id	sid	tid	token	lemma	pos
brj-23918	1	1	peer	peer	NOUN
brj-23918	1	2	-	-	PUNCT
brj-23918	1	3	review	review	NOUN
brj-23918	1	4	article	article	NOUN
brj-23918	1	5	peer	peer	NOUN
brj-23918	1	6	-	-	PUNCT
brj-23918	1	7	reviewed	review	VERB
brj-23918	1	8	article	article	NOUN
brj-23918	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	1	10	dong	dong	PROPN
brj-23918	1	11	et	et	PROPN
brj-23918	1	12	al	al	PROPN
brj-23918	1	13	.	.	PROPN
brj-23918	2	1	(	(	PUNCT
brj-23918	2	2	2025	2025	NUM
brj-23918	2	3	)	)	PUNCT
brj-23918	2	4	.	.	PUNCT
brj-23918	3	1	“	"	PUNCT
brj-23918	3	2	cracks	crack	NOUN
brj-23918	3	3	in	in	ADP
brj-23918	3	4	sawn	sawn	NOUN
brj-23918	3	5	wood	wood	NOUN
brj-23918	3	6	,	,	PUNCT
brj-23918	3	7	”	"	PUNCT
brj-23918	3	8	bioresources	bioresource	NOUN
brj-23918	3	9	20(2	20(2	NUM
brj-23918	3	10	)	)	PUNCT
brj-23918	3	11	,	,	PUNCT
brj-23918	3	12	3545	3545	NUM
brj-23918	3	13	-	-	SYM
brj-23918	3	14	3556	3556	NUM
brj-23918	3	15	.	.	PUNCT
brj-23918	4	1	3545	3545	NUM
brj-23918	4	2	iecau	iecau	NOUN
brj-23918	4	3	-	-	PUNCT
brj-23918	4	4	net	net	NOUN
brj-23918	4	5	:	:	PUNCT
brj-23918	4	6	a	a	DET
brj-23918	4	7	wood	wood	NOUN
brj-23918	4	8	defects	defect	VERB
brj-23918	4	9	image	image	NOUN
brj-23918	4	10	segmentation	segmentation	NOUN
brj-23918	4	11	network	network	NOUN
brj-23918	4	12	based	base	VERB
brj-23918	4	13	on	on	ADP
brj-23918	4	14	improved	improved	ADJ
brj-23918	4	15	attention	attention	NOUN
brj-23918	4	16	u	u	NOUN
brj-23918	4	17	-	-	NOUN
brj-23918	4	18	net	net	ADJ
brj-23918	4	19	and	and	CCONJ
brj-23918	4	20	attention	attention	NOUN
brj-23918	4	21	mechanism	mechanism	NOUN
brj-23918	4	22	yingda	yingda	NOUN
brj-23918	4	23	dong,1	dong,1	NOUN
brj-23918	4	24	chunguang	chunguang	PROPN
brj-23918	4	25	he,1	he,1	PROPN
brj-23918	4	26	xiaoyang	xiaoyang	PROPN
brj-23918	4	27	xiang	xiang	PROPN
brj-23918	4	28	,	,	PUNCT
brj-23918	4	29	yuhan	yuhan	PROPN
brj-23918	4	30	cui	cui	PROPN
brj-23918	4	31	,	,	PUNCT
brj-23918	4	32	yongkang	yongkang	PROPN
brj-23918	4	33	kang	kang	PROPN
brj-23918	4	34	,	,	PUNCT
brj-23918	4	35	anning	anne	VERB
brj-23918	4	36	ding	ding	NOUN
brj-23918	4	37	,	,	PUNCT
brj-23918	4	38	huaqiong	huaqiong	PROPN
brj-23918	4	39	duo	duo	PROPN
brj-23918	4	40	,	,	PUNCT
brj-23918	4	41	*	*	PUNCT
brj-23918	4	42	and	and	CCONJ
brj-23918	4	43	ximing	xime	VERB
brj-23918	4	44	wang	wang	PROPN
brj-23918	4	45	*	*	PUNCT
brj-23918	4	46	saw	see	VERB
brj-23918	4	47	wood	wood	NOUN
brj-23918	4	48	cracks	crack	NOUN
brj-23918	4	49	are	be	AUX
brj-23918	4	50	defects	defect	NOUN
brj-23918	4	51	that	that	PRON
brj-23918	4	52	affect	affect	VERB
brj-23918	4	53	the	the	DET
brj-23918	4	54	appearance	appearance	NOUN
brj-23918	4	55	and	and	CCONJ
brj-23918	4	56	mechanical	mechanical	ADJ
brj-23918	4	57	strength	strength	NOUN
brj-23918	4	58	of	of	ADP
brj-23918	4	59	sawn	sawn	ADJ
brj-23918	4	60	wood	wood	NOUN
brj-23918	4	61	.	.	PUNCT
brj-23918	5	1	crack	crack	VERB
brj-23918	5	2	defects	defect	NOUN
brj-23918	5	3	in	in	ADP
brj-23918	5	4	the	the	DET
brj-23918	5	5	surface	surface	NOUN
brj-23918	5	6	of	of	ADP
brj-23918	5	7	sawn	sawn	NOUN
brj-23918	5	8	wood	wood	NOUN
brj-23918	5	9	can	can	AUX
brj-23918	5	10	be	be	AUX
brj-23918	5	11	readily	readily	ADV
brj-23918	5	12	detected	detect	VERB
brj-23918	5	13	.	.	PUNCT
brj-23918	6	1	decisions	decision	NOUN
brj-23918	6	2	regarding	regard	VERB
brj-23918	6	3	the	the	DET
brj-23918	6	4	presence	presence	NOUN
brj-23918	6	5	and	and	CCONJ
brj-23918	6	6	severity	severity	NOUN
brj-23918	6	7	of	of	ADP
brj-23918	6	8	such	such	ADJ
brj-23918	6	9	defects	defect	NOUN
brj-23918	6	10	can	can	AUX
brj-23918	6	11	affect	affect	VERB
brj-23918	6	12	the	the	DET
brj-23918	6	13	utilization	utilization	NOUN
brj-23918	6	14	rate	rate	NOUN
brj-23918	6	15	of	of	ADP
brj-23918	6	16	sawn	sawn	NOUN
brj-23918	6	17	timber	timber	NOUN
brj-23918	6	18	.	.	PUNCT
brj-23918	7	1	due	due	ADP
brj-23918	7	2	to	to	ADP
brj-23918	7	3	the	the	DET
brj-23918	7	4	heavy	heavy	ADJ
brj-23918	7	5	workload	workload	NOUN
brj-23918	7	6	,	,	PUNCT
brj-23918	7	7	low	low	ADJ
brj-23918	7	8	efficiency	efficiency	NOUN
brj-23918	7	9	,	,	PUNCT
brj-23918	7	10	and	and	CCONJ
brj-23918	7	11	low	low	ADJ
brj-23918	7	12	accuracy	accuracy	NOUN
brj-23918	7	13	of	of	ADP
brj-23918	7	14	manual	manual	ADJ
brj-23918	7	15	inspection	inspection	NOUN
brj-23918	7	16	,	,	PUNCT
brj-23918	7	17	traditional	traditional	ADJ
brj-23918	7	18	machine	machine	NOUN
brj-23918	7	19	learning	learning	NOUN
brj-23918	7	20	methods	method	NOUN
brj-23918	7	21	have	have	VERB
brj-23918	7	22	strong	strong	ADJ
brj-23918	7	23	specialization	specialization	NOUN
brj-23918	7	24	,	,	PUNCT
brj-23918	7	25	complex	complex	ADJ
brj-23918	7	26	methods	method	NOUN
brj-23918	7	27	,	,	PUNCT
brj-23918	7	28	and	and	CCONJ
brj-23918	7	29	high	high	ADJ
brj-23918	7	30	costs	cost	NOUN
brj-23918	7	31	.	.	PUNCT
brj-23918	8	1	by	by	ADP
brj-23918	8	2	studying	study	VERB
brj-23918	8	3	the	the	DET
brj-23918	8	4	semantic	semantic	ADJ
brj-23918	8	5	segmentation	segmentation	NOUN
brj-23918	8	6	model	model	NOUN
brj-23918	8	7	of	of	ADP
brj-23918	8	8	surface	surface	NOUN
brj-23918	8	9	crack	crack	NOUN
brj-23918	8	10	defects	defect	NOUN
brj-23918	8	11	in	in	ADP
brj-23918	8	12	sawn	sawn	NOUN
brj-23918	8	13	timber	timber	NOUN
brj-23918	8	14	based	base	VERB
brj-23918	8	15	on	on	ADP
brj-23918	8	16	deep	deep	ADJ
brj-23918	8	17	learning	learning	NOUN
brj-23918	8	18	,	,	PUNCT
brj-23918	8	19	the	the	DET
brj-23918	8	20	optimal	optimal	ADJ
brj-23918	8	21	model	model	NOUN
brj-23918	8	22	for	for	ADP
brj-23918	8	23	segmentation	segmentation	NOUN
brj-23918	8	24	and	and	CCONJ
brj-23918	8	25	detection	detection	NOUN
brj-23918	8	26	of	of	ADP
brj-23918	8	27	surface	surface	NOUN
brj-23918	8	28	cracks	crack	NOUN
brj-23918	8	29	in	in	ADP
brj-23918	8	30	sawn	sawn	NOUN
brj-23918	8	31	timber	timber	NOUN
brj-23918	8	32	was	be	AUX
brj-23918	8	33	established	establish	VERB
brj-23918	8	34	.	.	PUNCT
brj-23918	9	1	the	the	DET
brj-23918	9	2	improved	improve	VERB
brj-23918	9	3	attention	attention	NOUN
brj-23918	9	4	u	u	ADJ
brj-23918	9	5	-	-	ADJ
brj-23918	9	6	net	net	ADJ
brj-23918	9	7	model	model	NOUN
brj-23918	9	8	encoding	encoding	NOUN
brj-23918	9	9	stage	stage	NOUN
brj-23918	9	10	was	be	AUX
brj-23918	9	11	introduced	introduce	VERB
brj-23918	9	12	into	into	ADP
brj-23918	9	13	cbam	cbam	NOUN
brj-23918	9	14	,	,	PUNCT
brj-23918	9	15	and	and	CCONJ
brj-23918	9	16	adamw	adamw	NOUN
brj-23918	9	17	optimization	optimization	NOUN
brj-23918	9	18	was	be	AUX
brj-23918	9	19	used	use	VERB
brj-23918	9	20	instead	instead	ADV
brj-23918	9	21	of	of	ADP
brj-23918	9	22	sgd	sgd	PROPN
brj-23918	9	23	and	and	CCONJ
brj-23918	9	24	adam	adam	PROPN
brj-23918	9	25	to	to	PART
brj-23918	9	26	achieve	achieve	VERB
brj-23918	9	27	better	well	ADJ
brj-23918	9	28	crack	crack	VERB
brj-23918	9	29	semantic	semantic	ADJ
brj-23918	9	30	segmentation	segmentation	NOUN
brj-23918	9	31	results	result	NOUN
brj-23918	9	32	.	.	PUNCT
brj-23918	10	1	the	the	DET
brj-23918	10	2	eca	eca	NOUN
brj-23918	10	3	module	module	NOUN
brj-23918	10	4	was	be	AUX
brj-23918	10	5	introduced	introduce	VERB
brj-23918	10	6	in	in	ADP
brj-23918	10	7	the	the	DET
brj-23918	10	8	skip	skip	ADJ
brj-23918	10	9	connection	connection	NOUN
brj-23918	10	10	part	part	NOUN
brj-23918	10	11	,	,	PUNCT
brj-23918	10	12	and	and	CCONJ
brj-23918	10	13	the	the	DET
brj-23918	10	14	weighted	weight	VERB
brj-23918	10	15	fusion	fusion	NOUN
brj-23918	10	16	multi	multi	ADJ
brj-23918	10	17	loss	loss	NOUN
brj-23918	10	18	function	function	NOUN
brj-23918	10	19	was	be	AUX
brj-23918	10	20	used	use	VERB
brj-23918	10	21	instead	instead	ADV
brj-23918	10	22	of	of	ADP
brj-23918	10	23	the	the	DET
brj-23918	10	24	original	original	ADJ
brj-23918	10	25	cross	cross	NOUN
brj-23918	10	26	entropy	entropy	PROPN
brj-23918	10	27	loss	loss	NOUN
brj-23918	10	28	function	function	NOUN
brj-23918	10	29	.	.	PUNCT
brj-23918	11	1	the	the	DET
brj-23918	11	2	positions	position	NOUN
brj-23918	11	3	of	of	ADP
brj-23918	11	4	the	the	DET
brj-23918	11	5	two	two	NUM
brj-23918	11	6	modules	module	NOUN
brj-23918	11	7	were	be	AUX
brj-23918	11	8	replaced	replace	VERB
brj-23918	11	9	to	to	PART
brj-23918	11	10	improve	improve	VERB
brj-23918	11	11	the	the	DET
brj-23918	11	12	accuracy	accuracy	NOUN
brj-23918	11	13	of	of	ADP
brj-23918	11	14	semantic	semantic	ADJ
brj-23918	11	15	segmentation	segmentation	NOUN
brj-23918	11	16	of	of	ADP
brj-23918	11	17	surface	surface	NOUN
brj-23918	11	18	cracks	crack	NOUN
brj-23918	11	19	in	in	ADP
brj-23918	11	20	sawn	sawn	NOUN
brj-23918	11	21	timber	timber	NOUN
brj-23918	11	22	.	.	PUNCT
brj-23918	12	1	through	through	ADP
brj-23918	12	2	comparative	comparative	ADJ
brj-23918	12	3	experiments	experiment	NOUN
brj-23918	12	4	,	,	PUNCT
brj-23918	12	5	the	the	DET
brj-23918	12	6	improved	improved	ADJ
brj-23918	12	7	model	model	NOUN
brj-23918	12	8	also	also	ADV
brj-23918	12	9	achieved	achieve	VERB
brj-23918	12	10	higher	high	ADJ
brj-23918	12	11	scores	score	NOUN
brj-23918	12	12	in	in	ADP
brj-23918	12	13	semantic	semantic	ADJ
brj-23918	12	14	segmentation	segmentation	NOUN
brj-23918	12	15	indicators	indicator	NOUN
brj-23918	12	16	for	for	ADP
brj-23918	12	17	surface	surface	NOUN
brj-23918	12	18	cracks	crack	NOUN
brj-23918	12	19	in	in	ADP
brj-23918	12	20	sawn	sawn	NOUN
brj-23918	12	21	timber	timber	NOUN
brj-23918	12	22	compared	compare	VERB
brj-23918	12	23	to	to	ADP
brj-23918	12	24	other	other	ADJ
brj-23918	12	25	models	model	NOUN
brj-23918	12	26	.	.	PUNCT
brj-23918	13	1	doi	doi	NOUN
brj-23918	13	2	:	:	PUNCT
brj-23918	13	3	10.15376	10.15376	NUM
brj-23918	13	4	/	/	SYM
brj-23918	13	5	biores.20.2.3545	biores.20.2.3545	PROPN
brj-23918	13	6	-	-	PUNCT
brj-23918	13	7	3556	3556	NUM
brj-23918	13	8	keywords	keyword	NOUN
brj-23918	13	9	:	:	PUNCT
brj-23918	13	10	surface	surface	NOUN
brj-23918	13	11	crack	crack	NOUN
brj-23918	13	12	detection	detection	NOUN
brj-23918	13	13	of	of	ADP
brj-23918	13	14	sawn	sawn	NOUN
brj-23918	13	15	timber	timber	NOUN
brj-23918	13	16	;	;	PUNCT
brj-23918	13	17	deep	deep	ADJ
brj-23918	13	18	learning	learning	NOUN
brj-23918	13	19	;	;	PUNCT
brj-23918	13	20	semantic	semantic	ADJ
brj-23918	13	21	segmentation	segmentation	NOUN
brj-23918	13	22	;	;	PUNCT
brj-23918	13	23	u	u	NOUN
brj-23918	13	24	-	-	ADJ
brj-23918	13	25	net	net	ADJ
brj-23918	13	26	contact	contact	NOUN
brj-23918	13	27	information	information	NOUN
brj-23918	13	28	:	:	PUNCT
brj-23918	13	29	college	college	NOUN
brj-23918	13	30	of	of	ADP
brj-23918	13	31	material	material	NOUN
brj-23918	13	32	science	science	NOUN
brj-23918	13	33	and	and	CCONJ
brj-23918	13	34	art	art	NOUN
brj-23918	13	35	design	design	NOUN
brj-23918	13	36	,	,	PUNCT
brj-23918	13	37	inner	inner	PROPN
brj-23918	13	38	mongolia	mongolia	PROPN
brj-23918	13	39	key	key	PROPN
brj-23918	13	40	laboratory	laboratory	NOUN
brj-23918	13	41	of	of	ADP
brj-23918	13	42	sandy	sandy	ADJ
brj-23918	13	43	shrubs	shrub	NOUN
brj-23918	13	44	fibrosis	fibrosis	NOUN
brj-23918	13	45	and	and	CCONJ
brj-23918	13	46	energy	energy	NOUN
brj-23918	13	47	development	development	NOUN
brj-23918	13	48	and	and	CCONJ
brj-23918	13	49	utilization	utilization	NOUN
brj-23918	13	50	,	,	PUNCT
brj-23918	13	51	inner	inner	PROPN
brj-23918	13	52	mongolia	mongolia	PROPN
brj-23918	13	53	agricultural	agricultural	PROPN
brj-23918	13	54	university	university	PROPN
brj-23918	13	55	,	,	PUNCT
brj-23918	13	56	hohhot	hohhot	ADJ
brj-23918	13	57	,	,	PUNCT
brj-23918	13	58	china	china	PROPN
brj-23918	13	59	;	;	PUNCT
brj-23918	13	60	1	1	X
brj-23918	13	61	:	:	PUNCT
brj-23918	13	62	these	these	DET
brj-23918	13	63	authors	author	NOUN
brj-23918	13	64	contributed	contribute	VERB
brj-23918	13	65	equally	equally	ADV
brj-23918	13	66	to	to	ADP
brj-23918	13	67	this	this	DET
brj-23918	13	68	work	work	NOUN
brj-23918	13	69	.	.	PUNCT
brj-23918	14	1	*	*	PUNCT
brj-23918	14	2	correspondence	correspondence	NOUN
brj-23918	14	3	authors	author	NOUN
brj-23918	14	4	:	:	PUNCT
brj-23918	14	5	duohuaqiong@163.com	duohuaqiong@163.com	NOUN
brj-23918	14	6	;	;	PUNCT
brj-23918	14	7	wangximing@imau.edu.cn	wangximing@imau.edu.cn	NOUN
brj-23918	14	8	introduction	introduction	NOUN
brj-23918	14	9	in	in	ADP
brj-23918	14	10	the	the	DET
brj-23918	14	11	face	face	NOUN
brj-23918	14	12	of	of	ADP
brj-23918	14	13	the	the	DET
brj-23918	14	14	imbalance	imbalance	NOUN
brj-23918	14	15	between	between	ADP
brj-23918	14	16	supply	supply	NOUN
brj-23918	14	17	and	and	CCONJ
brj-23918	14	18	demand	demand	NOUN
brj-23918	14	19	,	,	PUNCT
brj-23918	14	20	where	where	SCONJ
brj-23918	14	21	there	there	PRON
brj-23918	14	22	is	be	VERB
brj-23918	14	23	a	a	DET
brj-23918	14	24	substantial	substantial	ADJ
brj-23918	14	25	demand	demand	NOUN
brj-23918	14	26	for	for	ADP
brj-23918	14	27	sawn	sawn	NOUN
brj-23918	14	28	timber	timber	NOUN
brj-23918	14	29	yet	yet	CCONJ
brj-23918	14	30	a	a	DET
brj-23918	14	31	shortage	shortage	NOUN
brj-23918	14	32	of	of	ADP
brj-23918	14	33	sawn	sawn	NOUN
brj-23918	14	34	timber	timber	NOUN
brj-23918	14	35	resources	resource	NOUN
brj-23918	14	36	,	,	PUNCT
brj-23918	14	37	it	it	PRON
brj-23918	14	38	is	be	AUX
brj-23918	14	39	necessary	necessary	ADJ
brj-23918	14	40	to	to	PART
brj-23918	14	41	classify	classify	VERB
brj-23918	14	42	and	and	CCONJ
brj-23918	14	43	use	use	VERB
brj-23918	14	44	sawn	sawn	NOUN
brj-23918	14	45	timber	timber	NOUN
brj-23918	14	46	in	in	ADP
brj-23918	14	47	order	order	NOUN
brj-23918	14	48	to	to	PART
brj-23918	14	49	fully	fully	ADV
brj-23918	14	50	and	and	CCONJ
brj-23918	14	51	efficiently	efficiently	ADV
brj-23918	14	52	utilize	utilize	VERB
brj-23918	14	53	the	the	DET
brj-23918	14	54	limited	limited	ADJ
brj-23918	14	55	sawn	sawn	NOUN
brj-23918	14	56	timber	timber	NOUN
brj-23918	14	57	.	.	PUNCT
brj-23918	15	1	the	the	DET
brj-23918	15	2	surface	surface	NOUN
brj-23918	15	3	of	of	ADP
brj-23918	15	4	sawn	sawn	NOUN
brj-23918	15	5	timber	timber	NOUN
brj-23918	15	6	may	may	AUX
brj-23918	15	7	have	have	VERB
brj-23918	15	8	defects	defect	NOUN
brj-23918	15	9	such	such	ADJ
brj-23918	15	10	as	as	ADP
brj-23918	15	11	knots	knot	NOUN
brj-23918	15	12	,	,	PUNCT
brj-23918	15	13	decay	decay	NOUN
brj-23918	15	14	,	,	PUNCT
brj-23918	15	15	and	and	CCONJ
brj-23918	15	16	cracks	crack	NOUN
brj-23918	15	17	,	,	PUNCT
brj-23918	15	18	which	which	PRON
brj-23918	15	19	are	be	AUX
brj-23918	15	20	also	also	ADV
brj-23918	15	21	the	the	DET
brj-23918	15	22	key	key	NOUN
brj-23918	15	23	to	to	ADP
brj-23918	15	24	grading	grade	VERB
brj-23918	15	25	sawn	sawn	NOUN
brj-23918	15	26	timber	timber	NOUN
brj-23918	15	27	.	.	PUNCT
brj-23918	16	1	among	among	ADP
brj-23918	16	2	these	these	DET
brj-23918	16	3	defects	defect	NOUN
brj-23918	16	4	,	,	PUNCT
brj-23918	16	5	surface	surface	NOUN
brj-23918	16	6	cracks	crack	NOUN
brj-23918	16	7	are	be	AUX
brj-23918	16	8	serious	serious	ADJ
brj-23918	16	9	imperfections	imperfection	NOUN
brj-23918	16	10	.	.	PUNCT
brj-23918	17	1	they	they	PRON
brj-23918	17	2	impact	impact	VERB
brj-23918	17	3	the	the	DET
brj-23918	17	4	appearance	appearance	NOUN
brj-23918	17	5	quality	quality	NOUN
brj-23918	17	6	and	and	CCONJ
brj-23918	17	7	mechanical	mechanical	ADJ
brj-23918	17	8	strength	strength	NOUN
brj-23918	17	9	of	of	ADP
brj-23918	17	10	sawn	sawn	NOUN
brj-23918	17	11	timber	timber	NOUN
brj-23918	17	12	and	and	CCONJ
brj-23918	17	13	are	be	AUX
brj-23918	17	14	also	also	ADV
brj-23918	17	15	a	a	DET
brj-23918	17	16	major	major	ADJ
brj-23918	17	17	criterion	criterion	NOUN
brj-23918	17	18	for	for	ADP
brj-23918	17	19	grading	grade	VERB
brj-23918	17	20	.	.	PUNCT
brj-23918	18	1	the	the	DET
brj-23918	18	2	accuracy	accuracy	NOUN
brj-23918	18	3	of	of	ADP
brj-23918	18	4	surface	surface	NOUN
brj-23918	18	5	crack	crack	NOUN
brj-23918	18	6	detection	detection	NOUN
brj-23918	18	7	in	in	ADP
brj-23918	18	8	sawn	sawn	NOUN
brj-23918	18	9	timber	timber	NOUN
brj-23918	18	10	poses	pose	VERB
brj-23918	18	11	a	a	DET
brj-23918	18	12	challenge	challenge	NOUN
brj-23918	18	13	due	due	ADP
brj-23918	18	14	to	to	ADP
brj-23918	18	15	its	its	PRON
brj-23918	18	16	susceptibility	susceptibility	NOUN
brj-23918	18	17	to	to	PART
brj-23918	18	18	interference	interference	VERB
brj-23918	18	19	from	from	ADP
brj-23918	18	20	texture	texture	ADJ
brj-23918	18	21	and	and	CCONJ
brj-23918	18	22	other	other	ADJ
brj-23918	18	23	defects	defect	NOUN
brj-23918	18	24	.	.	PUNCT
brj-23918	19	1	by	by	ADP
brj-23918	19	2	accurately	accurately	ADV
brj-23918	19	3	detecting	detect	VERB
brj-23918	19	4	surface	surface	NOUN
brj-23918	19	5	cracks	crack	NOUN
brj-23918	19	6	in	in	ADP
brj-23918	19	7	sawn	sawn	NOUN
brj-23918	19	8	timber	timber	NOUN
brj-23918	19	9	,	,	PUNCT
brj-23918	19	10	there	there	PRON
brj-23918	19	11	is	be	VERB
brj-23918	19	12	potential	potential	ADJ
brj-23918	19	13	to	to	PART
brj-23918	19	14	better	well	ADV
brj-23918	19	15	understand	understand	VERB
brj-23918	19	16	the	the	DET
brj-23918	19	17	properties	property	NOUN
brj-23918	19	18	of	of	ADP
brj-23918	19	19	sawn	sawn	NOUN
brj-23918	19	20	timber	timber	NOUN
brj-23918	19	21	and	and	CCONJ
brj-23918	19	22	make	make	VERB
brj-23918	19	23	rational	rational	ADJ
brj-23918	19	24	use	use	NOUN
brj-23918	19	25	of	of	ADP
brj-23918	19	26	it	it	PRON
brj-23918	19	27	.	.	PUNCT
brj-23918	20	1	at	at	ADP
brj-23918	20	2	the	the	DET
brj-23918	20	3	same	same	ADJ
brj-23918	20	4	time	time	NOUN
brj-23918	20	5	,	,	PUNCT
brj-23918	20	6	it	it	PRON
brj-23918	20	7	provides	provide	VERB
brj-23918	20	8	a	a	DET
brj-23918	20	9	basis	basis	NOUN
brj-23918	20	10	for	for	ADP
brj-23918	20	11	subsequent	subsequent	ADJ
brj-23918	20	12	processing	processing	NOUN
brj-23918	20	13	to	to	PART
brj-23918	20	14	ensure	ensure	VERB
brj-23918	20	15	the	the	DET
brj-23918	20	16	quality	quality	NOUN
brj-23918	20	17	and	and	CCONJ
brj-23918	20	18	mailto:wangximing@imau.edu.cn	mailto:wangximing@imau.edu.cn	NOUN
brj-23918	20	19	peer	peer	NOUN
brj-23918	20	20	-	-	PUNCT
brj-23918	20	21	reviewed	review	VERB
brj-23918	20	22	article	article	NOUN
brj-23918	20	23	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	21	1	dong	dong	PROPN
brj-23918	21	2	et	et	PROPN
brj-23918	21	3	al	al	PROPN
brj-23918	21	4	.	.	PROPN
brj-23918	21	5	(	(	PUNCT
brj-23918	21	6	2025	2025	NUM
brj-23918	21	7	)	)	PUNCT
brj-23918	21	8	.	.	PUNCT
brj-23918	22	1	“	"	PUNCT
brj-23918	22	2	cracks	crack	NOUN
brj-23918	22	3	in	in	ADP
brj-23918	22	4	sawn	sawn	NOUN
brj-23918	22	5	wood	wood	NOUN
brj-23918	22	6	,	,	PUNCT
brj-23918	22	7	”	"	PUNCT
brj-23918	22	8	bioresources	bioresource	NOUN
brj-23918	22	9	20(2	20(2	NUM
brj-23918	22	10	)	)	PUNCT
brj-23918	22	11	,	,	PUNCT
brj-23918	22	12	3545	3545	NUM
brj-23918	22	13	-	-	SYM
brj-23918	22	14	3556	3556	NUM
brj-23918	22	15	.	.	PUNCT
brj-23918	23	1	3546	3546	NUM
brj-23918	23	2	appearance	appearance	NOUN
brj-23918	23	3	of	of	ADP
brj-23918	23	4	sawn	sawn	NOUN
brj-23918	23	5	timber	timber	NOUN
brj-23918	23	6	.	.	PUNCT
brj-23918	24	1	according	accord	VERB
brj-23918	24	2	to	to	ADP
brj-23918	24	3	the	the	DET
brj-23918	24	4	current	current	ADJ
brj-23918	24	5	research	research	NOUN
brj-23918	24	6	status	status	NOUN
brj-23918	24	7	of	of	ADP
brj-23918	24	8	defects	defect	NOUN
brj-23918	24	9	such	such	ADJ
brj-23918	24	10	as	as	ADP
brj-23918	24	11	wood	wood	NOUN
brj-23918	24	12	cracks	crack	NOUN
brj-23918	24	13	,	,	PUNCT
brj-23918	24	14	there	there	PRON
brj-23918	24	15	are	be	VERB
brj-23918	24	16	still	still	ADV
brj-23918	24	17	problems	problem	NOUN
brj-23918	24	18	in	in	ADP
brj-23918	24	19	the	the	DET
brj-23918	24	20	detection	detection	NOUN
brj-23918	24	21	of	of	ADP
brj-23918	24	22	surface	surface	NOUN
brj-23918	24	23	cracks	crack	NOUN
brj-23918	24	24	in	in	ADP
brj-23918	24	25	wood	wood	NOUN
brj-23918	24	26	.	.	PUNCT
brj-23918	25	1	in	in	ADP
brj-23918	25	2	traditional	traditional	ADJ
brj-23918	25	3	wood	wood	NOUN
brj-23918	25	4	crack	crack	NOUN
brj-23918	25	5	defect	defect	NOUN
brj-23918	25	6	detection	detection	NOUN
brj-23918	25	7	algorithms	algorithm	NOUN
brj-23918	25	8	,	,	PUNCT
brj-23918	25	9	an	an	DET
brj-23918	25	10	excessive	excessive	ADJ
brj-23918	25	11	dependence	dependence	NOUN
brj-23918	25	12	on	on	ADP
brj-23918	25	13	equipment	equipment	NOUN
brj-23918	25	14	detection	detection	NOUN
brj-23918	25	15	accuracy	accuracy	NOUN
brj-23918	25	16	gives	give	VERB
brj-23918	25	17	rise	rise	NOUN
brj-23918	25	18	to	to	ADP
brj-23918	25	19	high	high	ADJ
brj-23918	25	20	complexity	complexity	NOUN
brj-23918	25	21	and	and	CCONJ
brj-23918	25	22	low	low	ADJ
brj-23918	25	23	efficiency	efficiency	NOUN
brj-23918	25	24	in	in	ADP
brj-23918	25	25	processing	process	VERB
brj-23918	25	26	wood	wood	NOUN
brj-23918	25	27	crack	crack	NOUN
brj-23918	25	28	defect	defect	NOUN
brj-23918	25	29	data	datum	NOUN
brj-23918	25	30	.	.	PUNCT
brj-23918	26	1	although	although	SCONJ
brj-23918	26	2	the	the	DET
brj-23918	26	3	focus	focus	NOUN
brj-23918	26	4	of	of	ADP
brj-23918	26	5	subsequent	subsequent	ADJ
brj-23918	26	6	defect	defect	NOUN
brj-23918	26	7	detection	detection	NOUN
brj-23918	26	8	is	be	AUX
brj-23918	26	9	shifting	shift	VERB
brj-23918	26	10	towards	towards	ADP
brj-23918	26	11	machine	machine	NOUN
brj-23918	26	12	learning	learning	NOUN
brj-23918	26	13	,	,	PUNCT
brj-23918	26	14	the	the	DET
brj-23918	26	15	task	task	NOUN
brj-23918	26	16	of	of	ADP
brj-23918	26	17	wood	wood	NOUN
brj-23918	26	18	defect	defect	NOUN
brj-23918	26	19	detection	detection	NOUN
brj-23918	26	20	is	be	AUX
brj-23918	26	21	still	still	ADV
brj-23918	26	22	complex	complex	ADJ
brj-23918	26	23	,	,	PUNCT
brj-23918	26	24	requiring	require	VERB
brj-23918	26	25	wood	wood	NOUN
brj-23918	26	26	crack	crack	NOUN
brj-23918	26	27	image	image	NOUN
brj-23918	26	28	segmentation	segmentation	NOUN
brj-23918	26	29	,	,	PUNCT
brj-23918	26	30	feature	feature	NOUN
brj-23918	26	31	extraction	extraction	NOUN
brj-23918	26	32	,	,	PUNCT
brj-23918	26	33	classifier	classifier	NOUN
brj-23918	26	34	classification	classification	NOUN
brj-23918	26	35	and	and	CCONJ
brj-23918	26	36	recognition	recognition	NOUN
brj-23918	26	37	,	,	PUNCT
brj-23918	26	38	as	as	ADV
brj-23918	26	39	well	well	ADV
brj-23918	26	40	as	as	ADP
brj-23918	26	41	a	a	DET
brj-23918	26	42	significant	significant	ADJ
brj-23918	26	43	investment	investment	NOUN
brj-23918	26	44	in	in	ADP
brj-23918	26	45	labor	labor	NOUN
brj-23918	26	46	costs	cost	NOUN
brj-23918	26	47	.	.	PUNCT
brj-23918	27	1	in	in	ADP
brj-23918	27	2	the	the	DET
brj-23918	27	3	application	application	NOUN
brj-23918	27	4	of	of	ADP
brj-23918	27	5	deep	deep	ADJ
brj-23918	27	6	learning	learning	NOUN
brj-23918	27	7	algorithms	algorithm	NOUN
brj-23918	27	8	for	for	ADP
brj-23918	27	9	detecting	detect	VERB
brj-23918	27	10	wood	wood	NOUN
brj-23918	27	11	cracks	crack	NOUN
brj-23918	27	12	and	and	CCONJ
brj-23918	27	13	other	other	ADJ
brj-23918	27	14	defects	defect	NOUN
brj-23918	27	15	,	,	PUNCT
brj-23918	27	16	in	in	ADP
brj-23918	27	17	contrast	contrast	NOUN
brj-23918	27	18	to	to	ADP
brj-23918	27	19	traditional	traditional	ADJ
brj-23918	27	20	machine	machine	NOUN
brj-23918	27	21	learning	learning	NOUN
brj-23918	27	22	based	base	VERB
brj-23918	27	23	detection	detection	NOUN
brj-23918	27	24	methods	method	NOUN
brj-23918	27	25	,	,	PUNCT
brj-23918	27	26	the	the	DET
brj-23918	27	27	steps	step	NOUN
brj-23918	27	28	of	of	ADP
brj-23918	27	29	wood	wood	NOUN
brj-23918	27	30	crack	crack	NOUN
brj-23918	27	31	segmentation	segmentation	NOUN
brj-23918	27	32	and	and	CCONJ
brj-23918	27	33	feature	feature	NOUN
brj-23918	27	34	extraction	extraction	NOUN
brj-23918	27	35	are	be	AUX
brj-23918	27	36	reduced	reduce	VERB
brj-23918	27	37	.	.	PUNCT
brj-23918	28	1	once	once	SCONJ
brj-23918	28	2	the	the	DET
brj-23918	28	3	dataset	dataset	NOUN
brj-23918	28	4	is	be	AUX
brj-23918	28	5	established	establish	VERB
brj-23918	28	6	,	,	PUNCT
brj-23918	28	7	it	it	PRON
brj-23918	28	8	can	can	AUX
brj-23918	28	9	be	be	AUX
brj-23918	28	10	input	input	VERB
brj-23918	28	11	into	into	ADP
brj-23918	28	12	the	the	DET
brj-23918	28	13	network	network	NOUN
brj-23918	28	14	for	for	ADP
brj-23918	28	15	training	training	NOUN
brj-23918	28	16	,	,	PUNCT
brj-23918	28	17	and	and	CCONJ
brj-23918	28	18	the	the	DET
brj-23918	28	19	deep	deep	ADJ
brj-23918	28	20	neural	neural	ADJ
brj-23918	28	21	network	network	NOUN
brj-23918	28	22	automatically	automatically	ADV
brj-23918	28	23	completes	complete	VERB
brj-23918	28	24	the	the	DET
brj-23918	28	25	feature	feature	NOUN
brj-23918	28	26	extraction	extraction	NOUN
brj-23918	28	27	and	and	CCONJ
brj-23918	28	28	other	other	ADJ
brj-23918	28	29	tasks	task	NOUN
brj-23918	28	30	,	,	PUNCT
brj-23918	28	31	ultimately	ultimately	ADV
brj-23918	28	32	generating	generate	VERB
brj-23918	28	33	the	the	DET
brj-23918	28	34	recognition	recognition	NOUN
brj-23918	28	35	and	and	CCONJ
brj-23918	28	36	segmentation	segmentation	NOUN
brj-23918	28	37	of	of	ADP
brj-23918	28	38	wood	wood	NOUN
brj-23918	28	39	defect	defect	NOUN
brj-23918	28	40	images	image	NOUN
brj-23918	28	41	.	.	PUNCT
brj-23918	29	1	however	however	ADV
brj-23918	29	2	,	,	PUNCT
brj-23918	29	3	while	while	SCONJ
brj-23918	29	4	object	object	NOUN
brj-23918	29	5	detection	detection	NOUN
brj-23918	29	6	algorithms	algorithm	NOUN
brj-23918	29	7	are	be	AUX
brj-23918	29	8	more	more	ADV
brj-23918	29	9	prevalently	prevalently	ADV
brj-23918	29	10	employed	employ	VERB
brj-23918	29	11	in	in	ADP
brj-23918	29	12	detecting	detect	VERB
brj-23918	29	13	defects	defect	NOUN
brj-23918	29	14	such	such	ADJ
brj-23918	29	15	as	as	ADP
brj-23918	29	16	wood	wood	NOUN
brj-23918	29	17	cracks	crack	NOUN
brj-23918	29	18	,	,	PUNCT
brj-23918	29	19	semantic	semantic	ADJ
brj-23918	29	20	segmentation	segmentation	NOUN
brj-23918	29	21	algorithms	algorithm	NOUN
brj-23918	29	22	for	for	ADP
brj-23918	29	23	such	such	ADJ
brj-23918	29	24	defects	defect	NOUN
brj-23918	29	25	are	be	AUX
brj-23918	29	26	relatively	relatively	ADV
brj-23918	29	27	scarce	scarce	ADJ
brj-23918	29	28	.	.	PUNCT
brj-23918	30	1	even	even	ADV
brj-23918	30	2	with	with	ADP
brj-23918	30	3	the	the	DET
brj-23918	30	4	use	use	NOUN
brj-23918	30	5	of	of	ADP
brj-23918	30	6	semantic	semantic	ADJ
brj-23918	30	7	segmentation	segmentation	NOUN
brj-23918	30	8	algorithms	algorithm	NOUN
brj-23918	30	9	,	,	PUNCT
brj-23918	30	10	there	there	PRON
brj-23918	30	11	is	be	VERB
brj-23918	30	12	still	still	ADV
brj-23918	30	13	some	some	DET
brj-23918	30	14	distance	distance	NOUN
brj-23918	30	15	to	to	PART
brj-23918	30	16	go	go	VERB
brj-23918	30	17	in	in	ADP
brj-23918	30	18	the	the	DET
brj-23918	30	19	subsequent	subsequent	ADJ
brj-23918	30	20	classification	classification	NOUN
brj-23918	30	21	of	of	ADP
brj-23918	30	22	wood	wood	NOUN
brj-23918	30	23	.	.	PUNCT
brj-23918	31	1	cracks	crack	NOUN
brj-23918	31	2	can	can	AUX
brj-23918	31	3	not	not	PART
brj-23918	31	4	be	be	AUX
brj-23918	31	5	separated	separate	VERB
brj-23918	31	6	due	due	ADJ
brj-23918	31	7	to	to	ADP
brj-23918	31	8	similarity	similarity	NOUN
brj-23918	31	9	in	in	ADP
brj-23918	31	10	background	background	NOUN
brj-23918	31	11	color	color	NOUN
brj-23918	31	12	,	,	PUNCT
brj-23918	31	13	similarity	similarity	NOUN
brj-23918	31	14	in	in	ADP
brj-23918	31	15	wood	wood	NOUN
brj-23918	31	16	texture	texture	NOUN
brj-23918	31	17	to	to	ADP
brj-23918	31	18	cracks	crack	NOUN
brj-23918	31	19	,	,	PUNCT
brj-23918	31	20	and	and	CCONJ
brj-23918	31	21	segmentation	segmentation	NOUN
brj-23918	31	22	errors	error	NOUN
brj-23918	31	23	in	in	ADP
brj-23918	31	24	both	both	DET
brj-23918	31	25	cracks	crack	NOUN
brj-23918	31	26	and	and	CCONJ
brj-23918	31	27	some	some	DET
brj-23918	31	28	joints	joint	NOUN
brj-23918	31	29	.	.	PUNCT
brj-23918	32	1	this	this	DET
brj-23918	32	2	article	article	NOUN
brj-23918	32	3	addresses	address	VERB
brj-23918	32	4	the	the	DET
brj-23918	32	5	issues	issue	NOUN
brj-23918	32	6	raised	raise	VERB
brj-23918	32	7	above	above	ADV
brj-23918	32	8	by	by	ADP
brj-23918	32	9	using	use	VERB
brj-23918	32	10	a	a	DET
brj-23918	32	11	deep	deep	ADJ
brj-23918	32	12	learning	learning	NOUN
brj-23918	32	13	model	model	NOUN
brj-23918	32	14	based	base	VERB
brj-23918	32	15	on	on	ADP
brj-23918	32	16	an	an	DET
brj-23918	32	17	improved	improved	ADJ
brj-23918	32	18	u	u	NOUN
brj-23918	32	19	-	-	NOUN
brj-23918	32	20	net	net	ADJ
brj-23918	32	21	to	to	PART
brj-23918	32	22	perform	perform	VERB
brj-23918	32	23	semantic	semantic	ADJ
brj-23918	32	24	segmentation	segmentation	NOUN
brj-23918	32	25	of	of	ADP
brj-23918	32	26	surface	surface	NOUN
brj-23918	32	27	cracks	crack	NOUN
brj-23918	32	28	in	in	ADP
brj-23918	32	29	sawn	sawn	NOUN
brj-23918	32	30	timber	timber	NOUN
brj-23918	32	31	.	.	PUNCT
brj-23918	33	1	then	then	ADV
brj-23918	33	2	,	,	PUNCT
brj-23918	33	3	a	a	DET
brj-23918	33	4	classification	classification	NOUN
brj-23918	33	5	system	system	NOUN
brj-23918	33	6	for	for	ADP
brj-23918	33	7	detecting	detect	VERB
brj-23918	33	8	and	and	CCONJ
brj-23918	33	9	grading	grade	VERB
brj-23918	33	10	surface	surface	NOUN
brj-23918	33	11	cracks	crack	NOUN
brj-23918	33	12	in	in	ADP
brj-23918	33	13	sawn	sawn	NOUN
brj-23918	33	14	timber	timber	NOUN
brj-23918	33	15	is	be	AUX
brj-23918	33	16	established	establish	VERB
brj-23918	33	17	to	to	PART
brj-23918	33	18	achieve	achieve	VERB
brj-23918	33	19	the	the	DET
brj-23918	33	20	classification	classification	NOUN
brj-23918	33	21	of	of	ADP
brj-23918	33	22	surface	surface	NOUN
brj-23918	33	23	cracks	crack	NOUN
brj-23918	33	24	in	in	ADP
brj-23918	33	25	sawn	sawn	NOUN
brj-23918	33	26	timber	timber	NOUN
brj-23918	33	27	.	.	PUNCT
brj-23918	34	1	for	for	ADP
brj-23918	34	2	semantic	semantic	ADJ
brj-23918	34	3	segmentation	segmentation	NOUN
brj-23918	34	4	of	of	ADP
brj-23918	34	5	surface	surface	NOUN
brj-23918	34	6	crack	crack	NOUN
brj-23918	34	7	images	image	NOUN
brj-23918	34	8	in	in	ADP
brj-23918	34	9	sawn	sawn	NOUN
brj-23918	34	10	timber	timber	NOUN
brj-23918	34	11	,	,	PUNCT
brj-23918	34	12	the	the	DET
brj-23918	34	13	u	u	NOUN
brj-23918	34	14	-	-	ADJ
brj-23918	34	15	net	net	ADJ
brj-23918	34	16	deep	deep	ADJ
brj-23918	34	17	learning	learning	NOUN
brj-23918	34	18	model	model	NOUN
brj-23918	34	19	is	be	AUX
brj-23918	34	20	mainly	mainly	ADV
brj-23918	34	21	used	use	VERB
brj-23918	34	22	.	.	PUNCT
brj-23918	35	1	based	base	VERB
brj-23918	35	2	on	on	ADP
brj-23918	35	3	this	this	PRON
brj-23918	35	4	,	,	PUNCT
brj-23918	35	5	the	the	DET
brj-23918	35	6	network	network	NOUN
brj-23918	35	7	framework	framework	NOUN
brj-23918	35	8	is	be	AUX
brj-23918	35	9	improved	improve	VERB
brj-23918	35	10	,	,	PUNCT
brj-23918	35	11	and	and	CCONJ
brj-23918	35	12	the	the	DET
brj-23918	35	13	main	main	ADJ
brj-23918	35	14	research	research	NOUN
brj-23918	35	15	focuses	focus	VERB
brj-23918	35	16	on	on	ADP
brj-23918	35	17	adding	add	VERB
brj-23918	35	18	convolutional	convolutional	ADJ
brj-23918	35	19	block	block	NOUN
brj-23918	35	20	attention	attention	NOUN
brj-23918	35	21	module	module	NOUN
brj-23918	35	22	(	(	PUNCT
brj-23918	35	23	cbam	cbam	NOUN
brj-23918	35	24	)	)	PUNCT
brj-23918	35	25	,	,	PUNCT
brj-23918	35	26	attention	attention	NOUN
brj-23918	35	27	gate	gate	NOUN
brj-23918	35	28	(	(	PUNCT
brj-23918	35	29	ag	ag	PROPN
brj-23918	35	30	)	)	PUNCT
brj-23918	35	31	,	,	PUNCT
brj-23918	35	32	and	and	CCONJ
brj-23918	35	33	efficient	efficient	ADJ
brj-23918	35	34	channel	channel	NOUN
brj-23918	35	35	attention	attention	NOUN
brj-23918	35	36	(	(	PUNCT
brj-23918	35	37	eca	eca	NOUN
brj-23918	35	38	)	)	PUNCT
brj-23918	35	39	modules	module	NOUN
brj-23918	35	40	to	to	PART
brj-23918	35	41	exchange	exchange	VERB
brj-23918	35	42	the	the	DET
brj-23918	35	43	positions	position	NOUN
brj-23918	35	44	of	of	ADP
brj-23918	35	45	each	each	DET
brj-23918	35	46	module	module	NOUN
brj-23918	35	47	,	,	PUNCT
brj-23918	35	48	optimize	optimize	VERB
brj-23918	35	49	the	the	DET
brj-23918	35	50	loss	loss	NOUN
brj-23918	35	51	function	function	NOUN
brj-23918	35	52	,	,	PUNCT
brj-23918	35	53	and	and	CCONJ
brj-23918	35	54	adjust	adjust	VERB
brj-23918	35	55	the	the	DET
brj-23918	35	56	parameters	parameter	NOUN
brj-23918	35	57	of	of	ADP
brj-23918	35	58	the	the	DET
brj-23918	35	59	network	network	NOUN
brj-23918	35	60	model	model	NOUN
brj-23918	35	61	to	to	PART
brj-23918	35	62	obtain	obtain	VERB
brj-23918	35	63	the	the	DET
brj-23918	35	64	best	good	ADJ
brj-23918	35	65	sawn	sawn	NOUN
brj-23918	35	66	timber	timber	NOUN
brj-23918	35	67	surface	surface	NOUN
brj-23918	35	68	crack	crack	VERB
brj-23918	35	69	semantic	semantic	ADJ
brj-23918	35	70	segmentation	segmentation	NOUN
brj-23918	35	71	model	model	NOUN
brj-23918	35	72	.	.	PUNCT
brj-23918	36	1	the	the	DET
brj-23918	36	2	goal	goal	NOUN
brj-23918	36	3	is	be	AUX
brj-23918	36	4	to	to	PART
brj-23918	36	5	apply	apply	VERB
brj-23918	36	6	the	the	DET
brj-23918	36	7	improved	improved	ADJ
brj-23918	36	8	u	u	ADJ
brj-23918	36	9	-	-	ADJ
brj-23918	36	10	net	net	ADJ
brj-23918	36	11	model	model	NOUN
brj-23918	36	12	for	for	ADP
brj-23918	36	13	optimal	optimal	ADJ
brj-23918	36	14	surface	surface	NOUN
brj-23918	36	15	cracking	cracking	NOUN
brj-23918	36	16	of	of	ADP
brj-23918	36	17	sawn	sawn	NOUN
brj-23918	36	18	timber	timber	NOUN
brj-23918	36	19	to	to	PART
brj-23918	36	20	sawn	sawn	VERB
brj-23918	36	21	timber	timber	NOUN
brj-23918	36	22	grading	grade	VERB
brj-23918	36	23	to	to	PART
brj-23918	36	24	improve	improve	VERB
brj-23918	36	25	the	the	DET
brj-23918	36	26	accuracy	accuracy	NOUN
brj-23918	36	27	of	of	ADP
brj-23918	36	28	detection	detection	NOUN
brj-23918	36	29	.	.	PUNCT
brj-23918	37	1	related	relate	VERB
brj-23918	37	2	work	work	NOUN
brj-23918	37	3	in	in	ADP
brj-23918	37	4	the	the	DET
brj-23918	37	5	domain	domain	NOUN
brj-23918	37	6	of	of	ADP
brj-23918	37	7	wood	wood	NOUN
brj-23918	37	8	surface	surface	NOUN
brj-23918	37	9	defect	defect	NOUN
brj-23918	37	10	image	image	NOUN
brj-23918	37	11	segmentation	segmentation	NOUN
brj-23918	37	12	,	,	PUNCT
brj-23918	37	13	traditional	traditional	ADJ
brj-23918	37	14	image	image	NOUN
brj-23918	37	15	segmentation	segmentation	NOUN
brj-23918	37	16	approaches	approach	NOUN
brj-23918	37	17	mainly	mainly	ADV
brj-23918	37	18	encompass	encompass	VERB
brj-23918	37	19	segmentation	segmentation	NOUN
brj-23918	37	20	methods	method	NOUN
brj-23918	37	21	based	base	VERB
brj-23918	37	22	on	on	ADP
brj-23918	37	23	thresholding	thresholding	NOUN
brj-23918	37	24	,	,	PUNCT
brj-23918	37	25	region	region	NOUN
brj-23918	37	26	,	,	PUNCT
brj-23918	37	27	edge	edge	NOUN
brj-23918	37	28	detection	detection	NOUN
brj-23918	37	29	,	,	PUNCT
brj-23918	37	30	clustering	clustering	NOUN
brj-23918	37	31	,	,	PUNCT
brj-23918	37	32	graph	graph	NOUN
brj-23918	37	33	theory	theory	NOUN
brj-23918	37	34	,	,	PUNCT
brj-23918	37	35	and	and	CCONJ
brj-23918	37	36	specific	specific	ADJ
brj-23918	37	37	theories	theory	NOUN
brj-23918	37	38	.	.	PUNCT
brj-23918	38	1	currently	currently	ADV
brj-23918	38	2	,	,	PUNCT
brj-23918	38	3	the	the	DET
brj-23918	38	4	more	more	ADV
brj-23918	38	5	prevalent	prevalent	ADJ
brj-23918	38	6	methods	method	NOUN
brj-23918	38	7	are	be	AUX
brj-23918	38	8	those	those	PRON
brj-23918	38	9	of	of	ADP
brj-23918	38	10	deep	deep	ADJ
brj-23918	38	11	learning	learning	NOUN
brj-23918	38	12	.	.	PUNCT
brj-23918	39	1	among	among	ADP
brj-23918	39	2	them	they	PRON
brj-23918	39	3	,	,	PUNCT
brj-23918	39	4	semantic	semantic	ADJ
brj-23918	39	5	segmentation	segmentation	NOUN
brj-23918	39	6	is	be	AUX
brj-23918	39	7	aimed	aim	VERB
brj-23918	39	8	at	at	ADP
brj-23918	39	9	partitioning	partition	VERB
brj-23918	39	10	an	an	DET
brj-23918	39	11	image	image	NOUN
brj-23918	39	12	into	into	ADP
brj-23918	39	13	regions	region	NOUN
brj-23918	39	14	with	with	ADP
brj-23918	39	15	semantic	semantic	ADJ
brj-23918	39	16	information	information	NOUN
brj-23918	39	17	,	,	PUNCT
brj-23918	39	18	and	and	CCONJ
brj-23918	39	19	each	each	DET
brj-23918	39	20	region	region	NOUN
brj-23918	39	21	is	be	AUX
brj-23918	39	22	assigned	assign	VERB
brj-23918	39	23	a	a	DET
brj-23918	39	24	class	class	NOUN
brj-23918	39	25	label	label	NOUN
brj-23918	39	26	.	.	PUNCT
brj-23918	40	1	in	in	ADP
brj-23918	40	2	contrast	contrast	NOUN
brj-23918	40	3	to	to	AUX
brj-23918	40	4	object	object	VERB
brj-23918	40	5	detection	detection	NOUN
brj-23918	40	6	,	,	PUNCT
brj-23918	40	7	semantic	semantic	ADJ
brj-23918	40	8	segmentation	segmentation	NOUN
brj-23918	40	9	not	not	PART
brj-23918	40	10	only	only	ADV
brj-23918	40	11	pays	pay	VERB
brj-23918	40	12	attention	attention	NOUN
brj-23918	40	13	to	to	ADP
brj-23918	40	14	the	the	DET
brj-23918	40	15	location	location	NOUN
brj-23918	40	16	of	of	ADP
brj-23918	40	17	the	the	DET
brj-23918	40	18	object	object	NOUN
brj-23918	40	19	but	but	CCONJ
brj-23918	40	20	also	also	ADV
brj-23918	40	21	focuses	focus	VERB
brj-23918	40	22	on	on	ADP
brj-23918	40	23	the	the	DET
brj-23918	40	24	pixel	pixel	ADJ
brj-23918	40	25	-	-	PUNCT
brj-23918	40	26	level	level	NOUN
brj-23918	40	27	boundary	boundary	NOUN
brj-23918	40	28	of	of	ADP
brj-23918	40	29	the	the	DET
brj-23918	40	30	object	object	NOUN
brj-23918	40	31	.	.	PUNCT
brj-23918	41	1	typical	typical	ADJ
brj-23918	41	2	algorithms	algorithm	NOUN
brj-23918	41	3	for	for	ADP
brj-23918	41	4	semantic	semantic	ADJ
brj-23918	41	5	segmentation	segmentation	NOUN
brj-23918	41	6	include	include	VERB
brj-23918	41	7	fcn	fcn	PROPN
brj-23918	41	8	(	(	PUNCT
brj-23918	41	9	2017	2017	NUM
brj-23918	41	10	)	)	PUNCT
brj-23918	41	11	(	(	PUNCT
brj-23918	41	12	fully	fully	ADV
brj-23918	41	13	convolutional	convolutional	ADJ
brj-23918	41	14	network	network	NOUN
brj-23918	41	15	)	)	PUNCT
brj-23918	41	16	,	,	PUNCT
brj-23918	41	17	u	u	NOUN
brj-23918	41	18	-	-	NOUN
brj-23918	41	19	net	net	ADJ
brj-23918	41	20	(	(	PUNCT
brj-23918	41	21	2017	2017	NUM
brj-23918	41	22	)	)	PUNCT
brj-23918	41	23	,	,	PUNCT
brj-23918	41	24	seg	seg	PROPN
brj-23918	41	25	net	net	PROPN
brj-23918	41	26	(	(	PUNCT
brj-23918	41	27	2017	2017	NUM
brj-23918	41	28	)	)	PUNCT
brj-23918	41	29	,	,	PUNCT
brj-23918	41	30	mask	mask	VERB
brj-23918	41	31	r	r	NOUN
brj-23918	41	32	-	-	PUNCT
brj-23918	41	33	cnn	cnn	PROPN
brj-23918	41	34	(	(	PUNCT
brj-23918	41	35	2017	2017	NUM
brj-23918	41	36	)	)	PUNCT
brj-23918	41	37	,	,	PUNCT
brj-23918	41	38	etc	etc	X
brj-23918	41	39	.	.	X
brj-23918	42	1	xie	xie	PROPN
brj-23918	42	2	(	(	PUNCT
brj-23918	42	3	2023	2023	NUM
brj-23918	42	4	)	)	PUNCT
brj-23918	42	5	redesigned	redesign	VERB
brj-23918	42	6	the	the	DET
brj-23918	42	7	residual	residual	ADJ
brj-23918	42	8	layering	layering	NOUN
brj-23918	42	9	module	module	NOUN
brj-23918	42	10	and	and	CCONJ
brj-23918	42	11	convolution	convolution	NOUN
brj-23918	42	12	method	method	NOUN
brj-23918	42	13	of	of	ADP
brj-23918	42	14	the	the	DET
brj-23918	42	15	mask	mask	NOUN
brj-23918	42	16	rcnn	rcnn	PROPN
brj-23918	42	17	model	model	PROPN
brj-23918	42	18	to	to	PART
brj-23918	42	19	optimize	optimize	VERB
brj-23918	42	20	the	the	DET
brj-23918	42	21	detection	detection	NOUN
brj-23918	42	22	and	and	CCONJ
brj-23918	42	23	segmentation	segmentation	NOUN
brj-23918	42	24	of	of	ADP
brj-23918	42	25	wood	wood	NOUN
brj-23918	42	26	defects	defect	NOUN
brj-23918	42	27	such	such	ADJ
brj-23918	42	28	as	as	ADP
brj-23918	42	29	cracks	crack	NOUN
brj-23918	42	30	.	.	PUNCT
brj-23918	43	1	however	however	ADV
brj-23918	43	2	,	,	PUNCT
brj-23918	43	3	the	the	DET
brj-23918	43	4	efficiency	efficiency	NOUN
brj-23918	43	5	of	of	ADP
brj-23918	43	6	detection	detection	NOUN
brj-23918	43	7	and	and	CCONJ
brj-23918	43	8	segmentation	segmentation	NOUN
brj-23918	43	9	needs	need	VERB
brj-23918	43	10	to	to	PART
brj-23918	43	11	be	be	AUX
brj-23918	43	12	improved	improve	VERB
brj-23918	43	13	.	.	PUNCT
brj-23918	44	1	lin	lin	PROPN
brj-23918	44	2	et	et	PROPN
brj-23918	44	3	al	al	PROPN
brj-23918	44	4	.	.	PROPN
brj-23918	45	1	(	(	PUNCT
brj-23918	45	2	2023	2023	NUM
brj-23918	45	3	)	)	PUNCT
brj-23918	45	4	proposed	propose	VERB
brj-23918	45	5	a	a	DET
brj-23918	45	6	data	data	NOUN
brj-23918	45	7	-	-	PUNCT
brj-23918	45	8	driven	drive	VERB
brj-23918	45	9	semantic	semantic	ADJ
brj-23918	45	10	segmentation	segmentation	NOUN
brj-23918	45	11	network	network	NOUN
brj-23918	45	12	based	base	VERB
brj-23918	45	13	on	on	ADP
brj-23918	45	14	u	u	NOUN
brj-23918	45	15	-	-	NOUN
brj-23918	45	16	net	net	ADJ
brj-23918	45	17	for	for	ADP
brj-23918	45	18	wood	wood	NOUN
brj-23918	45	19	crack	crack	NOUN
brj-23918	45	20	detection	detection	NOUN
brj-23918	45	21	,	,	PUNCT
brj-23918	45	22	which	which	PRON
brj-23918	45	23	achieved	achieve	VERB
brj-23918	45	24	more	more	ADV
brj-23918	45	25	accurate	accurate	ADJ
brj-23918	45	26	segmentation	segmentation	NOUN
brj-23918	45	27	results	result	NOUN
brj-23918	45	28	.	.	PUNCT
brj-23918	46	1	peer	peer	NOUN
brj-23918	46	2	-	-	PUNCT
brj-23918	46	3	reviewed	review	VERB
brj-23918	46	4	article	article	NOUN
brj-23918	46	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	46	6	dong	dong	PROPN
brj-23918	46	7	et	et	PROPN
brj-23918	46	8	al	al	PROPN
brj-23918	46	9	.	.	PROPN
brj-23918	47	1	(	(	PUNCT
brj-23918	47	2	2025	2025	NUM
brj-23918	47	3	)	)	PUNCT
brj-23918	47	4	.	.	PUNCT
brj-23918	48	1	“	"	PUNCT
brj-23918	48	2	cracks	crack	NOUN
brj-23918	48	3	in	in	ADP
brj-23918	48	4	sawn	sawn	NOUN
brj-23918	48	5	wood	wood	NOUN
brj-23918	48	6	,	,	PUNCT
brj-23918	48	7	”	"	PUNCT
brj-23918	48	8	bioresources	bioresource	NOUN
brj-23918	48	9	20(2	20(2	NUM
brj-23918	48	10	)	)	PUNCT
brj-23918	48	11	,	,	PUNCT
brj-23918	48	12	3545	3545	NUM
brj-23918	48	13	-	-	SYM
brj-23918	48	14	3556	3556	NUM
brj-23918	48	15	.	.	PUNCT
brj-23918	49	1	3547	3547	NUM
brj-23918	49	2	attention	attention	NOUN
brj-23918	49	3	gate	gate	NOUN
brj-23918	49	4	(	(	PUNCT
brj-23918	49	5	ag	ag	PROPN
brj-23918	49	6	)	)	PUNCT
brj-23918	49	7	the	the	DET
brj-23918	49	8	ag	ag	PROPN
brj-23918	49	9	in	in	ADP
brj-23918	49	10	the	the	DET
brj-23918	49	11	u	u	NOUN
brj-23918	49	12	-	-	ADJ
brj-23918	49	13	net	net	ADJ
brj-23918	49	14	model	model	NOUN
brj-23918	49	15	was	be	AUX
brj-23918	49	16	first	first	ADV
brj-23918	49	17	proposed	propose	VERB
brj-23918	49	18	by	by	ADP
brj-23918	49	19	oktay	oktay	PROPN
brj-23918	49	20	et	et	PROPN
brj-23918	49	21	al	al	PROPN
brj-23918	49	22	.	.	PROPN
brj-23918	50	1	(	(	PUNCT
brj-23918	50	2	2018	2018	NUM
brj-23918	50	3	)	)	PUNCT
brj-23918	50	4	.	.	PUNCT
brj-23918	51	1	the	the	DET
brj-23918	51	2	ag	ag	PROPN
brj-23918	51	3	attention	attention	NOUN
brj-23918	51	4	module	module	NOUN
brj-23918	51	5	adapts	adapt	VERB
brj-23918	51	6	and	and	CCONJ
brj-23918	51	7	automatically	automatically	ADV
brj-23918	51	8	learns	learn	VERB
brj-23918	51	9	to	to	PART
brj-23918	51	10	focus	focus	VERB
brj-23918	51	11	on	on	ADP
brj-23918	51	12	target	target	NOUN
brj-23918	51	13	structures	structure	NOUN
brj-23918	51	14	of	of	ADP
brj-23918	51	15	different	different	ADJ
brj-23918	51	16	shapes	shape	NOUN
brj-23918	51	17	and	and	CCONJ
brj-23918	51	18	sizes	size	NOUN
brj-23918	51	19	in	in	ADP
brj-23918	51	20	medical	medical	ADJ
brj-23918	51	21	images	image	NOUN
brj-23918	51	22	.	.	PUNCT
brj-23918	52	1	the	the	DET
brj-23918	52	2	model	model	NOUN
brj-23918	52	3	using	use	VERB
brj-23918	52	4	ag	ag	PROPN
brj-23918	52	5	implicitly	implicitly	ADV
brj-23918	52	6	learns	learn	VERB
brj-23918	52	7	to	to	PART
brj-23918	52	8	highlight	highlight	VERB
brj-23918	52	9	salient	salient	NOUN
brj-23918	52	10	features	feature	NOUN
brj-23918	52	11	that	that	PRON
brj-23918	52	12	are	be	AUX
brj-23918	52	13	useful	useful	ADJ
brj-23918	52	14	for	for	ADP
brj-23918	52	15	specific	specific	ADJ
brj-23918	52	16	tasks	task	NOUN
brj-23918	52	17	while	while	SCONJ
brj-23918	52	18	suppressing	suppress	VERB
brj-23918	52	19	irrelevant	irrelevant	ADJ
brj-23918	52	20	regions	region	NOUN
brj-23918	52	21	in	in	ADP
brj-23918	52	22	the	the	DET
brj-23918	52	23	input	input	NOUN
brj-23918	52	24	image	image	NOUN
brj-23918	52	25	.	.	PUNCT
brj-23918	53	1	tong	tong	PROPN
brj-23918	53	2	et	et	PROPN
brj-23918	53	3	al	al	PROPN
brj-23918	53	4	.	.	PROPN
brj-23918	54	1	(	(	PUNCT
brj-23918	54	2	2021	2021	NUM
brj-23918	54	3	)	)	PUNCT
brj-23918	54	4	extracted	extract	VERB
brj-23918	54	5	relevant	relevant	ADJ
brj-23918	54	6	information	information	NOUN
brj-23918	54	7	from	from	ADP
brj-23918	54	8	coarse	coarse	ADJ
brj-23918	54	9	scales	scale	NOUN
brj-23918	54	10	to	to	PART
brj-23918	54	11	determine	determine	VERB
brj-23918	54	12	gating	gate	VERB
brj-23918	54	13	to	to	PART
brj-23918	54	14	eliminate	eliminate	VERB
brj-23918	54	15	noise	noise	NOUN
brj-23918	54	16	and	and	CCONJ
brj-23918	54	17	irrelevant	irrelevant	ADJ
brj-23918	54	18	responses	response	NOUN
brj-23918	54	19	caused	cause	VERB
brj-23918	54	20	by	by	ADP
brj-23918	54	21	skip	skip	ADJ
brj-23918	54	22	connections	connection	NOUN
brj-23918	54	23	.	.	PUNCT
brj-23918	55	1	before	before	ADP
brj-23918	55	2	the	the	DET
brj-23918	55	3	connection	connection	NOUN
brj-23918	55	4	operation	operation	NOUN
brj-23918	55	5	,	,	PUNCT
brj-23918	55	6	ag	ag	PROPN
brj-23918	55	7	only	only	ADV
brj-23918	55	8	performs	perform	VERB
brj-23918	55	9	the	the	DET
brj-23918	55	10	merging	merging	NOUN
brj-23918	55	11	of	of	ADP
brj-23918	55	12	related	related	ADJ
brj-23918	55	13	activations	activation	NOUN
brj-23918	55	14	,	,	PUNCT
brj-23918	55	15	filtering	filter	VERB
brj-23918	55	16	out	out	ADP
brj-23918	55	17	neuron	neuron	NOUN
brj-23918	55	18	activations	activation	NOUN
brj-23918	55	19	for	for	ADP
brj-23918	55	20	forward	forward	ADV
brj-23918	55	21	and	and	CCONJ
brj-23918	55	22	backward	backward	ADJ
brj-23918	55	23	transmission	transmission	NOUN
brj-23918	55	24	.	.	PUNCT
brj-23918	56	1	ag	ag	PROPN
brj-23918	56	2	performs	perform	VERB
brj-23918	56	3	linear	linear	PROPN
brj-23918	56	4	transformation	transformation	NOUN
brj-23918	56	5	without	without	ADP
brj-23918	56	6	any	any	DET
brj-23918	56	7	spatial	spatial	ADJ
brj-23918	56	8	support	support	NOUN
brj-23918	56	9	,	,	PUNCT
brj-23918	56	10	downsampling	downsample	VERB
brj-23918	56	11	the	the	DET
brj-23918	56	12	gate	gate	NOUN
brj-23918	56	13	signal	signal	NOUN
brj-23918	56	14	to	to	PART
brj-23918	56	15	reduce	reduce	VERB
brj-23918	56	16	the	the	DET
brj-23918	56	17	resolution	resolution	NOUN
brj-23918	56	18	of	of	ADP
brj-23918	56	19	input	input	NOUN
brj-23918	56	20	feature	feature	NOUN
brj-23918	56	21	mapping	mapping	NOUN
brj-23918	56	22	,	,	PUNCT
brj-23918	56	23	thereby	thereby	ADV
brj-23918	56	24	reducing	reduce	VERB
brj-23918	56	25	the	the	DET
brj-23918	56	26	parameter	parameter	NOUN
brj-23918	56	27	and	and	CCONJ
brj-23918	56	28	computational	computational	ADJ
brj-23918	56	29	resource	resource	NOUN
brj-23918	56	30	consumption	consumption	NOUN
brj-23918	56	31	of	of	ADP
brj-23918	56	32	the	the	DET
brj-23918	56	33	network	network	NOUN
brj-23918	56	34	model	model	NOUN
brj-23918	56	35	.	.	PUNCT
brj-23918	57	1	wang	wang	PROPN
brj-23918	57	2	et	et	PROPN
brj-23918	57	3	al	al	PROPN
brj-23918	57	4	.	.	PROPN
brj-23918	57	5	(	(	PUNCT
brj-23918	57	6	2020a	2020a	NUM
brj-23918	57	7	,	,	PUNCT
brj-23918	57	8	b	b	NOUN
brj-23918	57	9	)	)	PUNCT
brj-23918	57	10	used	use	VERB
brj-23918	57	11	attention	attention	NOUN
brj-23918	57	12	gate	gate	NOUN
brj-23918	57	13	(	(	PUNCT
brj-23918	57	14	ag	ag	PROPN
brj-23918	57	15	)	)	PUNCT
brj-23918	57	16	instead	instead	ADV
brj-23918	57	17	of	of	ADP
brj-23918	57	18	direct	direct	ADJ
brj-23918	57	19	addition	addition	NOUN
brj-23918	57	20	operation	operation	NOUN
brj-23918	57	21	in	in	ADP
brj-23918	57	22	the	the	DET
brj-23918	57	23	process	process	NOUN
brj-23918	57	24	of	of	ADP
brj-23918	57	25	connecting	connect	VERB
brj-23918	57	26	the	the	DET
brj-23918	57	27	encoder	encoder	NOUN
brj-23918	57	28	and	and	CCONJ
brj-23918	57	29	decoder	decoder	NOUN
brj-23918	57	30	to	to	PART
brj-23918	57	31	extract	extract	VERB
brj-23918	57	32	low	low	ADJ
brj-23918	57	33	-	-	PUNCT
brj-23918	57	34	level	level	NOUN
brj-23918	57	35	features	feature	NOUN
brj-23918	57	36	of	of	ADP
brj-23918	57	37	the	the	DET
brj-23918	57	38	image	image	NOUN
brj-23918	57	39	and	and	CCONJ
brj-23918	57	40	reduce	reduce	VERB
brj-23918	57	41	the	the	DET
brj-23918	57	42	loss	loss	NOUN
brj-23918	57	43	of	of	ADP
brj-23918	57	44	detail	detail	NOUN
brj-23918	57	45	information	information	NOUN
brj-23918	57	46	.	.	PUNCT
brj-23918	58	1	ren	ren	NOUN
brj-23918	58	2	et	et	PROPN
brj-23918	58	3	al	al	PROPN
brj-23918	58	4	.	.	PROPN
brj-23918	59	1	(	(	PUNCT
brj-23918	59	2	2024	2024	NUM
brj-23918	59	3	)	)	PUNCT
brj-23918	59	4	introduced	introduce	VERB
brj-23918	59	5	an	an	DET
brj-23918	59	6	attention	attention	NOUN
brj-23918	59	7	gating	gate	VERB
brj-23918	59	8	module	module	NOUN
brj-23918	59	9	at	at	ADP
brj-23918	59	10	the	the	DET
brj-23918	59	11	skip	skip	ADJ
brj-23918	59	12	connection	connection	NOUN
brj-23918	59	13	to	to	PART
brj-23918	59	14	implicitly	implicitly	ADV
brj-23918	59	15	suppress	suppress	VERB
brj-23918	59	16	irrelevant	irrelevant	ADJ
brj-23918	59	17	features	feature	NOUN
brj-23918	59	18	in	in	ADP
brj-23918	59	19	the	the	DET
brj-23918	59	20	input	input	NOUN
brj-23918	59	21	image	image	NOUN
brj-23918	59	22	,	,	PUNCT
brj-23918	59	23	improve	improve	VERB
brj-23918	59	24	sensitivity	sensitivity	NOUN
brj-23918	59	25	to	to	PART
brj-23918	59	26	target	target	VERB
brj-23918	59	27	objects	object	NOUN
brj-23918	59	28	,	,	PUNCT
brj-23918	59	29	and	and	CCONJ
brj-23918	59	30	enhance	enhance	VERB
brj-23918	59	31	extraction	extraction	NOUN
brj-23918	59	32	ability	ability	NOUN
brj-23918	59	33	in	in	ADP
brj-23918	59	34	complex	complex	ADJ
brj-23918	59	35	scenes	scene	NOUN
brj-23918	59	36	.	.	PUNCT
brj-23918	60	1	xu	xu	PROPN
brj-23918	60	2	(	(	PUNCT
brj-23918	60	3	2023	2023	NUM
brj-23918	60	4	)	)	PUNCT
brj-23918	60	5	added	add	VERB
brj-23918	60	6	the	the	DET
brj-23918	60	7	attentiongate	attentiongate	ADJ
brj-23918	60	8	structure	structure	NOUN
brj-23918	60	9	to	to	ADP
brj-23918	60	10	the	the	DET
brj-23918	60	11	network	network	NOUN
brj-23918	60	12	model	model	NOUN
brj-23918	60	13	,	,	PUNCT
brj-23918	60	14	which	which	PRON
brj-23918	60	15	improved	improve	VERB
brj-23918	60	16	the	the	DET
brj-23918	60	17	response	response	NOUN
brj-23918	60	18	speed	speed	NOUN
brj-23918	60	19	of	of	ADP
brj-23918	60	20	each	each	DET
brj-23918	60	21	layer	layer	NOUN
brj-23918	60	22	of	of	ADP
brj-23918	60	23	the	the	DET
brj-23918	60	24	network	network	NOUN
brj-23918	60	25	to	to	ADP
brj-23918	60	26	the	the	DET
brj-23918	60	27	region	region	NOUN
brj-23918	60	28	of	of	ADP
brj-23918	60	29	interest	interest	NOUN
brj-23918	60	30	,	,	PUNCT
brj-23918	60	31	especially	especially	ADV
brj-23918	60	32	in	in	ADP
brj-23918	60	33	the	the	DET
brj-23918	60	34	ert	ert	NOUN
brj-23918	60	35	image	image	NOUN
brj-23918	60	36	reconstruction	reconstruction	NOUN
brj-23918	60	37	task	task	NOUN
brj-23918	60	38	,	,	PUNCT
brj-23918	60	39	where	where	SCONJ
brj-23918	60	40	the	the	DET
brj-23918	60	41	edge	edge	NOUN
brj-23918	60	42	processing	processing	NOUN
brj-23918	60	43	of	of	ADP
brj-23918	60	44	the	the	DET
brj-23918	60	45	reconstructed	reconstructed	ADJ
brj-23918	60	46	image	image	NOUN
brj-23918	60	47	was	be	AUX
brj-23918	60	48	improved	improve	VERB
brj-23918	60	49	,	,	PUNCT
brj-23918	60	50	while	while	SCONJ
brj-23918	60	51	effectively	effectively	ADV
brj-23918	60	52	reducing	reduce	VERB
brj-23918	60	53	the	the	DET
brj-23918	60	54	redundant	redundant	ADJ
brj-23918	60	55	background	background	NOUN
brj-23918	60	56	medium	medium	ADJ
brj-23918	60	57	response	response	NOUN
brj-23918	60	58	caused	cause	VERB
brj-23918	60	59	by	by	ADP
brj-23918	60	60	network	network	NOUN
brj-23918	60	61	deepening	deepening	NOUN
brj-23918	60	62	.	.	PUNCT
brj-23918	61	1	convolutional	convolutional	ADJ
brj-23918	61	2	block	block	NOUN
brj-23918	61	3	attention	attention	NOUN
brj-23918	61	4	module	module	NOUN
brj-23918	61	5	(	(	PUNCT
brj-23918	61	6	cbam	cbam	NOUN
brj-23918	61	7	)	)	PUNCT
brj-23918	61	8	cbam	cbam	NOUN
brj-23918	61	9	mainly	mainly	ADV
brj-23918	61	10	includes	include	VERB
brj-23918	61	11	channel	channel	NOUN
brj-23918	61	12	attention	attention	NOUN
brj-23918	61	13	mechanism	mechanism	NOUN
brj-23918	61	14	and	and	CCONJ
brj-23918	61	15	spatial	spatial	ADJ
brj-23918	61	16	attention	attention	NOUN
brj-23918	61	17	mechanism	mechanism	NOUN
brj-23918	61	18	,	,	PUNCT
brj-23918	61	19	which	which	PRON
brj-23918	61	20	respectively	respectively	ADV
brj-23918	61	21	capture	capture	VERB
brj-23918	61	22	the	the	DET
brj-23918	61	23	dependency	dependency	NOUN
brj-23918	61	24	relationship	relationship	NOUN
brj-23918	61	25	between	between	ADP
brj-23918	61	26	channels	channel	NOUN
brj-23918	61	27	and	and	CCONJ
brj-23918	61	28	spatial	spatial	ADJ
brj-23918	61	29	pixel	pixel	PROPN
brj-23918	61	30	level	level	NOUN
brj-23918	61	31	relationship	relationship	NOUN
brj-23918	61	32	.	.	PUNCT
brj-23918	62	1	the	the	DET
brj-23918	62	2	mixture	mixture	NOUN
brj-23918	62	3	of	of	ADP
brj-23918	62	4	the	the	DET
brj-23918	62	5	two	two	NUM
brj-23918	62	6	can	can	AUX
brj-23918	62	7	adaptively	adaptively	ADV
brj-23918	62	8	extract	extract	VERB
brj-23918	62	9	channel	channel	NOUN
brj-23918	62	10	and	and	CCONJ
brj-23918	62	11	spatial	spatial	ADJ
brj-23918	62	12	features	feature	NOUN
brj-23918	62	13	,	,	PUNCT
brj-23918	62	14	which	which	PRON
brj-23918	62	15	is	be	AUX
brj-23918	62	16	a	a	DET
brj-23918	62	17	soft	soft	ADJ
brj-23918	62	18	attention	attention	NOUN
brj-23918	62	19	mechanism	mechanism	NOUN
brj-23918	62	20	.	.	PUNCT
brj-23918	63	1	sheng	sheng	PROPN
brj-23918	63	2	et	et	PROPN
brj-23918	63	3	al	al	PROPN
brj-23918	63	4	.	.	PROPN
brj-23918	63	5	(	(	PUNCT
brj-23918	63	6	2023	2023	NUM
brj-23918	63	7	)	)	PUNCT
brj-23918	63	8	proposed	propose	VERB
brj-23918	63	9	introducing	introduce	VERB
brj-23918	63	10	the	the	DET
brj-23918	63	11	cbam	cbam	NOUN
brj-23918	63	12	attention	attention	NOUN
brj-23918	63	13	mechanism	mechanism	NOUN
brj-23918	63	14	in	in	ADP
brj-23918	63	15	feature	feature	NOUN
brj-23918	63	16	extraction	extraction	NOUN
brj-23918	63	17	networks	network	NOUN
brj-23918	63	18	,	,	PUNCT
brj-23918	63	19	combining	combine	VERB
brj-23918	63	20	spatial	spatial	ADJ
brj-23918	63	21	attention	attention	NOUN
brj-23918	63	22	module	module	NOUN
brj-23918	63	23	and	and	CCONJ
brj-23918	63	24	channel	channel	NOUN
brj-23918	63	25	attention	attention	NOUN
brj-23918	63	26	module	module	NOUN
brj-23918	63	27	to	to	PART
brj-23918	63	28	filter	filter	VERB
brj-23918	63	29	information	information	NOUN
brj-23918	63	30	,	,	PUNCT
brj-23918	63	31	focusing	focus	VERB
brj-23918	63	32	on	on	ADP
brj-23918	63	33	the	the	DET
brj-23918	63	34	local	local	ADJ
brj-23918	63	35	effective	effective	ADJ
brj-23918	63	36	information	information	NOUN
brj-23918	63	37	of	of	ADP
brj-23918	63	38	feature	feature	NOUN
brj-23918	63	39	images	image	NOUN
brj-23918	63	40	,	,	PUNCT
brj-23918	63	41	and	and	CCONJ
brj-23918	63	42	improving	improve	VERB
brj-23918	63	43	the	the	DET
brj-23918	63	44	detection	detection	NOUN
brj-23918	63	45	ability	ability	NOUN
brj-23918	63	46	of	of	ADP
brj-23918	63	47	occluded	occluded	ADJ
brj-23918	63	48	or	or	CCONJ
brj-23918	63	49	truncated	truncated	ADJ
brj-23918	63	50	targets	target	NOUN
brj-23918	63	51	.	.	PUNCT
brj-23918	64	1	yang	yang	PROPN
brj-23918	64	2	et	et	PROPN
brj-23918	64	3	al	al	PROPN
brj-23918	64	4	.	.	PROPN
brj-23918	64	5	(	(	PUNCT
brj-23918	64	6	2023	2023	NUM
brj-23918	64	7	)	)	PUNCT
brj-23918	64	8	introduced	introduce	VERB
brj-23918	64	9	the	the	DET
brj-23918	64	10	convolutional	convolutional	ADJ
brj-23918	64	11	block	block	NOUN
brj-23918	64	12	attention	attention	NOUN
brj-23918	64	13	module	module	NOUN
brj-23918	64	14	(	(	PUNCT
brj-23918	64	15	cbam	cbam	NOUN
brj-23918	64	16	)	)	PUNCT
brj-23918	64	17	theory	theory	NOUN
brj-23918	64	18	to	to	PART
brj-23918	64	19	optimize	optimize	VERB
brj-23918	64	20	the	the	DET
brj-23918	64	21	yolox	yolox	NOUN
brj-23918	64	22	network	network	NOUN
brj-23918	64	23	in	in	ADP
brj-23918	64	24	their	their	PRON
brj-23918	64	25	study	study	NOUN
brj-23918	64	26	,	,	PUNCT
brj-23918	64	27	further	far	ADV
brj-23918	64	28	improving	improve	VERB
brj-23918	64	29	the	the	DET
brj-23918	64	30	performance	performance	NOUN
brj-23918	64	31	of	of	ADP
brj-23918	64	32	the	the	DET
brj-23918	64	33	network	network	NOUN
brj-23918	64	34	model	model	NOUN
brj-23918	64	35	.	.	PUNCT
brj-23918	65	1	the	the	DET
brj-23918	65	2	experimental	experimental	ADJ
brj-23918	65	3	results	result	NOUN
brj-23918	65	4	showed	show	VERB
brj-23918	65	5	that	that	SCONJ
brj-23918	65	6	the	the	DET
brj-23918	65	7	introduction	introduction	NOUN
brj-23918	65	8	of	of	ADP
brj-23918	65	9	cbam	cbam	NOUN
brj-23918	65	10	improved	improve	VERB
brj-23918	65	11	the	the	DET
brj-23918	65	12	detection	detection	NOUN
brj-23918	65	13	accuracy	accuracy	NOUN
brj-23918	65	14	of	of	ADP
brj-23918	65	15	yolox	yolox	NOUN
brj-23918	65	16	on	on	ADP
brj-23918	65	17	the	the	DET
brj-23918	65	18	insulator	insulator	NOUN
brj-23918	65	19	dataset	dataset	VERB
brj-23918	65	20	by	by	ADP
brj-23918	65	21	about	about	ADV
brj-23918	65	22	3	3	NUM
brj-23918	65	23	%	%	NOUN
brj-23918	65	24	,	,	PUNCT
brj-23918	65	25	and	and	CCONJ
brj-23918	65	26	the	the	DET
brj-23918	65	27	performance	performance	NOUN
brj-23918	65	28	of	of	ADP
brj-23918	65	29	the	the	DET
brj-23918	65	30	model	model	NOUN
brj-23918	65	31	was	be	AUX
brj-23918	65	32	optimized	optimize	VERB
brj-23918	65	33	to	to	ADP
brj-23918	65	34	a	a	DET
brj-23918	65	35	certain	certain	ADJ
brj-23918	65	36	extent	extent	NOUN
brj-23918	65	37	.	.	PUNCT
brj-23918	66	1	jia	jia	PROPN
brj-23918	66	2	et	et	PROPN
brj-23918	66	3	al	al	PROPN
brj-23918	66	4	.	.	PROPN
brj-23918	66	5	(	(	PUNCT
brj-23918	66	6	2022	2022	NUM
brj-23918	66	7	)	)	PUNCT
brj-23918	66	8	screened	screen	VERB
brj-23918	66	9	the	the	DET
brj-23918	66	10	optimal	optimal	ADJ
brj-23918	66	11	model	model	NOUN
brj-23918	66	12	for	for	ADP
brj-23918	66	13	bamboo	bamboo	NOUN
brj-23918	66	14	stick	stick	NOUN
brj-23918	66	15	recognition	recognition	NOUN
brj-23918	66	16	and	and	CCONJ
brj-23918	66	17	then	then	ADV
brj-23918	66	18	adopted	adopt	VERB
brj-23918	66	19	the	the	DET
brj-23918	66	20	convolutional	convolutional	ADJ
brj-23918	66	21	block	block	NOUN
brj-23918	66	22	attention	attention	NOUN
brj-23918	66	23	module	module	NOUN
brj-23918	66	24	(	(	PUNCT
brj-23918	66	25	cbam	cbam	NOUN
brj-23918	66	26	)	)	PUNCT
brj-23918	66	27	attention	attention	NOUN
brj-23918	66	28	mechanism	mechanism	NOUN
brj-23918	66	29	to	to	PART
brj-23918	66	30	replace	replace	VERB
brj-23918	66	31	the	the	DET
brj-23918	66	32	squeeze	squeeze	NOUN
brj-23918	66	33	excitation	excitation	NOUN
brj-23918	66	34	(	(	PUNCT
brj-23918	66	35	se	se	ADJ
brj-23918	66	36	)	)	PUNCT
brj-23918	66	37	attention	attention	NOUN
brj-23918	66	38	mechanism	mechanism	NOUN
brj-23918	66	39	in	in	ADP
brj-23918	66	40	the	the	DET
brj-23918	66	41	mobilenetv3	mobilenetv3	PROPN
brj-23918	66	42	network	network	NOUN
brj-23918	66	43	structure	structure	NOUN
brj-23918	66	44	,	,	PUNCT
brj-23918	66	45	enabling	enable	VERB
brj-23918	66	46	the	the	DET
brj-23918	66	47	network	network	NOUN
brj-23918	66	48	to	to	PART
brj-23918	66	49	extract	extract	VERB
brj-23918	66	50	features	feature	NOUN
brj-23918	66	51	in	in	ADP
brj-23918	66	52	both	both	DET
brj-23918	66	53	channel	channel	NOUN
brj-23918	66	54	and	and	CCONJ
brj-23918	66	55	spatial	spatial	ADJ
brj-23918	66	56	dimensions	dimension	NOUN
brj-23918	66	57	,	,	PUNCT
brj-23918	66	58	thereby	thereby	ADV
brj-23918	66	59	improving	improve	VERB
brj-23918	66	60	recognition	recognition	NOUN
brj-23918	66	61	accuracy	accuracy	NOUN
brj-23918	66	62	.	.	PUNCT
brj-23918	67	1	li	li	PROPN
brj-23918	67	2	et	et	PROPN
brj-23918	67	3	al	al	PROPN
brj-23918	67	4	.	.	PROPN
brj-23918	67	5	(	(	PUNCT
brj-23918	67	6	2023	2023	NUM
brj-23918	67	7	)	)	PUNCT
brj-23918	67	8	proposed	propose	VERB
brj-23918	67	9	a	a	DET
brj-23918	67	10	repvgg	repvgg	NOUN
brj-23918	67	11	-	-	PUNCT
brj-23918	67	12	cbam	cbam	NOUN
brj-23918	67	13	model	model	NOUN
brj-23918	67	14	with	with	ADP
brj-23918	67	15	cbam	cbam	NOUN
brj-23918	67	16	attention	attention	NOUN
brj-23918	67	17	mechanism	mechanism	NOUN
brj-23918	67	18	and	and	CCONJ
brj-23918	67	19	applied	apply	VERB
brj-23918	67	20	the	the	DET
brj-23918	67	21	model	model	NOUN
brj-23918	67	22	to	to	PART
brj-23918	67	23	classify	classify	VERB
brj-23918	67	24	10	10	NUM
brj-23918	67	25	types	type	NOUN
brj-23918	67	26	of	of	ADP
brj-23918	67	27	surface	surface	NOUN
brj-23918	67	28	defects	defect	NOUN
brj-23918	67	29	on	on	ADP
brj-23918	67	30	aluminum	aluminum	NOUN
brj-23918	67	31	profiles	profile	NOUN
brj-23918	67	32	.	.	PUNCT
brj-23918	68	1	experimental	experimental	ADJ
brj-23918	68	2	results	result	NOUN
brj-23918	68	3	have	have	AUX
brj-23918	68	4	shown	show	VERB
brj-23918	68	5	that	that	SCONJ
brj-23918	68	6	adding	add	VERB
brj-23918	68	7	cbam	cbam	NOUN
brj-23918	68	8	attention	attention	NOUN
brj-23918	68	9	mechanism	mechanism	NOUN
brj-23918	68	10	after	after	SCONJ
brj-23918	68	11	repvgg	repvgg	NOUN
brj-23918	68	12	stages	stage	NOUN
brj-23918	68	13	1	1	NUM
brj-23918	68	14	to	to	PART
brj-23918	68	15	4	4	NUM
brj-23918	68	16	results	result	NOUN
brj-23918	68	17	in	in	ADP
brj-23918	68	18	the	the	DET
brj-23918	68	19	strongest	strong	ADJ
brj-23918	68	20	classification	classification	NOUN
brj-23918	68	21	ability	ability	NOUN
brj-23918	68	22	.	.	PUNCT
brj-23918	69	1	ge	ge	PROPN
brj-23918	69	2	et	et	PROPN
brj-23918	69	3	al	al	PROPN
brj-23918	69	4	.	.	PROPN
brj-23918	70	1	(	(	PUNCT
brj-23918	70	2	2024	2024	NUM
brj-23918	70	3	)	)	PUNCT
brj-23918	70	4	introduced	introduce	VERB
brj-23918	70	5	cbam	cbam	NOUN
brj-23918	70	6	in	in	ADP
brj-23918	70	7	the	the	DET
brj-23918	70	8	study	study	NOUN
brj-23918	70	9	of	of	ADP
brj-23918	70	10	wood	wood	NOUN
brj-23918	70	11	ring	ring	NOUN
brj-23918	70	12	segmentation	segmentation	NOUN
brj-23918	70	13	to	to	PART
brj-23918	70	14	solve	solve	VERB
brj-23918	70	15	the	the	DET
brj-23918	70	16	loss	loss	NOUN
brj-23918	70	17	of	of	ADP
brj-23918	70	18	downsampling	downsample	VERB
brj-23918	70	19	information	information	NOUN
brj-23918	70	20	and	and	CCONJ
brj-23918	70	21	thereby	thereby	ADV
brj-23918	70	22	improve	improve	VERB
brj-23918	70	23	the	the	DET
brj-23918	70	24	performance	performance	NOUN
brj-23918	70	25	of	of	ADP
brj-23918	70	26	daf	daf	PROPN
brj-23918	70	27	net++	net++	PROPN
brj-23918	70	28	.	.	PUNCT
brj-23918	71	1	peer	peer	NOUN
brj-23918	71	2	-	-	PUNCT
brj-23918	71	3	reviewed	review	VERB
brj-23918	71	4	article	article	NOUN
brj-23918	71	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	71	6	dong	dong	PROPN
brj-23918	71	7	et	et	PROPN
brj-23918	71	8	al	al	PROPN
brj-23918	71	9	.	.	PROPN
brj-23918	72	1	(	(	PUNCT
brj-23918	72	2	2025	2025	NUM
brj-23918	72	3	)	)	PUNCT
brj-23918	72	4	.	.	PUNCT
brj-23918	73	1	“	"	PUNCT
brj-23918	73	2	cracks	crack	NOUN
brj-23918	73	3	in	in	ADP
brj-23918	73	4	sawn	sawn	NOUN
brj-23918	73	5	wood	wood	NOUN
brj-23918	73	6	,	,	PUNCT
brj-23918	73	7	”	"	PUNCT
brj-23918	73	8	bioresources	bioresource	NOUN
brj-23918	73	9	20(2	20(2	NUM
brj-23918	73	10	)	)	PUNCT
brj-23918	73	11	,	,	PUNCT
brj-23918	73	12	3545	3545	NUM
brj-23918	73	13	-	-	SYM
brj-23918	73	14	3556	3556	NUM
brj-23918	73	15	.	.	PUNCT
brj-23918	74	1	3548	3548	NUM
brj-23918	74	2	efficient	efficient	ADJ
brj-23918	74	3	channel	channel	NOUN
brj-23918	74	4	attention	attention	NOUN
brj-23918	74	5	(	(	PUNCT
brj-23918	74	6	eca	eca	PROPN
brj-23918	74	7	)	)	PUNCT
brj-23918	74	8	zhang	zhang	PROPN
brj-23918	74	9	et	et	PROPN
brj-23918	74	10	al	al	PROPN
brj-23918	74	11	.	.	PROPN
brj-23918	74	12	(	(	PUNCT
brj-23918	74	13	2023	2023	NUM
brj-23918	74	14	)	)	PUNCT
brj-23918	74	15	introduced	introduce	VERB
brj-23918	74	16	a	a	DET
brj-23918	74	17	lightweight	lightweight	ADJ
brj-23918	74	18	attention	attention	NOUN
brj-23918	74	19	mechanism	mechanism	NOUN
brj-23918	74	20	called	call	VERB
brj-23918	74	21	efficient	efficient	ADJ
brj-23918	74	22	channel	channel	NOUN
brj-23918	74	23	attention	attention	NOUN
brj-23918	74	24	(	(	PUNCT
brj-23918	74	25	eca	eca	NOUN
brj-23918	74	26	)	)	PUNCT
brj-23918	74	27	to	to	PART
brj-23918	74	28	address	address	VERB
brj-23918	74	29	the	the	DET
brj-23918	74	30	issue	issue	NOUN
brj-23918	74	31	of	of	ADP
brj-23918	74	32	background	background	NOUN
brj-23918	74	33	interference	interference	NOUN
brj-23918	74	34	and	and	CCONJ
brj-23918	74	35	increase	increase	VERB
brj-23918	74	36	the	the	DET
brj-23918	74	37	importance	importance	NOUN
brj-23918	74	38	of	of	ADP
brj-23918	74	39	insulator	insulator	NOUN
brj-23918	74	40	features	feature	NOUN
brj-23918	74	41	.	.	PUNCT
brj-23918	75	1	after	after	ADP
brj-23918	75	2	adding	add	VERB
brj-23918	75	3	eca	eca	NOUN
brj-23918	75	4	,	,	PUNCT
brj-23918	75	5	the	the	DET
brj-23918	75	6	background	background	NOUN
brj-23918	75	7	noise	noise	NOUN
brj-23918	75	8	is	be	AUX
brj-23918	75	9	effectively	effectively	ADV
brj-23918	75	10	suppressed	suppress	VERB
brj-23918	75	11	,	,	PUNCT
brj-23918	75	12	and	and	CCONJ
brj-23918	75	13	more	more	ADJ
brj-23918	75	14	attention	attention	NOUN
brj-23918	75	15	is	be	AUX
brj-23918	75	16	paid	pay	VERB
brj-23918	75	17	to	to	ADP
brj-23918	75	18	the	the	DET
brj-23918	75	19	insulator	insulator	NOUN
brj-23918	75	20	area	area	NOUN
brj-23918	75	21	,	,	PUNCT
brj-23918	75	22	thereby	thereby	ADV
brj-23918	75	23	improving	improve	VERB
brj-23918	75	24	the	the	DET
brj-23918	75	25	accurate	accurate	ADJ
brj-23918	75	26	detection	detection	NOUN
brj-23918	75	27	ability	ability	NOUN
brj-23918	75	28	of	of	ADP
brj-23918	75	29	the	the	DET
brj-23918	75	30	model	model	NOUN
brj-23918	75	31	for	for	ADP
brj-23918	75	32	insulator	insulator	NOUN
brj-23918	75	33	defects	defect	NOUN
brj-23918	75	34	.	.	PUNCT
brj-23918	76	1	sheng	sheng	PROPN
brj-23918	76	2	et	et	PROPN
brj-23918	76	3	al	al	PROPN
brj-23918	76	4	.	.	PROPN
brj-23918	76	5	(	(	PUNCT
brj-23918	76	6	2023	2023	NUM
brj-23918	76	7	)	)	PUNCT
brj-23918	76	8	designed	design	VERB
brj-23918	76	9	eca	eca	NOUN
brj-23918	76	10	convblock	convblock	PROPN
brj-23918	76	11	in	in	ADP
brj-23918	76	12	the	the	DET
brj-23918	76	13	backbone	backbone	NOUN
brj-23918	76	14	feature	feature	NOUN
brj-23918	76	15	extraction	extraction	NOUN
brj-23918	76	16	network	network	NOUN
brj-23918	76	17	to	to	PART
brj-23918	76	18	prevent	prevent	VERB
brj-23918	76	19	dimensionality	dimensionality	NOUN
brj-23918	76	20	reduction	reduction	NOUN
brj-23918	76	21	in	in	ADP
brj-23918	76	22	model	model	NOUN
brj-23918	76	23	channel	channel	NOUN
brj-23918	76	24	importance	importance	NOUN
brj-23918	76	25	prediction	prediction	NOUN
brj-23918	76	26	,	,	PUNCT
brj-23918	76	27	to	to	PART
brj-23918	76	28	enhance	enhance	VERB
brj-23918	76	29	high	high	ADJ
brj-23918	76	30	-	-	PUNCT
brj-23918	76	31	quality	quality	NOUN
brj-23918	76	32	expression	expression	NOUN
brj-23918	76	33	of	of	ADP
brj-23918	76	34	target	target	NOUN
brj-23918	76	35	features	feature	NOUN
brj-23918	76	36	,	,	PUNCT
brj-23918	76	37	and	and	CCONJ
brj-23918	76	38	to	to	PART
brj-23918	76	39	effectively	effectively	ADV
brj-23918	76	40	improve	improve	VERB
brj-23918	76	41	defect	defect	NOUN
brj-23918	76	42	detection	detection	NOUN
brj-23918	76	43	accuracy	accuracy	NOUN
brj-23918	76	44	.	.	PUNCT
brj-23918	77	1	li	li	PROPN
brj-23918	77	2	et	et	PROPN
brj-23918	77	3	al	al	PROPN
brj-23918	77	4	.	.	PROPN
brj-23918	77	5	(	(	PUNCT
brj-23918	77	6	2022	2022	NUM
brj-23918	77	7	)	)	PUNCT
brj-23918	77	8	made	make	VERB
brj-23918	77	9	some	some	DET
brj-23918	77	10	modifications	modification	NOUN
brj-23918	77	11	to	to	ADP
brj-23918	77	12	the	the	DET
brj-23918	77	13	yolov5	yolov5	NOUN
brj-23918	77	14	model	model	NOUN
brj-23918	77	15	and	and	CCONJ
brj-23918	77	16	introduced	introduce	VERB
brj-23918	77	17	the	the	DET
brj-23918	77	18	eca	eca	NOUN
brj-23918	77	19	mechanism	mechanism	NOUN
brj-23918	77	20	,	,	PUNCT
brj-23918	77	21	assigning	assign	VERB
brj-23918	77	22	higher	high	ADJ
brj-23918	77	23	weights	weight	NOUN
brj-23918	77	24	to	to	ADP
brj-23918	77	25	important	important	ADJ
brj-23918	77	26	feature	feature	NOUN
brj-23918	77	27	information	information	NOUN
brj-23918	77	28	and	and	CCONJ
brj-23918	77	29	achieving	achieve	VERB
brj-23918	77	30	good	good	ADJ
brj-23918	77	31	detection	detection	NOUN
brj-23918	77	32	accuracy	accuracy	NOUN
brj-23918	77	33	.	.	PUNCT
brj-23918	78	1	wang	wang	PROPN
brj-23918	78	2	et	et	PROPN
brj-23918	78	3	al	al	PROPN
brj-23918	78	4	.	.	PROPN
brj-23918	78	5	(	(	PUNCT
brj-23918	78	6	2022	2022	NUM
brj-23918	78	7	)	)	PUNCT
brj-23918	78	8	introduced	introduce	VERB
brj-23918	78	9	a	a	DET
brj-23918	78	10	lightweight	lightweight	ADJ
brj-23918	78	11	eca	eca	NOUN
brj-23918	78	12	attention	attention	NOUN
brj-23918	78	13	mechanism	mechanism	NOUN
brj-23918	78	14	in	in	ADP
brj-23918	78	15	the	the	DET
brj-23918	78	16	feature	feature	NOUN
brj-23918	78	17	pyramid	pyramid	NOUN
brj-23918	78	18	model	model	NOUN
brj-23918	78	19	panet	panet	PROPN
brj-23918	78	20	,	,	PUNCT
brj-23918	78	21	which	which	PRON
brj-23918	78	22	enables	enable	VERB
brj-23918	78	23	weight	weight	NOUN
brj-23918	78	24	analysis	analysis	NOUN
brj-23918	78	25	of	of	ADP
brj-23918	78	26	the	the	DET
brj-23918	78	27	importance	importance	NOUN
brj-23918	78	28	of	of	ADP
brj-23918	78	29	different	different	ADJ
brj-23918	78	30	channel	channel	NOUN
brj-23918	78	31	feature	feature	NOUN
brj-23918	78	32	maps	map	NOUN
brj-23918	78	33	through	through	ADP
brj-23918	78	34	cross	cross	NOUN
brj-23918	78	35	channel	channel	NOUN
brj-23918	78	36	interaction	interaction	NOUN
brj-23918	78	37	,	,	PUNCT
brj-23918	78	38	enabling	enable	VERB
brj-23918	78	39	the	the	DET
brj-23918	78	40	network	network	NOUN
brj-23918	78	41	to	to	PART
brj-23918	78	42	extract	extract	VERB
brj-23918	78	43	more	more	ADV
brj-23918	78	44	prominent	prominent	ADJ
brj-23918	78	45	features	feature	NOUN
brj-23918	78	46	to	to	PART
brj-23918	78	47	distinguish	distinguish	VERB
brj-23918	78	48	categories	category	NOUN
brj-23918	78	49	.	.	PUNCT
brj-23918	79	1	wu	wu	PROPN
brj-23918	79	2	and	and	CCONJ
brj-23918	79	3	gu	gu	PROPN
brj-23918	79	4	(	(	PUNCT
brj-23918	79	5	2023	2023	NUM
brj-23918	79	6	)	)	PUNCT
brj-23918	79	7	proposed	propose	VERB
brj-23918	79	8	a	a	DET
brj-23918	79	9	gait	gait	ADJ
brj-23918	79	10	classification	classification	NOUN
brj-23918	79	11	model	model	NOUN
brj-23918	79	12	based	base	VERB
brj-23918	79	13	on	on	ADP
brj-23918	79	14	convolutional	convolutional	ADJ
brj-23918	79	15	neural	neural	ADJ
brj-23918	79	16	network	network	NOUN
brj-23918	79	17	(	(	PUNCT
brj-23918	79	18	cnn	cnn	PROPN
brj-23918	79	19	)	)	PUNCT
brj-23918	79	20	and	and	CCONJ
brj-23918	79	21	efficient	efficient	ADJ
brj-23918	79	22	channel	channel	NOUN
brj-23918	79	23	attention	attention	NOUN
brj-23918	79	24	(	(	PUNCT
brj-23918	79	25	eca	eca	NOUN
brj-23918	79	26	)	)	PUNCT
brj-23918	79	27	module	module	NOUN
brj-23918	79	28	for	for	ADP
brj-23918	79	29	gait	gait	NOUN
brj-23918	79	30	detection	detection	NOUN
brj-23918	79	31	applications	application	NOUN
brj-23918	79	32	based	base	VERB
brj-23918	79	33	on	on	ADP
brj-23918	79	34	surface	surface	NOUN
brj-23918	79	35	electromyography	electromyography	NOUN
brj-23918	79	36	(	(	PUNCT
brj-23918	79	37	semg	semg	NOUN
brj-23918	79	38	)	)	PUNCT
brj-23918	79	39	signals	signal	NOUN
brj-23918	79	40	.	.	PUNCT
brj-23918	80	1	cnn	cnn	PROPN
brj-23918	80	2	was	be	AUX
brj-23918	80	3	used	use	VERB
brj-23918	80	4	to	to	PART
brj-23918	80	5	extract	extract	VERB
brj-23918	80	6	features	feature	NOUN
brj-23918	80	7	from	from	ADP
brj-23918	80	8	onedimensional	onedimensional	ADJ
brj-23918	80	9	input	input	NOUN
brj-23918	80	10	surface	surface	NOUN
brj-23918	80	11	electromyography	electromyography	NOUN
brj-23918	80	12	signals	signal	VERB
brj-23918	80	13	to	to	PART
brj-23918	80	14	obtain	obtain	VERB
brj-23918	80	15	feature	feature	NOUN
brj-23918	80	16	vectors	vector	NOUN
brj-23918	80	17	,	,	PUNCT
brj-23918	80	18	which	which	PRON
brj-23918	80	19	were	be	AUX
brj-23918	80	20	then	then	ADV
brj-23918	80	21	input	input	VERB
brj-23918	80	22	into	into	ADP
brj-23918	80	23	the	the	DET
brj-23918	80	24	eca	eca	NOUN
brj-23918	80	25	module	module	NOUN
brj-23918	80	26	for	for	ADP
brj-23918	80	27	cross	cross	NOUN
brj-23918	80	28	channel	channel	NOUN
brj-23918	80	29	interaction	interaction	NOUN
brj-23918	80	30	.	.	PUNCT
brj-23918	81	1	wang	wang	PROPN
brj-23918	81	2	et	et	PROPN
brj-23918	81	3	al	al	PROPN
brj-23918	81	4	.	.	PROPN
brj-23918	81	5	(	(	PUNCT
brj-23918	81	6	2023	2023	NUM
brj-23918	81	7	)	)	PUNCT
brj-23918	81	8	solved	solve	VERB
brj-23918	81	9	the	the	DET
brj-23918	81	10	problem	problem	NOUN
brj-23918	81	11	of	of	ADP
brj-23918	81	12	low	low	ADJ
brj-23918	81	13	target	target	NOUN
brj-23918	81	14	recognition	recognition	NOUN
brj-23918	81	15	accuracy	accuracy	NOUN
brj-23918	81	16	and	and	CCONJ
brj-23918	81	17	high	high	ADJ
brj-23918	81	18	model	model	NOUN
brj-23918	81	19	complexity	complexity	NOUN
brj-23918	81	20	in	in	ADP
brj-23918	81	21	the	the	DET
brj-23918	81	22	field	field	NOUN
brj-23918	81	23	of	of	ADP
brj-23918	81	24	3d	3d	NUM
brj-23918	81	25	object	object	NOUN
brj-23918	81	26	detection	detection	NOUN
brj-23918	81	27	.	.	PUNCT
brj-23918	82	1	referring	refer	VERB
brj-23918	82	2	to	to	ADP
brj-23918	82	3	the	the	DET
brj-23918	82	4	voxelnet	voxelnet	NOUN
brj-23918	82	5	model	model	NOUN
brj-23918	82	6	,	,	PUNCT
brj-23918	82	7	the	the	DET
brj-23918	82	8	eca	eca	NOUN
brj-23918	82	9	mechanism	mechanism	NOUN
brj-23918	82	10	was	be	AUX
brj-23918	82	11	introduced	introduce	VERB
brj-23918	82	12	to	to	PART
brj-23918	82	13	reduce	reduce	VERB
brj-23918	82	14	the	the	DET
brj-23918	82	15	complexity	complexity	NOUN
brj-23918	82	16	of	of	ADP
brj-23918	82	17	the	the	DET
brj-23918	82	18	model	model	NOUN
brj-23918	82	19	while	while	SCONJ
brj-23918	82	20	maintaining	maintain	VERB
brj-23918	82	21	good	good	ADJ
brj-23918	82	22	performance	performance	NOUN
brj-23918	82	23	.	.	PUNCT
brj-23918	83	1	experimental	experimental	ADJ
brj-23918	83	2	method	method	NOUN
brj-23918	83	3	framework	framework	NOUN
brj-23918	83	4	to	to	PART
brj-23918	83	5	solve	solve	VERB
brj-23918	83	6	the	the	DET
brj-23918	83	7	problems	problem	NOUN
brj-23918	83	8	of	of	ADP
brj-23918	83	9	cracks	crack	NOUN
brj-23918	83	10	being	be	AUX
brj-23918	83	11	unable	unable	ADJ
brj-23918	83	12	to	to	PART
brj-23918	83	13	be	be	AUX
brj-23918	83	14	segmented	segment	VERB
brj-23918	83	15	due	due	ADP
brj-23918	83	16	to	to	ADP
brj-23918	83	17	similarity	similarity	NOUN
brj-23918	83	18	in	in	ADP
brj-23918	83	19	background	background	NOUN
brj-23918	83	20	color	color	NOUN
brj-23918	83	21	,	,	PUNCT
brj-23918	83	22	wood	wood	NOUN
brj-23918	83	23	texture	texture	NOUN
brj-23918	83	24	being	be	AUX
brj-23918	83	25	similar	similar	ADJ
brj-23918	83	26	to	to	ADP
brj-23918	83	27	cracks	crack	NOUN
brj-23918	83	28	,	,	PUNCT
brj-23918	83	29	and	and	CCONJ
brj-23918	83	30	segmentation	segmentation	NOUN
brj-23918	83	31	errors	error	NOUN
brj-23918	83	32	occurring	occur	VERB
brj-23918	83	33	simultaneously	simultaneously	ADV
brj-23918	83	34	with	with	ADP
brj-23918	83	35	some	some	DET
brj-23918	83	36	joints	joint	NOUN
brj-23918	84	1	,	,	PUNCT
brj-23918	84	2	this	this	DET
brj-23918	84	3	paper	paper	NOUN
brj-23918	84	4	proposes	propose	VERB
brj-23918	84	5	an	an	DET
brj-23918	84	6	improved	improved	ADJ
brj-23918	84	7	attention	attention	NOUN
brj-23918	84	8	u	u	NOUN
brj-23918	84	9	-	-	NOUN
brj-23918	84	10	net	net	ADJ
brj-23918	84	11	,	,	PUNCT
brj-23918	84	12	namely	namely	ADV
brj-23918	84	13	ieca	ieca	VERB
brj-23918	84	14	u	u	NOUN
brj-23918	84	15	-	-	NOUN
brj-23918	84	16	net	net	ADJ
brj-23918	84	17	(	(	PUNCT
brj-23918	84	18	improved	improved	ADJ
brj-23918	84	19	eca	eca	NOUN
brj-23918	84	20	-	-	PUNCT
brj-23918	84	21	cbam	cbam	NOUN
brj-23918	84	22	-	-	PUNCT
brj-23918	84	23	agu	agu	NOUN
brj-23918	84	24	-	-	PUNCT
brj-23918	84	25	net	net	NOUN
brj-23918	84	26	)	)	PUNCT
brj-23918	84	27	.	.	PUNCT
brj-23918	85	1	the	the	DET
brj-23918	85	2	architecture	architecture	NOUN
brj-23918	85	3	diagram	diagram	NOUN
brj-23918	85	4	of	of	ADP
brj-23918	85	5	ieca	ieca	NOUN
brj-23918	85	6	unet	unet	NOUN
brj-23918	85	7	is	be	AUX
brj-23918	85	8	shown	show	VERB
brj-23918	85	9	in	in	ADP
brj-23918	85	10	fig	fig	NOUN
brj-23918	85	11	.	.	PUNCT
brj-23918	86	1	1	1	X
brj-23918	86	2	.	.	NUM
brj-23918	86	3	improved	improve	VERB
brj-23918	86	4	attention	attention	NOUN
brj-23918	86	5	gate	gate	NOUN
brj-23918	86	6	in	in	ADP
brj-23918	86	7	the	the	DET
brj-23918	86	8	face	face	NOUN
brj-23918	86	9	of	of	ADP
brj-23918	86	10	cnn	cnn	PROPN
brj-23918	86	11	’s	’s	PART
brj-23918	86	12	fall	fall	NOUN
brj-23918	86	13	pose	pose	VERB
brj-23918	86	14	(	(	PUNCT
brj-23918	86	15	fp	fp	X
brj-23918	86	16	)	)	PUNCT
brj-23918	86	17	prediction	prediction	NOUN
brj-23918	86	18	problem	problem	NOUN
brj-23918	86	19	for	for	ADP
brj-23918	86	20	small	small	ADJ
brj-23918	86	21	targets	target	NOUN
brj-23918	86	22	with	with	ADP
brj-23918	86	23	large	large	ADJ
brj-23918	86	24	deformation	deformation	NOUN
brj-23918	86	25	,	,	PUNCT
brj-23918	86	26	locating	locating	NOUN
brj-23918	86	27	is	be	AUX
brj-23918	86	28	usually	usually	ADV
brj-23918	86	29	done	do	VERB
brj-23918	86	30	first	first	ADV
brj-23918	86	31	,	,	PUNCT
brj-23918	86	32	followed	follow	VERB
brj-23918	86	33	by	by	ADP
brj-23918	86	34	segmenting	segment	VERB
brj-23918	86	35	.	.	PUNCT
brj-23918	87	1	oktay	oktay	PROPN
brj-23918	87	2	et	et	PROPN
brj-23918	87	3	al	al	PROPN
brj-23918	87	4	.	.	PROPN
brj-23918	88	1	(	(	PUNCT
brj-23918	88	2	2018	2018	NUM
brj-23918	88	3	)	)	PUNCT
brj-23918	88	4	added	add	VERB
brj-23918	88	5	the	the	DET
brj-23918	88	6	attention	attention	NOUN
brj-23918	88	7	gate	gate	NOUN
brj-23918	88	8	(	(	PUNCT
brj-23918	88	9	ag	ag	PROPN
brj-23918	88	10	)	)	PUNCT
brj-23918	88	11	module	module	NOUN
brj-23918	88	12	to	to	ADP
brj-23918	88	13	u	u	NOUN
brj-23918	88	14	-	-	NOUN
brj-23918	88	15	net	net	ADJ
brj-23918	88	16	and	and	CCONJ
brj-23918	88	17	proposed	propose	VERB
brj-23918	88	18	the	the	DET
brj-23918	88	19	attention	attention	NOUN
brj-23918	88	20	u	u	ADJ
brj-23918	88	21	-	-	ADJ
brj-23918	88	22	net	net	ADJ
brj-23918	88	23	model	model	NOUN
brj-23918	88	24	,	,	PUNCT
brj-23918	88	25	which	which	PRON
brj-23918	88	26	directly	directly	ADV
brj-23918	88	27	achieves	achieve	VERB
brj-23918	88	28	integrated	integrate	VERB
brj-23918	88	29	localization	localization	NOUN
brj-23918	88	30	and	and	CCONJ
brj-23918	88	31	segmentation	segmentation	NOUN
brj-23918	88	32	without	without	ADP
brj-23918	88	33	the	the	DET
brj-23918	88	34	need	need	NOUN
brj-23918	88	35	to	to	PART
brj-23918	88	36	train	train	VERB
brj-23918	88	37	multiple	multiple	ADJ
brj-23918	88	38	models	model	NOUN
brj-23918	88	39	and	and	CCONJ
brj-23918	88	40	a	a	DET
brj-23918	88	41	large	large	ADJ
brj-23918	88	42	number	number	NOUN
brj-23918	88	43	of	of	ADP
brj-23918	88	44	additional	additional	ADJ
brj-23918	88	45	parameters	parameter	NOUN
brj-23918	88	46	,	,	PUNCT
brj-23918	88	47	while	while	SCONJ
brj-23918	88	48	suppressing	suppress	VERB
brj-23918	88	49	irrelevant	irrelevant	ADJ
brj-23918	88	50	background	background	NOUN
brj-23918	88	51	region	region	NOUN
brj-23918	88	52	responses	response	NOUN
brj-23918	88	53	and	and	CCONJ
brj-23918	88	54	eliminating	eliminate	VERB
brj-23918	88	55	the	the	DET
brj-23918	88	56	need	need	NOUN
brj-23918	88	57	to	to	PART
brj-23918	88	58	crop	crop	VERB
brj-23918	88	59	rois	rois	NOUN
brj-23918	88	60	across	across	ADP
brj-23918	88	61	networks	network	NOUN
brj-23918	88	62	.	.	PUNCT
brj-23918	89	1	compared	compare	VERB
brj-23918	89	2	to	to	ADP
brj-23918	89	3	the	the	DET
brj-23918	89	4	prototype	prototype	NOUN
brj-23918	89	5	u	u	NOUN
brj-23918	89	6	-	-	NOUN
brj-23918	89	7	net	net	ADJ
brj-23918	89	8	,	,	PUNCT
brj-23918	89	9	there	there	PRON
brj-23918	89	10	was	be	VERB
brj-23918	89	11	significant	significant	ADJ
brj-23918	89	12	improvement	improvement	NOUN
brj-23918	89	13	in	in	ADP
brj-23918	89	14	performance	performance	NOUN
brj-23918	89	15	.	.	PUNCT
brj-23918	90	1	to	to	PART
brj-23918	90	2	adapt	adapt	VERB
brj-23918	90	3	to	to	ADP
brj-23918	90	4	the	the	DET
brj-23918	90	5	surface	surface	NOUN
brj-23918	90	6	image	image	NOUN
brj-23918	90	7	of	of	ADP
brj-23918	90	8	two	two	NUM
brj-23918	90	9	-	-	PUNCT
brj-23918	90	10	dimensional	dimensional	ADJ
brj-23918	90	11	sawn	sawn	NOUN
brj-23918	90	12	timber	timber	NOUN
brj-23918	90	13	,	,	PUNCT
brj-23918	90	14	ag	ag	PROPN
brj-23918	90	15	was	be	AUX
brj-23918	90	16	improved	improve	VERB
brj-23918	90	17	by	by	ADP
brj-23918	90	18	removing	remove	VERB
brj-23918	90	19	depth	depth	NOUN
brj-23918	90	20	channels	channel	NOUN
brj-23918	90	21	and	and	CCONJ
brj-23918	90	22	resampler	resampler	NOUN
brj-23918	90	23	.	.	PUNCT
brj-23918	91	1	in	in	ADP
brj-23918	91	2	the	the	DET
brj-23918	91	3	improved	improved	ADJ
brj-23918	91	4	ag	ag	PROPN
brj-23918	91	5	,	,	PUNCT
brj-23918	91	6	the	the	DET
brj-23918	91	7	inputs	input	NOUN
brj-23918	91	8	x	x	X
brj-23918	91	9	and	and	CCONJ
brj-23918	91	10	g	g	PROPN
brj-23918	91	11	are	be	AUX
brj-23918	91	12	convolved	convolve	VERB
brj-23918	91	13	and	and	CCONJ
brj-23918	91	14	added	add	VERB
brj-23918	91	15	separately	separately	ADV
brj-23918	91	16	,	,	PUNCT
brj-23918	91	17	and	and	CCONJ
brj-23918	91	18	then	then	ADV
brj-23918	91	19	the	the	DET
brj-23918	91	20	weight	weight	NOUN
brj-23918	91	21	map	map	NOUN
brj-23918	91	22	is	be	AUX
brj-23918	91	23	obtained	obtain	VERB
brj-23918	91	24	through	through	ADP
brj-23918	91	25	continuous	continuous	ADJ
brj-23918	91	26	relu	relu	NOUN
brj-23918	91	27	,	,	PUNCT
brj-23918	91	28	convolution	convolution	NOUN
brj-23918	91	29	,	,	PUNCT
brj-23918	91	30	and	and	CCONJ
brj-23918	91	31	sigmoid	sigmoid	NOUN
brj-23918	91	32	.	.	PUNCT
brj-23918	92	1	finally	finally	ADV
brj-23918	92	2	,	,	PUNCT
brj-23918	92	3	the	the	DET
brj-23918	92	4	input	input	NOUN
brj-23918	92	5	x	x	PUNCT
brj-23918	92	6	is	be	AUX
brj-23918	92	7	multiplied	multiply	VERB
brj-23918	92	8	,	,	PUNCT
brj-23918	92	9	as	as	SCONJ
brj-23918	92	10	shown	show	VERB
brj-23918	92	11	in	in	ADP
brj-23918	92	12	fig	fig	NOUN
brj-23918	92	13	.	.	PUNCT
brj-23918	93	1	2	2	NUM
brj-23918	93	2	.	.	X
brj-23918	93	3	peer	peer	NOUN
brj-23918	93	4	-	-	PUNCT
brj-23918	93	5	reviewed	review	VERB
brj-23918	93	6	article	article	NOUN
brj-23918	93	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	93	8	dong	dong	PROPN
brj-23918	93	9	et	et	PROPN
brj-23918	93	10	al	al	PROPN
brj-23918	93	11	.	.	PROPN
brj-23918	94	1	(	(	PUNCT
brj-23918	94	2	2025	2025	NUM
brj-23918	94	3	)	)	PUNCT
brj-23918	94	4	.	.	PUNCT
brj-23918	95	1	“	"	PUNCT
brj-23918	95	2	cracks	crack	NOUN
brj-23918	95	3	in	in	ADP
brj-23918	95	4	sawn	sawn	NOUN
brj-23918	95	5	wood	wood	NOUN
brj-23918	95	6	,	,	PUNCT
brj-23918	95	7	”	"	PUNCT
brj-23918	95	8	bioresources	bioresource	NOUN
brj-23918	95	9	20(2	20(2	NUM
brj-23918	95	10	)	)	PUNCT
brj-23918	95	11	,	,	PUNCT
brj-23918	95	12	3545	3545	NUM
brj-23918	95	13	-	-	SYM
brj-23918	95	14	3556	3556	NUM
brj-23918	95	15	.	.	PUNCT
brj-23918	96	1	3549	3549	NUM
brj-23918	96	2	fig	fig	NOUN
brj-23918	96	3	.	.	PUNCT
brj-23918	97	1	1	1	X
brj-23918	97	2	.	.	X
brj-23918	97	3	network	network	NOUN
brj-23918	97	4	framework	framework	NOUN
brj-23918	97	5	for	for	ADP
brj-23918	97	6	semantic	semantic	ADJ
brj-23918	97	7	segmentation	segmentation	NOUN
brj-23918	97	8	of	of	ADP
brj-23918	97	9	sawn	sawn	NOUN
brj-23918	97	10	timber	timber	NOUN
brj-23918	97	11	surface	surface	NOUN
brj-23918	97	12	crack	crack	NOUN
brj-23918	97	13	image	image	NOUN
brj-23918	97	14	based	base	VERB
brj-23918	97	15	on	on	ADP
brj-23918	97	16	attention	attention	NOUN
brj-23918	97	17	u	u	NOUN
brj-23918	97	18	-	-	NOUN
brj-23918	97	19	net	net	ADJ
brj-23918	97	20	with	with	ADP
brj-23918	97	21	cbam	cbam	NOUN
brj-23918	97	22	fig	fig	NOUN
brj-23918	97	23	.	.	PUNCT
brj-23918	98	1	2	2	X
brj-23918	98	2	.	.	X
brj-23918	98	3	ag	ag	PROPN
brj-23918	98	4	network	network	NOUN
brj-23918	98	5	framework	framework	NOUN
brj-23918	98	6	in	in	ADP
brj-23918	98	7	fig	fig	NOUN
brj-23918	98	8	.	.	PUNCT
brj-23918	99	1	2	2	NUM
brj-23918	99	2	,	,	PUNCT
brj-23918	99	3	the	the	DET
brj-23918	99	4	attention	attention	NOUN
brj-23918	99	5	coefficient	coefficient	VERB
brj-23918	99	6	α	α	PROPN
brj-23918	99	7	∈	∈	PROPN
brj-23918	99	8	(	(	PUNCT
brj-23918	99	9	0,1	0,1	NUM
brj-23918	99	10	)	)	PUNCT
brj-23918	99	11	highlights	highlight	NOUN
brj-23918	99	12	the	the	DET
brj-23918	99	13	roi	roi	NOUN
brj-23918	99	14	and	and	CCONJ
brj-23918	99	15	suppresses	suppress	VERB
brj-23918	99	16	irrelevant	irrelevant	ADJ
brj-23918	99	17	background	background	NOUN
brj-23918	99	18	feature	feature	NOUN
brj-23918	99	19	responses	response	NOUN
brj-23918	99	20	.	.	PUNCT
brj-23918	100	1	the	the	DET
brj-23918	100	2	multiplication	multiplication	NOUN
brj-23918	100	3	of	of	ADP
brj-23918	100	4	alpha	alpha	NOUN
brj-23918	100	5	and	and	CCONJ
brj-23918	100	6	feature	feature	NOUN
brj-23918	100	7	maps	map	NOUN
brj-23918	100	8	is	be	AUX
brj-23918	100	9	based	base	VERB
brj-23918	100	10	on	on	ADP
brj-23918	100	11	element	element	ADJ
brj-23918	100	12	-	-	ADJ
brj-23918	100	13	wise	wise	ADJ
brj-23918	100	14	multiplication	multiplication	NOUN
brj-23918	100	15	.	.	PUNCT
brj-23918	101	1	the	the	DET
brj-23918	101	2	above	above	ADJ
brj-23918	101	3	figure	figure	NOUN
brj-23918	101	4	uses	use	VERB
brj-23918	101	5	additive	additive	ADJ
brj-23918	101	6	attention	attention	NOUN
brj-23918	101	7	to	to	PART
brj-23918	101	8	obtain	obtain	VERB
brj-23918	101	9	gating	gate	VERB
brj-23918	101	10	coefficients	coefficient	NOUN
brj-23918	101	11	,	,	PUNCT
brj-23918	101	12	which	which	PRON
brj-23918	101	13	performs	perform	VERB
brj-23918	101	14	better	well	ADV
brj-23918	101	15	in	in	ADP
brj-23918	101	16	experiments	experiment	NOUN
brj-23918	101	17	compared	compare	VERB
brj-23918	101	18	to	to	ADP
brj-23918	101	19	multiplicative	multiplicative	ADJ
brj-23918	101	20	attention	attention	NOUN
brj-23918	101	21	.	.	PUNCT
brj-23918	102	1	σ	σ	NOUN
brj-23918	102	2	1	1	NUM
brj-23918	102	3	and	and	CCONJ
brj-23918	102	4	σ	σ	PROPN
brj-23918	102	5	2	2	NUM
brj-23918	102	6	are	be	AUX
brj-23918	102	7	relu	relu	NOUN
brj-23918	102	8	and	and	CCONJ
brj-23918	102	9	sigmoid	sigmoid	NOUN
brj-23918	102	10	functions	function	NOUN
brj-23918	102	11	,	,	PUNCT
brj-23918	102	12	respectively	respectively	ADV
brj-23918	102	13	,	,	PUNCT
brj-23918	102	14	and	and	CCONJ
brj-23918	102	15	w1	w1	NOUN
brj-23918	102	16	,	,	PUNCT
brj-23918	102	17	w2	w2	NOUN
brj-23918	102	18	,	,	PUNCT
brj-23918	102	19	and	and	CCONJ
brj-23918	102	20	ψ	ψ	NOUN
brj-23918	102	21	are	be	AUX
brj-23918	102	22	all	all	PRON
brj-23918	102	23	convolution	convolution	NOUN
brj-23918	102	24	operations	operation	NOUN
brj-23918	102	25	.	.	PUNCT
brj-23918	103	1	among	among	ADP
brj-23918	103	2	them	they	PRON
brj-23918	103	3	,	,	PUNCT
brj-23918	103	4	the	the	DET
brj-23918	103	5	sigmoid	sigmoid	NOUN
brj-23918	103	6	function	function	NOUN
brj-23918	103	7	can	can	AUX
brj-23918	103	8	make	make	VERB
brj-23918	103	9	the	the	DET
brj-23918	103	10	training	training	NOUN
brj-23918	103	11	converge	converge	VERB
brj-23918	103	12	better	well	ADV
brj-23918	103	13	.	.	PUNCT
brj-23918	104	1	improved	improve	VERB
brj-23918	104	2	convolutional	convolutional	ADJ
brj-23918	104	3	block	block	NOUN
brj-23918	104	4	attention	attention	NOUN
brj-23918	104	5	module	module	NOUN
brj-23918	104	6	on	on	ADP
brj-23918	104	7	the	the	DET
brj-23918	104	8	basis	basis	NOUN
brj-23918	104	9	of	of	ADP
brj-23918	104	10	the	the	DET
brj-23918	104	11	original	original	ADJ
brj-23918	104	12	attention	attention	NOUN
brj-23918	104	13	u	u	NOUN
brj-23918	104	14	-	-	NOUN
brj-23918	104	15	net	net	ADJ
brj-23918	104	16	,	,	PUNCT
brj-23918	104	17	the	the	DET
brj-23918	104	18	convolutional	convolutional	ADJ
brj-23918	104	19	block	block	NOUN
brj-23918	104	20	attention	attention	NOUN
brj-23918	104	21	module	module	NOUN
brj-23918	104	22	(	(	PUNCT
brj-23918	104	23	cbam	cbam	NOUN
brj-23918	104	24	)	)	PUNCT
brj-23918	104	25	proposed	propose	VERB
brj-23918	104	26	by	by	ADP
brj-23918	104	27	woo	woo	PROPN
brj-23918	104	28	et	et	PROPN
brj-23918	104	29	al	al	PROPN
brj-23918	104	30	.	.	PROPN
brj-23918	105	1	(	(	PUNCT
brj-23918	105	2	2018	2018	NUM
brj-23918	105	3	)	)	PUNCT
brj-23918	105	4	was	be	AUX
brj-23918	105	5	added	add	VERB
brj-23918	105	6	during	during	ADP
brj-23918	105	7	the	the	DET
brj-23918	105	8	downsampling	downsample	VERB
brj-23918	105	9	process	process	NOUN
brj-23918	105	10	.	.	PUNCT
brj-23918	106	1	cbam	cbam	NOUN
brj-23918	106	2	is	be	AUX
brj-23918	106	3	a	a	DET
brj-23918	106	4	lightweight	lightweight	ADJ
brj-23918	106	5	and	and	CCONJ
brj-23918	106	6	general	general	ADJ
brj-23918	106	7	-	-	PUNCT
brj-23918	106	8	purpose	purpose	NOUN
brj-23918	106	9	module	module	NOUN
brj-23918	106	10	with	with	ADP
brj-23918	106	11	few	few	ADJ
brj-23918	106	12	parameters	parameter	NOUN
brj-23918	106	13	.	.	PUNCT
brj-23918	107	1	it	it	PRON
brj-23918	107	2	is	be	AUX
brj-23918	107	3	easy	easy	ADJ
brj-23918	107	4	to	to	PART
brj-23918	107	5	use	use	VERB
brj-23918	107	6	and	and	CCONJ
brj-23918	107	7	can	can	AUX
brj-23918	107	8	be	be	AUX
brj-23918	107	9	well	well	ADV
brj-23918	107	10	combined	combine	VERB
brj-23918	107	11	with	with	ADP
brj-23918	107	12	the	the	DET
brj-23918	107	13	cnn	cnn	PROPN
brj-23918	107	14	of	of	ADP
brj-23918	107	15	the	the	DET
brj-23918	107	16	original	original	ADJ
brj-23918	107	17	model	model	NOUN
brj-23918	107	18	.	.	PUNCT
brj-23918	108	1	cbam	cbam	NOUN
brj-23918	108	2	mainly	mainly	ADV
brj-23918	108	3	includes	include	VERB
brj-23918	108	4	channel	channel	NOUN
brj-23918	108	5	attention	attention	NOUN
brj-23918	108	6	mechanism	mechanism	NOUN
brj-23918	108	7	and	and	CCONJ
brj-23918	108	8	spatial	spatial	ADJ
brj-23918	108	9	attention	attention	NOUN
brj-23918	108	10	mechanism	mechanism	NOUN
brj-23918	108	11	,	,	PUNCT
brj-23918	108	12	which	which	PRON
brj-23918	108	13	respectively	respectively	ADV
brj-23918	108	14	capture	capture	VERB
brj-23918	108	15	the	the	DET
brj-23918	108	16	dependency	dependency	NOUN
brj-23918	108	17	relationship	relationship	NOUN
brj-23918	108	18	between	between	ADP
brj-23918	108	19	channels	channel	NOUN
brj-23918	108	20	and	and	CCONJ
brj-23918	108	21	spatial	spatial	ADJ
brj-23918	108	22	pixel	pixel	PROPN
brj-23918	108	23	level	level	NOUN
brj-23918	108	24	relationship	relationship	NOUN
brj-23918	108	25	.	.	PUNCT
brj-23918	109	1	the	the	DET
brj-23918	109	2	mixture	mixture	NOUN
brj-23918	109	3	of	of	ADP
brj-23918	109	4	the	the	DET
brj-23918	109	5	two	two	NUM
brj-23918	109	6	can	can	AUX
brj-23918	109	7	adaptively	adaptively	ADV
brj-23918	109	8	extract	extract	VERB
brj-23918	109	9	channel	channel	NOUN
brj-23918	109	10	and	and	CCONJ
brj-23918	109	11	spatial	spatial	ADJ
brj-23918	109	12	features	feature	NOUN
brj-23918	109	13	,	,	PUNCT
brj-23918	109	14	which	which	PRON
brj-23918	109	15	is	be	AUX
brj-23918	109	16	a	a	DET
brj-23918	109	17	soft	soft	ADJ
brj-23918	109	18	attention	attention	NOUN
brj-23918	109	19	mechanism	mechanism	NOUN
brj-23918	109	20	.	.	PUNCT
brj-23918	110	1	in	in	ADP
brj-23918	110	2	the	the	DET
brj-23918	110	3	channel	channel	NOUN
brj-23918	110	4	attention	attention	NOUN
brj-23918	110	5	module	module	NOUN
brj-23918	110	6	,	,	PUNCT
brj-23918	110	7	the	the	DET
brj-23918	110	8	input	input	NOUN
brj-23918	110	9	feature	feature	NOUN
brj-23918	110	10	map	map	NOUN
brj-23918	110	11	is	be	AUX
brj-23918	110	12	first	first	ADJ
brj-23918	110	13	peer	peer	NOUN
brj-23918	110	14	-	-	PUNCT
brj-23918	110	15	reviewed	review	VERB
brj-23918	110	16	article	article	NOUN
brj-23918	110	17	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	110	18	dong	dong	PROPN
brj-23918	110	19	et	et	PROPN
brj-23918	110	20	al	al	PROPN
brj-23918	110	21	.	.	PROPN
brj-23918	111	1	(	(	PUNCT
brj-23918	111	2	2025	2025	NUM
brj-23918	111	3	)	)	PUNCT
brj-23918	111	4	.	.	PUNCT
brj-23918	112	1	“	"	PUNCT
brj-23918	112	2	cracks	crack	NOUN
brj-23918	112	3	in	in	ADP
brj-23918	112	4	sawn	sawn	NOUN
brj-23918	112	5	wood	wood	NOUN
brj-23918	112	6	,	,	PUNCT
brj-23918	112	7	”	"	PUNCT
brj-23918	112	8	bioresources	bioresource	NOUN
brj-23918	112	9	20(2	20(2	NUM
brj-23918	112	10	)	)	PUNCT
brj-23918	112	11	,	,	PUNCT
brj-23918	112	12	3545	3545	NUM
brj-23918	112	13	-	-	SYM
brj-23918	112	14	3556	3556	NUM
brj-23918	112	15	.	.	PUNCT
brj-23918	113	1	3550	3550	NUM
brj-23918	113	2	compressed	compress	VERB
brj-23918	113	3	through	through	ADP
brj-23918	113	4	max	max	PROPN
brj-23918	113	5	pooling	pooling	NOUN
brj-23918	113	6	and	and	CCONJ
brj-23918	113	7	average	average	ADJ
brj-23918	113	8	pooling	pooling	NOUN
brj-23918	113	9	,	,	PUNCT
brj-23918	113	10	and	and	CCONJ
brj-23918	113	11	then	then	ADV
brj-23918	113	12	the	the	DET
brj-23918	113	13	outputs	output	NOUN
brj-23918	113	14	of	of	ADP
brj-23918	113	15	the	the	DET
brj-23918	113	16	two	two	NUM
brj-23918	113	17	pooling	pooling	NOUN
brj-23918	113	18	features	feature	NOUN
brj-23918	113	19	are	be	AUX
brj-23918	113	20	used	use	VERB
brj-23918	113	21	as	as	ADP
brj-23918	113	22	inputs	input	NOUN
brj-23918	113	23	to	to	ADP
brj-23918	113	24	the	the	DET
brj-23918	113	25	multilayer	multilayer	PROPN
brj-23918	113	26	perceptron	perceptron	PROPN
brj-23918	113	27	,	,	PUNCT
brj-23918	113	28	which	which	PRON
brj-23918	113	29	learns	learn	VERB
brj-23918	113	30	parameters	parameter	NOUN
brj-23918	113	31	.	.	PUNCT
brj-23918	114	1	finally	finally	ADV
brj-23918	114	2	,	,	PUNCT
brj-23918	114	3	the	the	DET
brj-23918	114	4	sigmoid	sigmoid	NOUN
brj-23918	114	5	function	function	NOUN
brj-23918	114	6	is	be	AUX
brj-23918	114	7	used	use	VERB
brj-23918	114	8	to	to	PART
brj-23918	114	9	obtain	obtain	VERB
brj-23918	114	10	the	the	DET
brj-23918	114	11	importance	importance	NOUN
brj-23918	114	12	of	of	ADP
brj-23918	114	13	the	the	DET
brj-23918	114	14	features	feature	NOUN
brj-23918	114	15	,	,	PUNCT
brj-23918	114	16	and	and	CCONJ
brj-23918	114	17	the	the	DET
brj-23918	114	18	importance	importance	NOUN
brj-23918	114	19	probability	probability	NOUN
brj-23918	114	20	of	of	ADP
brj-23918	114	21	each	each	DET
brj-23918	114	22	channel	channel	NOUN
brj-23918	114	23	is	be	AUX
brj-23918	114	24	calculated	calculate	VERB
brj-23918	114	25	to	to	PART
brj-23918	114	26	increase	increase	VERB
brj-23918	114	27	the	the	DET
brj-23918	114	28	weight	weight	NOUN
brj-23918	114	29	of	of	ADP
brj-23918	114	30	some	some	DET
brj-23918	114	31	channels	channel	NOUN
brj-23918	114	32	.	.	PUNCT
brj-23918	115	1	in	in	ADP
brj-23918	115	2	the	the	DET
brj-23918	115	3	spatial	spatial	ADJ
brj-23918	115	4	attention	attention	NOUN
brj-23918	115	5	module	module	NOUN
brj-23918	115	6	,	,	PUNCT
brj-23918	115	7	the	the	DET
brj-23918	115	8	feature	feature	NOUN
brj-23918	115	9	map	map	NOUN
brj-23918	115	10	output	output	NOUN
brj-23918	115	11	by	by	ADP
brj-23918	115	12	the	the	DET
brj-23918	115	13	channel	channel	NOUN
brj-23918	115	14	attention	attention	NOUN
brj-23918	115	15	module	module	NOUN
brj-23918	115	16	is	be	AUX
brj-23918	115	17	used	use	VERB
brj-23918	115	18	as	as	ADP
brj-23918	115	19	the	the	DET
brj-23918	115	20	input	input	NOUN
brj-23918	115	21	feature	feature	NOUN
brj-23918	115	22	map	map	NOUN
brj-23918	115	23	of	of	ADP
brj-23918	115	24	the	the	DET
brj-23918	115	25	spatial	spatial	ADJ
brj-23918	115	26	attention	attention	NOUN
brj-23918	115	27	module	module	NOUN
brj-23918	115	28	.	.	PUNCT
brj-23918	116	1	the	the	DET
brj-23918	116	2	image	image	NOUN
brj-23918	116	3	is	be	AUX
brj-23918	116	4	compressed	compress	VERB
brj-23918	116	5	into	into	ADP
brj-23918	116	6	a	a	DET
brj-23918	116	7	single	single	ADJ
brj-23918	116	8	channel	channel	NOUN
brj-23918	116	9	along	along	ADP
brj-23918	116	10	each	each	DET
brj-23918	116	11	channel	channel	NOUN
brj-23918	116	12	through	through	ADP
brj-23918	116	13	max	max	PROPN
brj-23918	116	14	pooling	pooling	NOUN
brj-23918	116	15	and	and	CCONJ
brj-23918	116	16	average	average	ADJ
brj-23918	116	17	pooling	pooling	NOUN
brj-23918	116	18	at	at	ADP
brj-23918	116	19	the	the	DET
brj-23918	116	20	channel	channel	NOUN
brj-23918	116	21	scale	scale	NOUN
brj-23918	116	22	.	.	PUNCT
brj-23918	117	1	then	then	ADV
brj-23918	117	2	,	,	PUNCT
brj-23918	117	3	parameters	parameter	NOUN
brj-23918	117	4	are	be	AUX
brj-23918	117	5	learned	learn	VERB
brj-23918	117	6	through	through	ADP
brj-23918	117	7	a	a	DET
brj-23918	117	8	1x1	1x1	NUM
brj-23918	117	9	convolutional	convolutional	ADJ
brj-23918	117	10	layer	layer	NOUN
brj-23918	117	11	,	,	PUNCT
brj-23918	117	12	and	and	CCONJ
brj-23918	117	13	the	the	DET
brj-23918	117	14	importance	importance	NOUN
brj-23918	117	15	of	of	ADP
brj-23918	117	16	pixels	pixel	NOUN
brj-23918	117	17	is	be	AUX
brj-23918	117	18	obtained	obtain	VERB
brj-23918	117	19	through	through	ADP
brj-23918	117	20	sigmoid	sigmoid	NOUN
brj-23918	117	21	to	to	PART
brj-23918	117	22	generate	generate	VERB
brj-23918	117	23	spatial	spatial	ADJ
brj-23918	117	24	attention	attention	NOUN
brj-23918	117	25	features	feature	NOUN
brj-23918	117	26	,	,	PUNCT
brj-23918	117	27	as	as	SCONJ
brj-23918	117	28	shown	show	VERB
brj-23918	117	29	in	in	ADP
brj-23918	117	30	eq	eq	ADJ
brj-23918	117	31	.	.	PROPN
brj-23918	117	32	1	1	NUM
brj-23918	117	33	.	.	PUNCT
brj-23918	118	1	(	(	PUNCT
brj-23918	118	2	)	)	PUNCT
brj-23918	118	3	(	(	PUNCT
brj-23918	118	4	)	)	PUNCT
brj-23918	118	5	(	(	PUNCT
brj-23918	118	6	)	)	PUNCT
brj-23918	118	7	(	(	PUNCT
brj-23918	118	8	)	)	PUNCT
brj-23918	118	9	(	(	PUNCT
brj-23918	118	10	)	)	PUNCT
brj-23918	118	11	(	(	PUNCT
brj-23918	118	12	)	)	PUNCT
brj-23918	118	13	(	(	PUNCT
brj-23918	118	14	)	)	PUNCT
brj-23918	118	15	(	(	PUNCT
brj-23918	118	16	)	)	PUNCT
brj-23918	118	17	(	(	PUNCT
brj-23918	118	18	)	)	PUNCT
brj-23918	118	19			NOUN
brj-23918	118	20			PROPN
brj-23918	118	21	(	(	PUNCT
brj-23918	118	22	)	)	PUNCT
brj-23918	118	23	(	(	PUNCT
brj-23918	118	24	)	)	PUNCT
brj-23918	118	25	(	(	PUNCT
brj-23918	118	26	)	)	PUNCT
brj-23918	118	27	(	(	PUNCT
brj-23918	118	28	)	)	PUNCT
brj-23918	118	29	(	(	PUNCT
brj-23918	118	30	)	)	PUNCT
brj-23918	118	31	(	(	PUNCT
brj-23918	118	32	)	)	PUNCT
brj-23918	118	33	ffmffmmf	ffmffmmf	ADJ
brj-23918	118	34	fmaxpoolfpoolaffm	fmaxpoolfpoolaffm	PROPN
brj-23918	118	35	fmaxpoolmlpfgpoolamlpfm	fmaxpoolmlpfgpoolamlpfm	PROPN
brj-23918	118	36	ccs	ccs	PROPN
brj-23918	118	37	s	s	PROPN
brj-23918	118	38	c	c	NOUN
brj-23918	118	39	=	=	X
brj-23918	118	40	=	=	PUNCT
brj-23918	119	1	+	+	NOUN
brj-23918	119	2	=	=	SYM
brj-23918	119	3			NOUN
brj-23918	119	4	'	'	PUNCT
brj-23918	119	5	;	;	PUNCT
brj-23918	120	1	vg	vg	ADP
brj-23918	120	2	v	v	ADP
brj-23918	120	3	77	77	NUM
brj-23918	120	4			X
brj-23918	120	5	(	(	PUNCT
brj-23918	120	6	1	1	NUM
brj-23918	120	7	)	)	PUNCT
brj-23918	121	1	where	where	SCONJ
brj-23918	122	1	f	f	PROPN
brj-23918	122	2			PROPN
brj-23918	122	3	is	be	AUX
brj-23918	122	4	the	the	DET
brj-23918	122	5	final	final	ADJ
brj-23918	122	6	refined	refined	ADJ
brj-23918	122	7	output	output	NOUN
brj-23918	122	8	,	,	PUNCT
brj-23918	122	9	f	f	PROPN
brj-23918	122	10			PROPN
brj-23918	122	11	rch	rch	PROPN
brj-23918	122	12	w	w	PROPN
brj-23918	122	13	is	be	AUX
brj-23918	122	14	the	the	DET
brj-23918	122	15	intermediate	intermediate	ADJ
brj-23918	122	16	feature	feature	NOUN
brj-23918	122	17	as	as	ADP
brj-23918	122	18	input	input	NOUN
brj-23918	122	19	and	and	CCONJ
brj-23918	122	20			NOUN
brj-23918	122	21	represents	represent	VERB
brj-23918	122	22	element	element	ADJ
brj-23918	122	23	multiplication	multiplication	NOUN
brj-23918	122	24	,	,	PUNCT
brj-23918	122	25	avgpool	avgpool	NOUN
brj-23918	122	26	,	,	PUNCT
brj-23918	122	27	maxpool	maxpool	NOUN
brj-23918	122	28	,	,	PUNCT
brj-23918	122	29	and	and	CCONJ
brj-23918	122	30	mlp	mlp	PROPN
brj-23918	122	31	represent	represent	VERB
brj-23918	122	32	average	average	ADJ
brj-23918	122	33	pooling	pooling	NOUN
brj-23918	122	34	,	,	PUNCT
brj-23918	122	35	maximum	maximum	ADJ
brj-23918	122	36	pooling	pooling	NOUN
brj-23918	122	37	,	,	PUNCT
brj-23918	122	38	and	and	CCONJ
brj-23918	122	39	multilayer	multilayer	ADJ
brj-23918	122	40	sensing	sensing	NOUN
brj-23918	122	41	,	,	PUNCT
brj-23918	122	42	respectively	respectively	ADV
brj-23918	122	43	.	.	PUNCT
brj-23918	123	1	improved	improve	VERB
brj-23918	123	2	efficient	efficient	ADJ
brj-23918	123	3	channel	channel	NOUN
brj-23918	123	4	attention	attention	NOUN
brj-23918	123	5	this	this	DET
brj-23918	123	6	section	section	NOUN
brj-23918	123	7	continues	continue	VERB
brj-23918	123	8	to	to	PART
brj-23918	123	9	optimize	optimize	VERB
brj-23918	123	10	and	and	CCONJ
brj-23918	123	11	improve	improve	VERB
brj-23918	123	12	the	the	DET
brj-23918	123	13	attention	attention	NOUN
brj-23918	123	14	u	u	NOUN
brj-23918	123	15	-	-	NOUN
brj-23918	123	16	net	net	ADJ
brj-23918	123	17	by	by	ADP
brj-23918	123	18	adding	add	VERB
brj-23918	123	19	the	the	DET
brj-23918	123	20	efficient	efficient	ADJ
brj-23918	123	21	channel	channel	NOUN
brj-23918	123	22	attention	attention	NOUN
brj-23918	123	23	(	(	PUNCT
brj-23918	123	24	eca	eca	NOUN
brj-23918	123	25	)	)	PUNCT
brj-23918	123	26	module	module	NOUN
brj-23918	123	27	(	(	PUNCT
brj-23918	123	28	2023	2023	NUM
brj-23918	123	29	)	)	PUNCT
brj-23918	123	30	to	to	ADP
brj-23918	123	31	the	the	DET
brj-23918	123	32	skip	skip	ADJ
brj-23918	123	33	connections	connection	NOUN
brj-23918	123	34	of	of	ADP
brj-23918	123	35	the	the	DET
brj-23918	123	36	attention	attention	NOUN
brj-23918	123	37	u	u	ADJ
brj-23918	123	38	-	-	ADJ
brj-23918	123	39	net	net	ADJ
brj-23918	123	40	network	network	NOUN
brj-23918	123	41	(	(	PUNCT
brj-23918	123	42	referred	refer	VERB
brj-23918	123	43	to	to	ADP
brj-23918	123	44	as	as	ADP
brj-23918	123	45	cbam	cbam	NOUN
brj-23918	123	46	-	-	PUNCT
brj-23918	123	47	eca	eca	NOUN
brj-23918	123	48	-	-	PUNCT
brj-23918	123	49	ag	ag	PROPN
brj-23918	123	50	)	)	PUNCT
brj-23918	123	51	.	.	PUNCT
brj-23918	124	1	the	the	DET
brj-23918	124	2	eca	eca	NOUN
brj-23918	124	3	module	module	NOUN
brj-23918	124	4	is	be	AUX
brj-23918	124	5	a	a	DET
brj-23918	124	6	channel	channel	NOUN
brj-23918	124	7	attention	attention	NOUN
brj-23918	124	8	module	module	NOUN
brj-23918	124	9	that	that	PRON
brj-23918	124	10	supports	support	VERB
brj-23918	124	11	plug	plug	NOUN
brj-23918	124	12	and	and	CCONJ
brj-23918	124	13	play	play	NOUN
brj-23918	124	14	,	,	PUNCT
brj-23918	124	15	which	which	PRON
brj-23918	124	16	can	can	AUX
brj-23918	124	17	enhance	enhance	VERB
brj-23918	124	18	the	the	DET
brj-23918	124	19	channel	channel	NOUN
brj-23918	124	20	features	feature	NOUN
brj-23918	124	21	of	of	ADP
brj-23918	124	22	the	the	DET
brj-23918	124	23	input	input	NOUN
brj-23918	124	24	feature	feature	NOUN
brj-23918	124	25	map	map	NOUN
brj-23918	124	26	,	,	PUNCT
brj-23918	124	27	achieve	achieve	VERB
brj-23918	124	28	local	local	ADJ
brj-23918	124	29	channel	channel	NOUN
brj-23918	124	30	interaction	interaction	NOUN
brj-23918	124	31	without	without	ADP
brj-23918	124	32	dimensionality	dimensionality	NOUN
brj-23918	124	33	reduction	reduction	NOUN
brj-23918	124	34	,	,	PUNCT
brj-23918	124	35	and	and	CCONJ
brj-23918	124	36	significantly	significantly	ADV
brj-23918	124	37	reduce	reduce	VERB
brj-23918	124	38	the	the	DET
brj-23918	124	39	complexity	complexity	NOUN
brj-23918	124	40	of	of	ADP
brj-23918	124	41	the	the	DET
brj-23918	124	42	model	model	NOUN
brj-23918	124	43	.	.	PUNCT
brj-23918	125	1	figure	figure	NOUN
brj-23918	125	2	3	3	NUM
brj-23918	125	3	shows	show	VERB
brj-23918	125	4	the	the	DET
brj-23918	125	5	network	network	NOUN
brj-23918	125	6	framework	framework	NOUN
brj-23918	125	7	of	of	ADP
brj-23918	125	8	the	the	DET
brj-23918	125	9	eca	eca	NOUN
brj-23918	125	10	module	module	NOUN
brj-23918	125	11	.	.	PUNCT
brj-23918	126	1	fig	fig	NOUN
brj-23918	126	2	.	.	PUNCT
brj-23918	127	1	3	3	X
brj-23918	127	2	.	.	X
brj-23918	127	3	e	e	X
brj-23918	127	4	c	c	PROPN
brj-23918	127	5	a	a	DET
brj-23918	127	6	module	module	NOUN
brj-23918	127	7	network	network	NOUN
brj-23918	127	8	framework	framework	NOUN
brj-23918	127	9	peer	peer	NOUN
brj-23918	127	10	-	-	PUNCT
brj-23918	127	11	reviewed	review	VERB
brj-23918	127	12	article	article	NOUN
brj-23918	127	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	127	14	dong	dong	PROPN
brj-23918	127	15	et	et	PROPN
brj-23918	127	16	al	al	PROPN
brj-23918	127	17	.	.	PROPN
brj-23918	128	1	(	(	PUNCT
brj-23918	128	2	2025	2025	NUM
brj-23918	128	3	)	)	PUNCT
brj-23918	128	4	.	.	PUNCT
brj-23918	129	1	“	"	PUNCT
brj-23918	129	2	cracks	crack	NOUN
brj-23918	129	3	in	in	ADP
brj-23918	129	4	sawn	sawn	NOUN
brj-23918	129	5	wood	wood	NOUN
brj-23918	129	6	,	,	PUNCT
brj-23918	129	7	”	"	PUNCT
brj-23918	129	8	bioresources	bioresource	NOUN
brj-23918	129	9	20(2	20(2	NUM
brj-23918	129	10	)	)	PUNCT
brj-23918	129	11	,	,	PUNCT
brj-23918	129	12	3545	3545	NUM
brj-23918	129	13	-	-	SYM
brj-23918	129	14	3556	3556	NUM
brj-23918	129	15	.	.	PUNCT
brj-23918	129	16	3551	3551	NUM
brj-23918	129	17	in	in	ADP
brj-23918	129	18	the	the	DET
brj-23918	129	19	figure	figure	NOUN
brj-23918	129	20	,	,	PUNCT
brj-23918	129	21	“	"	PUNCT
brj-23918	129	22	gap	gap	NOUN
brj-23918	129	23	”	"	PUNCT
brj-23918	129	24	represents	represent	VERB
brj-23918	129	25	global	global	ADJ
brj-23918	129	26	aver/*-age	aver/*-age	NOUN
brj-23918	129	27	pooling	pooling	NOUN
brj-23918	129	28	and	and	CCONJ
brj-23918	129	29	“	"	PUNCT
brj-23918	129	30	⊗	⊗	PROPN
brj-23918	129	31	”	"	PUNCT
brj-23918	129	32	represents	represent	VERB
brj-23918	129	33	matrix	matrix	NOUN
brj-23918	129	34	dot	dot	NOUN
brj-23918	129	35	multiplication	multiplication	NOUN
brj-23918	129	36	,	,	PUNCT
brj-23918	129	37	which	which	PRON
brj-23918	129	38	involves	involve	VERB
brj-23918	129	39	multiplying	multiply	VERB
brj-23918	129	40	the	the	DET
brj-23918	129	41	elements	element	NOUN
brj-23918	129	42	at	at	ADP
brj-23918	129	43	the	the	DET
brj-23918	129	44	corresponding	corresponding	ADJ
brj-23918	129	45	positions	position	NOUN
brj-23918	129	46	in	in	ADP
brj-23918	129	47	the	the	DET
brj-23918	129	48	matrix	matrix	NOUN
brj-23918	129	49	.	.	PUNCT
brj-23918	130	1	firstly	firstly	ADV
brj-23918	130	2	,	,	PUNCT
brj-23918	130	3	input	input	VERB
brj-23918	130	4	a	a	DET
brj-23918	130	5	feature	feature	NOUN
brj-23918	130	6	map	map	NOUN
brj-23918	130	7	with	with	ADP
brj-23918	130	8	dimensions	dimension	NOUN
brj-23918	130	9	of	of	ADP
brj-23918	130	10	h×w×c	h×w×c	VERB
brj-23918	130	11	.	.	PUNCT
brj-23918	131	1	then	then	ADV
brj-23918	131	2	,	,	PUNCT
brj-23918	131	3	the	the	DET
brj-23918	131	4	global	global	ADJ
brj-23918	131	5	average	average	ADJ
brj-23918	131	6	pooling	pooling	NOUN
brj-23918	131	7	is	be	AUX
brj-23918	131	8	used	use	VERB
brj-23918	131	9	to	to	PART
brj-23918	131	10	finely	finely	ADV
brj-23918	131	11	compress	compress	VERB
brj-23918	131	12	the	the	DET
brj-23918	131	13	spatial	spatial	ADJ
brj-23918	131	14	features	feature	NOUN
brj-23918	131	15	of	of	ADP
brj-23918	131	16	the	the	DET
brj-23918	131	17	input	input	NOUN
brj-23918	131	18	feature	feature	NOUN
brj-23918	131	19	map	map	NOUN
brj-23918	131	20	to	to	PART
brj-23918	131	21	obtain	obtain	VERB
brj-23918	131	22	a	a	DET
brj-23918	131	23	1	1	NUM
brj-23918	131	24	×	×	NOUN
brj-23918	131	25	1	1	NUM
brj-23918	131	26	×	×	NOUN
brj-23918	131	27	c	c	NOUN
brj-23918	131	28	feature	feature	NOUN
brj-23918	131	29	map	map	NOUN
brj-23918	131	30	.	.	PUNCT
brj-23918	132	1	then	then	ADV
brj-23918	132	2	the	the	DET
brj-23918	132	3	original	original	ADJ
brj-23918	132	4	input	input	NOUN
brj-23918	132	5	feature	feature	NOUN
brj-23918	132	6	map	map	NOUN
brj-23918	132	7	h×w×c	h×w×c	ADJ
brj-23918	132	8	channel	channel	NOUN
brj-23918	132	9	is	be	AUX
brj-23918	132	10	multiplied	multiply	VERB
brj-23918	132	11	by	by	ADP
brj-23918	132	12	channel	channel	NOUN
brj-23918	132	13	to	to	PART
brj-23918	132	14	obtain	obtain	VERB
brj-23918	132	15	a	a	DET
brj-23918	132	16	feature	feature	NOUN
brj-23918	132	17	map	map	NOUN
brj-23918	132	18	with	with	ADP
brj-23918	132	19	channel	channel	NOUN
brj-23918	132	20	attention	attention	NOUN
brj-23918	132	21	.	.	PUNCT
brj-23918	133	1	when	when	SCONJ
brj-23918	133	2	using	use	VERB
brj-23918	133	3	one	one	NUM
brj-23918	133	4	-	-	PUNCT
brj-23918	133	5	dimensional	dimensional	ADJ
brj-23918	133	6	convolution	convolution	NOUN
brj-23918	133	7	,	,	PUNCT
brj-23918	133	8	adaptive	adaptive	ADJ
brj-23918	133	9	selection	selection	NOUN
brj-23918	133	10	of	of	ADP
brj-23918	133	11	one	one	NUM
brj-23918	133	12	-	-	PUNCT
brj-23918	133	13	dimensional	dimensional	ADJ
brj-23918	133	14	convolution	convolution	NOUN
brj-23918	133	15	kernel	kernel	NOUN
brj-23918	133	16	size	size	NOUN
brj-23918	133	17	is	be	AUX
brj-23918	133	18	used	use	VERB
brj-23918	133	19	to	to	PART
brj-23918	133	20	determine	determine	VERB
brj-23918	133	21	the	the	DET
brj-23918	133	22	coverage	coverage	NOUN
brj-23918	133	23	of	of	ADP
brj-23918	133	24	local	local	PROPN
brj-23918	133	25	cross	cross	PROPN
brj-23918	133	26	channel	channel	NOUN
brj-23918	133	27	interactions	interaction	NOUN
brj-23918	133	28	,	,	PUNCT
brj-23918	133	29	as	as	SCONJ
brj-23918	133	30	shown	show	VERB
brj-23918	133	31	in	in	ADP
brj-23918	133	32	eq	eq	ADJ
brj-23918	133	33	.	.	PROPN
brj-23918	133	34	2	2	NUM
brj-23918	133	35	,	,	PUNCT
brj-23918	133	36	odd	odd	ADJ
brj-23918	133	37	bc	bc	PROPN
brj-23918	133	38	c	c	PROPN
brj-23918	133	39			PROPN
brj-23918	134	1	+	+	PUNCT
brj-23918	134	2	=	=	NOUN
brj-23918	134	3	=	=	NOUN
brj-23918	134	4	)	)	PUNCT
brj-23918	134	5	(	(	PUNCT
brj-23918	134	6	log	log	NOUN
brj-23918	134	7	)	)	PUNCT
brj-23918	134	8	(	(	PUNCT
brj-23918	134	9	k	k	NOUN
brj-23918	134	10	2	2	NUM
brj-23918	134	11	(	(	PUNCT
brj-23918	134	12	2	2	NUM
brj-23918	134	13	)	)	PUNCT
brj-23918	134	14	where	where	SCONJ
brj-23918	134	15	k	k	PROPN
brj-23918	134	16	is	be	AUX
brj-23918	134	17	the	the	DET
brj-23918	134	18	size	size	NOUN
brj-23918	134	19	of	of	ADP
brj-23918	134	20	the	the	DET
brj-23918	134	21	convolution	convolution	NOUN
brj-23918	134	22	kernel	kernel	NOUN
brj-23918	134	23	,	,	PUNCT
brj-23918	134	24	c	c	PROPN
brj-23918	134	25	is	be	AUX
brj-23918	134	26	the	the	DET
brj-23918	134	27	number	number	NOUN
brj-23918	134	28	of	of	ADP
brj-23918	134	29	channels	channel	NOUN
brj-23918	134	30	,	,	PUNCT
brj-23918	134	31	which	which	PRON
brj-23918	134	32	are	be	AUX
brj-23918	134	33	used	use	VERB
brj-23918	134	34	to	to	PART
brj-23918	134	35	adjust	adjust	VERB
brj-23918	134	36	the	the	DET
brj-23918	134	37	ratio	ratio	NOUN
brj-23918	134	38	between	between	ADP
brj-23918	134	39	the	the	DET
brj-23918	134	40	number	number	NOUN
brj-23918	134	41	of	of	ADP
brj-23918	134	42	channels	channel	NOUN
brj-23918	134	43	c	c	NOUN
brj-23918	134	44	and	and	CCONJ
brj-23918	134	45	the	the	DET
brj-23918	134	46	size	size	NOUN
brj-23918	134	47	of	of	ADP
brj-23918	134	48	the	the	DET
brj-23918	134	49	convolution	convolution	NOUN
brj-23918	134	50	kernel	kernel	NOUN
brj-23918	134	51	,	,	PUNCT
brj-23918	134	52	where	where	SCONJ
brj-23918	134	53	2	2	NUM
brj-23918	134	54	and	and	CCONJ
brj-23918	134	55	1	1	NUM
brj-23918	134	56	are	be	AUX
brj-23918	134	57	taken	take	VERB
brj-23918	134	58	respectively	respectively	ADV
brj-23918	134	59	.	.	PUNCT
brj-23918	135	1	optimization	optimization	NOUN
brj-23918	135	2	of	of	ADP
brj-23918	135	3	loss	loss	NOUN
brj-23918	135	4	function	function	NOUN
brj-23918	135	5	in	in	ADP
brj-23918	135	6	semantic	semantic	ADJ
brj-23918	135	7	segmentation	segmentation	NOUN
brj-23918	135	8	tasks	task	NOUN
brj-23918	135	9	,	,	PUNCT
brj-23918	135	10	there	there	PRON
brj-23918	135	11	may	may	AUX
brj-23918	135	12	also	also	ADV
brj-23918	135	13	be	be	AUX
brj-23918	135	14	foreground	foreground	ADJ
brj-23918	135	15	regions	region	NOUN
brj-23918	135	16	(	(	PUNCT
brj-23918	135	17	in	in	ADP
brj-23918	135	18	this	this	DET
brj-23918	135	19	case	case	NOUN
brj-23918	135	20	,	,	PUNCT
brj-23918	135	21	surface	surface	NOUN
brj-23918	135	22	cracks	crack	NOUN
brj-23918	135	23	on	on	ADP
brj-23918	135	24	sawn	sawn	NOUN
brj-23918	135	25	timber	timber	NOUN
brj-23918	135	26	)	)	PUNCT
brj-23918	135	27	that	that	PRON
brj-23918	135	28	are	be	AUX
brj-23918	135	29	much	much	ADV
brj-23918	135	30	smaller	small	ADJ
brj-23918	135	31	than	than	ADP
brj-23918	135	32	background	background	NOUN
brj-23918	135	33	regions	region	NOUN
brj-23918	135	34	.	.	PUNCT
brj-23918	136	1	using	use	VERB
brj-23918	136	2	a	a	DET
brj-23918	136	3	cross	cross	NOUN
brj-23918	136	4	entropy	entropy	NOUN
brj-23918	136	5	loss	loss	NOUN
brj-23918	136	6	function	function	NOUN
brj-23918	136	7	may	may	AUX
brj-23918	136	8	result	result	VERB
brj-23918	136	9	in	in	ADP
brj-23918	136	10	poor	poor	ADJ
brj-23918	136	11	network	network	NOUN
brj-23918	136	12	performance	performance	NOUN
brj-23918	136	13	,	,	PUNCT
brj-23918	136	14	so	so	CCONJ
brj-23918	136	15	it	it	PRON
brj-23918	136	16	is	be	AUX
brj-23918	136	17	necessary	necessary	ADJ
brj-23918	136	18	to	to	PART
brj-23918	136	19	optimize	optimize	VERB
brj-23918	136	20	the	the	DET
brj-23918	136	21	loss	loss	NOUN
brj-23918	136	22	function	function	NOUN
brj-23918	136	23	.	.	PUNCT
brj-23918	137	1	this	this	DET
brj-23918	137	2	paper	paper	NOUN
brj-23918	137	3	uses	use	VERB
brj-23918	137	4	a	a	DET
brj-23918	137	5	multi	multi	ADJ
brj-23918	137	6	loss	loss	NOUN
brj-23918	137	7	function	function	NOUN
brj-23918	137	8	fusion	fusion	NOUN
brj-23918	137	9	method	method	NOUN
brj-23918	137	10	,	,	PUNCT
brj-23918	137	11	introducing	introduce	VERB
brj-23918	137	12	dice	dice	NOUN
brj-23918	137	13	loss	loss	NOUN
brj-23918	137	14	and	and	CCONJ
brj-23918	137	15	iou	iou	NOUN
brj-23918	137	16	loss	loss	NOUN
brj-23918	137	17	,	,	PUNCT
brj-23918	137	18	and	and	CCONJ
brj-23918	137	19	linearly	linearly	ADV
brj-23918	137	20	combining	combine	VERB
brj-23918	137	21	them	they	PRON
brj-23918	137	22	with	with	ADP
brj-23918	137	23	cross	cross	NOUN
brj-23918	137	24	loss	loss	NOUN
brj-23918	137	25	.	.	PUNCT
brj-23918	138	1	the	the	DET
brj-23918	138	2	enhanced	enhance	VERB
brj-23918	138	3	fusion	fusion	NOUN
brj-23918	138	4	method	method	NOUN
brj-23918	138	5	is	be	AUX
brj-23918	138	6	used	use	VERB
brj-23918	138	7	to	to	PART
brj-23918	138	8	assign	assign	VERB
brj-23918	138	9	different	different	ADJ
brj-23918	138	10	weights	weight	NOUN
brj-23918	138	11	to	to	ADP
brj-23918	138	12	each	each	DET
brj-23918	138	13	loss	loss	NOUN
brj-23918	138	14	function	function	NOUN
brj-23918	138	15	,	,	PUNCT
brj-23918	138	16	and	and	CCONJ
brj-23918	138	17	the	the	DET
brj-23918	138	18	weights	weight	NOUN
brj-23918	138	19	of	of	ADP
brj-23918	138	20	the	the	DET
brj-23918	138	21	loss	loss	NOUN
brj-23918	138	22	functions	function	NOUN
brj-23918	138	23	are	be	AUX
brj-23918	138	24	adjusted	adjust	VERB
brj-23918	138	25	according	accord	VERB
brj-23918	138	26	to	to	ADP
brj-23918	138	27	the	the	DET
brj-23918	138	28	different	different	ADJ
brj-23918	138	29	characteristics	characteristic	NOUN
brj-23918	138	30	of	of	ADP
brj-23918	138	31	the	the	DET
brj-23918	138	32	training	training	NOUN
brj-23918	138	33	data	datum	NOUN
brj-23918	138	34	to	to	PART
brj-23918	138	35	obtain	obtain	VERB
brj-23918	138	36	the	the	DET
brj-23918	138	37	best	good	ADJ
brj-23918	138	38	results	result	NOUN
brj-23918	138	39	.	.	PUNCT
brj-23918	139	1	among	among	ADP
brj-23918	139	2	them	they	PRON
brj-23918	139	3	,	,	PUNCT
brj-23918	139	4	dice	dice	NOUN
brj-23918	139	5	loss	loss	NOUN
brj-23918	139	6	is	be	AUX
brj-23918	139	7	determined	determine	VERB
brj-23918	139	8	by	by	ADP
brj-23918	139	9	the	the	DET
brj-23918	139	10	dice	dice	NOUN
brj-23918	139	11	coefficient	coefficient	NOUN
brj-23918	139	12	of	of	ADP
brj-23918	139	13	semantic	semantic	ADJ
brj-23918	139	14	segmentation	segmentation	NOUN
brj-23918	139	15	evaluation	evaluation	NOUN
brj-23918	139	16	index	index	NOUN
brj-23918	139	17	,	,	PUNCT
brj-23918	139	18	and	and	CCONJ
brj-23918	139	19	the	the	DET
brj-23918	139	20	specific	specific	ADJ
brj-23918	139	21	formula	formula	NOUN
brj-23918	139	22	is	be	AUX
brj-23918	139	23	:	:	PUNCT
brj-23918	139	24	tcoefficiendicelossd	tcoefficiendicelossd	NOUN
brj-23918	139	25	−=1ice	−=1ice	NOUN
brj-23918	139	26	(	(	PUNCT
brj-23918	139	27	3	3	X
brj-23918	139	28	)	)	PUNCT
brj-23918	139	29	the	the	DET
brj-23918	139	30	iou	iou	NOUN
brj-23918	139	31	loss	loss	NOUN
brj-23918	139	32	is	be	AUX
brj-23918	139	33	determined	determine	VERB
brj-23918	139	34	by	by	ADP
brj-23918	139	35	the	the	DET
brj-23918	139	36	iou	iou	NOUN
brj-23918	139	37	intersection	intersection	NOUN
brj-23918	139	38	to	to	ADP
brj-23918	139	39	union	union	NOUN
brj-23918	139	40	ratio	ratio	NOUN
brj-23918	139	41	,	,	PUNCT
brj-23918	139	42	and	and	CCONJ
brj-23918	139	43	the	the	DET
brj-23918	139	44	specific	specific	ADJ
brj-23918	139	45	formula	formula	NOUN
brj-23918	139	46	is	be	AUX
brj-23918	139	47	,	,	PUNCT
brj-23918	139	48	(	(	PUNCT
brj-23918	139	49	4	4	X
brj-23918	139	50	)	)	PUNCT
brj-23918	139	51	the	the	DET
brj-23918	139	52	loss	loss	NOUN
brj-23918	139	53	function	function	NOUN
brj-23918	139	54	is	be	AUX
brj-23918	139	55	expressed	express	VERB
brj-23918	139	56	as	as	ADP
brj-23918	139	57	:	:	PUNCT
brj-23918	139	58	ioulossdicelossbcelossl	ioulossdicelossbcelossl	ADJ
brj-23918	139	59	γoss	γoss	NOUN
brj-23918	140	1	+	+	PROPN
brj-23918	140	2	+	+	NOUN
brj-23918	140	3	=	=	NOUN
brj-23918	140	4			X
brj-23918	140	5	(	(	PUNCT
brj-23918	140	6	5	5	NUM
brj-23918	140	7	)	)	PUNCT
brj-23918	140	8	where	where	SCONJ
brj-23918	140	9	α	α	X
brj-23918	140	10	,	,	PUNCT
brj-23918	140	11	β	β	NOUN
brj-23918	140	12	,	,	PUNCT
brj-23918	140	13	and	and	CCONJ
brj-23918	140	14	γ	γ	NOUN
brj-23918	140	15	are	be	AUX
brj-23918	140	16	used	use	VERB
brj-23918	140	17	to	to	PART
brj-23918	140	18	adjust	adjust	VERB
brj-23918	140	19	the	the	DET
brj-23918	140	20	parameters	parameter	NOUN
brj-23918	140	21	of	of	ADP
brj-23918	140	22	the	the	DET
brj-23918	140	23	loss	loss	NOUN
brj-23918	140	24	function	function	NOUN
brj-23918	140	25	weight	weight	NOUN
brj-23918	140	26	.	.	PUNCT
brj-23918	141	1	results	result	NOUN
brj-23918	141	2	and	and	CCONJ
brj-23918	141	3	discussion	discussion	NOUN
brj-23918	141	4	implementation	implementation	NOUN
brj-23918	141	5	details	detail	NOUN
brj-23918	141	6	the	the	DET
brj-23918	141	7	proposed	propose	VERB
brj-23918	141	8	model	model	NOUN
brj-23918	141	9	was	be	AUX
brj-23918	141	10	built	build	VERB
brj-23918	141	11	upon	upon	SCONJ
brj-23918	141	12	the	the	DET
brj-23918	141	13	pytorch	pytorch	NOUN
brj-23918	141	14	library	library	NOUN
brj-23918	141	15	.	.	PUNCT
brj-23918	142	1	all	all	DET
brj-23918	142	2	experiments	experiment	NOUN
brj-23918	142	3	and	and	CCONJ
brj-23918	142	4	the	the	DET
brj-23918	142	5	model	model	NOUN
brj-23918	142	6	training	training	NOUN
brj-23918	142	7	were	be	AUX
brj-23918	142	8	done	do	VERB
brj-23918	142	9	on	on	ADP
brj-23918	142	10	a	a	DET
brj-23918	142	11	16	16	NUM
brj-23918	142	12	gb	gb	PROPN
brj-23918	142	13	nvidia	nvidia	PROPN
brj-23918	142	14	rtx4060ti	rtx4060ti	PROPN
brj-23918	142	15	gpu	gpu	PROPN
brj-23918	142	16	.	.	PUNCT
brj-23918	143	1	the	the	DET
brj-23918	143	2	training	training	NOUN
brj-23918	143	3	process	process	NOUN
brj-23918	143	4	employed	employ	VERB
brj-23918	143	5	the	the	DET
brj-23918	143	6	adam	adam	PROPN
brj-23918	143	7	optimizer	optimizer	NOUN
brj-23918	143	8	with	with	ADP
brj-23918	143	9	an	an	DET
brj-23918	143	10	initial	initial	ADJ
brj-23918	143	11	learning	learning	NOUN
brj-23918	143	12	rate	rate	NOUN
brj-23918	143	13	of	of	ADP
brj-23918	143	14	5e−4	5e−4	NUM
brj-23918	143	15	,	,	PUNCT
brj-23918	143	16	a	a	DET
brj-23918	143	17	batch	batch	NOUN
brj-23918	143	18	size	size	NOUN
brj-23918	143	19	of	of	ADP
brj-23918	143	20	8	8	NUM
brj-23918	143	21	,	,	PUNCT
brj-23918	143	22	and	and	CCONJ
brj-23918	143	23	100	100	NUM
brj-23918	143	24	iterations	iteration	NOUN
brj-23918	143	25	.	.	PUNCT
brj-23918	144	1	peer	peer	NOUN
brj-23918	144	2	-	-	PUNCT
brj-23918	144	3	reviewed	review	VERB
brj-23918	144	4	article	article	NOUN
brj-23918	144	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	144	6	dong	dong	PROPN
brj-23918	144	7	et	et	PROPN
brj-23918	144	8	al	al	PROPN
brj-23918	144	9	.	.	PROPN
brj-23918	145	1	(	(	PUNCT
brj-23918	145	2	2025	2025	NUM
brj-23918	145	3	)	)	PUNCT
brj-23918	145	4	.	.	PUNCT
brj-23918	146	1	“	"	PUNCT
brj-23918	146	2	cracks	crack	NOUN
brj-23918	146	3	in	in	ADP
brj-23918	146	4	sawn	sawn	NOUN
brj-23918	146	5	wood	wood	NOUN
brj-23918	146	6	,	,	PUNCT
brj-23918	146	7	”	"	PUNCT
brj-23918	146	8	bioresources	bioresource	NOUN
brj-23918	146	9	20(2	20(2	NUM
brj-23918	146	10	)	)	PUNCT
brj-23918	146	11	,	,	PUNCT
brj-23918	146	12	3545	3545	NUM
brj-23918	146	13	-	-	SYM
brj-23918	146	14	3556	3556	NUM
brj-23918	146	15	.	.	PUNCT
brj-23918	147	1	3552	3552	NUM
brj-23918	147	2	evaluation	evaluation	NOUN
brj-23918	147	3	index	index	NOUN
brj-23918	147	4	in	in	ADP
brj-23918	147	5	order	order	NOUN
brj-23918	147	6	to	to	PART
brj-23918	147	7	better	well	ADV
brj-23918	147	8	compare	compare	VERB
brj-23918	147	9	the	the	DET
brj-23918	147	10	performance	performance	NOUN
brj-23918	147	11	of	of	ADP
brj-23918	147	12	various	various	ADJ
brj-23918	147	13	models	model	NOUN
brj-23918	147	14	,	,	PUNCT
brj-23918	147	15	five	five	NUM
brj-23918	147	16	evaluation	evaluation	NOUN
brj-23918	147	17	indexes	index	NOUN
brj-23918	147	18	were	be	AUX
brj-23918	147	19	used	use	VERB
brj-23918	147	20	prc	prc	PROPN
brj-23918	147	21	(	(	PUNCT
brj-23918	147	22	percision.prc	percision.prc	PROPN
brj-23918	147	23	)	)	PUNCT
brj-23918	147	24	,	,	PUNCT
brj-23918	147	25	rec	rec	X
brj-23918	147	26	(	(	PUNCT
brj-23918	147	27	recall	recall	NOUN
brj-23918	147	28	,	,	PUNCT
brj-23918	147	29	rec	rec	NOUN
brj-23918	147	30	)	)	PUNCT
brj-23918	147	31	,	,	PUNCT
brj-23918	147	32	f1(f1	f1(f1	NOUN
brj-23918	147	33	score	score	NOUN
brj-23918	147	34	)	)	PUNCT
brj-23918	147	35	,	,	PUNCT
brj-23918	147	36	iouc	iouc	NOUN
brj-23918	147	37	(	(	PUNCT
brj-23918	147	38	intersection	intersection	NOUN
brj-23918	147	39	over	over	ADP
brj-23918	147	40	union	union	NOUN
brj-23918	147	41	,	,	PUNCT
brj-23918	147	42	iouc	iouc	NOUN
brj-23918	147	43	)	)	PUNCT
brj-23918	147	44	and	and	CCONJ
brj-23918	147	45	miou	miou	NOUN
brj-23918	147	46	(	(	PUNCT
brj-23918	147	47	mean	mean	VERB
brj-23918	147	48	intersection	intersection	NOUN
brj-23918	147	49	over	over	ADP
brj-23918	147	50	union	union	NOUN
brj-23918	147	51	,	,	PUNCT
brj-23918	147	52	miouc	miouc	PROPN
brj-23918	147	53	)	)	PUNCT
brj-23918	147	54	.	.	PUNCT
brj-23918	148	1	comparative	comparative	ADJ
brj-23918	148	2	analysis	analysis	NOUN
brj-23918	148	3	of	of	ADP
brj-23918	148	4	experimental	experimental	ADJ
brj-23918	148	5	results	result	NOUN
brj-23918	148	6	this	this	DET
brj-23918	148	7	model	model	NOUN
brj-23918	148	8	specifically	specifically	ADV
brj-23918	148	9	added	add	VERB
brj-23918	148	10	the	the	DET
brj-23918	148	11	efficient	efficient	ADJ
brj-23918	148	12	channel	channel	NOUN
brj-23918	148	13	attention	attention	NOUN
brj-23918	148	14	(	(	PUNCT
brj-23918	148	15	eca	eca	NOUN
brj-23918	148	16	)	)	PUNCT
brj-23918	148	17	module	module	NOUN
brj-23918	148	18	to	to	ADP
brj-23918	148	19	the	the	DET
brj-23918	148	20	skip	skip	ADJ
brj-23918	148	21	connections	connection	NOUN
brj-23918	148	22	of	of	ADP
brj-23918	148	23	the	the	DET
brj-23918	148	24	attention	attention	NOUN
brj-23918	148	25	u	u	ADJ
brj-23918	148	26	-	-	ADJ
brj-23918	148	27	net	net	ADJ
brj-23918	148	28	network	network	NOUN
brj-23918	148	29	(	(	PUNCT
brj-23918	148	30	referred	refer	VERB
brj-23918	148	31	to	to	ADP
brj-23918	148	32	as	as	ADP
brj-23918	148	33	cbam	cbam	NOUN
brj-23918	148	34	-	-	PUNCT
brj-23918	148	35	eca	eca	NOUN
brj-23918	148	36	-	-	PUNCT
brj-23918	148	37	ag	ag	PROPN
brj-23918	148	38	)	)	PUNCT
brj-23918	148	39	.	.	PUNCT
brj-23918	149	1	the	the	DET
brj-23918	149	2	eca	eca	NOUN
brj-23918	149	3	module	module	NOUN
brj-23918	149	4	is	be	AUX
brj-23918	149	5	a	a	DET
brj-23918	149	6	channel	channel	NOUN
brj-23918	149	7	attention	attention	NOUN
brj-23918	149	8	module	module	NOUN
brj-23918	149	9	that	that	PRON
brj-23918	149	10	supports	support	VERB
brj-23918	149	11	plug	plug	NOUN
brj-23918	149	12	and	and	CCONJ
brj-23918	149	13	play	play	NOUN
brj-23918	149	14	,	,	PUNCT
brj-23918	149	15	which	which	PRON
brj-23918	149	16	can	can	AUX
brj-23918	149	17	enhance	enhance	VERB
brj-23918	149	18	the	the	DET
brj-23918	149	19	channel	channel	NOUN
brj-23918	149	20	features	feature	NOUN
brj-23918	149	21	of	of	ADP
brj-23918	149	22	the	the	DET
brj-23918	149	23	input	input	NOUN
brj-23918	149	24	feature	feature	NOUN
brj-23918	149	25	map	map	NOUN
brj-23918	149	26	,	,	PUNCT
brj-23918	149	27	achieve	achieve	VERB
brj-23918	149	28	local	local	ADJ
brj-23918	149	29	channel	channel	NOUN
brj-23918	149	30	interaction	interaction	NOUN
brj-23918	149	31	without	without	ADP
brj-23918	149	32	dimensionality	dimensionality	NOUN
brj-23918	149	33	reduction	reduction	NOUN
brj-23918	149	34	,	,	PUNCT
brj-23918	149	35	and	and	CCONJ
brj-23918	149	36	significantly	significantly	ADV
brj-23918	149	37	reduce	reduce	VERB
brj-23918	149	38	the	the	DET
brj-23918	149	39	complexity	complexity	NOUN
brj-23918	149	40	of	of	ADP
brj-23918	149	41	the	the	DET
brj-23918	149	42	model	model	NOUN
brj-23918	149	43	.	.	PUNCT
brj-23918	150	1	by	by	ADP
brj-23918	150	2	conducting	conduct	VERB
brj-23918	150	3	ablation	ablation	NOUN
brj-23918	150	4	experiments	experiment	NOUN
brj-23918	150	5	,	,	PUNCT
brj-23918	150	6	replacing	replace	VERB
brj-23918	150	7	the	the	DET
brj-23918	150	8	cbam	cbam	NOUN
brj-23918	150	9	module	module	NOUN
brj-23918	150	10	and	and	CCONJ
brj-23918	150	11	eca	eca	NOUN
brj-23918	150	12	module	module	NOUN
brj-23918	150	13	,	,	PUNCT
brj-23918	150	14	optimizing	optimize	VERB
brj-23918	150	15	the	the	DET
brj-23918	150	16	loss	loss	NOUN
brj-23918	150	17	function	function	NOUN
brj-23918	150	18	and	and	CCONJ
brj-23918	150	19	other	other	ADJ
brj-23918	150	20	experimental	experimental	ADJ
brj-23918	150	21	parameters	parameter	NOUN
brj-23918	150	22	,	,	PUNCT
brj-23918	150	23	a	a	DET
brj-23918	150	24	model	model	NOUN
brj-23918	150	25	framework	framework	NOUN
brj-23918	150	26	was	be	AUX
brj-23918	150	27	determined	determine	VERB
brj-23918	150	28	that	that	SCONJ
brj-23918	150	29	can	can	AUX
brj-23918	150	30	segment	segment	VERB
brj-23918	150	31	surface	surface	NOUN
brj-23918	150	32	cracks	crack	NOUN
brj-23918	150	33	of	of	ADP
brj-23918	150	34	sawn	sawn	NOUN
brj-23918	150	35	timber	timber	NOUN
brj-23918	150	36	with	with	ADP
brj-23918	150	37	high	high	ADJ
brj-23918	150	38	segmentation	segmentation	NOUN
brj-23918	150	39	accuracy	accuracy	NOUN
brj-23918	150	40	.	.	PUNCT
brj-23918	151	1	finally	finally	ADV
brj-23918	151	2	,	,	PUNCT
brj-23918	151	3	comparative	comparative	ADJ
brj-23918	151	4	experiments	experiment	NOUN
brj-23918	151	5	were	be	AUX
brj-23918	151	6	conducted	conduct	VERB
brj-23918	151	7	with	with	ADP
brj-23918	151	8	other	other	ADJ
brj-23918	151	9	semantic	semantic	ADJ
brj-23918	151	10	segmentation	segmentation	NOUN
brj-23918	151	11	models	model	NOUN
brj-23918	151	12	(	(	PUNCT
brj-23918	151	13	fcn	fcn	PROPN
brj-23918	151	14	,	,	PUNCT
brj-23918	151	15	seg	seg	PROPN
brj-23918	151	16	net	net	PROPN
brj-23918	151	17	,	,	PUNCT
brj-23918	151	18	and	and	CCONJ
brj-23918	151	19	deeplabv3	deeplabv3	NOUN
brj-23918	151	20	+	+	PRON
brj-23918	151	21	)	)	PUNCT
brj-23918	151	22	to	to	PART
brj-23918	151	23	evaluate	evaluate	VERB
brj-23918	151	24	the	the	DET
brj-23918	151	25	performance	performance	NOUN
brj-23918	151	26	of	of	ADP
brj-23918	151	27	the	the	DET
brj-23918	151	28	improved	improve	VERB
brj-23918	151	29	attention	attention	NOUN
brj-23918	151	30	u	u	NOUN
brj-23918	151	31	-	-	NOUN
brj-23918	151	32	net	net	ADJ
brj-23918	151	33	compared	compare	VERB
brj-23918	151	34	to	to	ADP
brj-23918	151	35	other	other	ADJ
brj-23918	151	36	semantic	semantic	ADJ
brj-23918	151	37	models	model	NOUN
brj-23918	151	38	in	in	ADP
brj-23918	151	39	order	order	NOUN
brj-23918	151	40	for	for	SCONJ
brj-23918	151	41	the	the	DET
brj-23918	151	42	model	model	NOUN
brj-23918	151	43	to	to	PART
brj-23918	151	44	achieve	achieve	VERB
brj-23918	151	45	better	well	ADJ
brj-23918	151	46	segmentation	segmentation	NOUN
brj-23918	151	47	results	result	NOUN
brj-23918	151	48	on	on	ADP
brj-23918	151	49	the	the	DET
brj-23918	151	50	mask	mask	NOUN
brj-23918	151	51	image	image	NOUN
brj-23918	151	52	,	,	PUNCT
brj-23918	151	53	the	the	DET
brj-23918	151	54	authors	author	NOUN
brj-23918	151	55	will	will	AUX
brj-23918	151	56	continue	continue	VERB
brj-23918	151	57	to	to	PART
brj-23918	151	58	explore	explore	VERB
brj-23918	151	59	the	the	DET
brj-23918	151	60	impact	impact	NOUN
brj-23918	151	61	of	of	ADP
brj-23918	151	62	changes	change	NOUN
brj-23918	151	63	in	in	ADP
brj-23918	151	64	the	the	DET
brj-23918	151	65	weight	weight	NOUN
brj-23918	151	66	of	of	ADP
brj-23918	151	67	the	the	DET
brj-23918	151	68	loss	loss	NOUN
brj-23918	151	69	function	function	NOUN
brj-23918	151	70	.	.	PUNCT
brj-23918	152	1	table	table	NOUN
brj-23918	152	2	1	1	NUM
brj-23918	152	3	compares	compare	VERB
brj-23918	152	4	the	the	DET
brj-23918	152	5	semantic	semantic	ADJ
brj-23918	152	6	segmentation	segmentation	NOUN
brj-23918	152	7	evaluation	evaluation	NOUN
brj-23918	152	8	performance	performance	NOUN
brj-23918	152	9	of	of	ADP
brj-23918	152	10	eca	eca	NOUN
brj-23918	152	11	-	-	PUNCT
brj-23918	152	12	cbamagu	cbamagu	NOUN
brj-23918	152	13	-	-	ADJ
brj-23918	152	14	net	net	ADJ
brj-23918	152	15	models	model	NOUN
brj-23918	152	16	with	with	ADP
brj-23918	152	17	different	different	ADJ
brj-23918	152	18	fusion	fusion	NOUN
brj-23918	152	19	weights	weight	NOUN
brj-23918	152	20	of	of	ADP
brj-23918	152	21	the	the	DET
brj-23918	152	22	loss	loss	NOUN
brj-23918	152	23	function	function	NOUN
brj-23918	152	24	.	.	PUNCT
brj-23918	153	1	table	table	NOUN
brj-23918	153	2	1	1	NUM
brj-23918	153	3	.	.	PUNCT
brj-23918	154	1	influence	influence	NOUN
brj-23918	154	2	of	of	ADP
brj-23918	154	3	different	different	ADJ
brj-23918	154	4	fusion	fusion	NOUN
brj-23918	154	5	weights	weight	NOUN
brj-23918	154	6	of	of	ADP
brj-23918	154	7	eca	eca	NOUN
brj-23918	154	8	-	-	PUNCT
brj-23918	154	9	cbam	cbam	NOUN
brj-23918	154	10	-	-	PUNCT
brj-23918	154	11	ag	ag	NOUN
brj-23918	154	12	u	u	PROPN
brj-23918	154	13	-	-	ADJ
brj-23918	154	14	net	net	ADJ
brj-23918	154	15	model	model	NOUN
brj-23918	154	16	loss	loss	NOUN
brj-23918	154	17	function	function	NOUN
brj-23918	154	18	on	on	ADP
brj-23918	154	19	semantic	semantic	ADJ
brj-23918	154	20	segmentation	segmentation	NOUN
brj-23918	154	21	index	index	NOUN
brj-23918	154	22	of	of	ADP
brj-23918	154	23	sawn	sawn	NOUN
brj-23918	154	24	timber	timber	NOUN
brj-23918	154	25	surface	surface	NOUN
brj-23918	154	26	crack	crack	NOUN
brj-23918	154	27	model	model	NOUN
brj-23918	154	28	u	u	NOUN
brj-23918	154	29	-	-	NOUN
brj-23918	154	30	net+eca+ag+skipcbam	net+eca+ag+skipcbam	ADJ
brj-23918	154	31	(	(	PUNCT
brj-23918	154	32	lr5e-4,wd5e-5,ep100	lr5e-4,wd5e-5,ep100	PROPN
brj-23918	154	33	)	)	PUNCT
brj-23918	154	34	prc	prc	PROPN
brj-23918	154	35	rec	rec	PROPN
brj-23918	154	36	f1c	f1c	PROPN
brj-23918	154	37	iouc	iouc	PROPN
brj-23918	154	38	miou	miou	NOUN
brj-23918	154	39	0.2ce+0.5dice+0.3iou	0.2ce+0.5dice+0.3iou	VERB
brj-23918	154	40	82.41	82.41	NUM
brj-23918	154	41	63.84	63.84	NUM
brj-23918	154	42	73.06	73.06	NUM
brj-23918	154	43	57.56	57.56	NUM
brj-23918	154	44	78.09	78.09	NUM
brj-23918	154	45	0.3ce+0.5dice+0.2iou	0.3ce+0.5dice+0.2iou	NOUN
brj-23918	154	46	86.93	86.93	NUM
brj-23918	154	47	61.08	61.08	NUM
brj-23918	154	48	71.74	71.74	NUM
brj-23918	154	49	55.94	55.94	NUM
brj-23918	154	50	77.27	77.27	NUM
brj-23918	154	51	0.1ce+0.5dice+0.4iou	0.1ce+0.5dice+0.4iou	NOUN
brj-23918	154	52	87.84	87.84	NUM
brj-23918	154	53	58.94	58.94	NUM
brj-23918	154	54	70.54	70.54	NUM
brj-23918	154	55	54.49	54.49	NUM
brj-23918	154	56	76.52	76.52	NUM
brj-23918	154	57	0.4ce+0.2dice+0.4iou	0.4ce+0.2dice+0.4iou	NOUN
brj-23918	154	58	88.91	88.91	NUM
brj-23918	154	59	62.05	62.05	NUM
brj-23918	154	60	73.09	73.09	NUM
brj-23918	154	61	57.60	57.60	NUM
brj-23918	154	62	78.13	78.13	NUM
brj-23918	154	63	0.4ce+0.1dice+0.5iou	0.4ce+0.1dice+0.5iou	NOUN
brj-23918	154	64	90.09	90.09	NUM
brj-23918	154	65	58.37	58.37	NUM
brj-23918	154	66	70.85	70.85	NUM
brj-23918	154	67	54.85	54.85	NUM
brj-23918	154	68	76.72	76.72	NUM
brj-23918	154	69	0.3ce+0.5dice+0.2iou	0.3ce+0.5dice+0.2iou	NOUN
brj-23918	154	70	90.37	90.37	NUM
brj-23918	154	71	55.96	55.96	NUM
brj-23918	154	72	69.61	69.61	NUM
brj-23918	154	73	52.80	52.80	NUM
brj-23918	154	74	75.68	75.68	NUM
brj-23918	154	75	0.3ce+0.6dice+0.1iou	0.3ce+0.6dice+0.1iou	NOUN
brj-23918	154	76	84.86	84.86	NUM
brj-23918	154	77	63.63	63.63	NUM
brj-23918	154	78	72.73	72.73	NUM
brj-23918	154	79	61.38	61.38	NUM
brj-23918	154	80	77.88	77.88	NUM
brj-23918	154	81	0.4ce+0.3dice+0.3iou	0.4ce+0.3dice+0.3iou	VERB
brj-23918	154	82	88.96	88.96	NUM
brj-23918	154	83	65.48	65.48	NUM
brj-23918	154	84	75.43	75.43	NUM
brj-23918	154	85	60.55	60.55	NUM
brj-23918	154	86	79.65	79.65	NUM
brj-23918	154	87	0.25ce+0.5dice+0.25iou	0.25ce+0.5dice+0.25iou	NOUN
brj-23918	154	88	82.04	82.04	NUM
brj-23918	154	89	70.92	70.92	NUM
brj-23918	154	90	76.07	76.07	NUM
brj-23918	154	91	61.39	61.39	NUM
brj-23918	154	92	80.04	80.04	NUM
brj-23918	154	93	in	in	ADP
brj-23918	154	94	table	table	NOUN
brj-23918	154	95	1	1	NUM
brj-23918	154	96	,	,	PUNCT
brj-23918	154	97	the	the	DET
brj-23918	154	98	semantic	semantic	ADJ
brj-23918	154	99	segmentation	segmentation	NOUN
brj-23918	154	100	evaluation	evaluation	NOUN
brj-23918	154	101	index	index	NOUN
brj-23918	154	102	scores	score	NOUN
brj-23918	154	103	of	of	ADP
brj-23918	154	104	the	the	DET
brj-23918	154	105	eca	eca	NOUN
brj-23918	154	106	-	-	PUNCT
brj-23918	154	107	cbam	cbam	NOUN
brj-23918	154	108	agu	agu	NOUN
brj-23918	154	109	-	-	ADJ
brj-23918	154	110	net	net	ADJ
brj-23918	154	111	model	model	NOUN
brj-23918	154	112	vary	vary	VERB
brj-23918	154	113	with	with	ADP
brj-23918	154	114	different	different	ADJ
brj-23918	154	115	weights	weight	NOUN
brj-23918	154	116	of	of	ADP
brj-23918	154	117	α	α	NOUN
brj-23918	154	118	,	,	PUNCT
brj-23918	154	119	β	β	NOUN
brj-23918	154	120	,	,	PUNCT
brj-23918	154	121	and	and	CCONJ
brj-23918	154	122	γ	γ	NOUN
brj-23918	154	123	using	use	VERB
brj-23918	154	124	multiple	multiple	ADJ
brj-23918	154	125	loss	loss	NOUN
brj-23918	154	126	functions	function	NOUN
brj-23918	154	127	.	.	PUNCT
brj-23918	155	1	when	when	SCONJ
brj-23918	155	2	α=0.3	α=0.3	ADV
brj-23918	155	3	,	,	PUNCT
brj-23918	155	4	β=0.5	β=0.5	NOUN
brj-23918	155	5	,	,	PUNCT
brj-23918	155	6	and	and	CCONJ
brj-23918	155	7	γ=0.2	γ=0.2	ADV
brj-23918	155	8	,	,	PUNCT
brj-23918	155	9	prc	prc	PROPN
brj-23918	155	10	achieved	achieve	VERB
brj-23918	155	11	90.37	90.37	NUM
brj-23918	155	12	%	%	NOUN
brj-23918	155	13	,	,	PUNCT
brj-23918	155	14	which	which	PRON
brj-23918	155	15	is	be	AUX
brj-23918	155	16	the	the	DET
brj-23918	155	17	highest	high	ADJ
brj-23918	155	18	score	score	NOUN
brj-23918	155	19	in	in	ADP
brj-23918	155	20	prc	prc	PROPN
brj-23918	155	21	.	.	PUNCT
brj-23918	156	1	when	when	SCONJ
brj-23918	156	2	α=0.25	α=0.25	NOUN
brj-23918	156	3	,	,	PUNCT
brj-23918	156	4	β=0.5	β=0.5	NOUN
brj-23918	156	5	,	,	PUNCT
brj-23918	156	6	and	and	CCONJ
brj-23918	156	7	γ=0.25	γ=0.25	PROPN
brj-23918	156	8	,	,	PUNCT
brj-23918	156	9	the	the	DET
brj-23918	156	10	model	model	NOUN
brj-23918	156	11	achieves	achieve	VERB
brj-23918	156	12	70.92	70.92	NUM
brj-23918	156	13	%	%	NOUN
brj-23918	156	14	,	,	PUNCT
brj-23918	156	15	76.07	76.07	NUM
brj-23918	156	16	%	%	NOUN
brj-23918	156	17	,	,	PUNCT
brj-23918	156	18	61.39	61.39	NUM
brj-23918	156	19	%	%	NOUN
brj-23918	156	20	,	,	PUNCT
brj-23918	156	21	and	and	CCONJ
brj-23918	156	22	80.04	80.04	NUM
brj-23918	156	23	%	%	NOUN
brj-23918	156	24	in	in	ADP
brj-23918	156	25	rec	rec	NOUN
brj-23918	156	26	,	,	PUNCT
brj-23918	156	27	f1c	f1c	NOUN
brj-23918	156	28	,	,	PUNCT
brj-23918	156	29	iouc	iouc	NOUN
brj-23918	156	30	,	,	PUNCT
brj-23918	156	31	and	and	CCONJ
brj-23918	156	32	miou	miou	NOUN
brj-23918	156	33	,	,	PUNCT
brj-23918	156	34	respectively	respectively	ADV
brj-23918	156	35	.	.	PUNCT
brj-23918	157	1	from	from	ADP
brj-23918	157	2	the	the	DET
brj-23918	157	3	perspective	perspective	NOUN
brj-23918	157	4	of	of	ADP
brj-23918	157	5	semantic	semantic	ADJ
brj-23918	157	6	segmentation	segmentation	NOUN
brj-23918	157	7	indicators	indicator	NOUN
brj-23918	157	8	,	,	PUNCT
brj-23918	157	9	this	this	DET
brj-23918	157	10	weight	weight	NOUN
brj-23918	157	11	is	be	AUX
brj-23918	157	12	the	the	DET
brj-23918	157	13	optimal	optimal	ADJ
brj-23918	157	14	weight	weight	NOUN
brj-23918	157	15	.	.	PUNCT
brj-23918	158	1	the	the	DET
brj-23918	158	2	model	model	NOUN
brj-23918	158	3	developed	develop	VERB
brj-23918	158	4	in	in	ADP
brj-23918	158	5	this	this	DET
brj-23918	158	6	work	work	NOUN
brj-23918	158	7	was	be	AUX
brj-23918	158	8	compared	compare	VERB
brj-23918	158	9	with	with	ADP
brj-23918	158	10	other	other	ADJ
brj-23918	158	11	semantic	semantic	ADJ
brj-23918	158	12	segmentation	segmentation	NOUN
brj-23918	158	13	models	model	NOUN
brj-23918	158	14	(	(	PUNCT
brj-23918	158	15	fcn	fcn	PROPN
brj-23918	158	16	,	,	PUNCT
brj-23918	158	17	seg	seg	PROPN
brj-23918	158	18	net	net	PROPN
brj-23918	158	19	,	,	PUNCT
brj-23918	158	20	and	and	CCONJ
brj-23918	158	21	deeplabv3	deeplabv3	NOUN
brj-23918	158	22	+	+	NOUN
brj-23918	158	23	)	)	PUNCT
brj-23918	158	24	through	through	ADP
brj-23918	158	25	experiments	experiment	NOUN
brj-23918	158	26	.	.	PUNCT
brj-23918	159	1	among	among	ADP
brj-23918	159	2	them	they	PRON
brj-23918	159	3	,	,	PUNCT
brj-23918	159	4	fcn	fcn	PROPN
brj-23918	159	5	,	,	PUNCT
brj-23918	159	6	deeplabv3	deeplabv3	PUNCT
brj-23918	160	1	+	+	PROPN
brj-23918	160	2	,	,	PUNCT
brj-23918	160	3	seg	seg	PROPN
brj-23918	160	4	net	net	PROPN
brj-23918	160	5	uses	use	VERB
brj-23918	160	6	adam	adam	PROPN
brj-23918	160	7	optimizer	optimizer	NOUN
brj-23918	160	8	and	and	CCONJ
brj-23918	160	9	cross	cross	VERB
brj-23918	160	10	loss	loss	NOUN
brj-23918	160	11	function	function	NOUN
brj-23918	160	12	.	.	PUNCT
brj-23918	161	1	the	the	DET
brj-23918	161	2	remaining	remain	VERB
brj-23918	161	3	peer	peer	NOUN
brj-23918	161	4	-	-	PUNCT
brj-23918	161	5	reviewed	review	VERB
brj-23918	161	6	article	article	NOUN
brj-23918	161	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	161	8	dong	dong	PROPN
brj-23918	161	9	et	et	PROPN
brj-23918	161	10	al	al	PROPN
brj-23918	161	11	.	.	PROPN
brj-23918	162	1	(	(	PUNCT
brj-23918	162	2	2025	2025	NUM
brj-23918	162	3	)	)	PUNCT
brj-23918	162	4	.	.	PUNCT
brj-23918	163	1	“	"	PUNCT
brj-23918	163	2	cracks	crack	NOUN
brj-23918	163	3	in	in	ADP
brj-23918	163	4	sawn	sawn	NOUN
brj-23918	163	5	wood	wood	NOUN
brj-23918	163	6	,	,	PUNCT
brj-23918	163	7	”	"	PUNCT
brj-23918	163	8	bioresources	bioresource	NOUN
brj-23918	163	9	20(2	20(2	NUM
brj-23918	163	10	)	)	PUNCT
brj-23918	163	11	,	,	PUNCT
brj-23918	163	12	3545	3545	NUM
brj-23918	163	13	-	-	SYM
brj-23918	163	14	3556	3556	NUM
brj-23918	163	15	.	.	PUNCT
brj-23918	164	1	3553	3553	NUM
brj-23918	164	2	learning	learn	VERB
brj-23918	164	3	rates	rate	NOUN
brj-23918	164	4	lr	lr	NOUN
brj-23918	164	5	,	,	PUNCT
brj-23918	164	6	weight	weight	NOUN
brj-23918	164	7	decay	decay	NOUN
brj-23918	164	8	wd	wd	PROPN
brj-23918	164	9	,	,	PUNCT
brj-23918	164	10	and	and	CCONJ
brj-23918	164	11	iteration	iteration	NOUN
brj-23918	164	12	times	time	NOUN
brj-23918	164	13	ep	ep	PROPN
brj-23918	164	14	are	be	AUX
brj-23918	164	15	consistent	consistent	ADJ
brj-23918	164	16	with	with	ADP
brj-23918	164	17	eca	eca	NOUN
brj-23918	164	18	-	-	PUNCT
brj-23918	164	19	cbam	cbam	PROPN
brj-23918	164	20	agu	agu	PROPN
brj-23918	164	21	net	net	NOUN
brj-23918	164	22	.	.	PUNCT
brj-23918	164	23	table	table	NOUN
brj-23918	164	24	2	2	NUM
brj-23918	164	25	.	.	PUNCT
brj-23918	164	26	comparison	comparison	NOUN
brj-23918	164	27	of	of	ADP
brj-23918	164	28	different	different	ADJ
brj-23918	164	29	segmentation	segmentation	NOUN
brj-23918	164	30	models	model	NOUN
brj-23918	164	31	of	of	ADP
brj-23918	164	32	sawn	sawn	NOUN
brj-23918	164	33	timber	timber	NOUN
brj-23918	164	34	surface	surface	NOUN
brj-23918	164	35	cracks	crack	NOUN
brj-23918	164	36	model	model	VERB
brj-23918	164	37	prc	prc	PROPN
brj-23918	164	38	rec	rec	PROPN
brj-23918	164	39	f1c	f1c	PROPN
brj-23918	164	40	iouc	iouc	PROPN
brj-23918	164	41	miou	miou	PROPN
brj-23918	164	42	fcn	fcn	PROPN
brj-23918	164	43	(	(	PUNCT
brj-23918	164	44	adamlr5e-4wd5e-5	adamlr5e-4wd5e-5	PROPN
brj-23918	164	45	-	-	PUNCT
brj-23918	164	46	ep100	ep100	PROPN
brj-23918	164	47	)	)	PUNCT
brj-23918	164	48	83.01	83.01	NUM
brj-23918	164	49	62.36	62.36	NUM
brj-23918	164	50	71.22	71.22	NUM
brj-23918	164	51	55.30	55.30	NUM
brj-23918	164	52	76.92	76.92	NUM
brj-23918	164	53	deeplabv3	deeplabv3	NOUN
brj-23918	165	1	+	+	CCONJ
brj-23918	165	2	(	(	PUNCT
brj-23918	165	3	adamlr5e-4wd5e-5	adamlr5e-4wd5e-5	NUM
brj-23918	165	4	-	-	PUNCT
brj-23918	165	5	ep100	ep100	PROPN
brj-23918	165	6	)	)	PUNCT
brj-23918	166	1	84.52	84.52	NUM
brj-23918	166	2	63.44	63.44	NUM
brj-23918	166	3	72.47	72.47	NUM
brj-23918	166	4	56.84	56.84	NUM
brj-23918	166	5	77.71	77.71	NUM
brj-23918	166	6	segnet	segnet	NOUN
brj-23918	166	7	(	(	PUNCT
brj-23918	166	8	adamlr5e-4wd5e-5	adamlr5e-4wd5e-5	PROPN
brj-23918	166	9	-	-	PUNCT
brj-23918	166	10	ep100	ep100	PROPN
brj-23918	166	11	)	)	PUNCT
brj-23918	166	12	89.55	89.55	NUM
brj-23918	166	13	61.98	61.98	NUM
brj-23918	166	14	73.26	73.26	NUM
brj-23918	166	15	57.80	57.80	NUM
brj-23918	166	16	78.24	78.24	NUM
brj-23918	166	17	u	u	NOUN
brj-23918	166	18	-	-	NOUN
brj-23918	166	19	net+eca+ag+skipcbam	net+eca+ag+skipcbam	ADJ
brj-23918	166	20	(	(	PUNCT
brj-23918	166	21	lr5e-4,wd5e-5,ep100,0.4ce+0.3dice+0.3iou	lr5e-4,wd5e-5,ep100,0.4ce+0.3dice+0.3iou	PROPN
brj-23918	166	22	)	)	PUNCT
brj-23918	166	23	88.96	88.96	NUM
brj-23918	166	24	65.48	65.48	NUM
brj-23918	167	1	75.43	75.43	NUM
brj-23918	167	2	60.55	60.55	NUM
brj-23918	167	3	79.65	79.65	NUM
brj-23918	167	4	u	u	NOUN
brj-23918	167	5	-	-	NOUN
brj-23918	167	6	net+eca+ag+skipcbam	net+eca+ag+skipcbam	ADJ
brj-23918	167	7	(	(	PUNCT
brj-23918	167	8	lr5e-4,wd5e-5,ep100,0.25ce+0.5dice+0.25iou	lr5e-4,wd5e-5,ep100,0.25ce+0.5dice+0.25iou	NOUN
brj-23918	167	9	)	)	PUNCT
brj-23918	167	10	82.03	82.03	NUM
brj-23918	167	11	70.92	70.92	NUM
brj-23918	167	12	76.07	76.07	NUM
brj-23918	167	13	61.39	61.39	NUM
brj-23918	167	14	80.04	80.04	NUM
brj-23918	167	15	as	as	SCONJ
brj-23918	167	16	shown	show	VERB
brj-23918	167	17	in	in	ADP
brj-23918	167	18	table	table	NOUN
brj-23918	167	19	2	2	NUM
brj-23918	167	20	,	,	PUNCT
brj-23918	167	21	seg	seg	PROPN
brj-23918	167	22	-	-	PUNCT
brj-23918	167	23	net	net	NOUN
brj-23918	167	24	achieved	achieve	VERB
brj-23918	167	25	the	the	DET
brj-23918	167	26	highest	high	ADJ
brj-23918	167	27	score	score	NOUN
brj-23918	167	28	of	of	ADP
brj-23918	167	29	89.55	89.55	NUM
brj-23918	167	30	%	%	NOUN
brj-23918	167	31	in	in	ADP
brj-23918	167	32	prc	prc	PROPN
brj-23918	167	33	for	for	ADP
brj-23918	167	34	various	various	ADJ
brj-23918	167	35	semantic	semantic	ADJ
brj-23918	167	36	segmentation	segmentation	NOUN
brj-23918	167	37	evaluation	evaluation	NOUN
brj-23918	167	38	indicators	indicator	NOUN
brj-23918	167	39	,	,	PUNCT
brj-23918	167	40	but	but	CCONJ
brj-23918	167	41	the	the	DET
brj-23918	167	42	model	model	NOUN
brj-23918	167	43	was	be	AUX
brj-23918	167	44	lower	low	ADJ
brj-23918	167	45	than	than	ADP
brj-23918	167	46	the	the	DET
brj-23918	167	47	eca	eca	NOUN
brj-23918	167	48	-	-	PUNCT
brj-23918	167	49	cbam	cbam	NOUN
brj-23918	167	50	agu	agu	NOUN
brj-23918	167	51	-	-	PUNCT
brj-23918	167	52	net	net	NOUN
brj-23918	167	53	(	(	PUNCT
brj-23918	167	54	0.4ce+0.3dice+0.3iou	0.4ce+0.3dice+0.3iou	NOUN
brj-23918	167	55	)	)	PUNCT
brj-23918	167	56	model	model	NOUN
brj-23918	167	57	in	in	ADP
brj-23918	167	58	rec	rec	NOUN
brj-23918	167	59	,	,	PUNCT
brj-23918	167	60	f1c	f1c	NOUN
brj-23918	167	61	,	,	PUNCT
brj-23918	167	62	f1c	f1c	NOUN
brj-23918	167	63	,	,	PUNCT
brj-23918	167	64	iouc	iouc	NOUN
brj-23918	167	65	,	,	PUNCT
brj-23918	167	66	and	and	CCONJ
brj-23918	167	67	miou	miou	NOUN
brj-23918	167	68	,	,	PUNCT
brj-23918	167	69	and	and	CCONJ
brj-23918	167	70	the	the	DET
brj-23918	167	71	difference	difference	NOUN
brj-23918	167	72	in	in	ADP
brj-23918	167	73	prc	prc	PROPN
brj-23918	167	74	between	between	ADP
brj-23918	167	75	the	the	DET
brj-23918	167	76	two	two	NUM
brj-23918	167	77	was	be	AUX
brj-23918	167	78	not	not	PART
brj-23918	167	79	significant	significant	ADJ
brj-23918	167	80	.	.	PUNCT
brj-23918	168	1	from	from	ADP
brj-23918	168	2	the	the	DET
brj-23918	168	3	perspective	perspective	NOUN
brj-23918	168	4	of	of	ADP
brj-23918	168	5	evaluation	evaluation	NOUN
brj-23918	168	6	indicators	indicator	NOUN
brj-23918	168	7	,	,	PUNCT
brj-23918	168	8	eca	eca	NOUN
brj-23918	168	9	-	-	PUNCT
brj-23918	168	10	cbam	cbam	PROPN
brj-23918	168	11	ag	ag	PROPN
brj-23918	168	12	u	u	NOUN
brj-23918	168	13	-	-	NOUN
brj-23918	168	14	net	net	ADJ
brj-23918	168	15	(	(	PUNCT
brj-23918	168	16	0.25ce+0.5dice+0.25iou	0.25ce+0.5dice+0.25iou	NOUN
brj-23918	168	17	)	)	PUNCT
brj-23918	168	18	still	still	ADV
brj-23918	168	19	has	have	VERB
brj-23918	168	20	the	the	DET
brj-23918	168	21	highest	high	ADJ
brj-23918	168	22	scores	score	NOUN
brj-23918	168	23	in	in	ADP
brj-23918	168	24	rec	rec	NOUN
brj-23918	168	25	,	,	PUNCT
brj-23918	168	26	f1c	f1c	NOUN
brj-23918	168	27	,	,	PUNCT
brj-23918	168	28	iouc	iouc	NOUN
brj-23918	168	29	,	,	PUNCT
brj-23918	168	30	and	and	CCONJ
brj-23918	168	31	miou	miou	NOUN
brj-23918	168	32	indicators	indicator	NOUN
brj-23918	168	33	,	,	PUNCT
brj-23918	168	34	but	but	CCONJ
brj-23918	168	35	prc	prc	PROPN
brj-23918	168	36	is	be	AUX
brj-23918	168	37	indeed	indeed	ADV
brj-23918	168	38	the	the	DET
brj-23918	168	39	lowest	low	ADJ
brj-23918	168	40	.	.	PUNCT
brj-23918	169	1	fig	fig	NOUN
brj-23918	169	2	.	.	PUNCT
brj-23918	170	1	4	4	X
brj-23918	170	2	.	.	X
brj-23918	170	3	comparison	comparison	NOUN
brj-23918	170	4	of	of	ADP
brj-23918	170	5	segmentation	segmentation	NOUN
brj-23918	170	6	effects	effect	NOUN
brj-23918	170	7	of	of	ADP
brj-23918	170	8	different	different	ADJ
brj-23918	170	9	models	model	NOUN
brj-23918	170	10	on	on	ADP
brj-23918	170	11	surface	surface	NOUN
brj-23918	170	12	cracks	crack	NOUN
brj-23918	170	13	of	of	ADP
brj-23918	170	14	sawn	sawn	NOUN
brj-23918	170	15	timber	timber	NOUN
brj-23918	170	16	peer	peer	NOUN
brj-23918	170	17	-	-	PUNCT
brj-23918	170	18	reviewed	review	VERB
brj-23918	170	19	article	article	NOUN
brj-23918	170	20	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	170	21	dong	dong	PROPN
brj-23918	170	22	et	et	PROPN
brj-23918	170	23	al	al	PROPN
brj-23918	170	24	.	.	PROPN
brj-23918	171	1	(	(	PUNCT
brj-23918	171	2	2025	2025	NUM
brj-23918	171	3	)	)	PUNCT
brj-23918	171	4	.	.	PUNCT
brj-23918	172	1	“	"	PUNCT
brj-23918	172	2	cracks	crack	NOUN
brj-23918	172	3	in	in	ADP
brj-23918	172	4	sawn	sawn	NOUN
brj-23918	172	5	wood	wood	NOUN
brj-23918	172	6	,	,	PUNCT
brj-23918	172	7	”	"	PUNCT
brj-23918	172	8	bioresources	bioresource	NOUN
brj-23918	172	9	20(2	20(2	NUM
brj-23918	172	10	)	)	PUNCT
brj-23918	172	11	,	,	PUNCT
brj-23918	172	12	3545	3545	NUM
brj-23918	172	13	-	-	SYM
brj-23918	172	14	3556	3556	NUM
brj-23918	172	15	.	.	PUNCT
brj-23918	172	16	3554	3554	NUM
brj-23918	172	17	although	although	SCONJ
brj-23918	172	18	other	other	ADJ
brj-23918	172	19	models	model	NOUN
brj-23918	172	20	have	have	VERB
brj-23918	172	21	poorer	poor	ADJ
brj-23918	172	22	semantic	semantic	ADJ
brj-23918	172	23	segmentation	segmentation	NOUN
brj-23918	172	24	metrics	metric	NOUN
brj-23918	172	25	than	than	ADP
brj-23918	172	26	the	the	DET
brj-23918	172	27	authors	author	NOUN
brj-23918	172	28	’	’	PART
brj-23918	172	29	model	model	NOUN
brj-23918	172	30	in	in	ADP
brj-23918	172	31	the	the	DET
brj-23918	172	32	self	self	NOUN
brj-23918	172	33	-	-	PUNCT
brj-23918	172	34	made	make	VERB
brj-23918	172	35	surface	surface	NOUN
brj-23918	172	36	crack	crack	NOUN
brj-23918	172	37	pattern	pattern	NOUN
brj-23918	172	38	dataset	dataset	NOUN
brj-23918	172	39	,	,	PUNCT
brj-23918	172	40	it	it	PRON
brj-23918	172	41	does	do	AUX
brj-23918	172	42	not	not	PART
brj-23918	172	43	mean	mean	VERB
brj-23918	172	44	that	that	SCONJ
brj-23918	172	45	this	this	DET
brj-23918	172	46	model	model	NOUN
brj-23918	172	47	performs	perform	VERB
brj-23918	172	48	poorly	poorly	ADV
brj-23918	172	49	on	on	ADP
brj-23918	172	50	other	other	ADJ
brj-23918	172	51	datasets	dataset	NOUN
brj-23918	172	52	.	.	PUNCT
brj-23918	173	1	it	it	PRON
brj-23918	173	2	may	may	AUX
brj-23918	173	3	perform	perform	VERB
brj-23918	173	4	better	well	ADJ
brj-23918	173	5	than	than	ADP
brj-23918	173	6	our	our	PRON
brj-23918	173	7	model	model	NOUN
brj-23918	173	8	on	on	ADP
brj-23918	173	9	other	other	ADJ
brj-23918	173	10	datasets	dataset	NOUN
brj-23918	173	11	,	,	PUNCT
brj-23918	173	12	which	which	PRON
brj-23918	173	13	largely	largely	ADV
brj-23918	173	14	depends	depend	VERB
brj-23918	173	15	on	on	ADP
brj-23918	173	16	the	the	DET
brj-23918	173	17	quality	quality	NOUN
brj-23918	173	18	of	of	ADP
brj-23918	173	19	the	the	DET
brj-23918	173	20	dataset	dataset	NOUN
brj-23918	173	21	,	,	PUNCT
brj-23918	173	22	the	the	DET
brj-23918	173	23	number	number	NOUN
brj-23918	173	24	of	of	ADP
brj-23918	173	25	samples	sample	NOUN
brj-23918	173	26	,	,	PUNCT
brj-23918	173	27	and	and	CCONJ
brj-23918	173	28	the	the	DET
brj-23918	173	29	accuracy	accuracy	NOUN
brj-23918	173	30	of	of	ADP
brj-23918	173	31	data	datum	NOUN
brj-23918	173	32	labeling	labeling	NOUN
brj-23918	173	33	.	.	PUNCT
brj-23918	174	1	figure	figure	NOUN
brj-23918	174	2	4	4	NUM
brj-23918	174	3	compares	compare	VERB
brj-23918	174	4	the	the	DET
brj-23918	174	5	segmentation	segmentation	NOUN
brj-23918	174	6	effects	effect	NOUN
brj-23918	174	7	of	of	ADP
brj-23918	174	8	different	different	ADJ
brj-23918	174	9	models	model	NOUN
brj-23918	174	10	on	on	ADP
brj-23918	174	11	surface	surface	NOUN
brj-23918	174	12	cracks	crack	NOUN
brj-23918	174	13	in	in	ADP
brj-23918	174	14	sawn	sawn	NOUN
brj-23918	174	15	timber	timber	NOUN
brj-23918	174	16	.	.	PUNCT
brj-23918	175	1	in	in	ADP
brj-23918	175	2	the	the	DET
brj-23918	175	3	figure	figure	NOUN
brj-23918	175	4	,	,	PUNCT
brj-23918	175	5	fcn	fcn	PROPN
brj-23918	175	6	is	be	AUX
brj-23918	175	7	susceptible	susceptible	ADJ
brj-23918	175	8	to	to	ADP
brj-23918	175	9	the	the	DET
brj-23918	175	10	influence	influence	NOUN
brj-23918	175	11	of	of	ADP
brj-23918	175	12	background	background	NOUN
brj-23918	175	13	knots	knot	NOUN
brj-23918	175	14	,	,	PUNCT
brj-23918	175	15	which	which	PRON
brj-23918	175	16	severely	severely	ADV
brj-23918	175	17	respond	respond	VERB
brj-23918	175	18	as	as	ADP
brj-23918	175	19	cracks	crack	NOUN
brj-23918	175	20	.	.	PUNCT
brj-23918	176	1	deeplabv3+expands	deeplabv3+expand	VERB
brj-23918	176	2	the	the	DET
brj-23918	176	3	response	response	NOUN
brj-23918	176	4	area	area	NOUN
brj-23918	176	5	of	of	ADP
brj-23918	176	6	nodes	node	NOUN
brj-23918	176	7	and	and	CCONJ
brj-23918	176	8	does	do	AUX
brj-23918	176	9	not	not	PART
brj-23918	176	10	segment	segment	VERB
brj-23918	176	11	cracks	crack	NOUN
brj-23918	176	12	that	that	PRON
brj-23918	176	13	are	be	AUX
brj-23918	176	14	similar	similar	ADJ
brj-23918	176	15	to	to	ADP
brj-23918	176	16	the	the	DET
brj-23918	176	17	background	background	NOUN
brj-23918	176	18	.	.	PUNCT
brj-23918	177	1	seg	seg	PROPN
brj-23918	177	2	net	net	PROPN
brj-23918	177	3	only	only	ADV
brj-23918	177	4	segments	segment	NOUN
brj-23918	177	5	obvious	obvious	ADJ
brj-23918	177	6	cracks	crack	NOUN
brj-23918	177	7	,	,	PUNCT
brj-23918	177	8	and	and	CCONJ
brj-23918	177	9	when	when	SCONJ
brj-23918	177	10	there	there	PRON
brj-23918	177	11	are	be	VERB
brj-23918	177	12	knots	knot	NOUN
brj-23918	177	13	,	,	PUNCT
brj-23918	177	14	knots	knot	NOUN
brj-23918	177	15	,	,	PUNCT
brj-23918	177	16	and	and	CCONJ
brj-23918	177	17	cracks	crack	NOUN
brj-23918	177	18	that	that	PRON
brj-23918	177	19	exist	exist	VERB
brj-23918	177	20	simultaneously	simultaneously	ADV
brj-23918	177	21	and	and	CCONJ
brj-23918	177	22	are	be	AUX
brj-23918	177	23	similar	similar	ADJ
brj-23918	177	24	to	to	ADP
brj-23918	177	25	the	the	DET
brj-23918	177	26	background	background	NOUN
brj-23918	177	27	cracks	crack	NOUN
brj-23918	177	28	,	,	PUNCT
brj-23918	177	29	no	no	DET
brj-23918	177	30	cracks	crack	NOUN
brj-23918	177	31	are	be	AUX
brj-23918	177	32	segmented	segment	VERB
brj-23918	177	33	.	.	PUNCT
brj-23918	178	1	taking	take	VERB
brj-23918	178	2	all	all	DET
brj-23918	178	3	factors	factor	NOUN
brj-23918	178	4	into	into	ADP
brj-23918	178	5	consideration	consideration	NOUN
brj-23918	178	6	,	,	PUNCT
brj-23918	178	7	priority	priority	NOUN
brj-23918	178	8	should	should	AUX
brj-23918	178	9	be	be	AUX
brj-23918	178	10	given	give	VERB
brj-23918	178	11	to	to	ADP
brj-23918	178	12	using	use	VERB
brj-23918	178	13	loss	loss	NOUN
brj-23918	178	14	functions	function	NOUN
brj-23918	178	15	with	with	ADP
brj-23918	178	16	fusion	fusion	NOUN
brj-23918	178	17	weights	weight	NOUN
brj-23918	178	18	of	of	ADP
brj-23918	178	19	α=0.4	α=0.4	NOUN
brj-23918	178	20	,	,	PUNCT
brj-23918	178	21	β=0.3	β=0.3	NOUN
brj-23918	178	22	,	,	PUNCT
brj-23918	178	23	and	and	CCONJ
brj-23918	178	24	γ=0.3	γ=0.3	NOUN
brj-23918	178	25	.	.	PUNCT
brj-23918	179	1	although	although	SCONJ
brj-23918	179	2	the	the	DET
brj-23918	179	3	multi	multi	ADJ
brj-23918	179	4	loss	loss	NOUN
brj-23918	179	5	function	function	NOUN
brj-23918	179	6	fusion	fusion	NOUN
brj-23918	179	7	of	of	ADP
brj-23918	179	8	this	this	DET
brj-23918	179	9	weight	weight	NOUN
brj-23918	179	10	did	do	AUX
brj-23918	179	11	not	not	PART
brj-23918	179	12	reach	reach	VERB
brj-23918	179	13	the	the	DET
brj-23918	179	14	best	good	ADJ
brj-23918	179	15	level	level	NOUN
brj-23918	179	16	on	on	ADP
brj-23918	179	17	prc	prc	PROPN
brj-23918	179	18	,	,	PUNCT
brj-23918	179	19	it	it	PRON
brj-23918	179	20	is	be	AUX
brj-23918	179	21	only	only	ADV
brj-23918	179	22	second	second	ADJ
brj-23918	179	23	to	to	ADP
brj-23918	179	24	0.25ce+0.5dice+0.25iou	0.25ce+0.5dice+0.25iou	PROPN
brj-23918	179	25	in	in	ADP
brj-23918	179	26	rec	rec	NOUN
brj-23918	179	27	,	,	PUNCT
brj-23918	179	28	f1c	f1c	NOUN
brj-23918	179	29	,	,	PUNCT
brj-23918	179	30	iouc	iouc	NOUN
brj-23918	179	31	,	,	PUNCT
brj-23918	179	32	and	and	CCONJ
brj-23918	179	33	miou	miou	NOUN
brj-23918	179	34	indicators	indicator	NOUN
brj-23918	179	35	,	,	PUNCT
brj-23918	179	36	and	and	CCONJ
brj-23918	179	37	prc	prc	PROPN
brj-23918	179	38	has	have	AUX
brj-23918	179	39	increased	increase	VERB
brj-23918	179	40	by	by	ADP
brj-23918	179	41	6.92	6.92	NUM
brj-23918	179	42	percentage	percentage	NOUN
brj-23918	179	43	points	point	NOUN
brj-23918	179	44	compared	compare	VERB
brj-23918	179	45	to	to	ADP
brj-23918	179	46	the	the	DET
brj-23918	179	47	latter	latter	ADJ
brj-23918	179	48	.	.	PUNCT
brj-23918	180	1	at	at	ADP
brj-23918	180	2	the	the	DET
brj-23918	180	3	same	same	ADJ
brj-23918	180	4	time	time	NOUN
brj-23918	180	5	,	,	PUNCT
brj-23918	180	6	in	in	ADP
brj-23918	180	7	terms	term	NOUN
brj-23918	180	8	of	of	ADP
brj-23918	180	9	segmentation	segmentation	NOUN
brj-23918	180	10	performance	performance	NOUN
brj-23918	180	11	,	,	PUNCT
brj-23918	180	12	it	it	PRON
brj-23918	180	13	can	can	AUX
brj-23918	180	14	also	also	ADV
brj-23918	180	15	segment	segment	VERB
brj-23918	180	16	cracks	crack	NOUN
brj-23918	180	17	with	with	ADP
brj-23918	180	18	similar	similar	ADJ
brj-23918	180	19	backgrounds	background	NOUN
brj-23918	180	20	.	.	PUNCT
brj-23918	181	1	based	base	VERB
brj-23918	181	2	on	on	ADP
brj-23918	181	3	the	the	DET
brj-23918	181	4	comprehensive	comprehensive	ADJ
brj-23918	181	5	semantic	semantic	ADJ
brj-23918	181	6	segmentation	segmentation	NOUN
brj-23918	181	7	evaluation	evaluation	NOUN
brj-23918	181	8	index	index	NOUN
brj-23918	181	9	data	datum	NOUN
brj-23918	181	10	and	and	CCONJ
brj-23918	181	11	mask	mask	NOUN
brj-23918	181	12	prediction	prediction	NOUN
brj-23918	181	13	segmentation	segmentation	NOUN
brj-23918	181	14	performance	performance	NOUN
brj-23918	181	15	,	,	PUNCT
brj-23918	181	16	the	the	DET
brj-23918	181	17	eca	eca	NOUN
brj-23918	181	18	-	-	PUNCT
brj-23918	181	19	cbam	cbam	PROPN
brj-23918	181	20	ag	ag	PROPN
brj-23918	181	21	u	u	NOUN
brj-23918	181	22	-	-	NOUN
brj-23918	181	23	net	net	ADJ
brj-23918	181	24	(	(	PUNCT
brj-23918	181	25	0.4ce+0.3dice+0.3iou	0.4ce+0.3dice+0.3iou	NOUN
brj-23918	181	26	)	)	PUNCT
brj-23918	181	27	model	model	NOUN
brj-23918	181	28	is	be	AUX
brj-23918	181	29	the	the	DET
brj-23918	181	30	best	good	ADJ
brj-23918	181	31	.	.	PUNCT
brj-23918	182	1	conclusions	conclusion	NOUN
brj-23918	182	2	1	1	X
brj-23918	182	3	.	.	PUNCT
brj-23918	183	1	an	an	DET
brj-23918	183	2	improved	improved	ADJ
brj-23918	183	3	attention	attention	NOUN
brj-23918	183	4	u	u	ADJ
brj-23918	183	5	-	-	ADJ
brj-23918	183	6	net	net	ADJ
brj-23918	183	7	model	model	NOUN
brj-23918	183	8	was	be	AUX
brj-23918	183	9	applied	apply	VERB
brj-23918	183	10	in	in	ADP
brj-23918	183	11	a	a	DET
brj-23918	183	12	study	study	NOUN
brj-23918	183	13	of	of	ADP
brj-23918	183	14	semantic	semantic	ADJ
brj-23918	183	15	segmentation	segmentation	NOUN
brj-23918	183	16	of	of	ADP
brj-23918	183	17	surface	surface	NOUN
brj-23918	183	18	cracks	crack	NOUN
brj-23918	183	19	in	in	ADP
brj-23918	183	20	sawn	sawn	NOUN
brj-23918	183	21	timber	timber	NOUN
brj-23918	183	22	.	.	PUNCT
brj-23918	184	1	using	use	VERB
brj-23918	184	2	integrating	integrate	VERB
brj-23918	184	3	experimental	experimental	ADJ
brj-23918	184	4	and	and	CCONJ
brj-23918	184	5	comparative	comparative	ADJ
brj-23918	184	6	research	research	NOUN
brj-23918	184	7	,	,	PUNCT
brj-23918	184	8	the	the	DET
brj-23918	184	9	attention	attention	NOUN
brj-23918	184	10	u	u	NOUN
brj-23918	184	11	-	-	ADJ
brj-23918	184	12	net	net	ADJ
brj-23918	184	13	model	model	NOUN
brj-23918	184	14	was	be	AUX
brj-23918	184	15	optimized	optimize	VERB
brj-23918	184	16	and	and	CCONJ
brj-23918	184	17	improved	improve	VERB
brj-23918	184	18	to	to	PART
brj-23918	184	19	obtain	obtain	VERB
brj-23918	184	20	the	the	DET
brj-23918	184	21	optimal	optimal	ADJ
brj-23918	184	22	semantic	semantic	ADJ
brj-23918	184	23	segmentation	segmentation	NOUN
brj-23918	184	24	model	model	NOUN
brj-23918	184	25	for	for	ADP
brj-23918	184	26	surface	surface	NOUN
brj-23918	184	27	cracks	crack	NOUN
brj-23918	184	28	in	in	ADP
brj-23918	184	29	sawn	sawn	NOUN
brj-23918	184	30	timber	timber	NOUN
brj-23918	184	31	.	.	PUNCT
brj-23918	185	1	introducing	introduce	VERB
brj-23918	185	2	cbam	cbam	NOUN
brj-23918	185	3	in	in	ADP
brj-23918	185	4	the	the	DET
brj-23918	185	5	encoding	encoding	NOUN
brj-23918	185	6	stage	stage	NOUN
brj-23918	185	7	of	of	ADP
brj-23918	185	8	the	the	DET
brj-23918	185	9	attention	attention	NOUN
brj-23918	185	10	u	u	ADJ
brj-23918	185	11	-	-	ADJ
brj-23918	185	12	net	net	ADJ
brj-23918	185	13	model	model	NOUN
brj-23918	185	14	and	and	CCONJ
brj-23918	185	15	using	use	VERB
brj-23918	185	16	adamw	adamw	PROPN
brj-23918	185	17	optimization	optimization	NOUN
brj-23918	185	18	instead	instead	ADV
brj-23918	185	19	of	of	ADP
brj-23918	185	20	sgd	sgd	PROPN
brj-23918	185	21	and	and	CCONJ
brj-23918	185	22	adam	adam	PROPN
brj-23918	185	23	achieved	achieve	VERB
brj-23918	185	24	good	good	ADJ
brj-23918	185	25	crack	crack	NOUN
brj-23918	185	26	semantic	semantic	ADJ
brj-23918	185	27	segmentation	segmentation	NOUN
brj-23918	185	28	results	result	NOUN
brj-23918	185	29	,	,	PUNCT
brj-23918	185	30	which	which	PRON
brj-23918	185	31	can	can	AUX
brj-23918	185	32	segment	segment	VERB
brj-23918	185	33	most	most	ADV
brj-23918	185	34	obvious	obvious	ADJ
brj-23918	185	35	cracks	crack	NOUN
brj-23918	185	36	.	.	PUNCT
brj-23918	186	1	2	2	X
brj-23918	186	2	.	.	X
brj-23918	186	3	on	on	ADP
brj-23918	186	4	the	the	DET
brj-23918	186	5	basis	basis	NOUN
brj-23918	186	6	of	of	ADP
brj-23918	186	7	the	the	DET
brj-23918	186	8	above	above	NOUN
brj-23918	186	9	,	,	PUNCT
brj-23918	186	10	the	the	DET
brj-23918	186	11	eca	eca	NOUN
brj-23918	186	12	module	module	NOUN
brj-23918	186	13	was	be	AUX
brj-23918	186	14	introduced	introduce	VERB
brj-23918	186	15	to	to	ADP
brj-23918	186	16	the	the	DET
brj-23918	186	17	skip	skip	ADJ
brj-23918	186	18	connection	connection	NOUN
brj-23918	186	19	part	part	NOUN
brj-23918	186	20	of	of	ADP
brj-23918	186	21	the	the	DET
brj-23918	186	22	model	model	NOUN
brj-23918	186	23	,	,	PUNCT
brj-23918	186	24	and	and	CCONJ
brj-23918	186	25	multiple	multiple	ADJ
brj-23918	186	26	loss	loss	NOUN
brj-23918	186	27	functions	function	NOUN
brj-23918	186	28	with	with	ADP
brj-23918	186	29	different	different	ADJ
brj-23918	186	30	weights	weight	NOUN
brj-23918	186	31	were	be	AUX
brj-23918	186	32	fused	fuse	VERB
brj-23918	186	33	instead	instead	ADV
brj-23918	186	34	of	of	ADP
brj-23918	186	35	the	the	DET
brj-23918	186	36	original	original	ADJ
brj-23918	186	37	binary	binary	PROPN
brj-23918	186	38	cross	cross	PROPN
brj-23918	186	39	entropy	entropy	PROPN
brj-23918	186	40	loss	loss	NOUN
brj-23918	186	41	function	function	NOUN
brj-23918	186	42	.	.	PUNCT
brj-23918	187	1	the	the	DET
brj-23918	187	2	eca	eca	NOUN
brj-23918	187	3	module	module	NOUN
brj-23918	187	4	and	and	CCONJ
brj-23918	187	5	cbam	cbam	NOUN
brj-23918	187	6	module	module	NOUN
brj-23918	187	7	positions	position	NOUN
brj-23918	187	8	were	be	AUX
brj-23918	187	9	replaced	replace	VERB
brj-23918	187	10	,	,	PUNCT
brj-23918	187	11	and	and	CCONJ
brj-23918	187	12	the	the	DET
brj-23918	187	13	optimizer	optimizer	NOUN
brj-23918	187	14	parameters	parameter	NOUN
brj-23918	187	15	were	be	AUX
brj-23918	187	16	adjusted	adjust	VERB
brj-23918	187	17	(	(	PUNCT
brj-23918	187	18	including	include	VERB
brj-23918	187	19	weight	weight	NOUN
brj-23918	187	20	attenuation	attenuation	NOUN
brj-23918	187	21	of	of	ADP
brj-23918	187	22	5e-5	5e-5	NUM
brj-23918	187	23	during	during	ADP
brj-23918	187	24	training	training	NOUN
brj-23918	187	25	and	and	CCONJ
brj-23918	187	26	learning	learn	VERB
brj-23918	187	27	rate	rate	NOUN
brj-23918	187	28	adjusted	adjust	VERB
brj-23918	187	29	to	to	ADP
brj-23918	187	30	5e-4	5e-4	PROPN
brj-23918	187	31	)	)	PUNCT
brj-23918	187	32	.	.	PUNCT
brj-23918	188	1	the	the	DET
brj-23918	188	2	number	number	NOUN
brj-23918	188	3	of	of	ADP
brj-23918	188	4	iterations	iteration	NOUN
brj-23918	188	5	was	be	AUX
brj-23918	188	6	increased	increase	VERB
brj-23918	188	7	to	to	ADP
brj-23918	188	8	100	100	NUM
brj-23918	188	9	,	,	PUNCT
brj-23918	188	10	and	and	CCONJ
brj-23918	188	11	the	the	DET
brj-23918	188	12	ablation	ablation	NOUN
brj-23918	188	13	experiment	experiment	NOUN
brj-23918	188	14	was	be	AUX
brj-23918	188	15	carried	carry	VERB
brj-23918	188	16	out	out	ADP
brj-23918	188	17	.	.	PUNCT
brj-23918	189	1	compared	compare	VERB
brj-23918	189	2	with	with	ADP
brj-23918	189	3	the	the	DET
brj-23918	189	4	previously	previously	ADV
brj-23918	189	5	improved	improve	VERB
brj-23918	189	6	model	model	NOUN
brj-23918	189	7	,	,	PUNCT
brj-23918	189	8	the	the	DET
brj-23918	189	9	semantic	semantic	ADJ
brj-23918	189	10	segmentation	segmentation	NOUN
brj-23918	189	11	index	index	NOUN
brj-23918	189	12	of	of	ADP
brj-23918	189	13	surface	surface	NOUN
brj-23918	189	14	cracks	crack	NOUN
brj-23918	189	15	in	in	ADP
brj-23918	189	16	sawn	sawn	NOUN
brj-23918	189	17	timber	timber	NOUN
brj-23918	189	18	was	be	AUX
brj-23918	189	19	significantly	significantly	ADV
brj-23918	189	20	improved	improve	VERB
brj-23918	189	21	,	,	PUNCT
brj-23918	189	22	laying	lay	VERB
brj-23918	189	23	a	a	DET
brj-23918	189	24	foundation	foundation	NOUN
brj-23918	189	25	for	for	ADP
brj-23918	189	26	further	further	ADJ
brj-23918	189	27	work	work	NOUN
brj-23918	189	28	.	.	PUNCT
brj-23918	190	1	acknowledgments	acknowledgment	NOUN
brj-23918	190	2	the	the	DET
brj-23918	190	3	authors	author	NOUN
brj-23918	190	4	are	be	AUX
brj-23918	190	5	grateful	grateful	ADJ
brj-23918	190	6	for	for	ADP
brj-23918	190	7	the	the	DET
brj-23918	190	8	support	support	NOUN
brj-23918	190	9	of	of	ADP
brj-23918	190	10	the	the	DET
brj-23918	190	11	key	key	ADJ
brj-23918	190	12	r&d	r&d	NOUN
brj-23918	190	13	and	and	CCONJ
brj-23918	190	14	achievement	achievement	NOUN
brj-23918	190	15	transformation	transformation	NOUN
brj-23918	190	16	plan	plan	NOUN
brj-23918	190	17	of	of	ADP
brj-23918	190	18	inner	inner	PROPN
brj-23918	190	19	mongolia	mongolia	PROPN
brj-23918	190	20	autonomous	autonomous	PROPN
brj-23918	190	21	region	region	PROPN
brj-23918	190	22	,	,	PUNCT
brj-23918	190	23	project	project	NOUN
brj-23918	190	24	no	no	NOUN
brj-23918	190	25	.	.	PUNCT
brj-23918	191	1	2023yfhh0022	2023yfhh0022	NOUN
brj-23918	191	2	.	.	PUNCT
brj-23918	192	1	the	the	DET
brj-23918	192	2	authors	author	NOUN
brj-23918	192	3	are	be	AUX
brj-23918	192	4	grateful	grateful	ADJ
brj-23918	192	5	for	for	ADP
brj-23918	192	6	the	the	DET
brj-23918	192	7	supported	support	VERB
brj-23918	192	8	by	by	ADP
brj-23918	192	9	the	the	DET
brj-23918	192	10	“	"	PUNCT
brj-23918	192	11	fundamental	fundamental	ADJ
brj-23918	192	12	research	research	NOUN
brj-23918	192	13	funds	fund	NOUN
brj-23918	192	14	for	for	ADP
brj-23918	192	15	inner	inner	ADJ
brj-23918	192	16	mongolia	mongolia	PROPN
brj-23918	192	17	agricultural	agricultural	PROPN
brj-23918	192	18	university	university	PROPN
brj-23918	192	19	(	(	PUNCT
brj-23918	192	20	no	no	INTJ
brj-23918	192	21	.	.	PUNCT
brj-23918	193	1	br22	br22	PROPN
brj-23918	193	2	-	-	PUNCT
brj-23918	193	3	12	12	NUM
brj-23918	193	4	-	-	PUNCT
brj-23918	193	5	01	01	NUM
brj-23918	193	6	)	)	PUNCT
brj-23918	193	7	”	"	PUNCT
brj-23918	193	8	peer	peer	NOUN
brj-23918	193	9	-	-	PUNCT
brj-23918	193	10	reviewed	review	VERB
brj-23918	193	11	article	article	NOUN
brj-23918	193	12	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	193	13	dong	dong	PROPN
brj-23918	193	14	et	et	PROPN
brj-23918	193	15	al	al	PROPN
brj-23918	193	16	.	.	PROPN
brj-23918	194	1	(	(	PUNCT
brj-23918	194	2	2025	2025	NUM
brj-23918	194	3	)	)	PUNCT
brj-23918	194	4	.	.	PUNCT
brj-23918	195	1	“	"	PUNCT
brj-23918	195	2	cracks	crack	NOUN
brj-23918	195	3	in	in	ADP
brj-23918	195	4	sawn	sawn	NOUN
brj-23918	195	5	wood	wood	NOUN
brj-23918	195	6	,	,	PUNCT
brj-23918	195	7	”	"	PUNCT
brj-23918	195	8	bioresources	bioresource	NOUN
brj-23918	195	9	20(2	20(2	NUM
brj-23918	195	10	)	)	PUNCT
brj-23918	195	11	,	,	PUNCT
brj-23918	195	12	3545	3545	NUM
brj-23918	195	13	-	-	SYM
brj-23918	195	14	3556	3556	NUM
brj-23918	195	15	.	.	PUNCT
brj-23918	196	1	3555	3555	NUM
brj-23918	196	2	references	reference	NOUN
brj-23918	196	3	cited	cite	VERB
brj-23918	196	4	badrinarayanan	badrinarayanan	NOUN
brj-23918	196	5	,	,	PUNCT
brj-23918	196	6	v.	v.	PROPN
brj-23918	196	7	,	,	PUNCT
brj-23918	196	8	kendall	kendall	PROPN
brj-23918	196	9	,	,	PUNCT
brj-23918	196	10	a.	a.	PROPN
brj-23918	196	11	,	,	PUNCT
brj-23918	196	12	and	and	CCONJ
brj-23918	196	13	cipolla	cipolla	PROPN
brj-23918	196	14	,	,	PUNCT
brj-23918	196	15	r.	r.	PROPN
brj-23918	196	16	(	(	PUNCT
brj-23918	196	17	2015	2015	NUM
brj-23918	196	18	)	)	PUNCT
brj-23918	196	19	.	.	PUNCT
brj-23918	197	1	“	"	PUNCT
brj-23918	197	2	segnet	segnet	NOUN
brj-23918	197	3	:	:	PUNCT
brj-23918	197	4	a	a	DET
brj-23918	197	5	deep	deep	ADJ
brj-23918	197	6	convolutional	convolutional	ADJ
brj-23918	197	7	encoder	encoder	NOUN
brj-23918	197	8	-	-	PUNCT
brj-23918	197	9	decoder	decoder	NOUN
brj-23918	197	10	architecture	architecture	NOUN
brj-23918	197	11	for	for	ADP
brj-23918	197	12	image	image	NOUN
brj-23918	197	13	segmentation	segmentation	NOUN
brj-23918	197	14	,	,	PUNCT
brj-23918	197	15	”	"	PUNCT
brj-23918	197	16	ieee	ieee	NOUN
brj-23918	197	17	transactions	transaction	NOUN
brj-23918	197	18	on	on	ADP
brj-23918	197	19	pattern	pattern	NOUN
brj-23918	197	20	analysis	analysis	NOUN
brj-23918	197	21	and	and	CCONJ
brj-23918	197	22	machine	machine	NOUN
brj-23918	197	23	intelligence	intelligence	NOUN
brj-23918	197	24	39	39	NUM
brj-23918	197	25	,	,	PUNCT
brj-23918	197	26	2481	2481	NUM
brj-23918	197	27	-	-	SYM
brj-23918	197	28	2495	2495	NUM
brj-23918	197	29	.	.	PUNCT
brj-23918	198	1	doi	doi	NOUN
brj-23918	198	2	:	:	PUNCT
brj-23918	198	3	10.1109	10.1109	NUM
brj-23918	198	4	/	/	SYM
brj-23918	198	5	tpami.2016.2644615	tpami.2016.2644615	NUM
brj-23918	198	6	bai	bai	PROPN
brj-23918	198	7	,	,	PUNCT
brj-23918	198	8	s.	s.	PROPN
brj-23918	198	9	,	,	PUNCT
brj-23918	198	10	liang	liang	PROPN
brj-23918	198	11	,	,	PUNCT
brj-23918	198	12	c	c	PROPN
brj-23918	198	13	,	,	PUNCT
brj-23918	198	14	wang	wang	PROPN
brj-23918	198	15	,	,	PUNCT
brj-23918	198	16	z.	z.	PROPN
brj-23918	198	17	,	,	PUNCT
brj-23918	198	18	and	and	CCONJ
brj-23918	198	19	pan	pan	PROPN
brj-23918	198	20	w.	w.	PROPN
brj-23918	198	21	(	(	PUNCT
brj-23918	198	22	2020	2020	NUM
brj-23918	198	23	)	)	PUNCT
brj-23918	198	24	.	.	PUNCT
brj-23918	199	1	“	"	PUNCT
brj-23918	199	2	information	information	NOUN
brj-23918	199	3	entropy	entropy	NOUN
brj-23918	199	4	induced	induce	VERB
brj-23918	199	5	graph	graph	NOUN
brj-23918	199	6	convolutional	convolutional	ADJ
brj-23918	199	7	network	network	NOUN
brj-23918	199	8	for	for	ADP
brj-23918	199	9	semantic	semantic	ADJ
brj-23918	199	10	segmentation	segmentation	NOUN
brj-23918	199	11	,	,	PUNCT
brj-23918	199	12	”	"	PUNCT
brj-23918	199	13	journal	journal	NOUN
brj-23918	199	14	of	of	ADP
brj-23918	199	15	visual	visual	ADJ
brj-23918	199	16	communication	communication	NOUN
brj-23918	199	17	and	and	CCONJ
brj-23918	199	18	image	image	NOUN
brj-23918	199	19	representation	representation	NOUN
brj-23918	199	20	103	103	NUM
brj-23918	199	21	,	,	PUNCT
brj-23918	199	22	104217	104217	NUM
brj-23918	199	23	.	.	PUNCT
brj-23918	200	1	doi	doi	NOUN
brj-23918	200	2	:	:	PUNCT
brj-23918	200	3	10.1016	10.1016	NUM
brj-23918	200	4	/	/	SYM
brj-23918	200	5	j.jvcir.2024.104217	j.jvcir.2024.104217	PROPN
brj-23918	200	6	gb	gb	PROPN
brj-23918	200	7	/	/	SYM
brj-23918	200	8	t	t	NOUN
brj-23918	200	9	153	153	NUM
brj-23918	200	10	-	-	SYM
brj-23918	200	11	2019	2019	NUM
brj-23918	200	12	(	(	PUNCT
brj-23918	200	13	2019	2019	NUM
brj-23918	200	14	)	)	PUNCT
brj-23918	200	15	.	.	PUNCT
brj-23918	201	1	“	"	PUNCT
brj-23918	201	2	softwood	softwood	NOUN
brj-23918	201	3	lumber	lumber	NOUN
brj-23918	201	4	,	,	PUNCT
brj-23918	201	5	”	"	PUNCT
brj-23918	201	6	standards	standard	NOUN
brj-23918	201	7	press	press	NOUN
brj-23918	201	8	of	of	ADP
brj-23918	201	9	china	china	PROPN
brj-23918	201	10	,	,	PUNCT
brj-23918	201	11	beijing	beijing	PROPN
brj-23918	201	12	.	.	PUNCT
brj-23918	202	1	gb	gb	ADP
brj-23918	202	2	/	/	SYM
brj-23918	202	3	t	t	NOUN
brj-23918	202	4	4817	4817	NUM
brj-23918	202	5	-	-	SYM
brj-23918	202	6	2019	2019	NUM
brj-23918	202	7	(	(	PUNCT
brj-23918	202	8	2020	2020	NUM
brj-23918	202	9	)	)	PUNCT
brj-23918	202	10	.	.	PUNCT
brj-23918	203	1	“	"	PUNCT
brj-23918	203	2	broadleaf	broadleaf	NOUN
brj-23918	203	3	tree	tree	NOUN
brj-23918	203	4	lumber	lumber	NOUN
brj-23918	203	5	,	,	PUNCT
brj-23918	203	6	”	"	PUNCT
brj-23918	203	7	standards	standard	NOUN
brj-23918	203	8	press	press	NOUN
brj-23918	203	9	of	of	ADP
brj-23918	203	10	china	china	PROPN
brj-23918	203	11	,	,	PUNCT
brj-23918	203	12	beijing	beijing	PROPN
brj-23918	203	13	.	.	PUNCT
brj-23918	204	1	gb	gb	ADP
brj-23918	204	2	/	/	SYM
brj-23918	204	3	t	t	NOUN
brj-23918	204	4	4823	4823	NUM
brj-23918	204	5	-	-	SYM
brj-23918	204	6	2013	2013	NUM
brj-23918	204	7	(	(	PUNCT
brj-23918	204	8	2014	2014	NUM
brj-23918	204	9	)	)	PUNCT
brj-23918	204	10	.	.	PUNCT
brj-23918	205	1	“	"	PUNCT
brj-23918	205	2	lumber	lumber	NOUN
brj-23918	205	3	defects	defect	NOUN
brj-23918	205	4	,	,	PUNCT
brj-23918	205	5	”	"	PUNCT
brj-23918	205	6	standards	standard	NOUN
brj-23918	205	7	press	press	NOUN
brj-23918	205	8	of	of	ADP
brj-23918	205	9	china	china	PROPN
brj-23918	205	10	,	,	PUNCT
brj-23918	205	11	beijing	beijing	PROPN
brj-23918	205	12	.	.	PUNCT
brj-23918	206	1	gao	gao	PROPN
brj-23918	206	2	,	,	PUNCT
brj-23918	206	3	m.	m.	NOUN
brj-23918	206	4	,	,	PUNCT
brj-23918	206	5	guan	guan	PROPN
brj-23918	206	6	x.	x.	PROPN
brj-23918	206	7	,	,	PUNCT
brj-23918	206	8	fan	fan	PROPN
brj-23918	206	9	j.	j.	PROPN
brj-23918	206	10	,	,	PUNCT
brj-23918	206	11	and	and	CCONJ
brj-23918	206	12	yao	yao	PROPN
brj-23918	206	13	,	,	PUNCT
brj-23918	206	14	l.	l.	PROPN
brj-23918	206	15	(	(	PUNCT
brj-23918	206	16	2023	2023	NUM
brj-23918	206	17	)	)	PUNCT
brj-23918	206	18	.	.	PUNCT
brj-23918	207	1	“	"	PUNCT
brj-23918	207	2	intelligent	intelligent	ADJ
brj-23918	207	3	recognition	recognition	NOUN
brj-23918	207	4	of	of	ADP
brj-23918	207	5	road	road	NOUN
brj-23918	207	6	surface	surface	NOUN
brj-23918	207	7	cracks	crack	NOUN
brj-23918	207	8	based	base	VERB
brj-23918	207	9	on	on	ADP
brj-23918	207	10	improved	improve	VERB
brj-23918	207	11	u	u	ADJ
brj-23918	207	12	-	-	ADJ
brj-23918	207	13	net	net	ADJ
brj-23918	207	14	model	model	NOUN
brj-23918	207	15	,	,	PUNCT
brj-23918	207	16	”	"	PUNCT
brj-23918	207	17	information	information	NOUN
brj-23918	207	18	technology	technology	NOUN
brj-23918	207	19	and	and	CCONJ
brj-23918	207	20	informatization	informatization	NOUN
brj-23918	207	21	7	7	NUM
brj-23918	207	22	,	,	PUNCT
brj-23918	207	23	208	208	NUM
brj-23918	207	24	-	-	SYM
brj-23918	207	25	212	212	NUM
brj-23918	207	26	.	.	PUNCT
brj-23918	208	1	doi	doi	NOUN
brj-23918	208	2	:	:	PUNCT
brj-23918	208	3	10.3969	10.3969	NUM
brj-23918	208	4	/	/	SYM
brj-23918	208	5	j.issn.1006	j.issn.1006	PROPN
brj-23918	208	6	-	-	PUNCT
brj-23918	208	7	8023.2023.05.019	8023.2023.05.019	PROPN
brj-23918	208	8	ge	ge	PROPN
brj-23918	208	9	,	,	PUNCT
brj-23918	208	10	z.	z.	PROPN
brj-23918	208	11	,	,	PUNCT
brj-23918	208	12	zhang	zhang	PROPN
brj-23918	208	13	,	,	PUNCT
brj-23918	208	14	z.	z.	PROPN
brj-23918	208	15	,	,	PUNCT
brj-23918	208	16	shi	shi	PROPN
brj-23918	208	17	,	,	PUNCT
brj-23918	208	18	l.	l.	PROPN
brj-23918	208	19	,	,	PUNCT
brj-23918	208	20	liu	liu	PROPN
brj-23918	208	21	s.	s.	PROPN
brj-23918	208	22	,	,	PUNCT
brj-23918	208	23	gao	gao	PROPN
brj-23918	208	24	y.	y.	PROPN
brj-23918	208	25	,	,	PUNCT
brj-23918	208	26	zhou	zhou	PROPN
brj-23918	208	27	y.	y.	PROPN
brj-23918	208	28	,	,	PUNCT
brj-23918	208	29	and	and	CCONJ
brj-23918	208	30	sun	sun	PROPN
brj-23918	208	31	q.	q.	PROPN
brj-23918	208	32	(	(	PUNCT
brj-23918	208	33	2024	2024	NUM
brj-23918	208	34	)	)	PUNCT
brj-23918	208	35	.	.	PUNCT
brj-23918	209	1	“	"	PUNCT
brj-23918	209	2	an	an	DET
brj-23918	209	3	algorithm	algorithm	NOUN
brj-23918	209	4	based	base	VERB
brj-23918	209	5	on	on	ADP
brj-23918	209	6	daf	daf	PROPN
brj-23918	209	7	-	-	PUNCT
brj-23918	209	8	net++	net++	PROPN
brj-23918	209	9	model	model	NOUN
brj-23918	209	10	for	for	ADP
brj-23918	209	11	wood	wood	NOUN
brj-23918	209	12	annual	annual	ADJ
brj-23918	209	13	rings	ring	NOUN
brj-23918	209	14	segmentation	segmentation	NOUN
brj-23918	209	15	,	,	PUNCT
brj-23918	209	16	”	"	PUNCT
brj-23918	209	17	electronics	electronic	NOUN
brj-23918	209	18	12(14	12(14	NUM
brj-23918	209	19	)	)	PUNCT
brj-23918	209	20	,	,	PUNCT
brj-23918	209	21	3009	3009	NUM
brj-23918	209	22	.	.	PUNCT
brj-23918	210	1	doi	doi	NOUN
brj-23918	210	2	:	:	PUNCT
brj-23918	210	3	10.3390	10.3390	NUM
brj-23918	210	4	/	/	SYM
brj-23918	210	5	electronics12143009	electronics12143009	PROPN
brj-23918	210	6	jia	jia	PROPN
brj-23918	210	7	,	,	PUNCT
brj-23918	210	8	l.	l.	PROPN
brj-23918	210	9	,	,	PUNCT
brj-23918	210	10	wang	wang	PROPN
brj-23918	210	11	,	,	PUNCT
brj-23918	210	12	y.	y.	PROPN
brj-23918	210	13	,	,	PUNCT
brj-23918	210	14	zang	zang	PROPN
brj-23918	210	15	y.	y.	PROPN
brj-23918	210	16	,	,	PUNCT
brj-23918	210	17	zhang	zhang	PROPN
brj-23918	210	18	j.	j.	PROPN
brj-23918	210	19	,	,	PUNCT
brj-23918	210	20	leng	leng	PROPN
brj-23918	210	21	,	,	PUNCT
brj-23918	210	22	h.	h.	PROPN
brj-23918	210	23	,	,	PUNCT
brj-23918	210	24	and	and	CCONJ
brj-23918	210	25	xiao	xiao	PROPN
brj-23918	210	26	,	,	PUNCT
brj-23918	210	27	z.	z.	PROPN
brj-23918	210	28	(	(	PUNCT
brj-23918	210	29	2022	2022	NUM
brj-23918	210	30	)	)	PUNCT
brj-23918	210	31	“	"	PUNCT
brj-23918	210	32	mobilenetv3	mobilenetv3	NOUN
brj-23918	210	33	with	with	ADP
brj-23918	210	34	cbam	cbam	NOUN
brj-23918	210	35	for	for	ADP
brj-23918	210	36	bamboo	bamboo	NOUN
brj-23918	210	37	stick	stick	PROPN
brj-23918	210	38	counting	counting	NOUN
brj-23918	210	39	,	,	PUNCT
brj-23918	210	40	”	"	PUNCT
brj-23918	210	41	ieee	ieee	NOUN
brj-23918	210	42	access	access	NOUN
brj-23918	210	43	10	10	NUM
brj-23918	210	44	,	,	PUNCT
brj-23918	210	45	53963	53963	NUM
brj-23918	210	46	-	-	SYM
brj-23918	210	47	53971	53971	NUM
brj-23918	210	48	.	.	PUNCT
brj-23918	211	1	doi	doi	NOUN
brj-23918	211	2	:	:	PUNCT
brj-23918	211	3	10.1109	10.1109	NUM
brj-23918	211	4	/	/	SYM
brj-23918	211	5	access.2022.3175818	access.2022.3175818	PROPN
brj-23918	211	6	li	li	PROPN
brj-23918	211	7	,	,	PUNCT
brj-23918	211	8	z.	z.	PROPN
brj-23918	211	9	,	,	PUNCT
brj-23918	211	10	li	li	PROPN
brj-23918	211	11	,	,	PUNCT
brj-23918	211	12	b.	b.	PROPN
brj-23918	211	13	,	,	PUNCT
brj-23918	211	14	and	and	CCONJ
brj-23918	211	15	ni	ni	PROPN
brj-23918	211	16	,	,	PUNCT
brj-23918	211	17	h.	h.	PROPN
brj-23918	211	18	(	(	PUNCT
brj-23918	211	19	2022	2022	NUM
brj-23918	211	20	)	)	PUNCT
brj-23918	211	21	.	.	PUNCT
brj-23918	212	1	“	"	PUNCT
brj-23918	212	2	an	an	DET
brj-23918	212	3	effective	effective	ADJ
brj-23918	212	4	surface	surface	NOUN
brj-23918	212	5	defect	defect	NOUN
brj-23918	212	6	classification	classification	NOUN
brj-23918	212	7	method	method	NOUN
brj-23918	212	8	based	base	VERB
brj-23918	212	9	on	on	ADP
brj-23918	212	10	repvgg	repvgg	NOUN
brj-23918	212	11	with	with	ADP
brj-23918	212	12	cbam	cbam	NOUN
brj-23918	212	13	attention	attention	NOUN
brj-23918	212	14	mechanism	mechanism	NOUN
brj-23918	212	15	(	(	PUNCT
brj-23918	212	16	repvgg	repvgg	NOUN
brj-23918	212	17	-	-	PUNCT
brj-23918	212	18	cbam	cbam	NOUN
brj-23918	212	19	)	)	PUNCT
brj-23918	212	20	for	for	ADP
brj-23918	212	21	aluminum	aluminum	NOUN
brj-23918	212	22	profiles	profile	NOUN
brj-23918	212	23	,	,	PUNCT
brj-23918	212	24	”	"	PUNCT
brj-23918	212	25	metals	metal	NOUN
brj-23918	212	26	12(11	12(11	NUM
brj-23918	212	27	)	)	PUNCT
brj-23918	212	28	,	,	PUNCT
brj-23918	212	29	1809	1809	NUM
brj-23918	212	30	.	.	PUNCT
brj-23918	213	1	doi	doi	NOUN
brj-23918	213	2	:	:	PUNCT
brj-23918	213	3	10.3390	10.3390	NUM
brj-23918	213	4	/	/	SYM
brj-23918	213	5	met12111809	met12111809	NOUN
brj-23918	213	6	lin	lin	PROPN
brj-23918	213	7	,	,	PUNCT
brj-23918	213	8	y.	y.	PROPN
brj-23918	213	9	,	,	PUNCT
brj-23918	213	10	xu	xu	PROPN
brj-23918	213	11	,	,	PUNCT
brj-23918	213	12	z.	z.	PROPN
brj-23918	213	13	,	,	PUNCT
brj-23918	213	14	chen	chen	PROPN
brj-23918	213	15	,	,	PUNCT
brj-23918	213	16	d.	d.	PROPN
brj-23918	213	17	,	,	PUNCT
brj-23918	213	18	ai	ai	VERB
brj-23918	213	19	,	,	PUNCT
brj-23918	213	20	z.	z.	PROPN
brj-23918	213	21	,	,	PUNCT
brj-23918	213	22	qiu	qiu	PROPN
brj-23918	213	23	,	,	PUNCT
brj-23918	213	24	y.	y.	NOUN
brj-23918	213	25	,	,	PUNCT
brj-23918	213	26	and	and	CCONJ
brj-23918	213	27	yuan	yuan	NOUN
brj-23918	213	28	,	,	PUNCT
brj-23918	213	29	y.	y.	PROPN
brj-23918	213	30	(	(	PUNCT
brj-23918	213	31	2023	2023	NUM
brj-23918	213	32	)	)	PUNCT
brj-23918	213	33	.	.	PUNCT
brj-23918	214	1	“	"	PUNCT
brj-23918	214	2	wood	wood	NOUN
brj-23918	214	3	crack	crack	NOUN
brj-23918	214	4	detection	detection	NOUN
brj-23918	214	5	based	base	VERB
brj-23918	214	6	on	on	ADP
brj-23918	214	7	data	data	NOUN
brj-23918	214	8	-	-	PUNCT
brj-23918	214	9	driven	drive	VERB
brj-23918	214	10	semantic	semantic	ADJ
brj-23918	214	11	segmentation	segmentation	NOUN
brj-23918	214	12	network	network	NOUN
brj-23918	214	13	,	,	PUNCT
brj-23918	214	14	”	"	PUNCT
brj-23918	214	15	ieee	ieee	NOUN
brj-23918	214	16	/	/	SYM
brj-23918	214	17	caa	caa	PROPN
brj-23918	214	18	journal	journal	PROPN
brj-23918	214	19	of	of	ADP
brj-23918	214	20	automatica	automatica	PROPN
brj-23918	214	21	sinica	sinica	PROPN
brj-23918	214	22	10(6	10(6	PROPN
brj-23918	214	23	)	)	PUNCT
brj-23918	214	24	,	,	PUNCT
brj-23918	214	25	1510	1510	NUM
brj-23918	214	26	-	-	SYM
brj-23918	214	27	1512	1512	NUM
brj-23918	214	28	.	.	PUNCT
brj-23918	215	1	doi	doi	NOUN
brj-23918	215	2	:	:	PUNCT
brj-23918	215	3	10.1109	10.1109	NUM
brj-23918	215	4	/	/	SYM
brj-23918	215	5	jas.2023.123357	jas.2023.123357	PROPN
brj-23918	215	6	oktay	oktay	NOUN
brj-23918	215	7	,	,	PUNCT
brj-23918	215	8	o.	o.	PROPN
brj-23918	215	9	,	,	PUNCT
brj-23918	215	10	schlemper	schlemper	ADJ
brj-23918	215	11	,	,	PUNCT
brj-23918	215	12	j.	j.	PROPN
brj-23918	215	13	,	,	PUNCT
brj-23918	215	14	folgoc	folgoc	PROPN
brj-23918	215	15	,	,	PUNCT
brj-23918	215	16	l.l	l.l	PROPN
brj-23918	215	17	.	.	PROPN
brj-23918	215	18	,	,	PUNCT
brj-23918	215	19	lee	lee	PROPN
brj-23918	215	20	,	,	PUNCT
brj-23918	215	21	m.	m.	NOUN
brj-23918	215	22	,	,	PUNCT
brj-23918	215	23	heinrich	heinrich	NOUN
brj-23918	215	24	,	,	PUNCT
brj-23918	215	25	m.	m.	NOUN
brj-23918	215	26	,	,	PUNCT
brj-23918	215	27	misawa	misawa	PROPN
brj-23918	215	28	,	,	PUNCT
brj-23918	215	29	k.	k.	PROPN
brj-23918	215	30	,	,	PUNCT
brj-23918	215	31	mori	mori	PROPN
brj-23918	215	32	,	,	PUNCT
brj-23918	215	33	k.	k.	PROPN
brj-23918	215	34	,	,	PUNCT
brj-23918	215	35	mcdonagh	mcdonagh	PROPN
brj-23918	215	36	,	,	PUNCT
brj-23918	215	37	s.	s.	PROPN
brj-23918	215	38	,	,	PUNCT
brj-23918	215	39	hammerla	hammerla	PROPN
brj-23918	215	40	,	,	PUNCT
brj-23918	215	41	n.	n.	PROPN
brj-23918	215	42	y.	y.	PROPN
brj-23918	215	43	,	,	PUNCT
brj-23918	215	44	kainz	kainz	PROPN
brj-23918	215	45	,	,	PUNCT
brj-23918	215	46	b.	b.	PROPN
brj-23918	215	47	,	,	PUNCT
brj-23918	215	48	glocker	glocker	PROPN
brj-23918	215	49	,	,	PUNCT
brj-23918	215	50	b.	b.	PROPN
brj-23918	215	51	,	,	PUNCT
brj-23918	215	52	and	and	CCONJ
brj-23918	215	53	rueckert	rueckert	PROPN
brj-23918	215	54	,	,	PUNCT
brj-23918	215	55	d.	d.	PROPN
brj-23918	215	56	(	(	PUNCT
brj-23918	215	57	2018	2018	NUM
brj-23918	215	58	)	)	PUNCT
brj-23918	215	59	.	.	PUNCT
brj-23918	216	1	“	"	PUNCT
brj-23918	216	2	attention	attention	NOUN
brj-23918	216	3	u	u	NOUN
brj-23918	216	4	-	-	NOUN
brj-23918	216	5	net	net	ADJ
brj-23918	216	6	:	:	PUNCT
brj-23918	216	7	learning	learn	VERB
brj-23918	216	8	where	where	SCONJ
brj-23918	216	9	to	to	PART
brj-23918	216	10	look	look	VERB
brj-23918	216	11	for	for	ADP
brj-23918	216	12	the	the	DET
brj-23918	216	13	pancreas	pancrea	NOUN
brj-23918	216	14	,	,	PUNCT
brj-23918	216	15	”	"	PUNCT
brj-23918	216	16	in	in	ADP
brj-23918	216	17	:	:	PUNCT
brj-23918	216	18	ieee	ieee	NOUN
brj-23918	216	19	conference	conference	NOUN
brj-23918	216	20	on	on	ADP
brj-23918	216	21	computer	computer	NOUN
brj-23918	216	22	vision	vision	NOUN
brj-23918	216	23	and	and	CCONJ
brj-23918	216	24	pattern	pattern	NOUN
brj-23918	216	25	recognition	recognition	NOUN
brj-23918	216	26	.	.	PUNCT
brj-23918	217	1	doi	doi	NOUN
brj-23918	217	2	:	:	PUNCT
brj-23918	217	3	10.48550	10.48550	NUM
brj-23918	217	4	/	/	SYM
brj-23918	217	5	arxiv.1804.03999	arxiv.1804.03999	PROPN
brj-23918	217	6	ren	ren	PROPN
brj-23918	217	7	,	,	PUNCT
brj-23918	217	8	f.	f.	PROPN
brj-23918	217	9	,	,	PUNCT
brj-23918	217	10	fei	fei	PROPN
brj-23918	217	11	,	,	PUNCT
brj-23918	217	12	j.	j.	PROPN
brj-23918	217	13	,	,	PUNCT
brj-23918	217	14	li	li	PROPN
brj-23918	217	15	,	,	PUNCT
brj-23918	217	16	h.	h.	PROPN
brj-23918	217	17	,	,	PUNCT
brj-23918	217	18	doma	doma	PROPN
brj-23918	217	19	,	,	PUNCT
brj-23918	217	20	b.	b.	PROPN
brj-23918	217	21	t.	t.	PROPN
brj-23918	217	22	(	(	PUNCT
brj-23918	217	23	2024	2024	NUM
brj-23918	217	24	)	)	PUNCT
brj-23918	217	25	.	.	PUNCT
brj-23918	218	1	“	"	PUNCT
brj-23918	218	2	steel	steel	NOUN
brj-23918	218	3	surface	surface	NOUN
brj-23918	218	4	defect	defect	NOUN
brj-23918	218	5	detection	detection	NOUN
brj-23918	218	6	using	use	VERB
brj-23918	218	7	improved	improve	VERB
brj-23918	218	8	deep	deep	ADJ
brj-23918	218	9	learning	learning	NOUN
brj-23918	218	10	algorithm	algorithm	NOUN
brj-23918	218	11	:	:	PUNCT
brj-23918	218	12	eca	eca	NOUN
brj-23918	218	13	-	-	PUNCT
brj-23918	218	14	simsppf	simsppf	NOUN
brj-23918	218	15	-	-	PUNCT
brj-23918	218	16	siou	siou	NOUN
brj-23918	218	17	-	-	PUNCT
brj-23918	218	18	yolov5	yolov5	NOUN
brj-23918	218	19	,	,	PUNCT
brj-23918	218	20	”	"	PUNCT
brj-23918	218	21	ieee	ieee	NOUN
brj-23918	218	22	access	access	NOUN
brj-23918	218	23	12	12	NUM
brj-23918	218	24	,	,	PUNCT
brj-23918	218	25	32545	32545	NUM
brj-23918	218	26	-	-	SYM
brj-23918	218	27	32553	32553	NUM
brj-23918	218	28	.	.	PUNCT
brj-23918	219	1	sheng	sheng	PROPN
brj-23918	219	2	,	,	PUNCT
brj-23918	219	3	w.	w.	PROPN
brj-23918	219	4	,	,	PUNCT
brj-23918	219	5	yu	yu	PROPN
brj-23918	219	6	,	,	PUNCT
brj-23918	219	7	x.	x.	PROPN
brj-23918	219	8	,	,	PUNCT
brj-23918	219	9	lin	lin	PROPN
brj-23918	219	10	,	,	PUNCT
brj-23918	219	11	j.	j.	PROPN
brj-23918	219	12	and	and	CCONJ
brj-23918	219	13	chen	chen	PROPN
brj-23918	219	14	,	,	PUNCT
brj-23918	219	15	x.	x.	NOUN
brj-23918	219	16	(	(	PUNCT
brj-23918	219	17	2023	2023	NUM
brj-23918	219	18	)	)	PUNCT
brj-23918	219	19	.	.	PUNCT
brj-23918	220	1	“	"	PUNCT
brj-23918	220	2	faster	fast	ADJ
brj-23918	220	3	rcnn	rcnn	PROPN
brj-23918	220	4	target	target	PROPN
brj-23918	220	5	detection	detection	NOUN
brj-23918	220	6	algorithm	algorithm	NOUN
brj-23918	220	7	integrating	integrate	VERB
brj-23918	220	8	cbam	cbam	NOUN
brj-23918	220	9	and	and	CCONJ
brj-23918	220	10	fpn	fpn	PROPN
brj-23918	220	11	,	,	PUNCT
brj-23918	220	12	”	"	PUNCT
brj-23918	220	13	applied	apply	VERB
brj-23918	220	14	sciences	science	NOUN
brj-23918	220	15	13(12	13(12	NUM
brj-23918	220	16	)	)	PUNCT
brj-23918	220	17	,	,	PUNCT
brj-23918	220	18	6913	6913	NUM
brj-23918	220	19	.	.	PUNCT
brj-23918	221	1	doi	doi	NOUN
brj-23918	221	2	:	:	PUNCT
brj-23918	221	3	10.3390	10.3390	NUM
brj-23918	221	4	/	/	SYM
brj-23918	221	5	app13126913	app13126913	NOUN
brj-23918	221	6	tong	tong	PROPN
brj-23918	221	7	,	,	PUNCT
brj-23918	221	8	x.	x.	PROPN
brj-23918	221	9	,	,	PUNCT
brj-23918	221	10	wei	wei	PROPN
brj-23918	221	11	,	,	PUNCT
brj-23918	221	12	j.	j.	PROPN
brj-23918	221	13	,	,	PUNCT
brj-23918	221	14	sun	sun	PROPN
brj-23918	221	15	,	,	PUNCT
brj-23918	221	16	b.	b.	PROPN
brj-23918	221	17	,	,	PUNCT
brj-23918	221	18	su	su	PROPN
brj-23918	221	19	,	,	PUNCT
brj-23918	221	20	s.	s.	PROPN
brj-23918	221	21	,	,	PUNCT
brj-23918	221	22	zuo	zuo	PROPN
brj-23918	221	23	,	,	PUNCT
brj-23918	221	24	z.	z.	PROPN
brj-23918	221	25	,	,	PUNCT
brj-23918	221	26	and	and	CCONJ
brj-23918	221	27	wu	wu	PROPN
brj-23918	221	28	,	,	PUNCT
brj-23918	221	29	p.	p.	NOUN
brj-23918	221	30	(	(	PUNCT
brj-23918	221	31	2021	2021	NUM
brj-23918	221	32	)	)	PUNCT
brj-23918	221	33	.	.	PUNCT
brj-23918	222	1	“	"	PUNCT
brj-23918	222	2	ascu	ascu	NOUN
brj-23918	222	3	-	-	PUNCT
brj-23918	222	4	net	net	NOUN
brj-23918	222	5	:	:	PUNCT
brj-23918	222	6	attention	attention	NOUN
brj-23918	222	7	gate	gate	NOUN
brj-23918	222	8	,	,	PUNCT
brj-23918	222	9	spatial	spatial	ADJ
brj-23918	222	10	and	and	CCONJ
brj-23918	222	11	channel	channel	VERB
brj-23918	222	12	attention	attention	NOUN
brj-23918	222	13	u	u	NOUN
brj-23918	222	14	-	-	NOUN
brj-23918	222	15	net	net	ADJ
brj-23918	222	16	for	for	ADP
brj-23918	222	17	skin	skin	NOUN
brj-23918	222	18	lesion	lesion	NOUN
brj-23918	222	19	segmentation	segmentation	NOUN
brj-23918	222	20	,	,	PUNCT
brj-23918	222	21	”	"	PUNCT
brj-23918	222	22	diagnostics	diagnostic	NOUN
brj-23918	222	23	11(3	11(3	NUM
brj-23918	222	24	)	)	PUNCT
brj-23918	222	25	,	,	PUNCT
brj-23918	222	26	501	501	NUM
brj-23918	222	27	.	.	PUNCT
brj-23918	223	1	doi	doi	NOUN
brj-23918	223	2	:	:	PUNCT
brj-23918	223	3	10.3390	10.3390	NUM
brj-23918	223	4	/	/	SYM
brj-23918	223	5	diagnostics11030501	diagnostics11030501	PROPN
brj-23918	223	6	wang	wang	PROPN
brj-23918	223	7	,	,	PUNCT
brj-23918	223	8	q.	q.	PROPN
brj-23918	223	9	,	,	PUNCT
brj-23918	223	10	wu	wu	PROPN
brj-23918	223	11	,	,	PUNCT
brj-23918	223	12	b.	b.	PROPN
brj-23918	223	13	,	,	PUNCT
brj-23918	223	14	zhu	zhu	PROPN
brj-23918	223	15	,	,	PUNCT
brj-23918	223	16	p.	p.	PROPN
brj-23918	223	17	,	,	PUNCT
brj-23918	223	18	li	li	PROPN
brj-23918	223	19	,	,	PUNCT
brj-23918	223	20	p.	p.	NOUN
brj-23918	223	21	,	,	PUNCT
brj-23918	223	22	and	and	CCONJ
brj-23918	223	23	zuo	zuo	PROPN
brj-23918	223	24	,	,	PUNCT
brj-23918	223	25	w.	w.	NOUN
brj-23918	223	26	(	(	PUNCT
brj-23918	223	27	2020a	2020a	NUM
brj-23918	223	28	)	)	PUNCT
brj-23918	223	29	.	.	PUNCT
brj-23918	224	1	“	"	PUNCT
brj-23918	224	2	eca	eca	NOUN
brj-23918	224	3	-	-	PUNCT
brj-23918	224	4	net	net	NOUN
brj-23918	224	5	:	:	PUNCT
brj-23918	224	6	efficient	efficient	ADJ
brj-23918	224	7	channel	channel	NOUN
brj-23918	224	8	attention	attention	NOUN
brj-23918	224	9	for	for	ADP
brj-23918	224	10	deep	deep	ADJ
brj-23918	224	11	convolutional	convolutional	ADJ
brj-23918	224	12	neural	neural	ADJ
brj-23918	224	13	networks	network	NOUN
brj-23918	224	14	,	,	PUNCT
brj-23918	224	15	”	"	PUNCT
brj-23918	224	16	in	in	ADP
brj-23918	224	17	:	:	PUNCT
brj-23918	224	18	ieee	ieee	NOUN
brj-23918	224	19	/	/	SYM
brj-23918	224	20	cvf	cvf	NOUN
brj-23918	224	21	conference	conference	NOUN
brj-23918	224	22	on	on	ADP
brj-23918	224	23	computer	computer	NOUN
brj-23918	224	24	vision	vision	NOUN
brj-23918	224	25	and	and	CCONJ
brj-23918	224	26	pattern	pattern	NOUN
brj-23918	224	27	recognition	recognition	NOUN
brj-23918	224	28	,	,	PUNCT
brj-23918	224	29	pp	pp	ADP
brj-23918	224	30	.	.	PUNCT
brj-23918	224	31	11531	11531	NUM
brj-23918	224	32	-	-	SYM
brj-23918	224	33	11539	11539	NUM
brj-23918	224	34	.	.	PUNCT
brj-23918	225	1	doi	doi	NOUN
brj-23918	225	2	:	:	PUNCT
brj-23918	225	3	10.1109	10.1109	NUM
brj-23918	225	4	/	/	SYM
brj-23918	225	5	cvpr42600.2020.01155	cvpr42600.2020.01155	SYM
brj-23918	225	6	wang	wang	PROPN
brj-23918	225	7	,	,	PUNCT
brj-23918	225	8	q.	q.	PROPN
brj-23918	225	9	,	,	PUNCT
brj-23918	225	10	wu	wu	PROPN
brj-23918	225	11	,	,	PUNCT
brj-23918	225	12	b.	b.	PROPN
brj-23918	225	13	,	,	PUNCT
brj-23918	225	14	zhu	zhu	PROPN
brj-23918	225	15	,	,	PUNCT
brj-23918	225	16	p.	p.	PROPN
brj-23918	225	17	,	,	PUNCT
brj-23918	225	18	li	li	PROPN
brj-23918	225	19	,	,	PUNCT
brj-23918	225	20	p.	p.	PROPN
brj-23918	225	21	,	,	PUNCT
brj-23918	225	22	zuo	zuo	PROPN
brj-23918	225	23	,	,	PUNCT
brj-23918	225	24	w.	w.	PROPN
brj-23918	225	25	,	,	PUNCT
brj-23918	225	26	and	and	CCONJ
brj-23918	225	27	hu	hu	PROPN
brj-23918	225	28	,	,	PUNCT
brj-23918	225	29	q.	q.	PROPN
brj-23918	225	30	(	(	PUNCT
brj-23918	225	31	2020b	2020b	NUM
brj-23918	225	32	)	)	PUNCT
brj-23918	225	33	.	.	PUNCT
brj-23918	226	1	“	"	PUNCT
brj-23918	226	2	eca	eca	NOUN
brj-23918	226	3	-	-	PUNCT
brj-23918	226	4	net	net	NOUN
brj-23918	226	5	:	:	PUNCT
brj-23918	226	6	efficient	efficient	ADJ
brj-23918	226	7	channel	channel	NOUN
brj-23918	226	8	attention	attention	NOUN
brj-23918	226	9	for	for	ADP
brj-23918	226	10	deep	deep	ADJ
brj-23918	226	11	convolutional	convolutional	ADJ
brj-23918	226	12	neural	neural	ADJ
brj-23918	226	13	networks	network	NOUN
brj-23918	226	14	,	,	PUNCT
brj-23918	226	15	”	"	PUNCT
brj-23918	226	16	in	in	ADP
brj-23918	226	17	:	:	PUNCT
brj-23918	226	18	ieee	ieee	NOUN
brj-23918	226	19	/	/	SYM
brj-23918	226	20	cvf	cvf	NOUN
brj-23918	226	21	conference	conference	NOUN
brj-23918	226	22	on	on	ADP
brj-23918	226	23	computer	computer	NOUN
brj-23918	226	24	vision	vision	NOUN
brj-23918	226	25	and	and	CCONJ
brj-23918	226	26	pattern	pattern	NOUN
brj-23918	226	27	recognition	recognition	NOUN
brj-23918	226	28	(	(	PUNCT
brj-23918	226	29	cvpr	cvpr	NOUN
brj-23918	226	30	)	)	PUNCT
brj-23918	226	31	,	,	PUNCT
brj-23918	226	32	seattle	seattle	PROPN
brj-23918	226	33	,	,	PUNCT
brj-23918	226	34	wa	wa	PROPN
brj-23918	226	35	,	,	PUNCT
brj-23918	226	36	usa	usa	PROPN
brj-23918	226	37	,	,	PUNCT
brj-23918	226	38	pp	pp	ADJ
brj-23918	226	39	.	.	PUNCT
brj-23918	226	40	1153111539	1153111539	NUM
brj-23918	226	41	.	.	PUNCT
brj-23918	227	1	doi	doi	NOUN
brj-23918	227	2	:	:	PUNCT
brj-23918	227	3	10.1109	10.1109	NUM
brj-23918	227	4	/	/	SYM
brj-23918	227	5	cvpr42600.2020.01155	cvpr42600.2020.01155	NOUN
brj-23918	227	6	peer	peer	NOUN
brj-23918	227	7	-	-	PUNCT
brj-23918	227	8	reviewed	review	VERB
brj-23918	227	9	article	article	NOUN
brj-23918	227	10	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23918	227	11	dong	dong	PROPN
brj-23918	227	12	et	et	PROPN
brj-23918	227	13	al	al	PROPN
brj-23918	227	14	.	.	PROPN
brj-23918	228	1	(	(	PUNCT
brj-23918	228	2	2025	2025	NUM
brj-23918	228	3	)	)	PUNCT
brj-23918	228	4	.	.	PUNCT
brj-23918	229	1	“	"	PUNCT
brj-23918	229	2	cracks	crack	NOUN
brj-23918	229	3	in	in	ADP
brj-23918	229	4	sawn	sawn	NOUN
brj-23918	229	5	wood	wood	NOUN
brj-23918	229	6	,	,	PUNCT
brj-23918	229	7	”	"	PUNCT
brj-23918	229	8	bioresources	bioresource	NOUN
brj-23918	229	9	20(2	20(2	NUM
brj-23918	229	10	)	)	PUNCT
brj-23918	229	11	,	,	PUNCT
brj-23918	229	12	3545	3545	NUM
brj-23918	229	13	-	-	SYM
brj-23918	229	14	3556	3556	NUM
brj-23918	229	15	.	.	PUNCT
brj-23918	230	1	3556	3556	NUM
brj-23918	230	2	wang	wang	PROPN
brj-23918	230	3	,	,	PUNCT
brj-23918	230	4	c.	c.	PROPN
brj-23918	230	5	,	,	PUNCT
brj-23918	230	6	zhou	zhou	PROPN
brj-23918	230	7	,	,	PUNCT
brj-23918	230	8	y.	y.	PROPN
brj-23918	230	9	,	,	PUNCT
brj-23918	230	10	and	and	CCONJ
brj-23918	230	11	li	li	PROPN
brj-23918	230	12	,	,	PUNCT
brj-23918	230	13	j.	j.	PROPN
brj-23918	230	14	(	(	PUNCT
brj-23918	230	15	2022	2022	NUM
brj-23918	230	16	)	)	PUNCT
brj-23918	230	17	.	.	PUNCT
brj-23918	231	1	“	"	PUNCT
brj-23918	231	2	lightweight	lightweight	ADJ
brj-23918	231	3	yolov4	yolov4	PROPN
brj-23918	231	4	target	target	NOUN
brj-23918	231	5	detection	detection	NOUN
brj-23918	231	6	algorithm	algorithm	NOUN
brj-23918	231	7	fused	fuse	VERB
brj-23918	231	8	with	with	ADP
brj-23918	231	9	eca	eca	NOUN
brj-23918	231	10	mechanism	mechanism	NOUN
brj-23918	231	11	,	,	PUNCT
brj-23918	231	12	”	"	PUNCT
brj-23918	231	13	processes	process	VERB
brj-23918	231	14	10(7	10(7	NUM
brj-23918	231	15	)	)	PUNCT
brj-23918	231	16	,	,	PUNCT
brj-23918	231	17	1285	1285	NUM
brj-23918	231	18	.	.	PUNCT
brj-23918	232	1	doi	doi	NOUN
brj-23918	232	2	:	:	PUNCT
brj-23918	232	3	10.3390	10.3390	NUM
brj-23918	232	4	/	/	SYM
brj-23918	232	5	pr10071285	pr10071285	PROPN
brj-23918	232	6	wang	wang	PROPN
brj-23918	232	7	,	,	PUNCT
brj-23918	232	8	x.	x.	PROPN
brj-23918	232	9	,	,	PUNCT
brj-23918	232	10	jia	jia	PROPN
brj-23918	232	11	,	,	PUNCT
brj-23918	232	12	x.	x.	PROPN
brj-23918	232	13	,	,	PUNCT
brj-23918	232	14	zhang	zhang	PROPN
brj-23918	232	15	,	,	PUNCT
brj-23918	232	16	m.	m.	NOUN
brj-23918	232	17	,	,	PUNCT
brj-23918	232	18	and	and	CCONJ
brj-23918	232	19	lu	lu	PROPN
brj-23918	232	20	,	,	PUNCT
brj-23918	232	21	h.	h.	PROPN
brj-23918	232	22	(	(	PUNCT
brj-23918	232	23	2023	2023	NUM
brj-23918	232	24	)	)	PUNCT
brj-23918	232	25	.	.	PUNCT
brj-23918	233	1	“	"	PUNCT
brj-23918	233	2	object	object	VERB
brj-23918	233	3	detection	detection	NOUN
brj-23918	233	4	in	in	ADP
brj-23918	233	5	3d	3d	NUM
brj-23918	233	6	point	point	NOUN
brj-23918	233	7	cloud	cloud	NOUN
brj-23918	233	8	based	base	VERB
brj-23918	233	9	on	on	ADP
brj-23918	233	10	eca	eca	NOUN
brj-23918	233	11	mechanism	mechanism	NOUN
brj-23918	233	12	,	,	PUNCT
brj-23918	233	13	”	"	PUNCT
brj-23918	233	14	journal	journal	NOUN
brj-23918	233	15	of	of	ADP
brj-23918	233	16	circuits	circuit	NOUN
brj-23918	233	17	,	,	PUNCT
brj-23918	233	18	systems	system	NOUN
brj-23918	233	19	and	and	CCONJ
brj-23918	233	20	computers	computer	NOUN
brj-23918	233	21	32(05	32(05	PROPN
brj-23918	233	22	)	)	PUNCT
brj-23918	233	23	,	,	PUNCT
brj-23918	233	24	2350080	2350080	NUM
brj-23918	233	25	.	.	PUNCT
brj-23918	234	1	doi	doi	NOUN
brj-23918	234	2	:	:	PUNCT
brj-23918	234	3	10.1142	10.1142	NUM
brj-23918	234	4	/	/	SYM
brj-23918	234	5	s0218126623500809	s0218126623500809	PROPN
brj-23918	234	6	wu	wu	PROPN
brj-23918	234	7	,	,	PUNCT
brj-23918	234	8	z.	z.	PROPN
brj-23918	234	9	,	,	PUNCT
brj-23918	234	10	and	and	CCONJ
brj-23918	234	11	gu	gu	NOUN
brj-23918	234	12	,	,	PUNCT
brj-23918	234	13	m.	m.	NOUN
brj-23918	234	14	(	(	PUNCT
brj-23918	234	15	2023	2023	NUM
brj-23918	234	16	)	)	PUNCT
brj-23918	234	17	.	.	PUNCT
brj-23918	235	1	“	"	PUNCT
brj-23918	235	2	a	a	DET
brj-23918	235	3	novel	novel	ADJ
brj-23918	235	4	attention	attention	NOUN
brj-23918	235	5	-	-	PUNCT
brj-23918	235	6	guided	guide	VERB
brj-23918	235	7	eca	eca	NOUN
brj-23918	235	8	-	-	PUNCT
brj-23918	235	9	cnn	cnn	PROPN
brj-23918	235	10	architecture	architecture	NOUN
brj-23918	235	11	for	for	ADP
brj-23918	235	12	semgbased	semgbase	VERB
brj-23918	235	13	gait	gait	PROPN
brj-23918	235	14	classification	classification	NOUN
brj-23918	235	15	,	,	PUNCT
brj-23918	235	16	”	"	PUNCT
brj-23918	235	17	mathematical	mathematical	ADJ
brj-23918	235	18	biosciences	bioscience	NOUN
brj-23918	235	19	and	and	CCONJ
brj-23918	235	20	engineering	engineering	NOUN
brj-23918	235	21	:	:	PUNCT
brj-23918	235	22	mbe	mbe	PROPN
brj-23918	235	23	20(4	20(4	PROPN
brj-23918	235	24	)	)	PUNCT
brj-23918	235	25	,	,	PUNCT
brj-23918	235	26	7140	7140	NUM
brj-23918	235	27	-	-	SYM
brj-23918	235	28	7153	7153	NUM
brj-23918	235	29	.	.	PUNCT
brj-23918	236	1	doi	doi	NOUN
brj-23918	236	2	:	:	PUNCT
brj-23918	236	3	10.3934	10.3934	NUM
brj-23918	236	4	/	/	SYM
brj-23918	236	5	mbe.2023308	mbe.2023308	PROPN
brj-23918	236	6	xie	xie	PROPN
brj-23918	236	7	,	,	PUNCT
brj-23918	236	8	w.	w.	PROPN
brj-23918	236	9	(	(	PUNCT
brj-23918	236	10	2023	2023	NUM
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brj-23918	241	3	k.	k.	PROPN
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brj-23918	241	14	2023	2023	NUM
brj-23918	241	15	)	)	PUNCT
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brj-23918	242	1	“	"	PUNCT
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brj-23918	242	6	insulator	insulator	NOUN
brj-23918	242	7	detection	detection	NOUN
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brj-23918	242	9	transmission	transmission	NOUN
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brj-23918	242	12	”	"	PUNCT
brj-23918	242	13	multimedia	multimedia	NOUN
brj-23918	242	14	tools	tool	NOUN
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brj-23918	242	17	1	1	NUM
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brj-23918	244	1	“	"	PUNCT
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brj-23918	244	24	)	)	PUNCT
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brj-23918	246	1	“	"	PUNCT
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brj-23918	247	2	:	:	PUNCT
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brj-23918	247	45	.	.	PUNCT
brj-23918	248	1	doi	doi	NOUN
brj-23918	248	2	:	:	PUNCT
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