id	sid	tid	token	lemma	pos
brj-24732	1	1	peer	peer	NOUN
brj-24732	1	2	-	-	PUNCT
brj-24732	1	3	review	review	NOUN
brj-24732	1	4	article	article	NOUN
brj-24732	1	5	peer	peer	NOUN
brj-24732	1	6	-	-	PUNCT
brj-24732	1	7	reviewed	review	VERB
brj-24732	1	8	article	article	NOUN
brj-24732	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	1	10	he	he	PRON
brj-24732	1	11	et	et	PROPN
brj-24732	1	12	al	al	PROPN
brj-24732	1	13	.	.	PROPN
brj-24732	2	1	(	(	PUNCT
brj-24732	2	2	2025	2025	NUM
brj-24732	2	3	)	)	PUNCT
brj-24732	2	4	.	.	PUNCT
brj-24732	3	1	“	"	PUNCT
brj-24732	3	2	le	le	X
brj-24732	3	3	-	-	PROPN
brj-24732	3	4	yolo	yolo	PROPN
brj-24732	3	5	&	&	CCONJ
brj-24732	3	6	wood	wood	PROPN
brj-24732	3	7	surface	surface	NOUN
brj-24732	3	8	defects	defect	NOUN
brj-24732	3	9	,	,	PUNCT
brj-24732	3	10	”	"	PUNCT
brj-24732	3	11	bioresources	bioresource	NOUN
brj-24732	3	12	20(3	20(3	NOUN
brj-24732	3	13	)	)	PUNCT
brj-24732	3	14	,	,	PUNCT
brj-24732	3	15	7179	7179	NUM
brj-24732	3	16	-	-	SYM
brj-24732	3	17	7193	7193	NUM
brj-24732	3	18	.	.	PUNCT
brj-24732	4	1	7179	7179	NUM
brj-24732	4	2	le	le	X
brj-24732	4	3	-	-	PROPN
brj-24732	4	4	yolo	yolo	PROPN
brj-24732	4	5	:	:	PUNCT
brj-24732	4	6	a	a	DET
brj-24732	4	7	lightweight	lightweight	ADJ
brj-24732	4	8	and	and	CCONJ
brj-24732	4	9	enhanced	enhanced	ADJ
brj-24732	4	10	algorithm	algorithm	NOUN
brj-24732	4	11	for	for	ADP
brj-24732	4	12	detecting	detect	VERB
brj-24732	4	13	surface	surface	NOUN
brj-24732	4	14	defects	defect	NOUN
brj-24732	4	15	on	on	ADP
brj-24732	4	16	particleboard	particleboard	NOUN
brj-24732	5	1	chao	chao	PROPN
brj-24732	6	1	he	he	PRON
brj-24732	6	2	,	,	PUNCT
brj-24732	6	3	yongkang	yongkang	PROPN
brj-24732	6	4	kang	kang	PROPN
brj-24732	6	5	,	,	PUNCT
brj-24732	6	6	anning	anne	VERB
brj-24732	6	7	ding	ding	PROPN
brj-24732	6	8	,	,	PUNCT
brj-24732	6	9	wei	wei	PROPN
brj-24732	6	10	jia	jia	PROPN
brj-24732	6	11	,	,	PUNCT
brj-24732	6	12	and	and	CCONJ
brj-24732	6	13	huaqiong	huaqiong	PROPN
brj-24732	6	14	duo	duo	PROPN
brj-24732	6	15	*	*	PUNCT
brj-24732	6	16	current	current	ADJ
brj-24732	6	17	algorithms	algorithm	NOUN
brj-24732	6	18	for	for	ADP
brj-24732	6	19	surface	surface	NOUN
brj-24732	6	20	defect	defect	NOUN
brj-24732	6	21	detection	detection	NOUN
brj-24732	6	22	in	in	ADP
brj-24732	6	23	particleboard	particleboard	NOUN
brj-24732	6	24	often	often	ADV
brj-24732	6	25	encounter	encounter	VERB
brj-24732	6	26	limitations	limitation	NOUN
brj-24732	6	27	such	such	ADJ
brj-24732	6	28	as	as	ADP
brj-24732	6	29	high	high	ADJ
brj-24732	6	30	computational	computational	ADJ
brj-24732	6	31	complexity	complexity	NOUN
brj-24732	6	32	and	and	CCONJ
brj-24732	6	33	excessive	excessive	ADJ
brj-24732	6	34	parameter	parameter	NOUN
brj-24732	6	35	scale	scale	NOUN
brj-24732	6	36	.	.	PUNCT
brj-24732	7	1	to	to	PART
brj-24732	7	2	address	address	VERB
brj-24732	7	3	these	these	DET
brj-24732	7	4	challenges	challenge	NOUN
brj-24732	7	5	,	,	PUNCT
brj-24732	7	6	this	this	DET
brj-24732	7	7	study	study	NOUN
brj-24732	7	8	proposes	propose	VERB
brj-24732	7	9	the	the	DET
brj-24732	7	10	le	le	PROPN
brj-24732	7	11	-	-	ADJ
brj-24732	7	12	yolo	yolo	ADJ
brj-24732	7	13	model	model	NOUN
brj-24732	7	14	,	,	PUNCT
brj-24732	7	15	which	which	PRON
brj-24732	7	16	incorporates	incorporate	VERB
brj-24732	7	17	a	a	DET
brj-24732	7	18	normalized	normalize	VERB
brj-24732	7	19	wasserstein	wasserstein	NOUN
brj-24732	7	20	distance	distance	NOUN
brj-24732	7	21	into	into	ADP
brj-24732	7	22	the	the	DET
brj-24732	7	23	loss	loss	NOUN
brj-24732	7	24	function	function	NOUN
brj-24732	7	25	to	to	PART
brj-24732	7	26	enhance	enhance	VERB
brj-24732	7	27	the	the	DET
brj-24732	7	28	detection	detection	NOUN
brj-24732	7	29	capability	capability	NOUN
brj-24732	7	30	for	for	ADP
brj-24732	7	31	minute	minute	NOUN
brj-24732	7	32	surface	surface	NOUN
brj-24732	7	33	defects	defect	NOUN
brj-24732	7	34	.	.	PUNCT
brj-24732	8	1	a	a	DET
brj-24732	8	2	dynamic	dynamic	ADJ
brj-24732	8	3	mixed	mixed	ADJ
brj-24732	8	4	convolutional	convolutional	ADJ
brj-24732	8	5	network	network	NOUN
brj-24732	8	6	module	module	NOUN
brj-24732	8	7	is	be	AUX
brj-24732	8	8	introduced	introduce	VERB
brj-24732	8	9	to	to	PART
brj-24732	8	10	construct	construct	VERB
brj-24732	8	11	a	a	DET
brj-24732	8	12	lightweight	lightweight	ADJ
brj-24732	8	13	backbone	backbone	NOUN
brj-24732	8	14	architecture	architecture	NOUN
brj-24732	8	15	.	.	PUNCT
brj-24732	9	1	moreover	moreover	ADV
brj-24732	9	2	,	,	PUNCT
brj-24732	9	3	the	the	DET
brj-24732	9	4	shared	share	VERB
brj-24732	9	5	dilated	dilate	VERB
brj-24732	9	6	feature	feature	NOUN
brj-24732	9	7	pyramid	pyramid	NOUN
brj-24732	9	8	(	(	PUNCT
brj-24732	9	9	sdfp	sdfp	NOUN
brj-24732	9	10	)	)	PUNCT
brj-24732	9	11	module	module	NOUN
brj-24732	9	12	is	be	AUX
brj-24732	9	13	employed	employ	VERB
brj-24732	9	14	in	in	ADP
brj-24732	9	15	the	the	DET
brj-24732	9	16	neck	neck	NOUN
brj-24732	9	17	network	network	NOUN
brj-24732	9	18	,	,	PUNCT
brj-24732	9	19	effectively	effectively	ADV
brj-24732	9	20	reducing	reduce	VERB
brj-24732	9	21	computational	computational	ADJ
brj-24732	9	22	overhead	overhead	NOUN
brj-24732	9	23	while	while	SCONJ
brj-24732	9	24	preserving	preserve	VERB
brj-24732	9	25	detection	detection	NOUN
brj-24732	9	26	accuracy	accuracy	NOUN
brj-24732	9	27	.	.	PUNCT
brj-24732	10	1	a	a	DET
brj-24732	10	2	lightweight	lightweight	ADJ
brj-24732	10	3	detection	detection	NOUN
brj-24732	10	4	head	head	NOUN
brj-24732	10	5	was	be	AUX
brj-24732	10	6	further	far	ADV
brj-24732	10	7	designed	design	VERB
brj-24732	10	8	,	,	PUNCT
brj-24732	10	9	integrating	integrate	VERB
brj-24732	10	10	shared	share	VERB
brj-24732	10	11	convolutional	convolutional	ADJ
brj-24732	10	12	operations	operation	NOUN
brj-24732	10	13	with	with	ADP
brj-24732	10	14	a	a	DET
brj-24732	10	15	distribution	distribution	NOUN
brj-24732	10	16	-	-	PUNCT
brj-24732	10	17	aware	aware	ADJ
brj-24732	10	18	loss	loss	NOUN
brj-24732	10	19	function	function	NOUN
brj-24732	10	20	,	,	PUNCT
brj-24732	10	21	thereby	thereby	ADV
brj-24732	10	22	substantially	substantially	ADV
brj-24732	10	23	improving	improve	VERB
brj-24732	10	24	detection	detection	NOUN
brj-24732	10	25	performance	performance	NOUN
brj-24732	10	26	in	in	ADP
brj-24732	10	27	complex	complex	ADJ
brj-24732	10	28	textured	textured	ADJ
brj-24732	10	29	environments	environment	NOUN
brj-24732	10	30	.	.	PUNCT
brj-24732	11	1	experimental	experimental	ADJ
brj-24732	11	2	evaluations	evaluation	NOUN
brj-24732	11	3	conducted	conduct	VERB
brj-24732	11	4	on	on	ADP
brj-24732	11	5	the	the	DET
brj-24732	11	6	chipboardv1.0	chipboardv1.0	NUM
brj-24732	11	7	particleboard	particleboard	NOUN
brj-24732	11	8	surface	surface	NOUN
brj-24732	11	9	defect	defect	NOUN
brj-24732	11	10	dataset	dataset	NOUN
brj-24732	11	11	demonstrated	demonstrate	VERB
brj-24732	11	12	that	that	SCONJ
brj-24732	11	13	compared	compare	VERB
brj-24732	11	14	to	to	ADP
brj-24732	11	15	the	the	DET
brj-24732	11	16	baseline	baseline	PROPN
brj-24732	11	17	yolov11n	yolov11n	NOUN
brj-24732	11	18	model	model	NOUN
brj-24732	11	19	,	,	PUNCT
brj-24732	11	20	le	le	PROPN
brj-24732	11	21	-	-	ADJ
brj-24732	11	22	yolo	yolo	PROPN
brj-24732	11	23	achieved	achieve	VERB
brj-24732	11	24	a	a	DET
brj-24732	11	25	5	5	NUM
brj-24732	11	26	%	%	NOUN
brj-24732	11	27	improvement	improvement	NOUN
brj-24732	11	28	in	in	ADP
brj-24732	11	29	recall	recall	NOUN
brj-24732	11	30	,	,	PUNCT
brj-24732	11	31	a	a	DET
brj-24732	11	32	1	1	NUM
brj-24732	11	33	%	%	NOUN
brj-24732	11	34	increase	increase	NOUN
brj-24732	11	35	in	in	ADP
brj-24732	11	36	f1	f1	PROPN
brj-24732	11	37	score	score	NOUN
brj-24732	11	38	,	,	PUNCT
brj-24732	11	39	a	a	DET
brj-24732	11	40	4	4	NUM
brj-24732	11	41	%	%	NOUN
brj-24732	11	42	enhancement	enhancement	NOUN
brj-24732	11	43	in	in	ADP
brj-24732	11	44	map@50	map@50	NOUN
brj-24732	11	45	,	,	PUNCT
brj-24732	11	46	a	a	DET
brj-24732	11	47	6	6	NUM
brj-24732	11	48	%	%	NOUN
brj-24732	11	49	gain	gain	NOUN
brj-24732	11	50	in	in	ADP
brj-24732	11	51	map@50–95	map@50–95	NOUN
brj-24732	11	52	,	,	PUNCT
brj-24732	11	53	a	a	DET
brj-24732	11	54	12.69	12.69	NUM
brj-24732	11	55	%	%	NOUN
brj-24732	11	56	acceleration	acceleration	NOUN
brj-24732	11	57	in	in	ADP
brj-24732	11	58	inference	inference	NOUN
brj-24732	11	59	speed	speed	NOUN
brj-24732	11	60	,	,	PUNCT
brj-24732	11	61	and	and	CCONJ
brj-24732	11	62	an	an	DET
brj-24732	11	63	18.6	18.6	NUM
brj-24732	11	64	%	%	NOUN
brj-24732	11	65	reduction	reduction	NOUN
brj-24732	11	66	in	in	ADP
brj-24732	11	67	parameter	parameter	NOUN
brj-24732	11	68	count	count	NOUN
brj-24732	11	69	.	.	PUNCT
brj-24732	12	1	compared	compare	VERB
brj-24732	12	2	with	with	ADP
brj-24732	12	3	other	other	ADJ
brj-24732	12	4	models	model	NOUN
brj-24732	12	5	,	,	PUNCT
brj-24732	12	6	the	the	DET
brj-24732	12	7	proposed	propose	VERB
brj-24732	12	8	approach	approach	NOUN
brj-24732	12	9	not	not	PART
brj-24732	12	10	only	only	ADV
brj-24732	12	11	improved	improve	VERB
brj-24732	12	12	detection	detection	NOUN
brj-24732	12	13	precision	precision	NOUN
brj-24732	12	14	but	but	CCONJ
brj-24732	12	15	also	also	ADV
brj-24732	12	16	effectively	effectively	ADV
brj-24732	12	17	reduced	reduce	VERB
brj-24732	12	18	model	model	NOUN
brj-24732	12	19	complexity	complexity	NOUN
brj-24732	12	20	,	,	PUNCT
brj-24732	12	21	achieving	achieve	VERB
brj-24732	12	22	a	a	DET
brj-24732	12	23	lightweight	lightweight	ADJ
brj-24732	12	24	and	and	CCONJ
brj-24732	12	25	efficient	efficient	ADJ
brj-24732	12	26	detection	detection	NOUN
brj-24732	12	27	framework	framework	NOUN
brj-24732	12	28	.	.	PUNCT
brj-24732	13	1	doi	doi	NOUN
brj-24732	13	2	:	:	PUNCT
brj-24732	13	3	10.15376	10.15376	NUM
brj-24732	13	4	/	/	SYM
brj-24732	13	5	biores.20.3.7179	biores.20.3.7179	NOUN
brj-24732	13	6	-	-	PUNCT
brj-24732	13	7	7193	7193	NUM
brj-24732	13	8	keywords	keyword	NOUN
brj-24732	13	9	:	:	PUNCT
brj-24732	13	10	object	object	VERB
brj-24732	13	11	detection	detection	NOUN
brj-24732	13	12	;	;	PUNCT
brj-24732	13	13	lightweight	lightweight	ADJ
brj-24732	13	14	architecture	architecture	NOUN
brj-24732	13	15	;	;	PUNCT
brj-24732	13	16	yolo	yolo	ADJ
brj-24732	13	17	;	;	PUNCT
brj-24732	13	18	particleboard	particleboard	NOUN
brj-24732	13	19	surface	surface	NOUN
brj-24732	13	20	defects	defect	NOUN
brj-24732	13	21	;	;	PUNCT
brj-24732	14	1	deep	deep	ADJ
brj-24732	14	2	learning	learning	NOUN
brj-24732	14	3	;	;	PUNCT
brj-24732	14	4	feature	feature	NOUN
brj-24732	14	5	fusion	fusion	NOUN
brj-24732	14	6	contact	contact	NOUN
brj-24732	14	7	information	information	NOUN
brj-24732	14	8	:	:	PUNCT
brj-24732	14	9	college	college	NOUN
brj-24732	14	10	of	of	ADP
brj-24732	14	11	materials	material	NOUN
brj-24732	14	12	science	science	NOUN
brj-24732	14	13	and	and	CCONJ
brj-24732	14	14	art	art	NOUN
brj-24732	14	15	design	design	NOUN
brj-24732	14	16	,	,	PUNCT
brj-24732	14	17	inner	inner	PROPN
brj-24732	14	18	mongolia	mongolia	PROPN
brj-24732	14	19	agricultural	agricultural	PROPN
brj-24732	14	20	university	university	PROPN
brj-24732	14	21	,	,	PUNCT
brj-24732	14	22	hohhot	hohhot	ADJ
brj-24732	14	23	010018	010018	NUM
brj-24732	14	24	,	,	PUNCT
brj-24732	14	25	inner	inner	PROPN
brj-24732	14	26	mongolia	mongolia	PROPN
brj-24732	14	27	autonomous	autonomous	PROPN
brj-24732	14	28	region	region	PROPN
brj-24732	14	29	,	,	PUNCT
brj-24732	14	30	china	china	PROPN
brj-24732	14	31	;	;	PUNCT
brj-24732	14	32	*	*	PUNCT
brj-24732	14	33	corresponding	correspond	VERB
brj-24732	14	34	author	author	NOUN
brj-24732	14	35	:	:	PUNCT
brj-24732	14	36	duohuaqiong@163.com	duohuaqiong@163.com	X
brj-24732	14	37	introduction	introduction	NOUN
brj-24732	14	38	traditional	traditional	ADJ
brj-24732	14	39	wood	wood	NOUN
brj-24732	14	40	surface	surface	NOUN
brj-24732	14	41	defect	defect	NOUN
brj-24732	14	42	detection	detection	NOUN
brj-24732	14	43	methods	method	NOUN
brj-24732	14	44	have	have	AUX
brj-24732	14	45	predominantly	predominantly	ADV
brj-24732	14	46	relied	rely	VERB
brj-24732	14	47	on	on	ADP
brj-24732	14	48	handcrafted	handcraft	VERB
brj-24732	14	49	feature	feature	NOUN
brj-24732	14	50	extraction	extraction	NOUN
brj-24732	14	51	algorithms	algorithm	NOUN
brj-24732	14	52	.	.	PUNCT
brj-24732	15	1	for	for	ADP
brj-24732	15	2	example	example	NOUN
brj-24732	15	3	,	,	PUNCT
brj-24732	15	4	ji	ji	PROPN
brj-24732	15	5	et	et	PROPN
brj-24732	15	6	al	al	PROPN
brj-24732	15	7	.	.	PROPN
brj-24732	16	1	(	(	PUNCT
brj-24732	16	2	2019	2019	NUM
brj-24732	16	3	)	)	PUNCT
brj-24732	16	4	introduced	introduce	VERB
brj-24732	16	5	a	a	DET
brj-24732	16	6	wavelet	wavelet	NOUN
brj-24732	16	7	moment	moment	NOUN
brj-24732	16	8	-	-	PUNCT
brj-24732	16	9	based	base	VERB
brj-24732	16	10	feature	feature	NOUN
brj-24732	16	11	extraction	extraction	NOUN
brj-24732	16	12	algorithm	algorithm	NOUN
brj-24732	16	13	that	that	PRON
brj-24732	16	14	combines	combine	VERB
brj-24732	16	15	the	the	DET
brj-24732	16	16	advantages	advantage	NOUN
brj-24732	16	17	of	of	ADP
brj-24732	16	18	wavelet	wavelet	NOUN
brj-24732	16	19	energy	energy	NOUN
brj-24732	16	20	and	and	CCONJ
brj-24732	16	21	hu	hu	PROPN
brj-24732	16	22	invariant	invariant	ADJ
brj-24732	16	23	moments	moment	NOUN
brj-24732	16	24	to	to	PART
brj-24732	16	25	classify	classify	VERB
brj-24732	16	26	and	and	CCONJ
brj-24732	16	27	identify	identify	VERB
brj-24732	16	28	wood	wood	NOUN
brj-24732	16	29	defect	defect	NOUN
brj-24732	16	30	images	image	NOUN
brj-24732	16	31	.	.	PUNCT
brj-24732	17	1	compared	compare	VERB
brj-24732	17	2	to	to	ADP
brj-24732	17	3	conventional	conventional	ADJ
brj-24732	17	4	hu	hu	PROPN
brj-24732	17	5	moments	moment	NOUN
brj-24732	17	6	,	,	PUNCT
brj-24732	17	7	their	their	PRON
brj-24732	17	8	method	method	NOUN
brj-24732	17	9	significantly	significantly	ADV
brj-24732	17	10	improved	improve	VERB
brj-24732	17	11	recognition	recognition	NOUN
brj-24732	17	12	accuracy	accuracy	NOUN
brj-24732	17	13	.	.	PUNCT
brj-24732	18	1	however	however	ADV
brj-24732	18	2	,	,	PUNCT
brj-24732	18	3	this	this	DET
brj-24732	18	4	approach	approach	NOUN
brj-24732	18	5	suffers	suffer	VERB
brj-24732	18	6	from	from	ADP
brj-24732	18	7	high	high	ADJ
brj-24732	18	8	computational	computational	ADJ
brj-24732	18	9	complexity	complexity	NOUN
brj-24732	18	10	,	,	PUNCT
brj-24732	18	11	resulting	result	VERB
brj-24732	18	12	in	in	ADP
brj-24732	18	13	inefficiency	inefficiency	NOUN
brj-24732	18	14	when	when	SCONJ
brj-24732	18	15	processing	process	VERB
brj-24732	18	16	high	high	ADJ
brj-24732	18	17	-	-	PUNCT
brj-24732	18	18	resolution	resolution	NOUN
brj-24732	18	19	or	or	CCONJ
brj-24732	18	20	large	large	ADJ
brj-24732	18	21	-	-	PUNCT
brj-24732	18	22	scale	scale	NOUN
brj-24732	18	23	data	datum	NOUN
brj-24732	18	24	and	and	CCONJ
brj-24732	18	25	requiring	require	VERB
brj-24732	18	26	substantial	substantial	ADJ
brj-24732	18	27	hardware	hardware	NOUN
brj-24732	18	28	resources	resource	NOUN
brj-24732	18	29	.	.	PUNCT
brj-24732	19	1	additionally	additionally	ADV
brj-24732	19	2	,	,	PUNCT
brj-24732	19	3	parameter	parameter	NOUN
brj-24732	19	4	tuning	tuning	NOUN
brj-24732	19	5	is	be	AUX
brj-24732	19	6	challenging	challenging	ADJ
brj-24732	19	7	,	,	PUNCT
brj-24732	19	8	and	and	CCONJ
brj-24732	19	9	the	the	DET
brj-24732	19	10	algorithm	algorithm	NOUN
brj-24732	19	11	is	be	AUX
brj-24732	19	12	vulnerable	vulnerable	ADJ
brj-24732	19	13	to	to	ADP
brj-24732	19	14	noise	noise	NOUN
brj-24732	19	15	interference	interference	NOUN
brj-24732	19	16	,	,	PUNCT
brj-24732	19	17	which	which	PRON
brj-24732	19	18	compromises	compromise	VERB
brj-24732	19	19	real	real	ADJ
brj-24732	19	20	-	-	PUNCT
brj-24732	19	21	time	time	NOUN
brj-24732	19	22	application	application	NOUN
brj-24732	19	23	performance	performance	NOUN
brj-24732	19	24	.	.	PUNCT
brj-24732	20	1	peer	peer	NOUN
brj-24732	20	2	-	-	PUNCT
brj-24732	20	3	reviewed	review	VERB
brj-24732	20	4	article	article	NOUN
brj-24732	20	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	20	6	he	he	PRON
brj-24732	20	7	et	et	PROPN
brj-24732	20	8	al	al	PROPN
brj-24732	20	9	.	.	PROPN
brj-24732	21	1	(	(	PUNCT
brj-24732	21	2	2025	2025	NUM
brj-24732	21	3	)	)	PUNCT
brj-24732	21	4	.	.	PUNCT
brj-24732	22	1	“	"	PUNCT
brj-24732	22	2	le	le	X
brj-24732	22	3	-	-	PROPN
brj-24732	22	4	yolo	yolo	PROPN
brj-24732	22	5	&	&	CCONJ
brj-24732	22	6	wood	wood	PROPN
brj-24732	22	7	surface	surface	NOUN
brj-24732	22	8	defects	defect	NOUN
brj-24732	22	9	,	,	PUNCT
brj-24732	22	10	”	"	PUNCT
brj-24732	22	11	bioresources	bioresource	NOUN
brj-24732	22	12	20(3	20(3	NOUN
brj-24732	22	13	)	)	PUNCT
brj-24732	22	14	,	,	PUNCT
brj-24732	22	15	7179	7179	NUM
brj-24732	22	16	-	-	SYM
brj-24732	22	17	7193	7193	NUM
brj-24732	22	18	.	.	PUNCT
brj-24732	23	1	7180	7180	NUM
brj-24732	23	2	pan	pan	NOUN
brj-24732	23	3	et	et	PROPN
brj-24732	23	4	al	al	PROPN
brj-24732	23	5	.	.	PROPN
brj-24732	23	6	(	(	PUNCT
brj-24732	23	7	2022	2022	NUM
brj-24732	23	8	)	)	PUNCT
brj-24732	23	9	employed	employ	VERB
brj-24732	23	10	near	near	ADV
brj-24732	23	11	-	-	PUNCT
brj-24732	23	12	infrared	infrared	ADJ
brj-24732	23	13	spectroscopy	spectroscopy	NOUN
brj-24732	23	14	combined	combine	VERB
brj-24732	23	15	with	with	ADP
brj-24732	23	16	extreme	extreme	ADJ
brj-24732	23	17	learning	learning	NOUN
brj-24732	23	18	machine	machine	NOUN
brj-24732	23	19	(	(	PUNCT
brj-24732	23	20	elm	elm	NOUN
brj-24732	23	21	)	)	PUNCT
brj-24732	23	22	feature	feature	NOUN
brj-24732	23	23	extraction	extraction	NOUN
brj-24732	23	24	and	and	CCONJ
brj-24732	23	25	a	a	DET
brj-24732	23	26	whale	whale	NOUN
brj-24732	23	27	optimization	optimization	NOUN
brj-24732	23	28	algorithm	algorithm	NOUN
brj-24732	23	29	-	-	PUNCT
brj-24732	23	30	based	base	VERB
brj-24732	23	31	support	support	NOUN
brj-24732	23	32	vector	vector	NOUN
brj-24732	23	33	machine	machine	NOUN
brj-24732	23	34	(	(	PUNCT
brj-24732	23	35	woa	woa	NOUN
brj-24732	23	36	-	-	PUNCT
brj-24732	23	37	svm	svm	NOUN
brj-24732	23	38	)	)	PUNCT
brj-24732	23	39	to	to	PART
brj-24732	23	40	preprocess	preprocess	VERB
brj-24732	23	41	spectral	spectral	ADJ
brj-24732	23	42	data	datum	NOUN
brj-24732	23	43	and	and	CCONJ
brj-24732	23	44	identify	identify	VERB
brj-24732	23	45	wood	wood	NOUN
brj-24732	23	46	regions	region	NOUN
brj-24732	23	47	.	.	PUNCT
brj-24732	24	1	experimental	experimental	ADJ
brj-24732	24	2	results	result	NOUN
brj-24732	24	3	demonstrated	demonstrate	VERB
brj-24732	24	4	that	that	SCONJ
brj-24732	24	5	the	the	DET
brj-24732	24	6	elm	elm	NOUN
brj-24732	24	7	-	-	PUNCT
brj-24732	24	8	optimized	optimize	VERB
brj-24732	24	9	approach	approach	NOUN
brj-24732	24	10	effectively	effectively	ADV
brj-24732	24	11	enhanced	enhance	VERB
brj-24732	24	12	region	region	NOUN
brj-24732	24	13	recognition	recognition	NOUN
brj-24732	24	14	.	.	PUNCT
brj-24732	25	1	in	in	ADP
brj-24732	25	2	another	another	DET
brj-24732	25	3	study	study	NOUN
brj-24732	25	4	,	,	PUNCT
brj-24732	25	5	li	li	PROPN
brj-24732	25	6	et	et	PROPN
brj-24732	25	7	al	al	PROPN
brj-24732	25	8	.	.	PROPN
brj-24732	26	1	(	(	PUNCT
brj-24732	26	2	2025	2025	NUM
brj-24732	26	3	)	)	PUNCT
brj-24732	26	4	introduced	introduce	VERB
brj-24732	26	5	a	a	DET
brj-24732	26	6	supervised	supervised	ADJ
brj-24732	26	7	learning	learning	NOUN
brj-24732	26	8	-	-	PUNCT
brj-24732	26	9	based	base	VERB
brj-24732	26	10	image	image	NOUN
brj-24732	26	11	super	super	ADJ
brj-24732	26	12	-	-	ADJ
brj-24732	26	13	resolution	resolution	ADJ
brj-24732	26	14	method	method	NOUN
brj-24732	26	15	using	use	VERB
brj-24732	26	16	discrete	discrete	ADJ
brj-24732	26	17	wavelet	wavelet	NOUN
brj-24732	26	18	transform	transform	NOUN
brj-24732	26	19	(	(	PUNCT
brj-24732	26	20	dwt	dwt	NOUN
brj-24732	26	21	)	)	PUNCT
brj-24732	26	22	within	within	ADP
brj-24732	26	23	a	a	DET
brj-24732	26	24	u	u	ADJ
brj-24732	26	25	-	-	ADJ
brj-24732	26	26	net	net	ADJ
brj-24732	26	27	architecture	architecture	NOUN
brj-24732	26	28	,	,	PUNCT
brj-24732	26	29	incorporating	incorporate	VERB
brj-24732	26	30	dwt	dwt	NOUN
brj-24732	26	31	sampling	sampling	NOUN
brj-24732	26	32	and	and	CCONJ
brj-24732	26	33	channel	channel	NOUN
brj-24732	26	34	attention	attention	NOUN
brj-24732	26	35	residual	residual	ADJ
brj-24732	26	36	modules	module	NOUN
brj-24732	26	37	.	.	PUNCT
brj-24732	27	1	ablation	ablation	NOUN
brj-24732	27	2	studies	study	NOUN
brj-24732	27	3	and	and	CCONJ
brj-24732	27	4	comparative	comparative	ADJ
brj-24732	27	5	experiments	experiment	NOUN
brj-24732	27	6	validated	validate	VERB
brj-24732	27	7	the	the	DET
brj-24732	27	8	effectiveness	effectiveness	NOUN
brj-24732	27	9	of	of	ADP
brj-24732	27	10	each	each	DET
brj-24732	27	11	component	component	NOUN
brj-24732	27	12	,	,	PUNCT
brj-24732	27	13	showing	show	VERB
brj-24732	27	14	superior	superior	ADJ
brj-24732	27	15	performance	performance	NOUN
brj-24732	27	16	in	in	ADP
brj-24732	27	17	psnr	psnr	NOUN
brj-24732	27	18	and	and	CCONJ
brj-24732	27	19	ssim	ssim	NOUN
brj-24732	27	20	metrics	metric	NOUN
brj-24732	27	21	.	.	PUNCT
brj-24732	28	1	while	while	SCONJ
brj-24732	28	2	these	these	DET
brj-24732	28	3	traditional	traditional	ADJ
brj-24732	28	4	methods	method	NOUN
brj-24732	28	5	can	can	AUX
brj-24732	28	6	improve	improve	VERB
brj-24732	28	7	detection	detection	NOUN
brj-24732	28	8	accuracy	accuracy	NOUN
brj-24732	28	9	and	and	CCONJ
brj-24732	28	10	reduce	reduce	VERB
brj-24732	28	11	noise	noise	NOUN
brj-24732	28	12	interference	interference	NOUN
brj-24732	28	13	to	to	ADP
brj-24732	28	14	some	some	DET
brj-24732	28	15	extent	extent	NOUN
brj-24732	28	16	,	,	PUNCT
brj-24732	28	17	issues	issue	NOUN
brj-24732	28	18	,	,	PUNCT
brj-24732	28	19	such	such	ADJ
brj-24732	28	20	as	as	ADP
brj-24732	28	21	high	high	ADJ
brj-24732	28	22	computational	computational	ADJ
brj-24732	28	23	cost	cost	NOUN
brj-24732	28	24	,	,	PUNCT
brj-24732	28	25	complex	complex	ADJ
brj-24732	28	26	parameter	parameter	NOUN
brj-24732	28	27	optimization	optimization	NOUN
brj-24732	28	28	,	,	PUNCT
brj-24732	28	29	and	and	CCONJ
brj-24732	28	30	limited	limited	ADJ
brj-24732	28	31	real	real	ADJ
brj-24732	28	32	-	-	PUNCT
brj-24732	28	33	time	time	NOUN
brj-24732	28	34	applicability	applicability	NOUN
brj-24732	28	35	,	,	PUNCT
brj-24732	28	36	remain	remain	VERB
brj-24732	28	37	unresolved	unresolved	ADJ
brj-24732	28	38	.	.	PUNCT
brj-24732	29	1	in	in	ADP
brj-24732	29	2	contrast	contrast	NOUN
brj-24732	29	3	,	,	PUNCT
brj-24732	29	4	deep	deep	ADJ
brj-24732	29	5	learning	learning	NOUN
brj-24732	29	6	methods	method	NOUN
brj-24732	29	7	offer	offer	VERB
brj-24732	29	8	the	the	DET
brj-24732	29	9	advantage	advantage	NOUN
brj-24732	29	10	of	of	ADP
brj-24732	29	11	automatic	automatic	ADJ
brj-24732	29	12	feature	feature	NOUN
brj-24732	29	13	extraction	extraction	NOUN
brj-24732	29	14	and	and	CCONJ
brj-24732	29	15	end	end	NOUN
brj-24732	29	16	-	-	PUNCT
brj-24732	29	17	to	to	ADP
brj-24732	29	18	-	-	PUNCT
brj-24732	29	19	end	end	NOUN
brj-24732	29	20	learning	learning	NOUN
brj-24732	29	21	,	,	PUNCT
brj-24732	29	22	enabling	enable	VERB
brj-24732	29	23	greater	great	ADJ
brj-24732	29	24	adaptability	adaptability	NOUN
brj-24732	29	25	to	to	ADP
brj-24732	29	26	complex	complex	ADJ
brj-24732	29	27	data	datum	NOUN
brj-24732	29	28	.	.	PUNCT
brj-24732	30	1	compared	compare	VERB
brj-24732	30	2	to	to	ADP
brj-24732	30	3	traditional	traditional	ADJ
brj-24732	30	4	algorithms	algorithm	NOUN
brj-24732	30	5	,	,	PUNCT
brj-24732	30	6	deep	deep	ADJ
brj-24732	30	7	learning	learning	NOUN
brj-24732	30	8	-	-	PUNCT
brj-24732	30	9	based	base	VERB
brj-24732	30	10	approaches	approach	NOUN
brj-24732	30	11	successfully	successfully	ADV
brj-24732	30	12	overcome	overcome	VERB
brj-24732	30	13	the	the	DET
brj-24732	30	14	aforementioned	aforementioned	ADJ
brj-24732	30	15	limitations	limitation	NOUN
brj-24732	30	16	.	.	PUNCT
brj-24732	31	1	contemporary	contemporary	ADJ
brj-24732	31	2	object	object	NOUN
brj-24732	31	3	detection	detection	NOUN
brj-24732	31	4	networks	network	NOUN
brj-24732	31	5	are	be	AUX
brj-24732	31	6	generally	generally	ADV
brj-24732	31	7	categorized	categorize	VERB
brj-24732	31	8	into	into	ADP
brj-24732	31	9	two	two	NUM
brj-24732	31	10	types	type	NOUN
brj-24732	31	11	:	:	PUNCT
brj-24732	31	12	two	two	NUM
brj-24732	31	13	-	-	PUNCT
brj-24732	31	14	stage	stage	NOUN
brj-24732	31	15	and	and	CCONJ
brj-24732	31	16	one	one	NUM
brj-24732	31	17	-	-	PUNCT
brj-24732	31	18	stage	stage	NOUN
brj-24732	31	19	detectors	detector	NOUN
brj-24732	31	20	.	.	PUNCT
brj-24732	32	1	in	in	ADP
brj-24732	32	2	the	the	DET
brj-24732	32	3	two	two	NUM
brj-24732	32	4	-	-	PUNCT
brj-24732	32	5	stage	stage	NOUN
brj-24732	32	6	detection	detection	NOUN
brj-24732	32	7	domain	domain	NOUN
brj-24732	32	8	,	,	PUNCT
brj-24732	32	9	notable	notable	ADJ
brj-24732	32	10	contributions	contribution	NOUN
brj-24732	32	11	include	include	VERB
brj-24732	32	12	girshick	girshick	NOUN
brj-24732	32	13	et	et	PROPN
brj-24732	32	14	al	al	PROPN
brj-24732	32	15	.	.	PROPN
brj-24732	33	1	(	(	PUNCT
brj-24732	33	2	2014	2014	NUM
brj-24732	33	3	)	)	PUNCT
brj-24732	33	4	,	,	PUNCT
brj-24732	33	5	who	who	PRON
brj-24732	33	6	combined	combine	VERB
brj-24732	33	7	the	the	DET
brj-24732	33	8	region	region	NOUN
brj-24732	33	9	proposal	proposal	NOUN
brj-24732	33	10	algorithm	algorithm	NOUN
brj-24732	33	11	selective	selective	ADJ
brj-24732	33	12	search	search	NOUN
brj-24732	33	13	(	(	PUNCT
brj-24732	33	14	uijlings	uijling	NOUN
brj-24732	33	15	et	et	PROPN
brj-24732	33	16	al	al	PROPN
brj-24732	33	17	.	.	PROPN
brj-24732	33	18	2013	2013	NUM
brj-24732	33	19	)	)	PUNCT
brj-24732	33	20	with	with	ADP
brj-24732	33	21	convolutional	convolutional	ADJ
brj-24732	33	22	neural	neural	ADJ
brj-24732	33	23	networks	network	NOUN
brj-24732	33	24	(	(	PUNCT
brj-24732	33	25	cnns	cnns	PROPN
brj-24732	33	26	)	)	PUNCT
brj-24732	33	27	to	to	PART
brj-24732	33	28	develop	develop	VERB
brj-24732	33	29	the	the	DET
brj-24732	33	30	r	r	NOUN
brj-24732	33	31	-	-	PUNCT
brj-24732	33	32	cnn	cnn	PROPN
brj-24732	33	33	model	model	NOUN
brj-24732	33	34	,	,	PUNCT
brj-24732	33	35	achieving	achieve	VERB
brj-24732	33	36	significant	significant	ADJ
brj-24732	33	37	improvements	improvement	NOUN
brj-24732	33	38	in	in	ADP
brj-24732	33	39	detection	detection	NOUN
brj-24732	33	40	accuracy	accuracy	NOUN
brj-24732	33	41	.	.	PUNCT
brj-24732	34	1	subsequently	subsequently	ADV
brj-24732	34	2	,	,	PUNCT
brj-24732	34	3	girshick	girshick	PROPN
brj-24732	34	4	et	et	PROPN
brj-24732	34	5	al	al	PROPN
brj-24732	34	6	.	.	PROPN
brj-24732	35	1	(	(	PUNCT
brj-24732	35	2	2015	2015	NUM
brj-24732	35	3	)	)	PUNCT
brj-24732	35	4	introduced	introduce	VERB
brj-24732	35	5	the	the	DET
brj-24732	35	6	fast	fast	ADJ
brj-24732	35	7	r	r	NOUN
brj-24732	35	8	-	-	PUNCT
brj-24732	35	9	cnn	cnn	PROPN
brj-24732	35	10	model	model	NOUN
brj-24732	35	11	based	base	VERB
brj-24732	35	12	on	on	ADP
brj-24732	35	13	the	the	DET
brj-24732	35	14	spatial	spatial	ADJ
brj-24732	35	15	pyramid	pyramid	NOUN
brj-24732	35	16	pooling	pool	VERB
brj-24732	35	17	network	network	NOUN
brj-24732	35	18	(	(	PUNCT
brj-24732	35	19	sppnet	sppnet	PROPN
brj-24732	35	20	)	)	PUNCT
brj-24732	35	21	,	,	PUNCT
brj-24732	35	22	which	which	PRON
brj-24732	35	23	further	far	ADV
brj-24732	35	24	accelerated	accelerate	VERB
brj-24732	35	25	detection	detection	NOUN
brj-24732	35	26	speed	speed	NOUN
brj-24732	35	27	while	while	SCONJ
brj-24732	35	28	maintaining	maintain	VERB
brj-24732	35	29	high	high	ADJ
brj-24732	35	30	accuracy	accuracy	NOUN
brj-24732	35	31	.	.	PUNCT
brj-24732	36	1	more	more	ADV
brj-24732	36	2	recently	recently	ADV
brj-24732	36	3	,	,	PUNCT
brj-24732	36	4	zou	zou	PROPN
brj-24732	36	5	et	et	PROPN
brj-24732	36	6	al	al	PROPN
brj-24732	36	7	.	.	PROPN
brj-24732	36	8	(	(	PUNCT
brj-24732	36	9	2025	2025	NUM
brj-24732	36	10	)	)	PUNCT
brj-24732	36	11	enhanced	enhance	VERB
brj-24732	36	12	the	the	DET
brj-24732	36	13	faster	fast	ADJ
brj-24732	36	14	r	r	NOUN
brj-24732	36	15	-	-	PUNCT
brj-24732	36	16	cnn	cnn	NOUN
brj-24732	36	17	framework	framework	NOUN
brj-24732	36	18	by	by	ADP
brj-24732	36	19	integrating	integrate	VERB
brj-24732	36	20	an	an	DET
brj-24732	36	21	improved	improved	ADJ
brj-24732	36	22	resnet-50	resnet-50	NOUN
brj-24732	36	23	backbone	backbone	NOUN
brj-24732	36	24	,	,	PUNCT
brj-24732	36	25	a	a	DET
brj-24732	36	26	focal	focal	ADJ
brj-24732	36	27	loss	loss	NOUN
brj-24732	36	28	function	function	NOUN
brj-24732	36	29	,	,	PUNCT
brj-24732	36	30	and	and	CCONJ
brj-24732	36	31	soft	soft	ADJ
brj-24732	36	32	non	non	ADJ
brj-24732	36	33	-	-	ADJ
brj-24732	36	34	maximum	maximum	ADJ
brj-24732	36	35	suppression	suppression	NOUN
brj-24732	36	36	(	(	PUNCT
brj-24732	36	37	soft	soft	ADJ
brj-24732	36	38	-	-	PUNCT
brj-24732	36	39	nms	nms	NOUN
brj-24732	36	40	)	)	PUNCT
brj-24732	36	41	.	.	PUNCT
brj-24732	37	1	their	their	PRON
brj-24732	37	2	model	model	NOUN
brj-24732	37	3	achieved	achieve	VERB
brj-24732	37	4	a	a	DET
brj-24732	37	5	6.76	6.76	NUM
brj-24732	37	6	%	%	NOUN
brj-24732	37	7	improvement	improvement	NOUN
brj-24732	37	8	in	in	ADP
brj-24732	37	9	mean	mean	ADJ
brj-24732	37	10	average	average	ADJ
brj-24732	37	11	precision	precision	NOUN
brj-24732	37	12	(	(	PUNCT
brj-24732	37	13	map	map	NOUN
brj-24732	37	14	)	)	PUNCT
brj-24732	37	15	,	,	PUNCT
brj-24732	37	16	reaching	reach	VERB
brj-24732	37	17	67.80	67.80	NUM
brj-24732	37	18	%	%	NOUN
brj-24732	37	19	,	,	PUNCT
brj-24732	37	20	while	while	SCONJ
brj-24732	37	21	also	also	ADV
brj-24732	37	22	reducing	reduce	VERB
brj-24732	37	23	detection	detection	NOUN
brj-24732	37	24	time	time	NOUN
brj-24732	37	25	by	by	ADP
brj-24732	37	26	3.6	3.6	NUM
brj-24732	37	27	%	%	NOUN
brj-24732	37	28	,	,	PUNCT
brj-24732	37	29	thereby	thereby	ADV
brj-24732	37	30	improving	improve	VERB
brj-24732	37	31	detection	detection	NOUN
brj-24732	37	32	performance	performance	NOUN
brj-24732	37	33	in	in	ADP
brj-24732	37	34	wood	wood	NOUN
brj-24732	37	35	surface	surface	NOUN
brj-24732	37	36	defect	defect	NOUN
brj-24732	37	37	scenarios	scenario	NOUN
brj-24732	37	38	.	.	PUNCT
brj-24732	38	1	typical	typical	ADJ
brj-24732	38	2	one	one	NUM
brj-24732	38	3	-	-	PUNCT
brj-24732	38	4	stage	stage	NOUN
brj-24732	38	5	detectors	detector	NOUN
brj-24732	38	6	include	include	VERB
brj-24732	38	7	ssd	ssd	NOUN
brj-24732	38	8	(	(	PUNCT
brj-24732	38	9	single	single	ADJ
brj-24732	38	10	shot	shot	NOUN
brj-24732	38	11	multibox	multibox	NOUN
brj-24732	38	12	detector	detector	NOUN
brj-24732	38	13	)	)	PUNCT
brj-24732	38	14	and	and	CCONJ
brj-24732	38	15	the	the	DET
brj-24732	38	16	yolo	yolo	NOUN
brj-24732	38	17	(	(	PUNCT
brj-24732	38	18	you	you	PRON
brj-24732	38	19	only	only	ADV
brj-24732	38	20	look	look	VERB
brj-24732	38	21	once	once	ADV
brj-24732	38	22	)	)	PUNCT
brj-24732	38	23	series	series	NOUN
brj-24732	38	24	.	.	PUNCT
brj-24732	39	1	ding	de	VERB
brj-24732	39	2	et	et	PROPN
brj-24732	39	3	al	al	PROPN
brj-24732	39	4	.	.	PROPN
brj-24732	40	1	(	(	PUNCT
brj-24732	40	2	2020	2020	NUM
brj-24732	40	3	)	)	PUNCT
brj-24732	40	4	incorporated	incorporate	VERB
brj-24732	40	5	densenet	densenet	NOUN
brj-24732	40	6	into	into	ADP
brj-24732	40	7	the	the	DET
brj-24732	40	8	ssd	ssd	NOUN
brj-24732	40	9	framework	framework	NOUN
brj-24732	40	10	to	to	PART
brj-24732	40	11	improve	improve	VERB
brj-24732	40	12	deep	deep	ADJ
brj-24732	40	13	feature	feature	NOUN
brj-24732	40	14	extraction	extraction	NOUN
brj-24732	40	15	and	and	CCONJ
brj-24732	40	16	multi	multi	ADJ
brj-24732	40	17	-	-	ADJ
brj-24732	40	18	layer	layer	ADJ
brj-24732	40	19	feature	feature	NOUN
brj-24732	40	20	map	map	NOUN
brj-24732	40	21	fusion	fusion	NOUN
brj-24732	40	22	in	in	ADP
brj-24732	40	23	wood	wood	NOUN
brj-24732	40	24	imagery	imagery	NOUN
brj-24732	40	25	,	,	PUNCT
brj-24732	40	26	yielding	yield	VERB
brj-24732	40	27	superior	superior	ADJ
brj-24732	40	28	performance	performance	NOUN
brj-24732	40	29	over	over	ADP
brj-24732	40	30	the	the	DET
brj-24732	40	31	traditional	traditional	ADJ
brj-24732	40	32	ssd	ssd	NOUN
brj-24732	40	33	and	and	CCONJ
brj-24732	40	34	meeting	meet	VERB
brj-24732	40	35	real	real	ADJ
brj-24732	40	36	-	-	PUNCT
brj-24732	40	37	time	time	NOUN
brj-24732	40	38	demands	demand	NOUN
brj-24732	40	39	of	of	ADP
brj-24732	40	40	industrial	industrial	ADJ
brj-24732	40	41	wood	wood	NOUN
brj-24732	40	42	processing	processing	NOUN
brj-24732	40	43	.	.	PUNCT
brj-24732	41	1	the	the	DET
brj-24732	41	2	evolution	evolution	NOUN
brj-24732	41	3	of	of	ADP
brj-24732	41	4	yolo	yolo	NOUN
brj-24732	41	5	from	from	ADP
brj-24732	41	6	yolov1	yolov1	NOUN
brj-24732	41	7	to	to	ADP
brj-24732	41	8	yolov3	yolov3	PROPN
brj-24732	41	9	has	have	AUX
brj-24732	41	10	driven	drive	VERB
brj-24732	41	11	the	the	DET
brj-24732	41	12	development	development	NOUN
brj-24732	41	13	of	of	ADP
brj-24732	41	14	end	end	NOUN
brj-24732	41	15	-	-	PUNCT
brj-24732	41	16	to	to	ADP
brj-24732	41	17	-	-	PUNCT
brj-24732	41	18	end	end	NOUN
brj-24732	41	19	optimized	optimize	VERB
brj-24732	41	20	detectors	detector	NOUN
brj-24732	41	21	that	that	PRON
brj-24732	41	22	perform	perform	VERB
brj-24732	41	23	inference	inference	NOUN
brj-24732	41	24	in	in	ADP
brj-24732	41	25	a	a	DET
brj-24732	41	26	single	single	ADJ
brj-24732	41	27	pass	pass	NOUN
brj-24732	41	28	.	.	PUNCT
brj-24732	42	1	meng	meng	PROPN
brj-24732	42	2	and	and	CCONJ
brj-24732	42	3	yuan	yuan	PROPN
brj-24732	42	4	(	(	PUNCT
brj-24732	42	5	2023	2023	NUM
brj-24732	42	6	)	)	PUNCT
brj-24732	42	7	proposed	propose	VERB
brj-24732	42	8	sgnyolo	sgnyolo	NOUN
brj-24732	42	9	,	,	PUNCT
brj-24732	42	10	an	an	DET
brj-24732	42	11	improved	improved	ADJ
brj-24732	42	12	yolov5	yolov5	NOUN
brj-24732	42	13	-	-	PUNCT
brj-24732	42	14	based	base	VERB
brj-24732	42	15	model	model	NOUN
brj-24732	42	16	incorporating	incorporate	VERB
brj-24732	42	17	a	a	DET
brj-24732	42	18	semi	semi	ADJ
brj-24732	42	19	-	-	ADJ
brj-24732	42	20	global	global	ADJ
brj-24732	42	21	network	network	NOUN
brj-24732	42	22	(	(	PUNCT
brj-24732	42	23	sgn	sgn	NOUN
brj-24732	42	24	)	)	PUNCT
brj-24732	42	25	,	,	PUNCT
brj-24732	42	26	an	an	DET
brj-24732	42	27	enhanced	enhanced	ADJ
brj-24732	42	28	e	e	NOUN
brj-24732	42	29	-	-	NOUN
brj-24732	42	30	elan	elan	NOUN
brj-24732	42	31	module	module	NOUN
brj-24732	42	32	,	,	PUNCT
brj-24732	42	33	and	and	CCONJ
brj-24732	42	34	an	an	DET
brj-24732	42	35	eiou	eiou	NOUN
brj-24732	42	36	loss	loss	NOUN
brj-24732	42	37	function	function	NOUN
brj-24732	42	38	.	.	PUNCT
brj-24732	43	1	on	on	ADP
brj-24732	43	2	a	a	DET
brj-24732	43	3	public	public	ADJ
brj-24732	43	4	wood	wood	NOUN
brj-24732	43	5	defect	defect	NOUN
brj-24732	43	6	dataset	dataset	NOUN
brj-24732	43	7	,	,	PUNCT
brj-24732	43	8	the	the	DET
brj-24732	43	9	model	model	NOUN
brj-24732	43	10	achieved	achieve	VERB
brj-24732	43	11	an	an	DET
brj-24732	43	12	map	map	NOUN
brj-24732	43	13	of	of	ADP
brj-24732	43	14	86.4	86.4	NUM
brj-24732	43	15	%	%	NOUN
brj-24732	43	16	,	,	PUNCT
brj-24732	43	17	representing	represent	VERB
brj-24732	43	18	a	a	DET
brj-24732	43	19	3.1	3.1	NUM
brj-24732	43	20	%	%	NOUN
brj-24732	43	21	improvement	improvement	NOUN
brj-24732	43	22	over	over	ADP
brj-24732	43	23	the	the	DET
brj-24732	43	24	baseline	baseline	NOUN
brj-24732	43	25	,	,	PUNCT
brj-24732	43	26	with	with	ADP
brj-24732	43	27	ablation	ablation	NOUN
brj-24732	43	28	experiments	experiment	NOUN
brj-24732	43	29	validating	validate	VERB
brj-24732	43	30	the	the	DET
brj-24732	43	31	contribution	contribution	NOUN
brj-24732	43	32	of	of	ADP
brj-24732	43	33	each	each	DET
brj-24732	43	34	enhancement	enhancement	NOUN
brj-24732	43	35	.	.	PUNCT
brj-24732	44	1	nevertheless	nevertheless	ADV
brj-24732	44	2	,	,	PUNCT
brj-24732	44	3	challenges	challenge	NOUN
brj-24732	44	4	remain	remain	VERB
brj-24732	44	5	in	in	ADP
brj-24732	44	6	detecting	detect	VERB
brj-24732	44	7	small	small	ADJ
brj-24732	44	8	defects	defect	NOUN
brj-24732	44	9	and	and	CCONJ
brj-24732	44	10	ensuring	ensure	VERB
brj-24732	44	11	robust	robust	ADJ
brj-24732	44	12	dataset	dataset	NOUN
brj-24732	44	13	generalization	generalization	NOUN
brj-24732	44	14	.	.	PUNCT
brj-24732	45	1	wang	wang	PROPN
brj-24732	45	2	et	et	PROPN
brj-24732	45	3	al	al	PROPN
brj-24732	45	4	.	.	PROPN
brj-24732	46	1	(	(	PUNCT
brj-24732	46	2	2024	2024	NUM
brj-24732	46	3	)	)	PUNCT
brj-24732	46	4	further	far	ADV
brj-24732	46	5	improved	improve	VERB
brj-24732	46	6	the	the	DET
brj-24732	46	7	yolov7	yolov7	NOUN
brj-24732	46	8	architecture	architecture	NOUN
brj-24732	46	9	by	by	ADP
brj-24732	46	10	replacing	replace	VERB
brj-24732	46	11	standard	standard	ADJ
brj-24732	46	12	convolution	convolution	NOUN
brj-24732	46	13	in	in	ADP
brj-24732	46	14	the	the	DET
brj-24732	46	15	elan	elan	PROPN
brj-24732	46	16	module	module	NOUN
brj-24732	46	17	with	with	ADP
brj-24732	46	18	partial	partial	ADJ
brj-24732	46	19	convolution	convolution	NOUN
brj-24732	46	20	(	(	PUNCT
brj-24732	46	21	pconv	pconv	NOUN
brj-24732	46	22	)	)	PUNCT
brj-24732	46	23	,	,	PUNCT
brj-24732	46	24	forming	form	VERB
brj-24732	46	25	the	the	DET
brj-24732	46	26	p	p	PROPN
brj-24732	46	27	-	-	PUNCT
brj-24732	46	28	elan	elan	NOUN
brj-24732	46	29	module	module	NOUN
brj-24732	46	30	.	.	PUNCT
brj-24732	47	1	this	this	DET
brj-24732	47	2	modification	modification	NOUN
brj-24732	47	3	reduced	reduce	VERB
brj-24732	47	4	computational	computational	ADJ
brj-24732	47	5	redundancy	redundancy	NOUN
brj-24732	47	6	and	and	CCONJ
brj-24732	47	7	memory	memory	NOUN
brj-24732	47	8	usage	usage	NOUN
brj-24732	47	9	while	while	SCONJ
brj-24732	47	10	enhancing	enhance	VERB
brj-24732	47	11	detection	detection	NOUN
brj-24732	47	12	accuracy	accuracy	NOUN
brj-24732	47	13	.	.	PUNCT
brj-24732	48	1	despite	despite	SCONJ
brj-24732	48	2	the	the	DET
brj-24732	48	3	progress	progress	NOUN
brj-24732	48	4	of	of	ADP
brj-24732	48	5	deep	deep	ADJ
brj-24732	48	6	learning	learning	NOUN
brj-24732	48	7	in	in	ADP
brj-24732	48	8	object	object	NOUN
brj-24732	48	9	detection	detection	NOUN
brj-24732	48	10	,	,	PUNCT
brj-24732	48	11	real	real	ADJ
brj-24732	48	12	-	-	PUNCT
brj-24732	48	13	world	world	NOUN
brj-24732	48	14	applications	application	NOUN
brj-24732	48	15	in	in	ADP
brj-24732	48	16	wood	wood	NOUN
brj-24732	48	17	processing	processing	NOUN
brj-24732	48	18	environments	environment	NOUN
brj-24732	48	19	still	still	ADV
brj-24732	48	20	face	face	VERB
brj-24732	48	21	challenges	challenge	NOUN
brj-24732	48	22	due	due	ADJ
brj-24732	48	23	to	to	ADP
brj-24732	48	24	lighting	lighting	NOUN
brj-24732	48	25	variations	variation	NOUN
brj-24732	48	26	,	,	PUNCT
brj-24732	48	27	dust	dust	NOUN
brj-24732	48	28	interference	interference	NOUN
brj-24732	48	29	,	,	PUNCT
brj-24732	48	30	and	and	CCONJ
brj-24732	48	31	the	the	DET
brj-24732	48	32	complexity	complexity	NOUN
brj-24732	48	33	of	of	ADP
brj-24732	48	34	textures	texture	NOUN
brj-24732	48	35	.	.	PUNCT
brj-24732	49	1	issues	issue	NOUN
brj-24732	49	2	persist	persist	VERB
brj-24732	49	3	,	,	PUNCT
brj-24732	49	4	such	such	ADJ
brj-24732	49	5	as	as	ADP
brj-24732	49	6	missed	miss	VERB
brj-24732	49	7	detections	detection	NOUN
brj-24732	49	8	and	and	CCONJ
brj-24732	49	9	large	large	ADJ
brj-24732	49	10	model	model	NOUN
brj-24732	49	11	sizes	size	NOUN
brj-24732	49	12	.	.	PUNCT
brj-24732	50	1	consequently	consequently	ADV
brj-24732	50	2	,	,	PUNCT
brj-24732	50	3	there	there	PRON
brj-24732	50	4	remains	remain	VERB
brj-24732	50	5	considerable	considerable	ADJ
brj-24732	50	6	room	room	NOUN
brj-24732	50	7	for	for	ADP
brj-24732	50	8	improving	improve	VERB
brj-24732	50	9	the	the	DET
brj-24732	50	10	accuracy	accuracy	NOUN
brj-24732	50	11	and	and	CCONJ
brj-24732	50	12	efficiency	efficiency	NOUN
brj-24732	50	13	of	of	ADP
brj-24732	50	14	particleboard	particleboard	NOUN
brj-24732	50	15	surface	surface	NOUN
brj-24732	50	16	defect	defect	NOUN
brj-24732	50	17	detection	detection	NOUN
brj-24732	50	18	.	.	PUNCT
brj-24732	51	1	peer	peer	NOUN
brj-24732	51	2	-	-	PUNCT
brj-24732	51	3	reviewed	review	VERB
brj-24732	51	4	article	article	NOUN
brj-24732	51	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	51	6	he	he	PRON
brj-24732	51	7	et	et	PROPN
brj-24732	51	8	al	al	PROPN
brj-24732	51	9	.	.	PROPN
brj-24732	52	1	(	(	PUNCT
brj-24732	52	2	2025	2025	NUM
brj-24732	52	3	)	)	PUNCT
brj-24732	52	4	.	.	PUNCT
brj-24732	53	1	“	"	PUNCT
brj-24732	53	2	le	le	X
brj-24732	53	3	-	-	PROPN
brj-24732	53	4	yolo	yolo	PROPN
brj-24732	53	5	&	&	CCONJ
brj-24732	53	6	wood	wood	PROPN
brj-24732	53	7	surface	surface	NOUN
brj-24732	53	8	defects	defect	NOUN
brj-24732	53	9	,	,	PUNCT
brj-24732	53	10	”	"	PUNCT
brj-24732	53	11	bioresources	bioresource	NOUN
brj-24732	53	12	20(3	20(3	NOUN
brj-24732	53	13	)	)	PUNCT
brj-24732	53	14	,	,	PUNCT
brj-24732	53	15	7179	7179	NUM
brj-24732	53	16	-	-	SYM
brj-24732	53	17	7193	7193	NUM
brj-24732	53	18	.	.	PUNCT
brj-24732	54	1	7181	7181	NUM
brj-24732	54	2	to	to	PART
brj-24732	54	3	address	address	VERB
brj-24732	54	4	these	these	DET
brj-24732	54	5	challenges	challenge	NOUN
brj-24732	54	6	,	,	PUNCT
brj-24732	54	7	this	this	DET
brj-24732	54	8	study	study	NOUN
brj-24732	54	9	proposes	propose	VERB
brj-24732	54	10	a	a	DET
brj-24732	54	11	lightweight	lightweight	ADJ
brj-24732	54	12	and	and	CCONJ
brj-24732	54	13	enhanced	enhanced	ADJ
brj-24732	54	14	detection	detection	NOUN
brj-24732	54	15	model	model	NOUN
brj-24732	54	16	based	base	VERB
brj-24732	54	17	on	on	ADP
brj-24732	54	18	yolov11	yolov11	NOUN
brj-24732	54	19	(	(	PUNCT
brj-24732	54	20	khanam	khanam	NOUN
brj-24732	54	21	and	and	CCONJ
brj-24732	54	22	hussain	hussain	PROPN
brj-24732	54	23	2024	2024	NUM
brj-24732	54	24	)	)	PUNCT
brj-24732	54	25	,	,	PUNCT
brj-24732	54	26	named	name	VERB
brj-24732	54	27	le	le	NOUN
brj-24732	54	28	-	-	NOUN
brj-24732	54	29	yolo	yolo	PROPN
brj-24732	54	30	.	.	PUNCT
brj-24732	55	1	rather	rather	ADV
brj-24732	55	2	than	than	ADP
brj-24732	55	3	merely	merely	ADV
brj-24732	55	4	aggregating	aggregate	VERB
brj-24732	55	5	existing	exist	VERB
brj-24732	55	6	architectural	architectural	ADJ
brj-24732	55	7	components	component	NOUN
brj-24732	55	8	,	,	PUNCT
brj-24732	55	9	the	the	DET
brj-24732	55	10	model	model	NOUN
brj-24732	55	11	incorporates	incorporate	VERB
brj-24732	55	12	several	several	ADJ
brj-24732	55	13	targeted	target	VERB
brj-24732	55	14	innovations	innovation	NOUN
brj-24732	55	15	.	.	PUNCT
brj-24732	56	1	the	the	DET
brj-24732	56	2	primary	primary	ADJ
brj-24732	56	3	contributions	contribution	NOUN
brj-24732	56	4	of	of	ADP
brj-24732	56	5	this	this	DET
brj-24732	56	6	work	work	NOUN
brj-24732	56	7	are	be	AUX
brj-24732	56	8	as	as	SCONJ
brj-24732	56	9	follows	follow	VERB
brj-24732	56	10	:	:	PUNCT
brj-24732	56	11	1	1	X
brj-24732	56	12	.	.	X
brj-24732	56	13	design	design	NOUN
brj-24732	56	14	of	of	ADP
brj-24732	56	15	the	the	DET
brj-24732	56	16	adaptive	adaptive	ADJ
brj-24732	56	17	multi	multi	ADJ
brj-24732	56	18	-	-	ADJ
brj-24732	56	19	kernel	kernel	ADJ
brj-24732	56	20	depthwise	depthwise	NOUN
brj-24732	56	21	conv2d	conv2d	PROPN
brj-24732	56	22	(	(	PUNCT
brj-24732	56	23	amdc	amdc	ADJ
brj-24732	56	24	)	)	PUNCT
brj-24732	56	25	module	module	NOUN
brj-24732	56	26	.	.	PUNCT
brj-24732	57	1	integrated	integrate	VERB
brj-24732	57	2	into	into	ADP
brj-24732	57	3	the	the	DET
brj-24732	57	4	backbone	backbone	NOUN
brj-24732	57	5	network	network	NOUN
brj-24732	57	6	as	as	ADP
brj-24732	57	7	a	a	DET
brj-24732	57	8	replacement	replacement	NOUN
brj-24732	57	9	for	for	ADP
brj-24732	57	10	the	the	DET
brj-24732	57	11	c3k2	c3k2	NOUN
brj-24732	57	12	module	module	NOUN
brj-24732	57	13	in	in	ADP
brj-24732	57	14	yolov11	yolov11	NOUN
brj-24732	57	15	,	,	PUNCT
brj-24732	57	16	amdc	amdc	NOUN
brj-24732	57	17	leverages	leverage	VERB
brj-24732	57	18	multiple	multiple	ADJ
brj-24732	57	19	shapes	shape	NOUN
brj-24732	57	20	of	of	ADP
brj-24732	57	21	2d	2d	NUM
brj-24732	57	22	depthwise	depthwise	NOUN
brj-24732	57	23	separable	separable	ADJ
brj-24732	57	24	convolution	convolution	NOUN
brj-24732	57	25	kernels	kernel	NOUN
brj-24732	57	26	to	to	PART
brj-24732	57	27	adaptively	adaptively	ADV
brj-24732	57	28	extract	extract	VERB
brj-24732	57	29	features	feature	NOUN
brj-24732	57	30	.	.	PUNCT
brj-24732	58	1	this	this	DET
brj-24732	58	2	design	design	NOUN
brj-24732	58	3	reduces	reduce	VERB
brj-24732	58	4	the	the	DET
brj-24732	58	5	parameter	parameter	NOUN
brj-24732	58	6	count	count	NOUN
brj-24732	58	7	while	while	SCONJ
brj-24732	58	8	preserving	preserve	VERB
brj-24732	58	9	model	model	NOUN
brj-24732	58	10	expressiveness	expressiveness	NOUN
brj-24732	58	11	.	.	PUNCT
brj-24732	59	1	2	2	X
brj-24732	59	2	.	.	X
brj-24732	59	3	development	development	NOUN
brj-24732	59	4	of	of	ADP
brj-24732	59	5	the	the	DET
brj-24732	59	6	shared	share	VERB
brj-24732	59	7	dilated	dilate	VERB
brj-24732	59	8	feature	feature	NOUN
brj-24732	59	9	pyramid	pyramid	NOUN
brj-24732	59	10	(	(	PUNCT
brj-24732	59	11	sdfp	sdfp	NOUN
brj-24732	59	12	)	)	PUNCT
brj-24732	59	13	module	module	NOUN
brj-24732	59	14	.	.	PUNCT
brj-24732	60	1	by	by	ADP
brj-24732	60	2	concatenating	concatenate	VERB
brj-24732	60	3	feature	feature	NOUN
brj-24732	60	4	maps	map	NOUN
brj-24732	60	5	derived	derive	VERB
brj-24732	60	6	from	from	ADP
brj-24732	60	7	varying	vary	VERB
brj-24732	60	8	dilation	dilation	NOUN
brj-24732	60	9	rates	rate	NOUN
brj-24732	60	10	with	with	ADP
brj-24732	60	11	the	the	DET
brj-24732	60	12	original	original	ADJ
brj-24732	60	13	convolutional	convolutional	ADJ
brj-24732	60	14	output	output	NOUN
brj-24732	60	15	along	along	ADP
brj-24732	60	16	the	the	DET
brj-24732	60	17	channel	channel	NOUN
brj-24732	60	18	dimension	dimension	NOUN
brj-24732	60	19	,	,	PUNCT
brj-24732	60	20	the	the	DET
brj-24732	60	21	sdfp	sdfp	NOUN
brj-24732	60	22	module	module	NOUN
brj-24732	60	23	facilitates	facilitate	VERB
brj-24732	60	24	multi	multi	ADJ
brj-24732	60	25	-	-	ADJ
brj-24732	60	26	scale	scale	ADJ
brj-24732	60	27	object	object	NOUN
brj-24732	60	28	recognition	recognition	NOUN
brj-24732	60	29	and	and	CCONJ
brj-24732	60	30	improves	improve	VERB
brj-24732	60	31	detection	detection	NOUN
brj-24732	60	32	performance	performance	NOUN
brj-24732	60	33	across	across	ADP
brj-24732	60	34	defect	defect	ADJ
brj-24732	60	35	sizes	size	NOUN
brj-24732	60	36	.	.	PUNCT
brj-24732	61	1	3	3	X
brj-24732	61	2	.	.	X
brj-24732	61	3	proposal	proposal	NOUN
brj-24732	61	4	of	of	ADP
brj-24732	61	5	the	the	DET
brj-24732	61	6	lightweight	lightweight	ADJ
brj-24732	61	7	detection	detection	NOUN
brj-24732	61	8	head	head	NOUN
brj-24732	61	9	(	(	PUNCT
brj-24732	61	10	lwdethead	lwdethead	ADJ
brj-24732	61	11	)	)	PUNCT
brj-24732	61	12	.	.	PUNCT
brj-24732	62	1	this	this	DET
brj-24732	62	2	detection	detection	NOUN
brj-24732	62	3	head	head	NOUN
brj-24732	62	4	utilizes	utilize	VERB
brj-24732	62	5	shared	share	VERB
brj-24732	62	6	convolutions	convolution	NOUN
brj-24732	62	7	and	and	CCONJ
brj-24732	62	8	multi	multi	ADJ
brj-24732	62	9	-	-	ADJ
brj-24732	62	10	scale	scale	ADJ
brj-24732	62	11	feature	feature	NOUN
brj-24732	62	12	fusion	fusion	NOUN
brj-24732	62	13	to	to	PART
brj-24732	62	14	enhance	enhance	VERB
brj-24732	62	15	fine	fine	ADV
brj-24732	62	16	-	-	PUNCT
brj-24732	62	17	grained	grain	VERB
brj-24732	62	18	representation	representation	NOUN
brj-24732	62	19	.	.	PUNCT
brj-24732	63	1	additionally	additionally	ADV
brj-24732	63	2	,	,	PUNCT
brj-24732	63	3	it	it	PRON
brj-24732	63	4	incorporates	incorporate	VERB
brj-24732	63	5	a	a	DET
brj-24732	63	6	distributed	distribute	VERB
brj-24732	63	7	focal	focal	ADJ
brj-24732	63	8	loss	loss	NOUN
brj-24732	63	9	mechanism	mechanism	NOUN
brj-24732	63	10	,	,	PUNCT
brj-24732	63	11	enabling	enable	VERB
brj-24732	63	12	effective	effective	ADJ
brj-24732	63	13	detection	detection	NOUN
brj-24732	63	14	with	with	ADP
brj-24732	63	15	minimal	minimal	ADJ
brj-24732	63	16	parameter	parameter	NOUN
brj-24732	63	17	overhead	overhead	ADV
brj-24732	63	18	and	and	CCONJ
brj-24732	63	19	computational	computational	ADJ
brj-24732	63	20	cost	cost	NOUN
brj-24732	63	21	.	.	PUNCT
brj-24732	64	1	4	4	X
brj-24732	64	2	.	.	X
brj-24732	64	3	incorporation	incorporation	NOUN
brj-24732	64	4	of	of	ADP
brj-24732	64	5	the	the	DET
brj-24732	64	6	normalized	normalize	VERB
brj-24732	64	7	wasserstein	wasserstein	NOUN
brj-24732	64	8	distance	distance	NOUN
brj-24732	64	9	(	(	PUNCT
brj-24732	64	10	nwd	nwd	NOUN
brj-24732	64	11	)	)	PUNCT
brj-24732	64	12	in	in	ADP
brj-24732	64	13	the	the	DET
brj-24732	64	14	loss	loss	NOUN
brj-24732	64	15	function	function	NOUN
brj-24732	64	16	.	.	PUNCT
brj-24732	65	1	as	as	SCONJ
brj-24732	65	2	introduced	introduce	VERB
brj-24732	65	3	by	by	ADP
brj-24732	65	4	wang	wang	PROPN
brj-24732	65	5	et	et	PROPN
brj-24732	65	6	al	al	PROPN
brj-24732	65	7	.	.	PROPN
brj-24732	66	1	(	(	PUNCT
brj-24732	66	2	2022	2022	NUM
brj-24732	66	3	)	)	PUNCT
brj-24732	66	4	,	,	PUNCT
brj-24732	66	5	nwd	nwd	NOUN
brj-24732	66	6	effectively	effectively	ADV
brj-24732	66	7	measures	measure	VERB
brj-24732	66	8	distributional	distributional	ADJ
brj-24732	66	9	similarity	similarity	NOUN
brj-24732	66	10	with	with	ADP
brj-24732	66	11	reduced	reduced	ADJ
brj-24732	66	12	sensitivity	sensitivity	NOUN
brj-24732	66	13	to	to	PART
brj-24732	66	14	object	object	VERB
brj-24732	66	15	scale	scale	NOUN
brj-24732	66	16	,	,	PUNCT
brj-24732	66	17	making	make	VERB
brj-24732	66	18	it	it	PRON
brj-24732	66	19	particularly	particularly	ADV
brj-24732	66	20	suitable	suitable	ADJ
brj-24732	66	21	for	for	ADP
brj-24732	66	22	small	small	ADJ
brj-24732	66	23	object	object	NOUN
brj-24732	66	24	detection	detection	NOUN
brj-24732	66	25	tasks	task	NOUN
brj-24732	66	26	,	,	PUNCT
brj-24732	66	27	such	such	ADJ
brj-24732	66	28	as	as	ADP
brj-24732	66	29	tiny	tiny	ADJ
brj-24732	66	30	surface	surface	NOUN
brj-24732	66	31	defects	defect	NOUN
brj-24732	66	32	.	.	PUNCT
brj-24732	67	1	experimental	experimental	ADJ
brj-24732	67	2	wood	wood	NOUN
brj-24732	67	3	defects	defect	NOUN
brj-24732	67	4	dataset	dataset	VERB
brj-24732	67	5	the	the	DET
brj-24732	67	6	chipboardv1.0	chipboardv1.0	NUM
brj-24732	67	7	dataset	dataset	NOUN
brj-24732	67	8	,	,	PUNCT
brj-24732	67	9	collected	collect	VERB
brj-24732	67	10	from	from	ADP
brj-24732	67	11	shandong	shandong	PROPN
brj-24732	67	12	luli	luli	PROPN
brj-24732	67	13	wood	wood	PROPN
brj-24732	67	14	industry	industry	PROPN
brj-24732	67	15	co.	co.	PROPN
brj-24732	67	16	,	,	PUNCT
brj-24732	67	17	ltd	ltd	PROPN
brj-24732	67	18	.	.	PROPN
brj-24732	67	19	’s	’s	PART
brj-24732	67	20	automated	automate	VERB
brj-24732	67	21	production	production	NOUN
brj-24732	67	22	line	line	NOUN
brj-24732	67	23	,	,	PUNCT
brj-24732	67	24	contains	contain	VERB
brj-24732	67	25	three	three	NUM
brj-24732	67	26	common	common	ADJ
brj-24732	67	27	particleboard	particleboard	NOUN
brj-24732	67	28	surface	surface	NOUN
brj-24732	67	29	defects	defect	NOUN
brj-24732	67	30	:	:	PUNCT
brj-24732	67	31	large	large	ADJ
brj-24732	67	32	shavings	shaving	NOUN
brj-24732	67	33	,	,	PUNCT
brj-24732	67	34	sand	sand	NOUN
brj-24732	67	35	leakage	leakage	NOUN
brj-24732	67	36	,	,	PUNCT
brj-24732	67	37	and	and	CCONJ
brj-24732	67	38	black	black	ADJ
brj-24732	67	39	spots	spot	NOUN
brj-24732	67	40	,	,	PUNCT
brj-24732	67	41	with	with	ADP
brj-24732	67	42	a	a	DET
brj-24732	67	43	balanced	balanced	ADJ
brj-24732	67	44	class	class	NOUN
brj-24732	67	45	distribution	distribution	NOUN
brj-24732	67	46	of	of	ADP
brj-24732	67	47	33	33	NUM
brj-24732	67	48	%	%	NOUN
brj-24732	67	49	black	black	ADJ
brj-24732	67	50	spots	spot	NOUN
brj-24732	67	51	,	,	PUNCT
brj-24732	67	52	33	33	NUM
brj-24732	67	53	%	%	NOUN
brj-24732	67	54	large	large	ADJ
brj-24732	67	55	shavings	shaving	NOUN
brj-24732	67	56	,	,	PUNCT
brj-24732	67	57	and	and	CCONJ
brj-24732	67	58	34	34	NUM
brj-24732	67	59	%	%	NOUN
brj-24732	67	60	sand	sand	NOUN
brj-24732	67	61	leakage	leakage	NOUN
brj-24732	67	62	.	.	PUNCT
brj-24732	68	1	to	to	PART
brj-24732	68	2	enhance	enhance	VERB
brj-24732	68	3	model	model	NOUN
brj-24732	68	4	robustness	robustness	NOUN
brj-24732	68	5	and	and	CCONJ
brj-24732	68	6	generalization	generalization	NOUN
brj-24732	68	7	,	,	PUNCT
brj-24732	68	8	data	datum	NOUN
brj-24732	68	9	augmentation	augmentation	NOUN
brj-24732	68	10	techniques	technique	NOUN
brj-24732	68	11	such	such	ADJ
brj-24732	68	12	as	as	ADP
brj-24732	68	13	horizontal	horizontal	ADJ
brj-24732	68	14	scaling	scaling	NOUN
brj-24732	68	15	and	and	CCONJ
brj-24732	68	16	gaussian	gaussian	ADJ
brj-24732	68	17	noise	noise	NOUN
brj-24732	68	18	were	be	AUX
brj-24732	68	19	applied	apply	VERB
brj-24732	68	20	,	,	PUNCT
brj-24732	68	21	improving	improve	VERB
brj-24732	68	22	adaptability	adaptability	NOUN
brj-24732	68	23	to	to	ADP
brj-24732	68	24	complex	complex	ADJ
brj-24732	68	25	textures	texture	NOUN
brj-24732	68	26	and	and	CCONJ
brj-24732	68	27	varying	vary	VERB
brj-24732	68	28	lighting	lighting	NOUN
brj-24732	68	29	.	.	PUNCT
brj-24732	69	1	all	all	DET
brj-24732	69	2	images	image	NOUN
brj-24732	69	3	were	be	AUX
brj-24732	69	4	standardized	standardize	VERB
brj-24732	69	5	to	to	ADP
brj-24732	69	6	640×640	640×640	NUM
brj-24732	69	7	pixels	pixel	NOUN
brj-24732	69	8	and	and	CCONJ
brj-24732	69	9	captured	capture	VERB
brj-24732	69	10	under	under	ADP
brj-24732	69	11	different	different	ADJ
brj-24732	69	12	lighting	lighting	NOUN
brj-24732	69	13	conditions	condition	NOUN
brj-24732	69	14	,	,	PUNCT
brj-24732	69	15	including	include	VERB
brj-24732	69	16	normal	normal	ADJ
brj-24732	69	17	,	,	PUNCT
brj-24732	69	18	reflective	reflective	ADJ
brj-24732	69	19	,	,	PUNCT
brj-24732	69	20	and	and	CCONJ
brj-24732	69	21	low	low	ADJ
brj-24732	69	22	-	-	PUNCT
brj-24732	69	23	light	light	NOUN
brj-24732	69	24	environments	environment	NOUN
brj-24732	69	25	.	.	PUNCT
brj-24732	70	1	a	a	DET
brj-24732	70	2	stratified	stratified	ADJ
brj-24732	70	3	sampling	sampling	NOUN
brj-24732	70	4	strategy	strategy	NOUN
brj-24732	70	5	was	be	AUX
brj-24732	70	6	used	use	VERB
brj-24732	70	7	to	to	PART
brj-24732	70	8	ensure	ensure	VERB
brj-24732	70	9	class	class	NOUN
brj-24732	70	10	balance	balance	NOUN
brj-24732	70	11	,	,	PUNCT
brj-24732	70	12	with	with	ADP
brj-24732	70	13	3,000	3,000	NUM
brj-24732	70	14	images	image	NOUN
brj-24732	70	15	selected—2,400	selected—2,400	NOUN
brj-24732	70	16	for	for	ADP
brj-24732	70	17	training	training	NOUN
brj-24732	70	18	,	,	PUNCT
brj-24732	70	19	300	300	NUM
brj-24732	70	20	for	for	ADP
brj-24732	70	21	validation	validation	NOUN
brj-24732	70	22	,	,	PUNCT
brj-24732	70	23	and	and	CCONJ
brj-24732	70	24	300	300	NUM
brj-24732	70	25	for	for	ADP
brj-24732	70	26	testing	testing	NOUN
brj-24732	70	27	(	(	PUNCT
brj-24732	70	28	8:1:1	8:1:1	PROPN
brj-24732	70	29	split	split	NOUN
brj-24732	70	30	)	)	PUNCT
brj-24732	70	31	.	.	PUNCT
brj-24732	71	1	figure	figure	NOUN
brj-24732	71	2	1	1	NUM
brj-24732	71	3	shows	show	VERB
brj-24732	71	4	representative	representative	ADJ
brj-24732	71	5	examples	example	NOUN
brj-24732	71	6	of	of	ADP
brj-24732	71	7	each	each	DET
brj-24732	71	8	defect	defect	NOUN
brj-24732	71	9	type	type	NOUN
brj-24732	71	10	.	.	PUNCT
brj-24732	72	1	black	black	ADJ
brj-24732	72	2	spots	spot	NOUN
brj-24732	72	3	large	large	ADJ
brj-24732	72	4	shavings	shaving	NOUN
brj-24732	72	5	sand	sand	NOUN
brj-24732	72	6	leakage	leakage	NOUN
brj-24732	72	7	fig	fig	NOUN
brj-24732	72	8	.	.	PUNCT
brj-24732	73	1	1	1	X
brj-24732	73	2	.	.	X
brj-24732	73	3	representative	representative	ADJ
brj-24732	73	4	examples	example	NOUN
brj-24732	73	5	of	of	ADP
brj-24732	73	6	the	the	DET
brj-24732	73	7	three	three	NUM
brj-24732	73	8	surface	surface	NOUN
brj-24732	73	9	defect	defect	NOUN
brj-24732	73	10	types	type	NOUN
brj-24732	73	11	in	in	ADP
brj-24732	73	12	particleboard	particleboard	NOUN
brj-24732	73	13	peer	peer	NOUN
brj-24732	73	14	-	-	PUNCT
brj-24732	73	15	reviewed	review	VERB
brj-24732	73	16	article	article	NOUN
brj-24732	73	17	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	73	18	he	he	PRON
brj-24732	73	19	et	et	PROPN
brj-24732	73	20	al	al	PROPN
brj-24732	73	21	.	.	PROPN
brj-24732	74	1	(	(	PUNCT
brj-24732	74	2	2025	2025	NUM
brj-24732	74	3	)	)	PUNCT
brj-24732	74	4	.	.	PUNCT
brj-24732	75	1	“	"	PUNCT
brj-24732	75	2	le	le	X
brj-24732	75	3	-	-	PROPN
brj-24732	75	4	yolo	yolo	PROPN
brj-24732	75	5	&	&	CCONJ
brj-24732	75	6	wood	wood	PROPN
brj-24732	75	7	surface	surface	NOUN
brj-24732	75	8	defects	defect	NOUN
brj-24732	75	9	,	,	PUNCT
brj-24732	75	10	”	"	PUNCT
brj-24732	75	11	bioresources	bioresource	NOUN
brj-24732	75	12	20(3	20(3	NOUN
brj-24732	75	13	)	)	PUNCT
brj-24732	75	14	,	,	PUNCT
brj-24732	75	15	7179	7179	NUM
brj-24732	75	16	-	-	SYM
brj-24732	75	17	7193	7193	NUM
brj-24732	75	18	.	.	PUNCT
brj-24732	76	1	7182	7182	NUM
brj-24732	76	2	le	le	PROPN
brj-24732	76	3	-	-	PROPN
brj-24732	76	4	yolo	yolo	ADJ
brj-24732	76	5	yolov11	yolov11	NOUN
brj-24732	76	6	,	,	PUNCT
brj-24732	76	7	developed	develop	VERB
brj-24732	76	8	by	by	ADP
brj-24732	76	9	ultralytics	ultralytic	NOUN
brj-24732	76	10	and	and	CCONJ
brj-24732	76	11	released	release	VERB
brj-24732	76	12	in	in	ADP
brj-24732	76	13	late	late	ADJ
brj-24732	76	14	2024	2024	NUM
brj-24732	76	15	,	,	PUNCT
brj-24732	76	16	represents	represent	VERB
brj-24732	76	17	a	a	DET
brj-24732	76	18	nextgeneration	nextgeneration	NOUN
brj-24732	76	19	object	object	NOUN
brj-24732	76	20	detection	detection	NOUN
brj-24732	76	21	algorithm	algorithm	NOUN
brj-24732	76	22	that	that	PRON
brj-24732	76	23	builds	build	VERB
brj-24732	76	24	on	on	ADP
brj-24732	76	25	the	the	DET
brj-24732	76	26	strengths	strength	NOUN
brj-24732	76	27	of	of	ADP
brj-24732	76	28	its	its	PRON
brj-24732	76	29	predecessors	predecessor	NOUN
brj-24732	76	30	.	.	PUNCT
brj-24732	77	1	it	it	PRON
brj-24732	77	2	introduces	introduce	VERB
brj-24732	77	3	notable	notable	ADJ
brj-24732	77	4	advancements	advancement	NOUN
brj-24732	77	5	in	in	ADP
brj-24732	77	6	both	both	DET
brj-24732	77	7	network	network	NOUN
brj-24732	77	8	architecture	architecture	NOUN
brj-24732	77	9	and	and	CCONJ
brj-24732	77	10	training	training	NOUN
brj-24732	77	11	methodologies	methodology	NOUN
brj-24732	77	12	,	,	PUNCT
brj-24732	77	13	thereby	thereby	ADV
brj-24732	77	14	greatly	greatly	ADV
brj-24732	77	15	enhancing	enhance	VERB
brj-24732	77	16	feature	feature	NOUN
brj-24732	77	17	extraction	extraction	NOUN
brj-24732	77	18	capabilities	capability	NOUN
brj-24732	77	19	.	.	PUNCT
brj-24732	78	1	these	these	DET
brj-24732	78	2	improvements	improvement	NOUN
brj-24732	78	3	enable	enable	VERB
brj-24732	78	4	more	more	ADV
brj-24732	78	5	accurate	accurate	ADJ
brj-24732	78	6	detection	detection	NOUN
brj-24732	78	7	of	of	ADP
brj-24732	78	8	complex	complex	ADJ
brj-24732	78	9	features	feature	NOUN
brj-24732	78	10	,	,	PUNCT
brj-24732	78	11	even	even	ADV
brj-24732	78	12	under	under	ADP
brj-24732	78	13	challenging	challenge	VERB
brj-24732	78	14	environmental	environmental	ADJ
brj-24732	78	15	and	and	CCONJ
brj-24732	78	16	textural	textural	ADJ
brj-24732	78	17	conditions	condition	NOUN
brj-24732	78	18	.	.	PUNCT
brj-24732	79	1	to	to	PART
brj-24732	79	2	overcome	overcome	VERB
brj-24732	79	3	the	the	DET
brj-24732	79	4	limitations	limitation	NOUN
brj-24732	79	5	of	of	ADP
brj-24732	79	6	existing	exist	VERB
brj-24732	79	7	particleboard	particleboard	NOUN
brj-24732	79	8	surface	surface	NOUN
brj-24732	79	9	defect	defect	NOUN
brj-24732	79	10	detection	detection	NOUN
brj-24732	79	11	algorithms	algorithm	NOUN
brj-24732	79	12	—	—	PUNCT
brj-24732	79	13	specifically	specifically	ADV
brj-24732	79	14	,	,	PUNCT
brj-24732	79	15	high	high	ADJ
brj-24732	79	16	computational	computational	ADJ
brj-24732	79	17	complexity	complexity	NOUN
brj-24732	79	18	and	and	CCONJ
brj-24732	79	19	large	large	ADJ
brj-24732	79	20	model	model	NOUN
brj-24732	79	21	size	size	NOUN
brj-24732	79	22	—	—	PUNCT
brj-24732	79	23	this	this	DET
brj-24732	79	24	study	study	NOUN
brj-24732	79	25	proposes	propose	VERB
brj-24732	79	26	a	a	DET
brj-24732	79	27	lightweight	lightweight	ADJ
brj-24732	79	28	and	and	CCONJ
brj-24732	79	29	enhanced	enhanced	ADJ
brj-24732	79	30	detection	detection	NOUN
brj-24732	79	31	model	model	NOUN
brj-24732	79	32	based	base	VERB
brj-24732	79	33	on	on	ADP
brj-24732	79	34	the	the	DET
brj-24732	79	35	yolov11	yolov11	NOUN
brj-24732	79	36	framework	framework	NOUN
brj-24732	79	37	,	,	PUNCT
brj-24732	79	38	referred	refer	VERB
brj-24732	79	39	to	to	ADP
brj-24732	79	40	as	as	ADP
brj-24732	79	41	le	le	X
brj-24732	79	42	-	-	NOUN
brj-24732	79	43	yolo	yolo	PROPN
brj-24732	79	44	.	.	PUNCT
brj-24732	80	1	the	the	DET
brj-24732	80	2	overall	overall	ADJ
brj-24732	80	3	architecture	architecture	NOUN
brj-24732	80	4	of	of	ADP
brj-24732	80	5	the	the	DET
brj-24732	80	6	proposed	propose	VERB
brj-24732	80	7	model	model	NOUN
brj-24732	80	8	is	be	AUX
brj-24732	80	9	illustrated	illustrate	VERB
brj-24732	80	10	in	in	ADP
brj-24732	80	11	fig	fig	NOUN
brj-24732	80	12	.	.	PUNCT
brj-24732	81	1	2	2	X
brj-24732	81	2	.	.	X
brj-24732	81	3	this	this	DET
brj-24732	81	4	section	section	NOUN
brj-24732	81	5	outlines	outline	VERB
brj-24732	81	6	the	the	DET
brj-24732	81	7	core	core	ADJ
brj-24732	81	8	components	component	NOUN
brj-24732	81	9	of	of	ADP
brj-24732	81	10	le	le	PROPN
brj-24732	81	11	-	-	PROPN
brj-24732	81	12	yolo	yolo	PROPN
brj-24732	81	13	,	,	PUNCT
brj-24732	81	14	including	include	VERB
brj-24732	81	15	the	the	DET
brj-24732	81	16	adaptive	adaptive	ADJ
brj-24732	81	17	multi	multi	ADJ
brj-24732	81	18	-	-	ADJ
brj-24732	81	19	kernel	kernel	ADJ
brj-24732	81	20	depthwise	depthwise	NOUN
brj-24732	81	21	conv2d	conv2d	PROPN
brj-24732	81	22	(	(	PUNCT
brj-24732	81	23	amdc	amdc	ADJ
brj-24732	81	24	)	)	PUNCT
brj-24732	81	25	module	module	NOUN
brj-24732	81	26	,	,	PUNCT
brj-24732	81	27	the	the	DET
brj-24732	81	28	multi	multi	ADJ
brj-24732	81	29	-	-	ADJ
brj-24732	81	30	scale	scale	ADJ
brj-24732	81	31	feature	feature	NOUN
brj-24732	81	32	fusion	fusion	NOUN
brj-24732	81	33	module	module	NOUN
brj-24732	81	34	(	(	PUNCT
brj-24732	81	35	shared	share	VERB
brj-24732	81	36	dilated	dilate	VERB
brj-24732	81	37	feature	feature	NOUN
brj-24732	81	38	pyramid	pyramid	NOUN
brj-24732	81	39	,	,	PUNCT
brj-24732	81	40	sdfp	sdfp	NOUN
brj-24732	81	41	)	)	PUNCT
brj-24732	81	42	,	,	PUNCT
brj-24732	81	43	the	the	DET
brj-24732	81	44	lwdethead	lwdethead	NOUN
brj-24732	81	45	,	,	PUNCT
brj-24732	81	46	and	and	CCONJ
brj-24732	81	47	the	the	DET
brj-24732	81	48	integration	integration	NOUN
brj-24732	81	49	of	of	ADP
brj-24732	81	50	the	the	DET
brj-24732	81	51	nwd	nwd	NOUN
brj-24732	81	52	in	in	ADP
brj-24732	81	53	the	the	DET
brj-24732	81	54	loss	loss	NOUN
brj-24732	81	55	function	function	NOUN
brj-24732	81	56	.	.	PUNCT
brj-24732	82	1	conv	conv	PROPN
brj-24732	82	2	amdc	amdc	PROPN
brj-24732	82	3	sdfp	sdfp	PROPN
brj-24732	82	4	c2psa	c2psa	PROPN
brj-24732	82	5	upsample	upsample	PROPN
brj-24732	82	6	conv	conv	PROPN
brj-24732	82	7	amdc	amdc	PROPN
brj-24732	82	8	conv	conv	PROPN
brj-24732	82	9	conv	conv	PROPN
brj-24732	82	10	amdc	amdc	PROPN
brj-24732	82	11	conv	conv	ADJ
brj-24732	82	12	amdc	amdc	PROPN
brj-24732	82	13	input	input	NOUN
brj-24732	82	14	640x640x3	640x640x3	NUM
brj-24732	83	1	concat	concat	PROPN
brj-24732	83	2	amdc	amdc	PART
brj-24732	83	3	upsample	upsample	PROPN
brj-24732	83	4	concat	concat	PROPN
brj-24732	83	5	amdc	amdc	PRON
brj-24732	83	6	conv	conv	PROPN
brj-24732	83	7	concat	concat	PROPN
brj-24732	83	8	amdc	amdc	PRON
brj-24732	83	9	conv	conv	PROPN
brj-24732	83	10	concat	concat	PROPN
brj-24732	83	11	amdc	amdc	ADV
brj-24732	83	12	lwdethead	lwdethead	ADJ
brj-24732	84	1	lwdethead	lwdethead	ADJ
brj-24732	84	2	lwdethead	lwdethead	ADJ
brj-24732	84	3	backbone	backbone	NOUN
brj-24732	84	4	neck	neck	NOUN
brj-24732	84	5	head	head	NOUN
brj-24732	85	1	k=3	k=3	X
brj-24732	85	2	s=2	s=2	PUNCT
brj-24732	85	3	k=3	k=3	PROPN
brj-24732	85	4	s=2	s=2	X
brj-24732	85	5	n=2	n=2	ADV
brj-24732	85	6	k=3	k=3	VERB
brj-24732	85	7	s=2	s=2	PUNCT
brj-24732	85	8	k=3	k=3	X
brj-24732	85	9	s=2	s=2	PUNCT
brj-24732	85	10	k=3	k=3	PROPN
brj-24732	85	11	s=2	s=2	VERB
brj-24732	85	12	n=2	n=2	X
brj-24732	85	13	n=2	n=2	ADV
brj-24732	85	14	n=2	n=2	X
brj-24732	85	15	320x320x64	320x320x64	NUM
brj-24732	85	16	160x160x128	160x160x128	NUM
brj-24732	85	17	160x160x256	160x160x256	NUM
brj-24732	85	18	80x80x256	80x80x256	NUM
brj-24732	85	19	80x80x512	80x80x512	NUM
brj-24732	85	20	40x40x512	40x40x512	NUM
brj-24732	86	1	40x40x512	40x40x512	NUM
brj-24732	87	1	20x20x1024	20x20x1024	NUM
brj-24732	87	2	20x20x1024	20x20x1024	NUM
brj-24732	87	3	20x20x1024	20x20x1024	NUM
brj-24732	87	4	20x20x1024	20x20x1024	NUM
brj-24732	87	5	40x40x1024	40x40x1024	NOUN
brj-24732	87	6	40x40x1536	40x40x1536	NUM
brj-24732	87	7	40x40x512	40x40x512	NUM
brj-24732	87	8	80x80x512	80x80x512	NUM
brj-24732	87	9	80x80x1024	80x80x1024	NUM
brj-24732	87	10	80x80	80x80	NUM
brj-24732	87	11	40x40x256	40x40x256	NUM
brj-24732	88	1	40x40x768	40x40x768	NUM
brj-24732	88	2	40x40x512	40x40x512	NUM
brj-24732	88	3	40x40	40x40	NUM
brj-24732	88	4	20x20x512	20x20x512	NUM
brj-24732	88	5	20x20x1536	20x20x1536	NUM
brj-24732	88	6	20x20x1024	20x20x1024	NUM
brj-24732	88	7	20x20	20x20	NUM
brj-24732	88	8	80x80x256	80x80x256	NUM
brj-24732	88	9	n=3	n=3	PUNCT
brj-24732	88	10	n=2	n=2	PRON
brj-24732	88	11	n=2	n=2	ADV
brj-24732	88	12	n=2	n=2	ADV
brj-24732	88	13	n=2	n=2	ADV
brj-24732	88	14	k=3	k=3	VERB
brj-24732	88	15	s=2	s=2	PUNCT
brj-24732	88	16	k=3	k=3	PROPN
brj-24732	88	17	s=2	s=2	X
brj-24732	88	18	fig	fig	NOUN
brj-24732	88	19	.	.	PUNCT
brj-24732	89	1	2	2	X
brj-24732	89	2	.	.	X
brj-24732	89	3	le	le	PROPN
brj-24732	89	4	-	-	ADJ
brj-24732	89	5	yolo	yolo	ADJ
brj-24732	89	6	structure	structure	NOUN
brj-24732	89	7	diagram	diagram	NOUN
brj-24732	89	8	the	the	DET
brj-24732	89	9	k	k	PROPN
brj-24732	89	10	and	and	CCONJ
brj-24732	89	11	s	s	PROPN
brj-24732	89	12	in	in	ADP
brj-24732	89	13	conv	conv	ADJ
brj-24732	89	14	blocks	block	NOUN
brj-24732	89	15	represent	represent	VERB
brj-24732	89	16	the	the	DET
brj-24732	89	17	kernel	kernel	NOUN
brj-24732	89	18	size	size	NOUN
brj-24732	89	19	and	and	CCONJ
brj-24732	89	20	stride	stride	ADJ
brj-24732	89	21	size	size	NOUN
brj-24732	89	22	.	.	PUNCT
brj-24732	90	1	the	the	DET
brj-24732	90	2	n	n	NOUN
brj-24732	90	3	in	in	ADP
brj-24732	90	4	amdc	amdc	ADV
brj-24732	90	5	and	and	CCONJ
brj-24732	90	6	sdfp	sdfp	NOUN
brj-24732	90	7	represents	represent	VERB
brj-24732	90	8	the	the	DET
brj-24732	90	9	number	number	NOUN
brj-24732	90	10	of	of	ADP
brj-24732	90	11	bottlenecks	bottleneck	NOUN
brj-24732	90	12	.	.	PUNCT
brj-24732	91	1	640×640×3	640×640×3	NOUN
brj-24732	91	2	refers	refer	VERB
brj-24732	91	3	to	to	ADP
brj-24732	91	4	the	the	DET
brj-24732	91	5	size	size	NOUN
brj-24732	91	6	of	of	ADP
brj-24732	91	7	the	the	DET
brj-24732	91	8	input	input	NOUN
brj-24732	91	9	image	image	NOUN
brj-24732	91	10	,	,	PUNCT
brj-24732	91	11	and	and	CCONJ
brj-24732	91	12	subsequent	subsequent	ADJ
brj-24732	91	13	numbers	number	NOUN
brj-24732	91	14	located	locate	VERB
brj-24732	91	15	below	below	ADP
brj-24732	91	16	each	each	DET
brj-24732	91	17	block	block	NOUN
brj-24732	91	18	represent	represent	VERB
brj-24732	91	19	the	the	DET
brj-24732	91	20	dimension	dimension	NOUN
brj-24732	91	21	of	of	ADP
brj-24732	91	22	feature	feature	NOUN
brj-24732	91	23	maps	map	NOUN
brj-24732	91	24	.	.	PUNCT
brj-24732	92	1	adaptive	adaptive	ADJ
brj-24732	92	2	multi	multi	ADJ
brj-24732	92	3	-	-	ADJ
brj-24732	92	4	kernel	kernel	ADJ
brj-24732	92	5	depthwise	depthwise	NOUN
brj-24732	92	6	conv2d	conv2d	VERB
brj-24732	92	7	the	the	DET
brj-24732	92	8	original	original	ADJ
brj-24732	92	9	feature	feature	NOUN
brj-24732	92	10	extraction	extraction	NOUN
brj-24732	92	11	module	module	NOUN
brj-24732	92	12	c3k2	c3k2	NOUN
brj-24732	92	13	in	in	ADP
brj-24732	92	14	yolov11	yolov11	NOUN
brj-24732	92	15	employs	employ	VERB
brj-24732	92	16	a	a	DET
brj-24732	92	17	multi	multi	ADJ
brj-24732	92	18	-	-	ADJ
brj-24732	92	19	branch	branch	ADJ
brj-24732	92	20	and	and	CCONJ
brj-24732	92	21	multi	multi	ADJ
brj-24732	92	22	-	-	ADJ
brj-24732	92	23	layer	layer	ADJ
brj-24732	92	24	stacking	stacking	NOUN
brj-24732	92	25	strategy	strategy	NOUN
brj-24732	92	26	based	base	VERB
brj-24732	92	27	on	on	ADP
brj-24732	92	28	the	the	DET
brj-24732	92	29	c3k	c3k	PROPN
brj-24732	92	30	structure	structure	NOUN
brj-24732	92	31	.	.	PUNCT
brj-24732	93	1	while	while	SCONJ
brj-24732	93	2	this	this	DET
brj-24732	93	3	design	design	NOUN
brj-24732	93	4	improves	improve	VERB
brj-24732	93	5	feature	feature	NOUN
brj-24732	93	6	representation	representation	NOUN
brj-24732	93	7	,	,	PUNCT
brj-24732	93	8	it	it	PRON
brj-24732	93	9	also	also	ADV
brj-24732	93	10	introduces	introduce	VERB
brj-24732	93	11	a	a	DET
brj-24732	93	12	large	large	ADJ
brj-24732	93	13	number	number	NOUN
brj-24732	93	14	of	of	ADP
brj-24732	93	15	parameters	parameter	NOUN
brj-24732	93	16	and	and	CCONJ
brj-24732	93	17	increases	increase	VERB
brj-24732	93	18	computational	computational	ADJ
brj-24732	93	19	complexity	complexity	NOUN
brj-24732	93	20	,	,	PUNCT
brj-24732	93	21	resulting	result	VERB
brj-24732	93	22	in	in	ADP
brj-24732	93	23	reduced	reduce	VERB
brj-24732	93	24	inference	inference	NOUN
brj-24732	93	25	speed	speed	NOUN
brj-24732	93	26	.	.	PUNCT
brj-24732	94	1	in	in	ADP
brj-24732	94	2	addition	addition	NOUN
brj-24732	94	3	,	,	PUNCT
brj-24732	94	4	the	the	DET
brj-24732	94	5	reliance	reliance	NOUN
brj-24732	94	6	peer	peer	NOUN
brj-24732	94	7	-	-	PUNCT
brj-24732	94	8	reviewed	review	VERB
brj-24732	94	9	article	article	NOUN
brj-24732	94	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	94	11	he	he	PRON
brj-24732	94	12	et	et	PROPN
brj-24732	94	13	al	al	PROPN
brj-24732	94	14	.	.	PROPN
brj-24732	95	1	(	(	PUNCT
brj-24732	95	2	2025	2025	NUM
brj-24732	95	3	)	)	PUNCT
brj-24732	95	4	.	.	PUNCT
brj-24732	96	1	“	"	PUNCT
brj-24732	96	2	le	le	X
brj-24732	96	3	-	-	PROPN
brj-24732	96	4	yolo	yolo	PROPN
brj-24732	96	5	&	&	CCONJ
brj-24732	96	6	wood	wood	PROPN
brj-24732	96	7	surface	surface	NOUN
brj-24732	96	8	defects	defect	NOUN
brj-24732	96	9	,	,	PUNCT
brj-24732	96	10	”	"	PUNCT
brj-24732	96	11	bioresources	bioresource	NOUN
brj-24732	96	12	20(3	20(3	NOUN
brj-24732	96	13	)	)	PUNCT
brj-24732	96	14	,	,	PUNCT
brj-24732	96	15	7179	7179	NUM
brj-24732	96	16	-	-	SYM
brj-24732	96	17	7193	7193	NUM
brj-24732	96	18	.	.	PUNCT
brj-24732	97	1	7183	7183	NUM
brj-24732	97	2	on	on	ADP
brj-24732	97	3	fixed	fix	VERB
brj-24732	97	4	-	-	PUNCT
brj-24732	97	5	size	size	NOUN
brj-24732	97	6	convolution	convolution	NOUN
brj-24732	97	7	kernels	kernel	NOUN
brj-24732	97	8	limits	limit	VERB
brj-24732	97	9	the	the	DET
brj-24732	97	10	ability	ability	NOUN
brj-24732	97	11	to	to	PART
brj-24732	97	12	capture	capture	VERB
brj-24732	97	13	features	feature	NOUN
brj-24732	97	14	across	across	ADP
brj-24732	97	15	diverse	diverse	ADJ
brj-24732	97	16	spatial	spatial	ADJ
brj-24732	97	17	scales	scale	NOUN
brj-24732	97	18	,	,	PUNCT
brj-24732	97	19	affecting	affect	VERB
brj-24732	97	20	the	the	DET
brj-24732	97	21	model	model	NOUN
brj-24732	97	22	’s	’s	PART
brj-24732	97	23	adaptability	adaptability	NOUN
brj-24732	97	24	and	and	CCONJ
brj-24732	97	25	robustness	robustness	NOUN
brj-24732	97	26	in	in	ADP
brj-24732	97	27	complex	complex	ADJ
brj-24732	97	28	scenarios	scenario	NOUN
brj-24732	97	29	.	.	PUNCT
brj-24732	98	1	to	to	PART
brj-24732	98	2	address	address	VERB
brj-24732	98	3	the	the	DET
brj-24732	98	4	aforementioned	aforementioned	ADJ
brj-24732	98	5	limitations	limitation	NOUN
brj-24732	98	6	,	,	PUNCT
brj-24732	98	7	this	this	DET
brj-24732	98	8	study	study	NOUN
brj-24732	98	9	proposes	propose	VERB
brj-24732	98	10	the	the	DET
brj-24732	98	11	adaptive	adaptive	ADJ
brj-24732	98	12	multikernel	multikernel	ADJ
brj-24732	98	13	depthwise	depthwise	NOUN
brj-24732	98	14	conv2d	conv2d	PROPN
brj-24732	98	15	(	(	PUNCT
brj-24732	98	16	amdc	amdc	PROPN
brj-24732	98	17	)	)	PUNCT
brj-24732	98	18	module	module	NOUN
brj-24732	98	19	(	(	PUNCT
brj-24732	98	20	refer	refer	VERB
brj-24732	98	21	to	to	ADP
brj-24732	98	22	fig	fig	NOUN
brj-24732	98	23	.	.	PUNCT
brj-24732	99	1	3	3	NUM
brj-24732	99	2	for	for	ADP
brj-24732	99	3	the	the	DET
brj-24732	99	4	amdc	amdc	VERB
brj-24732	99	5	structure	structure	NOUN
brj-24732	99	6	diagram	diagram	NOUN
brj-24732	99	7	)	)	PUNCT
brj-24732	99	8	.	.	PUNCT
brj-24732	100	1	the	the	DET
brj-24732	100	2	amdc	amdc	VERB
brj-24732	100	3	module	module	NOUN
brj-24732	100	4	integrates	integrate	VERB
brj-24732	100	5	dynamic	dynamic	ADJ
brj-24732	100	6	kernel	kernel	NOUN
brj-24732	100	7	selection	selection	NOUN
brj-24732	100	8	,	,	PUNCT
brj-24732	100	9	cross	cross	ADJ
brj-24732	100	10	-	-	ADJ
brj-24732	100	11	layer	layer	ADJ
brj-24732	100	12	information	information	NOUN
brj-24732	100	13	fusion	fusion	NOUN
brj-24732	100	14	,	,	PUNCT
brj-24732	100	15	and	and	CCONJ
brj-24732	100	16	a	a	DET
brj-24732	100	17	computationally	computationally	ADV
brj-24732	100	18	efficient	efficient	ADJ
brj-24732	100	19	structure	structure	NOUN
brj-24732	100	20	,	,	PUNCT
brj-24732	100	21	effectively	effectively	ADV
brj-24732	100	22	enhancing	enhance	VERB
brj-24732	100	23	multi	multi	ADJ
brj-24732	100	24	-	-	ADJ
brj-24732	100	25	scale	scale	ADJ
brj-24732	100	26	feature	feature	NOUN
brj-24732	100	27	extraction	extraction	NOUN
brj-24732	100	28	and	and	CCONJ
brj-24732	100	29	improving	improve	VERB
brj-24732	100	30	the	the	DET
brj-24732	100	31	inference	inference	NOUN
brj-24732	100	32	efficiency	efficiency	NOUN
brj-24732	100	33	of	of	ADP
brj-24732	100	34	the	the	DET
brj-24732	100	35	model	model	NOUN
brj-24732	100	36	.	.	PUNCT
brj-24732	101	1	the	the	DET
brj-24732	101	2	amdc	amdc	VERB
brj-24732	101	3	module	module	NOUN
brj-24732	101	4	processes	process	VERB
brj-24732	101	5	the	the	DET
brj-24732	101	6	input	input	NOUN
brj-24732	101	7	through	through	ADP
brj-24732	101	8	three	three	NUM
brj-24732	101	9	parallel	parallel	ADJ
brj-24732	101	10	convolutional	convolutional	ADJ
brj-24732	101	11	paths	path	NOUN
brj-24732	101	12	,	,	PUNCT
brj-24732	101	13	each	each	PRON
brj-24732	101	14	with	with	ADP
brj-24732	101	15	a	a	DET
brj-24732	101	16	distinct	distinct	ADJ
brj-24732	101	17	kernel	kernel	NOUN
brj-24732	101	18	configuration	configuration	NOUN
brj-24732	101	19	to	to	PART
brj-24732	101	20	extract	extract	VERB
brj-24732	101	21	complementary	complementary	ADJ
brj-24732	101	22	features	feature	NOUN
brj-24732	101	23	.	.	PUNCT
brj-24732	102	1	the	the	DET
brj-24732	102	2	outputs	output	NOUN
brj-24732	102	3	of	of	ADP
brj-24732	102	4	these	these	DET
brj-24732	102	5	paths	path	NOUN
brj-24732	102	6	are	be	AUX
brj-24732	102	7	transformed	transform	VERB
brj-24732	102	8	via	via	ADP
brj-24732	102	9	a	a	DET
brj-24732	102	10	shared	share	VERB
brj-24732	102	11	weight	weight	NOUN
brj-24732	102	12	matrix	matrix	NOUN
brj-24732	102	13	(	(	PUNCT
brj-24732	102	14	w	w	NOUN
brj-24732	102	15	)	)	PUNCT
brj-24732	102	16	.	.	PUNCT
brj-24732	103	1	meanwhile	meanwhile	ADV
brj-24732	103	2	,	,	PUNCT
brj-24732	103	3	the	the	DET
brj-24732	103	4	original	original	ADJ
brj-24732	103	5	input	input	NOUN
brj-24732	103	6	feature	feature	NOUN
brj-24732	103	7	map	map	NOUN
brj-24732	103	8	undergoes	undergo	VERB
brj-24732	103	9	adaptive	adaptive	ADJ
brj-24732	103	10	average	average	ADJ
brj-24732	103	11	pooling	pooling	NOUN
brj-24732	103	12	to	to	PART
brj-24732	103	13	generate	generate	VERB
brj-24732	103	14	a	a	DET
brj-24732	103	15	low	low	ADJ
brj-24732	103	16	-	-	PUNCT
brj-24732	103	17	dimensional	dimensional	ADJ
brj-24732	103	18	context	context	NOUN
brj-24732	103	19	vector	vector	NOUN
brj-24732	103	20	.	.	PUNCT
brj-24732	104	1	this	this	DET
brj-24732	104	2	vector	vector	NOUN
brj-24732	104	3	is	be	AUX
brj-24732	104	4	then	then	ADV
brj-24732	104	5	passed	pass	VERB
brj-24732	104	6	through	through	ADP
brj-24732	104	7	a	a	DET
brj-24732	104	8	1	1	NUM
brj-24732	104	9	×	×	NOUN
brj-24732	104	10	1	1	NUM
brj-24732	104	11	convolution	convolution	NOUN
brj-24732	104	12	and	and	CCONJ
brj-24732	104	13	a	a	DET
brj-24732	104	14	softmax	softmax	NOUN
brj-24732	104	15	activation	activation	NOUN
brj-24732	104	16	to	to	PART
brj-24732	104	17	produce	produce	VERB
brj-24732	104	18	a	a	DET
brj-24732	104	19	set	set	NOUN
brj-24732	104	20	of	of	ADP
brj-24732	104	21	probability	probability	NOUN
brj-24732	104	22	weights	weight	NOUN
brj-24732	104	23	,	,	PUNCT
brj-24732	104	24	which	which	PRON
brj-24732	104	25	are	be	AUX
brj-24732	104	26	subsequently	subsequently	ADV
brj-24732	104	27	applied	apply	VERB
brj-24732	104	28	to	to	ADP
brj-24732	104	29	the	the	DET
brj-24732	104	30	corresponding	correspond	VERB
brj-24732	104	31	outputs	output	NOUN
brj-24732	104	32	from	from	ADP
brj-24732	104	33	the	the	DET
brj-24732	104	34	three	three	NUM
brj-24732	104	35	convolutional	convolutional	ADJ
brj-24732	104	36	branches	branch	NOUN
brj-24732	104	37	.	.	PUNCT
brj-24732	105	1	the	the	DET
brj-24732	105	2	weighted	weight	VERB
brj-24732	105	3	features	feature	NOUN
brj-24732	105	4	are	be	AUX
brj-24732	105	5	finally	finally	ADV
brj-24732	105	6	aggregated	aggregate	VERB
brj-24732	105	7	through	through	ADP
brj-24732	105	8	summation	summation	NOUN
brj-24732	105	9	,	,	PUNCT
brj-24732	105	10	resulting	result	VERB
brj-24732	105	11	in	in	ADP
brj-24732	105	12	an	an	DET
brj-24732	105	13	output	output	NOUN
brj-24732	105	14	feature	feature	NOUN
brj-24732	105	15	map	map	NOUN
brj-24732	105	16	with	with	ADP
brj-24732	105	17	dimensions	dimension	NOUN
brj-24732	105	18	of	of	ADP
brj-24732	105	19	c	c	NOUN
brj-24732	105	20	×	×	NOUN
brj-24732	105	21	h	h	NOUN
brj-24732	105	22	×	×	PROPN
brj-24732	105	23	w.	w.	NOUN
brj-24732	105	24	compared	compare	VERB
brj-24732	105	25	to	to	ADP
brj-24732	105	26	the	the	DET
brj-24732	105	27	original	original	ADJ
brj-24732	105	28	module	module	NOUN
brj-24732	105	29	in	in	ADP
brj-24732	105	30	yolov11	yolov11	NOUN
brj-24732	105	31	,	,	PUNCT
brj-24732	105	32	the	the	DET
brj-24732	105	33	amdc	amdc	NOUN
brj-24732	105	34	offers	offer	VERB
brj-24732	105	35	enhanced	enhance	VERB
brj-24732	105	36	flexibility	flexibility	NOUN
brj-24732	105	37	and	and	CCONJ
brj-24732	105	38	adaptability	adaptability	NOUN
brj-24732	105	39	,	,	PUNCT
brj-24732	105	40	while	while	SCONJ
brj-24732	105	41	substantially	substantially	ADV
brj-24732	105	42	improving	improve	VERB
brj-24732	105	43	computational	computational	ADJ
brj-24732	105	44	efficiency	efficiency	NOUN
brj-24732	105	45	.	.	PUNCT
brj-24732	106	1	these	these	DET
brj-24732	106	2	improvements	improvement	NOUN
brj-24732	106	3	make	make	VERB
brj-24732	106	4	it	it	PRON
brj-24732	106	5	more	more	ADV
brj-24732	106	6	suitable	suitable	ADJ
brj-24732	106	7	for	for	ADP
brj-24732	106	8	real	real	ADJ
brj-24732	106	9	-	-	PUNCT
brj-24732	106	10	time	time	NOUN
brj-24732	106	11	surface	surface	NOUN
brj-24732	106	12	defect	defect	NOUN
brj-24732	106	13	detection	detection	NOUN
brj-24732	106	14	tasks	task	NOUN
brj-24732	106	15	under	under	ADP
brj-24732	106	16	industrial	industrial	ADJ
brj-24732	106	17	conditions	condition	NOUN
brj-24732	106	18	.	.	PUNCT
brj-24732	107	1	conv	conv	INTJ
brj-24732	107	2	k	k	PROPN
brj-24732	108	1	x	x	PUNCT
brj-24732	108	2	k	k	PROPN
brj-24732	109	1	conv	conv	PROPN
brj-24732	109	2	m	m	PROPN
brj-24732	109	3	x	x	SYM
brj-24732	109	4	1	1	NUM
brj-24732	109	5	conv	conv	NOUN
brj-24732	109	6	1	1	NUM
brj-24732	109	7	x	x	SYM
brj-24732	109	8	m	m	VERB
brj-24732	109	9	w	w	ADP
brj-24732	109	10	a	a	DET
brj-24732	109	11	daptivea	daptivea	ADJ
brj-24732	109	12	vgpool	vgpool	NOUN
brj-24732	109	13	conv	conv	NOUN
brj-24732	109	14	1	1	NUM
brj-24732	109	15	x	x	SYM
brj-24732	109	16	1	1	NUM
brj-24732	109	17	w	w	PROPN
brj-24732	109	18	w	w	PROPN
brj-24732	109	19	h	h	PROPN
brj-24732	109	20	w	w	PROPN
brj-24732	109	21	c	c	PROPN
brj-24732	109	22	h	h	PROPN
brj-24732	110	1	c	c	PROPN
brj-24732	110	2	w	w	PROPN
brj-24732	110	3	cxhxw	cxhxw	PROPN
brj-24732	110	4	cxhxw	cxhxw	VERB
brj-24732	110	5	cxhxw	cxhxw	PROPN
brj-24732	110	6	softmax	softmax	PROPN
brj-24732	110	7	fig	fig	PROPN
brj-24732	110	8	.	.	PUNCT
brj-24732	111	1	3	3	X
brj-24732	111	2	.	.	X
brj-24732	111	3	amdc	amdc	PROPN
brj-24732	111	4	structure	structure	NOUN
brj-24732	111	5	diagram	diagram	NOUN
brj-24732	111	6	shared	share	VERB
brj-24732	111	7	dilated	dilated	ADJ
brj-24732	111	8	feature	feature	NOUN
brj-24732	111	9	pyramid	pyramid	NOUN
brj-24732	111	10	the	the	DET
brj-24732	111	11	spatial	spatial	ADJ
brj-24732	111	12	pyramid	pyramid	NOUN
brj-24732	111	13	pooling	pooling	NOUN
brj-24732	111	14	-	-	PUNCT
brj-24732	111	15	fast	fast	ADJ
brj-24732	111	16	(	(	PUNCT
brj-24732	111	17	sppf	sppf	ADJ
brj-24732	111	18	)	)	PUNCT
brj-24732	111	19	module	module	NOUN
brj-24732	111	20	uses	use	VERB
brj-24732	111	21	fixed	fix	VERB
brj-24732	111	22	-	-	PUNCT
brj-24732	111	23	size	size	NOUN
brj-24732	111	24	max	max	PROPN
brj-24732	111	25	pooling	pool	VERB
brj-24732	111	26	kernels	kernel	NOUN
brj-24732	111	27	to	to	PART
brj-24732	111	28	extract	extract	VERB
brj-24732	111	29	and	and	CCONJ
brj-24732	111	30	fuse	fuse	VERB
brj-24732	111	31	multi	multi	ADJ
brj-24732	111	32	-	-	ADJ
brj-24732	111	33	scale	scale	ADJ
brj-24732	111	34	contextual	contextual	ADJ
brj-24732	111	35	information	information	NOUN
brj-24732	111	36	.	.	PUNCT
brj-24732	112	1	although	although	SCONJ
brj-24732	112	2	effective	effective	ADJ
brj-24732	112	3	in	in	ADP
brj-24732	112	4	aggregating	aggregate	VERB
brj-24732	112	5	features	feature	NOUN
brj-24732	112	6	from	from	ADP
brj-24732	112	7	various	various	ADJ
brj-24732	112	8	receptive	receptive	ADJ
brj-24732	112	9	fields	field	NOUN
brj-24732	112	10	,	,	PUNCT
brj-24732	112	11	its	its	PRON
brj-24732	112	12	dependence	dependence	NOUN
brj-24732	112	13	on	on	ADP
brj-24732	112	14	fixed	fix	VERB
brj-24732	112	15	pooling	pool	VERB
brj-24732	112	16	sizes	size	NOUN
brj-24732	112	17	limits	limit	VERB
brj-24732	112	18	its	its	PRON
brj-24732	112	19	adaptability	adaptability	NOUN
brj-24732	112	20	to	to	ADP
brj-24732	112	21	objects	object	NOUN
brj-24732	112	22	with	with	ADP
brj-24732	112	23	significant	significant	ADJ
brj-24732	112	24	scale	scale	NOUN
brj-24732	112	25	variation	variation	NOUN
brj-24732	112	26	.	.	PUNCT
brj-24732	113	1	this	this	DET
brj-24732	113	2	constraint	constraint	NOUN
brj-24732	113	3	can	can	AUX
brj-24732	113	4	reduce	reduce	VERB
brj-24732	113	5	detection	detection	NOUN
brj-24732	113	6	accuracy	accuracy	NOUN
brj-24732	113	7	,	,	PUNCT
brj-24732	113	8	particularly	particularly	ADV
brj-24732	113	9	in	in	ADP
brj-24732	113	10	complex	complex	ADJ
brj-24732	113	11	scenes	scene	NOUN
brj-24732	113	12	where	where	SCONJ
brj-24732	113	13	object	object	NOUN
brj-24732	113	14	sizes	size	NOUN
brj-24732	113	15	vary	vary	VERB
brj-24732	113	16	widely	widely	ADV
brj-24732	113	17	.	.	PUNCT
brj-24732	114	1	to	to	PART
brj-24732	114	2	overcome	overcome	VERB
brj-24732	114	3	this	this	DET
brj-24732	114	4	limitation	limitation	NOUN
brj-24732	114	5	,	,	PUNCT
brj-24732	114	6	this	this	DET
brj-24732	114	7	study	study	NOUN
brj-24732	114	8	proposes	propose	VERB
brj-24732	114	9	the	the	DET
brj-24732	114	10	shared	share	VERB
brj-24732	114	11	dilated	dilate	VERB
brj-24732	114	12	feature	feature	NOUN
brj-24732	114	13	pyramid	pyramid	NOUN
brj-24732	114	14	(	(	PUNCT
brj-24732	114	15	sdpf	sdpf	NOUN
brj-24732	114	16	)	)	PUNCT
brj-24732	114	17	module	module	NOUN
brj-24732	114	18	,	,	PUNCT
brj-24732	114	19	designed	design	VERB
brj-24732	114	20	to	to	PART
brj-24732	114	21	enhance	enhance	VERB
brj-24732	114	22	multi	multi	ADJ
brj-24732	114	23	-	-	ADJ
brj-24732	114	24	scale	scale	ADJ
brj-24732	114	25	feature	feature	NOUN
brj-24732	114	26	representation	representation	NOUN
brj-24732	114	27	while	while	SCONJ
brj-24732	114	28	maintaining	maintain	VERB
brj-24732	114	29	computational	computational	ADJ
brj-24732	114	30	efficiency	efficiency	NOUN
brj-24732	114	31	.	.	PUNCT
brj-24732	115	1	specifically	specifically	ADV
brj-24732	115	2	,	,	PUNCT
brj-24732	115	3	the	the	DET
brj-24732	115	4	input	input	NOUN
brj-24732	115	5	feature	feature	NOUN
brj-24732	115	6	map	map	NOUN
brj-24732	115	7	first	first	ADV
brj-24732	115	8	passes	pass	VERB
brj-24732	115	9	through	through	ADP
brj-24732	115	10	a	a	DET
brj-24732	115	11	1×1	1×1	NUM
brj-24732	115	12	convolution	convolution	NOUN
brj-24732	115	13	to	to	PART
brj-24732	115	14	reduce	reduce	VERB
brj-24732	115	15	the	the	DET
brj-24732	115	16	number	number	NOUN
brj-24732	115	17	of	of	ADP
brj-24732	115	18	channels	channel	NOUN
brj-24732	115	19	by	by	ADP
brj-24732	115	20	half	half	NOUN
brj-24732	115	21	,	,	PUNCT
brj-24732	115	22	thereby	thereby	ADV
brj-24732	115	23	lowering	lower	VERB
brj-24732	115	24	computational	computational	ADJ
brj-24732	115	25	cost	cost	NOUN
brj-24732	115	26	.	.	PUNCT
brj-24732	116	1	next	next	ADJ
brj-24732	116	2	,	,	PUNCT
brj-24732	116	3	three	three	NUM
brj-24732	116	4	parallel	parallel	ADJ
brj-24732	116	5	max	max	PROPN
brj-24732	116	6	pooling	pool	VERB
brj-24732	116	7	operations	operation	NOUN
brj-24732	116	8	with	with	ADP
brj-24732	116	9	different	different	ADJ
brj-24732	116	10	kernel	kernel	NOUN
brj-24732	116	11	sizes	size	NOUN
brj-24732	116	12	are	be	AUX
brj-24732	116	13	applied	apply	VERB
brj-24732	116	14	to	to	PART
brj-24732	116	15	produce	produce	VERB
brj-24732	116	16	feature	feature	NOUN
brj-24732	116	17	maps	map	NOUN
brj-24732	116	18	at	at	ADP
brj-24732	116	19	multiple	multiple	ADJ
brj-24732	116	20	scales	scale	NOUN
brj-24732	116	21	.	.	PUNCT
brj-24732	117	1	these	these	DET
brj-24732	117	2	outputs	output	NOUN
brj-24732	117	3	are	be	AUX
brj-24732	117	4	concatenated	concatenate	VERB
brj-24732	117	5	with	with	ADP
brj-24732	117	6	the	the	DET
brj-24732	117	7	original	original	ADJ
brj-24732	117	8	feature	feature	NOUN
brj-24732	117	9	map	map	NOUN
brj-24732	117	10	along	along	ADP
brj-24732	117	11	the	the	DET
brj-24732	117	12	channel	channel	NOUN
brj-24732	117	13	dimension	dimension	NOUN
brj-24732	117	14	,	,	PUNCT
brj-24732	117	15	followed	follow	VERB
brj-24732	117	16	by	by	ADP
brj-24732	117	17	another	another	DET
brj-24732	117	18	1×1	1×1	ADJ
brj-24732	117	19	convolution	convolution	NOUN
brj-24732	117	20	to	to	PART
brj-24732	117	21	restore	restore	VERB
brj-24732	117	22	the	the	DET
brj-24732	117	23	desired	desire	VERB
brj-24732	117	24	output	output	NOUN
brj-24732	117	25	channel	channel	NOUN
brj-24732	117	26	dimensions	dimension	NOUN
brj-24732	117	27	.	.	PUNCT
brj-24732	118	1	despite	despite	SCONJ
brj-24732	118	2	the	the	DET
brj-24732	118	3	advantages	advantage	NOUN
brj-24732	118	4	of	of	ADP
brj-24732	118	5	max	max	PROPN
brj-24732	118	6	pooling	pool	VERB
brj-24732	118	7	peer	peer	NOUN
brj-24732	118	8	-	-	PUNCT
brj-24732	118	9	reviewed	review	VERB
brj-24732	118	10	article	article	NOUN
brj-24732	118	11	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	118	12	he	he	PRON
brj-24732	118	13	et	et	PROPN
brj-24732	118	14	al	al	PROPN
brj-24732	118	15	.	.	PROPN
brj-24732	119	1	(	(	PUNCT
brj-24732	119	2	2025	2025	NUM
brj-24732	119	3	)	)	PUNCT
brj-24732	119	4	.	.	PUNCT
brj-24732	120	1	“	"	PUNCT
brj-24732	120	2	le	le	X
brj-24732	120	3	-	-	PROPN
brj-24732	120	4	yolo	yolo	PROPN
brj-24732	120	5	&	&	CCONJ
brj-24732	120	6	wood	wood	PROPN
brj-24732	120	7	surface	surface	NOUN
brj-24732	120	8	defects	defect	NOUN
brj-24732	120	9	,	,	PUNCT
brj-24732	120	10	”	"	PUNCT
brj-24732	120	11	bioresources	bioresource	NOUN
brj-24732	120	12	20(3	20(3	NOUN
brj-24732	120	13	)	)	PUNCT
brj-24732	120	14	,	,	PUNCT
brj-24732	120	15	7179	7179	NUM
brj-24732	120	16	-	-	SYM
brj-24732	120	17	7193	7193	NUM
brj-24732	120	18	.	.	PUNCT
brj-24732	121	1	7184	7184	NUM
brj-24732	121	2	in	in	ADP
brj-24732	121	3	enlarging	enlarge	VERB
brj-24732	121	4	the	the	DET
brj-24732	121	5	receptive	receptive	ADJ
brj-24732	121	6	field	field	NOUN
brj-24732	121	7	,	,	PUNCT
brj-24732	121	8	it	it	PRON
brj-24732	121	9	inherently	inherently	ADV
brj-24732	121	10	introduces	introduce	VERB
brj-24732	121	11	downsampling	downsampling	NOUN
brj-24732	121	12	,	,	PUNCT
brj-24732	121	13	which	which	PRON
brj-24732	121	14	can	can	AUX
brj-24732	121	15	result	result	VERB
brj-24732	121	16	in	in	ADP
brj-24732	121	17	the	the	DET
brj-24732	121	18	loss	loss	NOUN
brj-24732	121	19	of	of	ADP
brj-24732	121	20	fine	fine	ADV
brj-24732	121	21	-	-	PUNCT
brj-24732	121	22	grained	grain	VERB
brj-24732	121	23	spatial	spatial	ADJ
brj-24732	121	24	information	information	NOUN
brj-24732	121	25	.	.	PUNCT
brj-24732	122	1	moreover	moreover	ADV
brj-24732	122	2	,	,	PUNCT
brj-24732	122	3	the	the	DET
brj-24732	122	4	repeated	repeat	VERB
brj-24732	122	5	pooling	pool	VERB
brj-24732	122	6	operations	operation	NOUN
brj-24732	122	7	impose	impose	VERB
brj-24732	122	8	additional	additional	ADJ
brj-24732	122	9	computational	computational	ADJ
brj-24732	122	10	burden	burden	NOUN
brj-24732	122	11	,	,	PUNCT
brj-24732	122	12	particularly	particularly	ADV
brj-24732	122	13	in	in	ADP
brj-24732	122	14	resource	resource	NOUN
brj-24732	122	15	-	-	PUNCT
brj-24732	122	16	constrained	constrain	VERB
brj-24732	122	17	deployment	deployment	NOUN
brj-24732	122	18	environments	environment	NOUN
brj-24732	122	19	.	.	PUNCT
brj-24732	123	1	the	the	DET
brj-24732	123	2	use	use	NOUN
brj-24732	123	3	of	of	ADP
brj-24732	123	4	fixed	fix	VERB
brj-24732	123	5	-	-	PUNCT
brj-24732	123	6	size	size	NOUN
brj-24732	123	7	pooling	pool	VERB
brj-24732	123	8	kernels	kernel	NOUN
brj-24732	123	9	further	far	ADV
brj-24732	123	10	limits	limit	VERB
brj-24732	123	11	the	the	DET
brj-24732	123	12	ability	ability	NOUN
brj-24732	123	13	to	to	PART
brj-24732	123	14	adaptively	adaptively	ADV
brj-24732	123	15	capture	capture	VERB
brj-24732	123	16	features	feature	NOUN
brj-24732	123	17	across	across	ADP
brj-24732	123	18	scales	scale	NOUN
brj-24732	123	19	.	.	PUNCT
brj-24732	124	1	in	in	ADP
brj-24732	124	2	contrast	contrast	NOUN
brj-24732	124	3	,	,	PUNCT
brj-24732	124	4	the	the	DET
brj-24732	124	5	proposed	propose	VERB
brj-24732	124	6	sdpf	sdpf	NOUN
brj-24732	124	7	module	module	NOUN
brj-24732	124	8	utilizes	utilize	VERB
brj-24732	124	9	shared	share	VERB
brj-24732	124	10	convolutional	convolutional	ADJ
brj-24732	124	11	layers	layer	NOUN
brj-24732	124	12	to	to	PART
brj-24732	124	13	minimize	minimize	VERB
brj-24732	124	14	memory	memory	NOUN
brj-24732	124	15	usage	usage	NOUN
brj-24732	124	16	and	and	CCONJ
brj-24732	124	17	computational	computational	ADJ
brj-24732	124	18	overhead	overhead	NOUN
brj-24732	124	19	.	.	PUNCT
brj-24732	125	1	through	through	ADP
brj-24732	125	2	incorporating	incorporate	VERB
brj-24732	125	3	dilated	dilated	ADJ
brj-24732	125	4	convolutions	convolution	NOUN
brj-24732	125	5	with	with	ADP
brj-24732	125	6	varying	vary	VERB
brj-24732	125	7	dilation	dilation	NOUN
brj-24732	125	8	rates	rate	NOUN
brj-24732	125	9	—	—	PUNCT
brj-24732	125	10	smaller	small	ADJ
brj-24732	125	11	rates	rate	NOUN
brj-24732	125	12	for	for	ADP
brj-24732	125	13	capturing	capture	VERB
brj-24732	125	14	local	local	ADJ
brj-24732	125	15	structural	structural	ADJ
brj-24732	125	16	details	detail	NOUN
brj-24732	125	17	and	and	CCONJ
brj-24732	125	18	larger	large	ADJ
brj-24732	125	19	rates	rate	NOUN
brj-24732	125	20	for	for	ADP
brj-24732	125	21	encoding	encode	VERB
brj-24732	125	22	broader	broad	ADJ
brj-24732	125	23	contextual	contextual	ADJ
brj-24732	125	24	semantics	semantic	NOUN
brj-24732	125	25	—	—	PUNCT
brj-24732	125	26	the	the	DET
brj-24732	125	27	module	module	NOUN
brj-24732	125	28	achieves	achieve	VERB
brj-24732	125	29	more	more	ADV
brj-24732	125	30	flexible	flexible	ADJ
brj-24732	125	31	and	and	CCONJ
brj-24732	125	32	effective	effective	ADJ
brj-24732	125	33	multi	multi	ADJ
brj-24732	125	34	-	-	ADJ
brj-24732	125	35	scale	scale	ADJ
brj-24732	125	36	feature	feature	NOUN
brj-24732	125	37	extraction	extraction	NOUN
brj-24732	125	38	.	.	PUNCT
brj-24732	126	1	unlike	unlike	ADP
brj-24732	126	2	max	max	PROPN
brj-24732	126	3	pooling	pooling	NOUN
brj-24732	126	4	,	,	PUNCT
brj-24732	126	5	this	this	DET
brj-24732	126	6	convolution	convolution	NOUN
brj-24732	126	7	-	-	PUNCT
brj-24732	126	8	based	base	VERB
brj-24732	126	9	approach	approach	NOUN
brj-24732	126	10	preserves	preserve	VERB
brj-24732	126	11	spatial	spatial	ADJ
brj-24732	126	12	resolution	resolution	NOUN
brj-24732	126	13	and	and	CCONJ
brj-24732	126	14	allows	allow	VERB
brj-24732	126	15	the	the	DET
brj-24732	126	16	network	network	NOUN
brj-24732	126	17	to	to	PART
brj-24732	126	18	retain	retain	VERB
brj-24732	126	19	fine	fine	ADV
brj-24732	126	20	-	-	PUNCT
brj-24732	126	21	grained	grain	VERB
brj-24732	126	22	features	feature	NOUN
brj-24732	126	23	,	,	PUNCT
brj-24732	126	24	thereby	thereby	ADV
brj-24732	126	25	enhancing	enhance	VERB
brj-24732	126	26	detection	detection	NOUN
brj-24732	126	27	performance	performance	NOUN
brj-24732	126	28	across	across	ADP
brj-24732	126	29	objects	object	NOUN
brj-24732	126	30	of	of	ADP
brj-24732	126	31	different	different	ADJ
brj-24732	126	32	sizes	size	NOUN
brj-24732	126	33	.	.	PUNCT
brj-24732	127	1	figure	figure	NOUN
brj-24732	127	2	4	4	NUM
brj-24732	127	3	illustrates	illustrate	VERB
brj-24732	127	4	the	the	DET
brj-24732	127	5	architectural	architectural	ADJ
brj-24732	127	6	differences	difference	NOUN
brj-24732	127	7	between	between	ADP
brj-24732	127	8	the	the	DET
brj-24732	127	9	sppf	sppf	ADJ
brj-24732	127	10	and	and	CCONJ
brj-24732	127	11	sdpf	sdpf	NOUN
brj-24732	127	12	modules	module	NOUN
brj-24732	127	13	,	,	PUNCT
brj-24732	127	14	highlighting	highlight	VERB
brj-24732	127	15	the	the	DET
brj-24732	127	16	underlying	underlie	VERB
brj-24732	127	17	design	design	NOUN
brj-24732	127	18	principles	principle	NOUN
brj-24732	127	19	and	and	CCONJ
brj-24732	127	20	improvements	improvement	NOUN
brj-24732	127	21	introduced	introduce	VERB
brj-24732	127	22	in	in	ADP
brj-24732	127	23	sdpf	sdpf	NOUN
brj-24732	127	24	.	.	PUNCT
brj-24732	128	1	conv	conv	PROPN
brj-24732	128	2	1x1	1x1	NUM
brj-24732	129	1	maxpool	maxpool	PROPN
brj-24732	129	2	maxpool	maxpool	PROPN
brj-24732	129	3	maxpool	maxpool	PROPN
brj-24732	129	4	concat	concat	PROPN
brj-24732	129	5	conv	conv	PROPN
brj-24732	129	6	1x1	1x1	NUM
brj-24732	129	7	sppf	sppf	ADJ
brj-24732	129	8	conv	conv	PROPN
brj-24732	129	9	1x1	1x1	NUM
brj-24732	129	10	concat	concat	NOUN
brj-24732	129	11	conv	conv	NOUN
brj-24732	129	12	1x1	1x1	NUM
brj-24732	129	13	conv	conv	ADJ
brj-24732	129	14	3x3	3x3	NUM
brj-24732	129	15	d=1	d=1	NOUN
brj-24732	129	16	conv	conv	NOUN
brj-24732	129	17	3x3	3x3	NUM
brj-24732	130	1	d=3	d=3	NOUN
brj-24732	130	2	conv	conv	VERB
brj-24732	130	3	3x3	3x3	NUM
brj-24732	130	4	d=5	d=5	NOUN
brj-24732	130	5	sdfp	sdfp	NOUN
brj-24732	130	6	(	(	PUNCT
brj-24732	130	7	a	a	X
brj-24732	130	8	)	)	PUNCT
brj-24732	130	9	sppf	sppf	ADJ
brj-24732	130	10	(	(	PUNCT
brj-24732	130	11	b	b	NOUN
brj-24732	130	12	)	)	PUNCT
brj-24732	130	13	sdfp	sdfp	NOUN
brj-24732	130	14	fig	fig	NOUN
brj-24732	130	15	.	.	PUNCT
brj-24732	131	1	4	4	X
brj-24732	131	2	.	.	X
brj-24732	131	3	(	(	PUNCT
brj-24732	131	4	a	a	X
brj-24732	131	5	)	)	PUNCT
brj-24732	131	6	sppf	sppf	ADJ
brj-24732	131	7	and	and	CCONJ
brj-24732	131	8	(	(	PUNCT
brj-24732	131	9	b	b	NOUN
brj-24732	131	10	)	)	PUNCT
brj-24732	131	11	sdfp	sdfp	NOUN
brj-24732	131	12	structure	structure	NOUN
brj-24732	131	13	comparison	comparison	NOUN
brj-24732	131	14	diagram	diagram	PROPN
brj-24732	131	15	lightweight	lightweight	NOUN
brj-24732	131	16	detection	detection	NOUN
brj-24732	131	17	head	head	NOUN
brj-24732	131	18	yolov11	yolov11	NOUN
brj-24732	131	19	's	's	PART
brj-24732	131	20	detection	detection	NOUN
brj-24732	131	21	head	head	NOUN
brj-24732	131	22	utilizes	utilize	VERB
brj-24732	131	23	a	a	DET
brj-24732	131	24	dual	dual	ADJ
brj-24732	131	25	-	-	PUNCT
brj-24732	131	26	label	label	NOUN
brj-24732	131	27	assignment	assignment	NOUN
brj-24732	131	28	strategy	strategy	NOUN
brj-24732	131	29	,	,	PUNCT
brj-24732	131	30	combining	combine	VERB
brj-24732	131	31	one	one	NUM
brj-24732	131	32	-	-	PUNCT
brj-24732	131	33	to	to	ADP
brj-24732	131	34	-	-	PUNCT
brj-24732	131	35	many	many	ADJ
brj-24732	131	36	assignments	assignment	NOUN
brj-24732	131	37	during	during	ADP
brj-24732	131	38	training	training	NOUN
brj-24732	131	39	and	and	CCONJ
brj-24732	131	40	one	one	NUM
brj-24732	131	41	-	-	PUNCT
brj-24732	131	42	to	to	ADP
brj-24732	131	43	-	-	PUNCT
brj-24732	131	44	one	one	NUM
brj-24732	131	45	assignment	assignment	NOUN
brj-24732	131	46	during	during	ADP
brj-24732	131	47	inference	inference	NOUN
brj-24732	131	48	.	.	PUNCT
brj-24732	132	1	this	this	DET
brj-24732	132	2	approach	approach	NOUN
brj-24732	132	3	improves	improve	VERB
brj-24732	132	4	detection	detection	NOUN
brj-24732	132	5	performance	performance	NOUN
brj-24732	132	6	but	but	CCONJ
brj-24732	132	7	introduces	introduce	NOUN
brj-24732	132	8	added	add	VERB
brj-24732	132	9	computational	computational	ADJ
brj-24732	132	10	complexity	complexity	NOUN
brj-24732	132	11	,	,	PUNCT
brj-24732	132	12	particularly	particularly	ADV
brj-24732	132	13	during	during	ADP
brj-24732	132	14	the	the	DET
brj-24732	132	15	training	training	NOUN
brj-24732	132	16	and	and	CCONJ
brj-24732	132	17	inference	inference	NOUN
brj-24732	132	18	stages	stage	NOUN
brj-24732	132	19	.	.	PUNCT
brj-24732	133	1	the	the	DET
brj-24732	133	2	increased	increase	VERB
brj-24732	133	3	computational	computational	ADJ
brj-24732	133	4	load	load	NOUN
brj-24732	133	5	may	may	AUX
brj-24732	133	6	hinder	hinder	VERB
brj-24732	133	7	real	real	ADJ
brj-24732	133	8	-	-	PUNCT
brj-24732	133	9	time	time	NOUN
brj-24732	133	10	deployment	deployment	NOUN
brj-24732	133	11	in	in	ADP
brj-24732	133	12	resource	resource	NOUN
brj-24732	133	13	-	-	PUNCT
brj-24732	133	14	constrained	constrain	VERB
brj-24732	133	15	environments	environment	NOUN
brj-24732	133	16	.	.	PUNCT
brj-24732	134	1	to	to	PART
brj-24732	134	2	address	address	VERB
brj-24732	134	3	this	this	PRON
brj-24732	134	4	,	,	PUNCT
brj-24732	134	5	a	a	DET
brj-24732	134	6	novel	novel	ADJ
brj-24732	134	7	lightweight	lightweight	ADJ
brj-24732	134	8	detection	detection	NOUN
brj-24732	134	9	head	head	NOUN
brj-24732	134	10	(	(	PUNCT
brj-24732	134	11	lwdethead	lwdethead	ADJ
brj-24732	134	12	)	)	PUNCT
brj-24732	134	13	is	be	AUX
brj-24732	134	14	proposed	propose	VERB
brj-24732	134	15	,	,	PUNCT
brj-24732	134	16	designed	design	VERB
brj-24732	134	17	to	to	PART
brj-24732	134	18	balance	balance	VERB
brj-24732	134	19	performance	performance	NOUN
brj-24732	134	20	and	and	CCONJ
brj-24732	134	21	efficiency	efficiency	NOUN
brj-24732	134	22	for	for	ADP
brj-24732	134	23	wood	wood	NOUN
brj-24732	134	24	surface	surface	NOUN
brj-24732	134	25	defect	defect	NOUN
brj-24732	134	26	detection	detection	NOUN
brj-24732	134	27	.	.	PUNCT
brj-24732	135	1	as	as	SCONJ
brj-24732	135	2	shown	show	VERB
brj-24732	135	3	in	in	ADP
brj-24732	135	4	fig	fig	NOUN
brj-24732	135	5	.	.	PUNCT
brj-24732	136	1	5	5	NUM
brj-24732	136	2	,	,	PUNCT
brj-24732	136	3	the	the	DET
brj-24732	136	4	lwdethead	lwdethead	ADJ
brj-24732	136	5	architecture	architecture	NOUN
brj-24732	136	6	integrates	integrate	VERB
brj-24732	136	7	multiple	multiple	ADJ
brj-24732	136	8	convolutional	convolutional	ADJ
brj-24732	136	9	modules	module	NOUN
brj-24732	136	10	and	and	CCONJ
brj-24732	136	11	scale	scale	NOUN
brj-24732	136	12	layers	layer	NOUN
brj-24732	136	13	to	to	PART
brj-24732	136	14	optimize	optimize	VERB
brj-24732	136	15	both	both	DET
brj-24732	136	16	feature	feature	NOUN
brj-24732	136	17	characterization	characterization	NOUN
brj-24732	136	18	and	and	CCONJ
brj-24732	136	19	computational	computational	ADJ
brj-24732	136	20	efficiency	efficiency	NOUN
brj-24732	136	21	.	.	PUNCT
brj-24732	137	1	a	a	DET
brj-24732	137	2	key	key	ADJ
brj-24732	137	3	innovation	innovation	NOUN
brj-24732	137	4	of	of	ADP
brj-24732	137	5	lwdethead	lwdethead	ADJ
brj-24732	137	6	is	be	AUX
brj-24732	137	7	the	the	DET
brj-24732	137	8	detail	detail	NOUN
brj-24732	137	9	-	-	PUNCT
brj-24732	137	10	enhanced	enhance	VERB
brj-24732	137	11	convolution	convolution	NOUN
brj-24732	137	12	(	(	PUNCT
brj-24732	137	13	deconv	deconv	PROPN
brj-24732	137	14	)	)	PUNCT
brj-24732	137	15	,	,	PUNCT
brj-24732	137	16	which	which	PRON
brj-24732	137	17	improves	improve	VERB
brj-24732	137	18	feature	feature	NOUN
brj-24732	137	19	representation	representation	NOUN
brj-24732	137	20	by	by	ADP
brj-24732	137	21	incorporating	incorporate	VERB
brj-24732	137	22	prior	prior	ADJ
brj-24732	137	23	knowledge	knowledge	NOUN
brj-24732	137	24	into	into	ADP
brj-24732	137	25	standard	standard	ADJ
brj-24732	137	26	convolution	convolution	NOUN
brj-24732	137	27	operations	operation	NOUN
brj-24732	137	28	.	.	PUNCT
brj-24732	138	1	during	during	ADP
brj-24732	138	2	inference	inference	NOUN
brj-24732	138	3	,	,	PUNCT
brj-24732	138	4	deconv	deconv	PROPN
brj-24732	138	5	is	be	AUX
brj-24732	138	6	reparameterized	reparameterize	VERB
brj-24732	138	7	into	into	ADP
brj-24732	138	8	a	a	DET
brj-24732	138	9	standard	standard	ADJ
brj-24732	138	10	convolution	convolution	NOUN
brj-24732	138	11	,	,	PUNCT
brj-24732	138	12	avoiding	avoid	VERB
brj-24732	138	13	additional	additional	ADJ
brj-24732	138	14	parameters	parameter	NOUN
brj-24732	138	15	or	or	CCONJ
brj-24732	138	16	computational	computational	ADJ
brj-24732	138	17	overhead	overhead	NOUN
brj-24732	138	18	and	and	CCONJ
brj-24732	138	19	ensuring	ensure	VERB
brj-24732	138	20	compatibility	compatibility	NOUN
brj-24732	138	21	with	with	ADP
brj-24732	138	22	lightweight	lightweight	ADJ
brj-24732	138	23	deployment	deployment	ADJ
brj-24732	138	24	scenarios	scenario	NOUN
brj-24732	138	25	.	.	PUNCT
brj-24732	139	1	to	to	PART
brj-24732	139	2	further	far	ADV
brj-24732	139	3	enhance	enhance	VERB
brj-24732	139	4	localization	localization	NOUN
brj-24732	139	5	and	and	CCONJ
brj-24732	139	6	classification	classification	NOUN
brj-24732	139	7	,	,	PUNCT
brj-24732	139	8	the	the	DET
brj-24732	139	9	architecture	architecture	NOUN
brj-24732	139	10	includes	include	VERB
brj-24732	139	11	a	a	DET
brj-24732	139	12	normalized	normalize	VERB
brj-24732	139	13	convolutional	convolutional	ADJ
brj-24732	139	14	layer	layer	NOUN
brj-24732	139	15	(	(	PUNCT
brj-24732	139	16	conv_gn	conv_gn	NOUN
brj-24732	139	17	)	)	PUNCT
brj-24732	139	18	with	with	ADP
brj-24732	139	19	group	group	NOUN
brj-24732	139	20	normalization	normalization	NOUN
brj-24732	139	21	.	.	PUNCT
brj-24732	140	1	this	this	PRON
brj-24732	140	2	stabilizes	stabilize	VERB
brj-24732	140	3	training	training	NOUN
brj-24732	140	4	and	and	CCONJ
brj-24732	140	5	reduces	reduce	VERB
brj-24732	140	6	the	the	DET
brj-24732	140	7	number	number	NOUN
brj-24732	140	8	of	of	ADP
brj-24732	140	9	learnable	learnable	ADJ
brj-24732	140	10	parameters	parameter	NOUN
brj-24732	140	11	.	.	PUNCT
brj-24732	141	1	lwdethead	lwdethead	ADJ
brj-24732	141	2	also	also	ADV
brj-24732	141	3	addresses	address	VERB
brj-24732	141	4	scale	scale	NOUN
brj-24732	141	5	variation	variation	NOUN
brj-24732	141	6	by	by	ADP
brj-24732	141	7	employing	employ	VERB
brj-24732	141	8	shared	share	VERB
brj-24732	141	9	convolutional	convolutional	ADJ
brj-24732	141	10	layers	layer	NOUN
brj-24732	141	11	(	(	PUNCT
brj-24732	141	12	conv_reg	conv_reg	NOUN
brj-24732	141	13	)	)	PUNCT
brj-24732	141	14	alongside	alongside	ADP
brj-24732	141	15	scale	scale	NOUN
brj-24732	141	16	layers	layer	NOUN
brj-24732	141	17	.	.	PUNCT
brj-24732	142	1	these	these	DET
brj-24732	142	2	components	component	NOUN
brj-24732	142	3	adaptively	adaptively	ADV
brj-24732	142	4	adjust	adjust	VERB
brj-24732	142	5	feature	feature	NOUN
brj-24732	142	6	map	map	NOUN
brj-24732	142	7	scales	scale	NOUN
brj-24732	142	8	to	to	PART
brj-24732	142	9	enhance	enhance	VERB
brj-24732	142	10	robustness	robustness	NOUN
brj-24732	142	11	against	against	ADP
brj-24732	142	12	object	object	NOUN
brj-24732	142	13	size	size	NOUN
brj-24732	142	14	differences	difference	NOUN
brj-24732	142	15	.	.	PUNCT
brj-24732	143	1	the	the	DET
brj-24732	143	2	conv_reg	conv_reg	NOUN
brj-24732	143	3	layer	layer	NOUN
brj-24732	143	4	improves	improve	VERB
brj-24732	143	5	regression	regression	NOUN
brj-24732	143	6	tasks	task	NOUN
brj-24732	143	7	,	,	PUNCT
brj-24732	143	8	while	while	SCONJ
brj-24732	143	9	the	the	DET
brj-24732	143	10	dedicated	dedicated	ADJ
brj-24732	143	11	conv_cls	conv_cls	PROPN
brj-24732	143	12	layer	layer	NOUN
brj-24732	143	13	focuses	focus	VERB
brj-24732	143	14	on	on	ADP
brj-24732	143	15	classification	classification	NOUN
brj-24732	143	16	,	,	PUNCT
brj-24732	143	17	ensuring	ensure	VERB
brj-24732	143	18	consistent	consistent	ADJ
brj-24732	143	19	object	object	NOUN
brj-24732	143	20	identification	identification	NOUN
brj-24732	143	21	across	across	ADP
brj-24732	143	22	peer	peer	NOUN
brj-24732	143	23	-	-	PUNCT
brj-24732	143	24	reviewed	review	VERB
brj-24732	143	25	article	article	NOUN
brj-24732	143	26	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	143	27	he	he	PRON
brj-24732	143	28	et	et	PROPN
brj-24732	143	29	al	al	PROPN
brj-24732	143	30	.	.	PROPN
brj-24732	144	1	(	(	PUNCT
brj-24732	144	2	2025	2025	NUM
brj-24732	144	3	)	)	PUNCT
brj-24732	144	4	.	.	PUNCT
brj-24732	145	1	“	"	PUNCT
brj-24732	145	2	le	le	X
brj-24732	145	3	-	-	PROPN
brj-24732	145	4	yolo	yolo	PROPN
brj-24732	145	5	&	&	CCONJ
brj-24732	145	6	wood	wood	PROPN
brj-24732	145	7	surface	surface	NOUN
brj-24732	145	8	defects	defect	NOUN
brj-24732	145	9	,	,	PUNCT
brj-24732	145	10	”	"	PUNCT
brj-24732	145	11	bioresources	bioresource	NOUN
brj-24732	145	12	20(3	20(3	NOUN
brj-24732	145	13	)	)	PUNCT
brj-24732	145	14	,	,	PUNCT
brj-24732	145	15	7179	7179	NUM
brj-24732	145	16	-	-	SYM
brj-24732	145	17	7193	7193	NUM
brj-24732	145	18	.	.	PUNCT
brj-24732	146	1	7185	7185	NUM
brj-24732	146	2	feature	feature	NOUN
brj-24732	146	3	levels	level	NOUN
brj-24732	146	4	.	.	PUNCT
brj-24732	147	1	through	through	ADP
brj-24732	147	2	combining	combine	VERB
brj-24732	147	3	shared	share	VERB
brj-24732	147	4	convolutions	convolution	NOUN
brj-24732	147	5	,	,	PUNCT
brj-24732	147	6	scale	scale	NOUN
brj-24732	147	7	-	-	PUNCT
brj-24732	147	8	aware	aware	ADJ
brj-24732	147	9	processing	processing	NOUN
brj-24732	147	10	,	,	PUNCT
brj-24732	147	11	and	and	CCONJ
brj-24732	147	12	taskspecific	taskspecific	NOUN
brj-24732	147	13	modules	module	NOUN
brj-24732	147	14	,	,	PUNCT
brj-24732	147	15	lwdethead	lwdethead	VERB
brj-24732	147	16	reduces	reduce	VERB
brj-24732	147	17	parameter	parameter	NOUN
brj-24732	147	18	count	count	NOUN
brj-24732	147	19	and	and	CCONJ
brj-24732	147	20	computational	computational	ADJ
brj-24732	147	21	complexity	complexity	NOUN
brj-24732	147	22	with	with	ADP
brj-24732	147	23	minimal	minimal	ADJ
brj-24732	147	24	impact	impact	NOUN
brj-24732	147	25	on	on	ADP
brj-24732	147	26	detection	detection	NOUN
brj-24732	147	27	performance	performance	NOUN
brj-24732	147	28	,	,	PUNCT
brj-24732	147	29	making	make	VERB
brj-24732	147	30	it	it	PRON
brj-24732	147	31	suitable	suitable	ADJ
brj-24732	147	32	for	for	ADP
brj-24732	147	33	real	real	ADJ
brj-24732	147	34	-	-	PUNCT
brj-24732	147	35	time	time	NOUN
brj-24732	147	36	deployment	deployment	NOUN
brj-24732	147	37	in	in	ADP
brj-24732	147	38	resource	resource	NOUN
brj-24732	147	39	-	-	PUNCT
brj-24732	147	40	constrained	constrain	VERB
brj-24732	147	41	environments	environment	NOUN
brj-24732	147	42	.	.	PUNCT
brj-24732	148	1	lwdethead	lwdethead	ADJ
brj-24732	148	2	conv_gn	conv_gn	NOUN
brj-24732	148	3	1x1	1x1	NUM
brj-24732	148	4	deconv_gndeconv_gn	deconv_gndeconv_gn	ADV
brj-24732	148	5	conv_gn	conv_gn	NOUN
brj-24732	148	6	1x1	1x1	NUM
brj-24732	148	7	conv_gn	conv_gn	NOUN
brj-24732	148	8	1x1	1x1	NUM
brj-24732	148	9	conv_cls	conv_cls	PROPN
brj-24732	148	10	conv_reg	conv_reg	NOUN
brj-24732	148	11	conv_cls	conv_cls	PROPN
brj-24732	148	12	conv_reg	conv_reg	NOUN
brj-24732	148	13	conv_reg	conv_reg	NOUN
brj-24732	148	14	scale	scale	NOUN
brj-24732	148	15	scale	scale	NOUN
brj-24732	148	16	scale	scale	NOUN
brj-24732	148	17	conv_cls	conv_cls	PROPN
brj-24732	148	18	fig	fig	NOUN
brj-24732	148	19	.	.	PUNCT
brj-24732	149	1	5	5	NUM
brj-24732	149	2	.	.	X
brj-24732	149	3	lwdethead	lwdethead	ADJ
brj-24732	149	4	structure	structure	NOUN
brj-24732	149	5	diagram	diagram	NOUN
brj-24732	149	6	nwd	nwd	NOUN
brj-24732	149	7	loss	loss	NOUN
brj-24732	149	8	in	in	ADP
brj-24732	149	9	the	the	DET
brj-24732	149	10	detection	detection	NOUN
brj-24732	149	11	of	of	ADP
brj-24732	149	12	wood	wood	NOUN
brj-24732	149	13	surface	surface	NOUN
brj-24732	149	14	defects	defect	NOUN
brj-24732	149	15	,	,	PUNCT
brj-24732	149	16	particularly	particularly	ADV
brj-24732	149	17	for	for	ADP
brj-24732	149	18	particleboard	particleboard	NOUN
brj-24732	149	19	,	,	PUNCT
brj-24732	149	20	the	the	DET
brj-24732	149	21	identification	identification	NOUN
brj-24732	149	22	of	of	ADP
brj-24732	149	23	minor	minor	ADJ
brj-24732	149	24	defect	defect	NOUN
brj-24732	149	25	categories	category	NOUN
brj-24732	149	26	often	often	ADV
brj-24732	149	27	presents	present	VERB
brj-24732	149	28	significant	significant	ADJ
brj-24732	149	29	challenges	challenge	NOUN
brj-24732	149	30	due	due	ADP
brj-24732	149	31	to	to	ADP
brj-24732	149	32	their	their	PRON
brj-24732	149	33	low	low	ADJ
brj-24732	149	34	pixel	pixel	NOUN
brj-24732	149	35	occupancy	occupancy	NOUN
brj-24732	149	36	and	and	CCONJ
brj-24732	149	37	high	high	ADJ
brj-24732	149	38	intra	intra	ADJ
brj-24732	149	39	-	-	ADJ
brj-24732	149	40	class	class	ADJ
brj-24732	149	41	variability	variability	NOUN
brj-24732	149	42	.	.	PUNCT
brj-24732	150	1	these	these	DET
brj-24732	150	2	small	small	ADJ
brj-24732	150	3	defects	defect	NOUN
brj-24732	150	4	typically	typically	ADV
brj-24732	150	5	occupy	occupy	VERB
brj-24732	150	6	only	only	ADV
brj-24732	150	7	a	a	DET
brj-24732	150	8	tiny	tiny	ADJ
brj-24732	150	9	fraction	fraction	NOUN
brj-24732	150	10	of	of	ADP
brj-24732	150	11	the	the	DET
brj-24732	150	12	image	image	NOUN
brj-24732	150	13	,	,	PUNCT
brj-24732	150	14	making	make	VERB
brj-24732	150	15	them	they	PRON
brj-24732	150	16	more	more	ADV
brj-24732	150	17	susceptible	susceptible	ADJ
brj-24732	150	18	to	to	ADP
brj-24732	150	19	background	background	NOUN
brj-24732	150	20	noise	noise	NOUN
brj-24732	150	21	and	and	CCONJ
brj-24732	150	22	difficult	difficult	ADJ
brj-24732	150	23	to	to	PART
brj-24732	150	24	distinguish	distinguish	VERB
brj-24732	150	25	from	from	ADP
brj-24732	150	26	one	one	NUM
brj-24732	150	27	another	another	DET
brj-24732	150	28	.	.	PUNCT
brj-24732	151	1	the	the	DET
brj-24732	151	2	ciou	ciou	NOUN
brj-24732	151	3	(	(	PUNCT
brj-24732	151	4	complete	complete	ADJ
brj-24732	151	5	-	-	PUNCT
brj-24732	151	6	iou	iou	NOUN
brj-24732	151	7	)	)	PUNCT
brj-24732	151	8	loss	loss	NOUN
brj-24732	151	9	function	function	NOUN
brj-24732	151	10	used	use	VERB
brj-24732	151	11	in	in	ADP
brj-24732	151	12	yolo	yolo	NOUN
brj-24732	151	13	-	-	PUNCT
brj-24732	151	14	based	base	VERB
brj-24732	151	15	algorithms	algorithm	NOUN
brj-24732	151	16	has	have	VERB
brj-24732	151	17	notable	notable	ADJ
brj-24732	151	18	limitations	limitation	NOUN
brj-24732	151	19	when	when	SCONJ
brj-24732	151	20	dealing	deal	VERB
brj-24732	151	21	with	with	ADP
brj-24732	151	22	such	such	ADJ
brj-24732	151	23	small	small	ADJ
brj-24732	151	24	targets	target	NOUN
brj-24732	151	25	.	.	PUNCT
brj-24732	152	1	specifically	specifically	ADV
brj-24732	152	2	,	,	PUNCT
brj-24732	152	3	the	the	DET
brj-24732	152	4	aspect	aspect	NOUN
brj-24732	152	5	ratio	ratio	NOUN
brj-24732	152	6	of	of	ADP
brj-24732	152	7	small	small	ADJ
brj-24732	152	8	objects	object	NOUN
brj-24732	152	9	is	be	AUX
brj-24732	152	10	highly	highly	ADV
brj-24732	152	11	susceptible	susceptible	ADJ
brj-24732	152	12	to	to	ADP
brj-24732	152	13	image	image	NOUN
brj-24732	152	14	noise	noise	NOUN
brj-24732	152	15	interference	interference	NOUN
brj-24732	152	16	,	,	PUNCT
brj-24732	152	17	which	which	PRON
brj-24732	152	18	may	may	AUX
brj-24732	152	19	cause	cause	VERB
brj-24732	152	20	the	the	DET
brj-24732	152	21	ciou	ciou	NOUN
brj-24732	152	22	loss	loss	NOUN
brj-24732	152	23	function	function	NOUN
brj-24732	152	24	to	to	PART
brj-24732	152	25	wrongly	wrongly	ADV
brj-24732	152	26	penalize	penalize	VERB
brj-24732	152	27	otherwise	otherwise	ADV
brj-24732	152	28	reasonable	reasonable	ADJ
brj-24732	152	29	predictions	prediction	NOUN
brj-24732	152	30	.	.	PUNCT
brj-24732	153	1	additionally	additionally	ADV
brj-24732	153	2	,	,	PUNCT
brj-24732	153	3	ciou	ciou	NOUN
brj-24732	153	4	’s	’s	PART
brj-24732	153	5	reliance	reliance	NOUN
brj-24732	153	6	on	on	ADP
brj-24732	153	7	a	a	DET
brj-24732	153	8	rectangular	rectangular	ADJ
brj-24732	153	9	assumption	assumption	NOUN
brj-24732	153	10	,	,	PUNCT
brj-24732	153	11	its	its	PRON
brj-24732	153	12	sensitivity	sensitivity	NOUN
brj-24732	153	13	to	to	PART
brj-24732	153	14	scale	scale	VERB
brj-24732	153	15	variations	variation	NOUN
brj-24732	153	16	,	,	PUNCT
brj-24732	153	17	and	and	CCONJ
brj-24732	153	18	its	its	PRON
brj-24732	153	19	dependence	dependence	NOUN
brj-24732	153	20	on	on	ADP
brj-24732	153	21	specific	specific	ADJ
brj-24732	153	22	parameters	parameter	NOUN
brj-24732	153	23	can	can	AUX
brj-24732	153	24	substantially	substantially	ADV
brj-24732	153	25	constrain	constrain	VERB
brj-24732	153	26	its	its	PRON
brj-24732	153	27	performance	performance	NOUN
brj-24732	153	28	in	in	ADP
brj-24732	153	29	complex	complex	ADJ
brj-24732	153	30	scenarios	scenario	NOUN
brj-24732	153	31	.	.	PUNCT
brj-24732	154	1	to	to	PART
brj-24732	154	2	address	address	VERB
brj-24732	154	3	these	these	DET
brj-24732	154	4	issues	issue	NOUN
brj-24732	154	5	,	,	PUNCT
brj-24732	154	6	especially	especially	ADV
brj-24732	154	7	the	the	DET
brj-24732	154	8	challenges	challenge	NOUN
brj-24732	154	9	associated	associate	VERB
brj-24732	154	10	with	with	ADP
brj-24732	154	11	low	low	ADJ
brj-24732	154	12	-	-	PUNCT
brj-24732	154	13	pixel	pixel	NOUN
brj-24732	154	14	-	-	PUNCT
brj-24732	154	15	ratio	ratio	NOUN
brj-24732	154	16	defects	defect	NOUN
brj-24732	154	17	and	and	CCONJ
brj-24732	154	18	high	high	ADJ
brj-24732	154	19	intra	intra	ADJ
brj-24732	154	20	-	-	ADJ
brj-24732	154	21	class	class	ADJ
brj-24732	154	22	divergence	divergence	NOUN
brj-24732	154	23	,	,	PUNCT
brj-24732	154	24	this	this	DET
brj-24732	154	25	study	study	NOUN
brj-24732	154	26	incorporates	incorporate	VERB
brj-24732	154	27	the	the	DET
brj-24732	154	28	normalized	normalize	VERB
brj-24732	154	29	wasserstein	wasserstein	NOUN
brj-24732	154	30	distance	distance	NOUN
brj-24732	154	31	(	(	PUNCT
brj-24732	154	32	nwd	nwd	NOUN
brj-24732	154	33	)	)	PUNCT
brj-24732	154	34	into	into	ADP
brj-24732	154	35	the	the	DET
brj-24732	154	36	loss	loss	NOUN
brj-24732	154	37	function	function	NOUN
brj-24732	154	38	,	,	PUNCT
brj-24732	154	39	aiming	aim	VERB
brj-24732	154	40	to	to	PART
brj-24732	154	41	enhance	enhance	VERB
brj-24732	154	42	overall	overall	ADJ
brj-24732	154	43	model	model	NOUN
brj-24732	154	44	performance	performance	NOUN
brj-24732	154	45	by	by	ADP
brj-24732	154	46	reinforcing	reinforce	VERB
brj-24732	154	47	feature	feature	NOUN
brj-24732	154	48	distribution	distribution	NOUN
brj-24732	154	49	alignment	alignment	NOUN
brj-24732	154	50	.	.	PUNCT
brj-24732	155	1	this	this	DET
brj-24732	155	2	alignment	alignment	NOUN
brj-24732	155	3	helps	help	VERB
brj-24732	155	4	to	to	PART
brj-24732	155	5	reduce	reduce	VERB
brj-24732	155	6	regression	regression	NOUN
brj-24732	155	7	deviation	deviation	NOUN
brj-24732	155	8	caused	cause	VERB
brj-24732	155	9	by	by	ADP
brj-24732	155	10	inconsistent	inconsistent	ADJ
brj-24732	155	11	defect	defect	NOUN
brj-24732	155	12	patterns	pattern	NOUN
brj-24732	155	13	,	,	PUNCT
brj-24732	155	14	thereby	thereby	ADV
brj-24732	155	15	improving	improve	VERB
brj-24732	155	16	the	the	DET
brj-24732	155	17	localization	localization	NOUN
brj-24732	155	18	accuracy	accuracy	NOUN
brj-24732	155	19	of	of	ADP
brj-24732	155	20	small	small	ADJ
brj-24732	155	21	targets	target	NOUN
brj-24732	155	22	.	.	PUNCT
brj-24732	156	1	to	to	PART
brj-24732	156	2	balance	balance	VERB
brj-24732	156	3	the	the	DET
brj-24732	156	4	contributions	contribution	NOUN
brj-24732	156	5	of	of	ADP
brj-24732	156	6	the	the	DET
brj-24732	156	7	ciou	ciou	NOUN
brj-24732	156	8	loss	loss	NOUN
brj-24732	156	9	and	and	CCONJ
brj-24732	156	10	nwd	nwd	NOUN
brj-24732	156	11	loss	loss	NOUN
brj-24732	156	12	,	,	PUNCT
brj-24732	156	13	a	a	DET
brj-24732	156	14	scale	scale	NOUN
brj-24732	156	15	factor	factor	NOUN
brj-24732	156	16	μ	μ	NOUN
brj-24732	156	17	is	be	AUX
brj-24732	156	18	introduced	introduce	VERB
brj-24732	156	19	.	.	PUNCT
brj-24732	157	1	the	the	DET
brj-24732	157	2	revised	revise	VERB
brj-24732	157	3	loss	loss	NOUN
brj-24732	157	4	function	function	NOUN
brj-24732	157	5	is	be	AUX
brj-24732	157	6	shown	show	VERB
brj-24732	157	7	in	in	ADP
brj-24732	157	8	eq	eq	ADJ
brj-24732	157	9	.	.	PROPN
brj-24732	157	10	1	1	NUM
brj-24732	157	11	,	,	PUNCT
brj-24732	157	12	where	where	SCONJ
brj-24732	157	13	loss_iou	loss_iou	AUX
brj-24732	157	14	denotes	denote	VERB
brj-24732	157	15	the	the	DET
brj-24732	157	16	iou	iou	NOUN
brj-24732	157	17	-	-	PUNCT
brj-24732	157	18	based	base	VERB
brj-24732	157	19	loss	loss	NOUN
brj-24732	157	20	,	,	PUNCT
brj-24732	157	21	and	and	CCONJ
brj-24732	157	22	nwd_loss	nwd_loss	PROPN
brj-24732	157	23	represents	represent	VERB
brj-24732	157	24	the	the	DET
brj-24732	157	25	nwd	nwd	NOUN
brj-24732	157	26	-	-	PUNCT
brj-24732	157	27	based	base	VERB
brj-24732	157	28	loss	loss	NOUN
brj-24732	157	29	:	:	PUNCT
brj-24732	157	30	loss_iou	loss_iou	VERB
brj-24732	157	31	=	=	SYM
brj-24732	157	32	μ	μ	NUM
brj-24732	157	33	×	×	NOUN
brj-24732	157	34	loss_iou	loss_iou	X
brj-24732	157	35	+	+	X
brj-24732	157	36	(	(	PUNCT
brj-24732	157	37	1	1	NUM
brj-24732	157	38	−	−	PROPN
brj-24732	157	39	μ	μ	NUM
brj-24732	157	40	)	)	PUNCT
brj-24732	157	41	×	×	PROPN
brj-24732	157	42	nwd_loss	nwd_loss	NOUN
brj-24732	157	43	(	(	PUNCT
brj-24732	157	44	1	1	X
brj-24732	157	45	)	)	PUNCT
brj-24732	157	46	the	the	DET
brj-24732	157	47	formula	formula	NOUN
brj-24732	157	48	for	for	ADP
brj-24732	157	49	nwd	nwd	NOUN
brj-24732	157	50	is	be	AUX
brj-24732	157	51	provided	provide	VERB
brj-24732	157	52	in	in	ADP
brj-24732	157	53	eq	eq	NOUN
brj-24732	157	54	.	.	PROPN
brj-24732	157	55	2	2	NUM
brj-24732	157	56	,	,	PUNCT
brj-24732	157	57	lnwd	lnwd	ADJ
brj-24732	157	58	=	=	SYM
brj-24732	157	59	w(p	w(p	NOUN
brj-24732	157	60	,	,	PUNCT
brj-24732	157	61	q	q	ADJ
brj-24732	157	62	)	)	PUNCT
brj-24732	157	63	normalization	normalization	NOUN
brj-24732	157	64	factor	factor	NOUN
brj-24732	157	65	(	(	PUNCT
brj-24732	157	66	2	2	NUM
brj-24732	157	67	)	)	PUNCT
brj-24732	157	68	where	where	SCONJ
brj-24732	157	69	w(p	w(p	NOUN
brj-24732	157	70	,	,	PUNCT
brj-24732	157	71	q	q	X
brj-24732	157	72	)	)	PUNCT
brj-24732	157	73	denotes	denote	VERB
brj-24732	157	74	the	the	DET
brj-24732	157	75	original	original	ADJ
brj-24732	157	76	wasserstein	wasserstein	NOUN
brj-24732	157	77	distance	distance	NOUN
brj-24732	157	78	between	between	ADP
brj-24732	157	79	the	the	DET
brj-24732	157	80	predicted	predict	VERB
brj-24732	157	81	and	and	CCONJ
brj-24732	157	82	ground	ground	NOUN
brj-24732	157	83	truth	truth	NOUN
brj-24732	157	84	boxes	box	NOUN
brj-24732	157	85	(	(	PUNCT
brj-24732	157	86	as	as	SCONJ
brj-24732	157	87	illustrated	illustrate	VERB
brj-24732	157	88	in	in	ADP
brj-24732	157	89	fig	fig	NOUN
brj-24732	157	90	.	.	PUNCT
brj-24732	158	1	6	6	NUM
brj-24732	158	2	)	)	PUNCT
brj-24732	158	3	,	,	PUNCT
brj-24732	158	4	and	and	CCONJ
brj-24732	158	5	the	the	DET
brj-24732	158	6	denominator	denominator	NOUN
brj-24732	158	7	is	be	AUX
brj-24732	158	8	a	a	DET
brj-24732	158	9	normalization	normalization	NOUN
brj-24732	158	10	term	term	NOUN
brj-24732	158	11	to	to	PART
brj-24732	158	12	ensure	ensure	VERB
brj-24732	158	13	scale	scale	NOUN
brj-24732	158	14	invariance	invariance	NOUN
brj-24732	158	15	.	.	PUNCT
brj-24732	159	1	peer	peer	NOUN
brj-24732	159	2	-	-	PUNCT
brj-24732	159	3	reviewed	review	VERB
brj-24732	159	4	article	article	NOUN
brj-24732	159	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	159	6	he	he	PRON
brj-24732	159	7	et	et	PROPN
brj-24732	159	8	al	al	PROPN
brj-24732	159	9	.	.	PROPN
brj-24732	160	1	(	(	PUNCT
brj-24732	160	2	2025	2025	NUM
brj-24732	160	3	)	)	PUNCT
brj-24732	160	4	.	.	PUNCT
brj-24732	161	1	“	"	PUNCT
brj-24732	161	2	le	le	X
brj-24732	161	3	-	-	PROPN
brj-24732	161	4	yolo	yolo	PROPN
brj-24732	161	5	&	&	CCONJ
brj-24732	161	6	wood	wood	PROPN
brj-24732	161	7	surface	surface	NOUN
brj-24732	161	8	defects	defect	NOUN
brj-24732	161	9	,	,	PUNCT
brj-24732	161	10	”	"	PUNCT
brj-24732	161	11	bioresources	bioresource	NOUN
brj-24732	161	12	20(3	20(3	NOUN
brj-24732	161	13	)	)	PUNCT
brj-24732	161	14	,	,	PUNCT
brj-24732	161	15	7179	7179	NUM
brj-24732	161	16	-	-	SYM
brj-24732	161	17	7193	7193	NUM
brj-24732	161	18	.	.	PUNCT
brj-24732	162	1	7186	7186	NUM
brj-24732	162	2	fig	fig	NOUN
brj-24732	162	3	.	.	PUNCT
brj-24732	163	1	6	6	NUM
brj-24732	163	2	.	.	X
brj-24732	163	3	schematic	schematic	ADJ
brj-24732	163	4	diagram	diagram	NOUN
brj-24732	163	5	of	of	ADP
brj-24732	163	6	wasserstein	wasserstein	NOUN
brj-24732	163	7	distance	distance	NOUN
brj-24732	163	8	to	to	PART
brj-24732	163	9	determine	determine	VERB
brj-24732	163	10	the	the	DET
brj-24732	163	11	optimal	optimal	ADJ
brj-24732	163	12	value	value	NOUN
brj-24732	163	13	of	of	ADP
brj-24732	163	14	μ	μ	NUM
brj-24732	163	15	,	,	PUNCT
brj-24732	163	16	experiments	experiment	NOUN
brj-24732	163	17	were	be	AUX
brj-24732	163	18	conducted	conduct	VERB
brj-24732	163	19	by	by	ADP
brj-24732	163	20	varying	vary	VERB
brj-24732	163	21	the	the	DET
brj-24732	163	22	scale	scale	NOUN
brj-24732	163	23	factor	factor	NOUN
brj-24732	163	24	in	in	ADP
brj-24732	163	25	increments	increment	NOUN
brj-24732	163	26	of	of	ADP
brj-24732	163	27	0.1	0.1	NUM
brj-24732	163	28	over	over	ADP
brj-24732	163	29	the	the	DET
brj-24732	163	30	range	range	NOUN
brj-24732	163	31	[	[	X
brj-24732	163	32	0	0	NUM
brj-24732	163	33	,	,	PUNCT
brj-24732	163	34	0.9	0.9	NUM
brj-24732	163	35	]	]	PUNCT
brj-24732	163	36	,	,	PUNCT
brj-24732	163	37	resulting	result	VERB
brj-24732	163	38	in	in	ADP
brj-24732	163	39	ten	ten	NUM
brj-24732	163	40	different	different	ADJ
brj-24732	163	41	configurations	configuration	NOUN
brj-24732	163	42	.	.	PUNCT
brj-24732	164	1	the	the	DET
brj-24732	164	2	detection	detection	NOUN
brj-24732	164	3	results	result	NOUN
brj-24732	164	4	of	of	ADP
brj-24732	164	5	the	the	DET
brj-24732	164	6	le	le	PROPN
brj-24732	164	7	-	-	ADJ
brj-24732	164	8	yolo	yolo	ADJ
brj-24732	164	9	model	model	NOUN
brj-24732	164	10	under	under	ADP
brj-24732	164	11	each	each	DET
brj-24732	164	12	configuration	configuration	NOUN
brj-24732	164	13	are	be	AUX
brj-24732	164	14	summarized	summarize	VERB
brj-24732	164	15	in	in	ADP
brj-24732	164	16	table	table	NOUN
brj-24732	164	17	1	1	NUM
brj-24732	164	18	,	,	PUNCT
brj-24732	164	19	with	with	ADP
brj-24732	164	20	the	the	DET
brj-24732	164	21	best	good	ADJ
brj-24732	164	22	performance	performance	NOUN
brj-24732	164	23	highlighted	highlight	VERB
brj-24732	164	24	in	in	ADP
brj-24732	164	25	bold	bold	ADJ
brj-24732	164	26	.	.	PUNCT
brj-24732	165	1	the	the	DET
brj-24732	165	2	results	result	NOUN
brj-24732	165	3	show	show	VERB
brj-24732	165	4	that	that	SCONJ
brj-24732	165	5	when	when	SCONJ
brj-24732	165	6	μ	μ	PROPN
brj-24732	165	7	=	=	SYM
brj-24732	165	8	0.5	0.5	NUM
brj-24732	165	9	,	,	PUNCT
brj-24732	165	10	the	the	DET
brj-24732	165	11	model	model	NOUN
brj-24732	165	12	achieves	achieve	VERB
brj-24732	165	13	the	the	DET
brj-24732	165	14	highest	high	ADJ
brj-24732	165	15	detection	detection	NOUN
brj-24732	165	16	accuracy	accuracy	NOUN
brj-24732	165	17	,	,	PUNCT
brj-24732	165	18	with	with	ADP
brj-24732	165	19	an	an	DET
brj-24732	165	20	map@50	map@50	NUM
brj-24732	165	21	of	of	ADP
brj-24732	165	22	90	90	NUM
brj-24732	165	23	%	%	NOUN
brj-24732	165	24	and	and	CCONJ
brj-24732	165	25	an	an	DET
brj-24732	165	26	map@50:95	map@50:95	NOUN
brj-24732	165	27	of	of	ADP
brj-24732	165	28	55	55	NUM
brj-24732	165	29	%	%	NOUN
brj-24732	165	30	.	.	PUNCT
brj-24732	166	1	table	table	NOUN
brj-24732	166	2	1	1	NUM
brj-24732	166	3	.	.	PUNCT
brj-24732	167	1	performance	performance	NOUN
brj-24732	167	2	comparison	comparison	NOUN
brj-24732	167	3	across	across	ADP
brj-24732	167	4	scaling	scale	VERB
brj-24732	167	5	factors	factor	NOUN
brj-24732	167	6	µ	µ	PROPN
brj-24732	167	7	µ	µ	X
brj-24732	167	8	0.0	0.0	NUM
brj-24732	167	9	0.1	0.1	NUM
brj-24732	167	10	0.2	0.2	NUM
brj-24732	167	11	0.3	0.3	NUM
brj-24732	167	12	0.4	0.4	NUM
brj-24732	167	13	0.5	0.5	NUM
brj-24732	167	14	0.6	0.6	NUM
brj-24732	167	15	0.7	0.7	NUM
brj-24732	167	16	0.8	0.8	NUM
brj-24732	167	17	0.9	0.9	NUM
brj-24732	167	18	map@50	map@50	NUM
brj-24732	167	19	0.86	0.86	NUM
brj-24732	167	20	0.85	0.85	NUM
brj-24732	167	21	0.85	0.85	NUM
brj-24732	167	22	0.87	0.87	NUM
brj-24732	167	23	0.87	0.87	NUM
brj-24732	167	24	0.90	0.90	NUM
brj-24732	167	25	0.90	0.90	NUM
brj-24732	167	26	0.85	0.85	NUM
brj-24732	167	27	0.87	0.87	NUM
brj-24732	167	28	0.87	0.87	NUM
brj-24732	167	29	map@50:95	map@50:95	NOUN
brj-24732	167	30	0.50	0.50	NUM
brj-24732	167	31	0.48	0.48	NUM
brj-24732	167	32	0.49	0.49	NUM
brj-24732	167	33	0.50	0.50	NUM
brj-24732	168	1	0.51	0.51	NUM
brj-24732	168	2	0.55	0.55	NUM
brj-24732	168	3	0.54	0.54	NUM
brj-24732	168	4	0.51	0.51	NUM
brj-24732	168	5	0.53	0.53	NUM
brj-24732	168	6	0.49	0.49	NUM
brj-24732	168	7	experimental	experimental	ADJ
brj-24732	168	8	details	detail	NOUN
brj-24732	168	9	the	the	DET
brj-24732	168	10	experiments	experiment	NOUN
brj-24732	168	11	reported	report	VERB
brj-24732	168	12	in	in	ADP
brj-24732	168	13	this	this	DET
brj-24732	168	14	study	study	NOUN
brj-24732	168	15	were	be	AUX
brj-24732	168	16	conducted	conduct	VERB
brj-24732	168	17	on	on	ADP
brj-24732	168	18	a	a	DET
brj-24732	168	19	windows	window	NOUN
brj-24732	168	20	10	10	NUM
brj-24732	168	21	system	system	NOUN
brj-24732	168	22	equipped	equip	VERB
brj-24732	168	23	with	with	ADP
brj-24732	168	24	a	a	DET
brj-24732	168	25	12th	12th	ADJ
brj-24732	168	26	gen	gen	PROPN
brj-24732	168	27	intel(r	intel(r	PROPN
brj-24732	168	28	)	)	PUNCT
brj-24732	168	29	core(tm	core(tm	NOUN
brj-24732	168	30	)	)	PUNCT
brj-24732	168	31	i5	i5	PROPN
brj-24732	168	32	-	-	PUNCT
brj-24732	168	33	12600kf	12600kf	ADJ
brj-24732	168	34	3.70	3.70	NUM
brj-24732	168	35	ghz	ghz	NOUN
brj-24732	168	36	cpu	cpu	NOUN
brj-24732	168	37	and	and	CCONJ
brj-24732	168	38	32	32	NUM
brj-24732	168	39	gb	gb	NOUN
brj-24732	168	40	ram	ram	NOUN
brj-24732	168	41	.	.	PUNCT
brj-24732	169	1	graphics	graphic	NOUN
brj-24732	169	2	processing	processing	NOUN
brj-24732	169	3	was	be	AUX
brj-24732	169	4	handled	handle	VERB
brj-24732	169	5	by	by	ADP
brj-24732	169	6	an	an	DET
brj-24732	169	7	nvidia	nvidia	PROPN
brj-24732	169	8	4060	4060	NUM
brj-24732	169	9	ti	ti	NOUN
brj-24732	169	10	gpu	gpu	NOUN
brj-24732	169	11	with	with	ADP
brj-24732	169	12	16	16	NUM
brj-24732	169	13	gb	gb	NOUN
brj-24732	169	14	of	of	ADP
brj-24732	169	15	vram	vram	NOUN
brj-24732	169	16	.	.	PUNCT
brj-24732	170	1	the	the	DET
brj-24732	170	2	model	model	NOUN
brj-24732	170	3	was	be	AUX
brj-24732	170	4	developed	develop	VERB
brj-24732	170	5	using	use	VERB
brj-24732	170	6	python	python	PROPN
brj-24732	170	7	3.10.14	3.10.14	PROPN
brj-24732	170	8	,	,	PUNCT
brj-24732	170	9	with	with	ADP
brj-24732	170	10	cuda	cuda	NOUN
brj-24732	170	11	12.1	12.1	NUM
brj-24732	170	12	and	and	CCONJ
brj-24732	170	13	pytorch	pytorch	NOUN
brj-24732	170	14	2.2.2	2.2.2	NUM
brj-24732	170	15	.	.	PUNCT
brj-24732	171	1	no	no	DET
brj-24732	171	2	pretrained	pretraine	VERB
brj-24732	171	3	weights	weight	NOUN
brj-24732	171	4	were	be	AUX
brj-24732	171	5	utilized	utilize	VERB
brj-24732	171	6	during	during	ADP
brj-24732	171	7	the	the	DET
brj-24732	171	8	experiments	experiment	NOUN
brj-24732	171	9	.	.	PUNCT
brj-24732	172	1	the	the	DET
brj-24732	172	2	experimental	experimental	ADJ
brj-24732	172	3	settings	setting	NOUN
brj-24732	172	4	were	be	AUX
brj-24732	172	5	as	as	SCONJ
brj-24732	172	6	follows	follow	VERB
brj-24732	172	7	:	:	PUNCT
brj-24732	172	8	the	the	DET
brj-24732	172	9	input	input	NOUN
brj-24732	172	10	image	image	NOUN
brj-24732	172	11	resolution	resolution	NOUN
brj-24732	172	12	was	be	AUX
brj-24732	172	13	set	set	VERB
brj-24732	172	14	to	to	ADP
brj-24732	172	15	640	640	NUM
brj-24732	172	16	×	×	NOUN
brj-24732	172	17	640	640	NUM
brj-24732	172	18	pixels	pixel	NOUN
brj-24732	172	19	;	;	PUNCT
brj-24732	172	20	training	training	NOUN
brj-24732	172	21	was	be	AUX
brj-24732	172	22	conducted	conduct	VERB
brj-24732	172	23	for	for	ADP
brj-24732	172	24	over	over	ADP
brj-24732	172	25	300	300	NUM
brj-24732	172	26	epochs	epoch	NOUN
brj-24732	172	27	with	with	ADP
brj-24732	172	28	a	a	DET
brj-24732	172	29	batch	batch	NOUN
brj-24732	172	30	size	size	NOUN
brj-24732	172	31	of	of	ADP
brj-24732	172	32	32	32	NUM
brj-24732	172	33	.	.	PUNCT
brj-24732	173	1	the	the	DET
brj-24732	173	2	initial	initial	ADJ
brj-24732	173	3	learning	learning	NOUN
brj-24732	173	4	rate	rate	NOUN
brj-24732	173	5	was	be	AUX
brj-24732	173	6	set	set	VERB
brj-24732	173	7	to	to	ADP
brj-24732	173	8	0.01	0.01	NUM
brj-24732	173	9	.	.	PUNCT
brj-24732	174	1	the	the	DET
brj-24732	174	2	stochastic	stochastic	ADJ
brj-24732	174	3	gradient	gradient	ADJ
brj-24732	174	4	descent	descent	NOUN
brj-24732	174	5	(	(	PUNCT
brj-24732	174	6	sgd	sgd	PROPN
brj-24732	174	7	)	)	PUNCT
brj-24732	174	8	momentum	momentum	NOUN
brj-24732	174	9	was	be	AUX
brj-24732	174	10	set	set	VERB
brj-24732	174	11	to	to	ADP
brj-24732	174	12	0.937	0.937	NUM
brj-24732	174	13	,	,	PUNCT
brj-24732	174	14	and	and	CCONJ
brj-24732	174	15	the	the	DET
brj-24732	174	16	weight	weight	NOUN
brj-24732	174	17	decay	decay	NOUN
brj-24732	174	18	coefficient	coefficient	NOUN
brj-24732	174	19	was	be	AUX
brj-24732	174	20	set	set	VERB
brj-24732	174	21	to	to	ADP
brj-24732	174	22	0.0005	0.0005	NUM
brj-24732	174	23	.	.	PUNCT
brj-24732	175	1	these	these	DET
brj-24732	175	2	hyperparameters	hyperparameter	NOUN
brj-24732	175	3	,	,	PUNCT
brj-24732	175	4	including	include	VERB
brj-24732	175	5	the	the	DET
brj-24732	175	6	learning	learning	NOUN
brj-24732	175	7	rate	rate	NOUN
brj-24732	175	8	and	and	CCONJ
brj-24732	175	9	momentum	momentum	NOUN
brj-24732	175	10	,	,	PUNCT
brj-24732	175	11	were	be	AUX
brj-24732	175	12	not	not	PART
brj-24732	175	13	arbitrarily	arbitrarily	ADV
brj-24732	175	14	selected	select	VERB
brj-24732	175	15	nor	nor	CCONJ
brj-24732	175	16	directly	directly	ADV
brj-24732	175	17	inherited	inherit	VERB
brj-24732	175	18	from	from	ADP
brj-24732	175	19	previous	previous	ADJ
brj-24732	175	20	studies	study	NOUN
brj-24732	175	21	;	;	PUNCT
brj-24732	175	22	peer	peer	NOUN
brj-24732	175	23	-	-	PUNCT
brj-24732	175	24	reviewed	review	VERB
brj-24732	175	25	article	article	NOUN
brj-24732	175	26	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	175	27	he	he	PRON
brj-24732	175	28	et	et	PROPN
brj-24732	175	29	al	al	PROPN
brj-24732	175	30	.	.	PROPN
brj-24732	176	1	(	(	PUNCT
brj-24732	176	2	2025	2025	NUM
brj-24732	176	3	)	)	PUNCT
brj-24732	176	4	.	.	PUNCT
brj-24732	177	1	“	"	PUNCT
brj-24732	177	2	le	le	X
brj-24732	177	3	-	-	PROPN
brj-24732	177	4	yolo	yolo	PROPN
brj-24732	177	5	&	&	CCONJ
brj-24732	177	6	wood	wood	PROPN
brj-24732	177	7	surface	surface	NOUN
brj-24732	177	8	defects	defect	NOUN
brj-24732	177	9	,	,	PUNCT
brj-24732	177	10	”	"	PUNCT
brj-24732	177	11	bioresources	bioresource	NOUN
brj-24732	177	12	20(3	20(3	NOUN
brj-24732	177	13	)	)	PUNCT
brj-24732	177	14	,	,	PUNCT
brj-24732	177	15	7179	7179	NUM
brj-24732	177	16	-	-	SYM
brj-24732	177	17	7193	7193	NUM
brj-24732	177	18	.	.	PUNCT
brj-24732	178	1	7187	7187	NUM
brj-24732	178	2	instead	instead	ADV
brj-24732	178	3	,	,	PUNCT
brj-24732	178	4	they	they	PRON
brj-24732	178	5	were	be	AUX
brj-24732	178	6	meticulously	meticulously	ADV
brj-24732	178	7	tuned	tune	VERB
brj-24732	178	8	through	through	ADP
brj-24732	178	9	a	a	DET
brj-24732	178	10	series	series	NOUN
brj-24732	178	11	of	of	ADP
brj-24732	178	12	preliminary	preliminary	ADJ
brj-24732	178	13	experiments	experiment	NOUN
brj-24732	178	14	to	to	PART
brj-24732	178	15	ensure	ensure	VERB
brj-24732	178	16	optimal	optimal	ADJ
brj-24732	178	17	training	training	NOUN
brj-24732	178	18	stability	stability	NOUN
brj-24732	178	19	and	and	CCONJ
brj-24732	178	20	model	model	NOUN
brj-24732	178	21	performance	performance	NOUN
brj-24732	178	22	.	.	PUNCT
brj-24732	179	1	all	all	DET
brj-24732	179	2	experiments	experiment	NOUN
brj-24732	179	3	were	be	AUX
brj-24732	179	4	conducted	conduct	VERB
brj-24732	179	5	using	use	VERB
brj-24732	179	6	a	a	DET
brj-24732	179	7	fixed	fix	VERB
brj-24732	179	8	random	random	ADJ
brj-24732	179	9	seed	seed	NOUN
brj-24732	179	10	(	(	PUNCT
brj-24732	179	11	42	42	NUM
brj-24732	179	12	)	)	PUNCT
brj-24732	179	13	and	and	CCONJ
brj-24732	179	14	consistent	consistent	ADJ
brj-24732	179	15	data	datum	NOUN
brj-24732	179	16	splits	split	VERB
brj-24732	179	17	to	to	PART
brj-24732	179	18	reduce	reduce	VERB
brj-24732	179	19	variability	variability	NOUN
brj-24732	179	20	.	.	PUNCT
brj-24732	180	1	the	the	DET
brj-24732	180	2	results	result	NOUN
brj-24732	180	3	showed	show	VERB
brj-24732	180	4	minimal	minimal	ADJ
brj-24732	180	5	fluctuations	fluctuation	NOUN
brj-24732	180	6	across	across	ADP
brj-24732	180	7	repeated	repeat	VERB
brj-24732	180	8	training	training	NOUN
brj-24732	180	9	runs	run	NOUN
brj-24732	180	10	,	,	PUNCT
brj-24732	180	11	indicating	indicate	VERB
brj-24732	180	12	stable	stable	ADJ
brj-24732	180	13	performance	performance	NOUN
brj-24732	180	14	.	.	PUNCT
brj-24732	181	1	evaluation	evaluation	NOUN
brj-24732	181	2	metrics	metric	NOUN
brj-24732	181	3	to	to	PART
brj-24732	181	4	comprehensively	comprehensively	ADV
brj-24732	181	5	assess	assess	VERB
brj-24732	181	6	model	model	NOUN
brj-24732	181	7	performance	performance	NOUN
brj-24732	181	8	,	,	PUNCT
brj-24732	181	9	accuracy	accuracy	NOUN
brj-24732	181	10	metrics	metric	NOUN
brj-24732	181	11	were	be	AUX
brj-24732	181	12	employed	employ	VERB
brj-24732	181	13	—	—	PUNCT
brj-24732	181	14	precision	precision	NOUN
brj-24732	181	15	,	,	PUNCT
brj-24732	181	16	recall	recall	NOUN
brj-24732	181	17	,	,	PUNCT
brj-24732	181	18	f1	f1	NOUN
brj-24732	181	19	score	score	NOUN
brj-24732	181	20	,	,	PUNCT
brj-24732	181	21	and	and	CCONJ
brj-24732	181	22	mean	mean	VERB
brj-24732	181	23	average	average	ADJ
brj-24732	181	24	precision	precision	NOUN
brj-24732	181	25	(	(	PUNCT
brj-24732	181	26	map)—together	map)—together	ADP
brj-24732	181	27	with	with	ADP
brj-24732	181	28	efficiency	efficiency	NOUN
brj-24732	181	29	metrics	metric	NOUN
brj-24732	181	30	including	include	VERB
brj-24732	181	31	model	model	NOUN
brj-24732	181	32	size	size	NOUN
brj-24732	181	33	,	,	PUNCT
brj-24732	181	34	parameter	parameter	NOUN
brj-24732	181	35	count	count	NOUN
brj-24732	181	36	,	,	PUNCT
brj-24732	181	37	and	and	CCONJ
brj-24732	181	38	gflops	gflop	NOUN
brj-24732	181	39	.	.	PUNCT
brj-24732	182	1	precision	precision	NOUN
brj-24732	182	2	and	and	CCONJ
brj-24732	182	3	recall	recall	NOUN
brj-24732	182	4	are	be	AUX
brj-24732	182	5	defined	define	VERB
brj-24732	182	6	as	as	SCONJ
brj-24732	182	7	shown	show	VERB
brj-24732	182	8	in	in	ADP
brj-24732	182	9	eq	eq	ADP
brj-24732	182	10	.	.	PROPN
brj-24732	182	11	3	3	NUM
brj-24732	182	12	and	and	CCONJ
brj-24732	182	13	eq	eq	NOUN
brj-24732	182	14	.	.	PROPN
brj-24732	182	15	4	4	NUM
brj-24732	182	16	,	,	PUNCT
brj-24732	182	17	respectively	respectively	ADV
brj-24732	182	18	,	,	PUNCT
brj-24732	182	19	precision	precision	NOUN
brj-24732	182	20	=	=	PUNCT
brj-24732	182	21	tp	tp	NOUN
brj-24732	182	22	tp	tp	ADP
brj-24732	182	23	+	+	CCONJ
brj-24732	182	24	fp	fp	X
brj-24732	182	25	(	(	PUNCT
brj-24732	182	26	3	3	NUM
brj-24732	182	27	)	)	PUNCT
brj-24732	182	28	recall	recall	NOUN
brj-24732	182	29	=	=	VERB
brj-24732	182	30	tp	tp	NOUN
brj-24732	182	31	tp	tp	ADP
brj-24732	182	32	+	+	CCONJ
brj-24732	183	1	fn	fn	PROPN
brj-24732	183	2	(	(	PUNCT
brj-24732	183	3	4	4	NUM
brj-24732	183	4	)	)	PUNCT
brj-24732	183	5	where	where	SCONJ
brj-24732	183	6	tp	tp	NOUN
brj-24732	183	7	represents	represent	VERB
brj-24732	183	8	the	the	DET
brj-24732	183	9	number	number	NOUN
brj-24732	183	10	of	of	ADP
brj-24732	183	11	true	true	ADJ
brj-24732	183	12	positives	positive	NOUN
brj-24732	183	13	,	,	PUNCT
brj-24732	183	14	fp	fp	X
brj-24732	183	15	refers	refer	VERB
brj-24732	183	16	to	to	ADP
brj-24732	183	17	false	false	ADJ
brj-24732	183	18	positives	positive	NOUN
brj-24732	183	19	,	,	PUNCT
brj-24732	183	20	and	and	CCONJ
brj-24732	183	21	fn	fn	NOUN
brj-24732	183	22	denotes	denote	NOUN
brj-24732	183	23	false	false	ADJ
brj-24732	183	24	negatives	negative	NOUN
brj-24732	183	25	.	.	PUNCT
brj-24732	184	1	the	the	DET
brj-24732	184	2	f1	f1	PROPN
brj-24732	184	3	score	score	NOUN
brj-24732	184	4	,	,	PUNCT
brj-24732	184	5	which	which	PRON
brj-24732	184	6	representing	represent	VERB
brj-24732	184	7	the	the	DET
brj-24732	184	8	harmonic	harmonic	ADJ
brj-24732	184	9	mean	mean	NOUN
brj-24732	184	10	of	of	ADP
brj-24732	184	11	precision	precision	NOUN
brj-24732	184	12	and	and	CCONJ
brj-24732	184	13	recall	recall	NOUN
brj-24732	184	14	,	,	PUNCT
brj-24732	184	15	is	be	AUX
brj-24732	184	16	calculated	calculate	VERB
brj-24732	184	17	as	as	SCONJ
brj-24732	184	18	shown	show	VERB
brj-24732	184	19	in	in	ADP
brj-24732	184	20	eq	eq	ADJ
brj-24732	184	21	.	.	PROPN
brj-24732	184	22	5	5	NUM
brj-24732	184	23	:	:	PUNCT
brj-24732	184	24	f1	f1	ADJ
brj-24732	184	25	-	-	PUNCT
brj-24732	184	26	score	score	NOUN
brj-24732	184	27	=	=	SYM
brj-24732	184	28	2	2	NUM
brj-24732	184	29	⋅	⋅	PROPN
brj-24732	184	30	precision	precision	NOUN
brj-24732	184	31	⋅	⋅	PROPN
brj-24732	184	32	recall	recall	PROPN
brj-24732	184	33	precision	precision	NOUN
brj-24732	184	34	+	+	CCONJ
brj-24732	184	35	recall	recall	NOUN
brj-24732	184	36	(	(	PUNCT
brj-24732	184	37	5	5	NUM
brj-24732	184	38	)	)	PUNCT
brj-24732	184	39	average	average	ADJ
brj-24732	184	40	precision	precision	NOUN
brj-24732	184	41	(	(	PUNCT
brj-24732	184	42	ap	ap	PROPN
brj-24732	184	43	)	)	PUNCT
brj-24732	184	44	quantifies	quantify	VERB
brj-24732	184	45	the	the	DET
brj-24732	184	46	area	area	NOUN
brj-24732	184	47	under	under	ADP
brj-24732	184	48	the	the	DET
brj-24732	184	49	precision	precision	NOUN
brj-24732	184	50	–	–	PUNCT
brj-24732	184	51	recall	recall	NOUN
brj-24732	184	52	curve	curve	NOUN
brj-24732	184	53	for	for	ADP
brj-24732	184	54	each	each	DET
brj-24732	184	55	class	class	NOUN
brj-24732	184	56	,	,	PUNCT
brj-24732	184	57	while	while	SCONJ
brj-24732	184	58	mean	mean	VERB
brj-24732	184	59	average	average	ADJ
brj-24732	184	60	precision	precision	NOUN
brj-24732	184	61	(	(	PUNCT
brj-24732	184	62	map	map	NOUN
brj-24732	184	63	)	)	PUNCT
brj-24732	184	64	is	be	AUX
brj-24732	184	65	the	the	DET
brj-24732	184	66	average	average	NOUN
brj-24732	184	67	across	across	ADP
brj-24732	184	68	all	all	PRON
brj-24732	185	1	n	n	DET
brj-24732	185	2	classes	class	NOUN
brj-24732	185	3	,	,	PUNCT
brj-24732	185	4	as	as	SCONJ
brj-24732	185	5	shown	show	VERB
brj-24732	185	6	in	in	ADP
brj-24732	185	7	eq	eq	ADJ
brj-24732	185	8	.	.	PROPN
brj-24732	186	1	6	6	NUM
brj-24732	186	2	:	:	PUNCT
brj-24732	186	3	mean	mean	ADJ
brj-24732	186	4	average	average	ADJ
brj-24732	186	5	precision	precision	NOUN
brj-24732	186	6	(	(	PUNCT
brj-24732	186	7	map	map	NOUN
brj-24732	186	8	)	)	PUNCT
brj-24732	186	9	=	=	SYM
brj-24732	186	10	1	1	NUM
brj-24732	186	11	n	n	NUM
brj-24732	186	12	∑	∑	ADV
brj-24732	186	13	api	api	PROPN
brj-24732	186	14	n	n	PROPN
brj-24732	186	15	i=1	i=1	PROPN
brj-24732	186	16	(	(	PUNCT
brj-24732	186	17	6	6	NUM
brj-24732	186	18	)	)	PUNCT
brj-24732	186	19	both	both	PRON
brj-24732	186	20	map@50	map@50	X
brj-24732	186	21	(	(	PUNCT
brj-24732	186	22	with	with	ADP
brj-24732	186	23	an	an	DET
brj-24732	186	24	iou	iou	NOUN
brj-24732	186	25	threshold	threshold	NOUN
brj-24732	186	26	of	of	ADP
brj-24732	186	27	0.5	0.5	NUM
brj-24732	186	28	)	)	PUNCT
brj-24732	186	29	and	and	CCONJ
brj-24732	186	30	map@50–95	map@50–95	ADP
brj-24732	186	31	(	(	PUNCT
brj-24732	186	32	averaged	average	VERB
brj-24732	186	33	over	over	ADP
brj-24732	186	34	thresholds	threshold	NOUN
brj-24732	186	35	ranging	range	VERB
brj-24732	186	36	from	from	ADP
brj-24732	186	37	0.5	0.5	NUM
brj-24732	186	38	to	to	PART
brj-24732	186	39	0.95	0.95	NUM
brj-24732	186	40	in	in	ADP
brj-24732	186	41	0.05	0.05	NUM
brj-24732	186	42	increments	increment	NOUN
brj-24732	186	43	)	)	PUNCT
brj-24732	186	44	are	be	AUX
brj-24732	186	45	reported	report	VERB
brj-24732	186	46	in	in	ADP
brj-24732	186	47	this	this	DET
brj-24732	186	48	work	work	NOUN
brj-24732	186	49	to	to	PART
brj-24732	186	50	assess	assess	VERB
brj-24732	186	51	detection	detection	NOUN
brj-24732	186	52	robustness	robustness	NOUN
brj-24732	186	53	.	.	PUNCT
brj-24732	187	1	to	to	PART
brj-24732	187	2	evaluate	evaluate	VERB
brj-24732	187	3	computational	computational	ADJ
brj-24732	187	4	complexity	complexity	NOUN
brj-24732	187	5	,	,	PUNCT
brj-24732	187	6	gflops	gflop	NOUN
brj-24732	187	7	is	be	AUX
brj-24732	187	8	calculated	calculate	VERB
brj-24732	187	9	as	as	SCONJ
brj-24732	187	10	shown	show	VERB
brj-24732	187	11	in	in	ADP
brj-24732	187	12	eq	eq	ADJ
brj-24732	187	13	.	.	PROPN
brj-24732	187	14	7	7	NUM
brj-24732	187	15	:	:	PUNCT
brj-24732	187	16	gflops	gflop	NOUN
brj-24732	187	17	=	=	PUNCT
brj-24732	188	1	h	h	NOUN
brj-24732	189	1	×	×	NOUN
brj-24732	189	2	w	w	NOUN
brj-24732	189	3	×	×	NOUN
brj-24732	189	4	cin	cin	PROPN
brj-24732	189	5	×	×	NOUN
brj-24732	189	6	cout	cout	NOUN
brj-24732	189	7	×	×	PROPN
brj-24732	190	1	k	k	PROPN
brj-24732	190	2	×	×	PROPN
brj-24732	190	3	k	k	PROPN
brj-24732	190	4	109	109	NUM
brj-24732	190	5	(	(	PUNCT
brj-24732	190	6	7	7	NUM
brj-24732	190	7	)	)	PUNCT
brj-24732	190	8	where	where	SCONJ
brj-24732	190	9	h	h	NOUN
brj-24732	190	10	and	and	CCONJ
brj-24732	190	11	w	w	PROPN
brj-24732	190	12	represent	represent	VERB
brj-24732	190	13	the	the	DET
brj-24732	190	14	feature	feature	NOUN
brj-24732	190	15	map	map	NOUN
brj-24732	190	16	's	's	PART
brj-24732	190	17	height	height	NOUN
brj-24732	190	18	and	and	CCONJ
brj-24732	190	19	width	width	NOUN
brj-24732	190	20	,	,	PUNCT
brj-24732	190	21	cinand	cinand	NOUN
brj-24732	190	22	cout	cout	PROPN
brj-24732	190	23	are	be	AUX
brj-24732	190	24	the	the	DET
brj-24732	190	25	input	input	NOUN
brj-24732	190	26	and	and	CCONJ
brj-24732	190	27	output	output	NOUN
brj-24732	190	28	channel	channel	NOUN
brj-24732	190	29	counts	count	NOUN
brj-24732	190	30	,	,	PUNCT
brj-24732	190	31	and	and	CCONJ
brj-24732	190	32	k	k	PROPN
brj-24732	190	33	is	be	AUX
brj-24732	190	34	the	the	DET
brj-24732	190	35	kernel	kernel	NOUN
brj-24732	190	36	size	size	NOUN
brj-24732	190	37	.	.	PUNCT
brj-24732	191	1	this	this	DET
brj-24732	191	2	evaluation	evaluation	NOUN
brj-24732	191	3	framework	framework	NOUN
brj-24732	191	4	offers	offer	VERB
brj-24732	191	5	a	a	DET
brj-24732	191	6	balanced	balanced	ADJ
brj-24732	191	7	view	view	NOUN
brj-24732	191	8	of	of	ADP
brj-24732	191	9	detection	detection	NOUN
brj-24732	191	10	performance	performance	NOUN
brj-24732	191	11	and	and	CCONJ
brj-24732	191	12	model	model	NOUN
brj-24732	191	13	efficiency	efficiency	NOUN
brj-24732	191	14	.	.	PUNCT
brj-24732	192	1	results	result	NOUN
brj-24732	192	2	and	and	CCONJ
brj-24732	192	3	discussion	discussion	NOUN
brj-24732	192	4	module	module	NOUN
brj-24732	192	5	comparative	comparative	ADJ
brj-24732	192	6	experiments	experiment	NOUN
brj-24732	192	7	to	to	PART
brj-24732	192	8	validate	validate	VERB
brj-24732	192	9	the	the	DET
brj-24732	192	10	effectiveness	effectiveness	NOUN
brj-24732	192	11	of	of	ADP
brj-24732	192	12	the	the	DET
brj-24732	192	13	proposed	propose	VERB
brj-24732	192	14	adaptive	adaptive	ADJ
brj-24732	192	15	multi	multi	ADJ
brj-24732	192	16	-	-	ADJ
brj-24732	192	17	kernel	kernel	ADJ
brj-24732	192	18	depthwise	depthwise	NOUN
brj-24732	192	19	conv2d	conv2d	PROPN
brj-24732	192	20	(	(	PUNCT
brj-24732	192	21	amdc	amdc	ADJ
brj-24732	192	22	)	)	PUNCT
brj-24732	192	23	module	module	NOUN
brj-24732	192	24	,	,	PUNCT
brj-24732	192	25	a	a	DET
brj-24732	192	26	series	series	NOUN
brj-24732	192	27	of	of	ADP
brj-24732	192	28	comparative	comparative	ADJ
brj-24732	192	29	experiments	experiment	NOUN
brj-24732	192	30	were	be	AUX
brj-24732	192	31	conducted	conduct	VERB
brj-24732	192	32	using	use	VERB
brj-24732	192	33	three	three	NUM
brj-24732	192	34	classical	classical	ADJ
brj-24732	192	35	feature	feature	NOUN
brj-24732	192	36	extraction	extraction	NOUN
brj-24732	192	37	modules	module	NOUN
brj-24732	192	38	and	and	CCONJ
brj-24732	192	39	a	a	DET
brj-24732	192	40	baseline	baseline	ADJ
brj-24732	192	41	model	model	NOUN
brj-24732	192	42	.	.	PUNCT
brj-24732	193	1	specifically	specifically	ADV
brj-24732	193	2	,	,	PUNCT
brj-24732	193	3	the	the	DET
brj-24732	193	4	original	original	ADJ
brj-24732	193	5	c3k2	c3k2	NOUN
brj-24732	193	6	module	module	NOUN
brj-24732	193	7	was	be	AUX
brj-24732	193	8	replaced	replace	VERB
brj-24732	193	9	with	with	ADP
brj-24732	193	10	c3k2	c3k2	NOUN
brj-24732	193	11	-	-	PUNCT
brj-24732	193	12	faster	fast	ADJ
brj-24732	193	13	(	(	PUNCT
brj-24732	193	14	chen	chen	PROPN
brj-24732	193	15	et	et	PROPN
brj-24732	193	16	al	al	PROPN
brj-24732	193	17	.	.	PROPN
brj-24732	193	18	2023	2023	NUM
brj-24732	193	19	)	)	PUNCT
brj-24732	193	20	,	,	PUNCT
brj-24732	193	21	c3k2	c3k2	NOUN
brj-24732	193	22	-	-	PUNCT
brj-24732	193	23	mambaout	mambaout	NOUN
brj-24732	193	24	(	(	PUNCT
brj-24732	193	25	yu	yu	PROPN
brj-24732	193	26	and	and	CCONJ
brj-24732	193	27	wang	wang	PROPN
brj-24732	193	28	2024	2024	NUM
brj-24732	193	29	)	)	PUNCT
brj-24732	193	30	,	,	PUNCT
brj-24732	193	31	and	and	CCONJ
brj-24732	193	32	c3k2	c3k2	NOUN
brj-24732	193	33	-	-	PUNCT
brj-24732	193	34	dbb	dbb	PROPN
brj-24732	193	35	(	(	PUNCT
brj-24732	193	36	ding	ding	NOUN
brj-24732	193	37	et	et	PROPN
brj-24732	193	38	al	al	PROPN
brj-24732	193	39	.	.	PROPN
brj-24732	193	40	2021	2021	NUM
brj-24732	193	41	)	)	PUNCT
brj-24732	193	42	,	,	PUNCT
brj-24732	193	43	respectively	respectively	ADV
brj-24732	193	44	.	.	PUNCT
brj-24732	194	1	the	the	DET
brj-24732	194	2	experimental	experimental	ADJ
brj-24732	194	3	results	result	NOUN
brj-24732	194	4	are	be	AUX
brj-24732	194	5	presented	present	VERB
brj-24732	194	6	in	in	ADP
brj-24732	194	7	table	table	NOUN
brj-24732	194	8	2	2	NUM
brj-24732	194	9	.	.	PUNCT
brj-24732	194	10	c3k2	c3k2	NOUN
brj-24732	194	11	-	-	PUNCT
brj-24732	194	12	faster	fast	ADJ
brj-24732	194	13	achieved	achieve	VERB
brj-24732	194	14	reductions	reduction	NOUN
brj-24732	194	15	in	in	ADP
brj-24732	194	16	model	model	NOUN
brj-24732	194	17	parameters	parameter	NOUN
brj-24732	194	18	and	and	CCONJ
brj-24732	194	19	peer	peer	NOUN
brj-24732	194	20	-	-	PUNCT
brj-24732	194	21	reviewed	review	VERB
brj-24732	194	22	article	article	NOUN
brj-24732	194	23	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	194	24	he	he	PRON
brj-24732	194	25	et	et	PROPN
brj-24732	194	26	al	al	PROPN
brj-24732	194	27	.	.	PROPN
brj-24732	195	1	(	(	PUNCT
brj-24732	195	2	2025	2025	NUM
brj-24732	195	3	)	)	PUNCT
brj-24732	195	4	.	.	PUNCT
brj-24732	196	1	“	"	PUNCT
brj-24732	196	2	le	le	X
brj-24732	196	3	-	-	PROPN
brj-24732	196	4	yolo	yolo	PROPN
brj-24732	196	5	&	&	CCONJ
brj-24732	196	6	wood	wood	PROPN
brj-24732	196	7	surface	surface	NOUN
brj-24732	196	8	defects	defect	NOUN
brj-24732	196	9	,	,	PUNCT
brj-24732	196	10	”	"	PUNCT
brj-24732	196	11	bioresources	bioresource	NOUN
brj-24732	196	12	20(3	20(3	NOUN
brj-24732	196	13	)	)	PUNCT
brj-24732	196	14	,	,	PUNCT
brj-24732	196	15	7179	7179	NUM
brj-24732	196	16	-	-	SYM
brj-24732	196	17	7193	7193	NUM
brj-24732	196	18	.	.	PUNCT
brj-24732	197	1	7188	7188	NUM
brj-24732	197	2	computational	computational	ADJ
brj-24732	197	3	cost	cost	NOUN
brj-24732	197	4	,	,	PUNCT
brj-24732	197	5	but	but	CCONJ
brj-24732	197	6	its	its	PRON
brj-24732	197	7	detection	detection	NOUN
brj-24732	197	8	performance	performance	NOUN
brj-24732	197	9	declined	decline	VERB
brj-24732	197	10	substantially	substantially	ADV
brj-24732	197	11	.	.	PUNCT
brj-24732	198	1	c3k2	c3k2	NOUN
brj-24732	198	2	-	-	PUNCT
brj-24732	198	3	mambaout	mambaout	NOUN
brj-24732	198	4	and	and	CCONJ
brj-24732	198	5	c3k2	c3k2	NOUN
brj-24732	198	6	-	-	PUNCT
brj-24732	198	7	dbb	dbb	PROPN
brj-24732	198	8	improved	improve	VERB
brj-24732	198	9	detection	detection	NOUN
brj-24732	198	10	accuracy	accuracy	NOUN
brj-24732	198	11	yet	yet	ADV
brj-24732	198	12	it	it	PRON
brj-24732	198	13	failed	fail	VERB
brj-24732	198	14	to	to	PART
brj-24732	198	15	reduce	reduce	VERB
brj-24732	198	16	the	the	DET
brj-24732	198	17	model	model	NOUN
brj-24732	198	18	’s	’s	PART
brj-24732	198	19	complexity	complexity	NOUN
brj-24732	198	20	.	.	PUNCT
brj-24732	199	1	in	in	ADP
brj-24732	199	2	contrast	contrast	NOUN
brj-24732	199	3	,	,	PUNCT
brj-24732	199	4	the	the	DET
brj-24732	199	5	proposed	propose	VERB
brj-24732	199	6	amdc	amdc	NOUN
brj-24732	199	7	module	module	NOUN
brj-24732	199	8	not	not	PART
brj-24732	199	9	only	only	ADV
brj-24732	199	10	improved	improved	ADJ
brj-24732	199	11	detection	detection	NOUN
brj-24732	199	12	performance	performance	NOUN
brj-24732	199	13	but	but	CCONJ
brj-24732	199	14	also	also	ADV
brj-24732	199	15	reduced	reduce	VERB
brj-24732	199	16	parameter	parameter	NOUN
brj-24732	199	17	count	count	NOUN
brj-24732	199	18	and	and	CCONJ
brj-24732	199	19	computational	computational	ADJ
brj-24732	199	20	overhead	overhead	NOUN
brj-24732	199	21	,	,	PUNCT
brj-24732	199	22	demonstrating	demonstrate	VERB
brj-24732	199	23	superior	superior	ADJ
brj-24732	199	24	efficiency	efficiency	NOUN
brj-24732	199	25	and	and	CCONJ
brj-24732	199	26	robustness	robustness	NOUN
brj-24732	199	27	.	.	PUNCT
brj-24732	200	1	table	table	NOUN
brj-24732	200	2	2	2	NUM
brj-24732	200	3	.	.	PUNCT
brj-24732	200	4	comparison	comparison	NOUN
brj-24732	200	5	of	of	ADP
brj-24732	200	6	feature	feature	NOUN
brj-24732	200	7	extraction	extraction	NOUN
brj-24732	200	8	modules	module	NOUN
brj-24732	200	9	model	model	NOUN
brj-24732	200	10	p	p	NOUN
brj-24732	200	11	r	r	NOUN
brj-24732	200	12	f1	f1	NOUN
brj-24732	200	13	map@50	map@50	PUNCT
brj-24732	200	14	map@50:95	map@50:95	NOUN
brj-24732	200	15	gflops	gflop	NOUN
brj-24732	200	16	(	(	PUNCT
brj-24732	200	17	g	g	NOUN
brj-24732	200	18	)	)	PUNCT
brj-24732	200	19	parameters	parameter	NOUN
brj-24732	200	20	(	(	PUNCT
brj-24732	200	21	m	m	NOUN
brj-24732	200	22	)	)	PUNCT
brj-24732	200	23	baseline	baseline	VERB
brj-24732	200	24	0.89	0.89	NUM
brj-24732	200	25	0.79	0.79	NUM
brj-24732	200	26	0.82	0.82	NUM
brj-24732	200	27	0.86	0.86	NUM
brj-24732	200	28	0.49	0.49	NUM
brj-24732	200	29	6.3	6.3	NUM
brj-24732	200	30	2.60	2.60	NUM
brj-24732	200	31	c3k2	c3k2	NOUN
brj-24732	200	32	-	-	PUNCT
brj-24732	200	33	faster	fast	ADJ
brj-24732	200	34	0.89	0.89	NUM
brj-24732	200	35	0.76	0.76	NUM
brj-24732	200	36	0.8	0.8	NUM
brj-24732	200	37	0.84	0.84	NUM
brj-24732	200	38	0.47	0.47	NUM
brj-24732	200	39	5.8	5.8	NUM
brj-24732	200	40	2.30	2.30	NUM
brj-24732	200	41	c3k2	c3k2	NOUN
brj-24732	200	42	-	-	PUNCT
brj-24732	200	43	mambaout	mambaout	NOUN
brj-24732	200	44	0.84	0.84	NUM
brj-24732	200	45	0.8	0.8	NUM
brj-24732	200	46	0.8	0.8	NUM
brj-24732	200	47	0.87	0.87	NUM
brj-24732	200	48	0.49	0.49	NUM
brj-24732	200	49	6.9	6.9	NUM
brj-24732	200	50	2.50	2.50	NUM
brj-24732	200	51	c3k2	c3k2	NOUN
brj-24732	200	52	-	-	PUNCT
brj-24732	200	53	dbb	dbb	PROPN
brj-24732	200	54	0.78	0.78	NUM
brj-24732	200	55	0.82	0.82	NUM
brj-24732	200	56	0.8	0.8	NUM
brj-24732	200	57	0.87	0.87	NUM
brj-24732	200	58	0.50	0.50	NUM
brj-24732	200	59	6.3	6.3	NUM
brj-24732	200	60	2.58	2.58	NUM
brj-24732	200	61	amdc	amdc	VERB
brj-24732	200	62	0.85	0.85	NUM
brj-24732	200	63	0.79	0.79	NUM
brj-24732	200	64	0.81	0.81	NUM
brj-24732	200	65	0.87	0.87	NUM
brj-24732	200	66	0.50	0.50	NUM
brj-24732	200	67	5.8	5.8	NUM
brj-24732	200	68	2.30	2.30	NUM
brj-24732	200	69	to	to	PART
brj-24732	200	70	further	far	ADV
brj-24732	200	71	assess	assess	VERB
brj-24732	200	72	the	the	DET
brj-24732	200	73	impact	impact	NOUN
brj-24732	200	74	of	of	ADP
brj-24732	200	75	attention	attention	NOUN
brj-24732	200	76	mechanisms	mechanism	NOUN
brj-24732	200	77	on	on	ADP
brj-24732	200	78	the	the	DET
brj-24732	200	79	performance	performance	NOUN
brj-24732	200	80	of	of	ADP
brj-24732	200	81	the	the	DET
brj-24732	200	82	shared	share	VERB
brj-24732	200	83	detection	detection	NOUN
brj-24732	200	84	head	head	NOUN
brj-24732	200	85	,	,	PUNCT
brj-24732	200	86	comparative	comparative	ADJ
brj-24732	200	87	experiments	experiment	NOUN
brj-24732	200	88	were	be	AUX
brj-24732	200	89	conducted	conduct	VERB
brj-24732	200	90	with	with	ADP
brj-24732	200	91	several	several	ADJ
brj-24732	200	92	state	state	NOUN
brj-24732	200	93	-	-	PUNCT
brj-24732	200	94	of	of	ADP
brj-24732	200	95	-	-	PUNCT
brj-24732	200	96	theart	theart	NOUN
brj-24732	200	97	attention	attention	NOUN
brj-24732	200	98	-	-	PUNCT
brj-24732	200	99	based	base	VERB
brj-24732	200	100	designs	design	NOUN
brj-24732	200	101	,	,	PUNCT
brj-24732	200	102	including	include	VERB
brj-24732	200	103	dyhead	dyhead	NOUN
brj-24732	200	104	(	(	PUNCT
brj-24732	200	105	dai	dai	PROPN
brj-24732	200	106	et	et	PROPN
brj-24732	200	107	al	al	PROPN
brj-24732	200	108	.	.	PROPN
brj-24732	200	109	2021	2021	NUM
brj-24732	200	110	)	)	PUNCT
brj-24732	200	111	,	,	PUNCT
brj-24732	200	112	efficienthead	efficienthead	NOUN
brj-24732	200	113	(	(	PUNCT
brj-24732	200	114	tan	tan	NOUN
brj-24732	200	115	et	et	PROPN
brj-24732	200	116	al	al	PROPN
brj-24732	200	117	.	.	PROPN
brj-24732	200	118	2020	2020	NUM
brj-24732	200	119	)	)	PUNCT
brj-24732	200	120	,	,	PUNCT
brj-24732	200	121	and	and	CCONJ
brj-24732	200	122	seam	seam	NOUN
brj-24732	200	123	head	head	NOUN
brj-24732	200	124	(	(	PUNCT
brj-24732	200	125	yu	yu	PROPN
brj-24732	200	126	et	et	PROPN
brj-24732	200	127	al	al	PROPN
brj-24732	200	128	.	.	PROPN
brj-24732	200	129	2022	2022	NUM
brj-24732	200	130	)	)	PUNCT
brj-24732	200	131	,	,	PUNCT
brj-24732	200	132	which	which	PRON
brj-24732	200	133	incorporates	incorporate	VERB
brj-24732	200	134	occlusion	occlusion	NOUN
brj-24732	200	135	-	-	PUNCT
brj-24732	200	136	aware	aware	ADJ
brj-24732	200	137	attention	attention	NOUN
brj-24732	200	138	.	.	PUNCT
brj-24732	201	1	a	a	DET
brj-24732	201	2	baseline	baseline	NOUN
brj-24732	201	3	model	model	NOUN
brj-24732	201	4	without	without	ADP
brj-24732	201	5	attention	attention	NOUN
brj-24732	201	6	mechanisms	mechanism	NOUN
brj-24732	201	7	in	in	ADP
brj-24732	201	8	the	the	DET
brj-24732	201	9	shared	share	VERB
brj-24732	201	10	convolution	convolution	NOUN
brj-24732	201	11	was	be	AUX
brj-24732	201	12	used	use	VERB
brj-24732	201	13	for	for	ADP
brj-24732	201	14	comparison	comparison	NOUN
brj-24732	201	15	.	.	PUNCT
brj-24732	202	1	the	the	DET
brj-24732	202	2	results	result	NOUN
brj-24732	202	3	are	be	AUX
brj-24732	202	4	summarized	summarize	VERB
brj-24732	202	5	in	in	ADP
brj-24732	202	6	table	table	NOUN
brj-24732	202	7	3	3	NUM
brj-24732	202	8	.	.	PUNCT
brj-24732	202	9	dyhead	dyhead	PROPN
brj-24732	202	10	introduced	introduce	VERB
brj-24732	202	11	only	only	ADV
brj-24732	202	12	limited	limited	ADJ
brj-24732	202	13	performance	performance	NOUN
brj-24732	202	14	gains	gain	NOUN
brj-24732	202	15	while	while	SCONJ
brj-24732	202	16	substantially	substantially	ADV
brj-24732	202	17	increasing	increase	VERB
brj-24732	202	18	model	model	NOUN
brj-24732	202	19	size	size	NOUN
brj-24732	202	20	and	and	CCONJ
brj-24732	202	21	computational	computational	ADJ
brj-24732	202	22	overhead	overhead	NOUN
brj-24732	202	23	.	.	PUNCT
brj-24732	203	1	although	although	SCONJ
brj-24732	203	2	efficienthead	efficienthead	ADJ
brj-24732	203	3	and	and	CCONJ
brj-24732	203	4	seam	seam	NOUN
brj-24732	203	5	head	head	NOUN
brj-24732	203	6	offered	offer	VERB
brj-24732	203	7	a	a	DET
brj-24732	203	8	better	well	ADJ
brj-24732	203	9	trade	trade	NOUN
brj-24732	203	10	-	-	PUNCT
brj-24732	203	11	off	off	NOUN
brj-24732	203	12	between	between	ADP
brj-24732	203	13	accuracy	accuracy	NOUN
brj-24732	203	14	and	and	CCONJ
brj-24732	203	15	efficiency	efficiency	NOUN
brj-24732	203	16	,	,	PUNCT
brj-24732	203	17	only	only	ADV
brj-24732	203	18	the	the	DET
brj-24732	203	19	proposed	propose	VERB
brj-24732	203	20	lwdethead	lwdethead	NOUN
brj-24732	203	21	achieved	achieve	VERB
brj-24732	203	22	the	the	DET
brj-24732	203	23	highest	high	ADJ
brj-24732	203	24	detection	detection	NOUN
brj-24732	203	25	performance	performance	NOUN
brj-24732	203	26	while	while	SCONJ
brj-24732	203	27	substantially	substantially	ADV
brj-24732	203	28	reducing	reduce	VERB
brj-24732	203	29	parameters	parameter	NOUN
brj-24732	203	30	and	and	CCONJ
brj-24732	203	31	maintaining	maintain	VERB
brj-24732	203	32	a	a	DET
brj-24732	203	33	low	low	ADJ
brj-24732	203	34	computational	computational	ADJ
brj-24732	203	35	cost	cost	NOUN
brj-24732	203	36	.	.	PUNCT
brj-24732	204	1	table	table	NOUN
brj-24732	204	2	3	3	NUM
brj-24732	204	3	.	.	PUNCT
brj-24732	204	4	comparative	comparative	ADJ
brj-24732	204	5	analysis	analysis	NOUN
brj-24732	204	6	of	of	ADP
brj-24732	204	7	detection	detection	NOUN
brj-24732	204	8	heads	head	NOUN
brj-24732	204	9	model	model	VERB
brj-24732	204	10	p	p	PROPN
brj-24732	204	11	r	r	NOUN
brj-24732	204	12	f1	f1	NOUN
brj-24732	204	13	map@50	map@50	PUNCT
brj-24732	204	14	map@50:95	map@50:95	NOUN
brj-24732	204	15	gflops	gflop	NOUN
brj-24732	204	16	(	(	PUNCT
brj-24732	204	17	g	g	NOUN
brj-24732	204	18	)	)	PUNCT
brj-24732	204	19	parameters	parameter	NOUN
brj-24732	204	20	(	(	PUNCT
brj-24732	204	21	m	m	NOUN
brj-24732	204	22	)	)	PUNCT
brj-24732	204	23	baseline	baseline	VERB
brj-24732	204	24	0.89	0.89	NUM
brj-24732	204	25	0.79	0.79	NUM
brj-24732	204	26	0.82	0.82	NUM
brj-24732	204	27	0.86	0.86	NUM
brj-24732	204	28	0.49	0.49	NUM
brj-24732	204	29	6.3	6.3	NUM
brj-24732	204	30	2.60	2.60	NUM
brj-24732	204	31	dyhead	dyhead	NOUN
brj-24732	204	32	0.80	0.80	NUM
brj-24732	204	33	0.80	0.80	NUM
brj-24732	204	34	0.79	0.79	NUM
brj-24732	204	35	0.85	0.85	NUM
brj-24732	204	36	0.47	0.47	NUM
brj-24732	204	37	7.4	7.4	NUM
brj-24732	204	38	3.10	3.10	NUM
brj-24732	204	39	efficienthead	efficienthead	NOUN
brj-24732	204	40	0.88	0.88	NUM
brj-24732	204	41	0.83	0.83	NUM
brj-24732	204	42	0.83	0.83	NUM
brj-24732	204	43	0.86	0.86	NUM
brj-24732	204	44	0.51	0.51	NUM
brj-24732	204	45	5.1	5.1	NUM
brj-24732	204	46	2.32	2.32	NUM
brj-24732	204	47	seamhead	seamhead	NOUN
brj-24732	204	48	0.85	0.85	NUM
brj-24732	204	49	0.82	0.82	NUM
brj-24732	204	50	0.82	0.82	NUM
brj-24732	204	51	0.88	0.88	NUM
brj-24732	204	52	0.52	0.52	NUM
brj-24732	204	53	5.8	5.8	NUM
brj-24732	204	54	2.50	2.50	NUM
brj-24732	204	55	lwdethead	lwdethead	VERB
brj-24732	204	56	0.85	0.85	NUM
brj-24732	204	57	0.82	0.82	NUM
brj-24732	204	58	0.83	0.83	NUM
brj-24732	204	59	0.88	0.88	NUM
brj-24732	204	60	0.52	0.52	NUM
brj-24732	204	61	6.0	6.0	NUM
brj-24732	204	62	2.26	2.26	NUM
brj-24732	204	63	ablation	ablation	NOUN
brj-24732	204	64	studies	study	NOUN
brj-24732	204	65	to	to	PART
brj-24732	204	66	evaluate	evaluate	VERB
brj-24732	204	67	the	the	DET
brj-24732	204	68	impact	impact	NOUN
brj-24732	204	69	of	of	ADP
brj-24732	204	70	the	the	DET
brj-24732	204	71	proposed	propose	VERB
brj-24732	204	72	enhancement	enhancement	NOUN
brj-24732	204	73	modules	module	NOUN
brj-24732	204	74	on	on	ADP
brj-24732	204	75	the	the	DET
brj-24732	204	76	model	model	NOUN
brj-24732	204	77	’s	’s	PART
brj-24732	204	78	performance	performance	NOUN
brj-24732	204	79	for	for	ADP
brj-24732	204	80	chipboard	chipboard	NOUN
brj-24732	204	81	surface	surface	NOUN
brj-24732	204	82	defect	defect	NOUN
brj-24732	204	83	detection	detection	NOUN
brj-24732	204	84	,	,	PUNCT
brj-24732	204	85	a	a	DET
brj-24732	204	86	series	series	NOUN
brj-24732	204	87	of	of	ADP
brj-24732	204	88	ablation	ablation	NOUN
brj-24732	204	89	experiments	experiment	NOUN
brj-24732	204	90	were	be	AUX
brj-24732	204	91	conducted	conduct	VERB
brj-24732	204	92	based	base	VERB
brj-24732	204	93	on	on	ADP
brj-24732	204	94	the	the	DET
brj-24732	204	95	yolov11n	yolov11n	NOUN
brj-24732	204	96	framework	framework	NOUN
brj-24732	204	97	.	.	PUNCT
brj-24732	205	1	the	the	DET
brj-24732	205	2	core	core	NOUN
brj-24732	205	3	evaluation	evaluation	NOUN
brj-24732	205	4	metrics	metric	NOUN
brj-24732	205	5	included	include	VERB
brj-24732	205	6	the	the	DET
brj-24732	205	7	f1	f1	PROPN
brj-24732	205	8	score	score	NOUN
brj-24732	205	9	,	,	PUNCT
brj-24732	205	10	map@50	map@50	NOUN
brj-24732	205	11	,	,	PUNCT
brj-24732	205	12	map@50:95	map@50:95	NOUN
brj-24732	205	13	,	,	PUNCT
brj-24732	205	14	gflops	gflop	NOUN
brj-24732	205	15	,	,	PUNCT
brj-24732	205	16	and	and	CCONJ
brj-24732	205	17	the	the	DET
brj-24732	205	18	number	number	NOUN
brj-24732	205	19	of	of	ADP
brj-24732	205	20	parameters	parameter	NOUN
brj-24732	205	21	.	.	PUNCT
brj-24732	206	1	the	the	DET
brj-24732	206	2	experimental	experimental	ADJ
brj-24732	206	3	results	result	NOUN
brj-24732	206	4	are	be	AUX
brj-24732	206	5	presented	present	VERB
brj-24732	206	6	in	in	ADP
brj-24732	206	7	table	table	NOUN
brj-24732	206	8	4	4	NUM
brj-24732	206	9	.	.	PUNCT
brj-24732	207	1	the	the	DET
brj-24732	207	2	assessment	assessment	NOUN
brj-24732	207	3	of	of	ADP
brj-24732	207	4	the	the	DET
brj-24732	207	5	proposed	propose	VERB
brj-24732	207	6	le	le	PROPN
brj-24732	207	7	-	-	ADJ
brj-24732	207	8	yolo	yolo	ADJ
brj-24732	207	9	model	model	NOUN
brj-24732	207	10	involved	involve	VERB
brj-24732	207	11	eight	eight	NUM
brj-24732	207	12	experimental	experimental	ADJ
brj-24732	207	13	configurations	configuration	NOUN
brj-24732	207	14	:	:	PUNCT
brj-24732	207	15	experiment	experiment	NOUN
brj-24732	207	16	1	1	NUM
brj-24732	207	17	:	:	PUNCT
brj-24732	207	18	baseline	baseline	VERB
brj-24732	207	19	yolov11n	yolov11n	NOUN
brj-24732	207	20	model	model	NOUN
brj-24732	207	21	;	;	PUNCT
brj-24732	207	22	experiment	experiment	NOUN
brj-24732	207	23	2	2	NUM
brj-24732	207	24	:	:	PUNCT
brj-24732	207	25	incorporation	incorporation	NOUN
brj-24732	207	26	of	of	ADP
brj-24732	207	27	the	the	DET
brj-24732	207	28	sdfp	sdfp	NOUN
brj-24732	207	29	module	module	NOUN
brj-24732	207	30	into	into	ADP
brj-24732	207	31	the	the	DET
brj-24732	207	32	baseline	baseline	NOUN
brj-24732	207	33	;	;	PUNCT
brj-24732	207	34	experiment	experiment	NOUN
brj-24732	207	35	3	3	NUM
brj-24732	207	36	:	:	PUNCT
brj-24732	207	37	replacement	replacement	NOUN
brj-24732	207	38	of	of	ADP
brj-24732	207	39	the	the	DET
brj-24732	207	40	c3k2	c3k2	NOUN
brj-24732	207	41	module	module	NOUN
brj-24732	207	42	in	in	ADP
brj-24732	207	43	the	the	DET
brj-24732	207	44	yolov11	yolov11	NOUN
brj-24732	207	45	backbone	backbone	NOUN
brj-24732	207	46	with	with	ADP
brj-24732	207	47	the	the	DET
brj-24732	207	48	amdc	amdc	VERB
brj-24732	207	49	module	module	NOUN
brj-24732	207	50	;	;	PUNCT
brj-24732	207	51	experiment	experiment	NOUN
brj-24732	207	52	4	4	NUM
brj-24732	207	53	:	:	PUNCT
brj-24732	207	54	redesign	redesign	NOUN
brj-24732	207	55	of	of	ADP
brj-24732	207	56	the	the	DET
brj-24732	207	57	detection	detection	NOUN
brj-24732	207	58	head	head	NOUN
brj-24732	207	59	using	use	VERB
brj-24732	207	60	the	the	DET
brj-24732	207	61	proposed	propose	VERB
brj-24732	207	62	lwdethead	lwdethead	NOUN
brj-24732	207	63	;	;	PUNCT
brj-24732	207	64	experiment	experiment	NOUN
brj-24732	207	65	5	5	NUM
brj-24732	207	66	:	:	PUNCT
brj-24732	207	67	integration	integration	NOUN
brj-24732	207	68	of	of	ADP
brj-24732	207	69	amdc	amdc	PRON
brj-24732	207	70	on	on	ADP
brj-24732	207	71	top	top	NOUN
brj-24732	207	72	of	of	ADP
brj-24732	207	73	the	the	DET
brj-24732	207	74	experiment	experiment	NOUN
brj-24732	207	75	2	2	NUM
brj-24732	207	76	configuration	configuration	NOUN
brj-24732	207	77	;	;	PUNCT
brj-24732	207	78	experiment	experiment	NOUN
brj-24732	207	79	6	6	NUM
brj-24732	207	80	:	:	PUNCT
brj-24732	207	81	integration	integration	NOUN
brj-24732	207	82	of	of	ADP
brj-24732	207	83	lwdethead	lwdethead	NOUN
brj-24732	207	84	on	on	ADP
brj-24732	207	85	top	top	NOUN
brj-24732	207	86	of	of	ADP
brj-24732	207	87	the	the	DET
brj-24732	207	88	experiment	experiment	NOUN
brj-24732	207	89	2	2	NUM
brj-24732	207	90	configuration	configuration	NOUN
brj-24732	207	91	;	;	PUNCT
brj-24732	207	92	experiment	experiment	NOUN
brj-24732	207	93	7	7	NUM
brj-24732	207	94	:	:	PUNCT
brj-24732	207	95	peer	peer	NOUN
brj-24732	207	96	-	-	PUNCT
brj-24732	207	97	reviewed	review	VERB
brj-24732	207	98	article	article	NOUN
brj-24732	207	99	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	207	100	he	he	PRON
brj-24732	207	101	et	et	PROPN
brj-24732	207	102	al	al	PROPN
brj-24732	207	103	.	.	PROPN
brj-24732	208	1	(	(	PUNCT
brj-24732	208	2	2025	2025	NUM
brj-24732	208	3	)	)	PUNCT
brj-24732	208	4	.	.	PUNCT
brj-24732	209	1	“	"	PUNCT
brj-24732	209	2	le	le	X
brj-24732	209	3	-	-	PROPN
brj-24732	209	4	yolo	yolo	PROPN
brj-24732	209	5	&	&	CCONJ
brj-24732	209	6	wood	wood	PROPN
brj-24732	209	7	surface	surface	NOUN
brj-24732	209	8	defects	defect	NOUN
brj-24732	209	9	,	,	PUNCT
brj-24732	209	10	”	"	PUNCT
brj-24732	209	11	bioresources	bioresource	NOUN
brj-24732	209	12	20(3	20(3	NOUN
brj-24732	209	13	)	)	PUNCT
brj-24732	209	14	,	,	PUNCT
brj-24732	209	15	7179	7179	NUM
brj-24732	209	16	-	-	SYM
brj-24732	209	17	7193	7193	NUM
brj-24732	209	18	.	.	PUNCT
brj-24732	210	1	7189	7189	NUM
brj-24732	210	2	integration	integration	NOUN
brj-24732	210	3	of	of	ADP
brj-24732	210	4	lwdethead	lwdethead	NOUN
brj-24732	210	5	on	on	ADP
brj-24732	210	6	top	top	NOUN
brj-24732	210	7	of	of	ADP
brj-24732	210	8	the	the	DET
brj-24732	210	9	experiment	experiment	NOUN
brj-24732	210	10	3	3	NUM
brj-24732	210	11	configuration	configuration	NOUN
brj-24732	210	12	;	;	PUNCT
brj-24732	210	13	experiment	experiment	NOUN
brj-24732	210	14	8	8	NUM
brj-24732	210	15	:	:	PUNCT
brj-24732	210	16	comprehensive	comprehensive	ADJ
brj-24732	210	17	application	application	NOUN
brj-24732	210	18	of	of	ADP
brj-24732	210	19	all	all	DET
brj-24732	210	20	proposed	propose	VERB
brj-24732	210	21	modules	module	NOUN
brj-24732	210	22	,	,	PUNCT
brj-24732	210	23	representing	represent	VERB
brj-24732	210	24	the	the	DET
brj-24732	210	25	final	final	ADJ
brj-24732	210	26	le	le	ADJ
brj-24732	210	27	-	-	ADJ
brj-24732	210	28	yolo	yolo	ADJ
brj-24732	210	29	architecture	architecture	NOUN
brj-24732	210	30	.	.	PUNCT
brj-24732	211	1	table	table	NOUN
brj-24732	211	2	4	4	NUM
brj-24732	211	3	.	.	PUNCT
brj-24732	211	4	ablation	ablation	NOUN
brj-24732	211	5	experiment	experiment	NOUN
brj-24732	211	6	method	method	NOUN
brj-24732	211	7	f1	f1	PROPN
brj-24732	211	8	map@50	map@50	PRON
brj-24732	211	9	map@50:95	map@50:95	NOUN
brj-24732	211	10	gflops(g	gflops(g	NOUN
brj-24732	211	11	)	)	PUNCT
brj-24732	211	12	parameters	parameter	NOUN
brj-24732	211	13	(	(	PUNCT
brj-24732	211	14	m	m	NOUN
brj-24732	211	15	)	)	PUNCT
brj-24732	211	16	method(1	method(1	PROPN
brj-24732	211	17	)	)	PUNCT
brj-24732	211	18	0.82	0.82	NUM
brj-24732	211	19	0.86	0.86	NUM
brj-24732	211	20	0.49	0.49	NUM
brj-24732	211	21	6.3	6.3	NUM
brj-24732	211	22	2.58	2.58	NUM
brj-24732	211	23	method(2	method(2	NOUN
brj-24732	211	24	)	)	PUNCT
brj-24732	211	25	0.83	0.83	NUM
brj-24732	211	26	0.87	0.87	NUM
brj-24732	211	27	0.50	0.50	NUM
brj-24732	211	28	6.3	6.3	NUM
brj-24732	211	29	2.63	2.63	NUM
brj-24732	211	30	method(3	method(3	NOUN
brj-24732	211	31	)	)	PUNCT
brj-24732	211	32	0.81	0.81	NUM
brj-24732	211	33	0.87	0.87	NUM
brj-24732	211	34	0.49	0.49	NUM
brj-24732	211	35	5.8	5.8	NUM
brj-24732	211	36	2.30	2.30	NUM
brj-24732	211	37	method(4	method(4	ADJ
brj-24732	211	38	)	)	PUNCT
brj-24732	211	39	0.82	0.82	NUM
brj-24732	211	40	0.88	0.88	NUM
brj-24732	211	41	0.52	0.52	NUM
brj-24732	211	42	6.0	6.0	NUM
brj-24732	211	43	2.26	2.26	NUM
brj-24732	211	44	method(5	method(5	NOUN
brj-24732	211	45	)	)	PUNCT
brj-24732	211	46	0.83	0.83	NUM
brj-24732	211	47	0.84	0.84	NUM
brj-24732	211	48	0.50	0.50	NUM
brj-24732	211	49	5.8	5.8	NUM
brj-24732	211	50	2.47	2.47	NUM
brj-24732	211	51	method(6	method(6	NOUN
brj-24732	211	52	)	)	PUNCT
brj-24732	211	53	0.83	0.83	NUM
brj-24732	211	54	0.88	0.88	NUM
brj-24732	211	55	0.50	0.50	NUM
brj-24732	211	56	6.0	6.0	NUM
brj-24732	211	57	2.31	2.31	NUM
brj-24732	211	58	method(7	method(7	NOUN
brj-24732	211	59	)	)	PUNCT
brj-24732	211	60	0.83	0.83	NUM
brj-24732	211	61	0.89	0.89	NUM
brj-24732	211	62	0.51	0.51	NUM
brj-24732	211	63	5.5	5.5	NUM
brj-24732	211	64	1.99	1.99	NUM
brj-24732	211	65	method(8	method(8	NOUN
brj-24732	211	66	)	)	PUNCT
brj-24732	211	67	0.84	0.84	NUM
brj-24732	211	68	0.90	0.90	NUM
brj-24732	211	69	0.55	0.55	NUM
brj-24732	211	70	5.5	5.5	NUM
brj-24732	211	71	2.10	2.10	NUM
brj-24732	211	72	the	the	DET
brj-24732	211	73	results	result	NOUN
brj-24732	211	74	show	show	VERB
brj-24732	211	75	that	that	SCONJ
brj-24732	211	76	,	,	PUNCT
brj-24732	211	77	except	except	SCONJ
brj-24732	211	78	for	for	ADP
brj-24732	211	79	the	the	DET
brj-24732	211	80	sdfp	sdfp	NOUN
brj-24732	211	81	module	module	NOUN
brj-24732	211	82	,	,	PUNCT
brj-24732	211	83	all	all	DET
brj-24732	211	84	additional	additional	ADJ
brj-24732	211	85	enhancement	enhancement	NOUN
brj-24732	211	86	modules	module	NOUN
brj-24732	211	87	contributed	contribute	VERB
brj-24732	211	88	to	to	ADP
brj-24732	211	89	a	a	DET
brj-24732	211	90	reduction	reduction	NOUN
brj-24732	211	91	in	in	ADP
brj-24732	211	92	model	model	NOUN
brj-24732	211	93	parameters	parameter	NOUN
brj-24732	211	94	.	.	PUNCT
brj-24732	212	1	although	although	SCONJ
brj-24732	212	2	the	the	DET
brj-24732	212	3	inclusion	inclusion	NOUN
brj-24732	212	4	of	of	ADP
brj-24732	212	5	sdfp	sdfp	NOUN
brj-24732	212	6	resulted	result	VERB
brj-24732	212	7	in	in	ADP
brj-24732	212	8	a	a	DET
brj-24732	212	9	slight	slight	ADJ
brj-24732	212	10	increase	increase	NOUN
brj-24732	212	11	of	of	ADP
brj-24732	212	12	approximately	approximately	ADV
brj-24732	212	13	50k	50k	NUM
brj-24732	212	14	parameters	parameter	NOUN
brj-24732	212	15	compared	compare	VERB
brj-24732	212	16	to	to	ADP
brj-24732	212	17	the	the	DET
brj-24732	212	18	baseline	baseline	NOUN
brj-24732	212	19	,	,	PUNCT
brj-24732	212	20	it	it	PRON
brj-24732	212	21	led	lead	VERB
brj-24732	212	22	to	to	ADP
brj-24732	212	23	a	a	DET
brj-24732	212	24	1	1	NUM
brj-24732	212	25	%	%	NOUN
brj-24732	212	26	improvement	improvement	NOUN
brj-24732	212	27	in	in	ADP
brj-24732	212	28	f1	f1	PROPN
brj-24732	212	29	score	score	NOUN
brj-24732	212	30	,	,	PUNCT
brj-24732	212	31	map@50	map@50	NOUN
brj-24732	212	32	,	,	PUNCT
brj-24732	212	33	and	and	CCONJ
brj-24732	212	34	map@50:95	map@50:95	NOUN
brj-24732	212	35	.	.	PUNCT
brj-24732	213	1	this	this	DET
brj-24732	213	2	highlights	highlight	VERB
brj-24732	213	3	the	the	DET
brj-24732	213	4	sdfp	sdfp	NOUN
brj-24732	213	5	module	module	NOUN
brj-24732	213	6	’s	’s	PART
brj-24732	213	7	effectiveness	effectiveness	NOUN
brj-24732	213	8	in	in	ADP
brj-24732	213	9	enhancing	enhance	VERB
brj-24732	213	10	detection	detection	NOUN
brj-24732	213	11	accuracy	accuracy	NOUN
brj-24732	213	12	through	through	ADP
brj-24732	213	13	multi	multi	ADJ
brj-24732	213	14	-	-	ADJ
brj-24732	213	15	scale	scale	ADJ
brj-24732	213	16	feature	feature	NOUN
brj-24732	213	17	fusion	fusion	NOUN
brj-24732	213	18	under	under	ADP
brj-24732	213	19	a	a	DET
brj-24732	213	20	lightweight	lightweight	ADJ
brj-24732	213	21	design	design	NOUN
brj-24732	213	22	constraint	constraint	NOUN
brj-24732	213	23	.	.	PUNCT
brj-24732	214	1	the	the	DET
brj-24732	214	2	integration	integration	NOUN
brj-24732	214	3	of	of	ADP
brj-24732	214	4	the	the	DET
brj-24732	214	5	amdc	amdc	VERB
brj-24732	214	6	module	module	NOUN
brj-24732	214	7	yielded	yield	VERB
brj-24732	214	8	a	a	DET
brj-24732	214	9	1	1	NUM
brj-24732	214	10	%	%	NOUN
brj-24732	214	11	decrease	decrease	NOUN
brj-24732	214	12	in	in	ADP
brj-24732	214	13	f1	f1	ADJ
brj-24732	214	14	score	score	NOUN
brj-24732	214	15	but	but	CCONJ
brj-24732	214	16	improved	improve	VERB
brj-24732	214	17	map@50	map@50	PRON
brj-24732	214	18	1	1	NUM
brj-24732	214	19	%	%	NOUN
brj-24732	214	20	,	,	PUNCT
brj-24732	214	21	increased	increase	VERB
brj-24732	214	22	inference	inference	NOUN
brj-24732	214	23	speed	speed	NOUN
brj-24732	214	24	7.9	7.9	NUM
brj-24732	214	25	%	%	NOUN
brj-24732	214	26	,	,	PUNCT
brj-24732	214	27	and	and	CCONJ
brj-24732	214	28	reduced	reduce	VERB
brj-24732	214	29	the	the	DET
brj-24732	214	30	parameter	parameter	NOUN
brj-24732	214	31	count	count	NOUN
brj-24732	214	32	by	by	ADP
brj-24732	214	33	280k	280k	NUM
brj-24732	214	34	compared	compare	VERB
brj-24732	214	35	to	to	ADP
brj-24732	214	36	the	the	DET
brj-24732	214	37	baseline	baseline	NOUN
brj-24732	214	38	.	.	PUNCT
brj-24732	215	1	these	these	DET
brj-24732	215	2	results	result	NOUN
brj-24732	215	3	demonstrate	demonstrate	VERB
brj-24732	215	4	the	the	DET
brj-24732	215	5	robustness	robustness	NOUN
brj-24732	215	6	and	and	CCONJ
brj-24732	215	7	computational	computational	ADJ
brj-24732	215	8	efficiency	efficiency	NOUN
brj-24732	215	9	of	of	ADP
brj-24732	215	10	amdc	amdc	ADV
brj-24732	215	11	.	.	PUNCT
brj-24732	216	1	replacing	replace	VERB
brj-24732	216	2	the	the	DET
brj-24732	216	3	original	original	ADJ
brj-24732	216	4	detection	detection	NOUN
brj-24732	216	5	head	head	NOUN
brj-24732	216	6	with	with	ADP
brj-24732	216	7	the	the	DET
brj-24732	216	8	proposed	propose	VERB
brj-24732	216	9	lwdethead	lwdethead	NOUN
brj-24732	216	10	led	lead	VERB
brj-24732	216	11	to	to	ADP
brj-24732	216	12	a	a	DET
brj-24732	216	13	2	2	NUM
brj-24732	216	14	%	%	NOUN
brj-24732	216	15	increase	increase	NOUN
brj-24732	216	16	in	in	ADP
brj-24732	216	17	map@50	map@50	NOUN
brj-24732	216	18	and	and	CCONJ
brj-24732	216	19	a	a	DET
brj-24732	216	20	3	3	NUM
brj-24732	216	21	%	%	NOUN
brj-24732	216	22	increase	increase	NOUN
brj-24732	216	23	in	in	ADP
brj-24732	216	24	map@50:95	map@50:95	NOUN
brj-24732	216	25	,	,	PUNCT
brj-24732	216	26	along	along	ADP
brj-24732	216	27	with	with	ADP
brj-24732	216	28	a	a	DET
brj-24732	216	29	4	4	NUM
brj-24732	216	30	%	%	NOUN
brj-24732	216	31	increase	increase	NOUN
brj-24732	216	32	in	in	ADP
brj-24732	216	33	inference	inference	NOUN
brj-24732	216	34	speed	speed	NOUN
brj-24732	216	35	and	and	CCONJ
brj-24732	216	36	a	a	DET
brj-24732	216	37	reduction	reduction	NOUN
brj-24732	216	38	of	of	ADP
brj-24732	216	39	320k	320k	NUM
brj-24732	216	40	parameters	parameter	NOUN
brj-24732	216	41	,	,	PUNCT
brj-24732	216	42	emphasizing	emphasize	VERB
brj-24732	216	43	the	the	DET
brj-24732	216	44	critical	critical	ADJ
brj-24732	216	45	role	role	NOUN
brj-24732	216	46	of	of	ADP
brj-24732	216	47	lwdethead	lwdethead	NOUN
brj-24732	216	48	in	in	ADP
brj-24732	216	49	achieving	achieve	VERB
brj-24732	216	50	model	model	NOUN
brj-24732	216	51	compression	compression	NOUN
brj-24732	216	52	without	without	ADP
brj-24732	216	53	sacrificing	sacrifice	VERB
brj-24732	216	54	accuracy	accuracy	NOUN
brj-24732	216	55	.	.	PUNCT
brj-24732	217	1	overall	overall	ADV
brj-24732	217	2	,	,	PUNCT
brj-24732	217	3	the	the	DET
brj-24732	217	4	results	result	NOUN
brj-24732	217	5	validate	validate	VERB
brj-24732	217	6	that	that	SCONJ
brj-24732	217	7	the	the	DET
brj-24732	217	8	proposed	propose	VERB
brj-24732	217	9	le	le	PROPN
brj-24732	217	10	-	-	ADJ
brj-24732	217	11	yolo	yolo	ADJ
brj-24732	217	12	model	model	NOUN
brj-24732	217	13	not	not	PART
brj-24732	217	14	only	only	ADV
brj-24732	217	15	enhances	enhance	VERB
brj-24732	217	16	detection	detection	NOUN
brj-24732	217	17	precision	precision	NOUN
brj-24732	217	18	but	but	CCONJ
brj-24732	217	19	also	also	ADV
brj-24732	217	20	achieves	achieve	VERB
brj-24732	217	21	significant	significant	ADJ
brj-24732	217	22	model	model	NOUN
brj-24732	217	23	compression	compression	NOUN
brj-24732	217	24	and	and	CCONJ
brj-24732	217	25	computational	computational	ADJ
brj-24732	217	26	efficiency	efficiency	NOUN
brj-24732	217	27	.	.	PUNCT
brj-24732	218	1	visual	visual	ADJ
brj-24732	218	2	analytics	analytic	NOUN
brj-24732	218	3	in	in	ADP
brj-24732	218	4	addition	addition	NOUN
brj-24732	218	5	to	to	ADP
brj-24732	218	6	these	these	DET
brj-24732	218	7	quantitative	quantitative	ADJ
brj-24732	218	8	results	result	NOUN
brj-24732	218	9	,	,	PUNCT
brj-24732	218	10	the	the	DET
brj-24732	218	11	visual	visual	ADJ
brj-24732	218	12	analysis	analysis	NOUN
brj-24732	218	13	of	of	ADP
brj-24732	218	14	the	the	DET
brj-24732	218	15	ablation	ablation	NOUN
brj-24732	218	16	study	study	NOUN
brj-24732	218	17	in	in	ADP
brj-24732	218	18	fig	fig	NOUN
brj-24732	218	19	.	.	PUNCT
brj-24732	219	1	7	7	NUM
brj-24732	219	2	further	far	ADV
brj-24732	219	3	demonstrates	demonstrate	VERB
brj-24732	219	4	the	the	DET
brj-24732	219	5	improvements	improvement	NOUN
brj-24732	219	6	in	in	ADP
brj-24732	219	7	detection	detection	NOUN
brj-24732	219	8	performance	performance	NOUN
brj-24732	219	9	and	and	CCONJ
brj-24732	219	10	efficiency	efficiency	NOUN
brj-24732	219	11	,	,	PUNCT
brj-24732	219	12	providing	provide	VERB
brj-24732	219	13	a	a	DET
brj-24732	219	14	clearer	clear	ADJ
brj-24732	219	15	depiction	depiction	NOUN
brj-24732	219	16	of	of	ADP
brj-24732	219	17	how	how	SCONJ
brj-24732	219	18	each	each	DET
brj-24732	219	19	enhancement	enhancement	NOUN
brj-24732	219	20	module	module	NOUN
brj-24732	219	21	contributes	contribute	VERB
brj-24732	219	22	to	to	ADP
brj-24732	219	23	the	the	DET
brj-24732	219	24	overall	overall	ADJ
brj-24732	219	25	capabilities	capability	NOUN
brj-24732	219	26	of	of	ADP
brj-24732	219	27	the	the	DET
brj-24732	219	28	model	model	NOUN
brj-24732	219	29	.	.	PUNCT
brj-24732	220	1	overall	overall	ADV
brj-24732	220	2	,	,	PUNCT
brj-24732	220	3	the	the	DET
brj-24732	220	4	results	result	NOUN
brj-24732	220	5	validate	validate	VERB
brj-24732	220	6	that	that	SCONJ
brj-24732	220	7	the	the	DET
brj-24732	220	8	proposed	propose	VERB
brj-24732	220	9	le	le	PROPN
brj-24732	220	10	-	-	ADJ
brj-24732	220	11	yolo	yolo	ADJ
brj-24732	220	12	model	model	NOUN
brj-24732	220	13	not	not	PART
brj-24732	220	14	only	only	ADV
brj-24732	220	15	enhances	enhance	VERB
brj-24732	220	16	detection	detection	NOUN
brj-24732	220	17	accuracy	accuracy	NOUN
brj-24732	220	18	but	but	CCONJ
brj-24732	220	19	also	also	ADV
brj-24732	220	20	achieves	achieve	VERB
brj-24732	220	21	substantial	substantial	ADJ
brj-24732	220	22	model	model	NOUN
brj-24732	220	23	compression	compression	NOUN
brj-24732	220	24	and	and	CCONJ
brj-24732	220	25	optimization	optimization	NOUN
brj-24732	220	26	in	in	ADP
brj-24732	220	27	computational	computational	ADJ
brj-24732	220	28	efficiency	efficiency	NOUN
brj-24732	220	29	.	.	PUNCT
brj-24732	221	1	to	to	ADP
brj-24732	221	2	both	both	CCONJ
brj-24732	221	3	intuitively	intuitively	ADV
brj-24732	221	4	and	and	CCONJ
brj-24732	221	5	quantitatively	quantitatively	ADV
brj-24732	221	6	assess	assess	VERB
brj-24732	221	7	the	the	DET
brj-24732	221	8	performance	performance	NOUN
brj-24732	221	9	differences	difference	NOUN
brj-24732	221	10	among	among	ADP
brj-24732	221	11	the	the	DET
brj-24732	221	12	proposed	propose	VERB
brj-24732	221	13	le	le	PROPN
brj-24732	221	14	-	-	ADJ
brj-24732	221	15	yolo	yolo	ADJ
brj-24732	221	16	model	model	NOUN
brj-24732	221	17	,	,	PUNCT
brj-24732	221	18	the	the	DET
brj-24732	221	19	baseline	baseline	NOUN
brj-24732	221	20	yolov11n	yolov11n	NOUN
brj-24732	221	21	,	,	PUNCT
brj-24732	221	22	and	and	CCONJ
brj-24732	221	23	the	the	DET
brj-24732	221	24	latest	late	ADJ
brj-24732	221	25	model	model	NOUN
brj-24732	221	26	in	in	ADP
brj-24732	221	27	the	the	DET
brj-24732	221	28	series	series	NOUN
brj-24732	221	29	,	,	PUNCT
brj-24732	221	30	yolov12n	yolov12n	NOUN
brj-24732	221	31	(	(	PUNCT
brj-24732	221	32	tian	tian	PROPN
brj-24732	221	33	et	et	PROPN
brj-24732	221	34	al	al	PROPN
brj-24732	221	35	.	.	PROPN
brj-24732	221	36	2025	2025	NUM
brj-24732	221	37	)	)	PUNCT
brj-24732	221	38	,	,	PUNCT
brj-24732	221	39	a	a	DET
brj-24732	221	40	heatmap	heatmap	NOUN
brj-24732	221	41	-	-	PUNCT
brj-24732	221	42	based	base	VERB
brj-24732	221	43	visual	visual	ADJ
brj-24732	221	44	analysis	analysis	NOUN
brj-24732	221	45	was	be	AUX
brj-24732	221	46	conducted	conduct	VERB
brj-24732	221	47	for	for	ADP
brj-24732	221	48	chipboard	chipboard	NOUN
brj-24732	221	49	surface	surface	NOUN
brj-24732	221	50	defect	defect	NOUN
brj-24732	221	51	detection	detection	NOUN
brj-24732	221	52	under	under	ADP
brj-24732	221	53	three	three	NUM
brj-24732	221	54	lighting	lighting	NOUN
brj-24732	221	55	conditions	condition	NOUN
brj-24732	221	56	:	:	PUNCT
brj-24732	221	57	normal	normal	ADJ
brj-24732	221	58	illumination	illumination	NOUN
brj-24732	221	59	,	,	PUNCT
brj-24732	221	60	strong	strong	ADJ
brj-24732	221	61	lighting	lighting	NOUN
brj-24732	221	62	,	,	PUNCT
brj-24732	221	63	and	and	CCONJ
brj-24732	221	64	low	low	ADJ
brj-24732	221	65	-	-	PUNCT
brj-24732	221	66	light	light	NOUN
brj-24732	221	67	environments	environment	NOUN
brj-24732	221	68	.	.	PUNCT
brj-24732	222	1	as	as	SCONJ
brj-24732	222	2	shown	show	VERB
brj-24732	222	3	in	in	ADP
brj-24732	222	4	fig	fig	NOUN
brj-24732	222	5	.	.	PUNCT
brj-24732	223	1	8	8	NUM
brj-24732	223	2	,	,	PUNCT
brj-24732	223	3	the	the	DET
brj-24732	223	4	heatmaps	heatmap	NOUN
brj-24732	223	5	visualize	visualize	VERB
brj-24732	223	6	pixel	pixel	ADJ
brj-24732	223	7	-	-	PUNCT
brj-24732	223	8	level	level	NOUN
brj-24732	223	9	response	response	NOUN
brj-24732	223	10	intensities	intensity	NOUN
brj-24732	223	11	,	,	PUNCT
brj-24732	223	12	effectively	effectively	ADV
brj-24732	223	13	visualizing	visualize	VERB
brj-24732	223	14	the	the	DET
brj-24732	223	15	regions	region	NOUN
brj-24732	223	16	of	of	ADP
brj-24732	223	17	focus	focus	NOUN
brj-24732	223	18	for	for	ADP
brj-24732	223	19	defect	defect	ADJ
brj-24732	223	20	feature	feature	NOUN
brj-24732	223	21	extraction	extraction	NOUN
brj-24732	223	22	.	.	PUNCT
brj-24732	224	1	peer	peer	NOUN
brj-24732	224	2	-	-	PUNCT
brj-24732	224	3	reviewed	review	VERB
brj-24732	224	4	article	article	NOUN
brj-24732	224	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	224	6	he	he	PRON
brj-24732	224	7	et	et	PROPN
brj-24732	224	8	al	al	PROPN
brj-24732	224	9	.	.	PROPN
brj-24732	225	1	(	(	PUNCT
brj-24732	225	2	2025	2025	NUM
brj-24732	225	3	)	)	PUNCT
brj-24732	225	4	.	.	PUNCT
brj-24732	226	1	“	"	PUNCT
brj-24732	226	2	le	le	X
brj-24732	226	3	-	-	PROPN
brj-24732	226	4	yolo	yolo	PROPN
brj-24732	226	5	&	&	CCONJ
brj-24732	226	6	wood	wood	PROPN
brj-24732	226	7	surface	surface	NOUN
brj-24732	226	8	defects	defect	NOUN
brj-24732	226	9	,	,	PUNCT
brj-24732	226	10	”	"	PUNCT
brj-24732	226	11	bioresources	bioresource	NOUN
brj-24732	226	12	20(3	20(3	NOUN
brj-24732	226	13	)	)	PUNCT
brj-24732	226	14	,	,	PUNCT
brj-24732	226	15	7179	7179	NUM
brj-24732	226	16	-	-	SYM
brj-24732	226	17	7193	7193	NUM
brj-24732	226	18	.	.	PUNCT
brj-24732	227	1	7190	7190	NUM
brj-24732	227	2	the	the	DET
brj-24732	227	3	detected	detect	VERB
brj-24732	227	4	image	image	NOUN
brj-24732	227	5	method(1	method(1	PROPN
brj-24732	227	6	)	)	PUNCT
brj-24732	227	7	method(2	method(2	NOUN
brj-24732	227	8	)	)	PUNCT
brj-24732	227	9	method(3	method(3	NOUN
brj-24732	227	10	)	)	PUNCT
brj-24732	227	11	method(4	method(4	ADJ
brj-24732	227	12	)	)	PUNCT
brj-24732	227	13	method(5	method(5	NOUN
brj-24732	227	14	)	)	PUNCT
brj-24732	227	15	method(6	method(6	ADJ
brj-24732	227	16	)	)	PUNCT
brj-24732	227	17	method(7	method(7	ADJ
brj-24732	227	18	)	)	PUNCT
brj-24732	227	19	method(8	method(8	PROPN
brj-24732	227	20	)	)	PUNCT
brj-24732	227	21	fig	fig	NOUN
brj-24732	227	22	.	.	PUNCT
brj-24732	228	1	7	7	X
brj-24732	228	2	.	.	X
brj-24732	228	3	the	the	DET
brj-24732	228	4	visual	visual	ADJ
brj-24732	228	5	analysis	analysis	NOUN
brj-24732	228	6	of	of	ADP
brj-24732	228	7	the	the	DET
brj-24732	228	8	ablation	ablation	NOUN
brj-24732	228	9	study	study	NOUN
brj-24732	228	10	image	image	NOUN
brj-24732	228	11	yolov11n	yolov11n	NOUN
brj-24732	228	12	yolov12n	yolov12n	NOUN
brj-24732	228	13	le	le	PROPN
brj-24732	228	14	-	-	ADJ
brj-24732	228	15	yolo	yolo	ADJ
brj-24732	228	16	normal	normal	ADJ
brj-24732	228	17	illumination	illumination	NOUN
brj-24732	228	18	high	high	ADJ
brj-24732	228	19	-	-	PUNCT
brj-24732	228	20	intensity	intensity	NOUN
brj-24732	228	21	illumination	illumination	NOUN
brj-24732	228	22	low	low	ADJ
brj-24732	228	23	-	-	PUNCT
brj-24732	228	24	light	light	NOUN
brj-24732	228	25	conditions	condition	NOUN
brj-24732	228	26	fig	fig	NOUN
brj-24732	228	27	.	.	PUNCT
brj-24732	229	1	8	8	X
brj-24732	229	2	.	.	X
brj-24732	229	3	heatmap	heatmap	PROPN
brj-24732	229	4	comparison	comparison	NOUN
brj-24732	229	5	peer	peer	NOUN
brj-24732	229	6	-	-	PUNCT
brj-24732	229	7	reviewed	review	VERB
brj-24732	229	8	article	article	NOUN
brj-24732	229	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	229	10	he	he	PRON
brj-24732	229	11	et	et	PROPN
brj-24732	229	12	al	al	PROPN
brj-24732	229	13	.	.	PROPN
brj-24732	230	1	(	(	PUNCT
brj-24732	230	2	2025	2025	NUM
brj-24732	230	3	)	)	PUNCT
brj-24732	230	4	.	.	PUNCT
brj-24732	231	1	“	"	PUNCT
brj-24732	231	2	le	le	X
brj-24732	231	3	-	-	PROPN
brj-24732	231	4	yolo	yolo	PROPN
brj-24732	231	5	&	&	CCONJ
brj-24732	231	6	wood	wood	PROPN
brj-24732	231	7	surface	surface	NOUN
brj-24732	231	8	defects	defect	NOUN
brj-24732	231	9	,	,	PUNCT
brj-24732	231	10	”	"	PUNCT
brj-24732	231	11	bioresources	bioresource	NOUN
brj-24732	231	12	20(3	20(3	NOUN
brj-24732	231	13	)	)	PUNCT
brj-24732	231	14	,	,	PUNCT
brj-24732	231	15	7179	7179	NUM
brj-24732	231	16	-	-	SYM
brj-24732	231	17	7193	7193	NUM
brj-24732	231	18	.	.	PUNCT
brj-24732	232	1	7191	7191	NUM
brj-24732	232	2	experimental	experimental	ADJ
brj-24732	232	3	results	result	NOUN
brj-24732	232	4	show	show	VERB
brj-24732	232	5	that	that	SCONJ
brj-24732	232	6	both	both	PRON
brj-24732	232	7	yolov11n	yolov11n	NOUN
brj-24732	232	8	and	and	CCONJ
brj-24732	232	9	yolov12n	yolov12n	NOUN
brj-24732	232	10	produce	produce	VERB
brj-24732	232	11	scattered	scatter	VERB
brj-24732	232	12	heatmap	heatmap	NOUN
brj-24732	232	13	responses	response	NOUN
brj-24732	232	14	,	,	PUNCT
brj-24732	232	15	which	which	PRON
brj-24732	232	16	are	be	AUX
brj-24732	232	17	notably	notably	ADV
brj-24732	232	18	influenced	influence	VERB
brj-24732	232	19	by	by	ADP
brj-24732	232	20	background	background	NOUN
brj-24732	232	21	textures	texture	NOUN
brj-24732	232	22	,	,	PUNCT
brj-24732	232	23	leading	lead	VERB
brj-24732	232	24	to	to	PART
brj-24732	232	25	redundant	redundant	VERB
brj-24732	232	26	and	and	CCONJ
brj-24732	232	27	inaccurate	inaccurate	ADJ
brj-24732	232	28	feature	feature	NOUN
brj-24732	232	29	representations	representation	NOUN
brj-24732	232	30	.	.	PUNCT
brj-24732	233	1	in	in	ADP
brj-24732	233	2	contrast	contrast	NOUN
brj-24732	233	3	,	,	PUNCT
brj-24732	233	4	the	the	DET
brj-24732	233	5	le	le	PROPN
brj-24732	233	6	-	-	ADJ
brj-24732	233	7	yolo	yolo	ADJ
brj-24732	233	8	model	model	NOUN
brj-24732	233	9	,	,	PUNCT
brj-24732	233	10	with	with	ADP
brj-24732	233	11	its	its	PRON
brj-24732	233	12	enhanced	enhanced	ADJ
brj-24732	233	13	feature	feature	NOUN
brj-24732	233	14	perception	perception	NOUN
brj-24732	233	15	mechanism	mechanism	NOUN
brj-24732	233	16	,	,	PUNCT
brj-24732	233	17	effectively	effectively	ADV
brj-24732	233	18	focuses	focus	VERB
brj-24732	233	19	on	on	ADP
brj-24732	233	20	defect	defect	ADJ
brj-24732	233	21	regions	region	NOUN
brj-24732	233	22	,	,	PUNCT
brj-24732	233	23	achieving	achieve	VERB
brj-24732	233	24	comprehensive	comprehensive	ADJ
brj-24732	233	25	detection	detection	NOUN
brj-24732	233	26	of	of	ADP
brj-24732	233	27	fine	fine	ADV
brj-24732	233	28	-	-	PUNCT
brj-24732	233	29	grained	grain	VERB
brj-24732	233	30	and	and	CCONJ
brj-24732	233	31	complex	complex	ADJ
brj-24732	233	32	surface	surface	NOUN
brj-24732	233	33	flaws	flaw	NOUN
brj-24732	233	34	.	.	PUNCT
brj-24732	234	1	it	it	PRON
brj-24732	234	2	demonstrates	demonstrate	VERB
brj-24732	234	3	superior	superior	ADJ
brj-24732	234	4	performance	performance	NOUN
brj-24732	234	5	in	in	ADP
brj-24732	234	6	both	both	CCONJ
brj-24732	234	7	coverage	coverage	NOUN
brj-24732	234	8	and	and	CCONJ
brj-24732	234	9	focus	focus	NOUN
brj-24732	234	10	.	.	PUNCT
brj-24732	235	1	this	this	DET
brj-24732	235	2	comparative	comparative	ADJ
brj-24732	235	3	analysis	analysis	NOUN
brj-24732	235	4	not	not	PART
brj-24732	235	5	only	only	ADV
brj-24732	235	6	highlights	highlight	VERB
brj-24732	235	7	the	the	DET
brj-24732	235	8	substantial	substantial	ADJ
brj-24732	235	9	performance	performance	NOUN
brj-24732	235	10	improvements	improvement	NOUN
brj-24732	235	11	of	of	ADP
brj-24732	235	12	le	le	X
brj-24732	235	13	-	-	NOUN
brj-24732	235	14	yolo	yolo	PROPN
brj-24732	235	15	but	but	CCONJ
brj-24732	235	16	also	also	ADV
brj-24732	235	17	emphasizes	emphasize	VERB
brj-24732	235	18	its	its	PRON
brj-24732	235	19	increased	increase	VERB
brj-24732	235	20	robustness	robustness	NOUN
brj-24732	235	21	and	and	CCONJ
brj-24732	235	22	resilience	resilience	NOUN
brj-24732	235	23	to	to	PART
brj-24732	235	24	interference	interference	NOUN
brj-24732	235	25	,	,	PUNCT
brj-24732	235	26	achieved	achieve	VERB
brj-24732	235	27	through	through	ADP
brj-24732	235	28	improved	improve	VERB
brj-24732	235	29	background	background	NOUN
brj-24732	235	30	suppression	suppression	NOUN
brj-24732	235	31	and	and	CCONJ
brj-24732	235	32	refined	refined	ADJ
brj-24732	235	33	feature	feature	NOUN
brj-24732	235	34	filtering	filter	VERB
brj-24732	235	35	capabilities	capability	NOUN
brj-24732	235	36	.	.	PUNCT
brj-24732	236	1	performance	performance	NOUN
brj-24732	236	2	comparison	comparison	NOUN
brj-24732	236	3	with	with	ADP
brj-24732	236	4	mainstream	mainstream	ADJ
brj-24732	236	5	algorithms	algorithm	NOUN
brj-24732	236	6	to	to	PART
brj-24732	236	7	further	far	ADV
brj-24732	236	8	verify	verify	VERB
brj-24732	236	9	the	the	DET
brj-24732	236	10	effectiveness	effectiveness	NOUN
brj-24732	236	11	of	of	ADP
brj-24732	236	12	the	the	DET
brj-24732	236	13	proposed	propose	VERB
brj-24732	236	14	method	method	NOUN
brj-24732	236	15	,	,	PUNCT
brj-24732	236	16	the	the	DET
brj-24732	236	17	authors	author	NOUN
brj-24732	236	18	compared	compare	VERB
brj-24732	236	19	le	le	PROPN
brj-24732	236	20	-	-	NOUN
brj-24732	236	21	yolo	yolo	PROPN
brj-24732	236	22	with	with	ADP
brj-24732	236	23	eight	eight	NUM
brj-24732	236	24	state	state	NOUN
brj-24732	236	25	-	-	PUNCT
brj-24732	236	26	of	of	ADP
brj-24732	236	27	-	-	PUNCT
brj-24732	236	28	the	the	DET
brj-24732	236	29	-	-	PUNCT
brj-24732	236	30	art	art	NOUN
brj-24732	236	31	object	object	NOUN
brj-24732	236	32	detection	detection	NOUN
brj-24732	236	33	algorithms	algorithm	NOUN
brj-24732	236	34	:	:	PUNCT
brj-24732	236	35	yolov5n	yolov5n	ADJ
brj-24732	236	36	,	,	PUNCT
brj-24732	236	37	yolov7tiny	yolov7tiny	PROPN
brj-24732	236	38	,	,	PUNCT
brj-24732	236	39	yolov8n	yolov8n	PROPN
brj-24732	236	40	,	,	PUNCT
brj-24732	236	41	yolov9n	yolov9n	NOUN
brj-24732	236	42	,	,	PUNCT
brj-24732	236	43	yolov10n	yolov10n	NOUN
brj-24732	236	44	,	,	PUNCT
brj-24732	236	45	yolov11n	yolov11n	NOUN
brj-24732	236	46	,	,	PUNCT
brj-24732	236	47	yolov12n	yolov12n	NOUN
brj-24732	236	48	,	,	PUNCT
brj-24732	236	49	and	and	CCONJ
brj-24732	236	50	rt	rt	PROPN
brj-24732	236	51	-	-	PUNCT
brj-24732	236	52	detr	detr	NOUN
brj-24732	236	53	.	.	PUNCT
brj-24732	237	1	all	all	DET
brj-24732	237	2	experiments	experiment	NOUN
brj-24732	237	3	were	be	AUX
brj-24732	237	4	conducted	conduct	VERB
brj-24732	237	5	on	on	ADP
brj-24732	237	6	the	the	DET
brj-24732	237	7	unified	unified	ADJ
brj-24732	237	8	chipboardv1.0	chipboardv1.0	NUM
brj-24732	237	9	dataset	dataset	VERB
brj-24732	237	10	to	to	PART
brj-24732	237	11	ensure	ensure	VERB
brj-24732	237	12	fair	fair	ADJ
brj-24732	237	13	and	and	CCONJ
brj-24732	237	14	consistent	consistent	ADJ
brj-24732	237	15	evaluation	evaluation	NOUN
brj-24732	237	16	.	.	PUNCT
brj-24732	238	1	the	the	DET
brj-24732	238	2	results	result	NOUN
brj-24732	238	3	,	,	PUNCT
brj-24732	238	4	presented	present	VERB
brj-24732	238	5	in	in	ADP
brj-24732	238	6	table	table	NOUN
brj-24732	238	7	5	5	NUM
brj-24732	238	8	,	,	PUNCT
brj-24732	238	9	demonstrate	demonstrate	VERB
brj-24732	238	10	that	that	SCONJ
brj-24732	238	11	the	the	DET
brj-24732	238	12	proposed	propose	VERB
brj-24732	238	13	method	method	NOUN
brj-24732	238	14	achieves	achieve	VERB
brj-24732	238	15	outstanding	outstanding	ADJ
brj-24732	238	16	detection	detection	NOUN
brj-24732	238	17	accuracy	accuracy	NOUN
brj-24732	238	18	.	.	PUNCT
brj-24732	239	1	notably	notably	ADV
brj-24732	239	2	,	,	PUNCT
brj-24732	239	3	le	le	PROPN
brj-24732	239	4	-	-	PUNCT
brj-24732	239	5	yolo	yolo	PROPN
brj-24732	239	6	’s	’s	PART
brj-24732	239	7	performance	performance	NOUN
brj-24732	239	8	also	also	ADV
brj-24732	239	9	surpasses	surpass	VERB
brj-24732	239	10	rt	rt	NOUN
brj-24732	239	11	-	-	PUNCT
brj-24732	239	12	detr	detr	NOUN
brj-24732	239	13	,	,	PUNCT
brj-24732	239	14	except	except	SCONJ
brj-24732	239	15	for	for	ADP
brj-24732	239	16	precision	precision	NOUN
brj-24732	239	17	,	,	PUNCT
brj-24732	239	18	le	le	X
brj-24732	239	19	-	-	ADJ
brj-24732	239	20	yolo	yolo	ADJ
brj-24732	239	21	outperforms	outperform	NOUN
brj-24732	239	22	all	all	DET
brj-24732	239	23	comparison	comparison	NOUN
brj-24732	239	24	models	model	NOUN
brj-24732	239	25	in	in	ADP
brj-24732	239	26	recall	recall	NOUN
brj-24732	239	27	,	,	PUNCT
brj-24732	239	28	f1	f1	NOUN
brj-24732	239	29	-	-	PUNCT
brj-24732	239	30	score	score	NOUN
brj-24732	239	31	,	,	PUNCT
brj-24732	239	32	map@50	map@50	NOUN
brj-24732	239	33	,	,	PUNCT
brj-24732	239	34	and	and	CCONJ
brj-24732	239	35	map@50:95	map@50:95	NOUN
brj-24732	239	36	.	.	PROPN
brj-24732	240	1	notably	notably	ADV
brj-24732	240	2	,	,	PUNCT
brj-24732	240	3	it	it	PRON
brj-24732	240	4	substantially	substantially	ADV
brj-24732	240	5	surpasses	surpass	VERB
brj-24732	240	6	yolov12n	yolov12n	NOUN
brj-24732	240	7	,	,	PUNCT
brj-24732	240	8	the	the	DET
brj-24732	240	9	latest	late	ADJ
brj-24732	240	10	model	model	NOUN
brj-24732	240	11	in	in	ADP
brj-24732	240	12	the	the	DET
brj-24732	240	13	yolo	yolo	ADJ
brj-24732	240	14	series	series	NOUN
brj-24732	240	15	,	,	PUNCT
brj-24732	240	16	further	far	ADV
brj-24732	240	17	confirming	confirm	VERB
brj-24732	240	18	the	the	DET
brj-24732	240	19	superiority	superiority	NOUN
brj-24732	240	20	of	of	ADP
brj-24732	240	21	leyolo	leyolo	NOUN
brj-24732	240	22	in	in	ADP
brj-24732	240	23	detection	detection	NOUN
brj-24732	240	24	precision	precision	NOUN
brj-24732	240	25	.	.	PUNCT
brj-24732	241	1	in	in	ADP
brj-24732	241	2	terms	term	NOUN
brj-24732	241	3	of	of	ADP
brj-24732	241	4	model	model	NOUN
brj-24732	241	5	lightweighting	lightweighting	NOUN
brj-24732	241	6	,	,	PUNCT
brj-24732	241	7	the	the	DET
brj-24732	241	8	proposed	propose	VERB
brj-24732	241	9	method	method	NOUN
brj-24732	241	10	exhibits	exhibit	VERB
brj-24732	241	11	lower	low	ADJ
brj-24732	241	12	computational	computational	ADJ
brj-24732	241	13	complexity	complexity	NOUN
brj-24732	241	14	and	and	CCONJ
brj-24732	241	15	fewer	few	ADJ
brj-24732	241	16	parameters	parameter	NOUN
brj-24732	241	17	than	than	ADP
brj-24732	241	18	all	all	DET
brj-24732	241	19	comparison	comparison	NOUN
brj-24732	241	20	algorithms	algorithm	NOUN
brj-24732	241	21	except	except	SCONJ
brj-24732	241	22	yolov5n	yolov5n	PROPN
brj-24732	241	23	.	.	PUNCT
brj-24732	242	1	compared	compare	VERB
brj-24732	242	2	with	with	ADP
brj-24732	242	3	yolov5n	yolov5n	PROPN
brj-24732	242	4	,	,	PUNCT
brj-24732	242	5	le	le	PROPN
brj-24732	242	6	-	-	ADJ
brj-24732	242	7	yolo	yolo	PROPN
brj-24732	242	8	improves	improve	VERB
brj-24732	242	9	recall	recall	NOUN
brj-24732	242	10	4	4	NUM
brj-24732	242	11	%	%	NOUN
brj-24732	242	12	,	,	PUNCT
brj-24732	242	13	f1	f1	NOUN
brj-24732	242	14	-	-	PUNCT
brj-24732	242	15	score	score	NOUN
brj-24732	242	16	by	by	ADP
brj-24732	242	17	1	1	NUM
brj-24732	242	18	%	%	NOUN
brj-24732	242	19	,	,	PUNCT
brj-24732	242	20	map@50	map@50	X
brj-24732	242	21	by	by	ADP
brj-24732	242	22	4	4	NUM
brj-24732	242	23	%	%	NOUN
brj-24732	242	24	,	,	PUNCT
brj-24732	242	25	and	and	CCONJ
brj-24732	242	26	map@50:95	map@50:95	VERB
brj-24732	242	27	by	by	ADP
brj-24732	242	28	8	8	NUM
brj-24732	242	29	%	%	NOUN
brj-24732	242	30	.	.	PUNCT
brj-24732	243	1	although	although	SCONJ
brj-24732	243	2	the	the	DET
brj-24732	243	3	precision	precision	NOUN
brj-24732	243	4	decreases	decrease	VERB
brj-24732	243	5	2	2	NUM
brj-24732	243	6	%	%	NOUN
brj-24732	243	7	,	,	PUNCT
brj-24732	243	8	this	this	PRON
brj-24732	243	9	is	be	AUX
brj-24732	243	10	accompanied	accompany	VERB
brj-24732	243	11	by	by	ADP
brj-24732	243	12	only	only	ADV
brj-24732	243	13	a	a	DET
brj-24732	243	14	slight	slight	ADJ
brj-24732	243	15	increase	increase	NOUN
brj-24732	243	16	in	in	ADP
brj-24732	243	17	resource	resource	NOUN
brj-24732	243	18	consumption	consumption	NOUN
brj-24732	243	19	.	.	PUNCT
brj-24732	244	1	these	these	DET
brj-24732	244	2	findings	finding	NOUN
brj-24732	244	3	demonstrate	demonstrate	VERB
brj-24732	244	4	that	that	SCONJ
brj-24732	244	5	the	the	DET
brj-24732	244	6	proposed	propose	VERB
brj-24732	244	7	algorithm	algorithm	NOUN
brj-24732	244	8	achieves	achieve	VERB
brj-24732	244	9	high	high	ADJ
brj-24732	244	10	-	-	PUNCT
brj-24732	244	11	precision	precision	NOUN
brj-24732	244	12	detection	detection	NOUN
brj-24732	244	13	while	while	SCONJ
brj-24732	244	14	effectively	effectively	ADV
brj-24732	244	15	balancing	balance	VERB
brj-24732	244	16	performance	performance	NOUN
brj-24732	244	17	with	with	ADP
brj-24732	244	18	lightweight	lightweight	ADJ
brj-24732	244	19	design	design	NOUN
brj-24732	244	20	,	,	PUNCT
brj-24732	244	21	exhibiting	exhibit	VERB
brj-24732	244	22	substantial	substantial	ADJ
brj-24732	244	23	comprehensive	comprehensive	ADJ
brj-24732	244	24	advantages	advantage	NOUN
brj-24732	244	25	.	.	PUNCT
brj-24732	245	1	table	table	NOUN
brj-24732	245	2	5	5	NUM
brj-24732	245	3	.	.	PUNCT
brj-24732	246	1	performance	performance	NOUN
brj-24732	246	2	comparison	comparison	NOUN
brj-24732	246	3	with	with	ADP
brj-24732	246	4	mainstream	mainstream	NOUN
brj-24732	246	5	algorithms	algorithm	NOUN
brj-24732	246	6	model	model	NOUN
brj-24732	246	7	p	p	PROPN
brj-24732	246	8	r	r	NOUN
brj-24732	246	9	f1	f1	NOUN
brj-24732	246	10	map@50	map@50	PUNCT
brj-24732	246	11	map@50:95	map@50:95	NOUN
brj-24732	246	12	gflops	gflop	NOUN
brj-24732	246	13	(	(	PUNCT
brj-24732	246	14	g	g	NOUN
brj-24732	246	15	)	)	PUNCT
brj-24732	246	16	parameters	parameter	NOUN
brj-24732	246	17	(	(	PUNCT
brj-24732	246	18	m	m	NOUN
brj-24732	246	19	)	)	PUNCT
brj-24732	247	1	yolov5n	yolov5n	NOUN
brj-24732	247	2	0.88	0.88	NUM
brj-24732	247	3	0.81	0.81	NUM
brj-24732	247	4	0.84	0.84	NUM
brj-24732	247	5	0.86	0.86	NUM
brj-24732	247	6	0.47	0.47	NUM
brj-24732	247	7	4.1	4.1	NUM
brj-24732	247	8	1.76	1.76	NUM
brj-24732	247	9	yolov7tiny	yolov7tiny	PROPN
brj-24732	247	10	0.89	0.89	NUM
brj-24732	247	11	0.81	0.81	NUM
brj-24732	247	12	0.84	0.84	NUM
brj-24732	247	13	0.89	0.89	NUM
brj-24732	247	14	0.49	0.49	NUM
brj-24732	247	15	13.0	13.0	NUM
brj-24732	247	16	6.01	6.01	NUM
brj-24732	247	17	yolov8n	yolov8n	NOUN
brj-24732	247	18	0.84	0.84	NUM
brj-24732	247	19	0.79	0.79	NUM
brj-24732	247	20	0.82	0.82	NUM
brj-24732	247	21	0.86	0.86	NUM
brj-24732	247	22	0.51	0.51	NUM
brj-24732	247	23	8.1	8.1	NUM
brj-24732	247	24	3.00	3.00	NUM
brj-24732	247	25	yolo10n	yolo10n	NOUN
brj-24732	247	26	0.83	0.83	NUM
brj-24732	247	27	0.81	0.81	NUM
brj-24732	247	28	0.82	0.82	NUM
brj-24732	247	29	0.87	0.87	NUM
brj-24732	247	30	0.50	0.50	NUM
brj-24732	247	31	8.2	8.2	NUM
brj-24732	247	32	2.70	2.70	NUM
brj-24732	247	33	yolov12n	yolov12n	NOUN
brj-24732	247	34	0.78	0.78	NUM
brj-24732	247	35	0.79	0.79	NUM
brj-24732	247	36	0.78	0.78	NUM
brj-24732	247	37	0.84	0.84	NUM
brj-24732	247	38	0.48	0.48	NUM
brj-24732	247	39	5.8	5.8	NUM
brj-24732	247	40	2.51	2.51	NUM
brj-24732	247	41	yolov11n	yolov11n	NOUN
brj-24732	247	42	0.89	0.89	NUM
brj-24732	247	43	0.80	0.80	NUM
brj-24732	247	44	0.84	0.84	NUM
brj-24732	247	45	0.86	0.86	NUM
brj-24732	247	46	0.49	0.49	NUM
brj-24732	247	47	6.3	6.3	NUM
brj-24732	247	48	2.58	2.58	NUM
brj-24732	247	49	rt	rt	PROPN
brj-24732	247	50	-	-	PUNCT
brj-24732	247	51	detr	detr	NOUN
brj-24732	247	52	0.89	0.89	NUM
brj-24732	247	53	0.73	0.73	NUM
brj-24732	247	54	0.79	0.79	NUM
brj-24732	247	55	0.80	0.80	NUM
brj-24732	247	56	0.44	0.44	NUM
brj-24732	247	57	56.9	56.9	NUM
brj-24732	247	58	19.9	19.9	NUM
brj-24732	247	59	le	le	NOUN
brj-24732	247	60	-	-	PROPN
brj-24732	247	61	yolo	yolo	ADJ
brj-24732	247	62	0.86	0.86	NUM
brj-24732	247	63	0.85	0.85	NUM
brj-24732	247	64	0.85	0.85	NUM
brj-24732	247	65	0.90	0.90	NUM
brj-24732	247	66	0.55	0.55	NUM
brj-24732	247	67	5.5	5.5	NUM
brj-24732	247	68	2.10	2.10	NUM
brj-24732	247	69	conclusions	conclusion	NOUN
brj-24732	247	70	1	1	NUM
brj-24732	247	71	.	.	PUNCT
brj-24732	248	1	this	this	DET
brj-24732	248	2	study	study	NOUN
brj-24732	248	3	proposed	propose	VERB
brj-24732	248	4	le	le	PROPN
brj-24732	248	5	-	-	PROPN
brj-24732	248	6	yolo	yolo	PROPN
brj-24732	248	7	,	,	PUNCT
brj-24732	248	8	a	a	DET
brj-24732	248	9	lightweight	lightweight	ADJ
brj-24732	248	10	and	and	CCONJ
brj-24732	248	11	enhanced	enhanced	ADJ
brj-24732	248	12	object	object	NOUN
brj-24732	248	13	detection	detection	NOUN
brj-24732	248	14	model	model	NOUN
brj-24732	248	15	based	base	VERB
brj-24732	248	16	on	on	ADP
brj-24732	248	17	yolov11	yolov11	NOUN
brj-24732	248	18	,	,	PUNCT
brj-24732	248	19	designed	design	VERB
brj-24732	248	20	specifically	specifically	ADV
brj-24732	248	21	for	for	ADP
brj-24732	248	22	detecting	detect	VERB
brj-24732	248	23	surface	surface	NOUN
brj-24732	248	24	defects	defect	NOUN
brj-24732	248	25	on	on	ADP
brj-24732	248	26	particleboard	particleboard	NOUN
brj-24732	248	27	.	.	PUNCT
brj-24732	249	1	through	through	ADP
brj-24732	249	2	integrating	integrate	VERB
brj-24732	249	3	the	the	DET
brj-24732	249	4	adaptive	adaptive	ADJ
brj-24732	249	5	multi	multi	ADJ
brj-24732	249	6	-	-	ADJ
brj-24732	249	7	kernel	kernel	ADJ
brj-24732	249	8	depthwise	depthwise	NOUN
brj-24732	249	9	conv2d	conv2d	VERB
brj-24732	249	10	peer	peer	NOUN
brj-24732	249	11	-	-	PUNCT
brj-24732	249	12	reviewed	review	VERB
brj-24732	249	13	article	article	NOUN
brj-24732	249	14	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	249	15	he	he	PRON
brj-24732	249	16	et	et	PROPN
brj-24732	249	17	al	al	PROPN
brj-24732	249	18	.	.	PROPN
brj-24732	250	1	(	(	PUNCT
brj-24732	250	2	2025	2025	NUM
brj-24732	250	3	)	)	PUNCT
brj-24732	250	4	.	.	PUNCT
brj-24732	251	1	“	"	PUNCT
brj-24732	251	2	le	le	X
brj-24732	251	3	-	-	PROPN
brj-24732	251	4	yolo	yolo	PROPN
brj-24732	251	5	&	&	CCONJ
brj-24732	251	6	wood	wood	PROPN
brj-24732	251	7	surface	surface	NOUN
brj-24732	251	8	defects	defect	NOUN
brj-24732	251	9	,	,	PUNCT
brj-24732	251	10	”	"	PUNCT
brj-24732	251	11	bioresources	bioresource	NOUN
brj-24732	251	12	20(3	20(3	NOUN
brj-24732	251	13	)	)	PUNCT
brj-24732	251	14	,	,	PUNCT
brj-24732	251	15	7179	7179	NUM
brj-24732	251	16	-	-	SYM
brj-24732	251	17	7193	7193	NUM
brj-24732	251	18	.	.	PUNCT
brj-24732	252	1	7192	7192	NUM
brj-24732	252	2	(	(	PUNCT
brj-24732	252	3	amdc	amdc	NUM
brj-24732	252	4	)	)	PUNCT
brj-24732	252	5	,	,	PUNCT
brj-24732	252	6	shared	share	VERB
brj-24732	252	7	dilated	dilate	VERB
brj-24732	252	8	feature	feature	NOUN
brj-24732	252	9	pyramid	pyramid	NOUN
brj-24732	252	10	(	(	PUNCT
brj-24732	252	11	sdfp	sdfp	PROPN
brj-24732	252	12	)	)	PUNCT
brj-24732	252	13	,	,	PUNCT
brj-24732	252	14	and	and	CCONJ
brj-24732	252	15	a	a	DET
brj-24732	252	16	lightweight	lightweight	ADJ
brj-24732	252	17	detection	detection	NOUN
brj-24732	252	18	head	head	NOUN
brj-24732	252	19	(	(	PUNCT
brj-24732	252	20	lwdethead	lwdethead	ADJ
brj-24732	252	21	)	)	PUNCT
brj-24732	252	22	,	,	PUNCT
brj-24732	252	23	and	and	CCONJ
brj-24732	252	24	introducing	introduce	VERB
brj-24732	252	25	the	the	DET
brj-24732	252	26	normalized	normalize	VERB
brj-24732	252	27	wasserstein	wasserstein	NOUN
brj-24732	252	28	distance	distance	NOUN
brj-24732	252	29	(	(	PUNCT
brj-24732	252	30	nwd	nwd	NOUN
brj-24732	252	31	)	)	PUNCT
brj-24732	252	32	into	into	ADP
brj-24732	252	33	the	the	DET
brj-24732	252	34	loss	loss	NOUN
brj-24732	252	35	function	function	NOUN
brj-24732	252	36	,	,	PUNCT
brj-24732	252	37	the	the	DET
brj-24732	252	38	model	model	NOUN
brj-24732	252	39	effectively	effectively	ADV
brj-24732	252	40	improves	improve	VERB
brj-24732	252	41	detection	detection	NOUN
brj-24732	252	42	accuracy	accuracy	NOUN
brj-24732	252	43	while	while	SCONJ
brj-24732	252	44	reducing	reduce	VERB
brj-24732	252	45	computational	computational	ADJ
brj-24732	252	46	cost	cost	NOUN
brj-24732	252	47	.	.	PUNCT
brj-24732	253	1	2	2	X
brj-24732	253	2	.	.	PUNCT
brj-24732	253	3	based	base	VERB
brj-24732	253	4	on	on	ADP
brj-24732	253	5	an	an	DET
brj-24732	253	6	extensive	extensive	ADJ
brj-24732	253	7	evaluation	evaluation	NOUN
brj-24732	253	8	on	on	ADP
brj-24732	253	9	the	the	DET
brj-24732	253	10	chipboardv1.0	chipboardv1.0	NUM
brj-24732	253	11	dataset	dataset	NOUN
brj-24732	253	12	,	,	PUNCT
brj-24732	253	13	the	the	PRON
brj-24732	253	14	le	le	PROPN
brj-24732	253	15	-	-	PROPN
brj-24732	253	16	yolo	yolo	PROPN
brj-24732	253	17	outperformed	outperform	VERB
brj-24732	253	18	the	the	DET
brj-24732	253	19	baseline	baseline	NOUN
brj-24732	253	20	yolov11n	yolov11n	NOUN
brj-24732	253	21	model	model	NOUN
brj-24732	253	22	in	in	ADP
brj-24732	253	23	detecting	detect	VERB
brj-24732	253	24	particleboard	particleboard	NOUN
brj-24732	253	25	surface	surface	NOUN
brj-24732	253	26	defects	defect	NOUN
brj-24732	253	27	,	,	PUNCT
brj-24732	253	28	especially	especially	ADV
brj-24732	253	29	small	small	ADJ
brj-24732	253	30	and	and	CCONJ
brj-24732	253	31	intricate	intricate	ADJ
brj-24732	253	32	flaws	flaw	NOUN
brj-24732	253	33	.	.	PUNCT
brj-24732	254	1	specifically	specifically	ADV
brj-24732	254	2	,	,	PUNCT
brj-24732	254	3	le	le	X
brj-24732	254	4	-	-	ADJ
brj-24732	254	5	yolo	yolo	PROPN
brj-24732	254	6	achieved	achieve	VERB
brj-24732	254	7	a	a	DET
brj-24732	254	8	4	4	NUM
brj-24732	254	9	%	%	NOUN
brj-24732	254	10	increase	increase	NOUN
brj-24732	254	11	in	in	ADP
brj-24732	254	12	map@50	map@50	NOUN
brj-24732	254	13	,	,	PUNCT
brj-24732	254	14	a	a	DET
brj-24732	254	15	6	6	NUM
brj-24732	254	16	%	%	NOUN
brj-24732	254	17	improvement	improvement	NOUN
brj-24732	254	18	in	in	ADP
brj-24732	254	19	map@50:95	map@50:95	NOUN
brj-24732	254	20	,	,	PUNCT
brj-24732	254	21	and	and	CCONJ
brj-24732	254	22	demonstrated	demonstrate	VERB
brj-24732	254	23	enhanced	enhanced	ADJ
brj-24732	254	24	robustness	robustness	NOUN
brj-24732	254	25	in	in	ADP
brj-24732	254	26	challenging	challenge	VERB
brj-24732	254	27	lighting	lighting	NOUN
brj-24732	254	28	conditions	condition	NOUN
brj-24732	254	29	,	,	PUNCT
brj-24732	254	30	such	such	ADJ
brj-24732	254	31	as	as	ADP
brj-24732	254	32	low	low	ADJ
brj-24732	254	33	-	-	PUNCT
brj-24732	254	34	light	light	ADJ
brj-24732	254	35	and	and	CCONJ
brj-24732	254	36	highreflection	highreflection	NOUN
brj-24732	254	37	environments	environment	NOUN
brj-24732	254	38	.	.	PUNCT
brj-24732	255	1	these	these	DET
brj-24732	255	2	results	result	NOUN
brj-24732	255	3	highlight	highlight	VERB
brj-24732	255	4	the	the	DET
brj-24732	255	5	model	model	NOUN
brj-24732	255	6	’s	’s	PART
brj-24732	255	7	ability	ability	NOUN
brj-24732	255	8	to	to	PART
brj-24732	255	9	handle	handle	VERB
brj-24732	255	10	complex	complex	ADJ
brj-24732	255	11	real	real	ADJ
brj-24732	255	12	-	-	PUNCT
brj-24732	255	13	world	world	NOUN
brj-24732	255	14	industrial	industrial	ADJ
brj-24732	255	15	scenarios	scenario	NOUN
brj-24732	255	16	.	.	PUNCT
brj-24732	256	1	moreover	moreover	ADV
brj-24732	256	2	,	,	PUNCT
brj-24732	256	3	le	le	X
brj-24732	256	4	-	-	ADJ
brj-24732	256	5	yolo	yolo	PROPN
brj-24732	256	6	showed	show	VERB
brj-24732	256	7	superior	superior	ADJ
brj-24732	256	8	detection	detection	NOUN
brj-24732	256	9	accuracy	accuracy	NOUN
brj-24732	256	10	with	with	ADP
brj-24732	256	11	a	a	DET
brj-24732	256	12	2	2	NUM
brj-24732	256	13	%	%	NOUN
brj-24732	256	14	increase	increase	NOUN
brj-24732	256	15	in	in	ADP
brj-24732	256	16	recall	recall	NOUN
brj-24732	256	17	and	and	CCONJ
brj-24732	256	18	a	a	DET
brj-24732	256	19	1	1	NUM
brj-24732	256	20	%	%	NOUN
brj-24732	256	21	boost	boost	NOUN
brj-24732	256	22	in	in	ADP
brj-24732	256	23	f1	f1	NOUN
brj-24732	256	24	-	-	PUNCT
brj-24732	256	25	score	score	NOUN
brj-24732	256	26	,	,	PUNCT
brj-24732	256	27	confirming	confirm	VERB
brj-24732	256	28	its	its	PRON
brj-24732	256	29	overall	overall	ADJ
brj-24732	256	30	effectiveness	effectiveness	NOUN
brj-24732	256	31	in	in	ADP
brj-24732	256	32	detecting	detect	VERB
brj-24732	256	33	defects	defect	NOUN
brj-24732	256	34	.	.	PUNCT
brj-24732	257	1	the	the	DET
brj-24732	257	2	model	model	NOUN
brj-24732	257	3	also	also	ADV
brj-24732	257	4	exhibited	exhibit	VERB
brj-24732	257	5	a	a	DET
brj-24732	257	6	7.9	7.9	NUM
brj-24732	257	7	%	%	NOUN
brj-24732	257	8	increase	increase	NOUN
brj-24732	257	9	in	in	ADP
brj-24732	257	10	inference	inference	NOUN
brj-24732	257	11	speed	speed	NOUN
brj-24732	257	12	,	,	PUNCT
brj-24732	257	13	while	while	SCONJ
brj-24732	257	14	maintaining	maintain	VERB
brj-24732	257	15	a	a	DET
brj-24732	257	16	low	low	ADJ
brj-24732	257	17	number	number	NOUN
brj-24732	257	18	of	of	ADP
brj-24732	257	19	parameters	parameter	NOUN
brj-24732	257	20	,	,	PUNCT
brj-24732	257	21	ensuring	ensure	VERB
brj-24732	257	22	its	its	PRON
brj-24732	257	23	suitability	suitability	NOUN
brj-24732	257	24	for	for	ADP
brj-24732	257	25	real	real	ADJ
brj-24732	257	26	-	-	PUNCT
brj-24732	257	27	time	time	NOUN
brj-24732	257	28	deployment	deployment	NOUN
brj-24732	257	29	with	with	ADP
brj-24732	257	30	limited	limited	ADJ
brj-24732	257	31	computational	computational	ADJ
brj-24732	257	32	resources	resource	NOUN
brj-24732	257	33	.	.	PUNCT
brj-24732	258	1	acknowledgments	acknowledgment	NOUN
brj-24732	258	2	the	the	DET
brj-24732	258	3	authors	author	NOUN
brj-24732	258	4	are	be	AUX
brj-24732	258	5	grateful	grateful	ADJ
brj-24732	258	6	for	for	ADP
brj-24732	258	7	the	the	DET
brj-24732	258	8	financial	financial	ADJ
brj-24732	258	9	support	support	NOUN
brj-24732	258	10	from	from	ADP
brj-24732	258	11	the	the	DET
brj-24732	258	12	inner	inner	PROPN
brj-24732	258	13	mongolia	mongolia	PROPN
brj-24732	258	14	natural	natural	PROPN
brj-24732	258	15	science	science	PROPN
brj-24732	258	16	foundation	foundation	NOUN
brj-24732	258	17	for	for	ADP
brj-24732	258	18	the	the	DET
brj-24732	258	19	project	project	NOUN
brj-24732	258	20	“	"	PUNCT
brj-24732	258	21	construction	construction	NOUN
brj-24732	258	22	of	of	ADP
brj-24732	258	23	an	an	DET
brj-24732	258	24	intelligent	intelligent	ADJ
brj-24732	258	25	inspection	inspection	NOUN
brj-24732	258	26	model	model	NOUN
brj-24732	258	27	for	for	ADP
brj-24732	258	28	surface	surface	NOUN
brj-24732	258	29	defects	defect	NOUN
brj-24732	258	30	of	of	ADP
brj-24732	258	31	particleboard	particleboard	NOUN
brj-24732	258	32	made	make	VERB
brj-24732	258	33	from	from	ADP
brj-24732	258	34	psammophytic	psammophytic	ADJ
brj-24732	258	35	shrubs	shrub	NOUN
brj-24732	258	36	”	"	PUNCT
brj-24732	258	37	(	(	PUNCT
brj-24732	258	38	project	project	NOUN
brj-24732	258	39	no	no	INTJ
brj-24732	258	40	.	.	PUNCT
brj-24732	259	1	2024lhms03063	2024lhms03063	NUM
brj-24732	259	2	)	)	PUNCT
brj-24732	259	3	.	.	PUNCT
brj-24732	260	1	references	reference	NOUN
brj-24732	260	2	cited	cite	VERB
brj-24732	260	3	chen	chen	PROPN
brj-24732	260	4	,	,	PUNCT
brj-24732	260	5	j.	j.	PROPN
brj-24732	260	6	,	,	PUNCT
brj-24732	260	7	kao	kao	PROPN
brj-24732	260	8	,	,	PUNCT
brj-24732	260	9	s.-h	s.-h	NOUN
brj-24732	260	10	.	.	PUNCT
brj-24732	261	1	,	,	PUNCT
brj-24732	261	2	he	he	PRON
brj-24732	261	3	,	,	PUNCT
brj-24732	261	4	h.	h.	PROPN
brj-24732	261	5	,	,	PUNCT
brj-24732	261	6	zhuo	zhuo	PROPN
brj-24732	261	7	,	,	PUNCT
brj-24732	261	8	w.	w.	PROPN
brj-24732	261	9	,	,	PUNCT
brj-24732	261	10	wen	wen	PROPN
brj-24732	261	11	,	,	PUNCT
brj-24732	261	12	s.	s.	PROPN
brj-24732	261	13	,	,	PUNCT
brj-24732	261	14	lee	lee	PROPN
brj-24732	261	15	,	,	PUNCT
brj-24732	261	16	c.-h	c.-h	PROPN
brj-24732	261	17	.	.	PUNCT
brj-24732	261	18	,	,	PUNCT
brj-24732	261	19	and	and	CCONJ
brj-24732	261	20	chan	chan	PROPN
brj-24732	261	21	,	,	PUNCT
brj-24732	261	22	s.-h	s.-h	NOUN
brj-24732	261	23	.	.	PUNCT
brj-24732	262	1	g.	g.	NOUN
brj-24732	262	2	(	(	PUNCT
brj-24732	262	3	2023	2023	NUM
brj-24732	262	4	)	)	PUNCT
brj-24732	262	5	.	.	PUNCT
brj-24732	263	1	“	"	PUNCT
brj-24732	263	2	run	run	VERB
brj-24732	263	3	,	,	PUNCT
brj-24732	263	4	do	do	AUX
brj-24732	263	5	n’t	not	PART
brj-24732	263	6	walk	walk	VERB
brj-24732	263	7	:	:	PUNCT
brj-24732	263	8	chasing	chase	VERB
brj-24732	263	9	higher	high	ADJ
brj-24732	263	10	flops	flop	NOUN
brj-24732	263	11	for	for	ADP
brj-24732	263	12	faster	fast	ADJ
brj-24732	263	13	neural	neural	ADJ
brj-24732	263	14	networks	network	NOUN
brj-24732	263	15	,	,	PUNCT
brj-24732	263	16	”	"	PUNCT
brj-24732	263	17	arxiv	arxiv	PROPN
brj-24732	263	18	preprint	preprint	NOUN
brj-24732	263	19	,	,	PUNCT
brj-24732	263	20	article	article	NOUN
brj-24732	263	21	i	i	PROPN
brj-24732	263	22	d	d	PROPN
brj-24732	263	23	2303.03667v3	2303.03667v3	PROPN
brj-24732	263	24	.	.	PUNCT
brj-24732	264	1	doi	doi	NOUN
brj-24732	264	2	:	:	PUNCT
brj-24732	264	3	10.48550	10.48550	NUM
brj-24732	264	4	/	/	SYM
brj-24732	264	5	arxiv.2303.03667v3	arxiv.2303.03667v3	NUM
brj-24732	264	6	dai	dai	PROPN
brj-24732	264	7	,	,	PUNCT
brj-24732	264	8	x.	x.	PROPN
brj-24732	264	9	,	,	PUNCT
brj-24732	264	10	chen	chen	PROPN
brj-24732	264	11	,	,	PUNCT
brj-24732	264	12	y.	y.	PROPN
brj-24732	264	13	,	,	PUNCT
brj-24732	264	14	xiao	xiao	PROPN
brj-24732	264	15	,	,	PUNCT
brj-24732	264	16	b.	b.	PROPN
brj-24732	264	17	,	,	PUNCT
brj-24732	264	18	chen	chen	PROPN
brj-24732	264	19	,	,	PUNCT
brj-24732	264	20	d.	d.	PROPN
brj-24732	264	21	,	,	PUNCT
brj-24732	264	22	liu	liu	PROPN
brj-24732	264	23	,	,	PUNCT
brj-24732	264	24	m.	m.	NOUN
brj-24732	264	25	,	,	PUNCT
brj-24732	264	26	yuan	yuan	PROPN
brj-24732	264	27	,	,	PUNCT
brj-24732	264	28	l.	l.	PROPN
brj-24732	264	29	,	,	PUNCT
brj-24732	264	30	and	and	CCONJ
brj-24732	264	31	zhang	zhang	PROPN
brj-24732	264	32	,	,	PUNCT
brj-24732	264	33	l.	l.	PROPN
brj-24732	264	34	(	(	PUNCT
brj-24732	264	35	2021	2021	NUM
brj-24732	264	36	)	)	PUNCT
brj-24732	264	37	.	.	PUNCT
brj-24732	265	1	“	"	PUNCT
brj-24732	265	2	dynamic	dynamic	ADJ
brj-24732	265	3	head	head	NOUN
brj-24732	265	4	:	:	PUNCT
brj-24732	265	5	unifying	unify	VERB
brj-24732	265	6	object	object	NOUN
brj-24732	265	7	detection	detection	NOUN
brj-24732	265	8	heads	head	NOUN
brj-24732	265	9	with	with	ADP
brj-24732	265	10	attentions	attention	NOUN
brj-24732	265	11	,	,	PUNCT
brj-24732	265	12	”	"	PUNCT
brj-24732	265	13	arxiv	arxiv	PROPN
brj-24732	265	14	preprint	preprint	NOUN
brj-24732	265	15	,	,	PUNCT
brj-24732	265	16	article	article	NOUN
brj-24732	265	17	i	i	PROPN
brj-24732	265	18	d	d	PROPN
brj-24732	265	19	2106.08322v1	2106.08322v1	PROPN
brj-24732	265	20	.	.	PUNCT
brj-24732	266	1	doi	doi	NOUN
brj-24732	266	2	:	:	PUNCT
brj-24732	266	3	10.48550	10.48550	NUM
brj-24732	266	4	/	/	SYM
brj-24732	266	5	arxiv.2106.08322v1	arxiv.2106.08322v1	PROPN
brj-24732	266	6	ding	ding	NOUN
brj-24732	266	7	,	,	PUNCT
brj-24732	266	8	f.	f.	PROPN
brj-24732	266	9	,	,	PUNCT
brj-24732	266	10	zhuang	zhuang	PROPN
brj-24732	266	11	,	,	PUNCT
brj-24732	266	12	z.	z.	PROPN
brj-24732	266	13	,	,	PUNCT
brj-24732	266	14	liu	liu	PROPN
brj-24732	266	15	,	,	PUNCT
brj-24732	266	16	y.	y.	PROPN
brj-24732	266	17	,	,	PUNCT
brj-24732	266	18	jiiang	jiiang	PROPN
brj-24732	266	19	,	,	PUNCT
brj-24732	266	20	d.	d.	PROPN
brj-24732	266	21	,	,	PUNCT
brj-24732	266	22	yan	yan	PROPN
brj-24732	266	23	,	,	PUNCT
brj-24732	266	24	x.	x.	NOUN
brj-24732	266	25	,	,	PUNCT
brj-24732	266	26	and	and	CCONJ
brj-24732	266	27	wang	wang	PROPN
brj-24732	266	28	,	,	PUNCT
brj-24732	266	29	z.	z.	PROPN
brj-24732	266	30	(	(	PUNCT
brj-24732	266	31	2020	2020	NUM
brj-24732	266	32	)	)	PUNCT
brj-24732	266	33	.	.	PUNCT
brj-24732	267	1	“	"	PUNCT
brj-24732	267	2	detecting	detect	VERB
brj-24732	267	3	defects	defect	NOUN
brj-24732	267	4	on	on	ADP
brj-24732	267	5	solid	solid	ADJ
brj-24732	267	6	wood	wood	NOUN
brj-24732	267	7	panels	panel	NOUN
brj-24732	267	8	based	base	VERB
brj-24732	267	9	on	on	ADP
brj-24732	267	10	an	an	DET
brj-24732	267	11	improved	improved	ADJ
brj-24732	267	12	ssd	ssd	NOUN
brj-24732	267	13	algorithm	algorithm	NOUN
brj-24732	267	14	,	,	PUNCT
brj-24732	267	15	”	"	PUNCT
brj-24732	267	16	sensors	sensor	NOUN
brj-24732	267	17	20(18	20(18	NUM
brj-24732	267	18	)	)	PUNCT
brj-24732	267	19	,	,	PUNCT
brj-24732	267	20	article	article	NOUN
brj-24732	267	21	e5315	e5315	PROPN
brj-24732	267	22	.	.	PUNCT
brj-24732	268	1	doi	doi	NOUN
brj-24732	268	2	:	:	PUNCT
brj-24732	268	3	10.3390	10.3390	NUM
brj-24732	268	4	/	/	SYM
brj-24732	268	5	s20185315	s20185315	PROPN
brj-24732	268	6	ding	ding	NOUN
brj-24732	268	7	,	,	PUNCT
brj-24732	268	8	x.	x.	PROPN
brj-24732	268	9	,	,	PUNCT
brj-24732	268	10	zhang	zhang	PROPN
brj-24732	268	11	,	,	PUNCT
brj-24732	268	12	x.	x.	PROPN
brj-24732	268	13	,	,	PUNCT
brj-24732	268	14	han	han	PROPN
brj-24732	268	15	,	,	PUNCT
brj-24732	268	16	j.	j.	PROPN
brj-24732	268	17	,	,	PUNCT
brj-24732	268	18	and	and	CCONJ
brj-24732	268	19	ding	ding	NOUN
brj-24732	268	20	,	,	PUNCT
brj-24732	268	21	g.	g.	PROPN
brj-24732	268	22	(	(	PUNCT
brj-24732	268	23	2021	2021	NUM
brj-24732	268	24	)	)	PUNCT
brj-24732	268	25	.	.	PUNCT
brj-24732	269	1	“	"	PUNCT
brj-24732	269	2	diverse	diverse	ADJ
brj-24732	269	3	branch	branch	NOUN
brj-24732	269	4	block	block	NOUN
brj-24732	269	5	:	:	PUNCT
brj-24732	269	6	building	build	VERB
brj-24732	269	7	a	a	DET
brj-24732	269	8	convolution	convolution	NOUN
brj-24732	269	9	as	as	ADP
brj-24732	269	10	an	an	DET
brj-24732	269	11	inception	inception	NOUN
brj-24732	269	12	-	-	PUNCT
brj-24732	269	13	like	like	ADJ
brj-24732	269	14	unit	unit	NOUN
brj-24732	269	15	,	,	PUNCT
brj-24732	269	16	”	"	PUNCT
brj-24732	269	17	arxiv	arxiv	PROPN
brj-24732	269	18	preprint	preprint	NOUN
brj-24732	269	19	,	,	PUNCT
brj-24732	269	20	article	article	NOUN
brj-24732	269	21	i	i	PROPN
brj-24732	269	22	d	d	PROPN
brj-24732	269	23	2103.13425v2	2103.13425v2	PROPN
brj-24732	269	24	.	.	PUNCT
brj-24732	270	1	doi	doi	NOUN
brj-24732	270	2	:	:	PUNCT
brj-24732	270	3	10.48550	10.48550	NUM
brj-24732	270	4	/	/	SYM
brj-24732	270	5	arxiv.2103.13425v2	arxiv.2103.13425v2	PROPN
brj-24732	270	6	girshick	girshick	NOUN
brj-24732	270	7	,	,	PUNCT
brj-24732	270	8	r.	r.	PROPN
brj-24732	270	9	(	(	PUNCT
brj-24732	270	10	2015	2015	NUM
brj-24732	270	11	)	)	PUNCT
brj-24732	270	12	.	.	PUNCT
brj-24732	271	1	“	"	PUNCT
brj-24732	271	2	fast	fast	ADJ
brj-24732	271	3	r	r	NOUN
brj-24732	271	4	-	-	PUNCT
brj-24732	271	5	cnn	cnn	PROPN
brj-24732	271	6	,	,	PUNCT
brj-24732	271	7	”	"	PUNCT
brj-24732	271	8	in	in	ADP
brj-24732	271	9	:	:	PUNCT
brj-24732	271	10	proceedings	proceeding	NOUN
brj-24732	271	11	of	of	ADP
brj-24732	271	12	the	the	DET
brj-24732	271	13	ieee	ieee	NOUN
brj-24732	271	14	international	international	PROPN
brj-24732	271	15	conference	conference	NOUN
brj-24732	271	16	on	on	ADP
brj-24732	271	17	computer	computer	NOUN
brj-24732	271	18	vision	vision	NOUN
brj-24732	271	19	,	,	PUNCT
brj-24732	271	20	santiago	santiago	PROPN
brj-24732	271	21	,	,	PUNCT
brj-24732	271	22	chile	chile	PROPN
brj-24732	271	23	,	,	PUNCT
brj-24732	271	24	pp	pp	ADP
brj-24732	271	25	.	.	PUNCT
brj-24732	271	26	1440	1440	NUM
brj-24732	271	27	-	-	SYM
brj-24732	271	28	1448	1448	NUM
brj-24732	271	29	.	.	PUNCT
brj-24732	272	1	girshick	girshick	PROPN
brj-24732	272	2	,	,	PUNCT
brj-24732	272	3	r.	r.	PROPN
brj-24732	272	4	,	,	PUNCT
brj-24732	272	5	donahue	donahue	PROPN
brj-24732	272	6	,	,	PUNCT
brj-24732	272	7	j.	j.	PROPN
brj-24732	272	8	,	,	PUNCT
brj-24732	272	9	darrell	darrell	PROPN
brj-24732	272	10	,	,	PUNCT
brj-24732	272	11	t.	t.	PROPN
brj-24732	272	12	,	,	PUNCT
brj-24732	272	13	and	and	CCONJ
brj-24732	272	14	malik	malik	PROPN
brj-24732	272	15	,	,	PUNCT
brj-24732	272	16	j.	j.	PROPN
brj-24732	272	17	(	(	PUNCT
brj-24732	272	18	2014	2014	NUM
brj-24732	272	19	)	)	PUNCT
brj-24732	272	20	.	.	PUNCT
brj-24732	273	1	“	"	PUNCT
brj-24732	273	2	rich	rich	ADJ
brj-24732	273	3	feature	feature	NOUN
brj-24732	273	4	hierarchies	hierarchy	NOUN
brj-24732	273	5	for	for	ADP
brj-24732	273	6	accurate	accurate	ADJ
brj-24732	273	7	object	object	NOUN
brj-24732	273	8	detection	detection	NOUN
brj-24732	273	9	and	and	CCONJ
brj-24732	273	10	semantic	semantic	ADJ
brj-24732	273	11	segmentation	segmentation	NOUN
brj-24732	273	12	,	,	PUNCT
brj-24732	273	13	”	"	PUNCT
brj-24732	273	14	in	in	ADP
brj-24732	273	15	:	:	PUNCT
brj-24732	273	16	proceedings	proceeding	NOUN
brj-24732	273	17	of	of	ADP
brj-24732	273	18	the	the	DET
brj-24732	273	19	ieee	ieee	NOUN
brj-24732	273	20	conference	conference	NOUN
brj-24732	273	21	on	on	ADP
brj-24732	273	22	computer	computer	NOUN
brj-24732	273	23	vision	vision	NOUN
brj-24732	273	24	and	and	CCONJ
brj-24732	273	25	pattern	pattern	NOUN
brj-24732	273	26	recognition	recognition	NOUN
brj-24732	273	27	,	,	PUNCT
brj-24732	273	28	columbus	columbus	PROPN
brj-24732	273	29	,	,	PUNCT
brj-24732	273	30	oh	oh	INTJ
brj-24732	273	31	,	,	PUNCT
brj-24732	273	32	usa	usa	PROPN
brj-24732	273	33	,	,	PUNCT
brj-24732	273	34	pp	pp	ADJ
brj-24732	273	35	.	.	PUNCT
brj-24732	274	1	580	580	NUM
brj-24732	274	2	-	-	SYM
brj-24732	274	3	587	587	NUM
brj-24732	274	4	.	.	PUNCT
brj-24732	275	1	ji	ji	X
brj-24732	275	2	,	,	PUNCT
brj-24732	275	3	x.	x.	PROPN
brj-24732	275	4	,	,	PUNCT
brj-24732	275	5	guo	guo	PROPN
brj-24732	275	6	,	,	PUNCT
brj-24732	275	7	h.	h.	PROPN
brj-24732	275	8	,	,	PUNCT
brj-24732	275	9	and	and	CCONJ
brj-24732	275	10	hu	hu	PROPN
brj-24732	275	11	,	,	PUNCT
brj-24732	275	12	m.	m.	NOUN
brj-24732	275	13	(	(	PUNCT
brj-24732	275	14	2019	2019	NUM
brj-24732	275	15	)	)	PUNCT
brj-24732	275	16	.	.	PUNCT
brj-24732	276	1	“	"	PUNCT
brj-24732	276	2	features	feature	VERB
brj-24732	276	3	extraction	extraction	NOUN
brj-24732	276	4	and	and	CCONJ
brj-24732	276	5	classification	classification	NOUN
brj-24732	276	6	of	of	ADP
brj-24732	276	7	wood	wood	NOUN
brj-24732	276	8	defect	defect	NOUN
brj-24732	276	9	based	base	VERB
brj-24732	276	10	on	on	ADP
brj-24732	276	11	hu	hu	PROPN
brj-24732	276	12	invariant	invariant	PROPN
brj-24732	276	13	moment	moment	NOUN
brj-24732	276	14	and	and	CCONJ
brj-24732	276	15	wavelet	wavelet	NOUN
brj-24732	276	16	moment	moment	NOUN
brj-24732	276	17	and	and	CCONJ
brj-24732	276	18	bp	bp	PROPN
brj-24732	276	19	neural	neural	ADJ
brj-24732	276	20	network	network	NOUN
brj-24732	276	21	,	,	PUNCT
brj-24732	276	22	”	"	PUNCT
brj-24732	276	23	in	in	ADP
brj-24732	276	24	:	:	PUNCT
brj-24732	276	25	https://arxiv.org/abs/2303.03667v3	https://arxiv.org/abs/2303.03667v3	NUM
brj-24732	277	1	https://doi.org/10.48550/arxiv.2303.03667v3	https://doi.org/10.48550/arxiv.2303.03667v3	NOUN
brj-24732	277	2	https://doi.org/10.48550/arxiv.2106.08322v1	https://doi.org/10.48550/arxiv.2106.08322v1	PROPN
brj-24732	277	3	peer	peer	NOUN
brj-24732	277	4	-	-	PUNCT
brj-24732	277	5	reviewed	review	VERB
brj-24732	277	6	article	article	NOUN
brj-24732	277	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24732	277	8	he	he	PRON
brj-24732	277	9	et	et	PROPN
brj-24732	277	10	al	al	PROPN
brj-24732	277	11	.	.	PROPN
brj-24732	278	1	(	(	PUNCT
brj-24732	278	2	2025	2025	NUM
brj-24732	278	3	)	)	PUNCT
brj-24732	278	4	.	.	PUNCT
brj-24732	279	1	“	"	PUNCT
brj-24732	279	2	le	le	X
brj-24732	279	3	-	-	PROPN
brj-24732	279	4	yolo	yolo	PROPN
brj-24732	279	5	&	&	CCONJ
brj-24732	279	6	wood	wood	PROPN
brj-24732	279	7	surface	surface	NOUN
brj-24732	279	8	defects	defect	NOUN
brj-24732	279	9	,	,	PUNCT
brj-24732	279	10	”	"	PUNCT
brj-24732	279	11	bioresources	bioresource	NOUN
brj-24732	279	12	20(3	20(3	NOUN
brj-24732	279	13	)	)	PUNCT
brj-24732	279	14	,	,	PUNCT
brj-24732	279	15	7179	7179	NUM
brj-24732	279	16	-	-	SYM
brj-24732	279	17	7193	7193	NUM
brj-24732	279	18	.	.	PUNCT
brj-24732	280	1	7193	7193	NUM
brj-24732	280	2	vinci	vinci	PROPN
brj-24732	280	3	'	'	PROPN
brj-24732	280	4	19	19	NUM
brj-24732	280	5	:	:	PUNCT
brj-24732	280	6	proceedings	proceeding	NOUN
brj-24732	280	7	of	of	ADP
brj-24732	280	8	the	the	DET
brj-24732	280	9	12th	12th	ADJ
brj-24732	280	10	international	international	ADJ
brj-24732	280	11	symposium	symposium	NOUN
brj-24732	280	12	on	on	ADP
brj-24732	280	13	visual	visual	ADJ
brj-24732	280	14	information	information	NOUN
brj-24732	280	15	communication	communication	NOUN
brj-24732	280	16	and	and	CCONJ
brj-24732	280	17	interaction	interaction	NOUN
brj-24732	280	18	,	,	PUNCT
brj-24732	280	19	shanghai	shanghai	PROPN
brj-24732	280	20	china	china	PROPN
brj-24732	280	21	,	,	PUNCT
brj-24732	280	22	pp	pp	PROPN
brj-24732	280	23	.	.	PUNCT
brj-24732	281	1	1	1	NUM
brj-24732	281	2	-	-	SYM
brj-24732	281	3	5	5	NUM
brj-24732	281	4	.	.	PUNCT
brj-24732	281	5	khanam	khanam	PROPN
brj-24732	281	6	,	,	PUNCT
brj-24732	281	7	r.	r.	PROPN
brj-24732	281	8	,	,	PUNCT
brj-24732	281	9	and	and	CCONJ
brj-24732	281	10	hussain	hussain	PROPN
brj-24732	281	11	,	,	PUNCT
brj-24732	281	12	m.	m.	NOUN
brj-24732	281	13	(	(	PUNCT
brj-24732	281	14	2024	2024	NUM
brj-24732	281	15	)	)	PUNCT
brj-24732	281	16	.	.	PUNCT
brj-24732	282	1	“	"	PUNCT
brj-24732	282	2	yolov11	yolov11	NOUN
brj-24732	282	3	:	:	PUNCT
brj-24732	282	4	an	an	DET
brj-24732	282	5	overview	overview	NOUN
brj-24732	282	6	of	of	ADP
brj-24732	282	7	the	the	DET
brj-24732	282	8	key	key	ADJ
brj-24732	282	9	architectural	architectural	ADJ
brj-24732	282	10	enhancements	enhancement	NOUN
brj-24732	282	11	,	,	PUNCT
brj-24732	282	12	”	"	PUNCT
brj-24732	282	13	arxiv	arxiv	PROPN
brj-24732	282	14	preprint	preprint	NOUN
brj-24732	282	15	,	,	PUNCT
brj-24732	282	16	article	article	NOUN
brj-24732	282	17	i	i	PROPN
brj-24732	282	18	d	d	PROPN
brj-24732	282	19	2410.17725	2410.17725	PROPN
brj-24732	282	20	.	.	PUNCT
brj-24732	283	1	li	li	PROPN
brj-24732	283	2	,	,	PUNCT
brj-24732	283	3	c.	c.	PROPN
brj-24732	283	4	,	,	PUNCT
brj-24732	283	5	yao	yao	PROPN
brj-24732	283	6	,	,	PUNCT
brj-24732	283	7	h.	h.	PROPN
brj-24732	283	8	,	,	PUNCT
brj-24732	283	9	and	and	CCONJ
brj-24732	283	10	jian	jian	PROPN
brj-24732	283	11	,	,	PUNCT
brj-24732	283	12	b.	b.	PROPN
brj-24732	283	13	(	(	PUNCT
brj-24732	283	14	2025	2025	NUM
brj-24732	283	15	)	)	PUNCT
brj-24732	283	16	.	.	PUNCT
brj-24732	284	1	“	"	PUNCT
brj-24732	284	2	discrete	discrete	ADJ
brj-24732	284	3	wavelet	wavelet	NOUN
brj-24732	284	4	transform	transform	NOUN
brj-24732	284	5	sampling	sample	VERB
brj-24732	284	6	for	for	ADP
brj-24732	284	7	image	image	NOUN
brj-24732	284	8	super	super	ADJ
brj-24732	284	9	resolution	resolution	NOUN
brj-24732	284	10	,	,	PUNCT
brj-24732	284	11	”	"	PUNCT
brj-24732	284	12	applied	apply	VERB
brj-24732	284	13	artificial	artificial	ADJ
brj-24732	284	14	intelligence	intelligence	NOUN
brj-24732	284	15	39(1	39(1	NUM
brj-24732	284	16	)	)	PUNCT
brj-24732	284	17	,	,	PUNCT
brj-24732	284	18	article	article	NOUN
brj-24732	284	19	i	i	PROPN
brj-24732	284	20	d	d	PROPN
brj-24732	284	21	2449296	2449296	NUM
brj-24732	284	22	.	.	PUNCT
brj-24732	285	1	doi	doi	NOUN
brj-24732	285	2	:	:	PUNCT
brj-24732	285	3	10.1080/08839514.2024.2449296	10.1080/08839514.2024.2449296	NUM
brj-24732	285	4	meng	meng	PROPN
brj-24732	285	5	,	,	PUNCT
brj-24732	285	6	w.	w.	PROPN
brj-24732	285	7	,	,	PUNCT
brj-24732	285	8	and	and	CCONJ
brj-24732	285	9	yuan	yuan	NOUN
brj-24732	285	10	,	,	PUNCT
brj-24732	285	11	y.	y.	PROPN
brj-24732	285	12	(	(	PUNCT
brj-24732	285	13	2023	2023	NUM
brj-24732	285	14	)	)	PUNCT
brj-24732	285	15	.	.	PUNCT
brj-24732	286	1	“	"	PUNCT
brj-24732	286	2	sgn	sgn	NOUN
brj-24732	286	3	-	-	PUNCT
brj-24732	286	4	yolo	yolo	NOUN
brj-24732	286	5	:	:	PUNCT
brj-24732	286	6	detecting	detect	VERB
brj-24732	286	7	wood	wood	NOUN
brj-24732	286	8	defects	defect	NOUN
brj-24732	286	9	with	with	ADP
brj-24732	286	10	improved	improved	ADJ
brj-24732	286	11	yolov5	yolov5	NOUN
brj-24732	286	12	based	base	VERB
brj-24732	286	13	on	on	ADP
brj-24732	286	14	semi	semi	ADJ
brj-24732	286	15	-	-	ADJ
brj-24732	286	16	global	global	ADJ
brj-24732	286	17	network	network	NOUN
brj-24732	286	18	,	,	PUNCT
brj-24732	286	19	”	"	PUNCT
brj-24732	286	20	sensors	sensor	NOUN
brj-24732	286	21	23(21	23(21	NUM
brj-24732	286	22	)	)	PUNCT
brj-24732	286	23	,	,	PUNCT
brj-24732	286	24	article	article	NOUN
brj-24732	286	25	8705	8705	NUM
brj-24732	286	26	.	.	PUNCT
brj-24732	287	1	doi	doi	NOUN
brj-24732	287	2	:	:	PUNCT
brj-24732	287	3	10.3390	10.3390	NUM
brj-24732	287	4	/	/	SYM
brj-24732	287	5	s23218705	s23218705	NUM
brj-24732	287	6	pan	pan	PROPN
brj-24732	287	7	,	,	PUNCT
brj-24732	287	8	s.	s.	PROPN
brj-24732	287	9	,	,	PUNCT
brj-24732	287	10	wang	wang	PROPN
brj-24732	287	11	,	,	PUNCT
brj-24732	287	12	k.	k.	PROPN
brj-24732	287	13	,	,	PUNCT
brj-24732	287	14	chen	chen	PROPN
brj-24732	287	15	,	,	PUNCT
brj-24732	287	16	j.	j.	PROPN
brj-24732	287	17	,	,	PUNCT
brj-24732	287	18	and	and	CCONJ
brj-24732	287	19	zhang	zhang	PROPN
brj-24732	287	20	,	,	PUNCT
brj-24732	287	21	y.	y.	PROPN
brj-24732	287	22	(	(	PUNCT
brj-24732	287	23	2022	2022	NUM
brj-24732	287	24	)	)	PUNCT
brj-24732	287	25	.	.	PUNCT
brj-24732	288	1	“	"	PUNCT
brj-24732	288	2	larch	larch	NOUN
brj-24732	288	3	wood	wood	NOUN
brj-24732	288	4	defect	defect	NOUN
brj-24732	288	5	definition	definition	NOUN
brj-24732	288	6	and	and	CCONJ
brj-24732	288	7	microscopic	microscopic	ADJ
brj-24732	288	8	inversion	inversion	NOUN
brj-24732	288	9	analysis	analysis	NOUN
brj-24732	288	10	using	use	VERB
brj-24732	288	11	the	the	DET
brj-24732	288	12	elm	elm	NOUN
brj-24732	288	13	near	near	ADV
brj-24732	288	14	-	-	PUNCT
brj-24732	288	15	infrared	infrare	VERB
brj-24732	288	16	spectrum	spectrum	NOUN
brj-24732	288	17	optimization	optimization	NOUN
brj-24732	288	18	along	along	ADP
brj-24732	288	19	with	with	ADP
brj-24732	288	20	woa	woa	ADJ
brj-24732	288	21	–	–	PUNCT
brj-24732	288	22	svm	svm	ADJ
brj-24732	288	23	,	,	PUNCT
brj-24732	288	24	”	"	PUNCT
brj-24732	288	25	bioresources	bioresource	NOUN
brj-24732	288	26	17(1	17(1	NUM
brj-24732	288	27	)	)	PUNCT
brj-24732	288	28	,	,	PUNCT
brj-24732	288	29	682	682	NUM
brj-24732	288	30	-	-	SYM
brj-24732	288	31	698	698	NUM
brj-24732	288	32	.	.	PUNCT
brj-24732	289	1	doi	doi	NOUN
brj-24732	289	2	:	:	PUNCT
brj-24732	289	3	10.15376	10.15376	NUM
brj-24732	289	4	/	/	SYM
brj-24732	289	5	biores.17.1.682	biores.17.1.682	ADV
brj-24732	289	6	-	-	SYM
brj-24732	289	7	698	698	NUM
brj-24732	289	8	tan	tan	PROPN
brj-24732	289	9	,	,	PUNCT
brj-24732	289	10	m.	m.	NOUN
brj-24732	289	11	,	,	PUNCT
brj-24732	289	12	pang	pang	NOUN
brj-24732	289	13	,	,	PUNCT
brj-24732	289	14	r.	r.	PROPN
brj-24732	289	15	,	,	PUNCT
brj-24732	289	16	and	and	CCONJ
brj-24732	289	17	le	le	X
brj-24732	289	18	,	,	PUNCT
brj-24732	289	19	q.	q.	PROPN
brj-24732	289	20	v.	v.	PROPN
brj-24732	289	21	(	(	PUNCT
brj-24732	289	22	2020	2020	NUM
brj-24732	289	23	)	)	PUNCT
brj-24732	289	24	.	.	PUNCT
brj-24732	290	1	“	"	PUNCT
brj-24732	290	2	efficientdet	efficientdet	NOUN
brj-24732	290	3	:	:	PUNCT
brj-24732	290	4	scalable	scalable	ADJ
brj-24732	290	5	and	and	CCONJ
brj-24732	290	6	efficient	efficient	ADJ
brj-24732	290	7	object	object	NOUN
brj-24732	290	8	detection	detection	NOUN
brj-24732	290	9	,	,	PUNCT
brj-24732	290	10	”	"	PUNCT
brj-24732	290	11	arxiv	arxiv	PROPN
brj-24732	290	12	preprint	preprint	NOUN
brj-24732	290	13	,	,	PUNCT
brj-24732	290	14	article	article	NOUN
brj-24732	290	15	i	i	PROPN
brj-24732	290	16	d	d	PROPN
brj-24732	290	17	1911.09070v7	1911.09070v7	PROPN
brj-24732	290	18	.	.	PUNCT
brj-24732	291	1	doi	doi	NOUN
brj-24732	291	2	:	:	PUNCT
brj-24732	291	3	10.48550	10.48550	NUM
brj-24732	291	4	/	/	SYM
brj-24732	291	5	arxiv.1911.09070v7	arxiv.1911.09070v7	NUM
brj-24732	291	6	tian	tian	ADJ
brj-24732	291	7	,	,	PUNCT
brj-24732	291	8	y.	y.	PROPN
brj-24732	291	9	,	,	PUNCT
brj-24732	291	10	ye	ye	PROPN
brj-24732	291	11	,	,	PUNCT
brj-24732	291	12	q.	q.	NOUN
brj-24732	291	13	,	,	PUNCT
brj-24732	291	14	and	and	CCONJ
brj-24732	291	15	doermann	doermann	PROPN
brj-24732	291	16	,	,	PUNCT
brj-24732	291	17	d.	d.	PROPN
brj-24732	291	18	(	(	PUNCT
brj-24732	291	19	2025	2025	NUM
brj-24732	291	20	)	)	PUNCT
brj-24732	291	21	.	.	PUNCT
brj-24732	292	1	“	"	PUNCT
brj-24732	292	2	yolov12	yolov12	ADJ
brj-24732	292	3	:	:	PUNCT
brj-24732	292	4	attention	attention	NOUN
brj-24732	292	5	-	-	PUNCT
brj-24732	292	6	centric	centric	NOUN
brj-24732	292	7	real	real	ADJ
brj-24732	292	8	-	-	PUNCT
brj-24732	292	9	time	time	NOUN
brj-24732	292	10	object	object	NOUN
brj-24732	292	11	detectors	detector	NOUN
brj-24732	292	12	,	,	PUNCT
brj-24732	292	13	”	"	PUNCT
brj-24732	292	14	arxiv	arxiv	PROPN
brj-24732	292	15	preprint	preprint	NOUN
brj-24732	292	16	,	,	PUNCT
brj-24732	292	17	article	article	NOUN
brj-24732	292	18	i	i	PROPN
brj-24732	292	19	d	d	PROPN
brj-24732	292	20	2502.12524v1	2502.12524v1	PROPN
brj-24732	292	21	.	.	PUNCT
brj-24732	293	1	doi	doi	NOUN
brj-24732	293	2	:	:	PUNCT
brj-24732	293	3	10.48550	10.48550	NUM
brj-24732	293	4	/	/	SYM
brj-24732	293	5	arxiv.2502.12524v1	arxiv.2502.12524v1	NUM
brj-24732	293	6	uijlings	uijling	NOUN
brj-24732	293	7	,	,	PUNCT
brj-24732	293	8	j.	j.	PROPN
brj-24732	293	9	,	,	PUNCT
brj-24732	293	10	vandesande	vandesande	VERB
brj-24732	293	11	,	,	PUNCT
brj-24732	293	12	k.	k.	PROPN
brj-24732	293	13	,	,	PUNCT
brj-24732	293	14	gevers	gever	NOUN
brj-24732	293	15	,	,	PUNCT
brj-24732	293	16	t.	t.	NOUN
brj-24732	293	17	,	,	PUNCT
brj-24732	293	18	and	and	CCONJ
brj-24732	293	19	smeulders	smeulder	NOUN
brj-24732	293	20	,	,	PUNCT
brj-24732	293	21	a.	a.	NOUN
brj-24732	293	22	(	(	PUNCT
brj-24732	293	23	2013	2013	NUM
brj-24732	293	24	)	)	PUNCT
brj-24732	293	25	.	.	PUNCT
brj-24732	294	1	“	"	PUNCT
brj-24732	294	2	selective	selective	ADJ
brj-24732	294	3	search	search	NOUN
brj-24732	294	4	for	for	ADP
brj-24732	294	5	object	object	NOUN
brj-24732	294	6	recognition	recognition	NOUN
brj-24732	294	7	,	,	PUNCT
brj-24732	294	8	”	"	PUNCT
brj-24732	294	9	international	international	ADJ
brj-24732	294	10	journal	journal	NOUN
brj-24732	294	11	of	of	ADP
brj-24732	294	12	computer	computer	NOUN
brj-24732	294	13	vision	vision	NOUN
brj-24732	294	14	104(2	104(2	NUM
brj-24732	294	15	)	)	PUNCT
brj-24732	294	16	,	,	PUNCT
brj-24732	294	17	154	154	NUM
brj-24732	294	18	-	-	SYM
brj-24732	294	19	171	171	NUM
brj-24732	294	20	.	.	PUNCT
brj-24732	295	1	doi	doi	NOUN
brj-24732	295	2	:	:	PUNCT
brj-24732	295	3	10.1007	10.1007	NUM
brj-24732	295	4	/	/	SYM
brj-24732	295	5	s11263	s11263	NUM
brj-24732	295	6	-	-	PUNCT
brj-24732	295	7	013	013	NUM
brj-24732	295	8	-	-	PUNCT
brj-24732	295	9	0620	0620	NUM
brj-24732	295	10	-	-	SYM
brj-24732	295	11	5	5	NUM
brj-24732	295	12	wang	wang	PROPN
brj-24732	295	13	,	,	PUNCT
brj-24732	295	14	j.	j.	PROPN
brj-24732	295	15	,	,	PUNCT
brj-24732	295	16	xu	xu	PROPN
brj-24732	295	17	,	,	PUNCT
brj-24732	295	18	c.	c.	PROPN
brj-24732	295	19	,	,	PUNCT
brj-24732	295	20	yang	yang	PROPN
brj-24732	295	21	,	,	PUNCT
brj-24732	295	22	w.	w.	PROPN
brj-24732	295	23	,	,	PUNCT
brj-24732	295	24	and	and	CCONJ
brj-24732	295	25	yu	yu	PROPN
brj-24732	295	26	,	,	PUNCT
brj-24732	295	27	l.	l.	PROPN
brj-24732	295	28	(	(	PUNCT
brj-24732	295	29	2022	2022	NUM
brj-24732	295	30	)	)	PUNCT
brj-24732	295	31	.	.	PUNCT
brj-24732	296	1	“	"	PUNCT
brj-24732	296	2	a	a	DET
brj-24732	296	3	normalized	normalize	VERB
brj-24732	296	4	gaussian	gaussian	ADJ
brj-24732	296	5	wasserstein	wasserstein	NOUN
brj-24732	296	6	distance	distance	NOUN
brj-24732	296	7	for	for	ADP
brj-24732	296	8	tiny	tiny	ADJ
brj-24732	296	9	object	object	NOUN
brj-24732	296	10	detection	detection	NOUN
brj-24732	296	11	,	,	PUNCT
brj-24732	296	12	”	"	PUNCT
brj-24732	296	13	arxiv	arxiv	PROPN
brj-24732	296	14	preprint	preprint	NOUN
brj-24732	296	15	,	,	PUNCT
brj-24732	296	16	article	article	NOUN
brj-24732	296	17	i	i	PROPN
brj-24732	296	18	d	d	PROPN
brj-24732	296	19	2110.13389v2	2110.13389v2	PROPN
brj-24732	296	20	.	.	PUNCT
brj-24732	296	21	doi	doi	NOUN
brj-24732	296	22	:	:	PUNCT
brj-24732	296	23	10.48550	10.48550	NUM
brj-24732	296	24	/	/	SYM
brj-24732	296	25	arxiv.2110.13389v2	arxiv.2110.13389v2	NUM
brj-24732	296	26	wang	wang	PROPN
brj-24732	296	27	,	,	PUNCT
brj-24732	296	28	r.	r.	PROPN
brj-24732	296	29	,	,	PUNCT
brj-24732	296	30	chen	chen	PROPN
brj-24732	296	31	,	,	PUNCT
brj-24732	296	32	y.	y.	PROPN
brj-24732	296	33	,	,	PUNCT
brj-24732	296	34	liang	liang	PROPN
brj-24732	296	35	,	,	PUNCT
brj-24732	296	36	f.	f.	PROPN
brj-24732	296	37	,	,	PUNCT
brj-24732	296	38	wang	wang	PROPN
brj-24732	296	39	,	,	PUNCT
brj-24732	296	40	b.	b.	PROPN
brj-24732	296	41	,	,	PUNCT
brj-24732	296	42	mou	mou	PROPN
brj-24732	296	43	,	,	PUNCT
brj-24732	296	44	x.	x.	NOUN
brj-24732	296	45	,	,	PUNCT
brj-24732	296	46	and	and	CCONJ
brj-24732	296	47	zhang	zhang	PROPN
brj-24732	296	48	,	,	PUNCT
brj-24732	296	49	g.	g.	PROPN
brj-24732	296	50	(	(	PUNCT
brj-24732	296	51	2024	2024	NUM
brj-24732	296	52	)	)	PUNCT
brj-24732	296	53	.	.	PUNCT
brj-24732	297	1	“	"	PUNCT
brj-24732	297	2	bpn	bpn	VERB
brj-24732	297	3	-	-	PUNCT
brj-24732	297	4	yolo	yolo	NOUN
brj-24732	297	5	:	:	PUNCT
brj-24732	297	6	a	a	DET
brj-24732	297	7	novel	novel	ADJ
brj-24732	297	8	method	method	NOUN
brj-24732	297	9	for	for	ADP
brj-24732	297	10	wood	wood	NOUN
brj-24732	297	11	defect	defect	NOUN
brj-24732	297	12	detection	detection	NOUN
brj-24732	297	13	based	base	VERB
brj-24732	297	14	on	on	ADP
brj-24732	297	15	yolov7	yolov7	NOUN
brj-24732	297	16	,	,	PUNCT
brj-24732	297	17	”	"	PUNCT
brj-24732	297	18	forests	forest	NOUN
brj-24732	297	19	15(7	15(7	NUM
brj-24732	297	20	)	)	PUNCT
brj-24732	297	21	,	,	PUNCT
brj-24732	297	22	article	article	NOUN
brj-24732	297	23	1096	1096	NUM
brj-24732	297	24	.	.	PUNCT
brj-24732	298	1	doi	doi	NOUN
brj-24732	298	2	:	:	PUNCT
brj-24732	298	3	10.3390	10.3390	NUM
brj-24732	298	4	/	/	SYM
brj-24732	298	5	f15071096	f15071096	PROPN
brj-24732	298	6	yu	yu	PROPN
brj-24732	298	7	,	,	PUNCT
brj-24732	298	8	w.	w.	PROPN
brj-24732	298	9	,	,	PUNCT
brj-24732	298	10	and	and	CCONJ
brj-24732	298	11	wang	wang	PROPN
brj-24732	298	12	,	,	PUNCT
brj-24732	298	13	x.	x.	NOUN
brj-24732	298	14	(	(	PUNCT
brj-24732	298	15	2024	2024	NUM
brj-24732	298	16	)	)	PUNCT
brj-24732	298	17	.	.	PUNCT
brj-24732	299	1	“	"	PUNCT
brj-24732	299	2	mambaout	mambaout	NOUN
brj-24732	299	3	:	:	PUNCT
brj-24732	299	4	do	do	AUX
brj-24732	299	5	we	we	PRON
brj-24732	299	6	really	really	ADV
brj-24732	299	7	need	need	VERB
brj-24732	299	8	mamba	mamba	NOUN
brj-24732	299	9	for	for	ADP
brj-24732	299	10	vision	vision	NOUN
brj-24732	299	11	?	?	PUNCT
brj-24732	299	12	,	,	PUNCT
brj-24732	299	13	”	"	PUNCT
brj-24732	299	14	arxiv	arxiv	PROPN
brj-24732	299	15	preprint	preprint	NOUN
brj-24732	299	16	article	article	NOUN
brj-24732	299	17	i	i	PROPN
brj-24732	299	18	d	d	PROPN
brj-24732	299	19	2405.07992v3	2405.07992v3	PROPN
brj-24732	299	20	.	.	PUNCT
brj-24732	299	21	doi	doi	PROPN
brj-24732	299	22	:	:	PUNCT
brj-24732	299	23	10.48550	10.48550	NUM
brj-24732	299	24	/	/	SYM
brj-24732	299	25	arxiv.2405.07992v3	arxiv.2405.07992v3	PROPN
brj-24732	299	26	yu	yu	PROPN
brj-24732	299	27	,	,	PUNCT
brj-24732	299	28	z.	z.	PROPN
brj-24732	299	29	,	,	PUNCT
brj-24732	299	30	huang	huang	PROPN
brj-24732	299	31	,	,	PUNCT
brj-24732	299	32	h.	h.	PROPN
brj-24732	299	33	,	,	PUNCT
brj-24732	299	34	chen	chen	PROPN
brj-24732	299	35	,	,	PUNCT
brj-24732	299	36	w.	w.	PROPN
brj-24732	299	37	,	,	PUNCT
brj-24732	299	38	su	su	PROPN
brj-24732	299	39	,	,	PUNCT
brj-24732	299	40	y.	y.	PROPN
brj-24732	299	41	,	,	PUNCT
brj-24732	299	42	liu	liu	PROPN
brj-24732	299	43	,	,	PUNCT
brj-24732	299	44	y.	y.	PROPN
brj-24732	299	45	,	,	PUNCT
brj-24732	299	46	and	and	CCONJ
brj-24732	299	47	wang	wang	PROPN
brj-24732	299	48	,	,	PUNCT
brj-24732	299	49	x.	x.	NOUN
brj-24732	299	50	(	(	PUNCT
brj-24732	299	51	2022	2022	NUM
brj-24732	299	52	)	)	PUNCT
brj-24732	299	53	.	.	PUNCT
brj-24732	300	1	“	"	PUNCT
brj-24732	300	2	yolo	yolo	NOUN
brj-24732	300	3	-	-	PUNCT
brj-24732	300	4	facev2	facev2	PROPN
brj-24732	300	5	:	:	PUNCT
brj-24732	300	6	a	a	DET
brj-24732	300	7	scale	scale	NOUN
brj-24732	300	8	and	and	CCONJ
brj-24732	300	9	occlusion	occlusion	NOUN
brj-24732	300	10	aware	aware	ADJ
brj-24732	300	11	face	face	NOUN
brj-24732	300	12	detector	detector	NOUN
brj-24732	300	13	,	,	PUNCT
brj-24732	300	14	”	"	PUNCT
brj-24732	300	15	arxiv	arxiv	PROPN
brj-24732	300	16	preprint	preprint	NOUN
brj-24732	300	17	,	,	PUNCT
brj-24732	300	18	article	article	NOUN
brj-24732	300	19	i	i	PROPN
brj-24732	300	20	d	d	PROPN
brj-24732	300	21	2208.02019v2	2208.02019v2	PROPN
brj-24732	300	22	.	.	PUNCT
brj-24732	300	23	doi	doi	NOUN
brj-24732	300	24	:	:	PUNCT
brj-24732	300	25	10.48550	10.48550	NUM
brj-24732	300	26	/	/	SYM
brj-24732	300	27	arxiv.2208.02019v2	arxiv.2208.02019v2	NUM
brj-24732	300	28	zou	zou	PROPN
brj-24732	300	29	,	,	PUNCT
brj-24732	300	30	x.	x.	PROPN
brj-24732	300	31	,	,	PUNCT
brj-24732	300	32	wu	wu	PROPN
brj-24732	300	33	,	,	PUNCT
brj-24732	300	34	c.	c.	PROPN
brj-24732	300	35	,	,	PUNCT
brj-24732	300	36	liu	liu	PROPN
brj-24732	300	37	,	,	PUNCT
brj-24732	300	38	h.	h.	PROPN
brj-24732	300	39	,	,	PUNCT
brj-24732	300	40	yu	yu	PROPN
brj-24732	300	41	,	,	PUNCT
brj-24732	300	42	z.	z.	PROPN
brj-24732	300	43	,	,	PUNCT
brj-24732	300	44	and	and	CCONJ
brj-24732	300	45	kuang	kuang	PROPN
brj-24732	300	46	,	,	PUNCT
brj-24732	300	47	x.	x.	NOUN
brj-24732	300	48	(	(	PUNCT
brj-24732	300	49	2025	2025	NUM
brj-24732	300	50	)	)	PUNCT
brj-24732	300	51	.	.	PUNCT
brj-24732	301	1	“	"	PUNCT
brj-24732	301	2	an	an	DET
brj-24732	301	3	accurate	accurate	ADJ
brj-24732	301	4	object	object	NOUN
brj-24732	301	5	detection	detection	NOUN
brj-24732	301	6	of	of	ADP
brj-24732	301	7	wood	wood	NOUN
brj-24732	301	8	defects	defect	NOUN
brj-24732	301	9	using	use	VERB
brj-24732	301	10	an	an	DET
brj-24732	301	11	improved	improve	VERB
brj-24732	301	12	faster	fast	ADV
brj-24732	301	13	r	r	NOUN
brj-24732	301	14	-	-	PUNCT
brj-24732	301	15	cnn	cnn	PROPN
brj-24732	301	16	model	model	NOUN
brj-24732	301	17	,	,	PUNCT
brj-24732	301	18	”	"	PUNCT
brj-24732	301	19	wood	wood	NOUN
brj-24732	301	20	material	material	NOUN
brj-24732	301	21	science	science	NOUN
brj-24732	301	22	and	and	CCONJ
brj-24732	301	23	engineering	engineering	NOUN
brj-24732	301	24	20(2	20(2	NUM
brj-24732	301	25	)	)	PUNCT
brj-24732	301	26	,	,	PUNCT
brj-24732	301	27	413	413	NUM
brj-24732	301	28	-	-	SYM
brj-24732	301	29	419	419	NUM
brj-24732	301	30	.	.	PUNCT
brj-24732	302	1	doi	doi	NOUN
brj-24732	302	2	:	:	PUNCT
brj-24732	302	3	10.1080/17480272.2024.2352605	10.1080/17480272.2024.2352605	NUM
brj-24732	302	4	article	article	NOUN
brj-24732	302	5	submitted	submit	VERB
brj-24732	302	6	:	:	PUNCT
brj-24732	302	7	april	april	PROPN
brj-24732	302	8	27	27	NUM
brj-24732	302	9	,	,	PUNCT
brj-24732	302	10	2025	2025	NUM
brj-24732	302	11	;	;	PUNCT
brj-24732	302	12	peer	peer	NOUN
brj-24732	302	13	review	review	NOUN
brj-24732	302	14	completed	complete	VERB
brj-24732	302	15	:	:	PUNCT
brj-24732	302	16	may	may	AUX
brj-24732	302	17	18	18	NUM
brj-24732	302	18	,	,	PUNCT
brj-24732	302	19	2025	2025	NUM
brj-24732	302	20	;	;	PUNCT
brj-24732	302	21	revised	revise	VERB
brj-24732	302	22	version	version	NOUN
brj-24732	302	23	received	receive	VERB
brj-24732	302	24	and	and	CCONJ
brj-24732	302	25	accepted	accept	VERB
brj-24732	302	26	:	:	PUNCT
brj-24732	302	27	july	july	PROPN
brj-24732	302	28	2	2	NUM
brj-24732	302	29	,	,	PUNCT
brj-24732	302	30	2025	2025	NUM
brj-24732	302	31	;	;	PUNCT
brj-24732	302	32	published	publish	VERB
brj-24732	302	33	:	:	PUNCT
brj-24732	302	34	july	july	PROPN
brj-24732	302	35	11	11	NUM
brj-24732	302	36	,	,	PUNCT
brj-24732	302	37	2025	2025	NUM
brj-24732	302	38	.	.	PUNCT
brj-24732	303	1	doi	doi	NOUN
brj-24732	303	2	:	:	PUNCT
brj-24732	303	3	10.15376	10.15376	NUM
brj-24732	303	4	/	/	SYM
brj-24732	303	5	biores.20.3.7179	biores.20.3.7179	NOUN
brj-24732	303	6	-	-	PUNCT
brj-24732	303	7	7193	7193	NUM
brj-24732	303	8	https://arxiv.org/abs/1911.09070v7	https://arxiv.org/abs/1911.09070v7	NOUN
brj-24732	303	9	https://arxiv.org/abs/2110.13389v2	https://arxiv.org/abs/2110.13389v2	X
brj-24732	303	10	https://www.zhizhen.com/s?sw=author%28wang%2c+rijun%3csup%3ea%2cb%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28wang%2c+rijun%3csup%3ea%2cb%3c%2fsup%3e%29	ADV
brj-24732	303	11	https://www.zhizhen.com/s?sw=author%28chen%2c+yesheng%3csup%3ea%2cb%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28chen%2c+yesheng%3csup%3ea%2cb%3c%2fsup%3e%29	NOUN
brj-24732	303	12	https://www.zhizhen.com/s?sw=author%28liang%2c+fulong%3csup%3ea%2cb%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28liang%2c+fulong%3csup%3ea%2cb%3c%2fsup%3e%29	NOUN
brj-24732	303	13	https://www.zhizhen.com/s?sw=author%28wang%2c+bo%3csup%3eb%2cc%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28wang%2c+bo%3csup%3eb%2cc%3c%2fsup%3e%29	PUNCT
brj-24732	303	14	https://www.zhizhen.com/s?sw=author%28mou%2c+xiangwei%3csup%3ea%2cb%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28mou%2c+xiangwei%3csup%3ea%2cb%3c%2fsup%3e%29	ADJ
brj-24732	303	15	https://www.zhizhen.com/s?sw=author%28zhang%2c+guanghao%3csup%3ea%2cb%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28zhang%2c+guanghao%3csup%3ea%2cb%3c%2fsup%3e%29	NOUN
brj-24732	303	16	https://arxiv.org/abs/2405.07992v3	https://arxiv.org/abs/2405.07992v3	NUM
brj-24732	303	17	https://arxiv.org/abs/2208.02019v2	https://arxiv.org/abs/2208.02019v2	ADP
brj-24732	303	18	https://www.zhizhen.com/s?sw=author%28+wu%2c+chongyang%3csup%3e1%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28+wu%2c+chongyang%3csup%3e1%3c%2fsup%3e%29	PROPN
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brj-24732	303	20	https://www.zhizhen.com/s?sw=author%28+yu%2c+zhangwei%3csup%3e2%3c%2fsup%3e%29	https://www.zhizhen.com/s?sw=author%28+yu%2c+zhangwei%3csup%3e2%3c%2fsup%3e%29	PROPN