-24141	222	17	%	%	INTJ
brj-24141	222	18	)	)	PUNCT
brj-24141	223	1	map50	map50	NOUN
brj-24141	223	2	-95	-95	PUNCT
brj-24141	223	3	(	(	PUNCT
brj-24141	223	4	%	%	INTJ
brj-24141	223	5	)	)	PUNCT
brj-24141	223	6	recall	recall	NOUN
brj-24141	223	7	(	(	PUNCT
brj-24141	223	8	%	%	NOUN
brj-24141	223	9	)	)	PUNCT
brj-24141	223	10	precision	precision	NOUN
brj-24141	223	11	(	(	PUNCT
brj-24141	223	12	%	%	INTJ
brj-24141	223	13	)	)	PUNCT
brj-24141	223	14	gflops	gflop	NOUN
brj-24141	223	15	(	(	PUNCT
brj-24141	223	16	g	g	NOUN
brj-24141	223	17	)	)	PUNCT
brj-24141	223	18	78.5	78.5	NUM
brj-24141	223	19	49	49	NUM
brj-24141	223	20	67	67	NUM
brj-24141	223	21	78.2	78.2	NUM
brj-24141	223	22	8.1	8.1	NUM
brj-24141	223	23	√	√	ADV
brj-24141	223	24	79.3	79.3	NUM
brj-24141	223	25	48.9	48.9	NUM
brj-24141	223	26	73.7	73.7	NUM
brj-24141	223	27	75	75	NUM
brj-24141	223	28	7.2	7.2	NUM
brj-24141	223	29	√	√	PROPN
brj-24141	223	30	81.4	81.4	NUM
brj-24141	223	31	49.5	49.5	NUM
brj-24141	223	32	71.6	71.6	NUM
brj-24141	223	33	79.3	79.3	NUM
brj-24141	223	34	9.1	9.1	NUM
brj-24141	223	35	√	√	PROPN
brj-24141	223	36	79.8	79.8	NUM
brj-24141	223	37	48.7	48.7	NUM
brj-24141	223	38	68.8	68.8	NUM
brj-24141	223	39	81.2	81.2	NUM
brj-24141	223	40	7.6	7.6	NUM
brj-24141	223	41	√	√	NUM
brj-24141	223	42	80.8	80.8	NUM
brj-24141	223	43	48.8	48.8	NUM
brj-24141	223	44	74.1	74.1	NUM
brj-24141	223	45	79.2	79.2	NUM
brj-24141	223	46	8.1	8.1	NUM
brj-24141	223	47	√	√	NUM
brj-24141	223	48	√	√	ADP
brj-24141	223	49	82.9	82.9	NUM
brj-24141	223	50	49.1	49.1	NUM
brj-24141	223	51	72.5	72.5	NUM
brj-24141	223	52	78.5	78.5	NUM
brj-24141	223	53	8.5	8.5	NUM
brj-24141	223	54	√	√	NUM
brj-24141	223	55	√	√	ADP
brj-24141	224	1	√	√	NUM
brj-24141	224	2	83.7	83.7	NUM
brj-24141	224	3	50.3	50.3	NUM
brj-24141	224	4	76.7	76.7	NUM
brj-24141	224	5	78.9	78.9	NUM
brj-24141	224	6	8.0	8.0	NUM
brj-24141	224	7	√	√	NUM
brj-24141	224	8	√	√	PROPN
brj-24141	224	9	√	√	NUM
brj-24141	224	10	√	√	NUM
brj-24141	224	11	84.8	84.8	NUM
brj-24141	224	12	50.1	50.1	NUM
brj-24141	224	13	77.5	77.5	NUM
brj-24141	224	14	84.7	84.7	NUM
brj-24141	224	15	8.0	8.0	NUM
brj-24141	225	1	the	the	DET
brj-24141	225	2	second	second	ADJ
brj-24141	225	3	set	set	NOUN
brj-24141	225	4	of	of	ADP
brj-24141	225	5	experiments	experiment	NOUN
brj-24141	225	6	significantly	significantly	ADV
brj-24141	225	7	reduced	reduce	VERB
brj-24141	225	8	computational	computational	ADJ
brj-24141	225	9	effort	effort	NOUN
brj-24141	225	10	by	by	ADP
brj-24141	225	11	adjusting	adjust	VERB
brj-24141	225	12	the	the	DET
brj-24141	225	13	downsampling	downsample	VERB
brj-24141	225	14	strategy	strategy	NOUN
brj-24141	225	15	and	and	CCONJ
brj-24141	225	16	replacing	replace	VERB
brj-24141	225	17	the	the	DET
brj-24141	225	18	traditional	traditional	ADJ
brj-24141	225	19	convolutional	convolutional	ADJ
brj-24141	225	20	operation	operation	NOUN
brj-24141	225	21	with	with	ADP
brj-24141	225	22	c	c	NOUN
brj-24141	225	23	-	-	PUNCT
brj-24141	225	24	adown	adown	VERB
brj-24141	225	25	,	,	PUNCT
brj-24141	225	26	while	while	SCONJ
brj-24141	225	27	preserving	preserve	VERB
brj-24141	225	28	feature	feature	NOUN
brj-24141	225	29	information	information	NOUN
brj-24141	225	30	.	.	PUNCT
brj-24141	226	1	this	this	PRON
brj-24141	226	2	resulted	result	VERB
brj-24141	226	3	in	in	ADP
brj-24141	226	4	a	a	DET
brj-24141	226	5	0.8	0.8	NUM
brj-24141	226	6	%	%	NOUN
brj-24141	226	7	improvement	improvement	NOUN
brj-24141	226	8	in	in	ADP
brj-24141	226	9	mean	mean	ADJ
brj-24141	226	10	average	average	ADJ
brj-24141	226	11	precision	precision	NOUN
brj-24141	226	12	(	(	PUNCT
brj-24141	226	13	map	map	NOUN
brj-24141	226	14	)	)	PUNCT
brj-24141	226	15	and	and	CCONJ
brj-24141	226	16	a	a	DET
brj-24141	226	17	significant	significant	ADJ
brj-24141	226	18	increase	increase	NOUN
brj-24141	226	19	in	in	ADP
brj-24141	226	20	recall	recall	NOUN
brj-24141	226	21	of	of	ADP
brj-24141	226	22	about	about	ADV
brj-24141	226	23	6.7	6.7	NUM
brj-24141	226	24	%	%	NOUN
brj-24141	226	25	.	.	PUNCT
brj-24141	227	1	the	the	DET
brj-24141	227	2	third	third	ADJ
brj-24141	227	3	set	set	NOUN
brj-24141	227	4	of	of	ADP
brj-24141	227	5	experiments	experiment	NOUN
brj-24141	227	6	improved	improve	VERB
brj-24141	227	7	the	the	DET
brj-24141	227	8	c2f	c2f	NOUN
brj-24141	227	9	structure	structure	NOUN
brj-24141	227	10	by	by	ADP
brj-24141	227	11	introducing	introduce	VERB
brj-24141	227	12	the	the	DET
brj-24141	227	13	dwr	dwr	NOUN
brj-24141	227	14	residual	residual	ADJ
brj-24141	227	15	join	join	NOUN
brj-24141	227	16	and	and	CCONJ
brj-24141	227	17	msda	msda	NOUN
brj-24141	227	18	attention	attention	NOUN
brj-24141	227	19	mechanism	mechanism	NOUN
brj-24141	227	20	,	,	PUNCT
brj-24141	227	21	enhancing	enhance	VERB
brj-24141	227	22	the	the	DET
brj-24141	227	23	ability	ability	NOUN
brj-24141	227	24	to	to	PART
brj-24141	227	25	capture	capture	VERB
brj-24141	227	26	multi	multi	ADJ
brj-24141	227	27	-	-	ADJ
brj-24141	227	28	scale	scale	ADJ
brj-24141	227	29	features	feature	NOUN
brj-24141	227	30	,	,	PUNCT
brj-24141	227	31	and	and	CCONJ
brj-24141	227	32	contributing	contribute	VERB
brj-24141	227	33	significantly	significantly	ADV
brj-24141	227	34	to	to	ADP
brj-24141	227	35	the	the	DET
brj-24141	227	36	improvement	improvement	NOUN
brj-24141	227	37	of	of	ADP
brj-24141	227	38	precision	precision	NOUN
brj-24141	227	39	and	and	CCONJ
brj-24141	227	40	map	map	NOUN
brj-24141	227	41	.	.	PUNCT
brj-24141	228	1	this	this	PRON
brj-24141	228	2	resulted	result	VERB
brj-24141	228	3	in	in	ADP
brj-24141	228	4	a	a	DET
brj-24141	228	5	4.4	4.4	NUM
brj-24141	228	6	%	%	NOUN
brj-24141	228	7	improvement	improvement	NOUN
brj-24141	228	8	in	in	ADP
brj-24141	228	9	map	map	NOUN
brj-24141	228	10	.	.	PUNCT
brj-24141	229	1	the	the	DET
brj-24141	229	2	fourth	fourth	ADJ
brj-24141	229	3	group	group	NOUN
brj-24141	229	4	of	of	ADP
brj-24141	229	5	experiments	experiment	NOUN
brj-24141	229	6	improved	improve	VERB
brj-24141	229	7	the	the	DET
brj-24141	229	8	neck	neck	NOUN
brj-24141	229	9	structure	structure	NOUN
brj-24141	229	10	by	by	ADP
brj-24141	229	11	incorporating	incorporate	VERB
brj-24141	229	12	the	the	DET
brj-24141	229	13	ccff	ccff	NOUN
brj-24141	229	14	,	,	PUNCT
brj-24141	229	15	which	which	PRON
brj-24141	229	16	effectively	effectively	ADV
brj-24141	229	17	fused	fuse	VERB
brj-24141	229	18	features	feature	NOUN
brj-24141	229	19	at	at	ADP
brj-24141	229	20	different	different	ADJ
brj-24141	229	21	scales	scale	NOUN
brj-24141	229	22	,	,	PUNCT
brj-24141	229	23	enhancing	enhance	VERB
brj-24141	229	24	feature	feature	NOUN
brj-24141	229	25	fusion	fusion	NOUN
brj-24141	229	26	while	while	SCONJ
brj-24141	229	27	reducing	reduce	VERB
brj-24141	229	28	computational	computational	ADJ
brj-24141	229	29	effort	effort	NOUN
brj-24141	229	30	.	.	PUNCT
brj-24141	230	1	the	the	DET
brj-24141	230	2	fifth	fifth	ADJ
brj-24141	230	3	group	group	NOUN
brj-24141	230	4	of	of	ADP
brj-24141	230	5	experiments	experiment	NOUN
brj-24141	230	6	improved	improve	VERB
brj-24141	230	7	the	the	DET
brj-24141	230	8	loss	loss	NOUN
brj-24141	230	9	function	function	NOUN
brj-24141	230	10	,	,	PUNCT
brj-24141	230	11	accelerating	accelerate	VERB
brj-24141	230	12	model	model	NOUN
brj-24141	230	13	convergence	convergence	NOUN
brj-24141	230	14	speed	speed	NOUN
brj-24141	230	15	without	without	ADP
brj-24141	230	16	increasing	increase	VERB
brj-24141	230	17	computational	computational	ADJ
brj-24141	230	18	cost	cost	NOUN
brj-24141	230	19	.	.	PUNCT
brj-24141	231	1	ultimately	ultimately	ADV
brj-24141	231	2	,	,	PUNCT
brj-24141	231	3	compared	compare	VERB
brj-24141	231	4	to	to	ADP
brj-24141	231	5	the	the	DET
brj-24141	231	6	base	base	ADJ
brj-24141	231	7	version	version	NOUN
brj-24141	231	8	of	of	ADP
brj-24141	231	9	yolov8	yolov8	PROPN
brj-24141	231	10	,	,	PUNCT
brj-24141	231	11	the	the	DET
brj-24141	231	12	improved	improved	ADJ
brj-24141	231	13	model	model	NOUN
brj-24141	231	14	achieved	achieve	VERB
brj-24141	231	15	a	a	DET
brj-24141	231	16	6.3	6.3	NUM
brj-24141	231	17	%	%	NOUN
brj-24141	231	18	improvement	improvement	NOUN
brj-24141	231	19	in	in	ADP
brj-24141	231	20	map	map	NOUN
brj-24141	231	21	,	,	PUNCT
brj-24141	231	22	a	a	DET
brj-24141	231	23	10.5	10.5	NUM
brj-24141	231	24	%	%	NOUN
brj-24141	231	25	improvement	improvement	NOUN
brj-24141	231	26	in	in	ADP
brj-24141	231	27	recall	recall	NOUN
brj-24141	231	28	,	,	PUNCT
brj-24141	231	29	and	and	CCONJ
brj-24141	231	30	a	a	DET
brj-24141	231	31	6.5	6.5	NUM
brj-24141	231	32	%	%	NOUN
brj-24141	231	33	improvement	improvement	NOUN
brj-24141	231	34	in	in	ADP
brj-24141	231	35	precision	precision	NOUN
brj-24141	231	36	.	.	PUNCT
brj-24141	232	1	these	these	DET
brj-24141	232	2	improvements	improvement	NOUN
brj-24141	232	3	effectively	effectively	ADV
brj-24141	232	4	enhanced	enhance	VERB
brj-24141	232	5	the	the	DET
brj-24141	232	6	model	model	NOUN
brj-24141	232	7	's	's	PART
brj-24141	232	8	accuracy	accuracy	NOUN
brj-24141	232	9	,	,	PUNCT
brj-24141	232	10	demonstrating	demonstrate	VERB
brj-24141	232	11	a	a	DET
brj-24141	232	12	good	good	ADJ
brj-24141	232	13	synergy	synergy	NOUN
brj-24141	232	14	between	between	ADP
brj-24141	232	15	the	the	DET
brj-24141	232	16	improved	improved	ADJ
brj-24141	232	17	modules	module	NOUN
brj-24141	232	18	.	.	PUNCT
brj-24141	233	1	loss	loss	NOUN
brj-24141	233	2	function	function	NOUN
brj-24141	233	3	comparison	comparison	NOUN
brj-24141	233	4	when	when	SCONJ
brj-24141	233	5	comparing	compare	VERB
brj-24141	233	6	the	the	DET
brj-24141	233	7	loss	loss	NOUN
brj-24141	233	8	functions	function	NOUN
brj-24141	233	9	of	of	ADP
brj-24141	233	10	the	the	DET
brj-24141	233	11	base	base	NOUN
brj-24141	233	12	and	and	CCONJ
brj-24141	233	13	improved	improved	ADJ
brj-24141	233	14	models	model	NOUN
brj-24141	233	15	,	,	PUNCT
brj-24141	233	16	as	as	SCONJ
brj-24141	233	17	shown	show	VERB
brj-24141	233	18	in	in	ADP
brj-24141	233	19	fig	fig	NOUN
brj-24141	233	20	.	.	PUNCT
brj-24141	234	1	8	8	NUM
brj-24141	234	2	,	,	PUNCT
brj-24141	234	3	the	the	DET
brj-24141	234	4	ciou	ciou	NOUN
brj-24141	234	5	loss	loss	NOUN
brj-24141	234	6	function	function	NOUN
brj-24141	234	7	had	have	VERB
brj-24141	234	8	limitations	limitation	NOUN
brj-24141	234	9	in	in	ADP
brj-24141	234	10	its	its	PRON
brj-24141	234	11	performance	performance	NOUN
brj-24141	234	12	due	due	ADP
brj-24141	234	13	to	to	ADP
brj-24141	234	14	its	its	PRON
brj-24141	234	15	insufficient	insufficient	ADJ
brj-24141	234	16	consideration	consideration	NOUN
brj-24141	234	17	of	of	ADP
brj-24141	234	18	the	the	DET
brj-24141	234	19	balance	balance	NOUN
brj-24141	234	20	of	of	ADP
brj-24141	234	21	sample	sample	NOUN
brj-24141	234	22	difficulty	difficulty	NOUN
brj-24141	234	23	.	.	PUNCT
brj-24141	235	1	by	by	ADP
brj-24141	235	2	introducing	introduce	VERB
brj-24141	235	3	the	the	DET
brj-24141	235	4	focaler	focaler	NOUN
brj-24141	235	5	-	-	PUNCT
brj-24141	235	6	iou	iou	NOUN
brj-24141	235	7	loss	loss	NOUN
brj-24141	235	8	function	function	NOUN
brj-24141	235	9	on	on	ADP
brj-24141	235	10	top	top	NOUN
brj-24141	235	11	of	of	ADP
brj-24141	235	12	ciou	ciou	NOUN
brj-24141	235	13	,	,	PUNCT
brj-24141	235	14	the	the	DET
brj-24141	235	15	model	model	NOUN
brj-24141	235	16	was	be	AUX
brj-24141	235	17	able	able	ADJ
brj-24141	235	18	to	to	PART
brj-24141	235	19	focus	focus	VERB
brj-24141	235	20	more	more	ADV
brj-24141	235	21	on	on	ADP
brj-24141	235	22	processing	process	VERB
brj-24141	235	23	difficult	difficult	ADJ
brj-24141	235	24	samples	sample	NOUN
brj-24141	235	25	.	.	PUNCT
brj-24141	236	1	this	this	DET
brj-24141	236	2	improvement	improvement	NOUN
brj-24141	236	3	significantly	significantly	ADV
brj-24141	236	4	accelerated	accelerate	VERB
brj-24141	236	5	the	the	DET
brj-24141	236	6	convergence	convergence	NOUN
brj-24141	236	7	of	of	ADP
brj-24141	236	8	the	the	DET
brj-24141	236	9	model	model	NOUN
brj-24141	236	10	and	and	CCONJ
brj-24141	236	11	reduced	reduce	VERB
brj-24141	236	12	the	the	DET
brj-24141	236	13	overall	overall	ADJ
brj-24141	236	14	loss	loss	NOUN
brj-24141	236	15	value	value	NOUN
brj-24141	236	16	,	,	PUNCT
brj-24141	236	17	thereby	thereby	ADV
brj-24141	236	18	enhancing	enhance	VERB
brj-24141	236	19	the	the	DET
brj-24141	236	20	overall	overall	ADJ
brj-24141	236	21	performance	performance	NOUN
brj-24141	236	22	.	.	PUNCT
brj-24141	237	1	peer	peer	NOUN
brj-24141	237	2	-	-	PUNCT
brj-24141	237	3	reviewed	review	VERB
brj-24141	237	4	article	article	NOUN
brj-24141	237	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	237	6	li	li	PROPN
brj-24141	237	7	et	et	PROPN
brj-24141	237	8	al	al	PROPN
brj-24141	237	9	.	.	PROPN
brj-24141	238	1	(	(	PUNCT
brj-24141	238	2	2025	2025	NUM
brj-24141	238	3	)	)	PUNCT
brj-24141	238	4	.	.	PUNCT
brj-24141	239	1	“	"	PUNCT
brj-24141	239	2	improved	improved	ADJ
brj-24141	239	3	panel	panel	NOUN
brj-24141	239	4	defect	defect	NOUN
brj-24141	239	5	detection	detection	NOUN
brj-24141	239	6	,	,	PUNCT
brj-24141	239	7	”	"	PUNCT
brj-24141	239	8	bioresources	bioresource	NOUN
brj-24141	239	9	20(2	20(2	NUM
brj-24141	239	10	)	)	PUNCT
brj-24141	239	11	,	,	PUNCT
brj-24141	239	12	2556	2556	NUM
brj-24141	239	13	-	-	SYM
brj-24141	239	14	2573	2573	NUM
brj-24141	239	15	.	.	PUNCT
brj-24141	239	16	2568	2568	NUM
brj-24141	239	17	fig	fig	NOUN
brj-24141	239	18	.	.	PUNCT
brj-24141	240	1	8	8	NUM
brj-24141	240	2	.	.	X
brj-24141	240	3	comparison	comparison	NOUN
brj-24141	240	4	of	of	ADP
brj-24141	240	5	training	training	NOUN
brj-24141	240	6	loss	loss	NOUN
brj-24141	240	7	functions	function	NOUN
brj-24141	240	8	comparison	comparison	NOUN
brj-24141	240	9	of	of	ADP
brj-24141	240	10	different	different	ADJ
brj-24141	240	11	defects	defect	NOUN
brj-24141	240	12	a	a	DET
brj-24141	240	13	confusion	confusion	NOUN
brj-24141	240	14	matrix	matrix	NOUN
brj-24141	240	15	was	be	AUX
brj-24141	240	16	generated	generate	VERB
brj-24141	240	17	by	by	ADP
brj-24141	240	18	comparing	compare	VERB
brj-24141	240	19	the	the	DET
brj-24141	240	20	actual	actual	ADJ
brj-24141	240	21	categories	category	NOUN
brj-24141	240	22	of	of	ADP
brj-24141	240	23	the	the	DET
brj-24141	240	24	validation	validation	NOUN
brj-24141	240	25	set	set	VERB
brj-24141	240	26	with	with	ADP
brj-24141	240	27	the	the	DET
brj-24141	240	28	predicted	predict	VERB
brj-24141	240	29	categories	category	NOUN
brj-24141	240	30	of	of	ADP
brj-24141	240	31	the	the	DET
brj-24141	240	32	model	model	NOUN
brj-24141	240	33	,	,	PUNCT
brj-24141	240	34	as	as	SCONJ
brj-24141	240	35	shown	show	VERB
brj-24141	240	36	in	in	ADP
brj-24141	240	37	fig	fig	NOUN
brj-24141	240	38	.	.	PUNCT
brj-24141	241	1	9	9	X
brj-24141	241	2	.	.	X
brj-24141	241	3	this	this	DET
brj-24141	241	4	figure	figure	NOUN
brj-24141	241	5	compares	compare	VERB
brj-24141	241	6	the	the	DET
brj-24141	241	7	confusion	confusion	NOUN
brj-24141	241	8	matrix	matrix	NOUN
brj-24141	241	9	plots	plot	NOUN
brj-24141	241	10	of	of	ADP
brj-24141	241	11	the	the	DET
brj-24141	241	12	different	different	ADJ
brj-24141	241	13	algorithms	algorithm	NOUN
brj-24141	241	14	.	.	PUNCT
brj-24141	242	1	the	the	DET
brj-24141	242	2	true	true	ADJ
brj-24141	242	3	-	-	PUNCT
brj-24141	242	4	negative	negative	ADJ
brj-24141	242	5	squares	square	NOUN
brj-24141	242	6	on	on	ADP
brj-24141	242	7	the	the	DET
brj-24141	242	8	diagonal	diagonal	ADJ
brj-24141	242	9	represent	represent	VERB
brj-24141	242	10	the	the	DET
brj-24141	242	11	categories	category	NOUN
brj-24141	242	12	correctly	correctly	ADV
brj-24141	242	13	predicted	predict	VERB
brj-24141	242	14	by	by	ADP
brj-24141	242	15	the	the	DET
brj-24141	242	16	model	model	NOUN
brj-24141	242	17	.	.	PUNCT
brj-24141	243	1	the	the	DET
brj-24141	243	2	other	other	ADJ
brj-24141	243	3	squares	square	NOUN
brj-24141	243	4	represent	represent	VERB
brj-24141	243	5	misdetections	misdetection	NOUN
brj-24141	243	6	and	and	CCONJ
brj-24141	243	7	false	false	ADJ
brj-24141	243	8	detections	detection	NOUN
brj-24141	243	9	.	.	PUNCT
brj-24141	244	1	the	the	DET
brj-24141	244	2	values	value	NOUN
brj-24141	244	3	on	on	ADP
brj-24141	244	4	the	the	DET
brj-24141	244	5	diagonal	diagonal	NOUN
brj-24141	244	6	of	of	ADP
brj-24141	244	7	fig	fig	NOUN
brj-24141	244	8	.	.	PUNCT
brj-24141	245	1	8(b	8(b	NUM
brj-24141	245	2	)	)	PUNCT
brj-24141	246	1	were	be	AUX
brj-24141	246	2	consistently	consistently	ADV
brj-24141	246	3	higher	high	ADJ
brj-24141	246	4	than	than	ADP
brj-24141	246	5	those	those	PRON
brj-24141	246	6	on	on	ADP
brj-24141	246	7	the	the	DET
brj-24141	246	8	diagonal	diagonal	NOUN
brj-24141	246	9	of	of	ADP
brj-24141	246	10	fig	fig	NOUN
brj-24141	246	11	.	.	PUNCT
brj-24141	247	1	8(a	8(a	NUM
brj-24141	247	2	)	)	PUNCT
brj-24141	247	3	,	,	PUNCT
brj-24141	247	4	especially	especially	ADV
brj-24141	247	5	for	for	ADP
brj-24141	247	6	live	live	ADJ
brj-24141	247	7	knots	knot	NOUN
brj-24141	247	8	and	and	CCONJ
brj-24141	247	9	crack	crack	VERB
brj-24141	247	10	defects	defect	NOUN
brj-24141	247	11	.	.	PUNCT
brj-24141	248	1	this	this	PRON
brj-24141	248	2	indicates	indicate	VERB
brj-24141	248	3	a	a	DET
brj-24141	248	4	more	more	ADV
brj-24141	248	5	significant	significant	ADJ
brj-24141	248	6	improvement	improvement	NOUN
brj-24141	248	7	of	of	ADP
brj-24141	248	8	the	the	DET
brj-24141	248	9	improved	improved	ADJ
brj-24141	248	10	algorithm	algorithm	NOUN
brj-24141	248	11	over	over	ADP
brj-24141	248	12	the	the	DET
brj-24141	248	13	original	original	ADJ
brj-24141	248	14	algorithm	algorithm	NOUN
brj-24141	248	15	.	.	PUNCT
brj-24141	249	1	a	a	DET
brj-24141	249	2	comparison	comparison	NOUN
brj-24141	249	3	of	of	ADP
brj-24141	249	4	the	the	DET
brj-24141	249	5	results	result	NOUN
brj-24141	249	6	is	be	AUX
brj-24141	249	7	shown	show	VERB
brj-24141	249	8	in	in	ADP
brj-24141	249	9	figs	fig	NOUN
brj-24141	249	10	.	.	PUNCT
brj-24141	250	1	10	10	NUM
brj-24141	250	2	and	and	CCONJ
brj-24141	250	3	11	11	NUM
brj-24141	250	4	.	.	PUNCT
brj-24141	250	5	comparison	comparison	NOUN
brj-24141	250	6	of	of	ADP
brj-24141	250	7	different	different	ADJ
brj-24141	250	8	algorithms	algorithm	NOUN
brj-24141	250	9	to	to	PART
brj-24141	250	10	demonstrate	demonstrate	VERB
brj-24141	250	11	the	the	DET
brj-24141	250	12	superiority	superiority	NOUN
brj-24141	250	13	of	of	ADP
brj-24141	250	14	the	the	DET
brj-24141	250	15	proposed	propose	VERB
brj-24141	250	16	algorithm	algorithm	NOUN
brj-24141	250	17	in	in	ADP
brj-24141	250	18	wood	wood	NOUN
brj-24141	250	19	panel	panel	NOUN
brj-24141	250	20	defect	defect	NOUN
brj-24141	250	21	detection	detection	NOUN
brj-24141	250	22	,	,	PUNCT
brj-24141	250	23	it	it	PRON
brj-24141	250	24	was	be	AUX
brj-24141	250	25	compared	compare	VERB
brj-24141	250	26	with	with	ADP
brj-24141	250	27	other	other	ADJ
brj-24141	250	28	mainstream	mainstream	ADJ
brj-24141	250	29	algorithms	algorithm	NOUN
brj-24141	250	30	.	.	PUNCT
brj-24141	251	1	the	the	DET
brj-24141	251	2	comparison	comparison	NOUN
brj-24141	251	3	results	result	NOUN
brj-24141	251	4	are	be	AUX
brj-24141	251	5	presented	present	VERB
brj-24141	251	6	in	in	ADP
brj-24141	251	7	table	table	NOUN
brj-24141	251	8	3	3	NUM
brj-24141	251	9	.	.	PUNCT
brj-24141	252	1	the	the	DET
brj-24141	252	2	proposed	propose	VERB
brj-24141	252	3	algorithm	algorithm	NOUN
brj-24141	252	4	outperformed	outperform	VERB
brj-24141	252	5	yolov3	yolov3	PROPN
brj-24141	252	6	-	-	PUNCT
brj-24141	252	7	tiny	tiny	ADJ
brj-24141	252	8	by	by	ADP
brj-24141	252	9	15.4	15.4	NUM
brj-24141	252	10	%	%	NOUN
brj-24141	252	11	in	in	ADP
brj-24141	252	12	accuracy	accuracy	NOUN
brj-24141	252	13	and	and	CCONJ
brj-24141	252	14	6.2	6.2	NUM
brj-24141	252	15	%	%	NOUN
brj-24141	252	16	in	in	ADP
brj-24141	252	17	map	map	NOUN
brj-24141	252	18	.	.	PUNCT
brj-24141	253	1	compared	compare	VERB
brj-24141	253	2	to	to	ADP
brj-24141	253	3	yolov5	yolov5	NOUN
brj-24141	253	4	,	,	PUNCT
brj-24141	253	5	it	it	PRON
brj-24141	253	6	achieved	achieve	VERB
brj-24141	253	7	a	a	DET
brj-24141	253	8	10	10	NUM
brj-24141	253	9	%	%	NOUN
brj-24141	253	10	increase	increase	NOUN
brj-24141	253	11	in	in	ADP
brj-24141	253	12	accuracy	accuracy	NOUN
brj-24141	253	13	and	and	CCONJ
brj-24141	253	14	a	a	DET
brj-24141	253	15	6.8	6.8	NUM
brj-24141	253	16	%	%	NOUN
brj-24141	253	17	increase	increase	NOUN
brj-24141	253	18	in	in	ADP
brj-24141	253	19	map	map	NOUN
brj-24141	253	20	,	,	PUNCT
brj-24141	253	21	with	with	ADP
brj-24141	253	22	a	a	DET
brj-24141	253	23	slight	slight	ADJ
brj-24141	253	24	increase	increase	NOUN
brj-24141	253	25	in	in	ADP
brj-24141	253	26	the	the	DET
brj-24141	253	27	number	number	NOUN
brj-24141	253	28	of	of	ADP
brj-24141	253	29	parameters	parameter	NOUN
brj-24141	253	30	.	.	PUNCT
brj-24141	254	1	when	when	SCONJ
brj-24141	254	2	compared	compare	VERB
brj-24141	254	3	to	to	ADP
brj-24141	254	4	the	the	DET
brj-24141	254	5	larger	large	ADJ
brj-24141	254	6	yolov8s	yolov8	NOUN
brj-24141	254	7	,	,	PUNCT
brj-24141	254	8	the	the	DET
brj-24141	254	9	proposed	propose	VERB
brj-24141	254	10	algorithm	algorithm	NOUN
brj-24141	254	11	exhibited	exhibit	VERB
brj-24141	254	12	a	a	DET
brj-24141	254	13	4.5	4.5	NUM
brj-24141	254	14	%	%	NOUN
brj-24141	254	15	increase	increase	NOUN
brj-24141	254	16	in	in	ADP
brj-24141	254	17	accuracy	accuracy	NOUN
brj-24141	254	18	and	and	CCONJ
brj-24141	254	19	a	a	DET
brj-24141	254	20	1.9	1.9	NUM
brj-24141	254	21	%	%	NOUN
brj-24141	254	22	increase	increase	NOUN
brj-24141	254	23	in	in	ADP
brj-24141	254	24	map	map	NOUN
brj-24141	254	25	.	.	PUNCT
brj-24141	255	1	while	while	SCONJ
brj-24141	255	2	the	the	DET
brj-24141	255	3	proposed	propose	VERB
brj-24141	255	4	algorithm	algorithm	NOUN
brj-24141	255	5	demonstrated	demonstrate	VERB
brj-24141	255	6	comparable	comparable	ADJ
brj-24141	255	7	performance	performance	NOUN
brj-24141	255	8	to	to	ADP
brj-24141	255	9	some	some	PRON
brj-24141	255	10	of	of	ADP
brj-24141	255	11	the	the	DET
brj-24141	255	12	improved	improved	ADJ
brj-24141	255	13	algorithms	algorithm	NOUN
brj-24141	255	14	,	,	PUNCT
brj-24141	255	15	it	it	PRON
brj-24141	255	16	surpassed	surpass	VERB
brj-24141	255	17	them	they	PRON
brj-24141	255	18	in	in	ADP
brj-24141	255	19	the	the	DET
brj-24141	255	20	detection	detection	NOUN
brj-24141	255	21	of	of	ADP
brj-24141	255	22	dead	dead	ADJ
brj-24141	255	23	knots	knot	NOUN
brj-24141	255	24	,	,	PUNCT
brj-24141	255	25	dry	dry	ADJ
brj-24141	255	26	scars	scar	NOUN
brj-24141	255	27	,	,	PUNCT
brj-24141	255	28	and	and	CCONJ
brj-24141	255	29	live	live	ADJ
brj-24141	255	30	knots	knot	NOUN
brj-24141	255	31	.	.	PUNCT
brj-24141	256	1	however	however	ADV
brj-24141	256	2	,	,	PUNCT
brj-24141	256	3	its	its	PRON
brj-24141	256	4	performance	performance	NOUN
brj-24141	256	5	in	in	ADP
brj-24141	256	6	crack	crack	NOUN
brj-24141	256	7	detection	detection	NOUN
brj-24141	256	8	was	be	AUX
brj-24141	256	9	slightly	slightly	ADV
brj-24141	256	10	lower	low	ADJ
brj-24141	256	11	than	than	ADP
brj-24141	256	12	that	that	PRON
brj-24141	256	13	of	of	ADP
brj-24141	256	14	wood	wood	NOUN
brj-24141	256	15	-	-	PUNCT
brj-24141	256	16	net	net	NOUN
brj-24141	256	17	.	.	PUNCT
brj-24141	257	1	in	in	ADP
brj-24141	257	2	summary	summary	NOUN
brj-24141	257	3	,	,	PUNCT
brj-24141	257	4	the	the	DET
brj-24141	257	5	proposed	propose	VERB
brj-24141	257	6	algorithm	algorithm	NOUN
brj-24141	257	7	exhibited	exhibit	VERB
brj-24141	257	8	strong	strong	ADJ
brj-24141	257	9	performance	performance	NOUN
brj-24141	257	10	compared	compare	VERB
brj-24141	257	11	to	to	ADP
brj-24141	257	12	both	both	CCONJ
brj-24141	257	13	mainstream	mainstream	VERB
brj-24141	257	14	yolo	yolo	ADJ
brj-24141	257	15	algorithms	algorithm	NOUN
brj-24141	257	16	and	and	CCONJ
brj-24141	257	17	other	other	ADJ
brj-24141	257	18	improved	improved	ADJ
brj-24141	257	19	algorithms	algorithm	NOUN
brj-24141	257	20	.	.	PUNCT
brj-24141	258	1	it	it	PRON
brj-24141	258	2	demonstrated	demonstrate	VERB
brj-24141	258	3	superior	superior	ADJ
brj-24141	258	4	accuracy	accuracy	NOUN
brj-24141	258	5	in	in	ADP
brj-24141	258	6	detecting	detect	VERB
brj-24141	258	7	scar	scar	NOUN
brj-24141	258	8	and	and	CCONJ
brj-24141	258	9	live	live	ADJ
brj-24141	258	10	knot	knot	NOUN
brj-24141	258	11	defects	defect	NOUN
brj-24141	258	12	table	table	NOUN
brj-24141	258	13	3	3	NUM
brj-24141	258	14	comparison	comparison	NOUN
brj-24141	258	15	of	of	ADP
brj-24141	258	16	different	different	ADJ
brj-24141	258	17	algorithms	algorithm	NOUN
brj-24141	258	18	.	.	PUNCT
brj-24141	259	1	(	(	PUNCT
brj-24141	259	2	a	a	X
brj-24141	259	3	)	)	PUNCT
brj-24141	259	4	peer	peer	NOUN
brj-24141	259	5	-	-	PUNCT
brj-24141	259	6	reviewed	review	VERB
brj-24141	259	7	article	article	NOUN
brj-24141	259	8	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	259	9	li	li	PROPN
brj-24141	259	10	et	et	PROPN
brj-24141	259	11	al	al	PROPN
brj-24141	259	12	.	.	PROPN
brj-24141	260	1	(	(	PUNCT
brj-24141	260	2	2025	2025	NUM
brj-24141	260	3	)	)	PUNCT
brj-24141	260	4	.	.	PUNCT
brj-24141	261	1	“	"	PUNCT
brj-24141	261	2	improved	improved	ADJ
brj-24141	261	3	panel	panel	NOUN
brj-24141	261	4	defect	defect	NOUN
brj-24141	261	5	detection	detection	NOUN
brj-24141	261	6	,	,	PUNCT
brj-24141	261	7	”	"	PUNCT
brj-24141	261	8	bioresources	bioresource	NOUN
brj-24141	261	9	20(2	20(2	NUM
brj-24141	261	10	)	)	PUNCT
brj-24141	261	11	,	,	PUNCT
brj-24141	261	12	2556	2556	NUM
brj-24141	261	13	-	-	SYM
brj-24141	261	14	2573	2573	NUM
brj-24141	261	15	.	.	PUNCT
brj-24141	262	1	2569	2569	NUM
brj-24141	262	2	(	(	PUNCT
brj-24141	262	3	b	b	X
brj-24141	262	4	)	)	PUNCT
brj-24141	262	5	fig	fig	NOUN
brj-24141	262	6	.	.	PUNCT
brj-24141	263	1	9	9	X
brj-24141	263	2	.	.	X
brj-24141	263	3	(	(	PUNCT
brj-24141	263	4	a	a	X
brj-24141	263	5	)	)	PUNCT
brj-24141	263	6	confusion	confusion	NOUN
brj-24141	263	7	matrix	matrix	NOUN
brj-24141	263	8	for	for	ADP
brj-24141	263	9	the	the	DET
brj-24141	263	10	yolov8	yolov8	NOUN
brj-24141	263	11	model	model	NOUN
brj-24141	263	12	(	(	PUNCT
brj-24141	263	13	b	b	NOUN
brj-24141	263	14	)	)	PUNCT
brj-24141	263	15	confusion	confusion	NOUN
brj-24141	263	16	matrix	matrix	NOUN
brj-24141	263	17	for	for	ADP
brj-24141	263	18	the	the	DET
brj-24141	263	19	improved	improved	ADJ
brj-24141	263	20	model	model	NOUN
brj-24141	263	21	fig	fig	NOUN
brj-24141	263	22	.	.	PUNCT
brj-24141	264	1	10	10	NUM
brj-24141	264	2	.	.	PUNCT
brj-24141	265	1	yolov8	yolov8	NOUN
brj-24141	265	2	defect	defect	PROPN
brj-24141	265	3	detection	detection	NOUN
brj-24141	265	4	results	result	VERB
brj-24141	265	5	peer	peer	NOUN
brj-24141	265	6	-	-	PUNCT
brj-24141	265	7	reviewed	review	VERB
brj-24141	265	8	article	article	NOUN
brj-24141	265	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	265	10	li	li	PROPN
brj-24141	265	11	et	et	PROPN
brj-24141	265	12	al	al	PROPN
brj-24141	265	13	.	.	PROPN
brj-24141	266	1	(	(	PUNCT
brj-24141	266	2	2025	2025	NUM
brj-24141	266	3	)	)	PUNCT
brj-24141	266	4	.	.	PUNCT
brj-24141	267	1	“	"	PUNCT
brj-24141	267	2	improved	improved	ADJ
brj-24141	267	3	panel	panel	NOUN
brj-24141	267	4	defect	defect	NOUN
brj-24141	267	5	detection	detection	NOUN
brj-24141	267	6	,	,	PUNCT
brj-24141	267	7	”	"	PUNCT
brj-24141	267	8	bioresources	bioresource	NOUN
brj-24141	267	9	20(2	20(2	NUM
brj-24141	267	10	)	)	PUNCT
brj-24141	267	11	,	,	PUNCT
brj-24141	267	12	2556	2556	NUM
brj-24141	267	13	-	-	SYM
brj-24141	267	14	2573	2573	NUM
brj-24141	267	15	.	.	PUNCT
brj-24141	267	16	2570	2570	NUM
brj-24141	267	17	fig	fig	NOUN
brj-24141	267	18	.	.	PUNCT
brj-24141	268	1	11	11	NUM
brj-24141	268	2	.	.	PUNCT
brj-24141	268	3	improved	improve	VERB
brj-24141	268	4	algorithm	algorithm	NOUN
brj-24141	268	5	defect	defect	NOUN
brj-24141	268	6	detection	detection	NOUN
brj-24141	268	7	results	result	VERB
brj-24141	268	8	table	table	VERB
brj-24141	268	9	3	3	NUM
brj-24141	268	10	.	.	PUNCT
brj-24141	268	11	comparison	comparison	NOUN
brj-24141	268	12	of	of	ADP
brj-24141	268	13	different	different	ADJ
brj-24141	268	14	algorithms	algorithm	NOUN
brj-24141	268	15	arithmetic	arithmetic	PROPN
brj-24141	268	16	ap	ap	PROPN
brj-24141	268	17	(	(	PUNCT
brj-24141	268	18	%	%	INTJ
brj-24141	268	19	)	)	PUNCT
brj-24141	269	1	map50	map50	PROPN
brj-24141	269	2	(	(	PUNCT
brj-24141	269	3	%	%	INTJ
brj-24141	269	4	)	)	PUNCT
brj-24141	270	1	map50	map50	PROPN
brj-24141	270	2	-95/%	-95/%	PROPN
brj-24141	270	3	precision	precision	NOUN
brj-24141	270	4	(	(	PUNCT
brj-24141	270	5	%	%	INTJ
brj-24141	270	6	)	)	PUNCT
brj-24141	270	7	recall	recall	NOUN
brj-24141	270	8	(	(	PUNCT
brj-24141	270	9	%	%	NOUN
brj-24141	270	10	)	)	PUNCT
brj-24141	270	11	gflops	gflop	NOUN
brj-24141	270	12	(	(	PUNCT
brj-24141	270	13	g	g	NOUN
brj-24141	270	14	)	)	PUNCT
brj-24141	270	15	fps	fps	VERB
brj-24141	270	16	dead	dead	ADJ
brj-24141	270	17	knot	knot	NOUN
brj-24141	270	18	scar	scar	NOUN
brj-24141	270	19	live	live	ADJ
brj-24141	270	20	knot	knot	NOUN
brj-24141	270	21	crack	crack	NOUN
brj-24141	270	22	ssd	ssd	NOUN
brj-24141	270	23	79.5	79.5	NUM
brj-24141	270	24	78.2	78.2	NUM
brj-24141	270	25	76	76	NUM
brj-24141	270	26	70.5	70.5	NUM
brj-24141	270	27	73.6	73.6	NUM
brj-24141	270	28	44.5	44.5	NUM
brj-24141	270	29	67.2	67.2	NUM
brj-24141	270	30	74.5	74.5	NUM
brj-24141	270	31	92	92	NUM
brj-24141	270	32	42.6	42.6	NUM
brj-24141	270	33	faster	fast	ADJ
brj-24141	270	34	-	-	PUNCT
brj-24141	270	35	rcnn	rcnn	NOUN
brj-24141	270	36	82.6	82.6	NUM
brj-24141	270	37	81.5	81.5	NUM
brj-24141	270	38	79.1	79.1	NUM
brj-24141	270	39	77.1	77.1	NUM
brj-24141	270	40	79.2	79.2	NUM
brj-24141	270	41	48.9	48.9	NUM
brj-24141	270	42	72.5	72.5	NUM
brj-24141	270	43	75.2	75.2	NUM
brj-24141	270	44	108	108	NUM
brj-24141	270	45	14.3	14.3	NUM
brj-24141	271	1	yolov3	yolov3	NOUN
brj-24141	271	2	tiny	tiny	ADJ
brj-24141	271	3	85.7	85.7	NUM
brj-24141	271	4	79.6	79.6	NUM
brj-24141	271	5	75.2	75.2	NUM
brj-24141	271	6	73.8	73.8	NUM
brj-24141	271	7	78.6	78.6	NUM
brj-24141	271	8	47.5	47.5	NUM
brj-24141	271	9	69.3	69.3	NUM
brj-24141	271	10	73.8	73.8	NUM
brj-24141	271	11	19	19	NUM
brj-24141	271	12	85.2	85.2	NUM
brj-24141	271	13	yolov5	yolov5	NOUN
brj-24141	271	14	84.3	84.3	NUM
brj-24141	271	15	75.9	75.9	NUM
brj-24141	271	16	77.1	77.1	NUM
brj-24141	271	17	74.5	74.5	NUM
brj-24141	271	18	78	78	NUM
brj-24141	271	19	47.4	47.4	NUM
brj-24141	271	20	74.7	74.7	NUM
brj-24141	271	21	70.3	70.3	NUM
brj-24141	271	22	7.1	7.1	NUM
brj-24141	271	23	54.3	54.3	NUM
brj-24141	271	24	yolov6	yolov6	NOUN
brj-24141	272	1	84.5	84.5	NUM
brj-24141	272	2	90	90	NUM
brj-24141	272	3	74.9	74.9	NUM
brj-24141	272	4	69.7	69.7	NUM
brj-24141	272	5	79.8	79.8	NUM
brj-24141	272	6	46.8	46.8	NUM
brj-24141	272	7	70.9	70.9	NUM
brj-24141	272	8	73.1	73.1	NUM
brj-24141	272	9	11.8	11.8	NUM
brj-24141	272	10	43.1	43.1	NUM
brj-24141	272	11	yolov7	yolov7	NOUN
brj-24141	272	12	85.7	85.7	NUM
brj-24141	272	13	79.6	79.6	NUM
brj-24141	272	14	75.2	75.2	NUM
brj-24141	272	15	73.8	73.8	NUM
brj-24141	272	16	78.6	78.6	NUM
brj-24141	272	17	48.6	48.6	NUM
brj-24141	272	18	69.3	69.3	NUM
brj-24141	272	19	73.8	73.8	NUM
brj-24141	272	20	19	19	NUM
brj-24141	272	21	44.5	44.5	NUM
brj-24141	272	22	yolov8n	yolov8n	NOUN
brj-24141	272	23	86.5	86.5	NUM
brj-24141	272	24	84.1	84.1	NUM
brj-24141	272	25	71.7	71.7	NUM
brj-24141	272	26	71.8	71.8	NUM
brj-24141	272	27	78.5	78.5	NUM
brj-24141	272	28	49	49	NUM
brj-24141	272	29	78.2	78.2	NUM
brj-24141	272	30	67.2	67.2	NUM
brj-24141	272	31	8.1	8.1	NUM
brj-24141	272	32	81.3	81.3	NUM
brj-24141	272	33	yolov8s	yolov8	NOUN
brj-24141	272	34	86.7	86.7	NUM
brj-24141	272	35	81.2	81.2	NUM
brj-24141	272	36	83	83	NUM
brj-24141	272	37	79.8	79.8	NUM
brj-24141	272	38	82.7	82.7	NUM
brj-24141	272	39	50.8	50.8	NUM
brj-24141	272	40	80.2	80.2	NUM
brj-24141	272	41	71.7	71.7	NUM
brj-24141	272	42	28.4	28.4	NUM
brj-24141	272	43	69.7	69.7	NUM
brj-24141	272	44	yolov5_hs	yolov5_hs	NUM
brj-24141	272	45	85.9	85.9	NUM
brj-24141	272	46	88.3	88.3	NUM
brj-24141	272	47	82.7	82.7	NUM
brj-24141	272	48	71.4	71.4	NUM
brj-24141	272	49	82.1	82.1	NUM
brj-24141	272	50	49.8	49.8	NUM
brj-24141	272	51	78.9	78.9	NUM
brj-24141	272	52	76	76	NUM
brj-24141	272	53	23.3	23.3	NUM
brj-24141	272	54	60.8	60.8	NUM
brj-24141	272	55	wood	wood	NOUN
brj-24141	272	56	-	-	PUNCT
brj-24141	272	57	net	net	NOUN
brj-24141	272	58	83.9	83.9	NUM
brj-24141	272	59	89.5	89.5	NUM
brj-24141	272	60	83.9	83.9	NUM
brj-24141	272	61	76.2	76.2	NUM
brj-24141	272	62	83.8	83.8	NUM
brj-24141	272	63	50.6	50.6	NUM
brj-24141	272	64	78.6	78.6	NUM
brj-24141	272	65	78.7	78.7	NUM
brj-24141	272	66	16.4	16.4	NUM
brj-24141	272	67	21.3	21.3	NUM
brj-24141	272	68	yolov8ncdc	yolov8ncdc	PROPN
brj-24141	272	69	88.7	88.7	NUM
brj-24141	272	70	92.2	92.2	NUM
brj-24141	272	71	85.3	85.3	NUM
brj-24141	272	72	75.9	75.9	NUM
brj-24141	272	73	84.8	84.8	NUM
brj-24141	272	74	50.1	50.1	NUM
brj-24141	272	75	84.7	84.7	NUM
brj-24141	272	76	77.5	77.5	NUM
brj-24141	272	77	8.0	8.0	NUM
brj-24141	272	78	58.4	58.4	NUM
brj-24141	272	79	comparison	comparison	NOUN
brj-24141	272	80	of	of	ADP
brj-24141	272	81	versatility	versatility	NOUN
brj-24141	272	82	to	to	PART
brj-24141	272	83	verify	verify	VERB
brj-24141	272	84	the	the	DET
brj-24141	272	85	generalization	generalization	NOUN
brj-24141	272	86	of	of	ADP
brj-24141	272	87	the	the	DET
brj-24141	272	88	improved	improved	ADJ
brj-24141	272	89	model	model	NOUN
brj-24141	272	90	,	,	PUNCT
brj-24141	272	91	comparative	comparative	ADJ
brj-24141	272	92	experiments	experiment	NOUN
brj-24141	272	93	were	be	AUX
brj-24141	272	94	conducted	conduct	VERB
brj-24141	272	95	on	on	ADP
brj-24141	272	96	the	the	DET
brj-24141	272	97	roboflow	roboflow	ADJ
brj-24141	272	98	public	public	ADJ
brj-24141	272	99	dataset	dataset	NOUN
brj-24141	272	100	.	.	PUNCT
brj-24141	273	1	while	while	SCONJ
brj-24141	273	2	maintaining	maintain	VERB
brj-24141	273	3	the	the	DET
brj-24141	273	4	training	training	NOUN
brj-24141	273	5	parameters	parameter	NOUN
brj-24141	273	6	of	of	ADP
brj-24141	273	7	the	the	DET
brj-24141	273	8	model	model	NOUN
brj-24141	273	9	,	,	PUNCT
brj-24141	273	10	comparative	comparative	ADJ
brj-24141	273	11	experiments	experiment	NOUN
brj-24141	273	12	were	be	AUX
brj-24141	273	13	also	also	ADV
brj-24141	273	14	performed	perform	VERB
brj-24141	273	15	on	on	ADP
brj-24141	273	16	related	related	ADJ
brj-24141	273	17	domain	domain	NOUN
brj-24141	273	18	public	public	ADJ
brj-24141	273	19	datasets	dataset	NOUN
brj-24141	273	20	.	.	PUNCT
brj-24141	274	1	the	the	DET
brj-24141	274	2	results	result	NOUN
brj-24141	274	3	of	of	ADP
brj-24141	274	4	these	these	DET
brj-24141	274	5	comparative	comparative	ADJ
brj-24141	274	6	experiments	experiment	NOUN
brj-24141	274	7	are	be	AUX
brj-24141	274	8	presented	present	VERB
brj-24141	274	9	in	in	ADP
brj-24141	274	10	tables	table	NOUN
brj-24141	274	11	4	4	NUM
brj-24141	274	12	and	and	CCONJ
brj-24141	274	13	5	5	NUM
brj-24141	274	14	.	.	PUNCT
brj-24141	275	1	dataset	dataset	VERB
brj-24141	275	2	1	1	NUM
brj-24141	275	3	(	(	PUNCT
brj-24141	275	4	https://universe.roboflow.com/rtech/wood-surface-defects	https://universe.roboflow.com/rtech/wood-surface-defect	NOUN
brj-24141	275	5	)	)	PUNCT
brj-24141	275	6	dataset	dataset	VERB
brj-24141	275	7	2	2	NUM
brj-24141	275	8	(	(	PUNCT
brj-24141	275	9	https://universe.roboflow.com/laila-hammad-cxqz8/wood-jxhd5	https://universe.roboflow.com/laila-hammad-cxqz8/wood-jxhd5	NOUN
brj-24141	275	10	)	)	PUNCT
brj-24141	275	11	peer	peer	NOUN
brj-24141	275	12	-	-	PUNCT
brj-24141	275	13	reviewed	review	VERB
brj-24141	275	14	article	article	NOUN
brj-24141	275	15	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	275	16	li	li	PROPN
brj-24141	275	17	et	et	PROPN
brj-24141	275	18	al	al	PROPN
brj-24141	275	19	.	.	PROPN
brj-24141	276	1	(	(	PUNCT
brj-24141	276	2	2025	2025	NUM
brj-24141	276	3	)	)	PUNCT
brj-24141	276	4	.	.	PUNCT
brj-24141	277	1	“	"	PUNCT
brj-24141	277	2	improved	improved	ADJ
brj-24141	277	3	panel	panel	NOUN
brj-24141	277	4	defect	defect	NOUN
brj-24141	277	5	detection	detection	NOUN
brj-24141	277	6	,	,	PUNCT
brj-24141	277	7	”	"	PUNCT
brj-24141	277	8	bioresources	bioresource	NOUN
brj-24141	277	9	20(2	20(2	NUM
brj-24141	277	10	)	)	PUNCT
brj-24141	277	11	,	,	PUNCT
brj-24141	277	12	2556	2556	NUM
brj-24141	277	13	-	-	SYM
brj-24141	277	14	2573	2573	NUM
brj-24141	277	15	.	.	PUNCT
brj-24141	278	1	2571	2571	NUM
brj-24141	278	2	table	table	NOUN
brj-24141	278	3	4	4	NUM
brj-24141	278	4	.	.	PUNCT
brj-24141	278	5	comparison	comparison	NOUN
brj-24141	278	6	of	of	ADP
brj-24141	278	7	generalizability	generalizability	NOUN
brj-24141	278	8	across	across	ADP
brj-24141	278	9	dataset	dataset	NOUN
brj-24141	278	10	1	1	NUM
brj-24141	278	11	arithmetic	arithmetic	ADJ
brj-24141	278	12	precision/%	precision/%	X
brj-24141	278	13	recall/%	recall/%	ADV
brj-24141	278	14	map50/%	map50/%	PROPN
brj-24141	278	15	yolov8n	yolov8n	NOUN
brj-24141	278	16	47.3	47.3	NUM
brj-24141	278	17	45.8	45.8	NUM
brj-24141	278	18	46.1	46.1	NUM
brj-24141	278	19	yolov8n	yolov8n	PROPN
brj-24141	278	20	-	-	PUNCT
brj-24141	278	21	cdc	cdc	PROPN
brj-24141	278	22	53.3	53.3	NUM
brj-24141	278	23	45	45	NUM
brj-24141	278	24	48.2	48.2	NUM
brj-24141	278	25	table	table	NOUN
brj-24141	278	26	5	5	NUM
brj-24141	278	27	.	.	PUNCT
brj-24141	278	28	comparison	comparison	NOUN
brj-24141	278	29	of	of	ADP
brj-24141	278	30	generalizability	generalizability	NOUN
brj-24141	278	31	across	across	ADP
brj-24141	278	32	dataset	dataset	NOUN
brj-24141	278	33	2	2	NUM
brj-24141	278	34	arithmetic	arithmetic	ADJ
brj-24141	278	35	precision/%	precision/%	X
brj-24141	278	36	recall/%	recall/%	ADV
brj-24141	278	37	map50/%	map50/%	NOUN
brj-24141	278	38	yolov8n	yolov8n	NOUN
brj-24141	278	39	33.7	33.7	NUM
brj-24141	278	40	48.9	48.9	NUM
brj-24141	278	41	41.8	41.8	NUM
brj-24141	278	42	yolov8n	yolov8n	PROPN
brj-24141	278	43	-	-	PUNCT
brj-24141	278	44	cdc	cdc	PROPN
brj-24141	278	45	34.9	34.9	NUM
brj-24141	278	46	55.6	55.6	NUM
brj-24141	278	47	43.6	43.6	NUM
brj-24141	278	48	conclusions	conclusion	NOUN
brj-24141	278	49	1	1	NUM
brj-24141	278	50	.	.	PUNCT
brj-24141	278	51	to	to	PART
brj-24141	278	52	address	address	VERB
brj-24141	278	53	the	the	DET
brj-24141	278	54	challenges	challenge	NOUN
brj-24141	278	55	of	of	ADP
brj-24141	278	56	defect	defect	ADJ
brj-24141	278	57	detection	detection	NOUN
brj-24141	278	58	in	in	ADP
brj-24141	278	59	wood	wood	NOUN
brj-24141	278	60	panels	panel	NOUN
brj-24141	278	61	caused	cause	VERB
brj-24141	278	62	by	by	ADP
brj-24141	278	63	leakage	leakage	NOUN
brj-24141	278	64	and	and	CCONJ
brj-24141	278	65	misdetection	misdetection	NOUN
brj-24141	278	66	,	,	PUNCT
brj-24141	278	67	an	an	DET
brj-24141	278	68	improved	improved	ADJ
brj-24141	278	69	yolov8	yolov8	NOUN
brj-24141	278	70	-	-	PUNCT
brj-24141	278	71	based	base	VERB
brj-24141	278	72	model	model	NOUN
brj-24141	278	73	was	be	AUX
brj-24141	278	74	proposed	propose	VERB
brj-24141	278	75	in	in	ADP
brj-24141	278	76	this	this	DET
brj-24141	278	77	study	study	NOUN
brj-24141	278	78	.	.	PUNCT
brj-24141	279	1	extensive	extensive	ADJ
brj-24141	279	2	ablation	ablation	NOUN
brj-24141	279	3	experiments	experiment	NOUN
brj-24141	279	4	and	and	CCONJ
brj-24141	279	5	comparative	comparative	ADJ
brj-24141	279	6	evaluations	evaluation	NOUN
brj-24141	279	7	with	with	ADP
brj-24141	279	8	various	various	ADJ
brj-24141	279	9	existing	exist	VERB
brj-24141	279	10	models	model	NOUN
brj-24141	279	11	were	be	AUX
brj-24141	279	12	conducted	conduct	VERB
brj-24141	279	13	to	to	PART
brj-24141	279	14	effectively	effectively	ADV
brj-24141	279	15	mitigate	mitigate	VERB
brj-24141	279	16	these	these	DET
brj-24141	279	17	issues	issue	NOUN
brj-24141	279	18	.	.	PUNCT
brj-24141	280	1	2	2	X
brj-24141	280	2	.	.	X
brj-24141	280	3	the	the	DET
brj-24141	280	4	proposed	propose	VERB
brj-24141	280	5	model	model	NOUN
brj-24141	280	6	replaces	replace	VERB
brj-24141	280	7	the	the	DET
brj-24141	280	8	traditional	traditional	ADJ
brj-24141	280	9	downsampling	downsample	VERB
brj-24141	280	10	approach	approach	NOUN
brj-24141	280	11	in	in	ADP
brj-24141	280	12	the	the	DET
brj-24141	280	13	backbone	backbone	NOUN
brj-24141	280	14	network	network	NOUN
brj-24141	280	15	with	with	ADP
brj-24141	280	16	c	c	NOUN
brj-24141	280	17	-	-	PUNCT
brj-24141	280	18	adown	adown	NOUN
brj-24141	280	19	and	and	CCONJ
brj-24141	280	20	integrates	integrate	VERB
brj-24141	280	21	the	the	DET
brj-24141	280	22	dwr	dwr	NOUN
brj-24141	280	23	module	module	NOUN
brj-24141	280	24	,	,	PUNCT
brj-24141	280	25	which	which	PRON
brj-24141	280	26	combines	combine	VERB
brj-24141	280	27	the	the	DET
brj-24141	280	28	msda	msda	NOUN
brj-24141	280	29	null	null	ADJ
brj-24141	280	30	attention	attention	NOUN
brj-24141	280	31	mechanism	mechanism	NOUN
brj-24141	280	32	to	to	PART
brj-24141	280	33	enhance	enhance	VERB
brj-24141	280	34	the	the	DET
brj-24141	280	35	c2f	c2f	NOUN
brj-24141	280	36	and	and	CCONJ
brj-24141	280	37	bottleneck	bottleneck	NOUN
brj-24141	280	38	modules	module	NOUN
brj-24141	280	39	in	in	ADP
brj-24141	280	40	yolov8	yolov8	PROPN
brj-24141	280	41	.	.	PUNCT
brj-24141	281	1	this	this	DET
brj-24141	281	2	modification	modification	NOUN
brj-24141	281	3	allows	allow	VERB
brj-24141	281	4	for	for	ADP
brj-24141	281	5	improved	improved	ADJ
brj-24141	281	6	accuracy	accuracy	NOUN
brj-24141	281	7	in	in	ADP
brj-24141	281	8	detecting	detect	VERB
brj-24141	281	9	features	feature	NOUN
brj-24141	281	10	across	across	ADP
brj-24141	281	11	multiple	multiple	ADJ
brj-24141	281	12	scales	scale	NOUN
brj-24141	281	13	.	.	PUNCT
brj-24141	282	1	additionally	additionally	ADV
brj-24141	282	2	,	,	PUNCT
brj-24141	282	3	the	the	DET
brj-24141	282	4	neck	neck	NOUN
brj-24141	282	5	structure	structure	NOUN
brj-24141	282	6	is	be	AUX
brj-24141	282	7	enhanced	enhance	VERB
brj-24141	282	8	by	by	ADP
brj-24141	282	9	introducing	introduce	VERB
brj-24141	282	10	the	the	DET
brj-24141	282	11	ccff	ccff	NOUN
brj-24141	282	12	hybrid	hybrid	ADJ
brj-24141	282	13	coding	code	VERB
brj-24141	282	14	framework	framework	NOUN
brj-24141	282	15	,	,	PUNCT
brj-24141	282	16	which	which	PRON
brj-24141	282	17	facilitates	facilitate	VERB
brj-24141	282	18	intra	intra	ADJ
brj-24141	282	19	-	-	ADJ
brj-24141	282	20	scalar	scalar	ADJ
brj-24141	282	21	,	,	PUNCT
brj-24141	282	22	inter	inter	ADJ
brj-24141	282	23	-	-	ADJ
brj-24141	282	24	scalar	scalar	ADJ
brj-24141	282	25	,	,	PUNCT
brj-24141	282	26	and	and	CCONJ
brj-24141	282	27	cross	cross	ADJ
brj-24141	282	28	-	-	ADJ
brj-24141	282	29	scalar	scalar	ADJ
brj-24141	282	30	feature	feature	NOUN
brj-24141	282	31	interactions	interaction	NOUN
brj-24141	282	32	.	.	PUNCT
brj-24141	283	1	the	the	DET
brj-24141	283	2	loss	loss	NOUN
brj-24141	283	3	function	function	NOUN
brj-24141	283	4	is	be	AUX
brj-24141	283	5	also	also	ADV
brj-24141	283	6	refined	refine	VERB
brj-24141	283	7	to	to	PART
brj-24141	283	8	further	far	ADV
brj-24141	283	9	optimize	optimize	VERB
brj-24141	283	10	the	the	DET
brj-24141	283	11	model	model	NOUN
brj-24141	283	12	’s	’s	PART
brj-24141	283	13	performance	performance	NOUN
brj-24141	283	14	.	.	PUNCT
brj-24141	284	1	3	3	X
brj-24141	284	2	.	.	X
brj-24141	284	3	future	future	ADJ
brj-24141	284	4	research	research	NOUN
brj-24141	284	5	will	will	AUX
brj-24141	284	6	aim	aim	VERB
brj-24141	284	7	to	to	PART
brj-24141	284	8	address	address	VERB
brj-24141	284	9	the	the	DET
brj-24141	284	10	limitations	limitation	NOUN
brj-24141	284	11	of	of	ADP
brj-24141	284	12	the	the	DET
brj-24141	284	13	detection	detection	NOUN
brj-24141	284	14	head	head	NOUN
brj-24141	284	15	,	,	PUNCT
brj-24141	284	16	particularly	particularly	ADV
brj-24141	284	17	the	the	DET
brj-24141	284	18	risk	risk	NOUN
brj-24141	284	19	of	of	ADP
brj-24141	284	20	overfitting	overfitting	NOUN
brj-24141	284	21	that	that	PRON
brj-24141	284	22	arises	arise	VERB
brj-24141	284	23	from	from	ADP
brj-24141	284	24	the	the	DET
brj-24141	284	25	relatively	relatively	ADV
brj-24141	284	26	small	small	ADJ
brj-24141	284	27	dataset	dataset	NOUN
brj-24141	284	28	size	size	NOUN
brj-24141	284	29	,	,	PUNCT
brj-24141	284	30	which	which	PRON
brj-24141	284	31	may	may	AUX
brj-24141	284	32	hinder	hinder	VERB
brj-24141	284	33	the	the	DET
brj-24141	284	34	model	model	NOUN
brj-24141	284	35	’s	’s	PART
brj-24141	284	36	generalization	generalization	NOUN
brj-24141	284	37	ability	ability	NOUN
brj-24141	284	38	,	,	PUNCT
brj-24141	284	39	especially	especially	ADV
brj-24141	284	40	when	when	SCONJ
brj-24141	284	41	applied	apply	VERB
brj-24141	284	42	to	to	PART
brj-24141	284	43	diverse	diverse	VERB
brj-24141	284	44	or	or	CCONJ
brj-24141	284	45	unseen	unseen	ADJ
brj-24141	284	46	data	datum	NOUN
brj-24141	284	47	.	.	PUNCT
brj-24141	285	1	additionally	additionally	ADV
brj-24141	285	2	,	,	PUNCT
brj-24141	285	3	future	future	ADJ
brj-24141	285	4	work	work	NOUN
brj-24141	285	5	will	will	AUX
brj-24141	285	6	explore	explore	VERB
brj-24141	285	7	more	more	ADV
brj-24141	285	8	effective	effective	ADJ
brj-24141	285	9	feature	feature	NOUN
brj-24141	285	10	fusion	fusion	NOUN
brj-24141	285	11	techniques	technique	NOUN
brj-24141	285	12	,	,	PUNCT
brj-24141	285	13	with	with	ADP
brj-24141	285	14	a	a	DET
brj-24141	285	15	focus	focus	NOUN
brj-24141	285	16	on	on	ADP
brj-24141	285	17	improving	improve	VERB
brj-24141	285	18	the	the	DET
brj-24141	285	19	model	model	NOUN
brj-24141	285	20	’s	’s	PART
brj-24141	285	21	ability	ability	NOUN
brj-24141	285	22	to	to	PART
brj-24141	285	23	detect	detect	VERB
brj-24141	285	24	small	small	ADJ
brj-24141	285	25	and	and	CCONJ
brj-24141	285	26	densely	densely	ADV
brj-24141	285	27	packed	pack	VERB
brj-24141	285	28	targets	target	NOUN
brj-24141	285	29	.	.	PUNCT
brj-24141	286	1	furthermore	furthermore	ADV
brj-24141	286	2	,	,	PUNCT
brj-24141	286	3	model	model	NOUN
brj-24141	286	4	compression	compression	NOUN
brj-24141	286	5	strategies	strategy	NOUN
brj-24141	286	6	,	,	PUNCT
brj-24141	286	7	such	such	ADJ
brj-24141	286	8	as	as	ADP
brj-24141	286	9	pruning	prune	VERB
brj-24141	286	10	and	and	CCONJ
brj-24141	286	11	distillation	distillation	NOUN
brj-24141	286	12	,	,	PUNCT
brj-24141	286	13	will	will	AUX
brj-24141	286	14	be	be	AUX
brj-24141	286	15	investigated	investigate	VERB
brj-24141	286	16	to	to	PART
brj-24141	286	17	enable	enable	VERB
brj-24141	286	18	real	real	ADJ
brj-24141	286	19	-	-	PUNCT
brj-24141	286	20	time	time	NOUN
brj-24141	286	21	deployment	deployment	NOUN
brj-24141	286	22	on	on	ADP
brj-24141	286	23	embedded	embed	VERB
brj-24141	286	24	devices	device	NOUN
brj-24141	286	25	.	.	PUNCT
brj-24141	287	1	however	however	ADV
brj-24141	287	2	,	,	PUNCT
brj-24141	287	3	the	the	DET
brj-24141	287	4	potential	potential	ADJ
brj-24141	287	5	trade	trade	NOUN
brj-24141	287	6	-	-	PUNCT
brj-24141	287	7	offs	off	NOUN
brj-24141	287	8	in	in	ADP
brj-24141	287	9	model	model	NOUN
brj-24141	287	10	accuracy	accuracy	NOUN
brj-24141	287	11	and	and	CCONJ
brj-24141	287	12	robustness	robustness	NOUN
brj-24141	287	13	will	will	AUX
brj-24141	287	14	be	be	AUX
brj-24141	287	15	carefully	carefully	ADV
brj-24141	287	16	evaluated	evaluate	VERB
brj-24141	287	17	,	,	PUNCT
brj-24141	287	18	as	as	SCONJ
brj-24141	287	19	these	these	DET
brj-24141	287	20	compression	compression	NOUN
brj-24141	287	21	techniques	technique	NOUN
brj-24141	287	22	could	could	AUX
brj-24141	287	23	compromise	compromise	VERB
brj-24141	287	24	the	the	DET
brj-24141	287	25	overall	overall	ADJ
brj-24141	287	26	performance	performance	NOUN
brj-24141	287	27	.	.	PUNCT
brj-24141	288	1	4	4	X
brj-24141	288	2	.	.	X
brj-24141	288	3	a	a	DET
brj-24141	288	4	critical	critical	ADJ
brj-24141	288	5	aspect	aspect	NOUN
brj-24141	288	6	of	of	ADP
brj-24141	288	7	the	the	DET
brj-24141	288	8	model	model	NOUN
brj-24141	288	9	’s	’s	PART
brj-24141	288	10	future	future	ADJ
brj-24141	288	11	development	development	NOUN
brj-24141	288	12	will	will	AUX
brj-24141	288	13	be	be	AUX
brj-24141	288	14	its	its	PRON
brj-24141	288	15	generalizability	generalizability	NOUN
brj-24141	288	16	across	across	ADP
brj-24141	288	17	various	various	ADJ
brj-24141	288	18	industrial	industrial	ADJ
brj-24141	288	19	domains	domain	NOUN
brj-24141	288	20	.	.	PUNCT
brj-24141	289	1	although	although	SCONJ
brj-24141	289	2	the	the	DET
brj-24141	289	3	current	current	ADJ
brj-24141	289	4	improvements	improvement	NOUN
brj-24141	289	5	provide	provide	VERB
brj-24141	289	6	promising	promising	ADJ
brj-24141	289	7	results	result	NOUN
brj-24141	289	8	for	for	ADP
brj-24141	289	9	wood	wood	NOUN
brj-24141	289	10	panel	panel	NOUN
brj-24141	289	11	defect	defect	NOUN
brj-24141	289	12	detection	detection	NOUN
brj-24141	289	13	,	,	PUNCT
brj-24141	289	14	the	the	DET
brj-24141	289	15	scalability	scalability	NOUN
brj-24141	289	16	of	of	ADP
brj-24141	289	17	the	the	DET
brj-24141	289	18	model	model	NOUN
brj-24141	289	19	to	to	ADP
brj-24141	289	20	other	other	ADJ
brj-24141	289	21	materials	material	NOUN
brj-24141	289	22	and	and	CCONJ
brj-24141	289	23	industries	industry	NOUN
brj-24141	289	24	will	will	AUX
brj-24141	289	25	require	require	VERB
brj-24141	289	26	thorough	thorough	ADJ
brj-24141	289	27	validation	validation	NOUN
brj-24141	289	28	.	.	PUNCT
brj-24141	290	1	the	the	DET
brj-24141	290	2	model	model	NOUN
brj-24141	290	3	’s	’s	PART
brj-24141	290	4	robustness	robustness	NOUN
brj-24141	290	5	in	in	ADP
brj-24141	290	6	handling	handle	VERB
brj-24141	290	7	data	datum	NOUN
brj-24141	290	8	from	from	ADP
brj-24141	290	9	different	different	ADJ
brj-24141	290	10	sources	source	NOUN
brj-24141	290	11	,	,	PUNCT
brj-24141	290	12	variations	variation	NOUN
brj-24141	290	13	in	in	ADP
brj-24141	290	14	environmental	environmental	ADJ
brj-24141	290	15	conditions	condition	NOUN
brj-24141	290	16	,	,	PUNCT
brj-24141	290	17	and	and	CCONJ
brj-24141	290	18	diverse	diverse	ADJ
brj-24141	290	19	defect	defect	NOUN
brj-24141	290	20	types	type	NOUN
brj-24141	290	21	needs	need	VERB
brj-24141	290	22	to	to	PART
brj-24141	290	23	be	be	AUX
brj-24141	290	24	systematically	systematically	ADV
brj-24141	290	25	assessed	assess	VERB
brj-24141	290	26	.	.	PUNCT
brj-24141	291	1	this	this	PRON
brj-24141	291	2	includes	include	VERB
brj-24141	291	3	extending	extend	VERB
brj-24141	291	4	its	its	PRON
brj-24141	291	5	application	application	NOUN
brj-24141	291	6	to	to	ADP
brj-24141	291	7	industries	industry	NOUN
brj-24141	291	8	such	such	ADJ
brj-24141	291	9	as	as	ADP
brj-24141	291	10	construction	construction	NOUN
brj-24141	291	11	,	,	PUNCT
brj-24141	291	12	furniture	furniture	NOUN
brj-24141	291	13	manufacturing	manufacturing	NOUN
brj-24141	291	14	,	,	PUNCT
brj-24141	291	15	and	and	CCONJ
brj-24141	291	16	packaging	packaging	NOUN
brj-24141	291	17	,	,	PUNCT
brj-24141	291	18	where	where	SCONJ
brj-24141	291	19	similar	similar	ADJ
brj-24141	291	20	challenges	challenge	NOUN
brj-24141	291	21	in	in	ADP
brj-24141	291	22	defect	defect	NOUN
brj-24141	291	23	detection	detection	NOUN
brj-24141	291	24	and	and	CCONJ
brj-24141	291	25	quality	quality	NOUN
brj-24141	291	26	control	control	NOUN
brj-24141	291	27	exist	exist	VERB
brj-24141	291	28	.	.	PUNCT
brj-24141	292	1	further	further	ADJ
brj-24141	292	2	refinement	refinement	NOUN
brj-24141	292	3	of	of	ADP
brj-24141	292	4	the	the	DET
brj-24141	292	5	model	model	NOUN
brj-24141	292	6	,	,	PUNCT
brj-24141	292	7	including	include	VERB
brj-24141	292	8	addressing	address	VERB
brj-24141	292	9	its	its	PRON
brj-24141	292	10	sensitivity	sensitivity	NOUN
brj-24141	292	11	to	to	ADP
brj-24141	292	12	data	datum	NOUN
brj-24141	292	13	variation	variation	NOUN
brj-24141	292	14	and	and	CCONJ
brj-24141	292	15	incorporating	incorporate	VERB
brj-24141	292	16	domain	domain	NOUN
brj-24141	292	17	adaptation	adaptation	NOUN
brj-24141	292	18	techniques	technique	NOUN
brj-24141	292	19	,	,	PUNCT
brj-24141	292	20	will	will	AUX
brj-24141	292	21	be	be	AUX
brj-24141	292	22	essential	essential	ADJ
brj-24141	292	23	to	to	PART
brj-24141	292	24	ensure	ensure	VERB
brj-24141	292	25	its	its	PRON
brj-24141	292	26	broad	broad	ADJ
brj-24141	292	27	applicability	applicability	NOUN
brj-24141	292	28	and	and	CCONJ
brj-24141	292	29	reliable	reliable	ADJ
brj-24141	292	30	performance	performance	NOUN
brj-24141	292	31	across	across	ADP
brj-24141	292	32	diverse	diverse	ADJ
brj-24141	292	33	operational	operational	ADJ
brj-24141	292	34	contexts	contexts	NOUN
brj-24141	292	35	.	.	PUNCT
brj-24141	293	1	peer	peer	NOUN
brj-24141	293	2	-	-	PUNCT
brj-24141	293	3	reviewed	review	VERB
brj-24141	293	4	article	article	NOUN
brj-24141	293	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	293	6	li	li	PROPN
brj-24141	293	7	et	et	PROPN
brj-24141	293	8	al	al	PROPN
brj-24141	293	9	.	.	PROPN
brj-24141	294	1	(	(	PUNCT
brj-24141	294	2	2025	2025	NUM
brj-24141	294	3	)	)	PUNCT
brj-24141	294	4	.	.	PUNCT
brj-24141	295	1	“	"	PUNCT
brj-24141	295	2	improved	improved	ADJ
brj-24141	295	3	panel	panel	NOUN
brj-24141	295	4	defect	defect	NOUN
brj-24141	295	5	detection	detection	NOUN
brj-24141	295	6	,	,	PUNCT
brj-24141	295	7	”	"	PUNCT
brj-24141	295	8	bioresources	bioresource	NOUN
brj-24141	295	9	20(2	20(2	NUM
brj-24141	295	10	)	)	PUNCT
brj-24141	295	11	,	,	PUNCT
brj-24141	295	12	2556	2556	NUM
brj-24141	295	13	-	-	SYM
brj-24141	295	14	2573	2573	NUM
brj-24141	295	15	.	.	PUNCT
brj-24141	296	1	2572	2572	NUM
brj-24141	296	2	references	reference	NOUN
brj-24141	296	3	cited	cite	VERB
brj-24141	296	4	cheng	cheng	PROPN
brj-24141	296	5	,	,	PUNCT
brj-24141	296	6	d.	d.	PROPN
brj-24141	296	7	(	(	PUNCT
brj-24141	296	8	2023	2023	NUM
brj-24141	296	9	)	)	PUNCT
brj-24141	296	10	.	.	PUNCT
brj-24141	297	1	research	research	NOUN
brj-24141	297	2	and	and	CCONJ
brj-24141	297	3	application	application	NOUN
brj-24141	297	4	of	of	ADP
brj-24141	297	5	deep	deep	ADJ
brj-24141	297	6	learning	learning	NOUN
brj-24141	297	7	based	base	VERB
brj-24141	297	8	wood	wood	NOUN
brj-24141	297	9	surface	surface	NOUN
brj-24141	297	10	defect	defect	NOUN
brj-24141	297	11	detection	detection	NOUN
brj-24141	297	12	,	,	PUNCT
brj-24141	297	13	qilu	qilu	PROPN
brj-24141	297	14	univ	univ	PROPN
brj-24141	297	15	.	.	PUNCT
brj-24141	298	1	technology	technology	NOUN
brj-24141	298	2	.	.	PUNCT
brj-24141	299	1	doi	doi	NOUN
brj-24141	299	2	:	:	PUNCT
brj-24141	299	3	10.27278	10.27278	NUM
brj-24141	299	4	/	/	SYM
brj-24141	299	5	d.cnki.gsdqc.2023.000196	d.cnki.gsdqc.2023.000196	ADJ
brj-24141	299	6	cheng	cheng	PROPN
brj-24141	299	7	,	,	PUNCT
brj-24141	299	8	r.	r.	PROPN
brj-24141	299	9	(	(	PUNCT
brj-24141	299	10	2020	2020	NUM
brj-24141	299	11	)	)	PUNCT
brj-24141	299	12	.	.	PUNCT
brj-24141	300	1	“	"	PUNCT
brj-24141	300	2	the	the	DET
brj-24141	300	3	influence	influence	NOUN
brj-24141	300	4	of	of	ADP
brj-24141	300	5	market	market	NOUN
brj-24141	300	6	orientation	orientation	NOUN
brj-24141	300	7	on	on	ADP
brj-24141	300	8	the	the	DET
brj-24141	300	9	development	development	NOUN
brj-24141	300	10	of	of	ADP
brj-24141	300	11	wood	wood	NOUN
brj-24141	300	12	processing	processing	NOUN
brj-24141	300	13	industry	industry	NOUN
brj-24141	300	14	and	and	CCONJ
brj-24141	300	15	countermeasures	countermeasure	NOUN
brj-24141	300	16	,	,	PUNCT
brj-24141	300	17	”	"	PUNCT
brj-24141	300	18	china	china	PROPN
brj-24141	300	19	forest	forest	PROPN
brj-24141	300	20	products	product	NOUN
brj-24141	300	21	industry	industry	NOUN
brj-24141	300	22	57(11	57(11	NUM
brj-24141	300	23	)	)	PUNCT
brj-24141	300	24	,	,	PUNCT
brj-24141	300	25	88	88	NUM
brj-24141	300	26	-	-	SYM
brj-24141	300	27	89	89	NUM
brj-24141	300	28	+	+	NOUN
brj-24141	300	29	92	92	NUM
brj-24141	300	30	.	.	PUNCT
brj-24141	301	1	doi	doi	NOUN
brj-24141	301	2	:	:	PUNCT
brj-24141	301	3	10.19531	10.19531	NUM
brj-24141	301	4	/	/	SYM
brj-24141	301	5	j.issn1001	j.issn1001	NOUN
brj-24141	301	6	-	-	PUNCT
brj-24141	301	7	5299.202011022	5299.202011022	NOUN
brj-24141	301	8	buehlmann	buehlmann	NOUN
brj-24141	301	9	,	,	PUNCT
brj-24141	301	10	u.	u.	PROPN
brj-24141	301	11	,	,	PUNCT
brj-24141	301	12	and	and	CCONJ
brj-24141	301	13	thomas	thomas	PROPN
brj-24141	301	14	,	,	PUNCT
brj-24141	301	15	r.	r.	PROPN
brj-24141	301	16	e.	e.	PROPN
brj-24141	301	17	(	(	PUNCT
brj-24141	301	18	2002	2002	NUM
brj-24141	301	19	)	)	PUNCT
brj-24141	301	20	,	,	PUNCT
brj-24141	301	21	“	"	PUNCT
brj-24141	301	22	impact	impact	NOUN
brj-24141	301	23	of	of	ADP
brj-24141	301	24	human	human	ADJ
brj-24141	301	25	error	error	NOUN
brj-24141	301	26	on	on	ADP
brj-24141	301	27	lumber	lumber	NOUN
brj-24141	301	28	yield	yield	NOUN
brj-24141	301	29	in	in	ADP
brj-24141	301	30	rough	rough	ADJ
brj-24141	301	31	mills	mill	NOUN
brj-24141	301	32	,	,	PUNCT
brj-24141	301	33	”	"	PUNCT
brj-24141	301	34	robotics	robotic	NOUN
brj-24141	301	35	and	and	CCONJ
brj-24141	301	36	computer	computer	NOUN
brj-24141	301	37	-	-	PUNCT
brj-24141	301	38	integrated	integrate	VERB
brj-24141	301	39	manufacturing	manufacturing	NOUN
brj-24141	301	40	,	,	PUNCT
brj-24141	301	41	18(3	18(3	NUM
brj-24141	301	42	-	-	SYM
brj-24141	301	43	4	4	NUM
brj-24141	301	44	)	)	PUNCT
brj-24141	301	45	,	,	PUNCT
brj-24141	301	46	197	197	NUM
brj-24141	301	47	-	-	SYM
brj-24141	301	48	203	203	NUM
brj-24141	301	49	.	.	PUNCT
brj-24141	302	1	doi	doi	NOUN
brj-24141	302	2	:	:	PUNCT
brj-24141	302	3	10.1016	10.1016	NUM
brj-24141	302	4	/	/	SYM
brj-24141	302	5	s0736	s0736	PROPN
brj-24141	302	6	-	-	PUNCT
brj-24141	302	7	5845(02)00010	5845(02)00010	NUM
brj-24141	302	8	-	-	SYM
brj-24141	302	9	8	8	NUM
brj-24141	302	10	jia	jia	PROPN
brj-24141	302	11	,	,	PUNCT
brj-24141	302	12	h.	h.	PROPN
brj-24141	302	13	,	,	PUNCT
brj-24141	302	14	xu	xu	PROPN
brj-24141	302	15	,	,	PUNCT
brj-24141	302	16	h.	h.	PROPN
brj-24141	302	17	,	,	PUNCT
brj-24141	302	18	wang	wang	PROPN
brj-24141	302	19	,	,	PUNCT
brj-24141	302	20	l.	l.	PROPN
brj-24141	302	21	,	,	PUNCT
brj-24141	302	22	zhang	zhang	PROPN
brj-24141	302	23	,	,	PUNCT
brj-24141	302	24	j.	j.	PROPN
brj-24141	302	25	,	,	PUNCT
brj-24141	302	26	chu	chu	PROPN
brj-24141	302	27	,	,	PUNCT
brj-24141	302	28	x.	x.	NOUN
brj-24141	302	29	,	,	PUNCT
brj-24141	302	30	and	and	CCONJ
brj-24141	302	31	tang	tang	PROPN
brj-24141	302	32	,	,	PUNCT
brj-24141	302	33	x.	x.	NOUN
brj-24141	302	34	(	(	PUNCT
brj-24141	302	35	2023	2023	NUM
brj-24141	302	36	)	)	PUNCT
brj-24141	302	37	.	.	PUNCT
brj-24141	303	1	“	"	PUNCT
brj-24141	303	2	quantitative	quantitative	ADJ
brj-24141	303	3	identification	identification	NOUN
brj-24141	303	4	of	of	ADP
brj-24141	303	5	surface	surface	NOUN
brj-24141	303	6	defects	defect	NOUN
brj-24141	303	7	in	in	ADP
brj-24141	303	8	wood	wood	NOUN
brj-24141	303	9	lumber	lumber	NOUN
brj-24141	303	10	based	base	VERB
brj-24141	303	11	on	on	ADP
brj-24141	303	12	improved	improved	ADJ
brj-24141	303	13	yolov5	yolov5	NOUN
brj-24141	303	14	,	,	PUNCT
brj-24141	303	15	”	"	PUNCT
brj-24141	303	16	j.	j.	PROPN
brj-24141	303	17	beijing	beijing	PROPN
brj-24141	303	18	forestry	forestry	PROPN
brj-24141	303	19	university	university	PROPN
brj-24141	303	20	45(04	45(04	PROPN
brj-24141	303	21	)	)	PUNCT
brj-24141	303	22	,	,	PUNCT
brj-24141	303	23	147	147	NUM
brj-24141	303	24	-	-	SYM
brj-24141	303	25	155	155	NUM
brj-24141	303	26	.	.	PUNCT
brj-24141	304	1	doi	doi	NOUN
brj-24141	304	2	:	:	PUNCT
brj-24141	304	3	10.12171	10.12171	NUM
brj-24141	304	4	/	/	SYM
brj-24141	304	5	j.1000−1522.20220419	j.1000−1522.20220419	X
brj-24141	304	6	jiang	jiang	PROPN
brj-24141	304	7	,	,	PUNCT
brj-24141	304	8	x.	x.	NOUN
brj-24141	304	9	,	,	PUNCT
brj-24141	304	10	and	and	CCONJ
brj-24141	304	11	zhao	zhao	NOUN
brj-24141	304	12	,	,	PUNCT
brj-24141	304	13	x.	x.	NOUN
brj-24141	304	14	(	(	PUNCT
brj-24141	304	15	2024	2024	NUM
brj-24141	304	16	)	)	PUNCT
brj-24141	304	17	.	.	PUNCT
brj-24141	305	1	“	"	PUNCT
brj-24141	305	2	improved	improve	VERB
brj-24141	305	3	yolov7	yolov7	NOUN
brj-24141	305	4	algorithm	algorithm	NOUN
brj-24141	305	5	for	for	ADP
brj-24141	305	6	wood	wood	NOUN
brj-24141	305	7	surface	surface	NOUN
brj-24141	305	8	defect	defect	NOUN
brj-24141	305	9	detection	detection	NOUN
brj-24141	305	10	,	,	PUNCT
brj-24141	305	11	”	"	PUNCT
brj-24141	305	12	computer	computer	NOUN
brj-24141	305	13	engineering	engineering	NOUN
brj-24141	305	14	and	and	CCONJ
brj-24141	305	15	applications	application	NOUN
brj-24141	305	16	60(07	60(07	NOUN
brj-24141	305	17	)	)	PUNCT
brj-24141	305	18	,	,	PUNCT
brj-24141	305	19	175	175	NUM
brj-24141	305	20	-	-	SYM
brj-24141	305	21	182	182	NUM
brj-24141	305	22	.	.	PUNCT
brj-24141	305	23	doi	doi	NOUN
brj-24141	305	24	:	:	PUNCT
brj-24141	305	25	10.3778	10.3778	NUM
brj-24141	305	26	/	/	SYM
brj-24141	305	27	j.issn.1002	j.issn.1002	ADV
brj-24141	305	28	-	-	PUNCT
brj-24141	305	29	8331.2309	8331.2309	NUM
brj-24141	305	30	-	-	PUNCT
brj-24141	305	31	0185	0185	NUM
brj-24141	305	32	jiao	jiao	PROPN
brj-24141	305	33	,	,	PUNCT
brj-24141	305	34	j.	j.	PROPN
brj-24141	305	35	,	,	PUNCT
brj-24141	305	36	y.-m	y.-m	PROPN
brj-24141	305	37	.	.	PUNCT
brj-24141	305	38	tang	tang	PROPN
brj-24141	305	39	,	,	PUNCT
brj-24141	305	40	k.-y	k.-y	NOUN
brj-24141	305	41	.	.	PUNCT
brj-24141	306	1	lin	lin	PROPN
brj-24141	306	2	,	,	PUNCT
brj-24141	306	3	y.	y.	PROPN
brj-24141	306	4	gao	gao	PROPN
brj-24141	306	5	,	,	PUNCT
brj-24141	306	6	a.	a.	PROPN
brj-24141	306	7	j.	j.	PROPN
brj-24141	306	8	ma	ma	PROPN
brj-24141	306	9	,	,	PUNCT
brj-24141	306	10	y.	y.	PROPN
brj-24141	306	11	wang	wang	PROPN
brj-24141	306	12	,	,	PUNCT
brj-24141	306	13	and	and	CCONJ
brj-24141	306	14	w.-s	w.-	NOUN
brj-24141	306	15	.	.	PUNCT
brj-24141	307	1	zheng	zheng	PROPN
brj-24141	307	2	.	.	PUNCT
brj-24141	308	1	(	(	PUNCT
brj-24141	308	2	2023	2023	NUM
brj-24141	308	3	)	)	PUNCT
brj-24141	308	4	.	.	PUNCT
brj-24141	309	1	“	"	PUNCT
brj-24141	309	2	dilateformer	dilateformer	NOUN
brj-24141	309	3	:	:	PUNCT
brj-24141	309	4	multi	multi	ADJ
brj-24141	309	5	-	-	ADJ
brj-24141	309	6	scale	scale	ADJ
brj-24141	309	7	dilated	dilated	ADJ
brj-24141	309	8	transformer	transformer	NOUN
brj-24141	309	9	for	for	ADP
brj-24141	309	10	visual	visual	ADJ
brj-24141	309	11	recognition	recognition	NOUN
brj-24141	309	12	,	,	PUNCT
brj-24141	309	13	”	"	PUNCT
brj-24141	309	14	ieee	ieee	NOUN
brj-24141	309	15	transactions	transaction	NOUN
brj-24141	309	16	on	on	ADP
brj-24141	309	17	multimedia	multimedia	NOUN
brj-24141	309	18	25	25	NUM
brj-24141	309	19	,	,	PUNCT
brj-24141	309	20	8906	8906	NUM
brj-24141	309	21	-	-	SYM
brj-24141	309	22	8919	8919	NUM
brj-24141	309	23	.	.	PUNCT
brj-24141	310	1	doi	doi	NOUN
brj-24141	310	2	:	:	PUNCT
brj-24141	310	3	10.1109	10.1109	NUM
brj-24141	310	4	/	/	SYM
brj-24141	310	5	tmm.2023.3243616	tmm.2023.3243616	NUM
brj-24141	310	6	liu	liu	PROPN
brj-24141	310	7	,	,	PUNCT
brj-24141	310	8	q.	q.	PROPN
brj-24141	310	9	,	,	PUNCT
brj-24141	310	10	yuan	yuan	PROPN
brj-24141	310	11	,	,	PUNCT
brj-24141	310	12	y.	y.	PROPN
brj-24141	310	13	,	,	PUNCT
brj-24141	310	14	xia	xia	PROPN
brj-24141	310	15	,	,	PUNCT
brj-24141	310	16	x.	x.	NOUN
brj-24141	310	17	,	,	PUNCT
brj-24141	310	18	si	si	PROPN
brj-24141	310	19	,	,	PUNCT
brj-24141	310	20	l.	l.	PROPN
brj-24141	310	21	,	,	PUNCT
brj-24141	310	22	and	and	CCONJ
brj-24141	310	23	duo	duo	NOUN
brj-24141	310	24	,	,	PUNCT
brj-24141	310	25	h.	h.	PROPN
brj-24141	310	26	(	(	PUNCT
brj-24141	310	27	2023	2023	NUM
brj-24141	310	28	)	)	PUNCT
brj-24141	310	29	.	.	PUNCT
brj-24141	311	1	“	"	PUNCT
brj-24141	311	2	research	research	NOUN
brj-24141	311	3	advances	advance	NOUN
brj-24141	311	4	in	in	ADP
brj-24141	311	5	wood	wood	NOUN
brj-24141	311	6	defect	defect	NOUN
brj-24141	311	7	detection	detection	NOUN
brj-24141	311	8	based	base	VERB
brj-24141	311	9	on	on	ADP
brj-24141	311	10	artificial	artificial	ADJ
brj-24141	311	11	intelligence	intelligence	NOUN
brj-24141	311	12	,	,	PUNCT
brj-24141	311	13	”	"	PUNCT
brj-24141	311	14	world	world	NOUN
brj-24141	311	15	forestry	forestry	PROPN
brj-24141	311	16	research	research	PROPN
brj-24141	311	17	36(01	36(01	NUM
brj-24141	311	18	)	)	PUNCT
brj-24141	311	19	,	,	PUNCT
brj-24141	311	20	66	66	NUM
brj-24141	311	21	-	-	SYM
brj-24141	311	22	71	71	NUM
brj-24141	311	23	.	.	PUNCT
brj-24141	312	1	doi	doi	NOUN
brj-24141	312	2	:	:	PUNCT
brj-24141	312	3	10.13348	10.13348	NUM
brj-24141	312	4	/	/	SYM
brj-24141	312	5	j.cnki.sjlyyj.2022.0101.y	j.cnki.sjlyyj.2022.0101.y	PROPN
brj-24141	312	6	lv	lv	PROPN
brj-24141	312	7	,	,	PUNCT
brj-24141	312	8	w.	w.	PROPN
brj-24141	312	9	,	,	PUNCT
brj-24141	312	10	y.	y.	PROPN
brj-24141	312	11	zhao	zhao	PROPN
brj-24141	312	12	,	,	PUNCT
brj-24141	312	13	q.	q.	PROPN
brj-24141	312	14	chang	chang	PROPN
brj-24141	312	15	,	,	PUNCT
brj-24141	312	16	k.	k.	PROPN
brj-24141	312	17	huang	huang	PROPN
brj-24141	312	18	,	,	PUNCT
brj-24141	312	19	g.	g.	PROPN
brj-24141	312	20	wang	wang	PROPN
brj-24141	312	21	,	,	PUNCT
brj-24141	312	22	and	and	CCONJ
brj-24141	312	23	y.	y.	PROPN
brj-24141	312	24	liu	liu	PROPN
brj-24141	312	25	(	(	PUNCT
brj-24141	312	26	2024	2024	NUM
brj-24141	312	27	)	)	PUNCT
brj-24141	312	28	.	.	PUNCT
brj-24141	313	1	“	"	PUNCT
brj-24141	313	2	rt	rt	NOUN
brj-24141	313	3	-	-	PUNCT
brj-24141	313	4	detrv2	detrv2	PROPN
brj-24141	313	5	:	:	PUNCT
brj-24141	313	6	improved	improve	VERB
brj-24141	313	7	baseline	baseline	NOUN
brj-24141	313	8	with	with	ADP
brj-24141	313	9	bag	bag	NOUN
brj-24141	313	10	-	-	PUNCT
brj-24141	313	11	of	of	ADP
brj-24141	313	12	-	-	PUNCT
brj-24141	313	13	freebies	freebie	NOUN
brj-24141	313	14	for	for	ADP
brj-24141	313	15	real	real	ADJ
brj-24141	313	16	-	-	PUNCT
brj-24141	313	17	time	time	NOUN
brj-24141	313	18	detection	detection	NOUN
brj-24141	313	19	transformer	transformer	NOUN
brj-24141	313	20	,	,	PUNCT
brj-24141	313	21	”	"	PUNCT
brj-24141	313	22	arxiv	arxiv	PROPN
brj-24141	313	23	preprint	preprint	NOUN
brj-24141	313	24	arxiv:2407.17140	arxiv:2407.17140	NOUN
brj-24141	313	25	.	.	PUNCT
brj-24141	314	1	doi	doi	NOUN
brj-24141	314	2	:	:	PUNCT
brj-24141	314	3	10.48550	10.48550	NUM
brj-24141	314	4	/	/	SYM
brj-24141	314	5	arxiv.2407.17140	arxiv.2407.17140	PROPN
brj-24141	314	6	nie	nie	PROPN
brj-24141	314	7	,	,	PUNCT
brj-24141	314	8	f.	f.	PROPN
brj-24141	314	9	,	,	PUNCT
brj-24141	314	10	li	li	PROPN
brj-24141	314	11	,	,	PUNCT
brj-24141	314	12	m.	m.	NOUN
brj-24141	314	13	,	,	PUNCT
brj-24141	314	14	zhou	zhou	PROPN
brj-24141	314	15	,	,	PUNCT
brj-24141	314	16	m.	m.	NOUN
brj-24141	314	17	,	,	PUNCT
brj-24141	314	18	dong	dong	PROPN
brj-24141	314	19	,	,	PUNCT
brj-24141	314	20	y.	y.	PROPN
brj-24141	314	21	,	,	PUNCT
brj-24141	314	22	li	li	PROPN
brj-24141	314	23	,	,	PUNCT
brj-24141	314	24	z.	z.	PROPN
brj-24141	314	25	,	,	PUNCT
brj-24141	314	26	and	and	CCONJ
brj-24141	314	27	li	li	PROPN
brj-24141	314	28	,	,	PUNCT
brj-24141	314	29	l.	l.	PROPN
brj-24141	314	30	(	(	PUNCT
brj-24141	314	31	2024	2024	NUM
brj-24141	314	32	)	)	PUNCT
brj-24141	314	33	.	.	PUNCT
brj-24141	315	1	“	"	PUNCT
brj-24141	315	2	multiscale	multiscale	ADJ
brj-24141	315	3	dilated	dilate	VERB
brj-24141	315	4	u	u	NOUN
brj-24141	315	5	-	-	ADJ
brj-24141	315	6	net	net	ADJ
brj-24141	315	7	based	base	VERB
brj-24141	315	8	multifocus	multifocus	ADJ
brj-24141	315	9	image	image	NOUN
brj-24141	315	10	fusion	fusion	NOUN
brj-24141	315	11	algorithm	algorithm	NOUN
brj-24141	315	12	,	,	PUNCT
brj-24141	315	13	”	"	PUNCT
brj-24141	315	14	laser	laser	NOUN
brj-24141	315	15	&	&	CCONJ
brj-24141	315	16	optoelectronics	optoelectronic	NOUN
brj-24141	315	17	progress	progress	VERB
brj-24141	315	18	61(14	61(14	PROPN
brj-24141	315	19	)	)	PUNCT
brj-24141	315	20	,	,	PUNCT
brj-24141	315	21	447	447	NUM
brj-24141	315	22	-	-	NOUN
brj-24141	315	23	456	456	NUM
brj-24141	315	24	.	.	PUNCT
brj-24141	316	1	doi	doi	NOUN
brj-24141	316	2	:	:	PUNCT
brj-24141	316	3	10.3788	10.3788	NUM
brj-24141	316	4	/	/	SYM
brj-24141	316	5	lop232443	lop232443	NOUN
brj-24141	316	6	saito	saito	PROPN
brj-24141	316	7	,	,	PUNCT
brj-24141	316	8	k.	k.	PROPN
brj-24141	316	9	,	,	PUNCT
brj-24141	316	10	y.	y.	PROPN
brj-24141	316	11	ushiku	ushiku	PROPN
brj-24141	316	12	,	,	PUNCT
brj-24141	316	13	t.	t.	PROPN
brj-24141	316	14	harada	harada	PROPN
brj-24141	316	15	,	,	PUNCT
brj-24141	316	16	and	and	CCONJ
brj-24141	316	17	k.	k.	PROPN
brj-24141	316	18	saenko	saenko	PROPN
brj-24141	316	19	(	(	PUNCT
brj-24141	316	20	2019	2019	NUM
brj-24141	316	21	)	)	PUNCT
brj-24141	316	22	.	.	PUNCT
brj-24141	317	1	“	"	PUNCT
brj-24141	317	2	strong	strong	ADJ
brj-24141	317	3	-	-	PUNCT
brj-24141	317	4	weak	weak	ADJ
brj-24141	317	5	distribution	distribution	NOUN
brj-24141	317	6	alignment	alignment	NOUN
brj-24141	317	7	for	for	ADP
brj-24141	317	8	adaptive	adaptive	ADJ
brj-24141	317	9	object	object	NOUN
brj-24141	317	10	detection	detection	NOUN
brj-24141	317	11	,	,	PUNCT
brj-24141	317	12	”	"	PUNCT
brj-24141	317	13	in	in	ADP
brj-24141	317	14	:	:	PUNCT
brj-24141	317	15	proceedings	proceeding	NOUN
brj-24141	317	16	of	of	ADP
brj-24141	317	17	the	the	DET
brj-24141	317	18	ieee	ieee	NOUN
brj-24141	317	19	/	/	SYM
brj-24141	317	20	cvf	cvf	NOUN
brj-24141	317	21	conference	conference	NOUN
brj-24141	317	22	on	on	ADP
brj-24141	317	23	computer	computer	NOUN
brj-24141	317	24	vision	vision	NOUN
brj-24141	317	25	and	and	CCONJ
brj-24141	317	26	pattern	pattern	NOUN
brj-24141	317	27	recognition	recognition	NOUN
brj-24141	317	28	,	,	PUNCT
brj-24141	317	29	pp	pp	PROPN
brj-24141	317	30	.	.	PUNCT
brj-24141	318	1	6956	6956	NUM
brj-24141	318	2	-	-	SYM
brj-24141	318	3	6965	6965	NUM
brj-24141	318	4	.	.	PUNCT
brj-24141	319	1	doi	doi	NOUN
brj-24141	319	2	:	:	PUNCT
brj-24141	319	3	10.48550	10.48550	NUM
brj-24141	319	4	/	/	SYM
brj-24141	319	5	arxiv.1812.04798	arxiv.1812.04798	PROPN
brj-24141	319	6	urbonas	urbonas	PROPN
brj-24141	319	7	,	,	PUNCT
brj-24141	319	8	a.	a.	NOUN
brj-24141	319	9	,	,	PUNCT
brj-24141	319	10	raudonis	raudonis	PROPN
brj-24141	319	11	,	,	PUNCT
brj-24141	319	12	v.	v.	PROPN
brj-24141	319	13	,	,	PUNCT
brj-24141	319	14	maskeliūnas	maskeliūnas	PROPN
brj-24141	319	15	,	,	PUNCT
brj-24141	319	16	r.	r.	PROPN
brj-24141	319	17	,	,	PUNCT
brj-24141	319	18	and	and	CCONJ
brj-24141	319	19	damaševičius	damaševičius	PROPN
brj-24141	319	20	r.	r.	PROPN
brj-24141	319	21	(	(	PUNCT
brj-24141	319	22	2019	2019	NUM
brj-24141	319	23	)	)	PUNCT
brj-24141	319	24	.	.	PUNCT
brj-24141	320	1	“	"	PUNCT
brj-24141	320	2	automated	automate	VERB
brj-24141	320	3	identification	identification	NOUN
brj-24141	320	4	of	of	ADP
brj-24141	320	5	wood	wood	NOUN
brj-24141	320	6	veneer	veneer	NOUN
brj-24141	320	7	surface	surface	NOUN
brj-24141	320	8	defects	defect	NOUN
brj-24141	320	9	using	use	VERB
brj-24141	320	10	faster	fast	ADJ
brj-24141	320	11	region	region	NOUN
brj-24141	320	12	-	-	PUNCT
brj-24141	320	13	based	base	VERB
brj-24141	320	14	convolutional	convolutional	ADJ
brj-24141	320	15	neural	neural	ADJ
brj-24141	320	16	network	network	NOUN
brj-24141	320	17	with	with	ADP
brj-24141	320	18	data	datum	NOUN
brj-24141	320	19	augmentation	augmentation	NOUN
brj-24141	320	20	and	and	CCONJ
brj-24141	320	21	transfer	transfer	NOUN
brj-24141	320	22	learning	learning	NOUN
brj-24141	320	23	,	,	PUNCT
brj-24141	320	24	”	"	PUNCT
brj-24141	320	25	applied	apply	VERB
brj-24141	320	26	sciences	science	NOUN
brj-24141	320	27	9(22	9(22	NUM
brj-24141	320	28	)	)	PUNCT
brj-24141	320	29	,	,	PUNCT
brj-24141	320	30	article	article	NOUN
brj-24141	320	31	4898	4898	NUM
brj-24141	320	32	.	.	PUNCT
brj-24141	321	1	doi	doi	NOUN
brj-24141	321	2	:	:	PUNCT
brj-24141	321	3	10.3390	10.3390	NUM
brj-24141	321	4	/	/	SYM
brj-24141	321	5	app9224898	app9224898	PROPN
brj-24141	321	6	varghese	varghese	PROPN
brj-24141	321	7	,	,	PUNCT
brj-24141	321	8	r.	r.	PROPN
brj-24141	321	9	,	,	PUNCT
brj-24141	321	10	and	and	CCONJ
brj-24141	321	11	sambath	sambath	NOUN
brj-24141	321	12	,	,	PUNCT
brj-24141	321	13	m.	m.	NOUN
brj-24141	321	14	(	(	PUNCT
brj-24141	321	15	2024	2024	NUM
brj-24141	321	16	)	)	PUNCT
brj-24141	321	17	.	.	PUNCT
brj-24141	322	1	“	"	PUNCT
brj-24141	322	2	yolov8	yolov8	NOUN
brj-24141	322	3	:	:	PUNCT
brj-24141	322	4	a	a	DET
brj-24141	322	5	novel	novel	ADJ
brj-24141	322	6	object	object	NOUN
brj-24141	322	7	detection	detection	NOUN
brj-24141	322	8	algorithm	algorithm	NOUN
brj-24141	322	9	with	with	ADP
brj-24141	322	10	enhanced	enhanced	ADJ
brj-24141	322	11	performance	performance	NOUN
brj-24141	322	12	and	and	CCONJ
brj-24141	322	13	robustness	robustness	NOUN
brj-24141	322	14	,	,	PUNCT
brj-24141	322	15	”	"	PUNCT
brj-24141	322	16	in	in	ADP
brj-24141	322	17	:	:	PUNCT
brj-24141	322	18	2024	2024	NUM
brj-24141	322	19	international	international	ADJ
brj-24141	322	20	conference	conference	NOUN
brj-24141	322	21	on	on	ADP
brj-24141	322	22	advances	advance	NOUN
brj-24141	322	23	in	in	ADP
brj-24141	322	24	data	datum	NOUN
brj-24141	322	25	engineering	engineering	NOUN
brj-24141	322	26	and	and	CCONJ
brj-24141	322	27	intelligent	intelligent	ADJ
brj-24141	322	28	computing	computing	NOUN
brj-24141	322	29	systems	system	NOUN
brj-24141	322	30	(	(	PUNCT
brj-24141	322	31	adics	adics	PROPN
brj-24141	322	32	)	)	PUNCT
brj-24141	322	33	.	.	PUNCT
brj-24141	323	1	doi	doi	NOUN
brj-24141	323	2	:	:	PUNCT
brj-24141	323	3	10.1109	10.1109	NUM
brj-24141	323	4	/	/	SYM
brj-24141	323	5	adics58448.2024.10533619	adics58448.2024.10533619	PROPN
brj-24141	323	6	wang	wang	PROPN
brj-24141	323	7	,	,	PUNCT
brj-24141	323	8	z.	z.	PROPN
brj-24141	323	9	,	,	PUNCT
brj-24141	323	10	jiang	jiang	PROPN
brj-24141	323	11	,	,	PUNCT
brj-24141	323	12	y.	y.	PROPN
brj-24141	323	13	,	,	PUNCT
brj-24141	323	14	yan	yan	PROPN
brj-24141	323	15	,	,	PUNCT
brj-24141	323	16	f.	f.	PROPN
brj-24141	323	17	,	,	PUNCT
brj-24141	323	18	sun	sun	PROPN
brj-24141	323	19	,	,	PUNCT
brj-24141	323	20	y.	y.	PROPN
brj-24141	323	21	,	,	PUNCT
brj-24141	323	22	zhang	zhang	PROPN
brj-24141	323	23	,	,	PUNCT
brj-24141	323	24	y.	y.	PROPN
brj-24141	323	25	,	,	PUNCT
brj-24141	323	26	and	and	CCONJ
brj-24141	323	27	zhang	zhang	PROPN
brj-24141	323	28	,	,	PUNCT
brj-24141	323	29	l.	l.	PROPN
brj-24141	323	30	(	(	PUNCT
brj-24141	323	31	2024a	2024a	NUM
brj-24141	323	32	)	)	PUNCT
brj-24141	323	33	.	.	PUNCT
brj-24141	324	1	“	"	PUNCT
brj-24141	324	2	research	research	NOUN
brj-24141	324	3	on	on	ADP
brj-24141	324	4	wood	wood	NOUN
brj-24141	324	5	defect	defect	NOUN
brj-24141	324	6	detection	detection	NOUN
brj-24141	324	7	model	model	NOUN
brj-24141	324	8	wood	wood	NOUN
brj-24141	324	9	-	-	PUNCT
brj-24141	324	10	net	net	NOUN
brj-24141	324	11	based	base	VERB
brj-24141	324	12	on	on	ADP
brj-24141	324	13	yolov7	yolov7	NOUN
brj-24141	324	14	,	,	PUNCT
brj-24141	324	15	”	"	PUNCT
brj-24141	324	16	journal	journal	NOUN
brj-24141	324	17	of	of	ADP
brj-24141	324	18	forestry	forestry	NOUN
brj-24141	324	19	engineering	engineering	NOUN
brj-24141	324	20	9(01	9(01	NUM
brj-24141	324	21	)	)	PUNCT
brj-24141	324	22	,	,	PUNCT
brj-24141	324	23	132	132	NUM
brj-24141	324	24	-	-	SYM
brj-24141	324	25	140	140	NUM
brj-24141	324	26	.	.	PUNCT
brj-24141	325	1	doi	doi	NOUN
brj-24141	325	2	:	:	PUNCT
brj-24141	325	3	10.13360	10.13360	NUM
brj-24141	325	4	/j.issn.2096	/j.issn.2096	SYM
brj-24141	325	5	-	-	SYM
brj-24141	325	6	1359.202305016	1359.202305016	NUM
brj-24141	325	7	wang	wang	PROPN
brj-24141	325	8	,	,	PUNCT
brj-24141	325	9	m.	m.	NOUN
brj-24141	325	10	,	,	PUNCT
brj-24141	325	11	xiang	xiang	PROPN
brj-24141	325	12	,	,	PUNCT
brj-24141	325	13	x.	x.	PROPN
brj-24141	325	14	,	,	PUNCT
brj-24141	325	15	cui	cui	PROPN
brj-24141	325	16	,	,	PUNCT
brj-24141	325	17	w.	w.	PROPN
brj-24141	325	18	,	,	PUNCT
brj-24141	325	19	yuan	yuan	PROPN
brj-24141	325	20	,	,	PUNCT
brj-24141	325	21	m.	m.	NOUN
brj-24141	325	22	,	,	PUNCT
brj-24141	325	23	and	and	CCONJ
brj-24141	325	24	duo	duo	NOUN
brj-24141	325	25	,	,	PUNCT
brj-24141	325	26	h.	h.	PROPN
brj-24141	325	27	(	(	PUNCT
brj-24141	325	28	2024b	2024b	NUM
brj-24141	325	29	)	)	PUNCT
brj-24141	325	30	.	.	PUNCT
brj-24141	326	1	“	"	PUNCT
brj-24141	326	2	research	research	NOUN
brj-24141	326	3	progress	progress	NOUN
brj-24141	326	4	and	and	CCONJ
brj-24141	326	5	prospect	prospect	NOUN
brj-24141	326	6	of	of	ADP
brj-24141	326	7	intelligent	intelligent	ADJ
brj-24141	326	8	detection	detection	NOUN
brj-24141	326	9	of	of	ADP
brj-24141	326	10	wood	wood	NOUN
brj-24141	326	11	defects	defect	NOUN
brj-24141	326	12	based	base	VERB
brj-24141	326	13	on	on	ADP
brj-24141	326	14	deep	deep	ADJ
brj-24141	326	15	learning	learning	NOUN
brj-24141	326	16	,	,	PUNCT
brj-24141	326	17	”	"	PUNCT
brj-24141	326	18	china	china	PROPN
brj-24141	326	19	forest	forest	PROPN
brj-24141	326	20	products	product	NOUN
brj-24141	326	21	industry	industry	NOUN
brj-24141	326	22	61(03	61(03	NOUN
brj-24141	326	23	)	)	PUNCT
brj-24141	326	24	,	,	PUNCT
brj-24141	326	25	38	38	NUM
brj-24141	326	26	-	-	SYM
brj-24141	326	27	44	44	NUM
brj-24141	326	28	.	.	PUNCT
brj-24141	327	1	doi	doi	NOUN
brj-24141	327	2	:	:	PUNCT
brj-24141	327	3	10.19531	10.19531	NUM
brj-24141	327	4	/	/	SYM
brj-24141	327	5	j.issn1001	j.issn1001	NOUN
brj-24141	327	6	-	-	PUNCT
brj-24141	327	7	5299.202403006	5299.202403006	NUM
brj-24141	327	8	wang	wang	PROPN
brj-24141	327	9	,	,	PUNCT
brj-24141	327	10	c.	c.	PROPN
brj-24141	327	11	,	,	PUNCT
brj-24141	327	12	yeh	yeh	PROPN
brj-24141	327	13	,	,	PUNCT
brj-24141	327	14	i.	i.	PROPN
brj-24141	327	15	,	,	PUNCT
brj-24141	327	16	and	and	CCONJ
brj-24141	327	17	liao	liao	PROPN
brj-24141	327	18	,	,	PUNCT
brj-24141	327	19	h.	h.	PROPN
brj-24141	327	20	(	(	PUNCT
brj-24141	327	21	2024c	2024c	NUM
brj-24141	327	22	)	)	PUNCT
brj-24141	327	23	.	.	PUNCT
brj-24141	328	1	“	"	PUNCT
brj-24141	328	2	yolov9	yolov9	NOUN
brj-24141	328	3	:	:	PUNCT
brj-24141	328	4	learning	learn	VERB
brj-24141	328	5	what	what	PRON
brj-24141	328	6	you	you	PRON
brj-24141	328	7	want	want	VERB
brj-24141	328	8	to	to	PART
brj-24141	328	9	learn	learn	VERB
brj-24141	328	10	peer	peer	NOUN
brj-24141	328	11	-	-	PUNCT
brj-24141	328	12	reviewed	review	VERB
brj-24141	328	13	article	article	NOUN
brj-24141	328	14	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	ADP
brj-24141	328	15	li	li	PROPN
brj-24141	328	16	et	et	PROPN
brj-24141	328	17	al	al	PROPN
brj-24141	328	18	.	.	PROPN
brj-24141	329	1	(	(	PUNCT
brj-24141	329	2	2025	2025	NUM
brj-24141	329	3	)	)	PUNCT
brj-24141	329	4	.	.	PUNCT
brj-24141	330	1	“	"	PUNCT
brj-24141	330	2	improved	improved	ADJ
brj-24141	330	3	panel	panel	NOUN
brj-24141	330	4	defect	defect	NOUN
brj-24141	330	5	detection	detection	NOUN
brj-24141	330	6	,	,	PUNCT
brj-24141	330	7	”	"	PUNCT
brj-24141	330	8	bioresources	bioresource	NOUN
brj-24141	330	9	20(2	20(2	NUM
brj-24141	330	10	)	)	PUNCT
brj-24141	330	11	,	,	PUNCT
brj-24141	330	12	2556	2556	NUM
brj-24141	330	13	-	-	SYM
brj-24141	330	14	2573	2573	NUM
brj-24141	330	15	.	.	PUNCT
brj-24141	330	16	2573	2573	NUM
brj-24141	330	17	using	use	VERB
brj-24141	330	18	programmable	programmable	ADJ
brj-24141	330	19	gradient	gradient	ADJ
brj-24141	330	20	information	information	NOUN
brj-24141	330	21	,	,	PUNCT
brj-24141	330	22	”	"	PUNCT
brj-24141	330	23	arxiv	arxiv	PROPN
brj-24141	330	24	preprint	preprint	NOUN
brj-24141	330	25	arxiv:2402.13616	arxiv:2402.13616	NOUN
brj-24141	330	26	.	.	PUNCT
brj-24141	331	1	doi	doi	NOUN
brj-24141	331	2	:	:	PUNCT
brj-24141	331	3	10.48550	10.48550	NUM
brj-24141	331	4	/	/	SYM
brj-24141	331	5	arxiv.2402.13616	arxiv.2402.13616	ADP
brj-24141	331	6	wang	wang	PROPN
brj-24141	331	7	,	,	PUNCT
brj-24141	331	8	j.	j.	PROPN
brj-24141	331	9	,	,	PUNCT
brj-24141	331	10	chen	chen	PROPN
brj-24141	331	11	,	,	PUNCT
brj-24141	331	12	x.	x.	NOUN
brj-24141	331	13	,	,	PUNCT
brj-24141	331	14	and	and	CCONJ
brj-24141	331	15	xia	xia	PROPN
brj-24141	331	16	,	,	PUNCT
brj-24141	331	17	d.	d.	PROPN
brj-24141	331	18	(	(	PUNCT
brj-24141	331	19	2013	2013	NUM
brj-24141	331	20	)	)	PUNCT
brj-24141	331	21	.	.	PUNCT
brj-24141	332	1	“	"	PUNCT
brj-24141	332	2	research	research	NOUN
brj-24141	332	3	progresses	progress	VERB
brj-24141	332	4	on	on	ADP
brj-24141	332	5	elasticity	elasticity	NOUN
brj-24141	332	6	modulus	modulus	NOUN
brj-24141	332	7	nondestructive	nondestructive	ADJ
brj-24141	332	8	examination	examination	NOUN
brj-24141	332	9	of	of	ADP
brj-24141	332	10	wood	wood	NOUN
brj-24141	332	11	and	and	CCONJ
brj-24141	332	12	glulam	glulam	NOUN
brj-24141	332	13	structures	structure	NOUN
brj-24141	332	14	,	,	PUNCT
brj-24141	332	15	”	"	PUNCT
brj-24141	332	16	j.	j.	PROPN
brj-24141	332	17	central	central	PROPN
brj-24141	332	18	south	south	PROPN
brj-24141	332	19	univ	univ	PROPN
brj-24141	332	20	.	.	PUNCT
brj-24141	332	21	of	of	ADP
brj-24141	332	22	forestry	forestry	PROPN
brj-24141	332	23	&	&	CCONJ
brj-24141	332	24	technol	technol	NOUN
brj-24141	332	25	.	.	PUNCT
brj-24141	333	1	33(11	33(11	NUM
brj-24141	333	2	)	)	PUNCT
brj-24141	333	3	,	,	PUNCT
brj-24141	333	4	149	149	NUM
brj-24141	333	5	-	-	SYM
brj-24141	333	6	153	153	NUM
brj-24141	333	7	.	.	PUNCT
brj-24141	334	1	doi	doi	NOUN
brj-24141	334	2	:	:	PUNCT
brj-24141	334	3	10.14067	10.14067	NUM
brj-24141	334	4	/	/	SYM
brj-24141	334	5	j.cnki.1673	j.cnki.1673	NOUN
brj-24141	334	6	-	-	PUNCT
brj-24141	334	7	923x.2013.11.005	923x.2013.11.005	NUM
brj-24141	334	8	wei	wei	PROPN
brj-24141	334	9	,	,	PUNCT
brj-24141	334	10	h.	h.	PROPN
brj-24141	334	11	,	,	PUNCT
brj-24141	334	12	x.	x.	PROPN
brj-24141	334	13	liu	liu	PROPN
brj-24141	334	14	,	,	PUNCT
brj-24141	334	15	s.	s.	PROPN
brj-24141	334	16	xu	xu	PROPN
brj-24141	334	17	,	,	PUNCT
brj-24141	334	18	z.	z.	PROPN
brj-24141	334	19	dai	dai	PROPN
brj-24141	334	20	,	,	PUNCT
brj-24141	334	21	y.	y.	PROPN
brj-24141	334	22	dai	dai	PROPN
brj-24141	334	23	,	,	PUNCT
brj-24141	334	24	and	and	CCONJ
brj-24141	334	25	x.	x.	NOUN
brj-24141	334	26	xu	xu	PROPN
brj-24141	334	27	.	.	PUNCT
brj-24141	335	1	(	(	PUNCT
brj-24141	335	2	2022	2022	NUM
brj-24141	335	3	)	)	PUNCT
brj-24141	335	4	.	.	PUNCT
brj-24141	336	1	“	"	PUNCT
brj-24141	336	2	dwrseg	dwrseg	PROPN
brj-24141	336	3	:	:	PUNCT
brj-24141	336	4	rethinking	rethink	VERB
brj-24141	336	5	efficient	efficient	ADJ
brj-24141	336	6	acquisition	acquisition	NOUN
brj-24141	336	7	of	of	ADP
brj-24141	336	8	multi	multi	ADJ
brj-24141	336	9	-	-	ADJ
brj-24141	336	10	scale	scale	ADJ
brj-24141	336	11	contextual	contextual	ADJ
brj-24141	336	12	information	information	NOUN
brj-24141	336	13	for	for	ADP
brj-24141	336	14	real	real	ADJ
brj-24141	336	15	-	-	PUNCT
brj-24141	336	16	time	time	NOUN
brj-24141	336	17	semantic	semantic	ADJ
brj-24141	336	18	segmentation	segmentation	NOUN
brj-24141	336	19	,	,	PUNCT
brj-24141	336	20	”	"	PUNCT
brj-24141	336	21	arxiv	arxiv	PROPN
brj-24141	336	22	preprint	preprint	NOUN
brj-24141	336	23	arxiv:2212.01173	arxiv:2212.01173	PROPN
brj-24141	336	24	.	.	PUNCT
brj-24141	337	1	doi	doi	NOUN
brj-24141	337	2	:	:	PUNCT
brj-24141	337	3	10.48550	10.48550	NUM
brj-24141	337	4	/	/	SYM
brj-24141	337	5	arxiv.2212.01173	arxiv.2212.01173	PROPN
brj-24141	337	6	yang	yang	PROPN
brj-24141	337	7	,	,	PUNCT
brj-24141	337	8	f.	f.	PROPN
brj-24141	337	9	,	,	PUNCT
brj-24141	337	10	yang	yang	PROPN
brj-24141	337	11	,	,	PUNCT
brj-24141	337	12	b.	b.	PROPN
brj-24141	337	13	,	,	PUNCT
brj-24141	337	14	and	and	CCONJ
brj-24141	337	15	li	li	PROPN
brj-24141	337	16	,	,	PUNCT
brj-24141	337	17	r.	r.	PROPN
brj-24141	337	18	(	(	PUNCT
brj-24141	337	19	2023	2023	NUM
brj-24141	337	20	)	)	PUNCT
brj-24141	337	21	.	.	PUNCT
brj-24141	338	1	“	"	PUNCT
brj-24141	338	2	surface	surface	NOUN
brj-24141	338	3	defect	defect	NOUN
brj-24141	338	4	detection	detection	NOUN
brj-24141	338	5	technology	technology	NOUN
brj-24141	338	6	of	of	ADP
brj-24141	338	7	woodbased	woodbased	ADJ
brj-24141	338	8	panel	panel	NOUN
brj-24141	338	9	based	base	VERB
brj-24141	338	10	on	on	ADP
brj-24141	338	11	image	image	NOUN
brj-24141	338	12	segmentation	segmentation	NOUN
brj-24141	338	13	and	and	CCONJ
brj-24141	338	14	deep	deep	ADJ
brj-24141	338	15	learning	learning	NOUN
brj-24141	338	16	,	,	PUNCT
brj-24141	338	17	”	"	PUNCT
brj-24141	338	18	journal	journal	NOUN
brj-24141	338	19	of	of	ADP
brj-24141	338	20	zhejiang	zhejiang	PROPN
brj-24141	338	21	a&f	a&f	PROPN
brj-24141	338	22	university	university	PROPN
brj-24141	338	23	41(01	41(01	NOUN
brj-24141	338	24	)	)	PUNCT
brj-24141	338	25	,	,	PUNCT
brj-24141	338	26	176	176	NUM
brj-24141	338	27	-	-	SYM
brj-24141	338	28	182	182	NUM
brj-24141	338	29	.	.	PUNCT
brj-24141	339	1	doi	doi	NOUN
brj-24141	339	2	:	:	PUNCT
brj-24141	339	3	10.11833	10.11833	NUM
brj-24141	339	4	/	/	SYM
brj-24141	339	5	j.issn.2095	j.issn.2095	NOUN
brj-24141	339	6	-	-	PUNCT
brj-24141	339	7	0756.20230280	0756.20230280	ADJ
brj-24141	339	8	zhang	zhang	PROPN
brj-24141	339	9	,	,	PUNCT
brj-24141	339	10	h.	h.	PROPN
brj-24141	339	11	,	,	PUNCT
brj-24141	339	12	and	and	CCONJ
brj-24141	339	13	zhang	zhang	PROPN
brj-24141	339	14	,	,	PUNCT
brj-24141	339	15	s.	s.	PROPN
brj-24141	339	16	(	(	PUNCT
brj-24141	339	17	2024	2024	NUM
brj-24141	339	18	)	)	PUNCT
brj-24141	339	19	.	.	PUNCT
brj-24141	340	1	“	"	PUNCT
brj-24141	340	2	focaler	focaler	NOUN
brj-24141	340	3	-	-	PUNCT
brj-24141	340	4	iou	iou	NOUN
brj-24141	340	5	:	:	PUNCT
brj-24141	340	6	more	more	ADV
brj-24141	340	7	focused	focused	ADJ
brj-24141	340	8	intersection	intersection	NOUN
brj-24141	340	9	over	over	ADP
brj-24141	340	10	union	union	NOUN
brj-24141	340	11	loss	loss	NOUN
brj-24141	340	12	,	,	PUNCT
brj-24141	340	13	”	"	PUNCT
brj-24141	340	14	arxiv	arxiv	PROPN
brj-24141	340	15	preprint	preprint	NOUN
brj-24141	340	16	arxiv:2401.10525	arxiv:2401.10525	PROPN
brj-24141	340	17	.	.	PUNCT
brj-24141	341	1	doi	doi	NOUN
brj-24141	341	2	:	:	PUNCT
brj-24141	341	3	10.48550	10.48550	NUM
brj-24141	341	4	/	/	SYM
brj-24141	341	5	arxiv.2401.10525	arxiv.2401.10525	NOUN
brj-24141	341	6	zhao	zhao	NOUN
brj-24141	341	7	,	,	PUNCT
brj-24141	341	8	w.	w.	PROPN
brj-24141	341	9	,	,	PUNCT
brj-24141	341	10	zhao	zhao	PROPN
brj-24141	341	11	,	,	PUNCT
brj-24141	341	12	z.	z.	PROPN
brj-24141	341	13	,	,	PUNCT
brj-24141	341	14	and	and	CCONJ
brj-24141	341	15	kong	kong	PROPN
brj-24141	341	16	,	,	PUNCT
brj-24141	341	17	j.	j.	PROPN
brj-24141	341	18	(	(	PUNCT
brj-24141	341	19	2024	2024	NUM
brj-24141	341	20	)	)	PUNCT
brj-24141	341	21	.	.	PUNCT
brj-24141	342	1	“	"	PUNCT
brj-24141	342	2	remote	remote	ADJ
brj-24141	342	3	sensing	sense	VERB
brj-24141	342	4	image	image	NOUN
brj-24141	342	5	object	object	NOUN
brj-24141	342	6	detection	detection	NOUN
brj-24141	342	7	based	base	VERB
brj-24141	342	8	on	on	ADP
brj-24141	342	9	inverted	inverted	ADJ
brj-24141	342	10	residual	residual	ADJ
brj-24141	342	11	self	self	NOUN
brj-24141	342	12	-	-	PUNCT
brj-24141	342	13	attention	attention	NOUN
brj-24141	342	14	mechanism	mechanism	NOUN
brj-24141	342	15	,	,	PUNCT
brj-24141	342	16	”	"	PUNCT
brj-24141	342	17	caai	caai	NOUN
brj-24141	342	18	transactions	transaction	NOUN
brj-24141	342	19	on	on	ADP
brj-24141	342	20	intelligent	intelligent	ADJ
brj-24141	342	21	systems	system	NOUN
brj-24141	342	22	1	1	NUM
brj-24141	342	23	-	-	SYM
brj-24141	342	24	10	10	NUM
brj-24141	342	25	.	.	PUNCT
brj-24141	343	1	doi	doi	NOUN
brj-24141	343	2	:	:	PUNCT
brj-24141	343	3	10.11992	10.11992	NUM
brj-24141	343	4	/	/	SYM
brj-24141	343	5	tis.202312001	tis.202312001	NUM
brj-24141	343	6	zheng	zheng	PROPN
brj-24141	343	7	,	,	PUNCT
brj-24141	343	8	z.	z.	PROPN
brj-24141	343	9	,	,	PUNCT
brj-24141	343	10	wang	wang	PROPN
brj-24141	343	11	,	,	PUNCT
brj-24141	343	12	p.	p.	PROPN
brj-24141	343	13	,	,	PUNCT
brj-24141	343	14	ren	ren	PROPN
brj-24141	343	15	,	,	PUNCT
brj-24141	343	16	d.	d.	PROPN
brj-24141	343	17	,	,	PUNCT
brj-24141	343	18	liu	liu	PROPN
brj-24141	343	19	,	,	PUNCT
brj-24141	343	20	w.	w.	PROPN
brj-24141	343	21	,	,	PUNCT
brj-24141	343	22	ye	ye	PROPN
brj-24141	343	23	,	,	PUNCT
brj-24141	343	24	r.	r.	PROPN
brj-24141	343	25	,	,	PUNCT
brj-24141	343	26	hu	hu	PROPN
brj-24141	343	27	,	,	PUNCT
brj-24141	343	28	q.	q.	PROPN
brj-24141	343	29	,	,	PUNCT
brj-24141	343	30	and	and	CCONJ
brj-24141	343	31	zuo	zuo	PROPN
brj-24141	343	32	,	,	PUNCT
brj-24141	343	33	w.	w.	NOUN
brj-24141	343	34	(	(	PUNCT
brj-24141	343	35	2021	2021	NUM
brj-24141	343	36	)	)	PUNCT
brj-24141	343	37	.	.	PUNCT
brj-24141	344	1	“	"	PUNCT
brj-24141	344	2	enhancing	enhance	VERB
brj-24141	344	3	geometric	geometric	ADJ
brj-24141	344	4	factors	factor	NOUN
brj-24141	344	5	in	in	ADP
brj-24141	344	6	model	model	NOUN
brj-24141	344	7	learning	learning	NOUN
brj-24141	344	8	and	and	CCONJ
brj-24141	344	9	inference	inference	NOUN
brj-24141	344	10	for	for	ADP
brj-24141	344	11	object	object	NOUN
brj-24141	344	12	detection	detection	NOUN
brj-24141	344	13	and	and	CCONJ
brj-24141	344	14	instance	instance	NOUN
brj-24141	344	15	segmentation	segmentation	NOUN
brj-24141	344	16	,	,	PUNCT
brj-24141	344	17	”	"	PUNCT
brj-24141	344	18	ieee	ieee	NOUN
brj-24141	344	19	transactions	transaction	NOUN
brj-24141	344	20	on	on	ADP
brj-24141	344	21	cybernetics	cybernetic	NOUN
brj-24141	344	22	52(8	52(8	NUM
brj-24141	344	23	)	)	PUNCT
brj-24141	344	24	,	,	PUNCT
brj-24141	344	25	8574	8574	NUM
brj-24141	344	26	-	-	SYM
brj-24141	344	27	8586	8586	NUM
brj-24141	344	28	.	.	PUNCT
brj-24141	345	1	doi	doi	NOUN
brj-24141	345	2	:	:	PUNCT
brj-24141	345	3	10.1109	10.1109	NUM
brj-24141	345	4	/	/	SYM
brj-24141	345	5	tcyb.2021.3095305	tcyb.2021.3095305	NOUN
brj-24141	345	6	article	article	NOUN
brj-24141	345	7	submitted	submit	VERB
brj-24141	345	8	:	:	PUNCT
brj-24141	345	9	october	october	PROPN
brj-24141	345	10	30	30	NUM
brj-24141	345	11	,	,	PUNCT
brj-24141	345	12	2024	2024	NUM
brj-24141	345	13	;	;	PUNCT
brj-24141	345	14	peer	peer	NOUN
brj-24141	345	15	review	review	NOUN
brj-24141	345	16	completed	complete	VERB
brj-24141	345	17	:	:	PUNCT
brj-24141	345	18	november	november	PROPN
brj-24141	345	19	23	23	NUM
brj-24141	345	20	,	,	PUNCT
brj-24141	345	21	2024	2024	NUM
brj-24141	345	22	;	;	PUNCT
brj-24141	345	23	revised	revise	VERB
brj-24141	345	24	version	version	NOUN
brj-24141	345	25	received	receive	VERB
brj-24141	345	26	and	and	CCONJ
brj-24141	345	27	accepted	accept	VERB
brj-24141	345	28	:	:	PUNCT
brj-24141	345	29	january	january	PROPN
brj-24141	345	30	29	29	NUM
brj-24141	345	31	,	,	PUNCT
brj-24141	345	32	2025	2025	NUM
brj-24141	345	33	;	;	PUNCT
brj-24141	345	34	published	publish	VERB
brj-24141	345	35	:	:	PUNCT
brj-24141	345	36	february	february	PROPN
brj-24141	345	37	10	10	NUM
brj-24141	345	38	,	,	PUNCT
brj-24141	345	39	2025	2025	NUM
brj-24141	345	40	.	.	PUNCT
brj-24141	346	1	doi	doi	NOUN
brj-24141	346	2	:	:	PUNCT
brj-24141	346	3	10.15376	10.15376	NUM
brj-24141	346	4	/	/	SYM
brj-24141	346	5	biores.20.2.2556	biores.20.2.2556	NOUN
brj-24141	346	6	-	-	PUNCT
brj-24141	346	7	2573	2573	NUM