id	sid	tid	token	lemma	pos
brj-22874	1	1	peer	peer	NOUN
brj-22874	1	2	-	-	PUNCT
brj-22874	1	3	review	review	NOUN
brj-22874	1	4	article	article	NOUN
brj-22874	1	5	peer	peer	NOUN
brj-22874	1	6	-	-	PUNCT
brj-22874	1	7	reviewed	review	VERB
brj-22874	1	8	article	article	NOUN
brj-22874	1	9	bioresources.com	bioresources.com	X
brj-22874	1	10	tian	tian	PROPN
brj-22874	1	11	et	et	PROPN
brj-22874	1	12	al	al	PROPN
brj-22874	1	13	.	.	PROPN
brj-22874	1	14	(	(	PUNCT
brj-22874	1	15	2023	2023	NUM
brj-22874	1	16	)	)	PUNCT
brj-22874	1	17	.	.	PUNCT
brj-22874	2	1	“	"	PUNCT
brj-22874	2	2	defect	defect	VERB
brj-22874	2	3	detection	detection	NOUN
brj-22874	2	4	with	with	ADP
brj-22874	2	5	yolov5	yolov5	NOUN
brj-22874	2	6	,	,	PUNCT
brj-22874	2	7	”	"	PUNCT
brj-22874	2	8	bioresources	bioresource	NOUN
brj-22874	2	9	18(4	18(4	NUM
brj-22874	2	10	)	)	PUNCT
brj-22874	2	11	,	,	PUNCT
brj-22874	2	12	7713	7713	NUM
brj-22874	2	13	-	-	SYM
brj-22874	2	14	7730	7730	NUM
brj-22874	2	15	.	.	PUNCT
brj-22874	3	1	7713	7713	NUM
brj-22874	3	2	surface	surface	NOUN
brj-22874	3	3	defect	defect	NOUN
brj-22874	3	4	detection	detection	NOUN
brj-22874	3	5	method	method	NOUN
brj-22874	3	6	of	of	ADP
brj-22874	3	7	wooden	wooden	ADJ
brj-22874	3	8	spoon	spoon	NOUN
brj-22874	3	9	based	base	VERB
brj-22874	3	10	on	on	ADP
brj-22874	3	11	improved	improved	ADJ
brj-22874	3	12	yolov5	yolov5	NOUN
brj-22874	3	13	algorithm	algorithm	PROPN
brj-22874	3	14	siqing	siqe	VERB
brj-22874	3	15	tian	tian	PROPN
brj-22874	3	16	,	,	PUNCT
brj-22874	3	17	a	a	DET
brj-22874	3	18	xiao	xiao	PROPN
brj-22874	3	19	li	li	PROPN
brj-22874	3	20	,	,	PUNCT
brj-22874	3	21	b	b	PROPN
brj-22874	3	22	xiaolin	xiaolin	PROPN
brj-22874	3	23	fang	fang	PROPN
brj-22874	3	24	,	,	PUNCT
brj-22874	3	25	b	b	PROPN
brj-22874	3	26	xiaozhong	xiaozhong	PROPN
brj-22874	3	27	qi	qi	PROPN
brj-22874	3	28	,	,	PUNCT
brj-22874	3	29	a	a	PRON
brj-22874	3	30	and	and	CCONJ
brj-22874	3	31	jichao	jichao	PROPN
brj-22874	3	32	li	li	PROPN
brj-22874	3	33	a	a	PROPN
brj-22874	3	34	,	,	PUNCT
brj-22874	3	35	*	*	PUNCT
brj-22874	3	36	the	the	DET
brj-22874	3	37	available	available	ADJ
brj-22874	3	38	surface	surface	NOUN
brj-22874	3	39	defect	defect	NOUN
brj-22874	3	40	detection	detection	NOUN
brj-22874	3	41	methods	method	NOUN
brj-22874	3	42	for	for	ADP
brj-22874	3	43	disposable	disposable	ADJ
brj-22874	3	44	wooden	wooden	ADJ
brj-22874	3	45	spoons	spoon	NOUN
brj-22874	3	46	still	still	ADV
brj-22874	3	47	involve	involve	VERB
brj-22874	3	48	screening	screen	VERB
brj-22874	3	49	with	with	ADP
brj-22874	3	50	the	the	DET
brj-22874	3	51	naked	naked	ADJ
brj-22874	3	52	eye	eye	NOUN
brj-22874	3	53	.	.	PUNCT
brj-22874	4	1	this	this	DET
brj-22874	4	2	detection	detection	NOUN
brj-22874	4	3	method	method	NOUN
brj-22874	4	4	is	be	AUX
brj-22874	4	5	not	not	PART
brj-22874	4	6	only	only	ADV
brj-22874	4	7	inefficient	inefficient	ADJ
brj-22874	4	8	but	but	CCONJ
brj-22874	4	9	also	also	ADV
brj-22874	4	10	accompanied	accompany	VERB
brj-22874	4	11	by	by	ADP
brj-22874	4	12	problems	problem	NOUN
brj-22874	4	13	such	such	ADJ
brj-22874	4	14	as	as	ADP
brj-22874	4	15	false	false	ADJ
brj-22874	4	16	detection	detection	NOUN
brj-22874	4	17	and	and	CCONJ
brj-22874	4	18	missed	miss	VERB
brj-22874	4	19	detection	detection	NOUN
brj-22874	4	20	.	.	PUNCT
brj-22874	5	1	therefore	therefore	ADV
brj-22874	5	2	,	,	PUNCT
brj-22874	5	3	this	this	DET
brj-22874	5	4	paper	paper	NOUN
brj-22874	5	5	proposes	propose	VERB
brj-22874	5	6	a	a	DET
brj-22874	5	7	detection	detection	NOUN
brj-22874	5	8	method	method	NOUN
brj-22874	5	9	based	base	VERB
brj-22874	5	10	on	on	ADP
brj-22874	5	11	an	an	DET
brj-22874	5	12	improved	improved	ADJ
brj-22874	5	13	yolov5	yolov5	NOUN
brj-22874	5	14	network	network	NOUN
brj-22874	5	15	model	model	NOUN
brj-22874	5	16	(	(	PUNCT
brj-22874	5	17	yolov5	yolov5	NOUN
brj-22874	5	18	-	-	NOUN
brj-22874	5	19	tspp	tspp	NOUN
brj-22874	5	20	)	)	PUNCT
brj-22874	5	21	.	.	PUNCT
brj-22874	6	1	this	this	DET
brj-22874	6	2	method	method	NOUN
brj-22874	6	3	uses	use	VERB
brj-22874	6	4	the	the	DET
brj-22874	6	5	k	k	NOUN
brj-22874	6	6	-	-	PUNCT
brj-22874	6	7	means	mean	VERB
brj-22874	6	8	+	+	ADJ
brj-22874	6	9	+	+	NUM
brj-22874	6	10	algorithm	algorithm	NOUN
brj-22874	6	11	to	to	PART
brj-22874	6	12	cluster	cluster	VERB
brj-22874	6	13	the	the	DET
brj-22874	6	14	target	target	NOUN
brj-22874	6	15	samples	sample	NOUN
brj-22874	6	16	in	in	ADP
brj-22874	6	17	the	the	DET
brj-22874	6	18	data	datum	NOUN
brj-22874	6	19	set	set	VERB
brj-22874	6	20	to	to	PART
brj-22874	6	21	obtain	obtain	VERB
brj-22874	6	22	anchor	anchor	NOUN
brj-22874	6	23	frames	frame	NOUN
brj-22874	6	24	that	that	PRON
brj-22874	6	25	are	be	AUX
brj-22874	6	26	more	more	ADV
brj-22874	6	27	in	in	ADP
brj-22874	6	28	line	line	NOUN
brj-22874	6	29	with	with	ADP
brj-22874	6	30	different	different	ADJ
brj-22874	6	31	target	target	NOUN
brj-22874	6	32	scales	scale	NOUN
brj-22874	6	33	.	.	PUNCT
brj-22874	7	1	the	the	DET
brj-22874	7	2	coordinate	coordinate	ADJ
brj-22874	7	3	attention	attention	NOUN
brj-22874	7	4	module	module	NOUN
brj-22874	7	5	is	be	AUX
brj-22874	7	6	added	add	VERB
brj-22874	7	7	to	to	ADP
brj-22874	7	8	the	the	DET
brj-22874	7	9	backbone	backbone	NOUN
brj-22874	7	10	network	network	NOUN
brj-22874	7	11	of	of	ADP
brj-22874	7	12	the	the	DET
brj-22874	7	13	yolov5	yolov5	NOUN
brj-22874	7	14	network	network	NOUN
brj-22874	7	15	model	model	NOUN
brj-22874	7	16	to	to	PART
brj-22874	7	17	improve	improve	VERB
brj-22874	7	18	the	the	DET
brj-22874	7	19	feature	feature	NOUN
brj-22874	7	20	extraction	extraction	NOUN
brj-22874	7	21	ability	ability	NOUN
brj-22874	7	22	of	of	ADP
brj-22874	7	23	the	the	DET
brj-22874	7	24	model	model	NOUN
brj-22874	7	25	.	.	PUNCT
brj-22874	8	1	a	a	DET
brj-22874	8	2	new	new	ADJ
brj-22874	8	3	spp	spp	NOUN
brj-22874	8	4	module	module	NOUN
brj-22874	8	5	is	be	AUX
brj-22874	8	6	added	add	VERB
brj-22874	8	7	to	to	ADP
brj-22874	8	8	the	the	DET
brj-22874	8	9	backbone	backbone	NOUN
brj-22874	8	10	network	network	NOUN
brj-22874	8	11	to	to	PART
brj-22874	8	12	increase	increase	VERB
brj-22874	8	13	the	the	DET
brj-22874	8	14	important	important	ADJ
brj-22874	8	15	features	feature	NOUN
brj-22874	8	16	in	in	ADP
brj-22874	8	17	the	the	DET
brj-22874	8	18	receptive	receptive	ADJ
brj-22874	8	19	field	field	NOUN
brj-22874	8	20	extraction	extraction	NOUN
brj-22874	8	21	network	network	NOUN
brj-22874	8	22	to	to	PART
brj-22874	8	23	improve	improve	VERB
brj-22874	8	24	the	the	DET
brj-22874	8	25	detection	detection	NOUN
brj-22874	8	26	accuracy	accuracy	NOUN
brj-22874	8	27	of	of	ADP
brj-22874	8	28	small	small	ADJ
brj-22874	8	29	targets	target	NOUN
brj-22874	8	30	.	.	PUNCT
brj-22874	9	1	the	the	DET
brj-22874	9	2	experimental	experimental	ADJ
brj-22874	9	3	results	result	NOUN
brj-22874	9	4	show	show	VERB
brj-22874	9	5	:	:	PUNCT
brj-22874	9	6	the	the	DET
brj-22874	9	7	yolov5	yolov5	NOUN
brj-22874	9	8	-	-	PUNCT
brj-22874	9	9	tspp	tspp	NOUN
brj-22874	9	10	algorithm	algorithm	NOUN
brj-22874	9	11	has	have	VERB
brj-22874	9	12	better	well	ADJ
brj-22874	9	13	detection	detection	NOUN
brj-22874	9	14	performance	performance	NOUN
brj-22874	9	15	and	and	CCONJ
brj-22874	9	16	the	the	DET
brj-22874	9	17	map	map	NOUN
brj-22874	9	18	of	of	ADP
brj-22874	9	19	defect	defect	ADJ
brj-22874	9	20	detection	detection	NOUN
brj-22874	9	21	reaches	reach	VERB
brj-22874	9	22	80.3	80.3	NUM
brj-22874	9	23	%	%	NOUN
brj-22874	9	24	,	,	PUNCT
brj-22874	9	25	which	which	PRON
brj-22874	9	26	is	be	AUX
brj-22874	9	27	9.2	9.2	NUM
brj-22874	9	28	%	%	NOUN
brj-22874	9	29	higher	high	ADJ
brj-22874	9	30	than	than	ADP
brj-22874	9	31	that	that	PRON
brj-22874	9	32	of	of	ADP
brj-22874	9	33	the	the	DET
brj-22874	9	34	yolov5	yolov5	NOUN
brj-22874	9	35	algorithm	algorithm	NOUN
brj-22874	9	36	.	.	PUNCT
brj-22874	10	1	among	among	ADP
brj-22874	10	2	them	they	PRON
brj-22874	10	3	,	,	PUNCT
brj-22874	10	4	the	the	DET
brj-22874	10	5	detection	detection	NOUN
brj-22874	10	6	accuracy	accuracy	NOUN
brj-22874	10	7	of	of	ADP
brj-22874	10	8	black	black	ADJ
brj-22874	10	9	knot	knot	NOUN
brj-22874	10	10	defect	defect	NOUN
brj-22874	10	11	reached	reach	VERB
brj-22874	10	12	98.6	98.6	NUM
brj-22874	10	13	%	%	NOUN
brj-22874	10	14	,	,	PUNCT
brj-22874	10	15	the	the	DET
brj-22874	10	16	detection	detection	NOUN
brj-22874	10	17	accuracy	accuracy	NOUN
brj-22874	10	18	of	of	ADP
brj-22874	10	19	back	back	ADJ
brj-22874	10	20	crack	crack	NOUN
brj-22874	10	21	defect	defect	NOUN
brj-22874	10	22	reached	reach	VERB
brj-22874	10	23	92.1	92.1	NUM
brj-22874	10	24	%	%	NOUN
brj-22874	10	25	,	,	PUNCT
brj-22874	10	26	and	and	CCONJ
brj-22874	10	27	the	the	DET
brj-22874	10	28	detection	detection	NOUN
brj-22874	10	29	accuracy	accuracy	NOUN
brj-22874	10	30	of	of	ADP
brj-22874	10	31	mineral	mineral	NOUN
brj-22874	10	32	line	line	NOUN
brj-22874	10	33	defect	defect	NOUN
brj-22874	10	34	reached	reach	VERB
brj-22874	10	35	92.3	92.3	NUM
brj-22874	10	36	%	%	NOUN
brj-22874	10	37	.	.	PUNCT
brj-22874	11	1	doi	doi	NOUN
brj-22874	11	2	:	:	PUNCT
brj-22874	11	3	10.15376	10.15376	NUM
brj-22874	11	4	/	/	SYM
brj-22874	11	5	biores.18.4.7713	biores.18.4.7713	PROPN
brj-22874	11	6	-	-	PUNCT
brj-22874	11	7	7730	7730	NUM
brj-22874	11	8	keywords	keyword	NOUN
brj-22874	11	9	:	:	PUNCT
brj-22874	11	10	deep	deep	ADJ
brj-22874	11	11	learning	learning	NOUN
brj-22874	11	12	;	;	PUNCT
brj-22874	11	13	defect	defect	ADJ
brj-22874	11	14	detection	detection	NOUN
brj-22874	11	15	;	;	PUNCT
brj-22874	11	16	yolov5	yolov5	NOUN
brj-22874	11	17	;	;	PUNCT
brj-22874	11	18	wooden	wooden	ADJ
brj-22874	11	19	spoon	spoon	NOUN
brj-22874	11	20	contact	contact	NOUN
brj-22874	11	21	information	information	NOUN
brj-22874	11	22	:	:	PUNCT
brj-22874	11	23	a	a	X
brj-22874	11	24	:	:	PUNCT
brj-22874	11	25	college	college	NOUN
brj-22874	11	26	of	of	ADP
brj-22874	11	27	information	information	NOUN
brj-22874	11	28	and	and	CCONJ
brj-22874	11	29	electronics	electronic	NOUN
brj-22874	11	30	technology	technology	NOUN
brj-22874	11	31	,	,	PUNCT
brj-22874	11	32	jiamusi	jiamusi	PROPN
brj-22874	11	33	university	university	PROPN
brj-22874	11	34	,	,	PUNCT
brj-22874	11	35	jiamusi	jiamusi	PROPN
brj-22874	11	36	154007	154007	NUM
brj-22874	11	37	,	,	PUNCT
brj-22874	11	38	china	china	PROPN
brj-22874	11	39	;	;	PUNCT
brj-22874	11	40	b	b	X
brj-22874	11	41	:	:	PUNCT
brj-22874	11	42	college	college	NOUN
brj-22874	11	43	of	of	ADP
brj-22874	11	44	mechanical	mechanical	ADJ
brj-22874	11	45	engineering	engineering	PROPN
brj-22874	11	46	jiamusi	jiamusi	PROPN
brj-22874	11	47	university	university	PROPN
brj-22874	11	48	,	,	PUNCT
brj-22874	11	49	jiamusi	jiamusi	PROPN
brj-22874	11	50	154007	154007	NUM
brj-22874	11	51	,	,	PUNCT
brj-22874	11	52	china	china	PROPN
brj-22874	11	53	;	;	PUNCT
brj-22874	11	54	*	*	PUNCT
brj-22874	11	55	corresponding	correspond	VERB
brj-22874	11	56	author	author	NOUN
brj-22874	11	57	:	:	PUNCT
brj-22874	11	58	jichao	jichao	PROPN
brj-22874	11	59	li	li	PROPN
brj-22874	11	60	(	(	PUNCT
brj-22874	11	61	m20210309@126.com	m20210309@126.com	NOUN
brj-22874	11	62	)	)	PUNCT
brj-22874	11	63	introduction	introduction	NOUN
brj-22874	11	64	the	the	DET
brj-22874	11	65	disposable	disposable	ADJ
brj-22874	11	66	wooden	wooden	ADJ
brj-22874	11	67	spoon	spoon	NOUN
brj-22874	11	68	is	be	AUX
brj-22874	11	69	mainly	mainly	ADV
brj-22874	11	70	made	make	VERB
brj-22874	11	71	of	of	ADP
brj-22874	11	72	birch	birch	NOUN
brj-22874	11	73	as	as	ADP
brj-22874	11	74	raw	raw	ADJ
brj-22874	11	75	material	material	NOUN
brj-22874	11	76	,	,	PUNCT
brj-22874	11	77	through	through	ADP
brj-22874	11	78	cutting	cutting	NOUN
brj-22874	11	79	,	,	PUNCT
brj-22874	11	80	soaking	soak	VERB
brj-22874	11	81	,	,	PUNCT
brj-22874	11	82	drying	dry	VERB
brj-22874	11	83	,	,	PUNCT
brj-22874	11	84	hot	hot	ADJ
brj-22874	11	85	pressing	pressing	ADJ
brj-22874	11	86	,	,	PUNCT
brj-22874	11	87	polishing	polishing	NOUN
brj-22874	11	88	,	,	PUNCT
brj-22874	11	89	sorting	sorting	NOUN
brj-22874	11	90	,	,	PUNCT
brj-22874	11	91	and	and	CCONJ
brj-22874	11	92	other	other	ADJ
brj-22874	11	93	processes	process	NOUN
brj-22874	11	94	.	.	PUNCT
brj-22874	12	1	the	the	DET
brj-22874	12	2	surface	surface	NOUN
brj-22874	12	3	defects	defect	NOUN
brj-22874	12	4	of	of	ADP
brj-22874	12	5	wooden	wooden	ADJ
brj-22874	12	6	spoons	spoon	NOUN
brj-22874	12	7	are	be	AUX
brj-22874	12	8	mainly	mainly	ADV
brj-22874	12	9	divided	divide	VERB
brj-22874	12	10	into	into	ADP
brj-22874	12	11	two	two	NUM
brj-22874	12	12	categories	category	NOUN
brj-22874	12	13	;	;	PUNCT
brj-22874	12	14	one	one	NUM
brj-22874	12	15	is	be	AUX
brj-22874	12	16	the	the	DET
brj-22874	12	17	natural	natural	ADJ
brj-22874	12	18	defects	defect	NOUN
brj-22874	12	19	of	of	ADP
brj-22874	12	20	wood	wood	NOUN
brj-22874	12	21	raw	raw	ADJ
brj-22874	12	22	materials	material	NOUN
brj-22874	12	23	and	and	CCONJ
brj-22874	12	24	the	the	DET
brj-22874	12	25	other	other	ADJ
brj-22874	12	26	is	be	AUX
brj-22874	12	27	the	the	DET
brj-22874	12	28	defects	defect	NOUN
brj-22874	12	29	formed	form	VERB
brj-22874	12	30	during	during	ADP
brj-22874	12	31	processing	processing	NOUN
brj-22874	12	32	.	.	PUNCT
brj-22874	13	1	the	the	DET
brj-22874	13	2	most	most	ADV
brj-22874	13	3	common	common	ADJ
brj-22874	13	4	defects	defect	NOUN
brj-22874	13	5	are	be	AUX
brj-22874	13	6	black	black	ADJ
brj-22874	13	7	knots	knot	NOUN
brj-22874	13	8	,	,	PUNCT
brj-22874	13	9	mineral	mineral	NOUN
brj-22874	13	10	lines	line	NOUN
brj-22874	13	11	,	,	PUNCT
brj-22874	13	12	pollution	pollution	NOUN
brj-22874	13	13	,	,	PUNCT
brj-22874	13	14	and	and	CCONJ
brj-22874	13	15	back	back	NOUN
brj-22874	13	16	cracks	crack	NOUN
brj-22874	13	17	.	.	PUNCT
brj-22874	14	1	these	these	DET
brj-22874	14	2	defects	defect	NOUN
brj-22874	14	3	affect	affect	VERB
brj-22874	14	4	the	the	DET
brj-22874	14	5	appearance	appearance	NOUN
brj-22874	14	6	and	and	CCONJ
brj-22874	14	7	quality	quality	NOUN
brj-22874	14	8	of	of	ADP
brj-22874	14	9	wooden	wooden	ADJ
brj-22874	14	10	spoons	spoon	NOUN
brj-22874	14	11	and	and	CCONJ
brj-22874	14	12	reduce	reduce	VERB
brj-22874	14	13	the	the	DET
brj-22874	14	14	export	export	NOUN
brj-22874	14	15	number	number	NOUN
brj-22874	14	16	of	of	ADP
brj-22874	14	17	wooden	wooden	ADJ
brj-22874	14	18	spoons	spoon	NOUN
brj-22874	14	19	.	.	PUNCT
brj-22874	15	1	the	the	DET
brj-22874	15	2	existing	exist	VERB
brj-22874	15	3	detection	detection	NOUN
brj-22874	15	4	methods	method	NOUN
brj-22874	15	5	mainly	mainly	ADV
brj-22874	15	6	rely	rely	VERB
brj-22874	15	7	on	on	ADP
brj-22874	15	8	manual	manual	ADJ
brj-22874	15	9	detection	detection	NOUN
brj-22874	15	10	.	.	PUNCT
brj-22874	16	1	according	accord	VERB
brj-22874	16	2	to	to	ADP
brj-22874	16	3	the	the	DET
brj-22874	16	4	texture	texture	NOUN
brj-22874	16	5	,	,	PUNCT
brj-22874	16	6	structural	structural	ADJ
brj-22874	16	7	characteristics	characteristic	NOUN
brj-22874	16	8	,	,	PUNCT
brj-22874	16	9	color	color	NOUN
brj-22874	16	10	of	of	ADP
brj-22874	16	11	the	the	DET
brj-22874	16	12	raw	raw	ADJ
brj-22874	16	13	materials	material	NOUN
brj-22874	16	14	of	of	ADP
brj-22874	16	15	wooden	wooden	ADJ
brj-22874	16	16	spoons	spoon	NOUN
brj-22874	16	17	,	,	PUNCT
brj-22874	16	18	and	and	CCONJ
brj-22874	16	19	surface	surface	NOUN
brj-22874	16	20	defects	defect	NOUN
brj-22874	16	21	,	,	PUNCT
brj-22874	16	22	the	the	DET
brj-22874	16	23	wooden	wooden	ADJ
brj-22874	16	24	spoons	spoon	NOUN
brj-22874	16	25	are	be	AUX
brj-22874	16	26	classified	classify	VERB
brj-22874	16	27	and	and	CCONJ
brj-22874	16	28	graded	grade	VERB
brj-22874	16	29	by	by	ADP
brj-22874	16	30	human	human	ADJ
brj-22874	16	31	eyes	eye	NOUN
brj-22874	16	32	(	(	PUNCT
brj-22874	16	33	gu	gu	NOUN
brj-22874	16	34	et	et	PROPN
brj-22874	16	35	al	al	PROPN
brj-22874	16	36	.	.	PROPN
brj-22874	16	37	2010	2010	NUM
brj-22874	16	38	)	)	PUNCT
brj-22874	16	39	.	.	PUNCT
brj-22874	17	1	this	this	DET
brj-22874	17	2	method	method	NOUN
brj-22874	17	3	requires	require	VERB
brj-22874	17	4	a	a	DET
brj-22874	17	5	lot	lot	NOUN
brj-22874	17	6	of	of	ADP
brj-22874	17	7	manual	manual	ADJ
brj-22874	17	8	participation	participation	NOUN
brj-22874	17	9	and	and	CCONJ
brj-22874	17	10	faces	face	VERB
brj-22874	17	11	problems	problem	NOUN
brj-22874	17	12	such	such	ADJ
brj-22874	17	13	as	as	ADP
brj-22874	17	14	low	low	ADJ
brj-22874	17	15	-	-	PUNCT
brj-22874	17	16	quality	quality	NOUN
brj-22874	17	17	inspection	inspection	NOUN
brj-22874	17	18	rates	rate	NOUN
brj-22874	17	19	and	and	CCONJ
brj-22874	17	20	excessive	excessive	ADJ
brj-22874	17	21	labor	labor	NOUN
brj-22874	17	22	input	input	NOUN
brj-22874	17	23	.	.	PUNCT
brj-22874	18	1	with	with	ADP
brj-22874	18	2	the	the	DET
brj-22874	18	3	continuous	continuous	ADJ
brj-22874	18	4	development	development	NOUN
brj-22874	18	5	of	of	ADP
brj-22874	18	6	computer	computer	NOUN
brj-22874	18	7	vision	vision	NOUN
brj-22874	18	8	technology	technology	NOUN
brj-22874	18	9	,	,	PUNCT
brj-22874	18	10	intelligent	intelligent	ADJ
brj-22874	18	11	methods	method	NOUN
brj-22874	18	12	have	have	AUX
brj-22874	18	13	become	become	AUX
brj-22874	18	14	increasingly	increasingly	ADV
brj-22874	18	15	used	use	VERB
brj-22874	18	16	for	for	ADP
brj-22874	18	17	defect	defect	ADJ
brj-22874	18	18	detection	detection	NOUN
brj-22874	18	19	.	.	PUNCT
brj-22874	19	1	yonghua	yonghua	PROPN
brj-22874	19	2	and	and	CCONJ
brj-22874	19	3	jin	jin	NOUN
brj-22874	19	4	-	-	PUNCT
brj-22874	19	5	cong	cong	NOUN
brj-22874	19	6	(	(	PUNCT
brj-22874	19	7	2015	2015	NUM
brj-22874	19	8	)	)	PUNCT
brj-22874	19	9	proposed	propose	VERB
brj-22874	19	10	a	a	DET
brj-22874	19	11	detection	detection	NOUN
brj-22874	19	12	method	method	NOUN
brj-22874	19	13	of	of	ADP
brj-22874	19	14	mixed	mixed	ADJ
brj-22874	19	15	surface	surface	NOUN
brj-22874	19	16	texture	texture	NOUN
brj-22874	19	17	features	feature	NOUN
brj-22874	19	18	,	,	PUNCT
brj-22874	19	19	which	which	PRON
brj-22874	19	20	ensured	ensure	VERB
brj-22874	19	21	the	the	DET
brj-22874	19	22	accuracy	accuracy	NOUN
brj-22874	19	23	and	and	CCONJ
brj-22874	19	24	robustness	robustness	NOUN
brj-22874	19	25	of	of	ADP
brj-22874	19	26	the	the	DET
brj-22874	19	27	model	model	NOUN
brj-22874	19	28	and	and	CCONJ
brj-22874	19	29	could	could	AUX
brj-22874	19	30	detect	detect	VERB
brj-22874	19	31	dead	dead	ADJ
brj-22874	19	32	knot	knot	NOUN
brj-22874	19	33	and	and	CCONJ
brj-22874	19	34	live	live	ADJ
brj-22874	19	35	knot	knot	NOUN
brj-22874	19	36	defects	defect	NOUN
brj-22874	19	37	.	.	PUNCT
brj-22874	20	1	song	song	NOUN
brj-22874	20	2	et	et	PROPN
brj-22874	20	3	al	al	PROPN
brj-22874	20	4	.	.	PROPN
brj-22874	21	1	(	(	PUNCT
brj-22874	21	2	2015	2015	NUM
brj-22874	21	3	)	)	PUNCT
brj-22874	21	4	proposed	propose	VERB
brj-22874	21	5	a	a	DET
brj-22874	21	6	method	method	NOUN
brj-22874	21	7	peer	peer	NOUN
brj-22874	21	8	-	-	PUNCT
brj-22874	21	9	reviewed	review	VERB
brj-22874	21	10	article	article	NOUN
brj-22874	21	11	bioresources.com	bioresources.com	X
brj-22874	21	12	tian	tian	PROPN
brj-22874	21	13	et	et	PROPN
brj-22874	21	14	al	al	PROPN
brj-22874	21	15	.	.	PROPN
brj-22874	22	1	(	(	PUNCT
brj-22874	22	2	2023	2023	NUM
brj-22874	22	3	)	)	PUNCT
brj-22874	22	4	.	.	PUNCT
brj-22874	23	1	“	"	PUNCT
brj-22874	23	2	defect	defect	VERB
brj-22874	23	3	detection	detection	NOUN
brj-22874	23	4	with	with	ADP
brj-22874	23	5	yolov5	yolov5	NOUN
brj-22874	23	6	,	,	PUNCT
brj-22874	23	7	”	"	PUNCT
brj-22874	23	8	bioresources	bioresource	NOUN
brj-22874	23	9	18(4	18(4	NUM
brj-22874	23	10	)	)	PUNCT
brj-22874	23	11	,	,	PUNCT
brj-22874	23	12	7713	7713	NUM
brj-22874	23	13	-	-	SYM
brj-22874	23	14	7730	7730	NUM
brj-22874	23	15	.	.	PUNCT
brj-22874	23	16	7714	7714	NUM
brj-22874	23	17	based	base	VERB
brj-22874	23	18	on	on	ADP
brj-22874	23	19	image	image	NOUN
brj-22874	23	20	block	block	NOUN
brj-22874	23	21	percentile	percentile	ADJ
brj-22874	23	22	color	color	NOUN
brj-22874	23	23	histogram	histogram	NOUN
brj-22874	23	24	and	and	CCONJ
brj-22874	23	25	feature	feature	NOUN
brj-22874	23	26	vector	vector	NOUN
brj-22874	23	27	texture	texture	NOUN
brj-22874	23	28	classification	classification	NOUN
brj-22874	23	29	,	,	PUNCT
brj-22874	23	30	which	which	PRON
brj-22874	23	31	can	can	AUX
brj-22874	23	32	detect	detect	VERB
brj-22874	23	33	knots	knot	NOUN
brj-22874	23	34	and	and	CCONJ
brj-22874	23	35	crack	crack	VERB
brj-22874	23	36	defects	defect	NOUN
brj-22874	23	37	.	.	PUNCT
brj-22874	24	1	zhang	zhang	PROPN
brj-22874	24	2	et	et	PROPN
brj-22874	24	3	al	al	PROPN
brj-22874	24	4	.	.	PROPN
brj-22874	24	5	(	(	PUNCT
brj-22874	24	6	2015	2015	NUM
brj-22874	24	7	)	)	PUNCT
brj-22874	24	8	combined	combine	VERB
brj-22874	24	9	principal	principal	ADJ
brj-22874	24	10	component	component	NOUN
brj-22874	24	11	analysis	analysis	NOUN
brj-22874	24	12	with	with	ADP
brj-22874	24	13	compressed	compressed	ADJ
brj-22874	24	14	sensing	sense	VERB
brj-22874	24	15	technology	technology	NOUN
brj-22874	24	16	and	and	CCONJ
brj-22874	24	17	achieved	achieve	VERB
brj-22874	24	18	high	high	ADJ
brj-22874	24	19	recognition	recognition	NOUN
brj-22874	24	20	accuracy	accuracy	NOUN
brj-22874	24	21	in	in	ADP
brj-22874	24	22	detecting	detect	VERB
brj-22874	24	23	wood	wood	NOUN
brj-22874	24	24	defects	defect	NOUN
brj-22874	24	25	.	.	PUNCT
brj-22874	25	1	mu	mu	PROPN
brj-22874	25	2	et	et	PROPN
brj-22874	25	3	al	al	PROPN
brj-22874	25	4	.	.	PROPN
brj-22874	26	1	(	(	PUNCT
brj-22874	26	2	2015	2015	NUM
brj-22874	26	3	)	)	PUNCT
brj-22874	26	4	combined	combine	VERB
brj-22874	26	5	fuzzy	fuzzy	ADJ
brj-22874	26	6	mathematics	mathematic	NOUN
brj-22874	26	7	with	with	ADP
brj-22874	26	8	a	a	DET
brj-22874	26	9	back	back	ADJ
brj-22874	26	10	propagation	propagation	NOUN
brj-22874	26	11	neural	neural	ADJ
brj-22874	26	12	network	network	NOUN
brj-22874	26	13	(	(	PUNCT
brj-22874	26	14	bp	bp	PROPN
brj-22874	26	15	)	)	PUNCT
brj-22874	26	16	to	to	PART
brj-22874	26	17	build	build	VERB
brj-22874	26	18	a	a	DET
brj-22874	26	19	fuzzy	fuzzy	ADJ
brj-22874	26	20	bp	bp	PROPN
brj-22874	26	21	neural	neural	ADJ
brj-22874	26	22	network	network	NOUN
brj-22874	26	23	(	(	PUNCT
brj-22874	26	24	fbp	fbp	PROPN
brj-22874	26	25	)	)	PUNCT
brj-22874	26	26	,	,	PUNCT
brj-22874	26	27	which	which	PRON
brj-22874	26	28	can	can	AUX
brj-22874	26	29	realize	realize	VERB
brj-22874	26	30	the	the	DET
brj-22874	26	31	automatic	automatic	ADJ
brj-22874	26	32	identification	identification	NOUN
brj-22874	26	33	of	of	ADP
brj-22874	26	34	wood	wood	NOUN
brj-22874	26	35	defects	defect	NOUN
brj-22874	26	36	.	.	PUNCT
brj-22874	27	1	abdullah	abdullah	PROPN
brj-22874	27	2	et	et	PROPN
brj-22874	27	3	al	al	PROPN
brj-22874	27	4	.	.	PROPN
brj-22874	27	5	(	(	PUNCT
brj-22874	27	6	2020	2020	NUM
brj-22874	27	7	)	)	PUNCT
brj-22874	27	8	used	use	VERB
brj-22874	27	9	gray	gray	ADJ
brj-22874	27	10	dependence	dependence	NOUN
brj-22874	27	11	matrix	matrix	NOUN
brj-22874	27	12	(	(	PUNCT
brj-22874	27	13	gldm	gldm	NOUN
brj-22874	27	14	)	)	PUNCT
brj-22874	27	15	for	for	ADP
brj-22874	27	16	feature	feature	NOUN
brj-22874	27	17	extraction	extraction	NOUN
brj-22874	27	18	and	and	CCONJ
brj-22874	27	19	feature	feature	NOUN
brj-22874	27	20	analysis	analysis	NOUN
brj-22874	27	21	to	to	PART
brj-22874	27	22	study	study	VERB
brj-22874	27	23	appropriate	appropriate	ADJ
brj-22874	27	24	displacement	displacement	NOUN
brj-22874	27	25	and	and	CCONJ
brj-22874	27	26	quantitative	quantitative	ADJ
brj-22874	27	27	parameters	parameter	NOUN
brj-22874	27	28	that	that	PRON
brj-22874	27	29	can	can	AUX
brj-22874	27	30	classify	classify	VERB
brj-22874	27	31	wood	wood	NOUN
brj-22874	27	32	defects	defect	NOUN
brj-22874	27	33	.	.	PUNCT
brj-22874	28	1	yang	yang	PROPN
brj-22874	28	2	and	and	CCONJ
brj-22874	28	3	yu	yu	PROPN
brj-22874	28	4	(	(	PUNCT
brj-22874	28	5	2017	2017	NUM
brj-22874	28	6	)	)	PUNCT
brj-22874	28	7	used	use	VERB
brj-22874	28	8	wavelet	wavelet	NOUN
brj-22874	28	9	-	-	PUNCT
brj-22874	28	10	based	base	VERB
brj-22874	28	11	ultrasonic	ultrasonic	ADJ
brj-22874	28	12	testing	testing	NOUN
brj-22874	28	13	to	to	PART
brj-22874	28	14	extract	extract	VERB
brj-22874	28	15	features	feature	NOUN
brj-22874	28	16	of	of	ADP
brj-22874	28	17	wood	wood	NOUN
brj-22874	28	18	hole	hole	NOUN
brj-22874	28	19	defects	defect	NOUN
brj-22874	28	20	.	.	PUNCT
brj-22874	29	1	aleksi	aleksi	PROPN
brj-22874	29	2	et	et	PROPN
brj-22874	29	3	al	al	PROPN
brj-22874	29	4	.	.	PROPN
brj-22874	30	1	(	(	PUNCT
brj-22874	30	2	2019	2019	NUM
brj-22874	30	3	)	)	PUNCT
brj-22874	30	4	detected	detect	VERB
brj-22874	30	5	the	the	DET
brj-22874	30	6	defects	defect	NOUN
brj-22874	30	7	on	on	ADP
brj-22874	30	8	wood	wood	NOUN
brj-22874	30	9	by	by	ADP
brj-22874	30	10	calculating	calculate	VERB
brj-22874	30	11	the	the	DET
brj-22874	30	12	vector	vector	NOUN
brj-22874	30	13	difference	difference	NOUN
brj-22874	30	14	between	between	ADP
brj-22874	30	15	the	the	DET
brj-22874	30	16	texture	texture	NOUN
brj-22874	30	17	without	without	ADP
brj-22874	30	18	defects	defect	NOUN
brj-22874	30	19	and	and	CCONJ
brj-22874	30	20	the	the	DET
brj-22874	30	21	texture	texture	NOUN
brj-22874	30	22	with	with	ADP
brj-22874	30	23	defects	defect	NOUN
brj-22874	30	24	.	.	PUNCT
brj-22874	31	1	however	however	ADV
brj-22874	31	2	,	,	PUNCT
brj-22874	31	3	the	the	DET
brj-22874	31	4	above	above	ADJ
brj-22874	31	5	detection	detection	NOUN
brj-22874	31	6	methods	method	NOUN
brj-22874	31	7	are	be	AUX
brj-22874	31	8	easily	easily	ADV
brj-22874	31	9	affected	affect	VERB
brj-22874	31	10	by	by	ADP
brj-22874	31	11	the	the	DET
brj-22874	31	12	shape	shape	NOUN
brj-22874	31	13	and	and	CCONJ
brj-22874	31	14	texture	texture	NOUN
brj-22874	31	15	of	of	ADP
brj-22874	31	16	the	the	DET
brj-22874	31	17	wood	wood	NOUN
brj-22874	31	18	itself	itself	PRON
brj-22874	31	19	and	and	CCONJ
brj-22874	31	20	the	the	DET
brj-22874	31	21	surrounding	surround	VERB
brj-22874	31	22	environment	environment	NOUN
brj-22874	31	23	(	(	PUNCT
brj-22874	31	24	light	light	ADJ
brj-22874	31	25	,	,	PUNCT
brj-22874	31	26	angle	angle	NOUN
brj-22874	31	27	,	,	PUNCT
brj-22874	31	28	etc	etc	X
brj-22874	31	29	.	.	X
brj-22874	31	30	)	)	PUNCT
brj-22874	31	31	,	,	PUNCT
brj-22874	31	32	such	such	ADJ
brj-22874	31	33	that	that	SCONJ
brj-22874	31	34	it	it	PRON
brj-22874	31	35	can	can	AUX
brj-22874	31	36	be	be	AUX
brj-22874	31	37	difficult	difficult	ADJ
brj-22874	31	38	to	to	PART
brj-22874	31	39	meet	meet	VERB
brj-22874	31	40	the	the	DET
brj-22874	31	41	needs	need	NOUN
brj-22874	31	42	of	of	ADP
brj-22874	31	43	defect	defect	ADJ
brj-22874	31	44	detection	detection	NOUN
brj-22874	31	45	in	in	ADP
brj-22874	31	46	complex	complex	ADJ
brj-22874	31	47	image	image	NOUN
brj-22874	31	48	backgrounds	background	NOUN
brj-22874	31	49	.	.	PUNCT
brj-22874	32	1	with	with	ADP
brj-22874	32	2	the	the	DET
brj-22874	32	3	development	development	NOUN
brj-22874	32	4	of	of	ADP
brj-22874	32	5	convolutional	convolutional	ADJ
brj-22874	32	6	neural	neural	ADJ
brj-22874	32	7	networks	network	NOUN
brj-22874	32	8	,	,	PUNCT
brj-22874	32	9	applying	apply	VERB
brj-22874	32	10	convolutional	convolutional	ADJ
brj-22874	32	11	networks	network	NOUN
brj-22874	32	12	to	to	PART
brj-22874	32	13	target	target	VERB
brj-22874	32	14	detection	detection	NOUN
brj-22874	32	15	can	can	AUX
brj-22874	32	16	allow	allow	VERB
brj-22874	32	17	the	the	DET
brj-22874	32	18	system	system	NOUN
brj-22874	32	19	to	to	PART
brj-22874	32	20	learn	learn	VERB
brj-22874	32	21	higher	high	ADJ
brj-22874	32	22	-	-	PUNCT
brj-22874	32	23	level	level	NOUN
brj-22874	32	24	features	feature	NOUN
brj-22874	32	25	of	of	ADP
brj-22874	32	26	images	image	NOUN
brj-22874	32	27	and	and	CCONJ
brj-22874	32	28	improve	improve	VERB
brj-22874	32	29	detection	detection	NOUN
brj-22874	32	30	efficiency	efficiency	NOUN
brj-22874	32	31	.	.	PUNCT
brj-22874	33	1	at	at	ADP
brj-22874	33	2	present	present	ADJ
brj-22874	33	3	,	,	PUNCT
brj-22874	33	4	defect	defect	ADJ
brj-22874	33	5	detection	detection	NOUN
brj-22874	33	6	based	base	VERB
brj-22874	33	7	on	on	ADP
brj-22874	33	8	deep	deep	ADJ
brj-22874	33	9	learning	learning	NOUN
brj-22874	33	10	is	be	AUX
brj-22874	33	11	mainly	mainly	ADV
brj-22874	33	12	divided	divide	VERB
brj-22874	33	13	into	into	ADP
brj-22874	33	14	two	two	NUM
brj-22874	33	15	categories	category	NOUN
brj-22874	33	16	:	:	PUNCT
brj-22874	33	17	one	one	NUM
brj-22874	33	18	is	be	AUX
brj-22874	33	19	based	base	VERB
brj-22874	33	20	on	on	ADP
brj-22874	33	21	region	region	NOUN
brj-22874	33	22	proposal	proposal	NOUN
brj-22874	33	23	,	,	PUNCT
brj-22874	33	24	such	such	ADJ
brj-22874	33	25	as	as	ADP
brj-22874	33	26	the	the	DET
brj-22874	33	27	faster	fast	ADJ
brj-22874	33	28	rcnn	rcnn	PROPN
brj-22874	33	29	model	model	PROPN
brj-22874	33	30	(	(	PUNCT
brj-22874	33	31	ren	ren	NOUN
brj-22874	33	32	et	et	PROPN
brj-22874	33	33	al	al	PROPN
brj-22874	33	34	.	.	PROPN
brj-22874	33	35	2015	2015	NUM
brj-22874	33	36	)	)	PUNCT
brj-22874	33	37	;	;	PUNCT
brj-22874	33	38	the	the	DET
brj-22874	33	39	other	other	ADJ
brj-22874	33	40	is	be	AUX
brj-22874	33	41	object	object	NOUN
brj-22874	33	42	-	-	PUNCT
brj-22874	33	43	based	base	VERB
brj-22874	33	44	regression	regression	NOUN
brj-22874	33	45	methods	method	NOUN
brj-22874	33	46	,	,	PUNCT
brj-22874	33	47	such	such	ADJ
brj-22874	33	48	as	as	ADP
brj-22874	33	49	ssd	ssd	NOUN
brj-22874	33	50	(	(	PUNCT
brj-22874	33	51	liu	liu	PROPN
brj-22874	33	52	et	et	PROPN
brj-22874	33	53	al	al	PROPN
brj-22874	33	54	.	.	PROPN
brj-22874	33	55	2016	2016	NUM
brj-22874	33	56	)	)	PUNCT
brj-22874	33	57	and	and	CCONJ
brj-22874	33	58	yolo	yolo	ADJ
brj-22874	33	59	model	model	NOUN
brj-22874	33	60	(	(	PUNCT
brj-22874	33	61	redmon	redmon	PROPN
brj-22874	33	62	et	et	PROPN
brj-22874	33	63	al	al	PROPN
brj-22874	33	64	.	.	PROPN
brj-22874	33	65	2016	2016	NUM
brj-22874	33	66	)	)	PUNCT
brj-22874	33	67	.	.	PUNCT
brj-22874	34	1	shi	shi	PROPN
brj-22874	34	2	et	et	PROPN
brj-22874	34	3	al	al	PROPN
brj-22874	34	4	.	.	PROPN
brj-22874	34	5	(	(	PUNCT
brj-22874	34	6	2020	2020	NUM
brj-22874	34	7	)	)	PUNCT
brj-22874	34	8	constructed	construct	VERB
brj-22874	34	9	a	a	DET
brj-22874	34	10	convolutional	convolutional	ADJ
brj-22874	34	11	neural	neural	ADJ
brj-22874	34	12	network	network	NOUN
brj-22874	34	13	and	and	CCONJ
brj-22874	34	14	then	then	ADV
brj-22874	34	15	used	use	VERB
brj-22874	34	16	multi	multi	ADJ
brj-22874	34	17	-	-	ADJ
brj-22874	34	18	channel	channel	ADJ
brj-22874	34	19	mask	mask	NOUN
brj-22874	34	20	r	r	NOUN
brj-22874	34	21	-	-	PUNCT
brj-22874	34	22	cnn	cnn	NOUN
brj-22874	34	23	to	to	PART
brj-22874	34	24	classify	classify	VERB
brj-22874	34	25	and	and	CCONJ
brj-22874	34	26	locate	locate	ADJ
brj-22874	34	27	defects	defect	NOUN
brj-22874	34	28	,	,	PUNCT
brj-22874	34	29	which	which	PRON
brj-22874	34	30	can	can	AUX
brj-22874	34	31	identify	identify	VERB
brj-22874	34	32	dead	dead	ADJ
brj-22874	34	33	knots	knot	NOUN
brj-22874	34	34	,	,	PUNCT
brj-22874	34	35	live	live	ADJ
brj-22874	34	36	knots	knot	NOUN
brj-22874	34	37	,	,	PUNCT
brj-22874	34	38	and	and	CCONJ
brj-22874	34	39	cracks	crack	NOUN
brj-22874	34	40	in	in	ADP
brj-22874	34	41	wood	wood	NOUN
brj-22874	34	42	.	.	PUNCT
brj-22874	35	1	wang	wang	PROPN
brj-22874	35	2	et	et	PROPN
brj-22874	35	3	al	al	PROPN
brj-22874	35	4	.	.	PROPN
brj-22874	36	1	(	(	PUNCT
brj-22874	36	2	2018	2018	NUM
brj-22874	36	3	)	)	PUNCT
brj-22874	36	4	used	use	VERB
brj-22874	36	5	the	the	DET
brj-22874	36	6	fuzzy	fuzzy	ADJ
brj-22874	36	7	pattern	pattern	NOUN
brj-22874	36	8	recognition	recognition	NOUN
brj-22874	36	9	method	method	NOUN
brj-22874	36	10	to	to	PART
brj-22874	36	11	detect	detect	VERB
brj-22874	36	12	the	the	DET
brj-22874	36	13	surface	surface	NOUN
brj-22874	36	14	defects	defect	NOUN
brj-22874	36	15	of	of	ADP
brj-22874	36	16	particleboard	particleboard	NOUN
brj-22874	36	17	in	in	ADP
brj-22874	36	18	motion	motion	NOUN
brj-22874	36	19	and	and	CCONJ
brj-22874	36	20	calculated	calculate	VERB
brj-22874	36	21	the	the	DET
brj-22874	36	22	number	number	NOUN
brj-22874	36	23	of	of	ADP
brj-22874	36	24	defects	defect	NOUN
brj-22874	36	25	,	,	PUNCT
brj-22874	36	26	defect	defect	ADJ
brj-22874	36	27	area	area	NOUN
brj-22874	36	28	,	,	PUNCT
brj-22874	36	29	and	and	CCONJ
brj-22874	36	30	damage	damage	NOUN
brj-22874	36	31	degree	degree	NOUN
brj-22874	36	32	.	.	PUNCT
brj-22874	37	1	yang	yang	PROPN
brj-22874	37	2	et	et	PROPN
brj-22874	37	3	al	al	PROPN
brj-22874	37	4	.	.	PROPN
brj-22874	38	1	(	(	PUNCT
brj-22874	38	2	2019	2019	NUM
brj-22874	38	3	)	)	PUNCT
brj-22874	38	4	used	use	VERB
brj-22874	38	5	a	a	DET
brj-22874	38	6	3d	3d	NUM
brj-22874	38	7	laser	laser	NOUN
brj-22874	38	8	sensor	sensor	NOUN
brj-22874	38	9	system	system	NOUN
brj-22874	38	10	to	to	PART
brj-22874	38	11	classify	classify	VERB
brj-22874	38	12	and	and	CCONJ
brj-22874	38	13	identify	identify	VERB
brj-22874	38	14	the	the	DET
brj-22874	38	15	surface	surface	NOUN
brj-22874	38	16	defects	defect	NOUN
brj-22874	38	17	of	of	ADP
brj-22874	38	18	wood	wood	NOUN
brj-22874	38	19	-	-	PUNCT
brj-22874	38	20	based	base	VERB
brj-22874	38	21	panels	panel	NOUN
brj-22874	38	22	and	and	CCONJ
brj-22874	38	23	obtained	obtain	VERB
brj-22874	38	24	a	a	DET
brj-22874	38	25	final	final	ADJ
brj-22874	38	26	classification	classification	NOUN
brj-22874	38	27	accuracy	accuracy	NOUN
brj-22874	38	28	of	of	ADP
brj-22874	38	29	94.7	94.7	NUM
brj-22874	38	30	%	%	NOUN
brj-22874	38	31	after	after	ADP
brj-22874	38	32	applying	apply	VERB
brj-22874	38	33	svm	svm	PROPN
brj-22874	38	34	.	.	PROPN
brj-22874	39	1	urbonas	urbonas	PROPN
brj-22874	39	2	et	et	PROPN
brj-22874	39	3	al	al	PROPN
brj-22874	39	4	.	.	PROPN
brj-22874	40	1	(	(	PUNCT
brj-22874	40	2	2019	2019	NUM
brj-22874	40	3	)	)	PUNCT
brj-22874	40	4	used	use	VERB
brj-22874	40	5	a	a	DET
brj-22874	40	6	faster	fast	ADJ
brj-22874	40	7	region	region	NOUN
brj-22874	40	8	-	-	PUNCT
brj-22874	40	9	based	base	VERB
brj-22874	40	10	convolutional	convolutional	ADJ
brj-22874	40	11	neural	neural	ADJ
brj-22874	40	12	network	network	NOUN
brj-22874	40	13	(	(	PUNCT
brj-22874	40	14	faster	fast	ADJ
brj-22874	40	15	r	r	X
brj-22874	40	16	-	-	PUNCT
brj-22874	40	17	cnn	cnn	NOUN
brj-22874	40	18	)	)	PUNCT
brj-22874	40	19	to	to	PART
brj-22874	40	20	identify	identify	VERB
brj-22874	40	21	defects	defect	NOUN
brj-22874	40	22	on	on	ADP
brj-22874	40	23	the	the	DET
brj-22874	40	24	surface	surface	NOUN
brj-22874	40	25	of	of	ADP
brj-22874	40	26	wood	wood	NOUN
brj-22874	40	27	veneers	veneer	NOUN
brj-22874	40	28	.	.	PUNCT
brj-22874	41	1	he	he	PRON
brj-22874	41	2	et	et	PROPN
brj-22874	41	3	al	al	PROPN
brj-22874	41	4	.	.	PROPN
brj-22874	42	1	(	(	PUNCT
brj-22874	42	2	2019	2019	NUM
brj-22874	42	3	)	)	PUNCT
brj-22874	42	4	proposed	propose	VERB
brj-22874	42	5	a	a	DET
brj-22874	42	6	hybrid	hybrid	NOUN
brj-22874	42	7	fully	fully	ADV
brj-22874	42	8	convolutional	convolutional	ADJ
brj-22874	42	9	neural	neural	ADJ
brj-22874	42	10	network	network	NOUN
brj-22874	42	11	(	(	PUNCT
brj-22874	42	12	mix	mix	NOUN
brj-22874	42	13	-	-	PUNCT
brj-22874	42	14	fcn	fcn	NOUN
brj-22874	42	15	)	)	PUNCT
brj-22874	42	16	to	to	PART
brj-22874	42	17	detect	detect	VERB
brj-22874	42	18	the	the	DET
brj-22874	42	19	location	location	NOUN
brj-22874	42	20	of	of	ADP
brj-22874	42	21	wood	wood	NOUN
brj-22874	42	22	defects	defect	NOUN
brj-22874	42	23	and	and	CCONJ
brj-22874	42	24	automatically	automatically	ADV
brj-22874	42	25	classify	classify	VERB
brj-22874	42	26	the	the	DET
brj-22874	42	27	types	type	NOUN
brj-22874	42	28	of	of	ADP
brj-22874	42	29	defects	defect	NOUN
brj-22874	42	30	from	from	ADP
brj-22874	42	31	wood	wood	NOUN
brj-22874	42	32	surface	surface	NOUN
brj-22874	42	33	images	image	NOUN
brj-22874	42	34	.	.	PUNCT
brj-22874	43	1	he	he	PRON
brj-22874	43	2	et	et	PROPN
brj-22874	43	3	al	al	PROPN
brj-22874	43	4	.	.	PROPN
brj-22874	43	5	(	(	PUNCT
brj-22874	43	6	2020	2020	NUM
brj-22874	43	7	)	)	PUNCT
brj-22874	43	8	used	use	VERB
brj-22874	43	9	deep	deep	ADJ
brj-22874	43	10	convolutional	convolutional	ADJ
brj-22874	43	11	neural	neural	ADJ
brj-22874	43	12	network	network	NOUN
brj-22874	43	13	(	(	PUNCT
brj-22874	43	14	dcnn	dcnn	PROPN
brj-22874	43	15	)	)	PUNCT
brj-22874	43	16	to	to	PART
brj-22874	43	17	identify	identify	VERB
brj-22874	43	18	and	and	CCONJ
brj-22874	43	19	detect	detect	VERB
brj-22874	43	20	defects	defect	NOUN
brj-22874	43	21	in	in	ADP
brj-22874	43	22	wood	wood	NOUN
brj-22874	43	23	images	image	NOUN
brj-22874	43	24	collected	collect	VERB
brj-22874	43	25	by	by	ADP
brj-22874	43	26	laser	laser	NOUN
brj-22874	43	27	scanners	scanner	NOUN
brj-22874	43	28	.	.	PUNCT
brj-22874	44	1	chen	chen	PROPN
brj-22874	44	2	et	et	PROPN
brj-22874	44	3	al	al	PROPN
brj-22874	44	4	.	.	PROPN
brj-22874	44	5	(	(	PUNCT
brj-22874	44	6	2022	2022	NUM
brj-22874	44	7	)	)	PUNCT
brj-22874	44	8	used	use	VERB
brj-22874	44	9	deep	deep	ADJ
brj-22874	44	10	learning	learning	NOUN
brj-22874	44	11	algorithms	algorithm	NOUN
brj-22874	44	12	to	to	PART
brj-22874	44	13	extract	extract	VERB
brj-22874	44	14	image	image	NOUN
brj-22874	44	15	features	feature	NOUN
brj-22874	44	16	of	of	ADP
brj-22874	44	17	the	the	DET
brj-22874	44	18	original	original	ADJ
brj-22874	44	19	image	image	NOUN
brj-22874	44	20	and	and	CCONJ
brj-22874	44	21	laser	laser	NOUN
brj-22874	44	22	alignment	alignment	NOUN
brj-22874	44	23	to	to	PART
brj-22874	44	24	achieve	achieve	VERB
brj-22874	44	25	higher	high	ADJ
brj-22874	44	26	accuracy	accuracy	NOUN
brj-22874	44	27	and	and	CCONJ
brj-22874	44	28	used	use	VERB
brj-22874	44	29	aoi	aoi	PROPN
brj-22874	44	30	to	to	PART
brj-22874	44	31	classify	classify	VERB
brj-22874	44	32	the	the	DET
brj-22874	44	33	final	final	ADJ
brj-22874	44	34	result	result	NOUN
brj-22874	44	35	defects	defect	NOUN
brj-22874	44	36	of	of	ADP
brj-22874	44	37	wdd	wdd	NOUN
brj-22874	44	38	-	-	PUNCT
brj-22874	44	39	dl	dl	PROPN
brj-22874	44	40	.	.	PROPN
brj-22874	44	41	sun	sun	PROPN
brj-22874	44	42	(	(	PUNCT
brj-22874	44	43	2022	2022	NUM
brj-22874	44	44	)	)	PUNCT
brj-22874	44	45	designed	design	VERB
brj-22874	44	46	and	and	CCONJ
brj-22874	44	47	developed	develop	VERB
brj-22874	44	48	an	an	DET
brj-22874	44	49	automatic	automatic	ADJ
brj-22874	44	50	detection	detection	NOUN
brj-22874	44	51	method	method	NOUN
brj-22874	44	52	for	for	ADP
brj-22874	44	53	wood	wood	NOUN
brj-22874	44	54	surface	surface	NOUN
brj-22874	44	55	defects	defect	NOUN
brj-22874	44	56	based	base	VERB
brj-22874	44	57	on	on	ADP
brj-22874	44	58	deep	deep	ADJ
brj-22874	44	59	learning	learning	NOUN
brj-22874	44	60	algorithm	algorithm	NOUN
brj-22874	44	61	and	and	CCONJ
brj-22874	44	62	multi	multi	ADJ
brj-22874	44	63	-	-	ADJ
brj-22874	44	64	criteria	criterion	NOUN
brj-22874	44	65	framework	framework	NOUN
brj-22874	44	66	.	.	PUNCT
brj-22874	45	1	based	base	VERB
brj-22874	45	2	on	on	ADP
brj-22874	45	3	digital	digital	ADJ
brj-22874	45	4	image	image	NOUN
brj-22874	45	5	processing	processing	NOUN
brj-22874	45	6	technology	technology	NOUN
brj-22874	45	7	,	,	PUNCT
brj-22874	45	8	ye	ye	PRON
brj-22874	45	9	et	et	NOUN
brj-22874	45	10	al	al	PROPN
brj-22874	45	11	.	.	PROPN
brj-22874	46	1	(	(	PUNCT
brj-22874	46	2	2022	2022	NUM
brj-22874	46	3	)	)	PUNCT
brj-22874	46	4	designed	design	VERB
brj-22874	46	5	a	a	DET
brj-22874	46	6	complete	complete	ADJ
brj-22874	46	7	set	set	NOUN
brj-22874	46	8	of	of	ADP
brj-22874	46	9	real	real	ADJ
brj-22874	46	10	-	-	PUNCT
brj-22874	46	11	time	time	NOUN
brj-22874	46	12	wood	wood	NOUN
brj-22874	46	13	classification	classification	NOUN
brj-22874	46	14	detection	detection	NOUN
brj-22874	46	15	algorithms	algorithm	NOUN
brj-22874	46	16	.	.	PUNCT
brj-22874	47	1	xia	xia	PROPN
brj-22874	47	2	et	et	PROPN
brj-22874	47	3	al	al	PROPN
brj-22874	47	4	.	.	PROPN
brj-22874	47	5	(	(	PUNCT
brj-22874	47	6	2022	2022	NUM
brj-22874	47	7	)	)	PUNCT
brj-22874	47	8	improved	improve	VERB
brj-22874	47	9	the	the	DET
brj-22874	47	10	faster	fast	ADJ
brj-22874	47	11	r	r	NOUN
brj-22874	47	12	-	-	PUNCT
brj-22874	47	13	cnn	cnn	NOUN
brj-22874	47	14	algorithm	algorithm	NOUN
brj-22874	47	15	and	and	CCONJ
brj-22874	47	16	proposed	propose	VERB
brj-22874	47	17	a	a	DET
brj-22874	47	18	surface	surface	NOUN
brj-22874	47	19	defect	defect	NOUN
brj-22874	47	20	detection	detection	NOUN
brj-22874	47	21	algorithm	algorithm	NOUN
brj-22874	47	22	based	base	VERB
brj-22874	47	23	on	on	ADP
brj-22874	47	24	the	the	DET
brj-22874	47	25	improved	improve	VERB
brj-22874	47	26	faster	fast	ADV
brj-22874	47	27	r	r	NOUN
brj-22874	47	28	-	-	PUNCT
brj-22874	47	29	cnn	cnn	PROPN
brj-22874	47	30	.	.	PUNCT
brj-22874	48	1	hacıefendioğlu	hacıefendioğlu	PROPN
brj-22874	48	2	et	et	PROPN
brj-22874	48	3	al	al	PROPN
brj-22874	48	4	.	.	PROPN
brj-22874	49	1	(	(	PUNCT
brj-22874	49	2	2022	2022	NUM
brj-22874	49	3	)	)	PUNCT
brj-22874	49	4	used	use	VERB
brj-22874	49	5	the	the	DET
brj-22874	49	6	deep	deep	ADJ
brj-22874	49	7	convolutional	convolutional	ADJ
brj-22874	49	8	neural	neural	ADJ
brj-22874	49	9	network	network	NOUN
brj-22874	49	10	(	(	PUNCT
brj-22874	49	11	dcnn	dcnn	PROPN
brj-22874	49	12	)	)	PUNCT
brj-22874	49	13	model	model	NOUN
brj-22874	49	14	using	use	VERB
brj-22874	49	15	the	the	DET
brj-22874	49	16	k	k	NOUN
brj-22874	49	17	-	-	PUNCT
brj-22874	49	18	means	mean	VERB
brj-22874	49	19	clustering	cluster	VERB
brj-22874	49	20	algorithm	algorithm	NOUN
brj-22874	49	21	to	to	PART
brj-22874	49	22	further	far	ADV
brj-22874	49	23	improve	improve	VERB
brj-22874	49	24	the	the	DET
brj-22874	49	25	detection	detection	NOUN
brj-22874	49	26	results	result	NOUN
brj-22874	49	27	in	in	ADP
brj-22874	49	28	terms	term	NOUN
brj-22874	49	29	of	of	ADP
brj-22874	49	30	wood	wood	NOUN
brj-22874	49	31	defect	defect	NOUN
brj-22874	49	32	classification	classification	NOUN
brj-22874	49	33	accuracy	accuracy	NOUN
brj-22874	49	34	.	.	PUNCT
brj-22874	50	1	the	the	DET
brj-22874	50	2	above	above	ADJ
brj-22874	50	3	detection	detection	NOUN
brj-22874	50	4	model	model	NOUN
brj-22874	50	5	is	be	AUX
brj-22874	50	6	complex	complex	ADJ
brj-22874	50	7	,	,	PUNCT
brj-22874	50	8	which	which	PRON
brj-22874	50	9	may	may	AUX
brj-22874	50	10	reduce	reduce	VERB
brj-22874	50	11	the	the	DET
brj-22874	50	12	detection	detection	NOUN
brj-22874	50	13	efficiency	efficiency	NOUN
brj-22874	50	14	and	and	CCONJ
brj-22874	50	15	accuracy	accuracy	NOUN
brj-22874	50	16	in	in	ADP
brj-22874	50	17	complex	complex	ADJ
brj-22874	50	18	scenes	scene	NOUN
brj-22874	50	19	.	.	PUNCT
brj-22874	51	1	in	in	ADP
brj-22874	51	2	this	this	DET
brj-22874	51	3	paper	paper	NOUN
brj-22874	51	4	,	,	PUNCT
brj-22874	51	5	a	a	DET
brj-22874	51	6	surface	surface	NOUN
brj-22874	51	7	defect	defect	NOUN
brj-22874	51	8	detection	detection	NOUN
brj-22874	51	9	algorithm	algorithm	NOUN
brj-22874	51	10	for	for	ADP
brj-22874	51	11	wooden	wooden	ADJ
brj-22874	51	12	spoons	spoon	NOUN
brj-22874	51	13	based	base	VERB
brj-22874	51	14	on	on	ADP
brj-22874	51	15	yolov5	yolov5	NOUN
brj-22874	51	16	is	be	AUX
brj-22874	51	17	proposed	propose	VERB
brj-22874	51	18	by	by	ADP
brj-22874	51	19	using	use	VERB
brj-22874	51	20	deep	deep	ADJ
brj-22874	51	21	learning	learning	NOUN
brj-22874	51	22	.	.	PUNCT
brj-22874	52	1	the	the	DET
brj-22874	52	2	k	k	NOUN
brj-22874	52	3	-	-	PUNCT
brj-22874	52	4	means	mean	VERB
brj-22874	52	5	+	+	ADJ
brj-22874	52	6	+	+	NUM
brj-22874	52	7	algorithm	algorithm	NOUN
brj-22874	52	8	is	be	AUX
brj-22874	52	9	used	use	VERB
brj-22874	52	10	to	to	PART
brj-22874	52	11	cluster	cluster	VERB
brj-22874	52	12	the	the	DET
brj-22874	52	13	target	target	NOUN
brj-22874	52	14	samples	sample	NOUN
brj-22874	52	15	in	in	ADP
brj-22874	52	16	the	the	DET
brj-22874	52	17	data	datum	NOUN
brj-22874	52	18	set	set	VERB
brj-22874	52	19	to	to	PART
brj-22874	52	20	obtain	obtain	VERB
brj-22874	52	21	anchor	anchor	NOUN
brj-22874	52	22	frames	frame	NOUN
brj-22874	52	23	that	that	PRON
brj-22874	52	24	are	be	AUX
brj-22874	52	25	more	more	ADV
brj-22874	52	26	in	in	ADP
brj-22874	52	27	line	line	NOUN
brj-22874	52	28	with	with	ADP
brj-22874	52	29	different	different	ADJ
brj-22874	52	30	target	target	NOUN
brj-22874	52	31	scales	scale	NOUN
brj-22874	52	32	and	and	CCONJ
brj-22874	52	33	improve	improve	VERB
brj-22874	52	34	the	the	DET
brj-22874	52	35	accuracy	accuracy	NOUN
brj-22874	52	36	of	of	ADP
brj-22874	52	37	multi	multi	ADJ
brj-22874	52	38	-	-	ADJ
brj-22874	52	39	target	target	NOUN
brj-22874	52	40	positioning	positioning	NOUN
brj-22874	52	41	and	and	CCONJ
brj-22874	52	42	entity	entity	NOUN
brj-22874	52	43	segmentation	segmentation	NOUN
brj-22874	52	44	.	.	PUNCT
brj-22874	53	1	the	the	DET
brj-22874	53	2	coordinate	coordinate	ADJ
brj-22874	53	3	attention	attention	NOUN
brj-22874	53	4	module	module	NOUN
brj-22874	53	5	is	be	AUX
brj-22874	53	6	added	add	VERB
brj-22874	53	7	to	to	ADP
brj-22874	53	8	the	the	DET
brj-22874	53	9	backbone	backbone	NOUN
brj-22874	53	10	network	network	NOUN
brj-22874	53	11	of	of	ADP
brj-22874	53	12	the	the	DET
brj-22874	53	13	yolov5	yolov5	NOUN
brj-22874	53	14	peer	peer	NOUN
brj-22874	53	15	-	-	PUNCT
brj-22874	53	16	reviewed	review	VERB
brj-22874	53	17	article	article	NOUN
brj-22874	53	18	bioresources.com	bioresources.com	X
brj-22874	53	19	tian	tian	PROPN
brj-22874	53	20	et	et	PROPN
brj-22874	53	21	al	al	PROPN
brj-22874	53	22	.	.	PROPN
brj-22874	54	1	(	(	PUNCT
brj-22874	54	2	2023	2023	NUM
brj-22874	54	3	)	)	PUNCT
brj-22874	54	4	.	.	PUNCT
brj-22874	55	1	“	"	PUNCT
brj-22874	55	2	defect	defect	VERB
brj-22874	55	3	detection	detection	NOUN
brj-22874	55	4	with	with	ADP
brj-22874	55	5	yolov5	yolov5	NOUN
brj-22874	55	6	,	,	PUNCT
brj-22874	55	7	”	"	PUNCT
brj-22874	55	8	bioresources	bioresource	NOUN
brj-22874	55	9	18(4	18(4	NUM
brj-22874	55	10	)	)	PUNCT
brj-22874	55	11	,	,	PUNCT
brj-22874	55	12	7713	7713	NUM
brj-22874	55	13	-	-	SYM
brj-22874	55	14	7730	7730	NUM
brj-22874	55	15	.	.	PUNCT
brj-22874	56	1	7715	7715	NUM
brj-22874	56	2	network	network	NOUN
brj-22874	56	3	model	model	NOUN
brj-22874	56	4	to	to	PART
brj-22874	56	5	improve	improve	VERB
brj-22874	56	6	the	the	DET
brj-22874	56	7	feature	feature	NOUN
brj-22874	56	8	extraction	extraction	NOUN
brj-22874	56	9	ability	ability	NOUN
brj-22874	56	10	of	of	ADP
brj-22874	56	11	the	the	DET
brj-22874	56	12	model	model	NOUN
brj-22874	56	13	.	.	PUNCT
brj-22874	57	1	a	a	DET
brj-22874	57	2	new	new	ADJ
brj-22874	57	3	sppnet	sppnet	NOUN
brj-22874	57	4	module	module	NOUN
brj-22874	57	5	is	be	AUX
brj-22874	57	6	added	add	VERB
brj-22874	57	7	to	to	ADP
brj-22874	57	8	the	the	DET
brj-22874	57	9	backbone	backbone	NOUN
brj-22874	57	10	network	network	NOUN
brj-22874	57	11	to	to	PART
brj-22874	57	12	increase	increase	VERB
brj-22874	57	13	the	the	DET
brj-22874	57	14	receptive	receptive	ADJ
brj-22874	57	15	field	field	NOUN
brj-22874	57	16	to	to	PART
brj-22874	57	17	extract	extract	VERB
brj-22874	57	18	important	important	ADJ
brj-22874	57	19	features	feature	NOUN
brj-22874	57	20	to	to	PART
brj-22874	57	21	improve	improve	VERB
brj-22874	57	22	the	the	DET
brj-22874	57	23	detection	detection	NOUN
brj-22874	57	24	accuracy	accuracy	NOUN
brj-22874	57	25	of	of	ADP
brj-22874	57	26	small	small	ADJ
brj-22874	57	27	targets	target	NOUN
brj-22874	57	28	.	.	PUNCT
brj-22874	58	1	the	the	DET
brj-22874	58	2	improved	improved	ADJ
brj-22874	58	3	network	network	NOUN
brj-22874	58	4	can	can	AUX
brj-22874	58	5	better	well	ADV
brj-22874	58	6	improve	improve	VERB
brj-22874	58	7	the	the	DET
brj-22874	58	8	recognition	recognition	NOUN
brj-22874	58	9	accuracy	accuracy	NOUN
brj-22874	58	10	of	of	ADP
brj-22874	58	11	surface	surface	NOUN
brj-22874	58	12	defects	defect	NOUN
brj-22874	58	13	of	of	ADP
brj-22874	58	14	wooden	wooden	ADJ
brj-22874	58	15	spoons	spoon	NOUN
brj-22874	58	16	and	and	CCONJ
brj-22874	58	17	then	then	ADV
brj-22874	58	18	improve	improve	VERB
brj-22874	58	19	the	the	DET
brj-22874	58	20	effective	effective	ADJ
brj-22874	58	21	utilization	utilization	NOUN
brj-22874	58	22	rate	rate	NOUN
brj-22874	58	23	of	of	ADP
brj-22874	58	24	wooden	wooden	ADJ
brj-22874	58	25	spoons	spoon	NOUN
brj-22874	58	26	.	.	PUNCT
brj-22874	59	1	experimental	experimental	ADJ
brj-22874	59	2	materials	material	NOUN
brj-22874	59	3	the	the	DET
brj-22874	59	4	data	datum	NOUN
brj-22874	59	5	set	set	VERB
brj-22874	59	6	of	of	ADP
brj-22874	59	7	wooden	wooden	ADJ
brj-22874	59	8	spoons	spoon	NOUN
brj-22874	59	9	was	be	AUX
brj-22874	59	10	obtained	obtain	VERB
brj-22874	59	11	from	from	ADP
brj-22874	59	12	image	image	NOUN
brj-22874	59	13	acquisition	acquisition	NOUN
brj-22874	59	14	and	and	CCONJ
brj-22874	59	15	data	datum	NOUN
brj-22874	59	16	enhancement	enhancement	NOUN
brj-22874	59	17	.	.	PUNCT
brj-22874	60	1	the	the	DET
brj-22874	60	2	image	image	NOUN
brj-22874	60	3	acquisition	acquisition	NOUN
brj-22874	60	4	imports	import	VERB
brj-22874	60	5	the	the	DET
brj-22874	60	6	image	image	NOUN
brj-22874	60	7	of	of	ADP
brj-22874	60	8	the	the	DET
brj-22874	60	9	wooden	wooden	ADJ
brj-22874	60	10	spoon	spoon	NOUN
brj-22874	60	11	through	through	ADP
brj-22874	60	12	the	the	DET
brj-22874	60	13	experimental	experimental	ADJ
brj-22874	60	14	platform	platform	NOUN
brj-22874	60	15	.	.	PUNCT
brj-22874	61	1	a	a	DET
brj-22874	61	2	representative	representative	ADJ
brj-22874	61	3	surface	surface	NOUN
brj-22874	61	4	defect	defect	NOUN
brj-22874	61	5	image	image	NOUN
brj-22874	61	6	is	be	AUX
brj-22874	61	7	shown	show	VERB
brj-22874	61	8	in	in	ADP
brj-22874	61	9	fig	fig	NOUN
brj-22874	61	10	.	.	PUNCT
brj-22874	62	1	1	1	X
brj-22874	62	2	.	.	X
brj-22874	62	3	the	the	DET
brj-22874	62	4	back	back	ADJ
brj-22874	62	5	crack	crack	NOUN
brj-22874	62	6	is	be	AUX
brj-22874	62	7	caused	cause	VERB
brj-22874	62	8	by	by	ADP
brj-22874	62	9	the	the	DET
brj-22874	62	10	splitting	splitting	NOUN
brj-22874	62	11	of	of	ADP
brj-22874	62	12	the	the	DET
brj-22874	62	13	back	back	NOUN
brj-22874	62	14	of	of	ADP
brj-22874	62	15	the	the	DET
brj-22874	62	16	wooden	wooden	ADJ
brj-22874	62	17	spoon	spoon	NOUN
brj-22874	62	18	along	along	ADP
brj-22874	62	19	the	the	DET
brj-22874	62	20	direction	direction	NOUN
brj-22874	62	21	of	of	ADP
brj-22874	62	22	the	the	DET
brj-22874	62	23	wood	wood	NOUN
brj-22874	62	24	texture	texture	NOUN
brj-22874	62	25	during	during	ADP
brj-22874	62	26	the	the	DET
brj-22874	62	27	processing	processing	NOUN
brj-22874	62	28	of	of	ADP
brj-22874	62	29	the	the	DET
brj-22874	62	30	wooden	wooden	ADJ
brj-22874	62	31	spoon	spoon	NOUN
brj-22874	62	32	,	,	PUNCT
brj-22874	62	33	such	such	ADJ
brj-22874	62	34	as	as	ADP
brj-22874	62	35	the	the	DET
brj-22874	62	36	(	(	PUNCT
brj-22874	62	37	a	a	PRON
brj-22874	62	38	)	)	PUNCT
brj-22874	62	39	red	red	PROPN
brj-22874	62	40	box	box	PROPN
brj-22874	62	41	mark	mark	PROPN
brj-22874	62	42	position	position	NOUN
brj-22874	62	43	in	in	ADP
brj-22874	62	44	fig	fig	NOUN
brj-22874	62	45	.	.	PUNCT
brj-22874	63	1	1	1	X
brj-22874	63	2	.	.	X
brj-22874	63	3	black	black	ADJ
brj-22874	63	4	knots	knot	NOUN
brj-22874	63	5	are	be	AUX
brj-22874	63	6	naturally	naturally	ADV
brj-22874	63	7	formed	form	VERB
brj-22874	63	8	during	during	ADP
brj-22874	63	9	the	the	DET
brj-22874	63	10	growth	growth	NOUN
brj-22874	63	11	of	of	ADP
brj-22874	63	12	trees	tree	NOUN
brj-22874	63	13	.	.	PUNCT
brj-22874	64	1	the	the	DET
brj-22874	64	2	wood	wood	NOUN
brj-22874	64	3	defect	defect	NOUN
brj-22874	64	4	is	be	AUX
brj-22874	64	5	obvious	obvious	ADJ
brj-22874	64	6	.	.	PUNCT
brj-22874	65	1	the	the	DET
brj-22874	65	2	color	color	NOUN
brj-22874	65	3	is	be	AUX
brj-22874	65	4	deep	deep	ADJ
brj-22874	65	5	,	,	PUNCT
brj-22874	65	6	and	and	CCONJ
brj-22874	65	7	it	it	PRON
brj-22874	65	8	is	be	AUX
brj-22874	65	9	patchy	patchy	ADJ
brj-22874	65	10	,	,	PUNCT
brj-22874	65	11	such	such	ADJ
brj-22874	65	12	as	as	ADP
brj-22874	65	13	the	the	DET
brj-22874	65	14	(	(	PUNCT
brj-22874	65	15	b	b	NOUN
brj-22874	65	16	)	)	PUNCT
brj-22874	65	17	red	red	PROPN
brj-22874	65	18	box	box	PROPN
brj-22874	65	19	mark	mark	PROPN
brj-22874	65	20	in	in	ADP
brj-22874	65	21	fig	fig	NOUN
brj-22874	65	22	.	.	PUNCT
brj-22874	66	1	1	1	NUM
brj-22874	66	2	;	;	PUNCT
brj-22874	66	3	mineral	mineral	NOUN
brj-22874	66	4	lines	line	NOUN
brj-22874	66	5	are	be	AUX
brj-22874	66	6	formed	form	VERB
brj-22874	66	7	when	when	SCONJ
brj-22874	66	8	trees	tree	NOUN
brj-22874	66	9	absorb	absorb	VERB
brj-22874	66	10	and	and	CCONJ
brj-22874	66	11	deposit	deposit	NOUN
brj-22874	66	12	minerals	mineral	NOUN
brj-22874	66	13	such	such	ADJ
brj-22874	66	14	as	as	ADP
brj-22874	66	15	carbonates	carbonate	NOUN
brj-22874	66	16	from	from	ADP
brj-22874	66	17	the	the	DET
brj-22874	66	18	soil	soil	NOUN
brj-22874	66	19	,	,	PUNCT
brj-22874	66	20	and	and	CCONJ
brj-22874	66	21	the	the	DET
brj-22874	66	22	defective	defective	ADJ
brj-22874	66	23	parts	part	NOUN
brj-22874	66	24	are	be	AUX
brj-22874	66	25	dark	dark	ADJ
brj-22874	66	26	strips	strip	NOUN
brj-22874	66	27	,	,	PUNCT
brj-22874	66	28	such	such	ADJ
brj-22874	66	29	as	as	ADP
brj-22874	66	30	the	the	DET
brj-22874	66	31	(	(	PUNCT
brj-22874	66	32	c	c	NOUN
brj-22874	66	33	)	)	PUNCT
brj-22874	66	34	red	red	PROPN
brj-22874	66	35	box	box	PROPN
brj-22874	66	36	mark	mark	PROPN
brj-22874	66	37	in	in	ADP
brj-22874	66	38	fig	fig	NOUN
brj-22874	66	39	.	.	PUNCT
brj-22874	67	1	1	1	X
brj-22874	67	2	.	.	X
brj-22874	67	3	defects	defect	NOUN
brj-22874	67	4	caused	cause	VERB
brj-22874	67	5	by	by	ADP
brj-22874	67	6	pollution	pollution	NOUN
brj-22874	67	7	include	include	VERB
brj-22874	67	8	oil	oil	NOUN
brj-22874	67	9	pollution	pollution	NOUN
brj-22874	67	10	or	or	CCONJ
brj-22874	67	11	an	an	DET
brj-22874	67	12	unclean	unclean	ADJ
brj-22874	67	13	area	area	NOUN
brj-22874	67	14	on	on	ADP
brj-22874	67	15	the	the	DET
brj-22874	67	16	production	production	NOUN
brj-22874	67	17	line	line	NOUN
brj-22874	67	18	during	during	ADP
brj-22874	67	19	the	the	DET
brj-22874	67	20	production	production	NOUN
brj-22874	67	21	and	and	CCONJ
brj-22874	67	22	processing	processing	NOUN
brj-22874	67	23	of	of	ADP
brj-22874	67	24	the	the	DET
brj-22874	67	25	wooden	wooden	ADJ
brj-22874	67	26	spoon	spoon	NOUN
brj-22874	67	27	.	.	PUNCT
brj-22874	68	1	defects	defect	NOUN
brj-22874	68	2	can	can	AUX
brj-22874	68	3	also	also	ADV
brj-22874	68	4	be	be	AUX
brj-22874	68	5	caused	cause	VERB
brj-22874	68	6	by	by	ADP
brj-22874	68	7	mildew	mildew	NOUN
brj-22874	68	8	which	which	PRON
brj-22874	68	9	shows	show	VERB
brj-22874	68	10	as	as	ADP
brj-22874	68	11	a	a	DET
brj-22874	68	12	black	black	ADJ
brj-22874	68	13	area	area	NOUN
brj-22874	68	14	on	on	ADP
brj-22874	68	15	the	the	DET
brj-22874	68	16	surface	surface	NOUN
brj-22874	68	17	of	of	ADP
brj-22874	68	18	the	the	DET
brj-22874	68	19	wooden	wooden	ADJ
brj-22874	68	20	spoon	spoon	NOUN
brj-22874	68	21	.	.	PUNCT
brj-22874	69	1	the	the	DET
brj-22874	69	2	defect	defect	NOUN
brj-22874	69	3	site	site	NOUN
brj-22874	69	4	mainly	mainly	ADV
brj-22874	69	5	presents	present	VERB
brj-22874	69	6	a	a	DET
brj-22874	69	7	dark	dark	ADJ
brj-22874	69	8	black	black	ADJ
brj-22874	69	9	oil	oil	NOUN
brj-22874	69	10	stain	stain	NOUN
brj-22874	69	11	state	state	NOUN
brj-22874	69	12	or	or	CCONJ
brj-22874	69	13	mildew	mildew	NOUN
brj-22874	69	14	state	state	NOUN
brj-22874	69	15	,	,	PUNCT
brj-22874	69	16	and	and	CCONJ
brj-22874	69	17	the	the	DET
brj-22874	69	18	shape	shape	NOUN
brj-22874	69	19	is	be	AUX
brj-22874	69	20	not	not	PART
brj-22874	69	21	fixed	fix	VERB
brj-22874	69	22	,	,	PUNCT
brj-22874	69	23	such	such	ADJ
brj-22874	69	24	as	as	ADP
brj-22874	69	25	the	the	DET
brj-22874	69	26	(	(	PUNCT
brj-22874	69	27	d	d	PROPN
brj-22874	69	28	)	)	PUNCT
brj-22874	69	29	red	red	PROPN
brj-22874	69	30	box	box	PROPN
brj-22874	69	31	mark	mark	PROPN
brj-22874	69	32	in	in	ADP
brj-22874	69	33	fig	fig	NOUN
brj-22874	69	34	.	.	PUNCT
brj-22874	70	1	1	1	X
brj-22874	70	2	.	.	X
brj-22874	70	3	the	the	DET
brj-22874	70	4	training	training	NOUN
brj-22874	70	5	of	of	ADP
brj-22874	70	6	convolutional	convolutional	ADJ
brj-22874	70	7	neural	neural	ADJ
brj-22874	70	8	networks	network	NOUN
brj-22874	70	9	requires	require	VERB
brj-22874	70	10	a	a	DET
brj-22874	70	11	large	large	ADJ
brj-22874	70	12	number	number	NOUN
brj-22874	70	13	of	of	ADP
brj-22874	70	14	samples	sample	NOUN
brj-22874	70	15	.	.	PUNCT
brj-22874	71	1	through	through	ADP
brj-22874	71	2	the	the	DET
brj-22874	71	3	learning	learning	NOUN
brj-22874	71	4	of	of	ADP
brj-22874	71	5	many	many	ADJ
brj-22874	71	6	samples	sample	NOUN
brj-22874	71	7	,	,	PUNCT
brj-22874	71	8	deep	deep	ADJ
brj-22874	71	9	and	and	CCONJ
brj-22874	71	10	specific	specific	ADJ
brj-22874	71	11	features	feature	NOUN
brj-22874	71	12	can	can	AUX
brj-22874	71	13	be	be	AUX
brj-22874	71	14	obtained	obtain	VERB
brj-22874	71	15	to	to	PART
brj-22874	71	16	improve	improve	VERB
brj-22874	71	17	the	the	DET
brj-22874	71	18	accuracy	accuracy	NOUN
brj-22874	71	19	of	of	ADP
brj-22874	71	20	defect	defect	ADJ
brj-22874	71	21	detection	detection	NOUN
brj-22874	71	22	(	(	PUNCT
brj-22874	71	23	hou	hou	NOUN
brj-22874	71	24	et	et	PROPN
brj-22874	71	25	al	al	PROPN
brj-22874	71	26	.	.	PROPN
brj-22874	71	27	2021	2021	NUM
brj-22874	71	28	)	)	PUNCT
brj-22874	71	29	.	.	PUNCT
brj-22874	72	1	to	to	PART
brj-22874	72	2	obtain	obtain	VERB
brj-22874	72	3	a	a	DET
brj-22874	72	4	large	large	ADJ
brj-22874	72	5	number	number	NOUN
brj-22874	72	6	of	of	ADP
brj-22874	72	7	sample	sample	NOUN
brj-22874	72	8	sets	set	NOUN
brj-22874	72	9	and	and	CCONJ
brj-22874	72	10	prevent	prevent	VERB
brj-22874	72	11	over	over	ADP
brj-22874	72	12	-	-	PUNCT
brj-22874	72	13	fitting	fit	VERB
brj-22874	72	14	during	during	ADP
brj-22874	72	15	network	network	NOUN
brj-22874	72	16	training	training	NOUN
brj-22874	72	17	,	,	PUNCT
brj-22874	72	18	the	the	DET
brj-22874	72	19	collected	collect	VERB
brj-22874	72	20	wooden	wooden	ADJ
brj-22874	72	21	spoon	spoon	NOUN
brj-22874	72	22	images	image	NOUN
brj-22874	72	23	are	be	AUX
brj-22874	72	24	enhanced	enhance	VERB
brj-22874	72	25	to	to	PART
brj-22874	72	26	improve	improve	VERB
brj-22874	72	27	the	the	DET
brj-22874	72	28	robustness	robustness	NOUN
brj-22874	72	29	of	of	ADP
brj-22874	72	30	the	the	DET
brj-22874	72	31	convolutional	convolutional	ADJ
brj-22874	72	32	neural	neural	ADJ
brj-22874	72	33	network	network	NOUN
brj-22874	72	34	.	.	PUNCT
brj-22874	73	1	data	datum	NOUN
brj-22874	73	2	enhancement	enhancement	NOUN
brj-22874	73	3	includes	include	VERB
brj-22874	73	4	rotation	rotation	NOUN
brj-22874	73	5	,	,	PUNCT
brj-22874	73	6	translation	translation	NOUN
brj-22874	73	7	,	,	PUNCT
brj-22874	73	8	cropping	cropping	NOUN
brj-22874	73	9	,	,	PUNCT
brj-22874	73	10	mirroring	mirroring	NOUN
brj-22874	73	11	,	,	PUNCT
brj-22874	73	12	and	and	CCONJ
brj-22874	73	13	brightness	brightness	NOUN
brj-22874	73	14	and	and	CCONJ
brj-22874	73	15	contrast	contrast	VERB
brj-22874	73	16	adjustment	adjustment	NOUN
brj-22874	73	17	of	of	ADP
brj-22874	73	18	the	the	DET
brj-22874	73	19	original	original	ADJ
brj-22874	73	20	image	image	NOUN
brj-22874	73	21	,	,	PUNCT
brj-22874	73	22	without	without	ADP
brj-22874	73	23	changing	change	VERB
brj-22874	73	24	the	the	DET
brj-22874	73	25	pixel	pixel	PROPN
brj-22874	73	26	value	value	NOUN
brj-22874	73	27	.	.	PUNCT
brj-22874	74	1	it	it	PRON
brj-22874	74	2	only	only	ADV
brj-22874	74	3	changes	change	VERB
brj-22874	74	4	the	the	DET
brj-22874	74	5	position	position	NOUN
brj-22874	74	6	of	of	ADP
brj-22874	74	7	the	the	DET
brj-22874	74	8	pixel	pixel	NOUN
brj-22874	74	9	,	,	PUNCT
brj-22874	74	10	so	so	SCONJ
brj-22874	74	11	that	that	SCONJ
brj-22874	74	12	the	the	DET
brj-22874	74	13	network	network	NOUN
brj-22874	74	14	model	model	NOUN
brj-22874	74	15	can	can	AUX
brj-22874	74	16	learn	learn	VERB
brj-22874	74	17	more	more	ADJ
brj-22874	74	18	image	image	NOUN
brj-22874	74	19	invariant	invariant	ADJ
brj-22874	74	20	features	feature	NOUN
brj-22874	74	21	and	and	CCONJ
brj-22874	74	22	avoid	avoid	VERB
brj-22874	74	23	overfitting	overfitte	VERB
brj-22874	74	24	(	(	PUNCT
brj-22874	74	25	li	li	PROPN
brj-22874	74	26	et	et	PROPN
brj-22874	74	27	al	al	PROPN
brj-22874	74	28	.	.	PROPN
brj-22874	74	29	2022	2022	NUM
brj-22874	74	30	)	)	PUNCT
brj-22874	74	31	.	.	PUNCT
brj-22874	75	1	a	a	DET
brj-22874	75	2	total	total	NOUN
brj-22874	75	3	of	of	ADP
brj-22874	75	4	3,178	3,178	NUM
brj-22874	75	5	images	image	NOUN
brj-22874	75	6	were	be	AUX
brj-22874	75	7	enhanced	enhance	VERB
brj-22874	75	8	from	from	ADP
brj-22874	75	9	the	the	DET
brj-22874	75	10	wooden	wooden	ADJ
brj-22874	75	11	spoon	spoon	NOUN
brj-22874	75	12	data	datum	NOUN
brj-22874	75	13	set	set	VERB
brj-22874	75	14	.	.	PUNCT
brj-22874	76	1	the	the	DET
brj-22874	76	2	labelimg	labelimg	ADJ
brj-22874	76	3	tool	tool	NOUN
brj-22874	76	4	was	be	AUX
brj-22874	76	5	used	use	VERB
brj-22874	76	6	to	to	PART
brj-22874	76	7	label	label	VERB
brj-22874	76	8	the	the	DET
brj-22874	76	9	wooden	wooden	ADJ
brj-22874	76	10	spoon	spoon	NOUN
brj-22874	76	11	.	.	PUNCT
brj-22874	77	1	finally	finally	ADV
brj-22874	77	2	,	,	PUNCT
brj-22874	77	3	the	the	DET
brj-22874	77	4	data	datum	NOUN
brj-22874	77	5	set	set	VERB
brj-22874	77	6	was	be	AUX
brj-22874	77	7	divided	divide	VERB
brj-22874	77	8	according	accord	VERB
brj-22874	77	9	to	to	ADP
brj-22874	77	10	the	the	DET
brj-22874	77	11	ratio	ratio	NOUN
brj-22874	77	12	of	of	ADP
brj-22874	77	13	training	training	NOUN
brj-22874	77	14	set	set	NOUN
brj-22874	77	15	:	:	PUNCT
brj-22874	77	16	verification	verification	NOUN
brj-22874	77	17	set	set	NOUN
brj-22874	77	18	:	:	PUNCT
brj-22874	77	19	test	test	NOUN
brj-22874	77	20	set	set	NOUN
brj-22874	77	21	=	=	NOUN
brj-22874	77	22	8	8	NUM
brj-22874	77	23	:	:	SYM
brj-22874	77	24	1	1	NUM
brj-22874	77	25	:	:	SYM
brj-22874	77	26	1	1	NUM
brj-22874	77	27	,	,	PUNCT
brj-22874	77	28	2542	2542	NUM
brj-22874	77	29	images	image	NOUN
brj-22874	77	30	were	be	AUX
brj-22874	77	31	used	use	VERB
brj-22874	77	32	as	as	ADP
brj-22874	77	33	the	the	DET
brj-22874	77	34	training	training	NOUN
brj-22874	77	35	set	set	NOUN
brj-22874	77	36	,	,	PUNCT
brj-22874	77	37	318	318	NUM
brj-22874	77	38	images	image	NOUN
brj-22874	77	39	were	be	AUX
brj-22874	77	40	used	use	VERB
brj-22874	77	41	as	as	ADP
brj-22874	77	42	the	the	DET
brj-22874	77	43	test	test	NOUN
brj-22874	77	44	set	set	NOUN
brj-22874	77	45	,	,	PUNCT
brj-22874	77	46	and	and	CCONJ
brj-22874	77	47	the	the	DET
brj-22874	77	48	remaining	remain	VERB
brj-22874	77	49	318	318	NUM
brj-22874	77	50	images	image	NOUN
brj-22874	77	51	were	be	AUX
brj-22874	77	52	used	use	VERB
brj-22874	77	53	as	as	ADP
brj-22874	77	54	the	the	DET
brj-22874	77	55	validation	validation	NOUN
brj-22874	77	56	set	set	NOUN
brj-22874	77	57	.	.	PUNCT
brj-22874	78	1	(	(	PUNCT
brj-22874	78	2	a	a	X
brj-22874	78	3	)	)	PUNCT
brj-22874	78	4	beilie	beilie	NOUN
brj-22874	78	5	defect	defect	NOUN
brj-22874	78	6	(	(	PUNCT
brj-22874	78	7	back	back	NOUN
brj-22874	78	8	crack	crack	NOUN
brj-22874	78	9	)	)	PUNCT
brj-22874	78	10	(	(	PUNCT
brj-22874	78	11	b	b	X
brj-22874	78	12	)	)	PUNCT
brj-22874	78	13	heijiezi	heijiezi	NOUN
brj-22874	78	14	defect	defect	NOUN
brj-22874	78	15	(	(	PUNCT
brj-22874	78	16	black	black	ADJ
brj-22874	78	17	knot	knot	NOUN
brj-22874	78	18	)	)	PUNCT
brj-22874	78	19	peer	peer	NOUN
brj-22874	78	20	-	-	PUNCT
brj-22874	78	21	reviewed	review	VERB
brj-22874	78	22	article	article	NOUN
brj-22874	78	23	bioresources.com	bioresources.com	X
brj-22874	78	24	tian	tian	PROPN
brj-22874	78	25	et	et	PROPN
brj-22874	78	26	al	al	PROPN
brj-22874	78	27	.	.	PROPN
brj-22874	79	1	(	(	PUNCT
brj-22874	79	2	2023	2023	NUM
brj-22874	79	3	)	)	PUNCT
brj-22874	79	4	.	.	PUNCT
brj-22874	80	1	“	"	PUNCT
brj-22874	80	2	defect	defect	VERB
brj-22874	80	3	detection	detection	NOUN
brj-22874	80	4	with	with	ADP
brj-22874	80	5	yolov5	yolov5	NOUN
brj-22874	80	6	,	,	PUNCT
brj-22874	80	7	”	"	PUNCT
brj-22874	80	8	bioresources	bioresource	NOUN
brj-22874	80	9	18(4	18(4	NUM
brj-22874	80	10	)	)	PUNCT
brj-22874	80	11	,	,	PUNCT
brj-22874	80	12	7713	7713	NUM
brj-22874	80	13	-	-	SYM
brj-22874	80	14	7730	7730	NUM
brj-22874	80	15	.	.	PUNCT
brj-22874	81	1	7716	7716	NUM
brj-22874	81	2	(	(	PUNCT
brj-22874	81	3	c	c	X
brj-22874	81	4	)	)	PUNCT
brj-22874	81	5	kuangwuxian	kuangwuxian	ADJ
brj-22874	81	6	defect	defect	NOUN
brj-22874	81	7	(	(	PUNCT
brj-22874	81	8	d	d	NOUN
brj-22874	81	9	)	)	PUNCT
brj-22874	81	10	wuran	wuran	NOUN
brj-22874	81	11	defect	defect	NOUN
brj-22874	81	12	(	(	PUNCT
brj-22874	81	13	pollution	pollution	NOUN
brj-22874	81	14	)	)	PUNCT
brj-22874	81	15	(	(	PUNCT
brj-22874	81	16	mineral	mineral	NOUN
brj-22874	81	17	line	line	NOUN
brj-22874	81	18	)	)	PUNCT
brj-22874	81	19	fig	fig	NOUN
brj-22874	81	20	.	.	PUNCT
brj-22874	82	1	1	1	X
brj-22874	82	2	.	.	PUNCT
brj-22874	82	3	the	the	DET
brj-22874	82	4	collected	collect	VERB
brj-22874	82	5	wooden	wooden	ADJ
brj-22874	82	6	spoon	spoon	NOUN
brj-22874	82	7	defect	defect	NOUN
brj-22874	82	8	samples	sample	NOUN
brj-22874	82	9	experimental	experimental	ADJ
brj-22874	82	10	platform	platform	NOUN
brj-22874	82	11	the	the	DET
brj-22874	82	12	image	image	NOUN
brj-22874	82	13	was	be	AUX
brj-22874	82	14	sourced	source	VERB
brj-22874	82	15	from	from	ADP
brj-22874	82	16	the	the	DET
brj-22874	82	17	image	image	NOUN
brj-22874	82	18	acquisition	acquisition	NOUN
brj-22874	82	19	experimental	experimental	ADJ
brj-22874	82	20	platform	platform	NOUN
brj-22874	82	21	.	.	PUNCT
brj-22874	83	1	the	the	DET
brj-22874	83	2	acquisition	acquisition	NOUN
brj-22874	83	3	experimental	experimental	ADJ
brj-22874	83	4	platform	platform	NOUN
brj-22874	83	5	mainly	mainly	ADV
brj-22874	83	6	includes	include	VERB
brj-22874	83	7	an	an	DET
brj-22874	83	8	industrial	industrial	ADJ
brj-22874	83	9	camera	camera	NOUN
brj-22874	83	10	,	,	PUNCT
brj-22874	83	11	lens	len	NOUN
brj-22874	83	12	,	,	PUNCT
brj-22874	83	13	and	and	CCONJ
brj-22874	83	14	light	light	ADJ
brj-22874	83	15	source	source	NOUN
brj-22874	83	16	.	.	PUNCT
brj-22874	84	1	considering	consider	VERB
brj-22874	84	2	that	that	SCONJ
brj-22874	84	3	the	the	DET
brj-22874	84	4	longest	long	ADJ
brj-22874	84	5	side	side	NOUN
brj-22874	84	6	of	of	ADP
brj-22874	84	7	the	the	DET
brj-22874	84	8	wooden	wooden	ADJ
brj-22874	84	9	spoon	spoon	NOUN
brj-22874	84	10	is	be	AUX
brj-22874	84	11	170	170	NUM
brj-22874	84	12	mm	mm	NOUN
brj-22874	84	13	,	,	PUNCT
brj-22874	84	14	the	the	DET
brj-22874	84	15	detection	detection	NOUN
brj-22874	84	16	accuracy	accuracy	NOUN
brj-22874	84	17	is	be	AUX
brj-22874	84	18	0.1	0.1	NUM
brj-22874	84	19	mm	mm	NOUN
brj-22874	84	20	,	,	PUNCT
brj-22874	84	21	the	the	DET
brj-22874	84	22	field	field	NOUN
brj-22874	84	23	of	of	ADP
brj-22874	84	24	view	view	NOUN
brj-22874	84	25	is	be	AUX
brj-22874	84	26	set	set	VERB
brj-22874	84	27	to	to	ADP
brj-22874	84	28	170	170	NUM
brj-22874	84	29	mm	mm	NOUN
brj-22874	84	30	*	*	PUNCT
brj-22874	84	31	170	170	NUM
brj-22874	84	32	mm	mm	NOUN
brj-22874	84	33	,	,	PUNCT
brj-22874	84	34	the	the	DET
brj-22874	84	35	target	target	NOUN
brj-22874	84	36	surface	surface	NOUN
brj-22874	84	37	size	size	NOUN
brj-22874	84	38	is	be	AUX
brj-22874	84	39	8.8	8.8	NUM
brj-22874	84	40	*	*	NUM
brj-22874	84	41	6.6	6.6	NUM
brj-22874	84	42	,	,	PUNCT
brj-22874	84	43	and	and	CCONJ
brj-22874	84	44	the	the	DET
brj-22874	84	45	working	work	VERB
brj-22874	84	46	distance	distance	NOUN
brj-22874	84	47	is	be	AUX
brj-22874	84	48	31.2	31.2	NUM
brj-22874	84	49	cm	cm	NOUN
brj-22874	84	50	.	.	PUNCT
brj-22874	85	1	thus	thus	ADV
brj-22874	85	2	,	,	PUNCT
brj-22874	85	3	a	a	DET
brj-22874	85	4	focal	focal	ADJ
brj-22874	85	5	length	length	NOUN
brj-22874	85	6	of	of	ADP
brj-22874	85	7	f	f	PROPN
brj-22874	85	8	=	=	SYM
brj-22874	85	9	1.6	1.6	NUM
brj-22874	85	10	mm	mm	NOUN
brj-22874	85	11	is	be	AUX
brj-22874	85	12	obtained	obtain	VERB
brj-22874	85	13	.	.	PUNCT
brj-22874	86	1	the	the	DET
brj-22874	86	2	hikvision	hikvision	NOUN
brj-22874	86	3	mvl	mvl	PROPN
brj-22874	86	4	-	-	PUNCT
brj-22874	86	5	hf1628m-6mpe	hf1628m-6mpe	PROPN
brj-22874	86	6	lens	lens	NOUN
brj-22874	86	7	was	be	AUX
brj-22874	86	8	selected	select	VERB
brj-22874	86	9	.	.	PUNCT
brj-22874	87	1	finally	finally	ADV
brj-22874	87	2	,	,	PUNCT
brj-22874	87	3	combined	combine	VERB
brj-22874	87	4	with	with	ADP
brj-22874	87	5	the	the	DET
brj-22874	87	6	above	above	ADJ
brj-22874	87	7	parameters	parameter	NOUN
brj-22874	87	8	,	,	PUNCT
brj-22874	87	9	the	the	DET
brj-22874	87	10	hikvision	hikvision	NOUN
brj-22874	87	11	mv	mv	PROPN
brj-22874	87	12	-	-	PUNCT
brj-22874	87	13	ca050	ca050	ADJ
brj-22874	87	14	-	-	PUNCT
brj-22874	87	15	gm	gm	PROPN
brj-22874	87	16	camera	camera	NOUN
brj-22874	87	17	was	be	AUX
brj-22874	87	18	selected	select	VERB
brj-22874	87	19	with	with	ADP
brj-22874	87	20	a	a	DET
brj-22874	87	21	resolution	resolution	NOUN
brj-22874	87	22	of	of	ADP
brj-22874	87	23	2448	2448	NUM
brj-22874	87	24	*	*	SYM
brj-22874	87	25	2048	2048	NUM
brj-22874	87	26	.	.	PUNCT
brj-22874	88	1	because	because	SCONJ
brj-22874	88	2	the	the	DET
brj-22874	88	3	defects	defect	NOUN
brj-22874	88	4	of	of	ADP
brj-22874	88	5	the	the	DET
brj-22874	88	6	wooden	wooden	ADJ
brj-22874	88	7	spoon	spoon	NOUN
brj-22874	88	8	mainly	mainly	ADV
brj-22874	88	9	exist	exist	VERB
brj-22874	88	10	on	on	ADP
brj-22874	88	11	the	the	DET
brj-22874	88	12	surface	surface	NOUN
brj-22874	88	13	,	,	PUNCT
brj-22874	88	14	the	the	DET
brj-22874	88	15	led	lead	VERB
brj-22874	88	16	front	front	ADJ
brj-22874	88	17	lighting	lighting	NOUN
brj-22874	88	18	source	source	NOUN
brj-22874	88	19	was	be	AUX
brj-22874	88	20	selected	select	VERB
brj-22874	88	21	.	.	PUNCT
brj-22874	89	1	the	the	DET
brj-22874	89	2	defects	defect	NOUN
brj-22874	89	3	on	on	ADP
brj-22874	89	4	the	the	DET
brj-22874	89	5	surface	surface	NOUN
brj-22874	89	6	of	of	ADP
brj-22874	89	7	the	the	DET
brj-22874	89	8	wooden	wooden	ADJ
brj-22874	89	9	spoon	spoon	NOUN
brj-22874	89	10	are	be	AUX
brj-22874	89	11	mainly	mainly	ADV
brj-22874	89	12	back	back	ADV
brj-22874	89	13	crack	crack	NOUN
brj-22874	89	14	,	,	PUNCT
brj-22874	89	15	black	black	ADJ
brj-22874	89	16	knot	knot	NOUN
brj-22874	89	17	,	,	PUNCT
brj-22874	89	18	mineral	mineral	NOUN
brj-22874	89	19	line	line	NOUN
brj-22874	89	20	,	,	PUNCT
brj-22874	89	21	and	and	CCONJ
brj-22874	89	22	pollution	pollution	NOUN
brj-22874	89	23	.	.	PUNCT
brj-22874	90	1	the	the	DET
brj-22874	90	2	defect	defect	NOUN
brj-22874	90	3	location	location	NOUN
brj-22874	90	4	was	be	AUX
brj-22874	90	5	not	not	PART
brj-22874	90	6	fixed	fix	VERB
brj-22874	90	7	,	,	PUNCT
brj-22874	90	8	the	the	DET
brj-22874	90	9	defect	defect	NOUN
brj-22874	90	10	size	size	NOUN
brj-22874	90	11	was	be	AUX
brj-22874	90	12	different	different	ADJ
brj-22874	90	13	,	,	PUNCT
brj-22874	90	14	and	and	CCONJ
brj-22874	90	15	the	the	DET
brj-22874	90	16	mineral	mineral	NOUN
brj-22874	90	17	line	line	NOUN
brj-22874	90	18	defect	defect	NOUN
brj-22874	90	19	was	be	AUX
brj-22874	90	20	similar	similar	ADJ
brj-22874	90	21	to	to	ADP
brj-22874	90	22	the	the	DET
brj-22874	90	23	wood	wood	NOUN
brj-22874	90	24	texture	texture	NOUN
brj-22874	90	25	shape	shape	NOUN
brj-22874	90	26	,	,	PUNCT
brj-22874	90	27	which	which	PRON
brj-22874	90	28	needs	need	VERB
brj-22874	90	29	to	to	PART
brj-22874	90	30	be	be	AUX
brj-22874	90	31	distinguished	distinguish	VERB
brj-22874	90	32	by	by	ADP
brj-22874	90	33	color	color	NOUN
brj-22874	90	34	.	.	PUNCT
brj-22874	91	1	in	in	ADP
brj-22874	91	2	order	order	NOUN
brj-22874	91	3	to	to	PART
brj-22874	91	4	reduce	reduce	VERB
brj-22874	91	5	the	the	DET
brj-22874	91	6	error	error	NOUN
brj-22874	91	7	,	,	PUNCT
brj-22874	91	8	the	the	DET
brj-22874	91	9	white	white	PROPN
brj-22874	91	10	led	lead	VERB
brj-22874	91	11	strip	strip	PROPN
brj-22874	91	12	light	light	ADJ
brj-22874	91	13	source	source	NOUN
brj-22874	91	14	was	be	AUX
brj-22874	91	15	finally	finally	ADV
brj-22874	91	16	selected	select	VERB
brj-22874	91	17	.	.	PUNCT
brj-22874	92	1	the	the	DET
brj-22874	92	2	specific	specific	ADJ
brj-22874	92	3	configuration	configuration	NOUN
brj-22874	92	4	of	of	ADP
brj-22874	92	5	the	the	DET
brj-22874	92	6	computer	computer	NOUN
brj-22874	92	7	used	use	VERB
brj-22874	92	8	was	be	AUX
brj-22874	92	9	xeon	xeon	PROPN
brj-22874	92	10	(	(	PUNCT
brj-22874	92	11	skylake	skylake	NOUN
brj-22874	92	12	,	,	PUNCT
brj-22874	92	13	ibrs	ibrs	ADJ
brj-22874	92	14	)	)	PUNCT
brj-22874	92	15	processor	processor	NOUN
brj-22874	92	16	,	,	PUNCT
brj-22874	92	17	tesla	tesla	PROPN
brj-22874	92	18	t4	t4	PROPN
brj-22874	92	19	display	display	PROPN
brj-22874	92	20	adapter	adapter	PROPN
brj-22874	92	21	,	,	PUNCT
brj-22874	92	22	and	and	CCONJ
brj-22874	92	23	16	16	NUM
brj-22874	92	24	gb	gb	NOUN
brj-22874	92	25	memory	memory	NOUN
brj-22874	92	26	.	.	PUNCT
brj-22874	93	1	the	the	DET
brj-22874	93	2	software	software	NOUN
brj-22874	93	3	environment	environment	NOUN
brj-22874	93	4	was	be	AUX
brj-22874	93	5	the	the	DET
brj-22874	93	6	ubantu18.04	ubantu18.04	ADJ
brj-22874	93	7	operating	operating	NOUN
brj-22874	93	8	system	system	NOUN
brj-22874	93	9	,	,	PUNCT
brj-22874	93	10	python3.8	python3.8	NOUN
brj-22874	93	11	programming	programming	NOUN
brj-22874	93	12	language	language	NOUN
brj-22874	93	13	,	,	PUNCT
brj-22874	93	14	and	and	CCONJ
brj-22874	93	15	the	the	DET
brj-22874	93	16	labelimg	labelimg	ADJ
brj-22874	93	17	annotation	annotation	NOUN
brj-22874	93	18	tool	tool	NOUN
brj-22874	93	19	was	be	AUX
brj-22874	93	20	used	use	VERB
brj-22874	93	21	to	to	PART
brj-22874	93	22	manually	manually	ADV
brj-22874	93	23	annotate	annotate	VERB
brj-22874	93	24	the	the	DET
brj-22874	93	25	defect	defect	NOUN
brj-22874	93	26	image	image	NOUN
brj-22874	93	27	.	.	PUNCT
brj-22874	94	1	the	the	DET
brj-22874	94	2	pytorch	pytorch	NOUN
brj-22874	94	3	deep	deep	ADJ
brj-22874	94	4	learning	learning	NOUN
brj-22874	94	5	framework	framework	NOUN
brj-22874	94	6	was	be	AUX
brj-22874	94	7	built	build	VERB
brj-22874	94	8	to	to	PART
brj-22874	94	9	train	train	VERB
brj-22874	94	10	and	and	CCONJ
brj-22874	94	11	test	test	VERB
brj-22874	94	12	the	the	DET
brj-22874	94	13	surface	surface	NOUN
brj-22874	94	14	defect	defect	NOUN
brj-22874	94	15	data	datum	NOUN
brj-22874	94	16	set	set	VERB
brj-22874	94	17	of	of	ADP
brj-22874	94	18	disposable	disposable	ADJ
brj-22874	94	19	wooden	wooden	ADJ
brj-22874	94	20	spoons	spoon	NOUN
brj-22874	94	21	.	.	PUNCT
brj-22874	95	1	the	the	DET
brj-22874	95	2	hyperparameter	hyperparameter	NOUN
brj-22874	95	3	settings	setting	VERB
brj-22874	95	4	in	in	ADP
brj-22874	95	5	the	the	DET
brj-22874	95	6	training	training	NOUN
brj-22874	95	7	phase	phase	NOUN
brj-22874	95	8	are	be	AUX
brj-22874	95	9	shown	show	VERB
brj-22874	95	10	in	in	ADP
brj-22874	95	11	table	table	NOUN
brj-22874	95	12	1	1	NUM
brj-22874	95	13	.	.	PUNCT
brj-22874	95	14	table	table	NOUN
brj-22874	95	15	1	1	NUM
brj-22874	95	16	.	.	PUNCT
brj-22874	96	1	hyperparameter	hyperparameter	NOUN
brj-22874	96	2	settings	setting	NOUN
brj-22874	96	3	parameter	parameter	VERB
brj-22874	96	4	numeric	numeric	ADJ
brj-22874	96	5	value	value	NOUN
brj-22874	96	6	initial	initial	ADJ
brj-22874	96	7	learning	learning	NOUN
brj-22874	96	8	rate	rate	NOUN
brj-22874	96	9	0.01	0.01	NUM
brj-22874	96	10	final	final	ADJ
brj-22874	96	11	decay	decay	NOUN
brj-22874	96	12	rate	rate	NOUN
brj-22874	96	13	5	5	NUM
brj-22874	96	14	×	×	NOUN
brj-22874	96	15	10	10	NUM
brj-22874	96	16	-	-	SYM
brj-22874	96	17	4	4	NUM
brj-22874	96	18	batch	batch	NOUN
brj-22874	96	19	8	8	NUM
brj-22874	96	20	number	number	NOUN
brj-22874	96	21	of	of	ADP
brj-22874	96	22	trainings	training	NOUN
brj-22874	96	23	150	150	NUM
brj-22874	96	24	momentum	momentum	NOUN
brj-22874	96	25	factor	factor	NOUN
brj-22874	96	26	0.937	0.937	NUM
brj-22874	97	1	yolov5	yolov5	NOUN
brj-22874	97	2	network	network	NOUN
brj-22874	97	3	structure	structure	NOUN
brj-22874	97	4	the	the	DET
brj-22874	97	5	yolov5	yolov5	NOUN
brj-22874	97	6	target	target	NOUN
brj-22874	97	7	detection	detection	NOUN
brj-22874	97	8	network	network	NOUN
brj-22874	97	9	consists	consist	VERB
brj-22874	97	10	of	of	ADP
brj-22874	97	11	four	four	NUM
brj-22874	97	12	versions	version	NOUN
brj-22874	97	13	,	,	PUNCT
brj-22874	97	14	namely	namely	ADV
brj-22874	97	15	yolov5s	yolov5s	PROPN
brj-22874	97	16	,	,	PUNCT
brj-22874	97	17	yolov5	yolov5	PROPN
brj-22874	97	18	m	m	PROPN
brj-22874	97	19	,	,	PUNCT
brj-22874	97	20	yolov5l	yolov5l	NOUN
brj-22874	97	21	,	,	PUNCT
brj-22874	97	22	and	and	CCONJ
brj-22874	97	23	yolov5x	yolov5x	PROPN
brj-22874	97	24	(	(	PUNCT
brj-22874	97	25	jocher	jocher	PROPN
brj-22874	97	26	et	et	PROPN
brj-22874	97	27	al	al	PROPN
brj-22874	97	28	.	.	PROPN
brj-22874	97	29	2020	2020	NUM
brj-22874	97	30	)	)	PUNCT
brj-22874	97	31	.	.	PUNCT
brj-22874	98	1	the	the	DET
brj-22874	98	2	weights	weight	NOUN
brj-22874	98	3	of	of	ADP
brj-22874	98	4	the	the	DET
brj-22874	98	5	four	four	NUM
brj-22874	98	6	models	model	NOUN
brj-22874	98	7	increase	increase	VERB
brj-22874	98	8	in	in	ADP
brj-22874	98	9	turn	turn	NOUN
brj-22874	98	10	,	,	PUNCT
brj-22874	98	11	and	and	CCONJ
brj-22874	98	12	the	the	DET
brj-22874	98	13	detection	detection	NOUN
brj-22874	98	14	accuracy	accuracy	NOUN
brj-22874	98	15	increases	increase	VERB
brj-22874	98	16	with	with	ADP
brj-22874	98	17	the	the	DET
brj-22874	98	18	weight	weight	NOUN
brj-22874	98	19	.	.	PUNCT
brj-22874	99	1	at	at	ADP
brj-22874	99	2	the	the	DET
brj-22874	99	3	same	same	ADJ
brj-22874	99	4	time	time	NOUN
brj-22874	99	5	,	,	PUNCT
brj-22874	99	6	the	the	DET
brj-22874	99	7	network	network	NOUN
brj-22874	99	8	training	training	NOUN
brj-22874	99	9	and	and	CCONJ
brj-22874	99	10	inference	inference	NOUN
brj-22874	99	11	time	time	NOUN
brj-22874	99	12	also	also	ADV
brj-22874	99	13	increase	increase	VERB
brj-22874	99	14	.	.	PUNCT
brj-22874	100	1	the	the	DET
brj-22874	100	2	detection	detection	NOUN
brj-22874	100	3	object	object	NOUN
brj-22874	100	4	of	of	ADP
brj-22874	100	5	this	this	DET
brj-22874	100	6	paper	paper	NOUN
brj-22874	100	7	is	be	AUX
brj-22874	100	8	small	small	ADJ
brj-22874	100	9	target	target	NOUN
brj-22874	100	10	defect	defect	NOUN
brj-22874	100	11	detection	detection	NOUN
brj-22874	100	12	,	,	PUNCT
brj-22874	100	13	and	and	CCONJ
brj-22874	100	14	the	the	DET
brj-22874	100	15	requirements	requirement	NOUN
brj-22874	100	16	for	for	ADP
brj-22874	100	17	detection	detection	NOUN
brj-22874	100	18	accuracy	accuracy	NOUN
brj-22874	100	19	are	be	AUX
brj-22874	100	20	peer	peer	NOUN
brj-22874	100	21	-	-	PUNCT
brj-22874	100	22	reviewed	review	VERB
brj-22874	100	23	article	article	NOUN
brj-22874	100	24	bioresources.com	bioresources.com	X
brj-22874	100	25	tian	tian	PROPN
brj-22874	100	26	et	et	PROPN
brj-22874	100	27	al	al	PROPN
brj-22874	100	28	.	.	PROPN
brj-22874	101	1	(	(	PUNCT
brj-22874	101	2	2023	2023	NUM
brj-22874	101	3	)	)	PUNCT
brj-22874	101	4	.	.	PUNCT
brj-22874	102	1	“	"	PUNCT
brj-22874	102	2	defect	defect	VERB
brj-22874	102	3	detection	detection	NOUN
brj-22874	102	4	with	with	ADP
brj-22874	102	5	yolov5	yolov5	NOUN
brj-22874	102	6	,	,	PUNCT
brj-22874	102	7	”	"	PUNCT
brj-22874	102	8	bioresources	bioresource	NOUN
brj-22874	102	9	18(4	18(4	NUM
brj-22874	102	10	)	)	PUNCT
brj-22874	102	11	,	,	PUNCT
brj-22874	102	12	7713	7713	NUM
brj-22874	102	13	-	-	SYM
brj-22874	102	14	7730	7730	NUM
brj-22874	102	15	.	.	PUNCT
brj-22874	103	1	7717	7717	NUM
brj-22874	103	2	relatively	relatively	ADV
brj-22874	103	3	high	high	ADJ
brj-22874	103	4	.	.	PUNCT
brj-22874	104	1	among	among	ADP
brj-22874	104	2	the	the	DET
brj-22874	104	3	four	four	NUM
brj-22874	104	4	models	model	NOUN
brj-22874	104	5	,	,	PUNCT
brj-22874	104	6	yolov5x	yolov5x	PROPN
brj-22874	104	7	has	have	VERB
brj-22874	104	8	the	the	DET
brj-22874	104	9	highest	high	ADJ
brj-22874	104	10	detection	detection	NOUN
brj-22874	104	11	accuracy	accuracy	NOUN
brj-22874	104	12	.	.	PUNCT
brj-22874	105	1	therefore	therefore	ADV
brj-22874	105	2	,	,	PUNCT
brj-22874	105	3	yolov5x	yolov5x	PROPN
brj-22874	105	4	was	be	AUX
brj-22874	105	5	selected	select	VERB
brj-22874	105	6	as	as	ADP
brj-22874	105	7	the	the	DET
brj-22874	105	8	detection	detection	NOUN
brj-22874	105	9	model	model	NOUN
brj-22874	105	10	.	.	PUNCT
brj-22874	106	1	the	the	DET
brj-22874	106	2	model	model	NOUN
brj-22874	106	3	structure	structure	NOUN
brj-22874	106	4	is	be	AUX
brj-22874	106	5	shown	show	VERB
brj-22874	106	6	in	in	ADP
brj-22874	106	7	fig	fig	NOUN
brj-22874	106	8	.	.	PUNCT
brj-22874	107	1	2	2	X
brj-22874	107	2	.	.	X
brj-22874	107	3	the	the	DET
brj-22874	107	4	network	network	NOUN
brj-22874	107	5	model	model	NOUN
brj-22874	107	6	includes	include	VERB
brj-22874	107	7	four	four	NUM
brj-22874	107	8	parts	part	NOUN
brj-22874	107	9	:	:	PUNCT
brj-22874	107	10	input	input	NOUN
brj-22874	107	11	,	,	PUNCT
brj-22874	107	12	backbone	backbone	NOUN
brj-22874	107	13	,	,	PUNCT
brj-22874	107	14	neck	neck	NOUN
brj-22874	107	15	,	,	PUNCT
brj-22874	107	16	and	and	CCONJ
brj-22874	107	17	head	head	NOUN
brj-22874	107	18	.	.	PUNCT
brj-22874	108	1	fig	fig	NOUN
brj-22874	108	2	.	.	PUNCT
brj-22874	109	1	2	2	X
brj-22874	109	2	.	.	X
brj-22874	109	3	yolov5	yolov5	NOUN
brj-22874	109	4	network	network	NOUN
brj-22874	109	5	structure	structure	NOUN
brj-22874	109	6	input	input	NOUN
brj-22874	109	7	mainly	mainly	ADV
brj-22874	109	8	includes	include	VERB
brj-22874	109	9	mosaic	mosaic	ADJ
brj-22874	109	10	data	datum	NOUN
brj-22874	109	11	enhancement	enhancement	NOUN
brj-22874	109	12	,	,	PUNCT
brj-22874	109	13	adaptive	adaptive	ADJ
brj-22874	109	14	anchor	anchor	NOUN
brj-22874	109	15	box	box	NOUN
brj-22874	109	16	calculation	calculation	NOUN
brj-22874	109	17	,	,	PUNCT
brj-22874	109	18	and	and	CCONJ
brj-22874	109	19	adaptive	adaptive	ADJ
brj-22874	109	20	image	image	NOUN
brj-22874	109	21	scaling	scaling	NOUN
brj-22874	109	22	.	.	PUNCT
brj-22874	110	1	the	the	DET
brj-22874	110	2	data	data	NOUN
brj-22874	110	3	enhancement	enhancement	NOUN
brj-22874	110	4	part	part	NOUN
brj-22874	110	5	can	can	AUX
brj-22874	110	6	enrich	enrich	VERB
brj-22874	110	7	the	the	DET
brj-22874	110	8	image	image	NOUN
brj-22874	110	9	background	background	NOUN
brj-22874	110	10	and	and	CCONJ
brj-22874	110	11	improve	improve	VERB
brj-22874	110	12	the	the	DET
brj-22874	110	13	generalization	generalization	NOUN
brj-22874	110	14	ability	ability	NOUN
brj-22874	110	15	of	of	ADP
brj-22874	110	16	the	the	DET
brj-22874	110	17	network	network	NOUN
brj-22874	110	18	by	by	ADP
brj-22874	110	19	randomly	randomly	ADV
brj-22874	110	20	scaling	scaling	NOUN
brj-22874	110	21	,	,	PUNCT
brj-22874	110	22	cropping	cropping	NOUN
brj-22874	110	23	,	,	PUNCT
brj-22874	110	24	and	and	CCONJ
brj-22874	110	25	arranging	arrange	VERB
brj-22874	110	26	the	the	DET
brj-22874	110	27	images	image	NOUN
brj-22874	110	28	and	and	CCONJ
brj-22874	110	29	then	then	ADV
brj-22874	110	30	splicing	splice	VERB
brj-22874	110	31	them	they	PRON
brj-22874	110	32	together	together	ADV
brj-22874	110	33	.	.	PUNCT
brj-22874	111	1	the	the	DET
brj-22874	111	2	adaptive	adaptive	ADJ
brj-22874	111	3	anchor	anchor	NOUN
brj-22874	111	4	frame	frame	NOUN
brj-22874	111	5	can	can	AUX
brj-22874	111	6	compare	compare	VERB
brj-22874	111	7	the	the	DET
brj-22874	111	8	initial	initial	ADJ
brj-22874	111	9	anchor	anchor	NOUN
brj-22874	111	10	frame	frame	NOUN
brj-22874	111	11	with	with	ADP
brj-22874	111	12	the	the	DET
brj-22874	111	13	real	real	ADJ
brj-22874	111	14	frame	frame	NOUN
brj-22874	111	15	through	through	ADP
brj-22874	111	16	training	training	NOUN
brj-22874	111	17	for	for	ADP
brj-22874	111	18	different	different	ADJ
brj-22874	111	19	data	datum	NOUN
brj-22874	111	20	sets	set	NOUN
brj-22874	111	21	,	,	PUNCT
brj-22874	111	22	reversely	reversely	ADV
brj-22874	111	23	updating	update	VERB
brj-22874	111	24	,	,	PUNCT
brj-22874	111	25	and	and	CCONJ
brj-22874	111	26	obtaining	obtain	VERB
brj-22874	111	27	the	the	DET
brj-22874	111	28	anchor	anchor	NOUN
brj-22874	111	29	frame	frame	NOUN
brj-22874	111	30	parameters	parameter	NOUN
brj-22874	111	31	that	that	PRON
brj-22874	111	32	are	be	AUX
brj-22874	111	33	more	more	ADV
brj-22874	111	34	suitable	suitable	ADJ
brj-22874	111	35	for	for	ADP
brj-22874	111	36	the	the	DET
brj-22874	111	37	sample	sample	NOUN
brj-22874	111	38	set	set	VERB
brj-22874	111	39	.	.	PUNCT
brj-22874	112	1	adaptive	adaptive	ADJ
brj-22874	112	2	image	image	NOUN
brj-22874	112	3	scaling	scaling	NOUN
brj-22874	112	4	scales	scale	VERB
brj-22874	112	5	the	the	DET
brj-22874	112	6	image	image	NOUN
brj-22874	112	7	of	of	ADP
brj-22874	112	8	the	the	DET
brj-22874	112	9	input	input	NOUN
brj-22874	112	10	network	network	NOUN
brj-22874	112	11	to	to	ADP
brj-22874	112	12	a	a	DET
brj-22874	112	13	unified	unify	VERB
brj-22874	112	14	standard	standard	ADJ
brj-22874	112	15	size	size	NOUN
brj-22874	112	16	and	and	CCONJ
brj-22874	112	17	sends	send	VERB
brj-22874	112	18	it	it	PRON
brj-22874	112	19	to	to	ADP
brj-22874	112	20	the	the	DET
brj-22874	112	21	network	network	NOUN
brj-22874	112	22	for	for	ADP
brj-22874	112	23	training	training	NOUN
brj-22874	112	24	.	.	PUNCT
brj-22874	113	1	the	the	DET
brj-22874	113	2	algorithm	algorithm	NOUN
brj-22874	113	3	can	can	AUX
brj-22874	113	4	reduce	reduce	VERB
brj-22874	113	5	the	the	DET
brj-22874	113	6	filling	filling	NOUN
brj-22874	113	7	amount	amount	NOUN
brj-22874	113	8	of	of	ADP
brj-22874	113	9	the	the	DET
brj-22874	113	10	scaled	scale	VERB
brj-22874	113	11	image	image	NOUN
brj-22874	113	12	to	to	PART
brj-22874	113	13	avoid	avoid	VERB
brj-22874	113	14	information	information	NOUN
brj-22874	113	15	redundancy	redundancy	NOUN
brj-22874	113	16	and	and	CCONJ
brj-22874	113	17	affect	affect	VERB
brj-22874	113	18	the	the	DET
brj-22874	113	19	inference	inference	NOUN
brj-22874	113	20	speed	speed	NOUN
brj-22874	113	21	.	.	PUNCT
brj-22874	114	1	the	the	DET
brj-22874	114	2	backbone	backbone	NOUN
brj-22874	114	3	part	part	NOUN
brj-22874	114	4	consists	consist	VERB
brj-22874	114	5	of	of	ADP
brj-22874	114	6	a	a	DET
brj-22874	114	7	series	series	NOUN
brj-22874	114	8	of	of	ADP
brj-22874	114	9	convolutional	convolutional	ADJ
brj-22874	114	10	neural	neural	ADJ
brj-22874	114	11	networks	network	NOUN
brj-22874	114	12	for	for	ADP
brj-22874	114	13	extracting	extract	VERB
brj-22874	114	14	image	image	NOUN
brj-22874	114	15	features	feature	NOUN
brj-22874	114	16	,	,	PUNCT
brj-22874	114	17	mainly	mainly	ADV
brj-22874	114	18	including	include	VERB
brj-22874	114	19	cbs	cbs	PROPN
brj-22874	114	20	,	,	PUNCT
brj-22874	114	21	c3	c3	PROPN
brj-22874	114	22	,	,	PUNCT
brj-22874	114	23	and	and	CCONJ
brj-22874	114	24	sppnet	sppnet	NOUN
brj-22874	114	25	.	.	PUNCT
brj-22874	115	1	focus	focus	PROPN
brj-22874	115	2	slice	slice	PROPN
brj-22874	115	3	operation	operation	NOUN
brj-22874	115	4	is	be	AUX
brj-22874	115	5	used	use	VERB
brj-22874	115	6	to	to	PART
brj-22874	115	7	convert	convert	VERB
brj-22874	115	8	the	the	DET
brj-22874	115	9	width	width	ADJ
brj-22874	115	10	and	and	CCONJ
brj-22874	115	11	height	height	NOUN
brj-22874	115	12	information	information	NOUN
brj-22874	115	13	to	to	ADP
brj-22874	115	14	the	the	DET
brj-22874	115	15	channel	channel	NOUN
brj-22874	115	16	dimension	dimension	NOUN
brj-22874	115	17	,	,	PUNCT
brj-22874	115	18	which	which	PRON
brj-22874	115	19	reduces	reduce	VERB
brj-22874	115	20	the	the	DET
brj-22874	115	21	information	information	NOUN
brj-22874	115	22	loss	loss	NOUN
brj-22874	115	23	caused	cause	VERB
brj-22874	115	24	by	by	ADP
brj-22874	115	25	feature	feature	NOUN
brj-22874	115	26	downsampling	downsampling	NOUN
brj-22874	115	27	.	.	PUNCT
brj-22874	116	1	the	the	DET
brj-22874	116	2	c3	c3	PROPN
brj-22874	116	3	module	module	NOUN
brj-22874	116	4	is	be	AUX
brj-22874	116	5	a	a	DET
brj-22874	116	6	replacement	replacement	NOUN
brj-22874	116	7	for	for	ADP
brj-22874	116	8	the	the	DET
brj-22874	116	9	csp	csp	PROPN
brj-22874	116	10	module	module	NOUN
brj-22874	116	11	,	,	PUNCT
brj-22874	116	12	which	which	PRON
brj-22874	116	13	can	can	AUX
brj-22874	116	14	effectively	effectively	ADV
brj-22874	116	15	reduce	reduce	VERB
brj-22874	116	16	the	the	DET
brj-22874	116	17	amount	amount	NOUN
brj-22874	116	18	of	of	ADP
brj-22874	116	19	calculation	calculation	NOUN
brj-22874	116	20	and	and	CCONJ
brj-22874	116	21	streamline	streamline	VERB
brj-22874	116	22	the	the	DET
brj-22874	116	23	network	network	NOUN
brj-22874	116	24	structure	structure	NOUN
brj-22874	116	25	.	.	PUNCT
brj-22874	117	1	the	the	DET
brj-22874	117	2	neck	neck	NOUN
brj-22874	117	3	part	part	NOUN
brj-22874	117	4	is	be	AUX
brj-22874	117	5	a	a	DET
brj-22874	117	6	feature	feature	NOUN
brj-22874	117	7	fusion	fusion	NOUN
brj-22874	117	8	network	network	NOUN
brj-22874	117	9	,	,	PUNCT
brj-22874	117	10	which	which	PRON
brj-22874	117	11	uses	use	VERB
brj-22874	117	12	panet	panet	NOUN
brj-22874	117	13	and	and	CCONJ
brj-22874	117	14	fpn	fpn	PROPN
brj-22874	117	15	.	.	PUNCT
brj-22874	118	1	the	the	DET
brj-22874	118	2	fpn	fpn	PROPN
brj-22874	118	3	module	module	NOUN
brj-22874	118	4	performs	perform	VERB
brj-22874	118	5	a	a	DET
brj-22874	118	6	top	top	ADJ
brj-22874	118	7	-	-	PUNCT
brj-22874	118	8	down	down	ADP
brj-22874	118	9	multi	multi	ADJ
brj-22874	118	10	-	-	ADJ
brj-22874	118	11	scale	scale	ADJ
brj-22874	118	12	fusion	fusion	NOUN
brj-22874	118	13	of	of	ADP
brj-22874	118	14	the	the	DET
brj-22874	118	15	multi	multi	ADJ
brj-22874	118	16	-	-	ADJ
brj-22874	118	17	scale	scale	ADJ
brj-22874	118	18	feature	feature	NOUN
brj-22874	118	19	maps	map	NOUN
brj-22874	118	20	output	output	NOUN
brj-22874	118	21	by	by	ADP
brj-22874	118	22	the	the	DET
brj-22874	118	23	feature	feature	NOUN
brj-22874	118	24	extraction	extraction	NOUN
brj-22874	118	25	network	network	NOUN
brj-22874	118	26	.	.	PUNCT
brj-22874	119	1	the	the	DET
brj-22874	119	2	panet	panet	NOUN
brj-22874	119	3	module	module	NOUN
brj-22874	119	4	performs	perform	VERB
brj-22874	119	5	a	a	DET
brj-22874	119	6	bottom	bottom	ADJ
brj-22874	119	7	-	-	PUNCT
brj-22874	119	8	up	up	ADP
brj-22874	119	9	multi	multi	ADJ
brj-22874	119	10	-	-	ADJ
brj-22874	119	11	scale	scale	ADJ
brj-22874	119	12	fusion	fusion	NOUN
brj-22874	119	13	of	of	ADP
brj-22874	119	14	the	the	DET
brj-22874	119	15	multi	multi	ADJ
brj-22874	119	16	-	-	ADJ
brj-22874	119	17	scale	scale	ADJ
brj-22874	119	18	feature	feature	NOUN
brj-22874	119	19	maps	map	NOUN
brj-22874	119	20	of	of	ADP
brj-22874	119	21	fpn	fpn	NOUN
brj-22874	119	22	and	and	CCONJ
brj-22874	119	23	finally	finally	ADV
brj-22874	119	24	outputs	output	VERB
brj-22874	119	25	a	a	DET
brj-22874	119	26	feature	feature	NOUN
brj-22874	119	27	map	map	NOUN
brj-22874	119	28	focus	focus	VERB
brj-22874	119	29	conv	conv	PROPN
brj-22874	119	30	c3	c3	PROPN
brj-22874	119	31	-	-	PUNCT
brj-22874	119	32	1	1	NUM
brj-22874	119	33	conv	conv	PROPN
brj-22874	119	34	c3	c3	PROPN
brj-22874	119	35	-	-	PUNCT
brj-22874	119	36	3	3	NUM
brj-22874	119	37	conv	conv	PROPN
brj-22874	119	38	c3	c3	PROPN
brj-22874	119	39	-	-	PUNCT
brj-22874	119	40	3	3	NUM
brj-22874	119	41	conv	conv	ADJ
brj-22874	119	42	spp	spp	PROPN
brj-22874	119	43	c3	c3	PROPN
brj-22874	119	44	-	-	PUNCT
brj-22874	119	45	1	1	NUM
brj-22874	119	46	conv	conv	PROPN
brj-22874	119	47	upsample	upsample	PROPN
brj-22874	119	48	concat	concat	PROPN
brj-22874	119	49	c3	c3	PROPN
brj-22874	119	50	-	-	PUNCT
brj-22874	119	51	1	1	NUM
brj-22874	119	52	conv	conv	PROPN
brj-22874	119	53	upsample	upsample	PROPN
brj-22874	119	54	concat	concat	PROPN
brj-22874	119	55	c3	c3	PROPN
brj-22874	119	56	-	-	PUNCT
brj-22874	119	57	1	1	NUM
brj-22874	119	58	conv	conv	NOUN
brj-22874	119	59	concat	concat	PROPN
brj-22874	119	60	c3	c3	PROPN
brj-22874	119	61	-	-	PUNCT
brj-22874	119	62	1	1	NUM
brj-22874	119	63	conv	conv	NOUN
brj-22874	119	64	concat	concat	PROPN
brj-22874	119	65	c3	c3	PROPN
brj-22874	119	66	-	-	PUNCT
brj-22874	119	67	1	1	NUM
brj-22874	119	68	detect	detect	NOUN
brj-22874	119	69	detect	detect	NOUN
brj-22874	119	70	detect	detect	NOUN
brj-22874	119	71	input	input	NOUN
brj-22874	119	72	backbone	backbone	NOUN
brj-22874	119	73	neck	neck	NOUN
brj-22874	119	74	head	head	NOUN
brj-22874	119	75	peer	peer	NOUN
brj-22874	119	76	-	-	PUNCT
brj-22874	119	77	reviewed	review	VERB
brj-22874	119	78	article	article	NOUN
brj-22874	119	79	bioresources.com	bioresources.com	X
brj-22874	119	80	tian	tian	PROPN
brj-22874	119	81	et	et	PROPN
brj-22874	119	82	al	al	PROPN
brj-22874	119	83	.	.	PROPN
brj-22874	120	1	(	(	PUNCT
brj-22874	120	2	2023	2023	NUM
brj-22874	120	3	)	)	PUNCT
brj-22874	120	4	.	.	PUNCT
brj-22874	121	1	“	"	PUNCT
brj-22874	121	2	defect	defect	VERB
brj-22874	121	3	detection	detection	NOUN
brj-22874	121	4	with	with	ADP
brj-22874	121	5	yolov5	yolov5	NOUN
brj-22874	121	6	,	,	PUNCT
brj-22874	121	7	”	"	PUNCT
brj-22874	121	8	bioresources	bioresource	NOUN
brj-22874	121	9	18(4	18(4	NUM
brj-22874	121	10	)	)	PUNCT
brj-22874	121	11	,	,	PUNCT
brj-22874	121	12	7713	7713	NUM
brj-22874	121	13	-	-	SYM
brj-22874	121	14	7730	7730	NUM
brj-22874	121	15	.	.	PUNCT
brj-22874	121	16	7718	7718	NUM
brj-22874	121	17	with	with	ADP
brj-22874	121	18	stronger	strong	ADJ
brj-22874	121	19	location	location	NOUN
brj-22874	121	20	information	information	NOUN
brj-22874	121	21	and	and	CCONJ
brj-22874	121	22	semantic	semantic	ADJ
brj-22874	121	23	information	information	NOUN
brj-22874	121	24	.	.	PUNCT
brj-22874	122	1	the	the	DET
brj-22874	122	2	head	head	NOUN
brj-22874	122	3	part	part	NOUN
brj-22874	122	4	is	be	AUX
brj-22874	122	5	the	the	DET
brj-22874	122	6	prediction	prediction	NOUN
brj-22874	122	7	network	network	NOUN
brj-22874	122	8	.	.	PUNCT
brj-22874	123	1	by	by	ADP
brj-22874	123	2	convolution	convolution	NOUN
brj-22874	123	3	operation	operation	NOUN
brj-22874	123	4	on	on	ADP
brj-22874	123	5	the	the	DET
brj-22874	123	6	three	three	NUM
brj-22874	123	7	outputs	output	NOUN
brj-22874	123	8	of	of	ADP
brj-22874	123	9	the	the	DET
brj-22874	123	10	neck	neck	NOUN
brj-22874	123	11	end	end	NOUN
brj-22874	123	12	,	,	PUNCT
brj-22874	123	13	three	three	NUM
brj-22874	123	14	sets	set	NOUN
brj-22874	123	15	of	of	ADP
brj-22874	123	16	feature	feature	NOUN
brj-22874	123	17	vectors	vector	NOUN
brj-22874	123	18	including	include	VERB
brj-22874	123	19	category	category	NOUN
brj-22874	123	20	prediction	prediction	NOUN
brj-22874	123	21	box	box	NOUN
brj-22874	123	22	,	,	PUNCT
brj-22874	123	23	confidence	confidence	NOUN
brj-22874	123	24	,	,	PUNCT
brj-22874	123	25	and	and	CCONJ
brj-22874	123	26	coordinate	coordinate	NOUN
brj-22874	123	27	position	position	NOUN
brj-22874	123	28	are	be	AUX
brj-22874	123	29	output	output	NOUN
brj-22874	123	30	.	.	PUNCT
brj-22874	124	1	yolov5	yolov5	NOUN
brj-22874	124	2	-	-	PUNCT
brj-22874	124	3	tspp	tspp	NOUN
brj-22874	124	4	network	network	NOUN
brj-22874	124	5	structure	structure	NOUN
brj-22874	124	6	coordinate	coordinate	NOUN
brj-22874	124	7	attention	attention	NOUN
brj-22874	124	8	mechanism	mechanism	NOUN
brj-22874	124	9	due	due	ADP
brj-22874	124	10	to	to	ADP
brj-22874	124	11	the	the	DET
brj-22874	124	12	visual	visual	ADJ
brj-22874	124	13	bottleneck	bottleneck	NOUN
brj-22874	124	14	of	of	ADP
brj-22874	124	15	human	human	ADJ
brj-22874	124	16	beings	being	NOUN
brj-22874	124	17	,	,	PUNCT
brj-22874	124	18	it	it	PRON
brj-22874	124	19	is	be	AUX
brj-22874	124	20	necessary	necessary	ADJ
brj-22874	124	21	to	to	PART
brj-22874	124	22	concentrate	concentrate	VERB
brj-22874	124	23	and	and	CCONJ
brj-22874	124	24	ignore	ignore	VERB
brj-22874	124	25	other	other	ADJ
brj-22874	124	26	secondary	secondary	ADJ
brj-22874	124	27	areas	area	NOUN
brj-22874	124	28	when	when	SCONJ
brj-22874	124	29	observing	observe	VERB
brj-22874	124	30	a	a	DET
brj-22874	124	31	specific	specific	ADJ
brj-22874	124	32	area	area	NOUN
brj-22874	124	33	.	.	PUNCT
brj-22874	125	1	this	this	DET
brj-22874	125	2	behavioral	behavioral	ADJ
brj-22874	125	3	action	action	NOUN
brj-22874	125	4	is	be	AUX
brj-22874	125	5	called	call	VERB
brj-22874	125	6	the	the	DET
brj-22874	125	7	attention	attention	NOUN
brj-22874	125	8	mechanism	mechanism	NOUN
brj-22874	125	9	,	,	PUNCT
brj-22874	125	10	which	which	PRON
brj-22874	125	11	has	have	AUX
brj-22874	125	12	been	be	AUX
brj-22874	125	13	helpful	helpful	ADJ
brj-22874	125	14	for	for	ADP
brj-22874	125	15	various	various	ADJ
brj-22874	125	16	computer	computer	NOUN
brj-22874	125	17	vision	vision	NOUN
brj-22874	125	18	tasks	task	NOUN
brj-22874	125	19	.	.	PUNCT
brj-22874	126	1	the	the	DET
brj-22874	126	2	attention	attention	NOUN
brj-22874	126	3	mechanism	mechanism	NOUN
brj-22874	126	4	mainly	mainly	ADV
brj-22874	126	5	further	further	ADJ
brj-22874	126	6	extracts	extract	NOUN
brj-22874	126	7	features	feature	NOUN
brj-22874	126	8	from	from	ADP
brj-22874	126	9	a	a	DET
brj-22874	126	10	given	give	VERB
brj-22874	126	11	intermediate	intermediate	ADJ
brj-22874	126	12	feature	feature	NOUN
brj-22874	126	13	map	map	NOUN
brj-22874	126	14	by	by	ADP
brj-22874	126	15	adding	add	VERB
brj-22874	126	16	a	a	DET
brj-22874	126	17	simple	simple	ADJ
brj-22874	126	18	and	and	CCONJ
brj-22874	126	19	effective	effective	ADJ
brj-22874	126	20	convolution	convolution	NOUN
brj-22874	126	21	attention	attention	NOUN
brj-22874	126	22	module	module	NOUN
brj-22874	126	23	,	,	PUNCT
brj-22874	126	24	aiming	aim	VERB
brj-22874	126	25	to	to	PART
brj-22874	126	26	improve	improve	VERB
brj-22874	126	27	the	the	DET
brj-22874	126	28	weight	weight	NOUN
brj-22874	126	29	of	of	ADP
brj-22874	126	30	beneficial	beneficial	ADJ
brj-22874	126	31	features	feature	NOUN
brj-22874	126	32	and	and	CCONJ
brj-22874	126	33	suppress	suppress	VERB
brj-22874	126	34	redundant	redundant	ADJ
brj-22874	126	35	features	feature	NOUN
brj-22874	126	36	.	.	PUNCT
brj-22874	127	1	common	common	ADJ
brj-22874	127	2	attention	attention	NOUN
brj-22874	127	3	mechanisms	mechanism	NOUN
brj-22874	127	4	include	include	VERB
brj-22874	127	5	senet	senet	PROPN
brj-22874	127	6	(	(	PUNCT
brj-22874	127	7	hu	hu	PROPN
brj-22874	127	8	et	et	PROPN
brj-22874	127	9	al	al	PROPN
brj-22874	127	10	.	.	PROPN
brj-22874	127	11	2020	2020	NUM
brj-22874	127	12	)	)	PUNCT
brj-22874	127	13	(	(	PUNCT
brj-22874	127	14	squeeze	squeeze	NOUN
brj-22874	127	15	-	-	PUNCT
brj-22874	127	16	and	and	CCONJ
brj-22874	127	17	-	-	PUNCT
brj-22874	127	18	excitation	excitation	NOUN
brj-22874	127	19	)	)	PUNCT
brj-22874	127	20	,	,	PUNCT
brj-22874	127	21	cbam	cbam	NOUN
brj-22874	127	22	(	(	PUNCT
brj-22874	127	23	woo	woo	INTJ
brj-22874	127	24	et	et	PROPN
brj-22874	127	25	al	al	PROPN
brj-22874	127	26	.	.	PROPN
brj-22874	127	27	2018	2018	NUM
brj-22874	127	28	)	)	PUNCT
brj-22874	127	29	(	(	PUNCT
brj-22874	127	30	convolutional	convolutional	ADJ
brj-22874	127	31	block	block	NOUN
brj-22874	127	32	attention	attention	NOUN
brj-22874	127	33	module	module	NOUN
brj-22874	127	34	)	)	PUNCT
brj-22874	127	35	attention	attention	NOUN
brj-22874	127	36	mechanism	mechanism	NOUN
brj-22874	127	37	,	,	PUNCT
brj-22874	127	38	and	and	CCONJ
brj-22874	127	39	ca	can	AUX
brj-22874	127	40	(	(	PUNCT
brj-22874	127	41	zhao	zhao	X
brj-22874	127	42	et	et	PROPN
brj-22874	127	43	al	al	PROPN
brj-22874	127	44	.	.	PROPN
brj-22874	127	45	2021	2021	NUM
brj-22874	127	46	)	)	PUNCT
brj-22874	127	47	(	(	PUNCT
brj-22874	127	48	coordinate	coordinate	VERB
brj-22874	127	49	attention	attention	NOUN
brj-22874	127	50	)	)	PUNCT
brj-22874	127	51	attention	attention	NOUN
brj-22874	127	52	mechanism	mechanism	NOUN
brj-22874	127	53	.	.	PUNCT
brj-22874	128	1	senet	senet	PROPN
brj-22874	128	2	uses	use	VERB
brj-22874	128	3	average	average	ADJ
brj-22874	128	4	pooling	pooling	NOUN
brj-22874	128	5	to	to	PART
brj-22874	128	6	extract	extract	VERB
brj-22874	128	7	channel	channel	NOUN
brj-22874	128	8	information	information	NOUN
brj-22874	128	9	.	.	PUNCT
brj-22874	129	1	senet	senet	PROPN
brj-22874	129	2	captures	capture	VERB
brj-22874	129	3	the	the	DET
brj-22874	129	4	weight	weight	NOUN
brj-22874	129	5	between	between	ADP
brj-22874	129	6	channels	channel	NOUN
brj-22874	129	7	through	through	ADP
brj-22874	129	8	two	two	NUM
brj-22874	129	9	fully	fully	ADV
brj-22874	129	10	connected	connected	ADJ
brj-22874	129	11	layers	layer	NOUN
brj-22874	129	12	.	.	PUNCT
brj-22874	130	1	senet	senet	PROPN
brj-22874	130	2	compresses	compress	VERB
brj-22874	130	3	global	global	ADJ
brj-22874	130	4	spatial	spatial	ADJ
brj-22874	130	5	information	information	NOUN
brj-22874	130	6	into	into	ADP
brj-22874	130	7	channel	channel	NOUN
brj-22874	130	8	descriptors	descriptor	NOUN
brj-22874	130	9	.	.	PUNCT
brj-22874	131	1	it	it	PRON
brj-22874	131	2	is	be	AUX
brj-22874	131	3	difficult	difficult	ADJ
brj-22874	131	4	to	to	PART
brj-22874	131	5	retain	retain	VERB
brj-22874	131	6	location	location	NOUN
brj-22874	131	7	information	information	NOUN
brj-22874	131	8	that	that	PRON
brj-22874	131	9	is	be	AUX
brj-22874	131	10	critical	critical	ADJ
brj-22874	131	11	to	to	PART
brj-22874	131	12	capture	capture	VERB
brj-22874	131	13	spatial	spatial	ADJ
brj-22874	131	14	structure	structure	NOUN
brj-22874	131	15	in	in	ADP
brj-22874	131	16	visual	visual	ADJ
brj-22874	131	17	tasks	task	NOUN
brj-22874	131	18	.	.	PUNCT
brj-22874	132	1	selecting	select	VERB
brj-22874	132	2	senet	senet	NOUN
brj-22874	132	3	channel	channel	NOUN
brj-22874	132	4	attention	attention	NOUN
brj-22874	132	5	alone	alone	ADV
brj-22874	132	6	will	will	AUX
brj-22874	132	7	lose	lose	VERB
brj-22874	132	8	location	location	NOUN
brj-22874	132	9	information	information	NOUN
brj-22874	132	10	.	.	PUNCT
brj-22874	133	1	cbam	cbam	NOUN
brj-22874	133	2	focuses	focus	VERB
brj-22874	133	3	on	on	ADP
brj-22874	133	4	the	the	DET
brj-22874	133	5	relationship	relationship	NOUN
brj-22874	133	6	between	between	ADP
brj-22874	133	7	different	different	ADJ
brj-22874	133	8	spaces	space	NOUN
brj-22874	133	9	.	.	PUNCT
brj-22874	134	1	by	by	ADP
brj-22874	134	2	reducing	reduce	VERB
brj-22874	134	3	the	the	DET
brj-22874	134	4	number	number	NOUN
brj-22874	134	5	of	of	ADP
brj-22874	134	6	channels	channel	NOUN
brj-22874	134	7	and	and	CCONJ
brj-22874	134	8	using	use	VERB
brj-22874	134	9	convolution	convolution	NOUN
brj-22874	134	10	to	to	PART
brj-22874	134	11	extract	extract	VERB
brj-22874	134	12	information	information	NOUN
brj-22874	134	13	,	,	PUNCT
brj-22874	134	14	it	it	PRON
brj-22874	134	15	pays	pay	VERB
brj-22874	134	16	more	more	ADJ
brj-22874	134	17	attention	attention	NOUN
brj-22874	134	18	to	to	ADP
brj-22874	134	19	the	the	DET
brj-22874	134	20	information	information	NOUN
brj-22874	134	21	in	in	ADP
brj-22874	134	22	the	the	DET
brj-22874	134	23	spatial	spatial	ADJ
brj-22874	134	24	direction	direction	NOUN
brj-22874	134	25	.	.	PUNCT
brj-22874	135	1	however	however	ADV
brj-22874	135	2	,	,	PUNCT
brj-22874	135	3	the	the	DET
brj-22874	135	4	convolution	convolution	NOUN
brj-22874	135	5	method	method	NOUN
brj-22874	135	6	only	only	ADV
brj-22874	135	7	extracts	extract	VERB
brj-22874	135	8	local	local	ADJ
brj-22874	135	9	relationships	relationship	NOUN
brj-22874	135	10	and	and	CCONJ
brj-22874	135	11	can	can	AUX
brj-22874	135	12	not	not	PART
brj-22874	135	13	extract	extract	VERB
brj-22874	135	14	long	long	ADJ
brj-22874	135	15	-	-	PUNCT
brj-22874	135	16	distance	distance	NOUN
brj-22874	135	17	relationships	relationship	NOUN
brj-22874	135	18	.	.	PUNCT
brj-22874	136	1	residual	residual	ADJ
brj-22874	136	2	input	input	NOUN
brj-22874	136	3	h	h	PROPN
brj-22874	136	4	w	w	PROPN
brj-22874	136	5	c	c	PROPN
brj-22874	136	6			NOUN
brj-22874	136	7	x	x	PUNCT
brj-22874	136	8	avg	avg	PROPN
brj-22874	136	9	pool	pool	PROPN
brj-22874	136	10	y	y	PROPN
brj-22874	136	11	avg	avg	PROPN
brj-22874	136	12	pool	pool	PROPN
brj-22874	136	13	concat+conv2d	concat+conv2d	PROPN
brj-22874	136	14	batchnorm+non	batchnorm+non	NOUN
brj-22874	136	15	-	-	PUNCT
brj-22874	136	16	linear	linear	ADJ
brj-22874	136	17	conv2d	conv2d	NOUN
brj-22874	136	18	conv2d	conv2d	VERB
brj-22874	136	19	sigmoid	sigmoid	NOUN
brj-22874	136	20	sigmoid	sigmoid	NOUN
brj-22874	136	21	re	re	NOUN
brj-22874	136	22	-	-	NOUN
brj-22874	136	23	weight	weight	ADJ
brj-22874	136	24	1	1	NUM
brj-22874	136	25	w	w	NOUN
brj-22874	136	26	c	c	PROPN
brj-22874	136	27			NOUN
brj-22874	136	28	1	1	NUM
brj-22874	136	29	w	w	PROPN
brj-22874	136	30	c	c	PROPN
brj-22874	136	31			NOUN
brj-22874	136	32	1	1	NUM
brj-22874	136	33	w	w	PROPN
brj-22874	136	34	c	c	PROPN
brj-22874	136	35			PROPN
brj-22874	136	36	h	h	PROPN
brj-22874	136	37	w	w	PROPN
brj-22874	136	38	c	c	PROPN
brj-22874	136	39			PROPN
brj-22874	136	40	1h	1h	NUM
brj-22874	136	41	c	c	PROPN
brj-22874	136	42			PROPN
brj-22874	136	43	1h	1h	NUM
brj-22874	136	44	c	c	PROPN
brj-22874	136	45			PROPN
brj-22874	136	46	1h	1h	NUM
brj-22874	136	47	c	c	PROPN
brj-22874	136	48			PROPN
brj-22874	136	49	1	1	NUM
brj-22874	136	50	(	(	PUNCT
brj-22874	136	51	)	)	PUNCT
brj-22874	136	52	/	/	SYM
brj-22874	136	53	rw	rw	PROPN
brj-22874	136	54	h	h	PROPN
brj-22874	136	55	c	c	PROPN
brj-22874	137	1	+	+	CCONJ
brj-22874	137	2			PROPN
brj-22874	137	3	1	1	NUM
brj-22874	137	4	(	(	PUNCT
brj-22874	137	5	)	)	PUNCT
brj-22874	137	6	/	/	SYM
brj-22874	137	7	rw	rw	PROPN
brj-22874	137	8	h	h	PROPN
brj-22874	137	9	c	c	PROPN
brj-22874	138	1	+	+	CCONJ
brj-22874	138	2			PROPN
brj-22874	138	3	output	output	NOUN
brj-22874	138	4	fig	fig	NOUN
brj-22874	138	5	.	.	PUNCT
brj-22874	139	1	3	3	X
brj-22874	139	2	.	.	X
brj-22874	139	3	coordinate	coordinate	NOUN
brj-22874	139	4	attention	attention	NOUN
brj-22874	139	5	mechanism	mechanism	NOUN
brj-22874	139	6	the	the	DET
brj-22874	139	7	ca	ca	NOUN
brj-22874	139	8	attention	attention	NOUN
brj-22874	139	9	mechanism	mechanism	NOUN
brj-22874	139	10	focuses	focus	VERB
brj-22874	139	11	on	on	ADP
brj-22874	139	12	the	the	DET
brj-22874	139	13	width	width	NOUN
brj-22874	139	14	and	and	CCONJ
brj-22874	139	15	height	height	NOUN
brj-22874	139	16	of	of	ADP
brj-22874	139	17	the	the	DET
brj-22874	139	18	image	image	NOUN
brj-22874	139	19	and	and	CCONJ
brj-22874	139	20	encodes	encode	NOUN
brj-22874	139	21	the	the	DET
brj-22874	139	22	accurate	accurate	ADJ
brj-22874	139	23	position	position	NOUN
brj-22874	139	24	information	information	NOUN
brj-22874	139	25	.	.	PUNCT
brj-22874	140	1	firstly	firstly	ADV
brj-22874	140	2	,	,	PUNCT
brj-22874	140	3	the	the	DET
brj-22874	140	4	input	input	NOUN
brj-22874	140	5	feature	feature	NOUN
brj-22874	140	6	map	map	NOUN
brj-22874	140	7	is	be	AUX
brj-22874	140	8	divided	divide	VERB
brj-22874	140	9	into	into	ADP
brj-22874	140	10	two	two	NUM
brj-22874	140	11	directions	direction	NOUN
brj-22874	140	12	of	of	ADP
brj-22874	140	13	width	width	NOUN
brj-22874	140	14	and	and	CCONJ
brj-22874	140	15	height	height	NOUN
brj-22874	140	16	for	for	ADP
brj-22874	140	17	global	global	ADJ
brj-22874	140	18	average	average	ADJ
brj-22874	140	19	pooling	pooling	NOUN
brj-22874	140	20	,	,	PUNCT
brj-22874	140	21	and	and	CCONJ
brj-22874	140	22	the	the	DET
brj-22874	140	23	feature	feature	NOUN
brj-22874	140	24	maps	map	NOUN
brj-22874	140	25	in	in	ADP
brj-22874	140	26	the	the	DET
brj-22874	140	27	two	two	NUM
brj-22874	140	28	directions	direction	NOUN
brj-22874	140	29	of	of	ADP
brj-22874	140	30	width	width	NOUN
brj-22874	140	31	and	and	CCONJ
brj-22874	140	32	height	height	NOUN
brj-22874	140	33	are	be	AUX
brj-22874	140	34	obtained	obtain	VERB
brj-22874	140	35	respectively	respectively	ADV
brj-22874	140	36	.	.	PUNCT
brj-22874	141	1	then	then	ADV
brj-22874	141	2	,	,	PUNCT
brj-22874	141	3	the	the	DET
brj-22874	141	4	feature	feature	NOUN
brj-22874	141	5	maps	map	NOUN
brj-22874	141	6	in	in	ADP
brj-22874	141	7	the	the	DET
brj-22874	141	8	two	two	NUM
brj-22874	141	9	directions	direction	NOUN
brj-22874	141	10	of	of	ADP
brj-22874	141	11	width	width	NOUN
brj-22874	141	12	and	and	CCONJ
brj-22874	141	13	height	height	NOUN
brj-22874	141	14	of	of	ADP
brj-22874	141	15	the	the	DET
brj-22874	141	16	global	global	ADJ
brj-22874	141	17	receptive	receptive	ADJ
brj-22874	141	18	field	field	NOUN
brj-22874	141	19	,	,	PUNCT
brj-22874	141	20	which	which	PRON
brj-22874	141	21	are	be	AUX
brj-22874	141	22	spliced	splice	VERB
brj-22874	141	23	together	together	ADV
brj-22874	141	24	and	and	CCONJ
brj-22874	141	25	peer	peer	NOUN
brj-22874	141	26	-	-	PUNCT
brj-22874	141	27	reviewed	review	VERB
brj-22874	141	28	article	article	NOUN
brj-22874	141	29	bioresources.com	bioresources.com	X
brj-22874	141	30	tian	tian	PROPN
brj-22874	141	31	et	et	PROPN
brj-22874	141	32	al	al	PROPN
brj-22874	141	33	.	.	PROPN
brj-22874	142	1	(	(	PUNCT
brj-22874	142	2	2023	2023	NUM
brj-22874	142	3	)	)	PUNCT
brj-22874	142	4	.	.	PUNCT
brj-22874	143	1	“	"	PUNCT
brj-22874	143	2	defect	defect	VERB
brj-22874	143	3	detection	detection	NOUN
brj-22874	143	4	with	with	ADP
brj-22874	143	5	yolov5	yolov5	NOUN
brj-22874	143	6	,	,	PUNCT
brj-22874	143	7	”	"	PUNCT
brj-22874	143	8	bioresources	bioresource	NOUN
brj-22874	143	9	18(4	18(4	NUM
brj-22874	143	10	)	)	PUNCT
brj-22874	143	11	,	,	PUNCT
brj-22874	143	12	7713	7713	NUM
brj-22874	143	13	-	-	SYM
brj-22874	143	14	7730	7730	NUM
brj-22874	143	15	.	.	PUNCT
brj-22874	144	1	7719	7719	NUM
brj-22874	144	2	then	then	ADV
brj-22874	144	3	they	they	PRON
brj-22874	144	4	are	be	AUX
brj-22874	144	5	sent	send	VERB
brj-22874	144	6	to	to	ADP
brj-22874	144	7	the	the	DET
brj-22874	144	8	shared	share	VERB
brj-22874	144	9	convolution	convolution	NOUN
brj-22874	144	10	module	module	NOUN
brj-22874	144	11	.	.	PUNCT
brj-22874	145	1	finally	finally	ADV
brj-22874	145	2	,	,	PUNCT
brj-22874	145	3	the	the	DET
brj-22874	145	4	weights	weight	NOUN
brj-22874	145	5	on	on	ADP
brj-22874	145	6	the	the	DET
brj-22874	145	7	width	width	NOUN
brj-22874	145	8	and	and	CCONJ
brj-22874	145	9	height	height	NOUN
brj-22874	145	10	are	be	AUX
brj-22874	145	11	obtained	obtain	VERB
brj-22874	145	12	through	through	ADP
brj-22874	145	13	the	the	DET
brj-22874	145	14	activation	activation	NOUN
brj-22874	145	15	function	function	NOUN
brj-22874	145	16	.	.	PUNCT
brj-22874	146	1	location	location	NOUN
brj-22874	146	2	information	information	NOUN
brj-22874	146	3	is	be	AUX
brj-22874	146	4	an	an	DET
brj-22874	146	5	essential	essential	ADJ
brj-22874	146	6	factor	factor	NOUN
brj-22874	146	7	for	for	ADP
brj-22874	146	8	generating	generate	VERB
brj-22874	146	9	spatial	spatial	ADJ
brj-22874	146	10	selective	selective	ADJ
brj-22874	146	11	attention	attention	NOUN
brj-22874	146	12	maps	map	NOUN
brj-22874	146	13	in	in	ADP
brj-22874	146	14	the	the	DET
brj-22874	146	15	detection	detection	NOUN
brj-22874	146	16	of	of	ADP
brj-22874	146	17	wooden	wooden	ADJ
brj-22874	146	18	spoon	spoon	NOUN
brj-22874	146	19	defects	defect	NOUN
brj-22874	146	20	.	.	PUNCT
brj-22874	147	1	therefore	therefore	ADV
brj-22874	147	2	,	,	PUNCT
brj-22874	147	3	a	a	DET
brj-22874	147	4	method	method	NOUN
brj-22874	147	5	of	of	ADP
brj-22874	147	6	introducing	introduce	VERB
brj-22874	147	7	the	the	DET
brj-22874	147	8	c	c	NOUN
brj-22874	147	9	attention	attention	NOUN
brj-22874	147	10	mechanism	mechanism	NOUN
brj-22874	147	11	is	be	AUX
brj-22874	147	12	proposed	propose	VERB
brj-22874	147	13	,	,	PUNCT
brj-22874	147	14	as	as	SCONJ
brj-22874	147	15	shown	show	VERB
brj-22874	147	16	in	in	ADP
brj-22874	147	17	fig	fig	NOUN
brj-22874	147	18	.	.	PUNCT
brj-22874	148	1	3	3	NUM
brj-22874	148	2	,	,	PUNCT
brj-22874	148	3	which	which	PRON
brj-22874	148	4	considers	consider	VERB
brj-22874	148	5	the	the	DET
brj-22874	148	6	relationship	relationship	NOUN
brj-22874	148	7	and	and	CCONJ
brj-22874	148	8	location	location	NOUN
brj-22874	148	9	information	information	NOUN
brj-22874	148	10	between	between	ADP
brj-22874	148	11	channels	channel	NOUN
brj-22874	148	12	.	.	PUNCT
brj-22874	149	1	to	to	PART
brj-22874	149	2	enable	enable	VERB
brj-22874	149	3	the	the	DET
brj-22874	149	4	attention	attention	NOUN
brj-22874	149	5	module	module	NOUN
brj-22874	149	6	to	to	PART
brj-22874	149	7	capture	capture	VERB
brj-22874	149	8	the	the	DET
brj-22874	149	9	feature	feature	NOUN
brj-22874	149	10	information	information	NOUN
brj-22874	149	11	with	with	ADP
brj-22874	149	12	accurate	accurate	ADJ
brj-22874	149	13	position	position	NOUN
brj-22874	149	14	,	,	PUNCT
brj-22874	149	15	the	the	DET
brj-22874	149	16	traditional	traditional	ADJ
brj-22874	149	17	global	global	ADJ
brj-22874	149	18	pooling	pooling	NOUN
brj-22874	149	19	method	method	NOUN
brj-22874	149	20	is	be	AUX
brj-22874	149	21	decomposed	decompose	VERB
brj-22874	149	22	into	into	ADP
brj-22874	149	23	two	two	NUM
brj-22874	149	24	one	one	NUM
brj-22874	149	25	-	-	PUNCT
brj-22874	149	26	dimensional	dimensional	ADJ
brj-22874	149	27	feature	feature	NOUN
brj-22874	149	28	codes	code	NOUN
brj-22874	149	29	.	.	PUNCT
brj-22874	150	1	specifically	specifically	ADV
brj-22874	150	2	,	,	PUNCT
brj-22874	150	3	given	give	VERB
brj-22874	150	4	the	the	DET
brj-22874	150	5	input	input	NOUN
brj-22874	150	6	x	x	PRON
brj-22874	150	7	,	,	PUNCT
brj-22874	150	8	each	each	DET
brj-22874	150	9	channel	channel	NOUN
brj-22874	150	10	is	be	AUX
brj-22874	150	11	encoded	encode	VERB
brj-22874	150	12	along	along	ADP
brj-22874	150	13	the	the	DET
brj-22874	150	14	horizontal	horizontal	ADJ
brj-22874	150	15	and	and	CCONJ
brj-22874	150	16	vertical	vertical	ADJ
brj-22874	150	17	coordinates	coordinate	NOUN
brj-22874	150	18	using	use	VERB
brj-22874	150	19	average	average	ADJ
brj-22874	150	20	pooling	pooling	NOUN
brj-22874	150	21	with	with	ADP
brj-22874	150	22	sizes	size	NOUN
brj-22874	150	23	(	(	PUNCT
brj-22874	150	24	h	h	NOUN
brj-22874	150	25	,	,	PUNCT
brj-22874	150	26	1	1	NUM
brj-22874	150	27	)	)	PUNCT
brj-22874	150	28	and	and	CCONJ
brj-22874	150	29	(	(	PUNCT
brj-22874	150	30	1	1	NUM
brj-22874	150	31	,	,	PUNCT
brj-22874	150	32	w	w	NOUN
brj-22874	150	33	)	)	PUNCT
brj-22874	150	34	,	,	PUNCT
brj-22874	150	35	respectively	respectively	ADV
brj-22874	150	36	.	.	PUNCT
brj-22874	151	1	therefore	therefore	ADV
brj-22874	151	2	,	,	PUNCT
brj-22874	151	3	the	the	DET
brj-22874	151	4	output	output	NOUN
brj-22874	151	5	of	of	ADP
brj-22874	151	6	the	the	DET
brj-22874	151	7	c	c	NOUN
brj-22874	151	8	-	-	PUNCT
brj-22874	151	9	th	th	VERB
brj-22874	151	10	channel	channel	NOUN
brj-22874	151	11	with	with	ADP
brj-22874	151	12	height	height	NOUN
brj-22874	151	13	(	(	PUNCT
brj-22874	151	14	h	h	NOUN
brj-22874	151	15	)	)	PUNCT
brj-22874	151	16	and	and	CCONJ
brj-22874	151	17	width	width	ADJ
brj-22874	151	18	(	(	PUNCT
brj-22874	151	19	w	w	NOUN
brj-22874	151	20	)	)	PUNCT
brj-22874	151	21	can	can	AUX
brj-22874	151	22	be	be	AUX
brj-22874	151	23	expressed	express	VERB
brj-22874	151	24	as	as	ADP
brj-22874	151	25	the	the	DET
brj-22874	151	26	following	follow	VERB
brj-22874	151	27	formula	formula	NOUN
brj-22874	151	28	respectively	respectively	ADV
brj-22874	151	29	.	.	PUNCT
brj-22874	152	1	where	where	SCONJ
brj-22874	152	2	h	h	NOUN
brj-22874	152	3	cz	cz	NOUN
brj-22874	152	4	represents	represent	VERB
brj-22874	152	5	the	the	DET
brj-22874	152	6	output	output	NOUN
brj-22874	152	7	of	of	ADP
brj-22874	152	8	the	the	DET
brj-22874	152	9	c	c	NOUN
brj-22874	152	10	-	-	PUNCT
brj-22874	152	11	channel	channel	NOUN
brj-22874	152	12	at	at	ADP
brj-22874	152	13	height	height	NOUN
brj-22874	152	14	h	h	NOUN
brj-22874	152	15	;	;	PUNCT
brj-22874	152	16	wzc	wzc	PROPN
brj-22874	152	17	represents	represent	VERB
brj-22874	152	18	the	the	DET
brj-22874	152	19	output	output	NOUN
brj-22874	152	20	of	of	ADP
brj-22874	152	21	channel	channel	NOUN
brj-22874	152	22	c	c	NOUN
brj-22874	152	23	at	at	ADP
brj-22874	152	24	the	the	DET
brj-22874	152	25	width	width	ADJ
brj-22874	152	26	w	w	NOUN
brj-22874	152	27	;	;	PUNCT
brj-22874	152	28	the	the	DET
brj-22874	152	29	input	input	NOUN
brj-22874	152	30	x	x	PUNCT
brj-22874	152	31	comes	come	VERB
brj-22874	152	32	directly	directly	ADV
brj-22874	152	33	from	from	ADP
brj-22874	152	34	the	the	DET
brj-22874	152	35	convolutional	convolutional	ADJ
brj-22874	152	36	layer	layer	NOUN
brj-22874	152	37	with	with	ADP
brj-22874	152	38	a	a	DET
brj-22874	152	39	fixed	fix	VERB
brj-22874	152	40	kernel	kernel	NOUN
brj-22874	152	41	size	size	NOUN
brj-22874	152	42	.	.	PUNCT
brj-22874	152	43	0	0	PUNCT
brj-22874	153	1	<	<	X
brj-22874	153	2	w	w	PROPN
brj-22874	153	3	1	1	NUM
brj-22874	153	4	(	(	PUNCT
brj-22874	153	5	)	)	PUNCT
brj-22874	153	6	=	=	SYM
brj-22874	153	7	(	(	PUNCT
brj-22874	153	8	,	,	PUNCT
brj-22874	153	9	)	)	PUNCT
brj-22874	153	10	h	h	NOUN
brj-22874	154	1	c	c	NOUN
brj-22874	154	2	c	c	NOUN
brj-22874	155	1	i	i	PRON
brj-22874	155	2	z	z	NOUN
brj-22874	155	3	h	h	NOUN
brj-22874	156	1	x	x	PUNCT
brj-22874	156	2	h	h	NOUN
brj-22874	157	1	i	i	NOUN
brj-22874	157	2	w	w	VERB
brj-22874	157	3			NUM
brj-22874	157	4			X
brj-22874	157	5	(	(	PUNCT
brj-22874	157	6	1	1	NUM
brj-22874	157	7	)	)	PUNCT
brj-22874	157	8	0	0	PUNCT
brj-22874	158	1	<	<	X
brj-22874	158	2	h	h	NOUN
brj-22874	158	3	1	1	NUM
brj-22874	158	4	(	(	PUNCT
brj-22874	158	5	)	)	PUNCT
brj-22874	158	6	=	=	SYM
brj-22874	158	7	(	(	PUNCT
brj-22874	158	8	,	,	PUNCT
brj-22874	158	9	)	)	PUNCT
brj-22874	158	10	w	w	PROPN
brj-22874	159	1	c	c	NOUN
brj-22874	159	2	c	c	PROPN
brj-22874	159	3	j	j	PROPN
brj-22874	159	4	z	z	PROPN
brj-22874	159	5	w	w	PROPN
brj-22874	159	6	x	x	X
brj-22874	159	7	j	j	PROPN
brj-22874	159	8	w	w	PROPN
brj-22874	159	9	h	h	PROPN
brj-22874	159	10			NUM
brj-22874	159	11			X
brj-22874	159	12	(	(	PUNCT
brj-22874	159	13	2	2	X
brj-22874	159	14	)	)	PUNCT
brj-22874	159	15	the	the	DET
brj-22874	159	16	above	above	ADJ
brj-22874	159	17	two	two	NUM
brj-22874	159	18	transformations	transformation	NOUN
brj-22874	159	19	aggregate	aggregate	ADJ
brj-22874	159	20	features	feature	NOUN
brj-22874	159	21	along	along	ADP
brj-22874	159	22	two	two	NUM
brj-22874	159	23	spatial	spatial	ADJ
brj-22874	159	24	directions	direction	NOUN
brj-22874	159	25	respectively	respectively	ADV
brj-22874	159	26	,	,	PUNCT
brj-22874	159	27	and	and	CCONJ
brj-22874	159	28	a	a	DET
brj-22874	159	29	pair	pair	NOUN
brj-22874	159	30	of	of	ADP
brj-22874	159	31	direction	direction	NOUN
brj-22874	159	32	-	-	PUNCT
brj-22874	159	33	aware	aware	ADJ
brj-22874	159	34	feature	feature	NOUN
brj-22874	159	35	maps	map	NOUN
brj-22874	159	36	are	be	AUX
brj-22874	159	37	obtained	obtain	VERB
brj-22874	159	38	.	.	PUNCT
brj-22874	160	1	the	the	DET
brj-22874	160	2	process	process	NOUN
brj-22874	160	3	also	also	ADV
brj-22874	160	4	allows	allow	VERB
brj-22874	160	5	the	the	DET
brj-22874	160	6	attention	attention	NOUN
brj-22874	160	7	module	module	NOUN
brj-22874	160	8	to	to	PART
brj-22874	160	9	capture	capture	VERB
brj-22874	160	10	long	long	ADJ
brj-22874	160	11	term	term	NOUN
brj-22874	160	12	dependencies	dependency	NOUN
brj-22874	160	13	along	along	ADP
brj-22874	160	14	one	one	NUM
brj-22874	160	15	spatial	spatial	ADJ
brj-22874	160	16	direction	direction	NOUN
brj-22874	160	17	and	and	CCONJ
brj-22874	160	18	save	save	VERB
brj-22874	160	19	accurate	accurate	ADJ
brj-22874	160	20	location	location	NOUN
brj-22874	160	21	information	information	NOUN
brj-22874	160	22	along	along	ADP
brj-22874	160	23	another	another	DET
brj-22874	160	24	spatial	spatial	ADJ
brj-22874	160	25	direction	direction	NOUN
brj-22874	160	26	.	.	PUNCT
brj-22874	161	1	this	this	PRON
brj-22874	161	2	helps	help	VERB
brj-22874	161	3	the	the	DET
brj-22874	161	4	network	network	NOUN
brj-22874	161	5	exclude	exclude	VERB
brj-22874	161	6	the	the	DET
brj-22874	161	7	interference	interference	NOUN
brj-22874	161	8	of	of	ADP
brj-22874	161	9	the	the	DET
brj-22874	161	10	image	image	NOUN
brj-22874	161	11	background	background	NOUN
brj-22874	161	12	and	and	CCONJ
brj-22874	161	13	more	more	ADV
brj-22874	161	14	accurately	accurately	ADV
brj-22874	161	15	locate	locate	VERB
brj-22874	161	16	the	the	DET
brj-22874	161	17	target	target	NOUN
brj-22874	161	18	of	of	ADP
brj-22874	161	19	interest	interest	NOUN
brj-22874	161	20	.	.	PUNCT
brj-22874	162	1	after	after	SCONJ
brj-22874	162	2	the	the	DET
brj-22874	162	3	transformation	transformation	NOUN
brj-22874	162	4	in	in	ADP
brj-22874	162	5	the	the	DET
brj-22874	162	6	information	information	NOUN
brj-22874	162	7	embedding	embed	VERB
brj-22874	162	8	,	,	PUNCT
brj-22874	162	9	the	the	DET
brj-22874	162	10	height	height	NOUN
brj-22874	162	11	and	and	CCONJ
brj-22874	162	12	width	width	NOUN
brj-22874	162	13	are	be	AUX
brj-22874	162	14	spliced	splice	VERB
brj-22874	162	15	,	,	PUNCT
brj-22874	162	16	and	and	CCONJ
brj-22874	162	17	the	the	DET
brj-22874	162	18	feature	feature	NOUN
brj-22874	162	19	map	map	NOUN
brj-22874	162	20	of	of	ADP
brj-22874	162	21	the	the	DET
brj-22874	162	22	spatial	spatial	ADJ
brj-22874	162	23	information	information	NOUN
brj-22874	162	24	in	in	ADP
brj-22874	162	25	the	the	DET
brj-22874	162	26	vertical	vertical	ADJ
brj-22874	162	27	and	and	CCONJ
brj-22874	162	28	horizontal	horizontal	ADJ
brj-22874	162	29	directions	direction	NOUN
brj-22874	162	30	is	be	AUX
brj-22874	162	31	generated	generate	VERB
brj-22874	162	32	through	through	ADP
brj-22874	162	33	the	the	DET
brj-22874	162	34	convolution	convolution	NOUN
brj-22874	162	35	operation	operation	NOUN
brj-22874	162	36	.	.	PUNCT
brj-22874	163	1	the	the	DET
brj-22874	163	2	following	follow	VERB
brj-22874	163	3	formula	formula	NOUN
brj-22874	163	4	is	be	AUX
brj-22874	163	5	shown	show	VERB
brj-22874	163	6	.	.	PUNCT
brj-22874	164	1	1	1	NUM
brj-22874	164	2	(	(	PUNCT
brj-22874	164	3	(	(	PUNCT
brj-22874	164	4	[	[	PUNCT
brj-22874	164	5	,	,	PUNCT
brj-22874	164	6	]	]	X
brj-22874	164	7	)	)	PUNCT
brj-22874	164	8	)	)	PUNCT
brj-22874	165	1	h	h	NOUN
brj-22874	165	2	wf	wf	PROPN
brj-22874	165	3	f	f	PROPN
brj-22874	165	4	z	z	PROPN
brj-22874	165	5	z=	z=	PROPN
brj-22874	165	6	(	(	PUNCT
brj-22874	165	7	3	3	NUM
brj-22874	165	8	)	)	PUNCT
brj-22874	165	9	then	then	ADV
brj-22874	165	10	it	it	PRON
brj-22874	165	11	is	be	AUX
brj-22874	165	12	decomposed	decompose	VERB
brj-22874	165	13	into	into	ADP
brj-22874	165	14	tensor	tensor	NOUN
brj-22874	165	15	/h	/h	NOUN
brj-22874	165	16	c	c	NOUN
brj-22874	165	17	r	r	NOUN
brj-22874	165	18	hf	hf	NOUN
brj-22874	165	19	r	r	NOUN
brj-22874	165	20			NOUN
brj-22874	165	21	and	and	CCONJ
brj-22874	165	22	tensor	tensor	NOUN
brj-22874	165	23	/w	/w	NOUN
brj-22874	165	24	c	c	NOUN
brj-22874	165	25	r	r	NOUN
brj-22874	165	26	wf	wf	PROPN
brj-22874	165	27	r	r	NOUN
brj-22874	165	28			NOUN
brj-22874	165	29	along	along	ADP
brj-22874	165	30	the	the	DET
brj-22874	165	31	spatial	spatial	ADJ
brj-22874	165	32	information	information	NOUN
brj-22874	165	33	.	.	PUNCT
brj-22874	166	1	among	among	ADP
brj-22874	166	2	them	they	PRON
brj-22874	166	3	,	,	PUNCT
brj-22874	166	4	r	r	NOUN
brj-22874	166	5	is	be	AUX
brj-22874	166	6	used	use	VERB
brj-22874	166	7	to	to	PART
brj-22874	166	8	control	control	VERB
brj-22874	166	9	the	the	DET
brj-22874	166	10	sampling	sample	VERB
brj-22874	166	11	size	size	NOUN
brj-22874	166	12	reduction	reduction	NOUN
brj-22874	166	13	rate	rate	NOUN
brj-22874	166	14	.	.	PUNCT
brj-22874	167	1	then	then	ADV
brj-22874	167	2	1	1	NUM
brj-22874	167	3	1	1	NUM
brj-22874	167	4	convolution	convolution	NOUN
brj-22874	167	5	transform	transform	VERB
brj-22874	167	6	hf	hf	ADV
brj-22874	167	7	and	and	CCONJ
brj-22874	167	8	wf	wf	PROPN
brj-22874	167	9	are	be	AUX
brj-22874	167	10	performed	perform	VERB
brj-22874	167	11	on	on	ADP
brj-22874	167	12	hf	hf	NOUN
brj-22874	167	13	and	and	CCONJ
brj-22874	167	14	wf	wf	PROPN
brj-22874	167	15	respectively	respectively	ADV
brj-22874	167	16	.	.	PUNCT
brj-22874	168	1	two	two	NUM
brj-22874	168	2	tensors	tensor	NOUN
brj-22874	168	3	with	with	ADP
brj-22874	168	4	the	the	DET
brj-22874	168	5	same	same	ADJ
brj-22874	168	6	number	number	NOUN
brj-22874	168	7	of	of	ADP
brj-22874	168	8	channels	channel	NOUN
brj-22874	168	9	are	be	AUX
brj-22874	168	10	obtained	obtain	VERB
brj-22874	168	11	as	as	ADP
brj-22874	168	12	inputs	input	NOUN
brj-22874	168	13	,	,	PUNCT
brj-22874	168	14	and	and	CCONJ
brj-22874	168	15	the	the	DET
brj-22874	168	16	sigmoid	sigmoid	NOUN
brj-22874	168	17	function	function	NOUN
brj-22874	168	18	transformation	transformation	NOUN
brj-22874	168	19	is	be	AUX
brj-22874	168	20	performed	perform	VERB
brj-22874	168	21	as	as	SCONJ
brj-22874	168	22	shown	show	VERB
brj-22874	168	23	in	in	ADP
brj-22874	168	24	the	the	DET
brj-22874	168	25	following	follow	VERB
brj-22874	168	26	formula	formula	NOUN
brj-22874	168	27	,	,	PUNCT
brj-22874	168	28	(	(	PUNCT
brj-22874	168	29	(	(	PUNCT
brj-22874	168	30	)	)	PUNCT
brj-22874	168	31	)	)	PUNCT
brj-22874	169	1	h	h	NOUN
brj-22874	169	2	h	h	NOUN
brj-22874	169	3	hg	hg	PROPN
brj-22874	169	4	f	f	PROPN
brj-22874	169	5	f=	f=	PROPN
brj-22874	169	6	(	(	PUNCT
brj-22874	169	7	4	4	NUM
brj-22874	169	8	)	)	PUNCT
brj-22874	169	9	(	(	PUNCT
brj-22874	169	10	(	(	PUNCT
brj-22874	169	11	)	)	PUNCT
brj-22874	169	12	)	)	PUNCT
brj-22874	170	1	w	w	PROPN
brj-22874	170	2	w	w	PROPN
brj-22874	170	3	wg	wg	PROPN
brj-22874	170	4	f	f	PROPN
brj-22874	170	5	f=	f=	PROPN
brj-22874	170	6	(	(	PUNCT
brj-22874	170	7	5	5	NUM
brj-22874	170	8	)	)	PUNCT
brj-22874	170	9	where	where	SCONJ
brj-22874	170	10			PROPN
brj-22874	170	11	is	be	AUX
brj-22874	170	12	the	the	DET
brj-22874	170	13	sigmoid	sigmoid	NOUN
brj-22874	170	14	activation	activation	NOUN
brj-22874	170	15	function	function	NOUN
brj-22874	170	16	.	.	PUNCT
brj-22874	171	1	the	the	DET
brj-22874	171	2	number	number	NOUN
brj-22874	171	3	of	of	ADP
brj-22874	171	4	channels	channel	NOUN
brj-22874	171	5	of	of	ADP
brj-22874	171	6	f	f	PROPN
brj-22874	171	7	is	be	AUX
brj-22874	171	8	reduced	reduce	VERB
brj-22874	171	9	by	by	ADP
brj-22874	171	10	the	the	DET
brj-22874	171	11	appropriate	appropriate	ADJ
brj-22874	171	12	reduction	reduction	NOUN
brj-22874	171	13	ratio	ratio	NOUN
brj-22874	171	14	r	r	NOUN
brj-22874	171	15	,	,	PUNCT
brj-22874	171	16	which	which	PRON
brj-22874	171	17	reduces	reduce	VERB
brj-22874	171	18	the	the	DET
brj-22874	171	19	calculation	calculation	NOUN
brj-22874	171	20	amount	amount	NOUN
brj-22874	171	21	and	and	CCONJ
brj-22874	171	22	complexity	complexity	NOUN
brj-22874	171	23	of	of	ADP
brj-22874	171	24	the	the	DET
brj-22874	171	25	model	model	NOUN
brj-22874	171	26	.	.	PUNCT
brj-22874	172	1	then	then	ADV
brj-22874	172	2	,	,	PUNCT
brj-22874	172	3	g	g	PROPN
brj-22874	172	4	h	h	NOUN
brj-22874	172	5	and	and	CCONJ
brj-22874	172	6	gw	gw	PROPN
brj-22874	172	7	are	be	AUX
brj-22874	172	8	extended	extend	VERB
brj-22874	172	9	as	as	ADP
brj-22874	172	10	attention	attention	NOUN
brj-22874	172	11	weights	weight	NOUN
brj-22874	172	12	,	,	PUNCT
brj-22874	172	13	respectively	respectively	ADV
brj-22874	172	14	,	,	PUNCT
brj-22874	172	15	and	and	CCONJ
brj-22874	172	16	the	the	DET
brj-22874	172	17	following	follow	VERB
brj-22874	172	18	formula	formula	NOUN
brj-22874	172	19	is	be	AUX
brj-22874	172	20	used	use	VERB
brj-22874	172	21	as	as	ADP
brj-22874	172	22	an	an	DET
brj-22874	172	23	output	output	NOUN
brj-22874	172	24	.	.	PUNCT
brj-22874	173	1	peer	peer	NOUN
brj-22874	173	2	-	-	PUNCT
brj-22874	173	3	reviewed	review	VERB
brj-22874	173	4	article	article	NOUN
brj-22874	173	5	bioresources.com	bioresources.com	X
brj-22874	173	6	tian	tian	PROPN
brj-22874	173	7	et	et	PROPN
brj-22874	173	8	al	al	PROPN
brj-22874	173	9	.	.	PROPN
brj-22874	173	10	(	(	PUNCT
brj-22874	173	11	2023	2023	NUM
brj-22874	173	12	)	)	PUNCT
brj-22874	173	13	.	.	PUNCT
brj-22874	174	1	“	"	PUNCT
brj-22874	174	2	defect	defect	VERB
brj-22874	174	3	detection	detection	NOUN
brj-22874	174	4	with	with	ADP
brj-22874	174	5	yolov5	yolov5	NOUN
brj-22874	174	6	,	,	PUNCT
brj-22874	174	7	”	"	PUNCT
brj-22874	174	8	bioresources	bioresource	NOUN
brj-22874	174	9	18(4	18(4	NUM
brj-22874	174	10	)	)	PUNCT
brj-22874	174	11	,	,	PUNCT
brj-22874	174	12	7713	7713	NUM
brj-22874	174	13	-	-	SYM
brj-22874	174	14	7730	7730	NUM
brj-22874	174	15	.	.	PUNCT
brj-22874	175	1	7720	7720	NUM
brj-22874	175	2	(	(	PUNCT
brj-22874	175	3	,	,	PUNCT
brj-22874	175	4	)	)	PUNCT
brj-22874	175	5	(	(	PUNCT
brj-22874	175	6	,	,	PUNCT
brj-22874	175	7	)	)	PUNCT
brj-22874	175	8	(	(	PUNCT
brj-22874	175	9	)	)	PUNCT
brj-22874	175	10	(	(	PUNCT
brj-22874	175	11	)	)	PUNCT
brj-22874	175	12	h	h	PROPN
brj-22874	176	1	w	w	NOUN
brj-22874	176	2	c	c	NOUN
brj-22874	176	3	c	c	NOUN
brj-22874	177	1	c	c	NOUN
brj-22874	177	2	cy	cy	INTJ
brj-22874	177	3	i	i	PRON
brj-22874	177	4	j	j	NOUN
brj-22874	177	5	x	x	VERB
brj-22874	178	1	i	i	PRON
brj-22874	178	2	j	j	NOUN
brj-22874	179	1	g	g	NOUN
brj-22874	179	2	i	i	PRON
brj-22874	179	3	g	g	PROPN
brj-22874	179	4	j=	j=	PROPN
brj-22874	179	5			PROPN
brj-22874	179	6			PROPN
brj-22874	179	7	(	(	PUNCT
brj-22874	179	8	6	6	NUM
brj-22874	179	9	)	)	PUNCT
brj-22874	179	10	the	the	DET
brj-22874	179	11	network	network	NOUN
brj-22874	179	12	structure	structure	NOUN
brj-22874	179	13	before	before	ADP
brj-22874	179	14	and	and	CCONJ
brj-22874	179	15	after	after	ADP
brj-22874	179	16	the	the	DET
brj-22874	179	17	introduction	introduction	NOUN
brj-22874	179	18	of	of	ADP
brj-22874	179	19	the	the	DET
brj-22874	179	20	ca	ca	NOUN
brj-22874	179	21	attention	attention	NOUN
brj-22874	179	22	mechanism	mechanism	NOUN
brj-22874	179	23	in	in	ADP
brj-22874	179	24	the	the	DET
brj-22874	179	25	backbone	backbone	NOUN
brj-22874	179	26	network	network	NOUN
brj-22874	179	27	is	be	AUX
brj-22874	179	28	shown	show	VERB
brj-22874	179	29	in	in	ADP
brj-22874	179	30	fig	fig	NOUN
brj-22874	179	31	.	.	PUNCT
brj-22874	180	1	4	4	X
brj-22874	180	2	.	.	X
brj-22874	180	3	focus	focus	VERB
brj-22874	180	4	conv	conv	PROPN
brj-22874	180	5	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	180	6	conv	conv	PROPN
brj-22874	180	7	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	180	8	conv	conv	PROPN
brj-22874	180	9	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	180	10	conv	conv	PROPN
brj-22874	180	11	spp	spp	PROPN
brj-22874	180	12	bottleneckcsp	bottleneckcsp	NOUN
brj-22874	180	13	focus	focus	VERB
brj-22874	180	14	conv	conv	ADJ
brj-22874	180	15	bottleneckcsp	bottleneckcsp	X
brj-22874	180	16	conv	conv	PROPN
brj-22874	180	17	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	180	18	conv	conv	PROPN
brj-22874	180	19	spp	spp	NOUN
brj-22874	180	20	bottleneckcsp	bottleneckcsp	NOUN
brj-22874	180	21	coordatt	coordatt	PROPN
brj-22874	180	22	coordatt	coordatt	PROPN
brj-22874	180	23	conv	conv	PROPN
brj-22874	180	24	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	180	25	fig	fig	NOUN
brj-22874	180	26	.	.	PUNCT
brj-22874	181	1	4	4	X
brj-22874	181	2	.	.	PUNCT
brj-22874	181	3	adds	add	VERB
brj-22874	181	4	the	the	DET
brj-22874	181	5	ca	ca	NOUN
brj-22874	181	6	attention	attention	NOUN
brj-22874	181	7	mechanism	mechanism	NOUN
brj-22874	181	8	k	k	NOUN
brj-22874	181	9	-	-	ADJ
brj-22874	181	10	means++	means++	ADV
brj-22874	181	11	clustering	clustering	ADJ
brj-22874	181	12	algorithm	algorithm	NOUN
brj-22874	181	13	the	the	DET
brj-22874	181	14	initial	initial	ADJ
brj-22874	181	15	anchor	anchor	NOUN
brj-22874	181	16	box	box	NOUN
brj-22874	181	17	in	in	ADP
brj-22874	181	18	yolov5	yolov5	NOUN
brj-22874	181	19	is	be	AUX
brj-22874	181	20	based	base	VERB
brj-22874	181	21	on	on	ADP
brj-22874	181	22	the	the	DET
brj-22874	181	23	data	data	NOUN
brj-22874	181	24	sets	set	NOUN
brj-22874	181	25	such	such	ADJ
brj-22874	181	26	as	as	ADP
brj-22874	181	27	coco	coco	PROPN
brj-22874	181	28	(	(	PUNCT
brj-22874	181	29	common	common	ADJ
brj-22874	181	30	objects	object	NOUN
brj-22874	181	31	in	in	ADP
brj-22874	181	32	context	context	NOUN
brj-22874	181	33	)	)	PUNCT
brj-22874	181	34	or	or	CCONJ
brj-22874	181	35	pascal	pascal	ADJ
brj-22874	181	36	voc	voc	NOUN
brj-22874	181	37	(	(	PUNCT
brj-22874	181	38	the	the	DET
brj-22874	181	39	pascal	pascal	ADJ
brj-22874	181	40	visual	visual	ADJ
brj-22874	181	41	object	object	NOUN
brj-22874	181	42	classes	class	NOUN
brj-22874	181	43	)	)	PUNCT
brj-22874	181	44	,	,	PUNCT
brj-22874	181	45	and	and	CCONJ
brj-22874	181	46	the	the	DET
brj-22874	181	47	initial	initial	ADJ
brj-22874	181	48	anchor	anchor	NOUN
brj-22874	181	49	box	box	PROPN
brj-22874	181	50	is	be	AUX
brj-22874	181	51	finally	finally	ADV
brj-22874	181	52	obtained	obtain	VERB
brj-22874	181	53	by	by	ADP
brj-22874	181	54	using	use	VERB
brj-22874	181	55	the	the	DET
brj-22874	181	56	k	k	ADJ
brj-22874	181	57	-	-	PUNCT
brj-22874	181	58	means	mean	VERB
brj-22874	181	59	clustering	clustering	ADJ
brj-22874	181	60	algorithm	algorithm	NOUN
brj-22874	181	61	.	.	PUNCT
brj-22874	182	1	the	the	DET
brj-22874	182	2	implementation	implementation	NOUN
brj-22874	182	3	steps	step	NOUN
brj-22874	182	4	are	be	AUX
brj-22874	182	5	as	as	SCONJ
brj-22874	182	6	follows	follow	VERB
brj-22874	182	7	:	:	PUNCT
brj-22874	182	8	a	a	X
brj-22874	182	9	)	)	PUNCT
brj-22874	182	10	randomly	randomly	ADV
brj-22874	182	11	select	select	ADJ
brj-22874	182	12	k	k	PROPN
brj-22874	182	13	samples	sample	NOUN
brj-22874	182	14	from	from	ADP
brj-22874	182	15	all	all	DET
brj-22874	182	16	samples	sample	NOUN
brj-22874	182	17	as	as	ADP
brj-22874	182	18	the	the	DET
brj-22874	182	19	initial	initial	ADJ
brj-22874	182	20	clustering	clustering	ADJ
brj-22874	182	21	center	center	NOUN
brj-22874	182	22	.	.	PUNCT
brj-22874	183	1	b	b	X
brj-22874	183	2	)	)	PUNCT
brj-22874	183	3	calculate	calculate	NOUN
brj-22874	183	4	the	the	DET
brj-22874	183	5	euclidean	euclidean	ADJ
brj-22874	183	6	distance	distance	NOUN
brj-22874	183	7	of	of	ADP
brj-22874	183	8	each	each	DET
brj-22874	183	9	sample	sample	NOUN
brj-22874	183	10	from	from	ADP
brj-22874	183	11	the	the	DET
brj-22874	183	12	cluster	cluster	NOUN
brj-22874	183	13	center	center	NOUN
brj-22874	183	14	,	,	PUNCT
brj-22874	183	15	and	and	CCONJ
brj-22874	183	16	then	then	ADV
brj-22874	183	17	divide	divide	VERB
brj-22874	183	18	the	the	DET
brj-22874	183	19	sample	sample	NOUN
brj-22874	183	20	into	into	ADP
brj-22874	183	21	the	the	DET
brj-22874	183	22	class	class	NOUN
brj-22874	183	23	closest	close	ADJ
brj-22874	183	24	to	to	ADP
brj-22874	183	25	it	it	PRON
brj-22874	183	26	.	.	PUNCT
brj-22874	184	1	c	c	X
brj-22874	184	2	)	)	PUNCT
brj-22874	184	3	the	the	DET
brj-22874	184	4	center	center	NOUN
brj-22874	184	5	point	point	NOUN
brj-22874	184	6	position	position	NOUN
brj-22874	184	7	of	of	ADP
brj-22874	184	8	each	each	DET
brj-22874	184	9	cluster	cluster	NOUN
brj-22874	184	10	is	be	AUX
brj-22874	184	11	recalculated	recalculate	VERB
brj-22874	184	12	according	accord	VERB
brj-22874	184	13	to	to	ADP
brj-22874	184	14	the	the	DET
brj-22874	184	15	clustering	clustering	ADJ
brj-22874	184	16	results	result	NOUN
brj-22874	184	17	.	.	PUNCT
brj-22874	185	1	d	d	X
brj-22874	185	2	)	)	PUNCT
brj-22874	185	3	repeat	repeat	NOUN
brj-22874	185	4	b	b	NOUN
brj-22874	185	5	)	)	PUNCT
brj-22874	185	6	to	to	ADP
brj-22874	185	7	c	c	PROPN
brj-22874	185	8	)	)	PUNCT
brj-22874	185	9	until	until	SCONJ
brj-22874	185	10	the	the	DET
brj-22874	185	11	internal	internal	ADJ
brj-22874	185	12	elements	element	NOUN
brj-22874	185	13	in	in	ADP
brj-22874	185	14	each	each	DET
brj-22874	185	15	cluster	cluster	NOUN
brj-22874	185	16	do	do	AUX
brj-22874	185	17	not	not	PART
brj-22874	185	18	change	change	VERB
brj-22874	185	19	,	,	PUNCT
brj-22874	185	20	and	and	CCONJ
brj-22874	185	21	all	all	DET
brj-22874	185	22	the	the	DET
brj-22874	185	23	final	final	ADJ
brj-22874	185	24	center	center	NOUN
brj-22874	185	25	point	point	NOUN
brj-22874	185	26	coordinates	coordinate	NOUN
brj-22874	185	27	are	be	AUX
brj-22874	185	28	the	the	DET
brj-22874	185	29	trained	train	VERB
brj-22874	185	30	parameter	parameter	NOUN
brj-22874	185	31	model	model	NOUN
brj-22874	185	32	.	.	PUNCT
brj-22874	186	1	start	start	VERB
brj-22874	186	2	clustering	cluster	VERB
brj-22874	186	3	set	set	VERB
brj-22874	186	4	the	the	DET
brj-22874	186	5	number	number	NOUN
brj-22874	186	6	of	of	ADP
brj-22874	186	7	clusters	cluster	NOUN
brj-22874	187	1	k	k	PROPN
brj-22874	187	2	one	one	NUM
brj-22874	187	3	cluster	cluster	NOUN
brj-22874	187	4	is	be	AUX
brj-22874	187	5	randomly	randomly	ADV
brj-22874	187	6	selected	select	VERB
brj-22874	187	7	as	as	SCONJ
brj-22874	187	8	the	the	DET
brj-22874	187	9	initial	initial	ADJ
brj-22874	187	10	cluster	cluster	NOUN
brj-22874	187	11	center	center	NOUN
brj-22874	187	12	allocate	allocate	VERB
brj-22874	187	13	each	each	DET
brj-22874	187	14	data	datum	NOUN
brj-22874	187	15	to	to	ADP
brj-22874	187	16	the	the	DET
brj-22874	187	17	nearest	near	ADJ
brj-22874	187	18	class	class	NOUN
brj-22874	187	19	recalculate	recalculate	NOUN
brj-22874	187	20	cluster	cluster	NOUN
brj-22874	187	21	centers	center	NOUN
brj-22874	187	22	maximum	maximum	ADJ
brj-22874	187	23	convergence	convergence	NOUN
brj-22874	187	24	is	be	AUX
brj-22874	187	25	achieved	achieve	VERB
brj-22874	187	26	output	output	NOUN
brj-22874	187	27	anchor	anchor	NOUN
brj-22874	187	28	clustering	cluster	VERB
brj-22874	187	29	results	result	NOUN
brj-22874	187	30	end	end	VERB
brj-22874	187	31	of	of	ADP
brj-22874	187	32	clustering	cluster	VERB
brj-22874	187	33	no	no	DET
brj-22874	187	34	yes	yes	INTJ
brj-22874	187	35	fig	fig	NOUN
brj-22874	187	36	.	.	PUNCT
brj-22874	188	1	5	5	NUM
brj-22874	188	2	.	.	X
brj-22874	189	1	k	k	ADJ
brj-22874	189	2	-	-	ADJ
brj-22874	189	3	means++	means++	ADJ
brj-22874	189	4	algorithm	algorithm	NOUN
brj-22874	189	5	flow	flow	NOUN
brj-22874	189	6	chart	chart	NOUN
brj-22874	189	7	peer	peer	NOUN
brj-22874	189	8	-	-	PUNCT
brj-22874	189	9	reviewed	review	VERB
brj-22874	189	10	article	article	NOUN
brj-22874	189	11	bioresources.com	bioresources.com	X
brj-22874	189	12	tian	tian	PROPN
brj-22874	189	13	et	et	PROPN
brj-22874	189	14	al	al	PROPN
brj-22874	189	15	.	.	PROPN
brj-22874	189	16	(	(	PUNCT
brj-22874	189	17	2023	2023	NUM
brj-22874	189	18	)	)	PUNCT
brj-22874	189	19	.	.	PUNCT
brj-22874	190	1	“	"	PUNCT
brj-22874	190	2	defect	defect	VERB
brj-22874	190	3	detection	detection	NOUN
brj-22874	190	4	with	with	ADP
brj-22874	190	5	yolov5	yolov5	NOUN
brj-22874	190	6	,	,	PUNCT
brj-22874	190	7	”	"	PUNCT
brj-22874	190	8	bioresources	bioresource	NOUN
brj-22874	190	9	18(4	18(4	NUM
brj-22874	190	10	)	)	PUNCT
brj-22874	190	11	,	,	PUNCT
brj-22874	190	12	7713	7713	NUM
brj-22874	190	13	-	-	SYM
brj-22874	190	14	7730	7730	NUM
brj-22874	190	15	.	.	PUNCT
brj-22874	190	16	7721	7721	NUM
brj-22874	190	17	in	in	ADP
brj-22874	190	18	yolov5	yolov5	NOUN
brj-22874	190	19	,	,	PUNCT
brj-22874	190	20	euclidean	euclidean	ADJ
brj-22874	190	21	distance	distance	NOUN
brj-22874	190	22	is	be	AUX
brj-22874	190	23	used	use	VERB
brj-22874	190	24	to	to	PART
brj-22874	190	25	identify	identify	VERB
brj-22874	190	26	the	the	DET
brj-22874	190	27	similarity	similarity	NOUN
brj-22874	190	28	between	between	ADP
brj-22874	190	29	the	the	DET
brj-22874	190	30	sample	sample	NOUN
brj-22874	190	31	marker	marker	NOUN
brj-22874	190	32	boxes	box	NOUN
brj-22874	190	33	,	,	PUNCT
brj-22874	190	34	which	which	PRON
brj-22874	190	35	can	can	AUX
brj-22874	190	36	easily	easily	ADV
brj-22874	190	37	cause	cause	VERB
brj-22874	190	38	the	the	DET
brj-22874	190	39	marker	marker	NOUN
brj-22874	190	40	boxes	box	NOUN
brj-22874	190	41	in	in	ADP
brj-22874	190	42	a	a	DET
brj-22874	190	43	certain	certain	ADJ
brj-22874	190	44	class	class	NOUN
brj-22874	190	45	to	to	PART
brj-22874	190	46	be	be	AUX
brj-22874	190	47	too	too	ADV
brj-22874	190	48	close	close	ADJ
brj-22874	190	49	to	to	ADP
brj-22874	190	50	the	the	DET
brj-22874	190	51	initial	initial	ADJ
brj-22874	190	52	anchor	anchor	NOUN
brj-22874	190	53	box	box	NOUN
brj-22874	190	54	size	size	NOUN
brj-22874	190	55	,	,	PUNCT
brj-22874	190	56	and	and	CCONJ
brj-22874	190	57	this	this	PRON
brj-22874	190	58	will	will	AUX
brj-22874	190	59	ultimately	ultimately	ADV
brj-22874	190	60	affect	affect	VERB
brj-22874	190	61	the	the	DET
brj-22874	190	62	clustering	clustering	ADJ
brj-22874	190	63	effect	effect	NOUN
brj-22874	190	64	.	.	PUNCT
brj-22874	191	1	given	give	VERB
brj-22874	191	2	the	the	DET
brj-22874	191	3	above	above	ADJ
brj-22874	191	4	problems	problem	NOUN
brj-22874	191	5	,	,	PUNCT
brj-22874	191	6	this	this	DET
brj-22874	191	7	paper	paper	NOUN
brj-22874	191	8	proposes	propose	VERB
brj-22874	191	9	a	a	DET
brj-22874	191	10	k	k	ADJ
brj-22874	191	11	-	-	ADJ
brj-22874	191	12	means++	means++	ADJ
brj-22874	191	13	clustering	clustering	ADJ
brj-22874	191	14	algorithm	algorithm	NOUN
brj-22874	191	15	.	.	PUNCT
brj-22874	192	1	the	the	DET
brj-22874	192	2	clustered	clustered	ADJ
brj-22874	192	3	prior	prior	ADJ
brj-22874	192	4	box	box	NOUN
brj-22874	192	5	is	be	AUX
brj-22874	192	6	closer	close	ADJ
brj-22874	192	7	to	to	ADP
brj-22874	192	8	the	the	DET
brj-22874	192	9	target	target	NOUN
brj-22874	192	10	box	box	NOUN
brj-22874	192	11	of	of	ADP
brj-22874	192	12	the	the	DET
brj-22874	192	13	wooden	wooden	ADJ
brj-22874	192	14	spoon	spoon	NOUN
brj-22874	192	15	image	image	NOUN
brj-22874	192	16	data	datum	NOUN
brj-22874	192	17	set	set	VERB
brj-22874	192	18	.	.	PUNCT
brj-22874	193	1	the	the	DET
brj-22874	193	2	specific	specific	ADJ
brj-22874	193	3	process	process	NOUN
brj-22874	193	4	of	of	ADP
brj-22874	193	5	the	the	DET
brj-22874	193	6	k	k	ADJ
brj-22874	193	7	-	-	ADJ
brj-22874	193	8	means++	means++	ADJ
brj-22874	193	9	algorithm	algorithm	NOUN
brj-22874	193	10	is	be	AUX
brj-22874	193	11	shown	show	VERB
brj-22874	193	12	in	in	ADP
brj-22874	193	13	fig	fig	NOUN
brj-22874	193	14	.	.	PUNCT
brj-22874	194	1	5	5	NUM
brj-22874	194	2	.	.	X
brj-22874	195	1	k	k	X
brj-22874	195	2	-	-	PUNCT
brj-22874	195	3	means++	means++	PROPN
brj-22874	195	4	is	be	AUX
brj-22874	195	5	based	base	VERB
brj-22874	195	6	on	on	ADP
brj-22874	195	7	the	the	DET
brj-22874	195	8	traditional	traditional	ADJ
brj-22874	195	9	k	k	ADJ
brj-22874	195	10	-	-	PUNCT
brj-22874	195	11	means	means	NOUN
brj-22874	195	12	algorithm	algorithm	NOUN
brj-22874	195	13	(	(	PUNCT
brj-22874	195	14	likas	likas	X
brj-22874	195	15	et	et	PROPN
brj-22874	195	16	al	al	PROPN
brj-22874	195	17	.	.	PROPN
brj-22874	195	18	2003	2003	NUM
brj-22874	195	19	)	)	PUNCT
brj-22874	195	20	to	to	PART
brj-22874	195	21	optimize	optimize	VERB
brj-22874	195	22	the	the	DET
brj-22874	195	23	selection	selection	NOUN
brj-22874	195	24	of	of	ADP
brj-22874	195	25	the	the	DET
brj-22874	195	26	initial	initial	ADJ
brj-22874	195	27	clustering	clustering	ADJ
brj-22874	195	28	center	center	NOUN
brj-22874	195	29	.	.	PUNCT
brj-22874	196	1	the	the	DET
brj-22874	196	2	implementation	implementation	NOUN
brj-22874	196	3	steps	step	NOUN
brj-22874	196	4	of	of	ADP
brj-22874	196	5	kmeans++	kmeans++	PRON
brj-22874	196	6	to	to	PART
brj-22874	196	7	optimize	optimize	VERB
brj-22874	196	8	the	the	DET
brj-22874	196	9	initialization	initialization	NOUN
brj-22874	196	10	centroid	centroid	NOUN
brj-22874	196	11	are	be	AUX
brj-22874	196	12	as	as	SCONJ
brj-22874	196	13	follows	follow	VERB
brj-22874	196	14	:	:	PUNCT
brj-22874	196	15	step	step	NOUN
brj-22874	196	16	1	1	NUM
brj-22874	196	17	:	:	PUNCT
brj-22874	196	18	set	set	VERB
brj-22874	196	19	the	the	DET
brj-22874	196	20	spatial	spatial	ADJ
brj-22874	196	21	data	datum	NOUN
brj-22874	196	22	set	set	VERB
brj-22874	196	23	1	1	NUM
brj-22874	196	24	2	2	NUM
brj-22874	196	25	{	{	PUNCT
brj-22874	196	26	,	,	PUNCT
brj-22874	196	27	,	,	PUNCT
brj-22874	196	28	...	...	PUNCT
brj-22874	196	29	,	,	PUNCT
brj-22874	196	30	}	}	PUNCT
brj-22874	197	1	np	np	ADP
brj-22874	197	2	p	p	X
brj-22874	197	3	p	p	X
brj-22874	197	4	p=	p=	NOUN
brj-22874	197	5	of	of	ADP
brj-22874	197	6	the	the	DET
brj-22874	197	7	input	input	NOUN
brj-22874	197	8	data	data	NOUN
brj-22874	197	9	point	point	NOUN
brj-22874	197	10	set	set	NOUN
brj-22874	197	11	,	,	PUNCT
brj-22874	197	12	and	and	CCONJ
brj-22874	197	13	randomly	randomly	ADV
brj-22874	197	14	select	select	VERB
brj-22874	197	15	a	a	DET
brj-22874	197	16	point	point	NOUN
brj-22874	197	17	ip	ip	NOUN
brj-22874	197	18	as	as	ADP
brj-22874	197	19	the	the	DET
brj-22874	197	20	first	first	ADJ
brj-22874	197	21	clustering	clustering	ADJ
brj-22874	197	22	center	center	NOUN
brj-22874	197	23	1k	1k	NUM
brj-22874	197	24	.	.	PUNCT
brj-22874	198	1	step	step	NOUN
brj-22874	198	2	2	2	NUM
brj-22874	198	3	:	:	PUNCT
brj-22874	198	4	for	for	ADP
brj-22874	198	5	each	each	DET
brj-22874	198	6	point	point	NOUN
brj-22874	198	7	in	in	ADP
brj-22874	198	8	the	the	DET
brj-22874	198	9	set	set	NOUN
brj-22874	198	10	p	p	NOUN
brj-22874	198	11	,	,	PUNCT
brj-22874	198	12	use	use	NOUN
brj-22874	198	13	formula	formula	NOUN
brj-22874	198	14	(	(	PUNCT
brj-22874	198	15	7	7	NUM
brj-22874	198	16	)	)	PUNCT
brj-22874	198	17	to	to	PART
brj-22874	198	18	calculate	calculate	VERB
brj-22874	198	19	the	the	DET
brj-22874	198	20	minimum	minimum	ADJ
brj-22874	198	21	distance	distance	NOUN
brj-22874	198	22	(	(	PUNCT
brj-22874	198	23	)	)	PUNCT
brj-22874	198	24	i	i	PROPN
brj-22874	198	25	d	d	PROPN
brj-22874	198	26	p	p	X
brj-22874	198	27	between	between	ADP
brj-22874	198	28	each	each	DET
brj-22874	198	29	object	object	NOUN
brj-22874	198	30	in	in	ADP
brj-22874	198	31	the	the	DET
brj-22874	198	32	set	set	NOUN
brj-22874	198	33	and	and	CCONJ
brj-22874	198	34	the	the	DET
brj-22874	198	35	current	current	ADJ
brj-22874	198	36	existing	exist	VERB
brj-22874	198	37	cluster	cluster	NOUN
brj-22874	198	38	center	center	NOUN
brj-22874	198	39	,	,	PUNCT
brj-22874	198	40	and	and	CCONJ
brj-22874	198	41	use	use	VERB
brj-22874	198	42	formula	formula	NOUN
brj-22874	198	43	(	(	PUNCT
brj-22874	198	44	8)	8)	NUM
brj-22874	198	45	to	to	PART
brj-22874	198	46	obtain	obtain	VERB
brj-22874	198	47	the	the	DET
brj-22874	198	48	sum	sum	NOUN
brj-22874	198	49	of	of	ADP
brj-22874	198	50	squares	square	NOUN
brj-22874	198	51	of	of	ADP
brj-22874	198	52	these	these	DET
brj-22874	198	53	distances	distance	NOUN
brj-22874	198	54	.	.	PUNCT
brj-22874	199	1	2	2	NUM
brj-22874	199	2	(	(	PUNCT
brj-22874	199	3	)	)	PUNCT
brj-22874	199	4	min	min	NOUN
brj-22874	199	5	{	{	PUNCT
brj-22874	199	6	(	(	PUNCT
brj-22874	199	7	(	(	PUNCT
brj-22874	199	8	)	)	PUNCT
brj-22874	199	9	)	)	PUNCT
brj-22874	199	10	}	}	PUNCT
brj-22874	200	1	i	i	PRON
brj-22874	200	2	i	i	PRON
brj-22874	200	3	nd	nd	VERB
brj-22874	200	4	p	p	X
brj-22874	200	5	p	p	X
brj-22874	200	6	k=	k=	X
brj-22874	200	7	−	−	PROPN
brj-22874	200	8	(	(	PUNCT
brj-22874	200	9	7	7	NUM
brj-22874	200	10	)	)	SYM
brj-22874	200	11	2	2	NUM
brj-22874	200	12	1	1	NUM
brj-22874	200	13	(	(	PUNCT
brj-22874	200	14	)	)	PUNCT
brj-22874	200	15	n	n	CCONJ
brj-22874	201	1	i	i	PRON
brj-22874	201	2	i	i	PRON
brj-22874	201	3	sum	sum	VERB
brj-22874	202	1	d	d	X
brj-22874	202	2	p	p	X
brj-22874	203	1	=	=	X
brj-22874	204	1	=	=	NOUN
brj-22874	204	2			X
brj-22874	204	3	(	(	PUNCT
brj-22874	204	4	8)	8)	NUM
brj-22874	204	5	step	step	NOUN
brj-22874	204	6	3	3	NUM
brj-22874	204	7	:	:	PUNCT
brj-22874	204	8	calculate	calculate	VERB
brj-22874	204	9	the	the	DET
brj-22874	204	10	probability	probability	NOUN
brj-22874	204	11	p	p	NOUN
brj-22874	204	12	of	of	ADP
brj-22874	204	13	each	each	DET
brj-22874	204	14	point	point	NOUN
brj-22874	204	15	being	be	AUX
brj-22874	204	16	selected	select	VERB
brj-22874	204	17	as	as	ADP
brj-22874	204	18	the	the	DET
brj-22874	204	19	next	next	ADJ
brj-22874	204	20	cluster	cluster	NOUN
brj-22874	204	21	center	center	NOUN
brj-22874	204	22	using	use	VERB
brj-22874	204	23	calculation	calculation	NOUN
brj-22874	204	24	formula	formula	NOUN
brj-22874	204	25	(	(	PUNCT
brj-22874	204	26	9	9	NUM
brj-22874	204	27	)	)	PUNCT
brj-22874	204	28	.	.	PUNCT
brj-22874	205	1	take	take	VERB
brj-22874	205	2	a	a	DET
brj-22874	205	3	random	random	ADJ
brj-22874	205	4	number	number	NOUN
brj-22874	205	5	ir	ir	NOUN
brj-22874	205	6	between	between	ADP
brj-22874	205	7	the	the	DET
brj-22874	205	8	interval	interval	NOUN
brj-22874	205	9	[	[	X
brj-22874	205	10	0,1	0,1	NOUN
brj-22874	205	11	]	]	PUNCT
brj-22874	205	12	,	,	PUNCT
brj-22874	205	13	subtract	subtract	VERB
brj-22874	205	14	1	1	NUM
brj-22874	205	15	2	2	NUM
brj-22874	205	16	{	{	PUNCT
brj-22874	205	17	,	,	PUNCT
brj-22874	205	18	,	,	PUNCT
brj-22874	205	19	...	...	PUNCT
brj-22874	205	20	,	,	PUNCT
brj-22874	205	21	}	}	PUNCT
brj-22874	205	22	ip	ip	VERB
brj-22874	205	23	p	p	X
brj-22874	205	24	p	p	NOUN
brj-22874	205	25	with	with	ADP
brj-22874	205	26	ir	ir	PROPN
brj-22874	205	27	in	in	ADP
brj-22874	205	28	turn	turn	NOUN
brj-22874	205	29	,	,	PUNCT
brj-22874	205	30	until	until	SCONJ
brj-22874	205	31	the	the	DET
brj-22874	205	32	result	result	NOUN
brj-22874	205	33	is	be	AUX
brj-22874	205	34	less	less	ADJ
brj-22874	205	35	than	than	ADP
brj-22874	205	36	0	0	NUM
brj-22874	205	37	.	.	PUNCT
brj-22874	206	1	the	the	DET
brj-22874	206	2	point	point	NOUN
brj-22874	206	3	corresponding	correspond	VERB
brj-22874	206	4	to	to	ADP
brj-22874	206	5	ip	ip	NOUN
brj-22874	206	6	is	be	AUX
brj-22874	206	7	the	the	DET
brj-22874	206	8	next	next	ADJ
brj-22874	206	9	cluster	cluster	NOUN
brj-22874	206	10	center	center	NOUN
brj-22874	206	11	.	.	PUNCT
brj-22874	207	1	2	2	NUM
brj-22874	207	2	(	(	PUNCT
brj-22874	207	3	)	)	PUNCT
brj-22874	207	4	(	(	PUNCT
brj-22874	207	5	)	)	PUNCT
brj-22874	208	1	i	i	PROPN
brj-22874	208	2	d	d	PROPN
brj-22874	209	1	p	p	X
brj-22874	209	2	p	p	X
brj-22874	210	1	i	i	PRON
brj-22874	210	2	sum	sum	NOUN
brj-22874	210	3	=	=	PUNCT
brj-22874	210	4	(	(	PUNCT
brj-22874	210	5	9	9	X
brj-22874	210	6	)	)	PUNCT
brj-22874	210	7	step	step	NOUN
brj-22874	210	8	4	4	NUM
brj-22874	210	9	:	:	PUNCT
brj-22874	210	10	repeat	repeat	NOUN
brj-22874	210	11	steps	step	NOUN
brj-22874	210	12	2	2	NUM
brj-22874	210	13	~	~	SYM
brj-22874	210	14	3	3	NUM
brj-22874	210	15	to	to	PART
brj-22874	210	16	find	find	VERB
brj-22874	210	17	the	the	DET
brj-22874	210	18	cluster	cluster	NOUN
brj-22874	210	19	center	center	NOUN
brj-22874	210	20	that	that	PRON
brj-22874	210	21	meets	meet	VERB
brj-22874	210	22	the	the	DET
brj-22874	210	23	requirements	requirement	NOUN
brj-22874	210	24	.	.	PUNCT
brj-22874	211	1	through	through	ADP
brj-22874	211	2	the	the	DET
brj-22874	211	3	above	above	ADJ
brj-22874	211	4	steps	step	NOUN
brj-22874	211	5	,	,	PUNCT
brj-22874	211	6	the	the	DET
brj-22874	211	7	optimized	optimize	VERB
brj-22874	211	8	initial	initial	ADJ
brj-22874	211	9	clustering	clustering	ADJ
brj-22874	211	10	center	center	NOUN
brj-22874	211	11	is	be	AUX
brj-22874	211	12	obtained	obtain	VERB
brj-22874	211	13	.	.	PUNCT
brj-22874	212	1	feature	feature	NOUN
brj-22874	212	2	extraction	extraction	NOUN
brj-22874	212	3	network	network	NOUN
brj-22874	212	4	sppnet	sppnet	NOUN
brj-22874	212	5	(	(	PUNCT
brj-22874	212	6	he	he	PRON
brj-22874	212	7	et	et	PROPN
brj-22874	212	8	al	al	PROPN
brj-22874	212	9	.	.	PROPN
brj-22874	212	10	2015	2015	NUM
brj-22874	212	11	)	)	PUNCT
brj-22874	212	12	is	be	AUX
brj-22874	212	13	placed	place	VERB
brj-22874	212	14	after	after	ADP
brj-22874	212	15	the	the	DET
brj-22874	212	16	last	last	ADJ
brj-22874	212	17	feature	feature	NOUN
brj-22874	212	18	layer	layer	NOUN
brj-22874	212	19	of	of	ADP
brj-22874	212	20	cspdarknet53	cspdarknet53	NOUN
brj-22874	212	21	.	.	PUNCT
brj-22874	213	1	after	after	ADP
brj-22874	213	2	three	three	NUM
brj-22874	213	3	convolutions	convolution	NOUN
brj-22874	213	4	of	of	ADP
brj-22874	213	5	the	the	DET
brj-22874	213	6	last	last	ADJ
brj-22874	213	7	feature	feature	NOUN
brj-22874	213	8	layer	layer	NOUN
brj-22874	213	9	,	,	PUNCT
brj-22874	213	10	it	it	PRON
brj-22874	213	11	is	be	AUX
brj-22874	213	12	processed	process	VERB
brj-22874	213	13	with	with	ADP
brj-22874	213	14	four	four	NUM
brj-22874	213	15	different	different	ADJ
brj-22874	213	16	sizes	size	NOUN
brj-22874	213	17	of	of	ADP
brj-22874	213	18	maximum	maximum	ADJ
brj-22874	213	19	pooling	pooling	NOUN
brj-22874	213	20	.	.	PUNCT
brj-22874	214	1	the	the	DET
brj-22874	214	2	sizes	size	NOUN
brj-22874	214	3	of	of	ADP
brj-22874	214	4	four	four	NUM
brj-22874	214	5	different	different	ADJ
brj-22874	214	6	sizes	size	NOUN
brj-22874	214	7	of	of	ADP
brj-22874	214	8	pooling	pool	VERB
brj-22874	214	9	kernels	kernel	NOUN
brj-22874	214	10	are	be	AUX
brj-22874	214	11	13	13	NUM
brj-22874	214	12	13	13	NUM
brj-22874	214	13	,	,	PUNCT
brj-22874	214	14	9	9	NUM
brj-22874	214	15	9	9	NUM
brj-22874	214	16	,	,	PUNCT
brj-22874	214	17	5	5	NUM
brj-22874	214	18	5	5	NUM
brj-22874	214	19	,	,	PUNCT
brj-22874	214	20	and	and	CCONJ
brj-22874	214	21	1	1	NUM
brj-22874	214	22	1	1	NUM
brj-22874	214	23	.	.	PUNCT
brj-22874	215	1	the	the	DET
brj-22874	215	2	structure	structure	NOUN
brj-22874	215	3	is	be	AUX
brj-22874	215	4	shown	show	VERB
brj-22874	215	5	in	in	ADP
brj-22874	215	6	fig	fig	NOUN
brj-22874	215	7	.	.	PUNCT
brj-22874	216	1	6	6	NUM
brj-22874	216	2	.	.	PUNCT
brj-22874	216	3	by	by	ADP
brj-22874	216	4	adding	add	VERB
brj-22874	216	5	spp	spp	NOUN
brj-22874	216	6	structure	structure	NOUN
brj-22874	216	7	in	in	ADP
brj-22874	216	8	yolov5	yolov5	NOUN
brj-22874	216	9	,	,	PUNCT
brj-22874	216	10	the	the	DET
brj-22874	216	11	receptive	receptive	ADJ
brj-22874	216	12	field	field	NOUN
brj-22874	216	13	is	be	AUX
brj-22874	216	14	increased	increase	VERB
brj-22874	216	15	,	,	PUNCT
brj-22874	216	16	the	the	DET
brj-22874	216	17	most	most	ADV
brj-22874	216	18	important	important	ADJ
brj-22874	216	19	contextual	contextual	ADJ
brj-22874	216	20	features	feature	NOUN
brj-22874	216	21	are	be	AUX
brj-22874	216	22	separated	separate	VERB
brj-22874	216	23	,	,	PUNCT
brj-22874	216	24	and	and	CCONJ
brj-22874	216	25	the	the	DET
brj-22874	216	26	detection	detection	NOUN
brj-22874	216	27	speed	speed	NOUN
brj-22874	216	28	is	be	AUX
brj-22874	216	29	not	not	PART
brj-22874	216	30	reduced	reduce	VERB
brj-22874	216	31	.	.	PUNCT
brj-22874	217	1	through	through	ADP
brj-22874	217	2	the	the	DET
brj-22874	217	3	analysis	analysis	NOUN
brj-22874	217	4	of	of	ADP
brj-22874	217	5	the	the	DET
brj-22874	217	6	sppnet	sppnet	NOUN
brj-22874	217	7	structure	structure	NOUN
brj-22874	217	8	,	,	PUNCT
brj-22874	217	9	it	it	PRON
brj-22874	217	10	is	be	AUX
brj-22874	217	11	concluded	conclude	VERB
brj-22874	217	12	that	that	SCONJ
brj-22874	217	13	the	the	DET
brj-22874	217	14	sppnet	sppnet	NOUN
brj-22874	217	15	used	use	VERB
brj-22874	217	16	in	in	ADP
brj-22874	217	17	the	the	DET
brj-22874	217	18	yolov5	yolov5	NOUN
brj-22874	217	19	structure	structure	NOUN
brj-22874	217	20	can	can	AUX
brj-22874	217	21	not	not	PART
brj-22874	217	22	effectively	effectively	ADV
brj-22874	217	23	extract	extract	VERB
brj-22874	217	24	the	the	DET
brj-22874	217	25	feature	feature	NOUN
brj-22874	217	26	information	information	NOUN
brj-22874	217	27	of	of	ADP
brj-22874	217	28	different	different	ADJ
brj-22874	217	29	scale	scale	NOUN
brj-22874	217	30	targets	target	NOUN
brj-22874	217	31	.	.	PUNCT
brj-22874	218	1	the	the	DET
brj-22874	218	2	sppnet	sppnet	NOUN
brj-22874	218	3	module	module	NOUN
brj-22874	218	4	is	be	AUX
brj-22874	218	5	used	use	VERB
brj-22874	218	6	as	as	ADP
brj-22874	218	7	a	a	DET
brj-22874	218	8	variable	variable	NOUN
brj-22874	218	9	and	and	CCONJ
brj-22874	218	10	added	add	VERB
brj-22874	218	11	to	to	ADP
brj-22874	218	12	different	different	ADJ
brj-22874	218	13	positions	position	NOUN
brj-22874	218	14	of	of	ADP
brj-22874	218	15	the	the	DET
brj-22874	218	16	backbone	backbone	NOUN
brj-22874	218	17	network	network	NOUN
brj-22874	218	18	to	to	PART
brj-22874	218	19	increase	increase	VERB
brj-22874	218	20	the	the	DET
brj-22874	218	21	receptive	receptive	ADJ
brj-22874	218	22	field	field	NOUN
brj-22874	218	23	to	to	PART
brj-22874	218	24	extract	extract	VERB
brj-22874	218	25	important	important	ADJ
brj-22874	218	26	defect	defect	NOUN
brj-22874	218	27	features	feature	NOUN
brj-22874	218	28	and	and	CCONJ
brj-22874	218	29	improve	improve	VERB
brj-22874	218	30	the	the	DET
brj-22874	218	31	accuracy	accuracy	NOUN
brj-22874	218	32	of	of	ADP
brj-22874	218	33	wood	wood	NOUN
brj-22874	218	34	spoon	spoon	NOUN
brj-22874	218	35	defect	defect	NOUN
brj-22874	218	36	detection	detection	NOUN
brj-22874	218	37	.	.	PUNCT
brj-22874	219	1	the	the	DET
brj-22874	219	2	improved	improved	ADJ
brj-22874	219	3	network	network	NOUN
brj-22874	219	4	structure	structure	NOUN
brj-22874	219	5	is	be	AUX
brj-22874	219	6	shown	show	VERB
brj-22874	219	7	in	in	ADP
brj-22874	219	8	fig	fig	NOUN
brj-22874	219	9	.	.	PUNCT
brj-22874	220	1	7	7	X
brj-22874	220	2	.	.	X
brj-22874	220	3	peer	peer	NOUN
brj-22874	220	4	-	-	PUNCT
brj-22874	220	5	reviewed	review	VERB
brj-22874	220	6	article	article	NOUN
brj-22874	220	7	bioresources.com	bioresources.com	X
brj-22874	220	8	tian	tian	PROPN
brj-22874	220	9	et	et	PROPN
brj-22874	220	10	al	al	PROPN
brj-22874	220	11	.	.	PROPN
brj-22874	220	12	(	(	PUNCT
brj-22874	220	13	2023	2023	NUM
brj-22874	220	14	)	)	PUNCT
brj-22874	220	15	.	.	PUNCT
brj-22874	221	1	“	"	PUNCT
brj-22874	221	2	defect	defect	VERB
brj-22874	221	3	detection	detection	NOUN
brj-22874	221	4	with	with	ADP
brj-22874	221	5	yolov5	yolov5	NOUN
brj-22874	221	6	,	,	PUNCT
brj-22874	221	7	”	"	PUNCT
brj-22874	221	8	bioresources	bioresource	NOUN
brj-22874	221	9	18(4	18(4	NUM
brj-22874	221	10	)	)	PUNCT
brj-22874	221	11	,	,	PUNCT
brj-22874	221	12	7713	7713	NUM
brj-22874	221	13	-	-	SYM
brj-22874	221	14	7730	7730	NUM
brj-22874	221	15	.	.	PUNCT
brj-22874	222	1	7722	7722	NUM
brj-22874	222	2	5	5	NUM
brj-22874	222	3	*	*	SYM
brj-22874	222	4	5	5	NUM
brj-22874	222	5	9	9	NUM
brj-22874	222	6	*	*	SYM
brj-22874	222	7	9	9	NUM
brj-22874	222	8	13	13	NUM
brj-22874	222	9	*	*	SYM
brj-22874	222	10	13	13	NUM
brj-22874	222	11	conv2d_bn_silu	conv2d_bn_silu	NOUN
brj-22874	222	12	conv2d_bn_silu	conv2d_bn_silu	PROPN
brj-22874	222	13	fig	fig	PROPN
brj-22874	222	14	.	.	PUNCT
brj-22874	223	1	6	6	X
brj-22874	223	2	.	.	X
brj-22874	223	3	structure	structure	NOUN
brj-22874	223	4	of	of	ADP
brj-22874	223	5	sppnet	sppnet	PROPN
brj-22874	223	6	focus	focus	VERB
brj-22874	223	7	conv	conv	PROPN
brj-22874	223	8	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	223	9	conv	conv	PROPN
brj-22874	223	10	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	223	11	conv	conv	PROPN
brj-22874	223	12	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	223	13	conv	conv	PROPN
brj-22874	223	14	spp	spp	PROPN
brj-22874	223	15	bottleneckcsp	bottleneckcsp	NOUN
brj-22874	223	16	focus	focus	VERB
brj-22874	223	17	conv	conv	ADJ
brj-22874	223	18	bottleneckcsp	bottleneckcsp	X
brj-22874	223	19	conv	conv	PROPN
brj-22874	223	20	bottleneckcsp	bottleneckcsp	PROPN
brj-22874	223	21	conv	conv	PROPN
brj-22874	223	22	spp	spp	PROPN
brj-22874	223	23	bottleneckcsp	bottleneckcsp	PUNCT
brj-22874	223	24	conv	conv	PROPN
brj-22874	223	25	bottleneckcsp	bottleneckcsp	NOUN
brj-22874	223	26	spp	spp	NOUN
brj-22874	223	27	fig	fig	NOUN
brj-22874	223	28	.	.	PUNCT
brj-22874	224	1	7	7	X
brj-22874	224	2	.	.	X
brj-22874	224	3	yolov5	yolov5	NOUN
brj-22874	224	4	-	-	PUNCT
brj-22874	224	5	tspp	tspp	NOUN
brj-22874	224	6	network	network	NOUN
brj-22874	224	7	structure	structure	NOUN
brj-22874	224	8	results	result	NOUN
brj-22874	224	9	and	and	CCONJ
brj-22874	224	10	discussion	discussion	NOUN
brj-22874	224	11	evaluating	evaluate	VERB
brj-22874	224	12	indicator	indicator	NOUN
brj-22874	224	13	according	accord	VERB
brj-22874	224	14	to	to	ADP
brj-22874	224	15	the	the	DET
brj-22874	224	16	combination	combination	NOUN
brj-22874	224	17	of	of	ADP
brj-22874	224	18	the	the	DET
brj-22874	224	19	real	real	ADJ
brj-22874	224	20	label	label	NOUN
brj-22874	224	21	and	and	CCONJ
brj-22874	224	22	the	the	DET
brj-22874	224	23	predicted	predict	VERB
brj-22874	224	24	label	label	NOUN
brj-22874	224	25	,	,	PUNCT
brj-22874	224	26	each	each	DET
brj-22874	224	27	picture	picture	NOUN
brj-22874	224	28	was	be	AUX
brj-22874	224	29	divided	divide	VERB
brj-22874	224	30	into	into	ADP
brj-22874	224	31	four	four	NUM
brj-22874	224	32	categories	category	NOUN
brj-22874	224	33	:	:	PUNCT
brj-22874	224	34	true	true	ADJ
brj-22874	224	35	positive	positive	ADJ
brj-22874	224	36	(	(	PUNCT
brj-22874	224	37	tp	tp	NOUN
brj-22874	224	38	)	)	PUNCT
brj-22874	224	39	,	,	PUNCT
brj-22874	224	40	true	true	ADJ
brj-22874	224	41	negative	negative	ADJ
brj-22874	224	42	(	(	PUNCT
brj-22874	224	43	tn	tn	NOUN
brj-22874	224	44	)	)	PUNCT
brj-22874	224	45	,	,	PUNCT
brj-22874	224	46	false	false	ADJ
brj-22874	224	47	positive	positive	ADJ
brj-22874	224	48	(	(	PUNCT
brj-22874	224	49	fp	fp	NOUN
brj-22874	224	50	)	)	PUNCT
brj-22874	224	51	,	,	PUNCT
brj-22874	224	52	and	and	CCONJ
brj-22874	224	53	false	false	ADJ
brj-22874	224	54	negative	negative	ADJ
brj-22874	224	55	(	(	PUNCT
brj-22874	224	56	fn	fn	NOUN
brj-22874	224	57	)	)	PUNCT
brj-22874	224	58	(	(	PUNCT
brj-22874	224	59	zhao	zhao	PROPN
brj-22874	224	60	et	et	PROPN
brj-22874	224	61	al	al	PROPN
brj-22874	224	62	.	.	PROPN
brj-22874	224	63	2021	2021	NUM
brj-22874	224	64	)	)	PUNCT
brj-22874	224	65	.	.	PUNCT
brj-22874	225	1	among	among	ADP
brj-22874	225	2	them	they	PRON
brj-22874	225	3	,	,	PUNCT
brj-22874	225	4	tp	tp	PART
brj-22874	225	5	is	be	AUX
brj-22874	225	6	the	the	DET
brj-22874	225	7	object	object	NOUN
brj-22874	225	8	existing	exist	VERB
brj-22874	225	9	in	in	ADP
brj-22874	225	10	the	the	DET
brj-22874	225	11	correctly	correctly	ADV
brj-22874	225	12	recognized	recognize	VERB
brj-22874	225	13	image	image	NOUN
brj-22874	225	14	,	,	PUNCT
brj-22874	225	15	tn	tn	PROPN
brj-22874	225	16	is	be	AUX
brj-22874	225	17	the	the	DET
brj-22874	225	18	object	object	NOUN
brj-22874	225	19	existing	exist	VERB
brj-22874	225	20	in	in	ADP
brj-22874	225	21	the	the	DET
brj-22874	225	22	image	image	NOUN
brj-22874	225	23	but	but	CCONJ
brj-22874	225	24	not	not	PART
brj-22874	225	25	detected	detect	VERB
brj-22874	225	26	,	,	PUNCT
brj-22874	225	27	fp	fp	X
brj-22874	225	28	is	be	AUX
brj-22874	225	29	the	the	DET
brj-22874	225	30	object	object	NOUN
brj-22874	225	31	existing	exist	VERB
brj-22874	225	32	in	in	ADP
brj-22874	225	33	the	the	DET
brj-22874	225	34	wrongly	wrongly	ADV
brj-22874	225	35	recognized	recognize	VERB
brj-22874	225	36	image	image	NOUN
brj-22874	225	37	,	,	PUNCT
brj-22874	225	38	and	and	CCONJ
brj-22874	225	39	fn	fn	NOUN
brj-22874	225	40	is	be	AUX
brj-22874	225	41	the	the	DET
brj-22874	225	42	object	object	NOUN
brj-22874	225	43	existing	exist	VERB
brj-22874	225	44	in	in	ADP
brj-22874	225	45	the	the	DET
brj-22874	225	46	image	image	NOUN
brj-22874	225	47	but	but	CCONJ
brj-22874	225	48	not	not	PART
brj-22874	225	49	detected	detect	VERB
brj-22874	225	50	.	.	PUNCT
brj-22874	226	1	precision	precision	NOUN
brj-22874	226	2	is	be	AUX
brj-22874	226	3	the	the	DET
brj-22874	226	4	ratio	ratio	NOUN
brj-22874	226	5	of	of	ADP
brj-22874	226	6	the	the	DET
brj-22874	226	7	number	number	NOUN
brj-22874	226	8	of	of	ADP
brj-22874	226	9	positive	positive	ADJ
brj-22874	226	10	samples	sample	NOUN
brj-22874	226	11	correctly	correctly	ADV
brj-22874	226	12	predicted	predict	VERB
brj-22874	226	13	to	to	ADP
brj-22874	226	14	the	the	DET
brj-22874	226	15	number	number	NOUN
brj-22874	226	16	of	of	ADP
brj-22874	226	17	positive	positive	ADJ
brj-22874	226	18	samples	sample	NOUN
brj-22874	226	19	predicted	predict	VERB
brj-22874	226	20	.	.	PUNCT
brj-22874	227	1	recall	recall	PROPN
brj-22874	227	2	represents	represent	VERB
brj-22874	227	3	the	the	DET
brj-22874	227	4	proportion	proportion	NOUN
brj-22874	227	5	of	of	ADP
brj-22874	227	6	the	the	DET
brj-22874	227	7	number	number	NOUN
brj-22874	227	8	of	of	ADP
brj-22874	227	9	positive	positive	ADJ
brj-22874	227	10	samples	sample	NOUN
brj-22874	227	11	correctly	correctly	ADV
brj-22874	227	12	determined	determined	ADJ
brj-22874	227	13	to	to	ADP
brj-22874	227	14	the	the	DET
brj-22874	227	15	total	total	ADJ
brj-22874	227	16	number	number	NOUN
brj-22874	227	17	of	of	ADP
brj-22874	227	18	positive	positive	ADJ
brj-22874	227	19	samples	sample	NOUN
brj-22874	227	20	.	.	PUNCT
brj-22874	228	1	the	the	DET
brj-22874	228	2	pr	pr	NOUN
brj-22874	228	3	curve	curve	NOUN
brj-22874	228	4	reflects	reflect	VERB
brj-22874	228	5	the	the	DET
brj-22874	228	6	relationship	relationship	NOUN
brj-22874	228	7	between	between	ADP
brj-22874	228	8	precision	precision	NOUN
brj-22874	228	9	and	and	CCONJ
brj-22874	228	10	recall	recall	NOUN
brj-22874	228	11	rate	rate	NOUN
brj-22874	228	12	.	.	PUNCT
brj-22874	229	1	the	the	PRON
brj-22874	229	2	higher	high	ADJ
brj-22874	229	3	the	the	DET
brj-22874	229	4	precision	precision	NOUN
brj-22874	229	5	and	and	CCONJ
brj-22874	229	6	recall	recall	NOUN
brj-22874	229	7	rate	rate	NOUN
brj-22874	229	8	of	of	ADP
brj-22874	229	9	the	the	DET
brj-22874	229	10	model	model	NOUN
brj-22874	229	11	,	,	PUNCT
brj-22874	229	12	the	the	PRON
brj-22874	229	13	larger	large	ADJ
brj-22874	229	14	the	the	DET
brj-22874	229	15	area	area	NOUN
brj-22874	229	16	surrounded	surround	VERB
brj-22874	229	17	by	by	ADP
brj-22874	229	18	the	the	DET
brj-22874	229	19	pr	pr	NOUN
brj-22874	229	20	curve	curve	NOUN
brj-22874	229	21	and	and	CCONJ
brj-22874	229	22	the	the	DET
brj-22874	229	23	x	x	NOUN
brj-22874	229	24	,	,	PUNCT
brj-22874	229	25	y	y	NOUN
brj-22874	229	26	-	-	PUNCT
brj-22874	229	27	axis	axis	NOUN
brj-22874	229	28	,	,	PUNCT
brj-22874	229	29	and	and	CCONJ
brj-22874	229	30	the	the	PRON
brj-22874	229	31	better	well	ADJ
brj-22874	229	32	the	the	DET
brj-22874	229	33	overall	overall	ADJ
brj-22874	229	34	performance	performance	NOUN
brj-22874	229	35	of	of	ADP
brj-22874	229	36	the	the	DET
brj-22874	229	37	model	model	NOUN
brj-22874	229	38	.	.	PUNCT
brj-22874	230	1	ap	ap	PROPN
brj-22874	230	2	is	be	AUX
brj-22874	230	3	the	the	DET
brj-22874	230	4	area	area	NOUN
brj-22874	230	5	below	below	ADP
brj-22874	230	6	the	the	DET
brj-22874	230	7	pr	pr	NOUN
brj-22874	230	8	curve	curve	NOUN
brj-22874	230	9	.	.	PUNCT
brj-22874	231	1	the	the	DET
brj-22874	231	2	larger	large	ADJ
brj-22874	231	3	the	the	DET
brj-22874	231	4	ap	ap	PROPN
brj-22874	231	5	,	,	PUNCT
brj-22874	231	6	the	the	PRON
brj-22874	231	7	higher	high	ADJ
brj-22874	231	8	the	the	DET
brj-22874	231	9	accuracy	accuracy	NOUN
brj-22874	231	10	of	of	ADP
brj-22874	231	11	the	the	DET
brj-22874	231	12	model	model	NOUN
brj-22874	231	13	.	.	PUNCT
brj-22874	232	1	the	the	DET
brj-22874	232	2	smaller	small	ADJ
brj-22874	232	3	the	the	DET
brj-22874	232	4	ap	ap	PROPN
brj-22874	232	5	,	,	PUNCT
brj-22874	232	6	the	the	PRON
brj-22874	232	7	worse	bad	ADJ
brj-22874	232	8	the	the	DET
brj-22874	232	9	performance	performance	NOUN
brj-22874	232	10	of	of	ADP
brj-22874	232	11	the	the	DET
brj-22874	232	12	model	model	NOUN
brj-22874	232	13	.	.	PUNCT
brj-22874	233	1	to	to	PART
brj-22874	233	2	evaluate	evaluate	VERB
brj-22874	233	3	the	the	DET
brj-22874	233	4	detection	detection	NOUN
brj-22874	233	5	effect	effect	NOUN
brj-22874	233	6	of	of	ADP
brj-22874	233	7	the	the	DET
brj-22874	233	8	model	model	NOUN
brj-22874	233	9	obtained	obtain	VERB
brj-22874	233	10	during	during	ADP
brj-22874	233	11	training	training	NOUN
brj-22874	233	12	,	,	PUNCT
brj-22874	233	13	the	the	DET
brj-22874	233	14	commonly	commonly	ADV
brj-22874	233	15	used	use	VERB
brj-22874	233	16	neural	neural	ADJ
brj-22874	233	17	network	network	NOUN
brj-22874	233	18	performance	performance	NOUN
brj-22874	233	19	peer	peer	NOUN
brj-22874	233	20	-	-	PUNCT
brj-22874	233	21	reviewed	review	VERB
brj-22874	233	22	article	article	NOUN
brj-22874	233	23	bioresources.com	bioresources.com	X
brj-22874	233	24	tian	tian	PROPN
brj-22874	233	25	et	et	PROPN
brj-22874	233	26	al	al	PROPN
brj-22874	233	27	.	.	PROPN
brj-22874	233	28	(	(	PUNCT
brj-22874	233	29	2023	2023	NUM
brj-22874	233	30	)	)	PUNCT
brj-22874	233	31	.	.	PUNCT
brj-22874	234	1	“	"	PUNCT
brj-22874	234	2	defect	defect	VERB
brj-22874	234	3	detection	detection	NOUN
brj-22874	234	4	with	with	ADP
brj-22874	234	5	yolov5	yolov5	NOUN
brj-22874	234	6	,	,	PUNCT
brj-22874	234	7	”	"	PUNCT
brj-22874	234	8	bioresources	bioresource	NOUN
brj-22874	234	9	18(4	18(4	NUM
brj-22874	234	10	)	)	PUNCT
brj-22874	234	11	,	,	PUNCT
brj-22874	234	12	7713	7713	NUM
brj-22874	234	13	-	-	SYM
brj-22874	234	14	7730	7730	NUM
brj-22874	234	15	.	.	PUNCT
brj-22874	235	1	7723	7723	NUM
brj-22874	235	2	evaluation	evaluation	NOUN
brj-22874	235	3	indicators	indicator	NOUN
brj-22874	235	4	are	be	AUX
brj-22874	235	5	used	use	VERB
brj-22874	235	6	in	in	ADP
brj-22874	235	7	the	the	DET
brj-22874	235	8	experiment	experiment	NOUN
brj-22874	235	9	:	:	PUNCT
brj-22874	235	10	precision	precision	NOUN
brj-22874	235	11	(	(	PUNCT
brj-22874	235	12	p	p	NOUN
brj-22874	235	13	)	)	PUNCT
brj-22874	235	14	,	,	PUNCT
brj-22874	235	15	recall	recall	INTJ
brj-22874	235	16	(	(	PUNCT
brj-22874	235	17	r	r	NOUN
brj-22874	235	18	)	)	PUNCT
brj-22874	235	19	,	,	PUNCT
brj-22874	235	20	and	and	CCONJ
brj-22874	235	21	average	average	ADJ
brj-22874	235	22	precision	precision	NOUN
brj-22874	235	23	(	(	PUNCT
brj-22874	235	24	ap	ap	PROPN
brj-22874	235	25	)	)	PUNCT
brj-22874	235	26	.	.	PUNCT
brj-22874	236	1	the	the	DET
brj-22874	236	2	calculation	calculation	NOUN
brj-22874	236	3	formula	formula	NOUN
brj-22874	236	4	is	be	AUX
brj-22874	236	5	as	as	SCONJ
brj-22874	236	6	follows	follow	VERB
brj-22874	236	7	:	:	PUNCT
brj-22874	236	8	tp	tp	ADP
brj-22874	236	9	p	p	NOUN
brj-22874	236	10	tp	tp	ADP
brj-22874	236	11	fp	fp	PROPN
brj-22874	236	12	=	=	PUNCT
brj-22874	237	1	+	+	CCONJ
brj-22874	237	2	(	(	PUNCT
brj-22874	237	3	10	10	NUM
brj-22874	237	4	)	)	PUNCT
brj-22874	237	5	tp	tp	ADP
brj-22874	237	6	r	r	NOUN
brj-22874	237	7	tp	tp	NOUN
brj-22874	237	8	fn	fn	NOUN
brj-22874	238	1	=	=	PUNCT
brj-22874	239	1	+	+	CCONJ
brj-22874	239	2	(	(	PUNCT
brj-22874	239	3	11	11	NUM
brj-22874	239	4	)	)	SYM
brj-22874	239	5	1	1	NUM
brj-22874	239	6	0	0	NUM
brj-22874	239	7	(	(	PUNCT
brj-22874	239	8	)	)	PUNCT
brj-22874	239	9	ap	ap	PROPN
brj-22874	239	10	p	p	NOUN
brj-22874	239	11	r	r	NOUN
brj-22874	239	12	dr=	dr=	VERB
brj-22874	239	13			X
brj-22874	239	14	(	(	PUNCT
brj-22874	239	15	12	12	NUM
brj-22874	239	16	)	)	PUNCT
brj-22874	239	17	ablation	ablation	NOUN
brj-22874	239	18	experiment	experiment	NOUN
brj-22874	239	19	to	to	PART
brj-22874	239	20	show	show	VERB
brj-22874	239	21	the	the	DET
brj-22874	239	22	performance	performance	NOUN
brj-22874	239	23	of	of	ADP
brj-22874	239	24	the	the	DET
brj-22874	239	25	proposed	propose	VERB
brj-22874	239	26	method	method	NOUN
brj-22874	239	27	in	in	ADP
brj-22874	239	28	the	the	DET
brj-22874	239	29	detection	detection	NOUN
brj-22874	239	30	and	and	CCONJ
brj-22874	239	31	recognition	recognition	NOUN
brj-22874	239	32	of	of	ADP
brj-22874	239	33	surface	surface	NOUN
brj-22874	239	34	defects	defect	NOUN
brj-22874	239	35	of	of	ADP
brj-22874	239	36	wooden	wooden	ADJ
brj-22874	239	37	spoons	spoon	NOUN
brj-22874	239	38	,	,	PUNCT
brj-22874	239	39	ablation	ablation	NOUN
brj-22874	239	40	experiments	experiment	NOUN
brj-22874	239	41	were	be	AUX
brj-22874	239	42	carried	carry	VERB
brj-22874	239	43	out	out	ADP
brj-22874	239	44	for	for	ADP
brj-22874	239	45	different	different	ADJ
brj-22874	239	46	defects	defect	NOUN
brj-22874	239	47	.	.	PUNCT
brj-22874	240	1	the	the	DET
brj-22874	240	2	average	average	ADJ
brj-22874	240	3	precision	precision	NOUN
brj-22874	240	4	(	(	PUNCT
brj-22874	240	5	ap	ap	PROPN
brj-22874	240	6	)	)	PUNCT
brj-22874	240	7	,	,	PUNCT
brj-22874	240	8	precision	precision	NOUN
brj-22874	240	9	(	(	PUNCT
brj-22874	240	10	precision	precision	NOUN
brj-22874	240	11	)	)	PUNCT
brj-22874	240	12	,	,	PUNCT
brj-22874	240	13	and	and	CCONJ
brj-22874	240	14	recall	recall	NOUN
brj-22874	240	15	(	(	PUNCT
brj-22874	240	16	recall	recall	NOUN
brj-22874	240	17	)	)	PUNCT
brj-22874	240	18	were	be	AUX
brj-22874	240	19	used	use	VERB
brj-22874	240	20	as	as	ADP
brj-22874	240	21	evaluation	evaluation	NOUN
brj-22874	240	22	indicators	indicator	NOUN
brj-22874	240	23	.	.	PUNCT
brj-22874	241	1	the	the	DET
brj-22874	241	2	network	network	NOUN
brj-22874	241	3	detection	detection	NOUN
brj-22874	241	4	model	model	NOUN
brj-22874	241	5	that	that	PRON
brj-22874	241	6	introduces	introduce	NOUN
brj-22874	241	7	improved	improve	VERB
brj-22874	241	8	kmeans++	kmeans++	PROPN
brj-22874	241	9	clustering	cluster	VERB
brj-22874	241	10	,	,	PUNCT
brj-22874	241	11	ca	can	AUX
brj-22874	241	12	coordinate	coordinate	VERB
brj-22874	241	13	attention	attention	NOUN
brj-22874	241	14	mechanism	mechanism	NOUN
brj-22874	241	15	,	,	PUNCT
brj-22874	241	16	and	and	CCONJ
brj-22874	241	17	re	re	VERB
brj-22874	241	18	-	-	VERB
brj-22874	241	19	adds	add	VERB
brj-22874	241	20	a	a	DET
brj-22874	241	21	sppnet	sppnet	NOUN
brj-22874	241	22	structure	structure	NOUN
brj-22874	241	23	has	have	VERB
brj-22874	241	24	different	different	ADJ
brj-22874	241	25	degrees	degree	NOUN
brj-22874	241	26	of	of	ADP
brj-22874	241	27	improvement	improvement	NOUN
brj-22874	241	28	in	in	ADP
brj-22874	241	29	accuracy	accuracy	NOUN
brj-22874	241	30	,	,	PUNCT
brj-22874	241	31	recall	recall	NOUN
brj-22874	241	32	,	,	PUNCT
brj-22874	241	33	and	and	CCONJ
brj-22874	241	34	average	average	ADJ
brj-22874	241	35	accuracy	accuracy	NOUN
brj-22874	241	36	compared	compare	VERB
brj-22874	241	37	with	with	ADP
brj-22874	241	38	the	the	DET
brj-22874	241	39	original	original	ADJ
brj-22874	241	40	model	model	NOUN
brj-22874	241	41	.	.	PUNCT
brj-22874	242	1	the	the	DET
brj-22874	242	2	experimental	experimental	ADJ
brj-22874	242	3	results	result	NOUN
brj-22874	242	4	show	show	VERB
brj-22874	242	5	that	that	SCONJ
brj-22874	242	6	the	the	DET
brj-22874	242	7	improved	improve	VERB
brj-22874	242	8	priori	priori	ADJ
brj-22874	242	9	box	box	PROPN
brj-22874	242	10	determined	determine	VERB
brj-22874	242	11	by	by	ADP
brj-22874	242	12	k	k	PROPN
brj-22874	242	13	-	-	ADJ
brj-22874	242	14	means++	means++	ADJ
brj-22874	242	15	clustering	clustering	NOUN
brj-22874	242	16	can	can	AUX
brj-22874	242	17	effectively	effectively	ADV
brj-22874	242	18	improve	improve	VERB
brj-22874	242	19	the	the	DET
brj-22874	242	20	learning	learning	NOUN
brj-22874	242	21	efficiency	efficiency	NOUN
brj-22874	242	22	of	of	ADP
brj-22874	242	23	the	the	DET
brj-22874	242	24	model	model	NOUN
brj-22874	242	25	for	for	ADP
brj-22874	242	26	the	the	DET
brj-22874	242	27	target	target	NOUN
brj-22874	242	28	detection	detection	NOUN
brj-22874	242	29	box	box	NOUN
brj-22874	242	30	.	.	PUNCT
brj-22874	243	1	secondly	secondly	ADV
brj-22874	243	2	,	,	PUNCT
brj-22874	243	3	because	because	SCONJ
brj-22874	243	4	ca	can	AUX
brj-22874	243	5	has	have	VERB
brj-22874	243	6	a	a	DET
brj-22874	243	7	longterm	longterm	ADJ
brj-22874	243	8	dependence	dependence	NOUN
brj-22874	243	9	on	on	ADP
brj-22874	243	10	location	location	NOUN
brj-22874	243	11	information	information	NOUN
brj-22874	243	12	and	and	CCONJ
brj-22874	243	13	channel	channel	NOUN
brj-22874	243	14	relationship	relationship	NOUN
brj-22874	243	15	,	,	PUNCT
brj-22874	243	16	the	the	DET
brj-22874	243	17	introduction	introduction	NOUN
brj-22874	243	18	of	of	ADP
brj-22874	243	19	ca	can	AUX
brj-22874	243	20	effectively	effectively	ADV
brj-22874	243	21	improves	improve	VERB
brj-22874	243	22	the	the	DET
brj-22874	243	23	efficiency	efficiency	NOUN
brj-22874	243	24	of	of	ADP
brj-22874	243	25	the	the	DET
brj-22874	243	26	model	model	NOUN
brj-22874	243	27	for	for	ADP
brj-22874	243	28	location	location	NOUN
brj-22874	243	29	information	information	NOUN
brj-22874	243	30	learning	learning	NOUN
brj-22874	243	31	and	and	CCONJ
brj-22874	243	32	improves	improve	VERB
brj-22874	243	33	the	the	DET
brj-22874	243	34	prediction	prediction	NOUN
brj-22874	243	35	effect	effect	NOUN
brj-22874	243	36	.	.	PUNCT
brj-22874	244	1	finally	finally	ADV
brj-22874	244	2	,	,	PUNCT
brj-22874	244	3	a	a	DET
brj-22874	244	4	sppnet	sppnet	NOUN
brj-22874	244	5	network	network	NOUN
brj-22874	244	6	structure	structure	NOUN
brj-22874	244	7	is	be	AUX
brj-22874	244	8	added	add	VERB
brj-22874	244	9	to	to	PART
brj-22874	244	10	realize	realize	VERB
brj-22874	244	11	the	the	DET
brj-22874	244	12	fusion	fusion	NOUN
brj-22874	244	13	of	of	ADP
brj-22874	244	14	local	local	ADJ
brj-22874	244	15	features	feature	NOUN
brj-22874	244	16	and	and	CCONJ
brj-22874	244	17	global	global	ADJ
brj-22874	244	18	features	feature	NOUN
brj-22874	244	19	,	,	PUNCT
brj-22874	244	20	improve	improve	VERB
brj-22874	244	21	the	the	DET
brj-22874	244	22	learning	learning	NOUN
brj-22874	244	23	efficiency	efficiency	NOUN
brj-22874	244	24	of	of	ADP
brj-22874	244	25	the	the	DET
brj-22874	244	26	model	model	NOUN
brj-22874	244	27	for	for	ADP
brj-22874	244	28	features	feature	NOUN
brj-22874	244	29	,	,	PUNCT
brj-22874	244	30	and	and	CCONJ
brj-22874	244	31	achieve	achieve	VERB
brj-22874	244	32	better	well	ADJ
brj-22874	244	33	detection	detection	NOUN
brj-22874	244	34	results	result	NOUN
brj-22874	244	35	.	.	PUNCT
brj-22874	245	1	the	the	DET
brj-22874	245	2	experimental	experimental	ADJ
brj-22874	245	3	results	result	NOUN
brj-22874	245	4	are	be	AUX
brj-22874	245	5	shown	show	VERB
brj-22874	245	6	in	in	ADP
brj-22874	245	7	table	table	NOUN
brj-22874	245	8	2	2	NUM
brj-22874	245	9	.	.	PUNCT
brj-22874	245	10	table	table	NOUN
brj-22874	245	11	2	2	NUM
brj-22874	245	12	.	.	PUNCT
brj-22874	245	13	ablation	ablation	NOUN
brj-22874	245	14	experiment	experiment	NOUN
brj-22874	245	15	model	model	NOUN
brj-22874	245	16	ma	ma	PROPN
brj-22874	245	17	p	p	PROPN
brj-22874	245	18	recall	recall	PROPN
brj-22874	245	19	precision	precision	PROPN
brj-22874	245	20	ap	ap	PROPN
brj-22874	246	1	b	b	PROPN
brj-22874	246	2	h	h	NOUN
brj-22874	247	1	k	k	PROPN
brj-22874	247	2	w	w	PROPN
brj-22874	247	3	b	b	PROPN
brj-22874	247	4	h	h	NOUN
brj-22874	248	1	k	k	PROPN
brj-22874	248	2	w	w	PROPN
brj-22874	248	3	b	b	PROPN
brj-22874	248	4	h	h	PROPN
brj-22874	249	1	k	k	PROPN
brj-22874	249	2	w	w	PROPN
brj-22874	249	3	yolov5x	yolov5x	PROPN
brj-22874	249	4	71	71	NUM
brj-22874	249	5	.	.	PUNCT
brj-22874	249	6	12	12	NUM
brj-22874	249	7	83	83	NUM
brj-22874	249	8	.	.	PUNCT
brj-22874	250	1	75	75	NUM
brj-22874	250	2	79	79	NUM
brj-22874	250	3	.	.	PUNCT
brj-22874	251	1	73	73	NUM
brj-22874	251	2	74	74	NUM
brj-22874	251	3	.	.	PUNCT
brj-22874	252	1	04	04	NUM
brj-22874	253	1	34	34	NUM
brj-22874	253	2	.	.	PUNCT
brj-22874	254	1	21	21	NUM
brj-22874	254	2	89	89	NUM
brj-22874	254	3	.	.	PUNCT
brj-22874	255	1	40	40	NUM
brj-22874	255	2	89	89	NUM
brj-22874	255	3	.	.	PUNCT
brj-22874	256	1	39	39	NUM
brj-22874	256	2	86	86	NUM
brj-22874	256	3	.	.	PUNCT
brj-22874	257	1	41	41	NUM
brj-22874	257	2	61	61	NUM
brj-22874	257	3	.	.	PUNCT
brj-22874	258	1	29	29	NUM
brj-22874	258	2	87	87	NUM
brj-22874	258	3	.	.	PUNCT
brj-22874	259	1	81	81	NUM
brj-22874	259	2	82	82	NUM
brj-22874	259	3	.	.	PUNCT
brj-22874	260	1	70	70	NUM
brj-22874	260	2	79	79	NUM
brj-22874	260	3	.	.	PUNCT
brj-22874	261	1	09	09	NUM
brj-22874	261	2	34	34	NUM
brj-22874	261	3	.	.	NOUN
brj-22874	262	1	88	88	NUM
brj-22874	262	2	yolov5x+k	yolov5x+k	PROPN
brj-22874	262	3	-means++	-means++	PROPN
brj-22874	262	4	71	71	NUM
brj-22874	262	5	.	.	PUNCT
brj-22874	263	1	73	73	NUM
brj-22874	263	2	84	84	NUM
brj-22874	263	3	.	.	PUNCT
brj-22874	264	1	42	42	NUM
brj-22874	264	2	79	79	NUM
brj-22874	264	3	.	.	PUNCT
brj-22874	265	1	80	80	NUM
brj-22874	265	2	74	74	NUM
brj-22874	265	3	.	.	PUNCT
brj-22874	266	1	38	38	NUM
brj-22874	266	2	34	34	NUM
brj-22874	266	3	.	.	PUNCT
brj-22874	267	1	53	53	NUM
brj-22874	267	2	90	90	NUM
brj-22874	267	3	.	.	PUNCT
brj-22874	268	1	56	56	NUM
brj-22874	268	2	92	92	NUM
brj-22874	268	3	.	.	PUNCT
brj-22874	269	1	19	19	NUM
brj-22874	269	2	86	86	NUM
brj-22874	269	3	.	.	PUNCT
brj-22874	270	1	63	63	NUM
brj-22874	270	2	61	61	NUM
brj-22874	270	3	.	.	PUNCT
brj-22874	271	1	88	88	NUM
brj-22874	271	2	88	88	NUM
brj-22874	271	3	.	.	PUNCT
brj-22874	272	1	57	57	NUM
brj-22874	272	2	84	84	NUM
brj-22874	272	3	.	.	PUNCT
brj-22874	273	1	12	12	NUM
brj-22874	273	2	79	79	NUM
brj-22874	273	3	.	.	PUNCT
brj-22874	274	1	16	16	NUM
brj-22874	274	2	35	35	NUM
brj-22874	274	3	.	.	PUNCT
brj-22874	275	1	07	07	NUM
brj-22874	275	2	yolov5x+	yolov5x+	NOUN
brj-22874	275	3	ca+kmeans++	ca+kmeans++	PROPN
brj-22874	275	4	75	75	NUM
brj-22874	275	5	.	.	PUNCT
brj-22874	275	6	08	08	NUM
brj-22874	275	7	87	87	NUM
brj-22874	275	8	.	.	PUNCT
brj-22874	276	1	13	13	NUM
brj-22874	276	2	83	83	NUM
brj-22874	276	3	.	.	PUNCT
brj-22874	277	1	78	78	NUM
brj-22874	277	2	80	80	NUM
brj-22874	277	3	.	.	PUNCT
brj-22874	278	1	37	37	NUM
brj-22874	278	2	43	43	NUM
brj-22874	278	3	.	.	PUNCT
brj-22874	279	1	21	21	NUM
brj-22874	279	2	91	91	NUM
brj-22874	279	3	.	.	PUNCT
brj-22874	280	1	21	21	NUM
brj-22874	280	2	96	96	NUM
brj-22874	280	3	.	.	PUNCT
brj-22874	281	1	88	88	NUM
brj-22874	281	2	90	90	NUM
brj-22874	281	3	.	.	PUNCT
brj-22874	282	1	79	79	NUM
brj-22874	282	2	63	63	NUM
brj-22874	282	3	.	.	PUNCT
brj-22874	283	1	16	16	NUM
brj-22874	283	2	89	89	NUM
brj-22874	283	3	.	.	PUNCT
brj-22874	283	4	11	11	NUM
brj-22874	283	5	85	85	NUM
brj-22874	283	6	.	.	PUNCT
brj-22874	284	1	65	65	NUM
brj-22874	284	2	82	82	NUM
brj-22874	284	3	.	.	PUNCT
brj-22874	285	1	74	74	NUM
brj-22874	285	2	42	42	NUM
brj-22874	285	3	.	.	PUNCT
brj-22874	286	1	47	47	NUM
brj-22874	286	2	yolov5x+	yolov5x+	NOUN
brj-22874	286	3	ca+kmeans+++s	ca+kmeans+++s	PROPN
brj-22874	286	4	ppnet	ppnet	VERB
brj-22874	286	5	80	80	NUM
brj-22874	286	6	.	.	PUNCT
brj-22874	287	1	30	30	NUM
brj-22874	287	2	87	87	NUM
brj-22874	287	3	.	.	PUNCT
brj-22874	288	1	73	73	NUM
brj-22874	288	2	90	90	NUM
brj-22874	288	3	.	.	PUNCT
brj-22874	289	1	79	79	NUM
brj-22874	289	2	83	83	NUM
brj-22874	289	3	.	.	PUNCT
brj-22874	289	4	72	72	NUM
brj-22874	289	5	55	55	NUM
brj-22874	289	6	.	.	PUNCT
brj-22874	290	1	83	83	NUM
brj-22874	290	2	92	92	NUM
brj-22874	290	3	.	.	PUNCT
brj-22874	291	1	12	12	NUM
brj-22874	291	2	98	98	NUM
brj-22874	291	3	.	.	PUNCT
brj-22874	292	1	57	57	NUM
brj-22874	292	2	92	92	NUM
brj-22874	292	3	.	.	PUNCT
brj-22874	293	1	31	31	NUM
brj-22874	293	2	69	69	NUM
brj-22874	293	3	.	.	PUNCT
brj-22874	294	1	53	53	NUM
brj-22874	294	2	89	89	NUM
brj-22874	294	3	.	.	PUNCT
brj-22874	294	4	21	21	NUM
brj-22874	294	5	92	92	NUM
brj-22874	294	6	.	.	PUNCT
brj-22874	295	1	24	24	NUM
brj-22874	295	2	85	85	NUM
brj-22874	295	3	.	.	PUNCT
brj-22874	296	1	32	32	NUM
brj-22874	296	2	54	54	NUM
brj-22874	296	3	.	.	PUNCT
brj-22874	297	1	43	43	NUM
brj-22874	297	2	note	note	NOUN
brj-22874	297	3	:	:	PUNCT
brj-22874	297	4	b	b	X
brj-22874	297	5	represents	represent	VERB
brj-22874	297	6	the	the	DET
brj-22874	297	7	back	back	ADJ
brj-22874	297	8	crack	crack	NOUN
brj-22874	297	9	defect	defect	NOUN
brj-22874	297	10	,	,	PUNCT
brj-22874	297	11	h	h	NOUN
brj-22874	297	12	represents	represent	VERB
brj-22874	297	13	the	the	DET
brj-22874	297	14	black	black	ADJ
brj-22874	297	15	knot	knot	NOUN
brj-22874	297	16	defect	defect	NOUN
brj-22874	297	17	,	,	PUNCT
brj-22874	297	18	k	k	PROPN
brj-22874	297	19	represents	represent	VERB
brj-22874	297	20	the	the	DET
brj-22874	297	21	mineral	mineral	NOUN
brj-22874	297	22	line	line	NOUN
brj-22874	297	23	defect	defect	NOUN
brj-22874	297	24	,	,	PUNCT
brj-22874	297	25	and	and	CCONJ
brj-22874	297	26	w	w	NOUN
brj-22874	297	27	represents	represent	VERB
brj-22874	297	28	the	the	DET
brj-22874	297	29	pollution	pollution	NOUN
brj-22874	297	30	defect	defect	NOUN
brj-22874	297	31	.	.	PUNCT
brj-22874	298	1	analysis	analysis	NOUN
brj-22874	298	2	of	of	ADP
brj-22874	298	3	table	table	NOUN
brj-22874	298	4	2	2	NUM
brj-22874	298	5	shows	show	VERB
brj-22874	298	6	that	that	SCONJ
brj-22874	298	7	after	after	ADP
brj-22874	298	8	adding	add	VERB
brj-22874	298	9	k	k	NOUN
brj-22874	298	10	-	-	PUNCT
brj-22874	298	11	means	mean	VERB
brj-22874	298	12	+	+	X
brj-22874	298	13	+	+	ADJ
brj-22874	298	14	,	,	PUNCT
brj-22874	298	15	ca	can	AUX
brj-22874	298	16	attention	attention	NOUN
brj-22874	298	17	mechanism	mechanism	NOUN
brj-22874	298	18	,	,	PUNCT
brj-22874	298	19	and	and	CCONJ
brj-22874	298	20	sppnet	sppnet	NOUN
brj-22874	298	21	,	,	PUNCT
brj-22874	298	22	the	the	DET
brj-22874	298	23	overall	overall	ADJ
brj-22874	298	24	detection	detection	NOUN
brj-22874	298	25	accuracy	accuracy	NOUN
brj-22874	298	26	of	of	ADP
brj-22874	298	27	the	the	DET
brj-22874	298	28	network	network	NOUN
brj-22874	298	29	model	model	NOUN
brj-22874	298	30	for	for	ADP
brj-22874	298	31	back	back	ADJ
brj-22874	298	32	crack	crack	NOUN
brj-22874	298	33	,	,	PUNCT
brj-22874	298	34	black	black	ADJ
brj-22874	298	35	knot	knot	NOUN
brj-22874	298	36	,	,	PUNCT
brj-22874	298	37	mineral	mineral	NOUN
brj-22874	298	38	line	line	NOUN
brj-22874	298	39	,	,	PUNCT
brj-22874	298	40	and	and	CCONJ
brj-22874	298	41	pollution	pollution	NOUN
brj-22874	298	42	is	be	AUX
brj-22874	298	43	significantly	significantly	ADV
brj-22874	298	44	improved	improve	VERB
brj-22874	298	45	.	.	PUNCT
brj-22874	299	1	due	due	ADP
brj-22874	299	2	to	to	ADP
brj-22874	299	3	the	the	DET
brj-22874	299	4	factors	factor	NOUN
brj-22874	299	5	of	of	ADP
brj-22874	299	6	the	the	DET
brj-22874	299	7	formation	formation	NOUN
brj-22874	299	8	of	of	ADP
brj-22874	299	9	peer	peer	NOUN
brj-22874	299	10	-	-	PUNCT
brj-22874	299	11	reviewed	review	VERB
brj-22874	299	12	article	article	NOUN
brj-22874	299	13	bioresources.com	bioresources.com	X
brj-22874	299	14	tian	tian	PROPN
brj-22874	299	15	et	et	PROPN
brj-22874	299	16	al	al	PROPN
brj-22874	299	17	.	.	PROPN
brj-22874	300	1	(	(	PUNCT
brj-22874	300	2	2023	2023	NUM
brj-22874	300	3	)	)	PUNCT
brj-22874	300	4	.	.	PUNCT
brj-22874	301	1	“	"	PUNCT
brj-22874	301	2	defect	defect	VERB
brj-22874	301	3	detection	detection	NOUN
brj-22874	301	4	with	with	ADP
brj-22874	301	5	yolov5	yolov5	NOUN
brj-22874	301	6	,	,	PUNCT
brj-22874	301	7	”	"	PUNCT
brj-22874	301	8	bioresources	bioresource	NOUN
brj-22874	301	9	18(4	18(4	NUM
brj-22874	301	10	)	)	PUNCT
brj-22874	301	11	,	,	PUNCT
brj-22874	301	12	7713	7713	NUM
brj-22874	301	13	-	-	SYM
brj-22874	301	14	7730	7730	NUM
brj-22874	301	15	.	.	PUNCT
brj-22874	301	16	7724	7724	NUM
brj-22874	301	17	pollution	pollution	NOUN
brj-22874	301	18	defects	defect	NOUN
brj-22874	301	19	and	and	CCONJ
brj-22874	301	20	the	the	DET
brj-22874	301	21	morphological	morphological	ADJ
brj-22874	301	22	reasons	reason	NOUN
brj-22874	301	23	of	of	ADP
brj-22874	301	24	the	the	DET
brj-22874	301	25	pollution	pollution	NOUN
brj-22874	301	26	defects	defect	NOUN
brj-22874	301	27	themselves	themselves	PRON
brj-22874	301	28	,	,	PUNCT
brj-22874	301	29	the	the	DET
brj-22874	301	30	pollution	pollution	NOUN
brj-22874	301	31	defects	defect	NOUN
brj-22874	301	32	are	be	AUX
brj-22874	301	33	easily	easily	ADV
brj-22874	301	34	confused	confuse	VERB
brj-22874	301	35	with	with	ADP
brj-22874	301	36	other	other	ADJ
brj-22874	301	37	defects	defect	NOUN
brj-22874	301	38	(	(	PUNCT
brj-22874	301	39	such	such	ADJ
brj-22874	301	40	as	as	ADP
brj-22874	301	41	short	short	ADJ
brj-22874	301	42	mineral	mineral	NOUN
brj-22874	301	43	lines	line	NOUN
brj-22874	301	44	)	)	PUNCT
brj-22874	301	45	and	and	CCONJ
brj-22874	301	46	the	the	DET
brj-22874	301	47	texture	texture	NOUN
brj-22874	301	48	features	feature	NOUN
brj-22874	301	49	of	of	ADP
brj-22874	301	50	the	the	DET
brj-22874	301	51	wooden	wooden	ADJ
brj-22874	301	52	spoon	spoon	NOUN
brj-22874	301	53	,	,	PUNCT
brj-22874	301	54	so	so	SCONJ
brj-22874	301	55	the	the	DET
brj-22874	301	56	network	network	NOUN
brj-22874	301	57	model	model	NOUN
brj-22874	301	58	has	have	VERB
brj-22874	301	59	a	a	DET
brj-22874	301	60	relatively	relatively	ADV
brj-22874	301	61	low	low	ADJ
brj-22874	301	62	detection	detection	NOUN
brj-22874	301	63	accuracy	accuracy	NOUN
brj-22874	301	64	for	for	ADP
brj-22874	301	65	pollution	pollution	NOUN
brj-22874	301	66	.	.	PUNCT
brj-22874	302	1	through	through	ADP
brj-22874	302	2	the	the	DET
brj-22874	302	3	analysis	analysis	NOUN
brj-22874	302	4	and	and	CCONJ
brj-22874	302	5	improvement	improvement	NOUN
brj-22874	302	6	of	of	ADP
brj-22874	302	7	the	the	DET
brj-22874	302	8	yolov5	yolov5	NOUN
brj-22874	302	9	detection	detection	NOUN
brj-22874	302	10	model	model	NOUN
brj-22874	302	11	,	,	PUNCT
brj-22874	302	12	the	the	DET
brj-22874	302	13	detection	detection	NOUN
brj-22874	302	14	precision	precision	NOUN
brj-22874	302	15	images	image	NOUN
brj-22874	302	16	of	of	ADP
brj-22874	302	17	the	the	DET
brj-22874	302	18	original	original	ADJ
brj-22874	302	19	model	model	NOUN
brj-22874	302	20	are	be	AUX
brj-22874	302	21	shown	show	VERB
brj-22874	302	22	in	in	ADP
brj-22874	302	23	fig	fig	NOUN
brj-22874	302	24	.	.	PUNCT
brj-22874	303	1	8	8	NUM
brj-22874	303	2	,	,	PUNCT
brj-22874	303	3	and	and	CCONJ
brj-22874	304	1	the	the	DET
brj-22874	304	2	detection	detection	NOUN
brj-22874	304	3	precision	precision	NOUN
brj-22874	304	4	images	image	NOUN
brj-22874	304	5	of	of	ADP
brj-22874	304	6	the	the	DET
brj-22874	304	7	improved	improved	ADJ
brj-22874	304	8	model	model	NOUN
brj-22874	304	9	are	be	AUX
brj-22874	304	10	shown	show	VERB
brj-22874	304	11	in	in	ADP
brj-22874	304	12	fig	fig	NOUN
brj-22874	304	13	.	.	PUNCT
brj-22874	305	1	9	9	X
brj-22874	305	2	.	.	X
brj-22874	305	3	the	the	DET
brj-22874	305	4	recall	recall	NOUN
brj-22874	305	5	images	image	NOUN
brj-22874	305	6	of	of	ADP
brj-22874	305	7	the	the	DET
brj-22874	305	8	original	original	ADJ
brj-22874	305	9	model	model	NOUN
brj-22874	305	10	are	be	AUX
brj-22874	305	11	shown	show	VERB
brj-22874	305	12	in	in	ADP
brj-22874	305	13	fig	fig	NOUN
brj-22874	305	14	.	.	PUNCT
brj-22874	306	1	10	10	NUM
brj-22874	306	2	,	,	PUNCT
brj-22874	306	3	and	and	CCONJ
brj-22874	306	4	the	the	DET
brj-22874	306	5	recall	recall	NOUN
brj-22874	306	6	images	image	NOUN
brj-22874	306	7	of	of	ADP
brj-22874	306	8	the	the	DET
brj-22874	306	9	improved	improved	ADJ
brj-22874	306	10	model	model	NOUN
brj-22874	306	11	are	be	AUX
brj-22874	306	12	shown	show	VERB
brj-22874	306	13	in	in	ADP
brj-22874	306	14	fig	fig	NOUN
brj-22874	306	15	.	.	PUNCT
brj-22874	307	1	11	11	NUM
brj-22874	307	2	.	.	PUNCT
brj-22874	308	1	the	the	DET
brj-22874	308	2	pr	pr	NOUN
brj-22874	308	3	images	image	NOUN
brj-22874	308	4	of	of	ADP
brj-22874	308	5	the	the	DET
brj-22874	308	6	original	original	ADJ
brj-22874	308	7	model	model	NOUN
brj-22874	308	8	are	be	AUX
brj-22874	308	9	shown	show	VERB
brj-22874	308	10	in	in	ADP
brj-22874	308	11	fig	fig	NOUN
brj-22874	308	12	.	.	PUNCT
brj-22874	309	1	12	12	NUM
brj-22874	309	2	,	,	PUNCT
brj-22874	309	3	and	and	CCONJ
brj-22874	309	4	the	the	DET
brj-22874	309	5	pr	pr	NOUN
brj-22874	309	6	images	image	NOUN
brj-22874	309	7	of	of	ADP
brj-22874	309	8	the	the	DET
brj-22874	309	9	improved	improved	ADJ
brj-22874	309	10	model	model	NOUN
brj-22874	309	11	are	be	AUX
brj-22874	309	12	shown	show	VERB
brj-22874	309	13	in	in	ADP
brj-22874	309	14	fig	fig	NOUN
brj-22874	309	15	.	.	PUNCT
brj-22874	310	1	13	13	NUM
brj-22874	310	2	.	.	X
brj-22874	310	3	fig	fig	NOUN
brj-22874	310	4	.	.	PUNCT
brj-22874	311	1	8	8	X
brj-22874	311	2	.	.	PUNCT
brj-22874	311	3	detection	detection	NOUN
brj-22874	311	4	precision	precision	NOUN
brj-22874	311	5	of	of	ADP
brj-22874	311	6	yolov5	yolov5	NOUN
brj-22874	311	7	for	for	ADP
brj-22874	311	8	four	four	NUM
brj-22874	311	9	defects	defect	NOUN
brj-22874	311	10	when	when	SCONJ
brj-22874	311	11	the	the	DET
brj-22874	311	12	detection	detection	NOUN
brj-22874	311	13	confidence	confidence	NOUN
brj-22874	311	14	is	be	AUX
brj-22874	311	15	greater	great	ADJ
brj-22874	311	16	than	than	ADP
brj-22874	311	17	or	or	CCONJ
brj-22874	311	18	equal	equal	ADJ
brj-22874	311	19	to	to	ADP
brj-22874	311	20	0.5	0.5	NUM
brj-22874	311	21	,	,	PUNCT
brj-22874	311	22	the	the	DET
brj-22874	311	23	detection	detection	NOUN
brj-22874	311	24	result	result	NOUN
brj-22874	311	25	can	can	AUX
brj-22874	311	26	be	be	AUX
brj-22874	311	27	reliable	reliable	ADJ
brj-22874	311	28	.	.	PUNCT
brj-22874	312	1	figures	figure	NOUN
brj-22874	312	2	8	8	NUM
brj-22874	312	3	and	and	CCONJ
brj-22874	312	4	9	9	NUM
brj-22874	312	5	show	show	VERB
brj-22874	312	6	that	that	SCONJ
brj-22874	312	7	when	when	SCONJ
brj-22874	312	8	the	the	DET
brj-22874	312	9	confidence	confidence	NOUN
brj-22874	312	10	level	level	NOUN
brj-22874	312	11	is	be	AUX
brj-22874	312	12	0.5	0.5	NUM
brj-22874	312	13	,	,	PUNCT
brj-22874	312	14	the	the	DET
brj-22874	312	15	detection	detection	NOUN
brj-22874	312	16	accuracy	accuracy	NOUN
brj-22874	312	17	of	of	ADP
brj-22874	312	18	back	back	ADJ
brj-22874	312	19	crack	crack	NOUN
brj-22874	312	20	,	,	PUNCT
brj-22874	312	21	black	black	ADJ
brj-22874	312	22	knot	knot	NOUN
brj-22874	312	23	,	,	PUNCT
brj-22874	312	24	mineral	mineral	NOUN
brj-22874	312	25	line	line	NOUN
brj-22874	312	26	,	,	PUNCT
brj-22874	312	27	and	and	CCONJ
brj-22874	312	28	pollution	pollution	NOUN
brj-22874	312	29	defect	defect	NOUN
brj-22874	312	30	is	be	AUX
brj-22874	312	31	89.40	89.40	NUM
brj-22874	312	32	%	%	NOUN
brj-22874	312	33	,	,	PUNCT
brj-22874	312	34	89.39	89.39	NUM
brj-22874	312	35	%	%	NOUN
brj-22874	312	36	,	,	PUNCT
brj-22874	312	37	86.41	86.41	NUM
brj-22874	312	38	%	%	NOUN
brj-22874	312	39	,	,	PUNCT
brj-22874	312	40	and	and	CCONJ
brj-22874	312	41	61.29	61.29	NUM
brj-22874	312	42	%	%	NOUN
brj-22874	312	43	.	.	PUNCT
brj-22874	313	1	by	by	ADP
brj-22874	313	2	using	use	VERB
brj-22874	313	3	the	the	DET
brj-22874	313	4	above	above	ADJ
brj-22874	313	5	three	three	NUM
brj-22874	313	6	improved	improved	ADJ
brj-22874	313	7	strategies	strategy	NOUN
brj-22874	313	8	,	,	PUNCT
brj-22874	313	9	the	the	DET
brj-22874	313	10	detection	detection	NOUN
brj-22874	313	11	accuracy	accuracy	NOUN
brj-22874	313	12	of	of	ADP
brj-22874	313	13	the	the	DET
brj-22874	313	14	yolov5	yolov5	NOUN
brj-22874	313	15	-	-	PUNCT
brj-22874	313	16	tspp	tspp	NOUN
brj-22874	313	17	detection	detection	NOUN
brj-22874	313	18	model	model	NOUN
brj-22874	313	19	for	for	ADP
brj-22874	313	20	back	back	ADJ
brj-22874	313	21	crack	crack	NOUN
brj-22874	313	22	,	,	PUNCT
brj-22874	313	23	black	black	ADJ
brj-22874	313	24	knot	knot	NOUN
brj-22874	313	25	,	,	PUNCT
brj-22874	313	26	mineral	mineral	NOUN
brj-22874	313	27	line	line	NOUN
brj-22874	313	28	,	,	PUNCT
brj-22874	313	29	and	and	CCONJ
brj-22874	313	30	pollution	pollution	NOUN
brj-22874	313	31	is	be	AUX
brj-22874	313	32	92.1	92.1	NUM
brj-22874	313	33	%	%	NOUN
brj-22874	313	34	,	,	PUNCT
brj-22874	313	35	98.6	98.6	NUM
brj-22874	313	36	%	%	NOUN
brj-22874	313	37	,	,	PUNCT
brj-22874	313	38	92.3	92.3	NUM
brj-22874	313	39	%	%	NOUN
brj-22874	313	40	and	and	CCONJ
brj-22874	313	41	69.5	69.5	NUM
brj-22874	313	42	%	%	NOUN
brj-22874	313	43	.	.	PUNCT
brj-22874	314	1	the	the	DET
brj-22874	314	2	detection	detection	NOUN
brj-22874	314	3	accuracy	accuracy	NOUN
brj-22874	314	4	of	of	ADP
brj-22874	314	5	the	the	DET
brj-22874	314	6	model	model	NOUN
brj-22874	314	7	for	for	ADP
brj-22874	314	8	various	various	ADJ
brj-22874	314	9	defects	defect	NOUN
brj-22874	314	10	has	have	AUX
brj-22874	314	11	improved	improve	VERB
brj-22874	314	12	,	,	PUNCT
brj-22874	314	13	indicating	indicate	VERB
brj-22874	314	14	that	that	SCONJ
brj-22874	314	15	the	the	DET
brj-22874	314	16	false	false	ADJ
brj-22874	314	17	detection	detection	NOUN
brj-22874	314	18	rate	rate	NOUN
brj-22874	314	19	of	of	ADP
brj-22874	314	20	the	the	DET
brj-22874	314	21	model	model	NOUN
brj-22874	314	22	for	for	ADP
brj-22874	314	23	various	various	ADJ
brj-22874	314	24	defects	defect	NOUN
brj-22874	314	25	is	be	AUX
brj-22874	314	26	decreasing	decrease	VERB
brj-22874	314	27	.	.	PUNCT
brj-22874	315	1	peer	peer	NOUN
brj-22874	315	2	-	-	PUNCT
brj-22874	315	3	reviewed	review	VERB
brj-22874	315	4	article	article	NOUN
brj-22874	315	5	bioresources.com	bioresources.com	X
brj-22874	315	6	tian	tian	PROPN
brj-22874	315	7	et	et	PROPN
brj-22874	315	8	al	al	PROPN
brj-22874	315	9	.	.	PROPN
brj-22874	315	10	(	(	PUNCT
brj-22874	315	11	2023	2023	NUM
brj-22874	315	12	)	)	PUNCT
brj-22874	315	13	.	.	PUNCT
brj-22874	316	1	“	"	PUNCT
brj-22874	316	2	defect	defect	VERB
brj-22874	316	3	detection	detection	NOUN
brj-22874	316	4	with	with	ADP
brj-22874	316	5	yolov5	yolov5	NOUN
brj-22874	316	6	,	,	PUNCT
brj-22874	316	7	”	"	PUNCT
brj-22874	316	8	bioresources	bioresource	NOUN
brj-22874	316	9	18(4	18(4	NUM
brj-22874	316	10	)	)	PUNCT
brj-22874	316	11	,	,	PUNCT
brj-22874	316	12	7713	7713	NUM
brj-22874	316	13	-	-	SYM
brj-22874	316	14	7730	7730	NUM
brj-22874	316	15	.	.	PUNCT
brj-22874	317	1	7725	7725	NUM
brj-22874	317	2	fig	fig	NOUN
brj-22874	317	3	.	.	PUNCT
brj-22874	318	1	9	9	X
brj-22874	318	2	.	.	X
brj-22874	318	3	detection	detection	NOUN
brj-22874	318	4	precision	precision	NOUN
brj-22874	318	5	of	of	ADP
brj-22874	318	6	yolov5	yolov5	NOUN
brj-22874	318	7	-	-	NOUN
brj-22874	318	8	tspp	tspp	NOUN
brj-22874	318	9	for	for	ADP
brj-22874	318	10	four	four	NUM
brj-22874	318	11	defects	defect	NOUN
brj-22874	318	12	fig	fig	NOUN
brj-22874	318	13	.	.	PUNCT
brj-22874	319	1	10	10	NUM
brj-22874	319	2	.	.	PUNCT
brj-22874	320	1	recall	recall	NOUN
brj-22874	320	2	of	of	ADP
brj-22874	320	3	yolov5	yolov5	NOUN
brj-22874	320	4	for	for	ADP
brj-22874	320	5	four	four	NUM
brj-22874	320	6	defects	defect	NOUN
brj-22874	320	7	peer	peer	NOUN
brj-22874	320	8	-	-	PUNCT
brj-22874	320	9	reviewed	review	VERB
brj-22874	320	10	article	article	NOUN
brj-22874	320	11	bioresources.com	bioresources.com	X
brj-22874	320	12	tian	tian	PROPN
brj-22874	320	13	et	et	PROPN
brj-22874	320	14	al	al	PROPN
brj-22874	320	15	.	.	PROPN
brj-22874	321	1	(	(	PUNCT
brj-22874	321	2	2023	2023	NUM
brj-22874	321	3	)	)	PUNCT
brj-22874	321	4	.	.	PUNCT
brj-22874	322	1	“	"	PUNCT
brj-22874	322	2	defect	defect	VERB
brj-22874	322	3	detection	detection	NOUN
brj-22874	322	4	with	with	ADP
brj-22874	322	5	yolov5	yolov5	NOUN
brj-22874	322	6	,	,	PUNCT
brj-22874	322	7	”	"	PUNCT
brj-22874	322	8	bioresources	bioresource	NOUN
brj-22874	322	9	18(4	18(4	NUM
brj-22874	322	10	)	)	PUNCT
brj-22874	322	11	,	,	PUNCT
brj-22874	322	12	7713	7713	NUM
brj-22874	322	13	-	-	SYM
brj-22874	322	14	7730	7730	NUM
brj-22874	322	15	.	.	PUNCT
brj-22874	323	1	7726	7726	NUM
brj-22874	323	2	fig	fig	NOUN
brj-22874	323	3	.	.	PUNCT
brj-22874	324	1	11	11	NUM
brj-22874	324	2	.	.	PUNCT
brj-22874	325	1	recall	recall	NOUN
brj-22874	325	2	of	of	ADP
brj-22874	325	3	yolov5	yolov5	NOUN
brj-22874	325	4	-	-	NOUN
brj-22874	325	5	tspp	tspp	NOUN
brj-22874	325	6	for	for	ADP
brj-22874	325	7	four	four	NUM
brj-22874	325	8	defects	defect	NOUN
brj-22874	325	9	fig	fig	NOUN
brj-22874	325	10	.	.	PUNCT
brj-22874	326	1	12	12	NUM
brj-22874	326	2	.	.	PUNCT
brj-22874	327	1	the	the	DET
brj-22874	327	2	average	average	ADJ
brj-22874	327	3	precision	precision	NOUN
brj-22874	327	4	of	of	ADP
brj-22874	327	5	yolov5	yolov5	NOUN
brj-22874	327	6	for	for	ADP
brj-22874	327	7	four	four	NUM
brj-22874	327	8	defects	defect	NOUN
brj-22874	327	9	peer	peer	NOUN
brj-22874	327	10	-	-	PUNCT
brj-22874	327	11	reviewed	review	VERB
brj-22874	327	12	article	article	NOUN
brj-22874	327	13	bioresources.com	bioresources.com	X
brj-22874	327	14	tian	tian	PROPN
brj-22874	327	15	et	et	PROPN
brj-22874	327	16	al	al	PROPN
brj-22874	327	17	.	.	PROPN
brj-22874	327	18	(	(	PUNCT
brj-22874	327	19	2023	2023	NUM
brj-22874	327	20	)	)	PUNCT
brj-22874	327	21	.	.	PUNCT
brj-22874	328	1	“	"	PUNCT
brj-22874	328	2	defect	defect	VERB
brj-22874	328	3	detection	detection	NOUN
brj-22874	328	4	with	with	ADP
brj-22874	328	5	yolov5	yolov5	NOUN
brj-22874	328	6	,	,	PUNCT
brj-22874	328	7	”	"	PUNCT
brj-22874	328	8	bioresources	bioresource	NOUN
brj-22874	328	9	18(4	18(4	NUM
brj-22874	328	10	)	)	PUNCT
brj-22874	328	11	,	,	PUNCT
brj-22874	328	12	7713	7713	NUM
brj-22874	328	13	-	-	SYM
brj-22874	328	14	7730	7730	NUM
brj-22874	328	15	.	.	PUNCT
brj-22874	329	1	7727	7727	NUM
brj-22874	329	2	fig	fig	NOUN
brj-22874	329	3	.	.	PUNCT
brj-22874	330	1	13	13	NUM
brj-22874	330	2	.	.	PUNCT
brj-22874	331	1	the	the	DET
brj-22874	331	2	average	average	ADJ
brj-22874	331	3	precision	precision	NOUN
brj-22874	331	4	of	of	ADP
brj-22874	331	5	yolov5	yolov5	NOUN
brj-22874	331	6	-	-	NOUN
brj-22874	331	7	tspp	tspp	NOUN
brj-22874	331	8	for	for	ADP
brj-22874	331	9	four	four	NUM
brj-22874	331	10	defects	defect	NOUN
brj-22874	331	11	by	by	ADP
brj-22874	331	12	comparing	compare	VERB
brj-22874	331	13	the	the	DET
brj-22874	331	14	recall	recall	NOUN
brj-22874	331	15	rate	rate	NOUN
brj-22874	331	16	images	image	NOUN
brj-22874	331	17	of	of	ADP
brj-22874	331	18	the	the	DET
brj-22874	331	19	yolov5	yolov5	NOUN
brj-22874	331	20	model	model	NOUN
brj-22874	331	21	and	and	CCONJ
brj-22874	331	22	yolov5	yolov5	NOUN
brj-22874	331	23	-	-	PUNCT
brj-22874	331	24	tspp	tspp	NOUN
brj-22874	331	25	model	model	NOUN
brj-22874	331	26	,	,	PUNCT
brj-22874	331	27	when	when	SCONJ
brj-22874	331	28	the	the	DET
brj-22874	331	29	confidence	confidence	NOUN
brj-22874	331	30	level	level	NOUN
brj-22874	331	31	is	be	AUX
brj-22874	331	32	0.5	0.5	NUM
brj-22874	331	33	,	,	PUNCT
brj-22874	331	34	the	the	DET
brj-22874	331	35	recall	recall	NOUN
brj-22874	331	36	rates	rate	NOUN
brj-22874	331	37	of	of	ADP
brj-22874	331	38	back	back	ADJ
brj-22874	331	39	crack	crack	NOUN
brj-22874	331	40	,	,	PUNCT
brj-22874	331	41	black	black	ADJ
brj-22874	331	42	knot	knot	NOUN
brj-22874	331	43	,	,	PUNCT
brj-22874	331	44	mineral	mineral	NOUN
brj-22874	331	45	line	line	NOUN
brj-22874	331	46	,	,	PUNCT
brj-22874	331	47	and	and	CCONJ
brj-22874	331	48	pollution	pollution	NOUN
brj-22874	331	49	in	in	ADP
brj-22874	331	50	yolov5	yolov5	NOUN
brj-22874	331	51	are	be	AUX
brj-22874	331	52	83.8	83.8	NUM
brj-22874	331	53	%	%	NOUN
brj-22874	331	54	,	,	PUNCT
brj-22874	331	55	79.7	79.7	NUM
brj-22874	331	56	%	%	NOUN
brj-22874	331	57	,	,	PUNCT
brj-22874	331	58	74.0	74.0	NUM
brj-22874	331	59	%	%	NOUN
brj-22874	331	60	,	,	PUNCT
brj-22874	331	61	and	and	CCONJ
brj-22874	331	62	34.2	34.2	NUM
brj-22874	331	63	%	%	NOUN
brj-22874	331	64	,	,	PUNCT
brj-22874	331	65	respectively	respectively	ADV
brj-22874	331	66	.	.	PUNCT
brj-22874	332	1	the	the	DET
brj-22874	332	2	recall	recall	NOUN
brj-22874	332	3	efficiencies	efficiency	NOUN
brj-22874	332	4	of	of	ADP
brj-22874	332	5	back	back	ADJ
brj-22874	332	6	crack	crack	NOUN
brj-22874	332	7	,	,	PUNCT
brj-22874	332	8	black	black	ADJ
brj-22874	332	9	knot	knot	NOUN
brj-22874	332	10	,	,	PUNCT
brj-22874	332	11	mineral	mineral	NOUN
brj-22874	332	12	line	line	NOUN
brj-22874	332	13	,	,	PUNCT
brj-22874	332	14	and	and	CCONJ
brj-22874	332	15	contamination	contamination	NOUN
brj-22874	332	16	in	in	ADP
brj-22874	332	17	yolov5tspp	yolov5tspp	PROPN
brj-22874	332	18	were	be	AUX
brj-22874	332	19	87.7	87.7	NUM
brj-22874	332	20	%	%	NOUN
brj-22874	332	21	,	,	PUNCT
brj-22874	332	22	90.8	90.8	NUM
brj-22874	332	23	%	%	NOUN
brj-22874	332	24	,	,	PUNCT
brj-22874	332	25	83.7	83.7	NUM
brj-22874	332	26	%	%	NOUN
brj-22874	332	27	,	,	PUNCT
brj-22874	332	28	and	and	CCONJ
brj-22874	332	29	55.8	55.8	NUM
brj-22874	332	30	%	%	NOUN
brj-22874	332	31	,	,	PUNCT
brj-22874	332	32	respectively	respectively	ADV
brj-22874	332	33	.	.	PUNCT
brj-22874	333	1	yolov5	yolov5	NOUN
brj-22874	333	2	-	-	PUNCT
brj-22874	333	3	tspp	tspp	NOUN
brj-22874	333	4	has	have	VERB
brj-22874	333	5	a	a	DET
brj-22874	333	6	lower	low	ADJ
brj-22874	333	7	missed	miss	VERB
brj-22874	333	8	detection	detection	NOUN
brj-22874	333	9	rate	rate	NOUN
brj-22874	333	10	than	than	ADP
brj-22874	333	11	yolov5	yolov5	NOUN
brj-22874	333	12	.	.	PUNCT
brj-22874	334	1	the	the	PRON
brj-22874	334	2	larger	large	ADJ
brj-22874	334	3	the	the	DET
brj-22874	334	4	area	area	NOUN
brj-22874	334	5	of	of	ADP
brj-22874	334	6	the	the	DET
brj-22874	334	7	p	p	ADJ
brj-22874	334	8	-	-	PUNCT
brj-22874	334	9	r	r	NOUN
brj-22874	334	10	curve	curve	NOUN
brj-22874	334	11	(	(	PUNCT
brj-22874	334	12	ap	ap	PROPN
brj-22874	334	13	)	)	PUNCT
brj-22874	334	14	,	,	PUNCT
brj-22874	334	15	the	the	PRON
brj-22874	334	16	better	well	ADJ
brj-22874	334	17	the	the	DET
brj-22874	334	18	performance	performance	NOUN
brj-22874	334	19	of	of	ADP
brj-22874	334	20	the	the	DET
brj-22874	334	21	corresponding	corresponding	ADJ
brj-22874	334	22	model	model	NOUN
brj-22874	334	23	.	.	PUNCT
brj-22874	335	1	when	when	SCONJ
brj-22874	335	2	the	the	DET
brj-22874	335	3	area	area	NOUN
brj-22874	335	4	reaches	reach	VERB
brj-22874	335	5	the	the	DET
brj-22874	335	6	maximum	maximum	ADJ
brj-22874	335	7	value	value	NOUN
brj-22874	335	8	,	,	PUNCT
brj-22874	335	9	the	the	DET
brj-22874	335	10	precision	precision	NOUN
brj-22874	335	11	and	and	CCONJ
brj-22874	335	12	recall	recall	NOUN
brj-22874	335	13	of	of	ADP
brj-22874	335	14	the	the	DET
brj-22874	335	15	model	model	NOUN
brj-22874	335	16	reaches	reach	VERB
brj-22874	335	17	the	the	DET
brj-22874	335	18	maximum	maximum	ADJ
brj-22874	335	19	value	value	NOUN
brj-22874	335	20	,	,	PUNCT
brj-22874	335	21	and	and	CCONJ
brj-22874	335	22	the	the	DET
brj-22874	335	23	false	false	ADJ
brj-22874	335	24	detection	detection	NOUN
brj-22874	335	25	rate	rate	NOUN
brj-22874	335	26	and	and	CCONJ
brj-22874	335	27	missed	miss	VERB
brj-22874	335	28	detection	detection	NOUN
brj-22874	335	29	rate	rate	NOUN
brj-22874	335	30	of	of	ADP
brj-22874	335	31	the	the	DET
brj-22874	335	32	model	model	NOUN
brj-22874	335	33	are	be	AUX
brj-22874	335	34	lower	low	ADJ
brj-22874	335	35	.	.	PUNCT
brj-22874	336	1	from	from	ADP
brj-22874	336	2	figs	fig	NOUN
brj-22874	336	3	.	.	PUNCT
brj-22874	337	1	12	12	NUM
brj-22874	337	2	and	and	CCONJ
brj-22874	337	3	13	13	NUM
brj-22874	337	4	,	,	PUNCT
brj-22874	337	5	the	the	DET
brj-22874	337	6	aps	ap	NOUN
brj-22874	337	7	of	of	ADP
brj-22874	337	8	back	back	ADJ
brj-22874	337	9	crack	crack	NOUN
brj-22874	337	10	,	,	PUNCT
brj-22874	337	11	black	black	ADJ
brj-22874	337	12	knot	knot	NOUN
brj-22874	337	13	,	,	PUNCT
brj-22874	337	14	mineral	mineral	NOUN
brj-22874	337	15	line	line	NOUN
brj-22874	337	16	,	,	PUNCT
brj-22874	337	17	and	and	CCONJ
brj-22874	337	18	contamination	contamination	NOUN
brj-22874	337	19	in	in	ADP
brj-22874	337	20	yolov5	yolov5	NOUN
brj-22874	337	21	were	be	AUX
brj-22874	337	22	87.8	87.8	NUM
brj-22874	337	23	%	%	NOUN
brj-22874	337	24	,	,	PUNCT
brj-22874	337	25	82.7	82.7	NUM
brj-22874	337	26	%	%	NOUN
brj-22874	337	27	,	,	PUNCT
brj-22874	337	28	79.1	79.1	NUM
brj-22874	337	29	%	%	NOUN
brj-22874	337	30	,	,	PUNCT
brj-22874	337	31	and	and	CCONJ
brj-22874	337	32	34.9	34.9	NUM
brj-22874	337	33	%	%	NOUN
brj-22874	337	34	,	,	PUNCT
brj-22874	337	35	respectively	respectively	ADV
brj-22874	337	36	.	.	PUNCT
brj-22874	338	1	the	the	DET
brj-22874	338	2	ap	ap	PROPN
brj-22874	338	3	of	of	ADP
brj-22874	338	4	back	back	NOUN
brj-22874	338	5	crack	crack	NOUN
brj-22874	338	6	,	,	PUNCT
brj-22874	338	7	black	black	ADJ
brj-22874	338	8	knot	knot	NOUN
brj-22874	338	9	,	,	PUNCT
brj-22874	338	10	mineral	mineral	NOUN
brj-22874	338	11	line	line	NOUN
brj-22874	338	12	,	,	PUNCT
brj-22874	338	13	and	and	CCONJ
brj-22874	338	14	contamination	contamination	NOUN
brj-22874	338	15	in	in	ADP
brj-22874	338	16	yolov5	yolov5	NOUN
brj-22874	338	17	-	-	PUNCT
brj-22874	338	18	tspp	tspp	NOUN
brj-22874	338	19	were	be	AUX
brj-22874	338	20	89.2	89.2	NUM
brj-22874	338	21	%	%	NOUN
brj-22874	338	22	,	,	PUNCT
brj-22874	338	23	92.2	92.2	NUM
brj-22874	338	24	%	%	NOUN
brj-22874	338	25	,	,	PUNCT
brj-22874	338	26	85.3	85.3	NUM
brj-22874	338	27	%	%	NOUN
brj-22874	338	28	and	and	CCONJ
brj-22874	338	29	54.4	54.4	NUM
brj-22874	338	30	%	%	NOUN
brj-22874	338	31	,	,	PUNCT
brj-22874	338	32	respectively	respectively	ADV
brj-22874	338	33	.	.	PUNCT
brj-22874	339	1	therefore	therefore	ADV
brj-22874	339	2	,	,	PUNCT
brj-22874	339	3	the	the	DET
brj-22874	339	4	performance	performance	NOUN
brj-22874	339	5	of	of	ADP
brj-22874	339	6	yolov5	yolov5	NOUN
brj-22874	339	7	-	-	PUNCT
brj-22874	339	8	tspp	tspp	NOUN
brj-22874	339	9	was	be	AUX
brj-22874	339	10	better	well	ADJ
brj-22874	339	11	than	than	ADP
brj-22874	339	12	that	that	PRON
brj-22874	339	13	of	of	ADP
brj-22874	339	14	yolov5	yolov5	PROPN
brj-22874	339	15	.	.	PUNCT
brj-22874	340	1	performance	performance	NOUN
brj-22874	340	2	comparison	comparison	NOUN
brj-22874	340	3	of	of	ADP
brj-22874	340	4	different	different	ADJ
brj-22874	340	5	models	model	NOUN
brj-22874	340	6	in	in	ADP
brj-22874	340	7	order	order	NOUN
brj-22874	340	8	to	to	PART
brj-22874	340	9	verify	verify	VERB
brj-22874	340	10	the	the	DET
brj-22874	340	11	performance	performance	NOUN
brj-22874	340	12	of	of	ADP
brj-22874	340	13	yolov5	yolov5	NOUN
brj-22874	340	14	-	-	PUNCT
brj-22874	340	15	tspp	tspp	NOUN
brj-22874	340	16	,	,	PUNCT
brj-22874	340	17	the	the	DET
brj-22874	340	18	same	same	ADJ
brj-22874	340	19	data	datum	NOUN
brj-22874	340	20	set	set	VERB
brj-22874	340	21	was	be	AUX
brj-22874	340	22	trained	train	VERB
brj-22874	340	23	and	and	CCONJ
brj-22874	340	24	tested	test	VERB
brj-22874	340	25	on	on	ADP
brj-22874	340	26	ssd	ssd	NOUN
brj-22874	340	27	and	and	CCONJ
brj-22874	340	28	centernet	centernet	NOUN
brj-22874	340	29	network	network	NOUN
brj-22874	340	30	models	model	NOUN
brj-22874	340	31	.	.	PUNCT
brj-22874	341	1	the	the	DET
brj-22874	341	2	map	map	NOUN
brj-22874	341	3	,	,	PUNCT
brj-22874	341	4	recall	recall	NOUN
brj-22874	341	5	,	,	PUNCT
brj-22874	341	6	and	and	CCONJ
brj-22874	341	7	precision	precision	NOUN
brj-22874	341	8	of	of	ADP
brj-22874	341	9	the	the	DET
brj-22874	341	10	models	model	NOUN
brj-22874	341	11	were	be	AUX
brj-22874	341	12	then	then	ADV
brj-22874	341	13	compared	compare	VERB
brj-22874	341	14	and	and	CCONJ
brj-22874	341	15	counted	count	VERB
brj-22874	341	16	.	.	PUNCT
brj-22874	342	1	the	the	DET
brj-22874	342	2	results	result	NOUN
brj-22874	342	3	are	be	AUX
brj-22874	342	4	shown	show	VERB
brj-22874	342	5	in	in	ADP
brj-22874	342	6	table	table	NOUN
brj-22874	342	7	3	3	NUM
brj-22874	342	8	,	,	PUNCT
brj-22874	342	9	where	where	SCONJ
brj-22874	342	10	b	b	NOUN
brj-22874	342	11	represents	represent	VERB
brj-22874	342	12	the	the	DET
brj-22874	342	13	back	back	ADJ
brj-22874	342	14	crack	crack	NOUN
brj-22874	342	15	defect	defect	NOUN
brj-22874	342	16	,	,	PUNCT
brj-22874	342	17	h	h	NOUN
brj-22874	342	18	represents	represent	VERB
brj-22874	342	19	the	the	DET
brj-22874	342	20	black	black	ADJ
brj-22874	342	21	knot	knot	NOUN
brj-22874	342	22	defect	defect	NOUN
brj-22874	342	23	,	,	PUNCT
brj-22874	342	24	k	k	PROPN
brj-22874	342	25	represents	represent	VERB
brj-22874	342	26	the	the	DET
brj-22874	342	27	mineral	mineral	NOUN
brj-22874	342	28	line	line	NOUN
brj-22874	342	29	defect	defect	NOUN
brj-22874	342	30	,	,	PUNCT
brj-22874	342	31	and	and	CCONJ
brj-22874	342	32	w	w	NOUN
brj-22874	342	33	represents	represent	VERB
brj-22874	342	34	the	the	DET
brj-22874	342	35	pollution	pollution	NOUN
brj-22874	342	36	defect	defect	NOUN
brj-22874	342	37	.	.	PUNCT
brj-22874	343	1	peer	peer	NOUN
brj-22874	343	2	-	-	PUNCT
brj-22874	343	3	reviewed	review	VERB
brj-22874	343	4	article	article	NOUN
brj-22874	343	5	bioresources.com	bioresources.com	X
brj-22874	343	6	tian	tian	PROPN
brj-22874	343	7	et	et	PROPN
brj-22874	343	8	al	al	PROPN
brj-22874	343	9	.	.	PROPN
brj-22874	343	10	(	(	PUNCT
brj-22874	343	11	2023	2023	NUM
brj-22874	343	12	)	)	PUNCT
brj-22874	343	13	.	.	PUNCT
brj-22874	344	1	“	"	PUNCT
brj-22874	344	2	defect	defect	VERB
brj-22874	344	3	detection	detection	NOUN
brj-22874	344	4	with	with	ADP
brj-22874	344	5	yolov5	yolov5	NOUN
brj-22874	344	6	,	,	PUNCT
brj-22874	344	7	”	"	PUNCT
brj-22874	344	8	bioresources	bioresource	NOUN
brj-22874	344	9	18(4	18(4	NUM
brj-22874	344	10	)	)	PUNCT
brj-22874	344	11	,	,	PUNCT
brj-22874	344	12	7713	7713	NUM
brj-22874	344	13	-	-	SYM
brj-22874	344	14	7730	7730	NUM
brj-22874	344	15	.	.	PUNCT
brj-22874	345	1	7728	7728	NUM
brj-22874	345	2	table	table	NOUN
brj-22874	345	3	3	3	NUM
brj-22874	345	4	.	.	PUNCT
brj-22874	345	5	performance	performance	NOUN
brj-22874	345	6	comparison	comparison	NOUN
brj-22874	345	7	of	of	ADP
brj-22874	345	8	different	different	ADJ
brj-22874	345	9	algorithm	algorithm	NOUN
brj-22874	345	10	network	network	NOUN
brj-22874	345	11	models	model	NOUN
brj-22874	345	12	model	model	NOUN
brj-22874	345	13	map	map	PROPN
brj-22874	345	14	recall	recall	PROPN
brj-22874	345	15	precision	precision	PROPN
brj-22874	345	16	b	b	PROPN
brj-22874	345	17	h	h	NOUN
brj-22874	346	1	k	k	PROPN
brj-22874	346	2	w	w	PROPN
brj-22874	346	3	b	b	PROPN
brj-22874	346	4	h	h	PROPN
brj-22874	346	5	k	k	PROPN
brj-22874	346	6	w	w	PROPN
brj-22874	346	7	ssd	ssd	PROPN
brj-22874	346	8	38.47	38.47	NUM
brj-22874	346	9	17.65	17.65	NUM
brj-22874	346	10	5.41	5.41	NUM
brj-22874	346	11	4.33	4.33	NUM
brj-22874	346	12	0.16	0.16	NUM
brj-22874	346	13	85.71	85.71	NUM
brj-22874	346	14	57.14	57.14	NUM
brj-22874	346	15	72.22	72.22	NUM
brj-22874	346	16	20.00	20.00	NUM
brj-22874	346	17	centernet	centernet	NOUN
brj-22874	346	18	62.85	62.85	NUM
brj-22874	346	19	66.74	66.74	NUM
brj-22874	346	20	72.97	72.97	NUM
brj-22874	346	21	62.23	62.23	NUM
brj-22874	346	22	39.09	39.09	NUM
brj-22874	346	23	90.21	90.21	NUM
brj-22874	346	24	88.52	88.52	NUM
brj-22874	346	25	85.19	85.19	NUM
brj-22874	346	26	56.60	56.60	NUM
brj-22874	346	27	yolov5	yolov5	NOUN
brj-22874	346	28	-	-	PUNCT
brj-22874	346	29	tspp	tspp	NOUN
brj-22874	346	30	80.30	80.30	NUM
brj-22874	346	31	87.73	87.73	NUM
brj-22874	346	32	90.79	90.79	NUM
brj-22874	346	33	83.72	83.72	NUM
brj-22874	346	34	55.83	55.83	NUM
brj-22874	346	35	92.12	92.12	NUM
brj-22874	346	36	98.57	98.57	NUM
brj-22874	346	37	92.31	92.31	NUM
brj-22874	346	38	69.53	69.53	NUM
brj-22874	346	39	compared	compare	VERB
brj-22874	346	40	with	with	ADP
brj-22874	346	41	the	the	DET
brj-22874	346	42	other	other	ADJ
brj-22874	346	43	two	two	NUM
brj-22874	346	44	models	model	NOUN
brj-22874	346	45	,	,	PUNCT
brj-22874	346	46	the	the	DET
brj-22874	346	47	yolov5	yolov5	NOUN
brj-22874	346	48	-	-	PUNCT
brj-22874	346	49	tspp	tspp	NOUN
brj-22874	346	50	model	model	NOUN
brj-22874	346	51	achieved	achieve	VERB
brj-22874	346	52	a	a	DET
brj-22874	346	53	better	well	ADJ
brj-22874	346	54	overall	overall	ADJ
brj-22874	346	55	detection	detection	NOUN
brj-22874	346	56	effect	effect	NOUN
brj-22874	346	57	,	,	PUNCT
brj-22874	346	58	and	and	CCONJ
brj-22874	346	59	the	the	DET
brj-22874	346	60	map	map	NOUN
brj-22874	346	61	was	be	AUX
brj-22874	346	62	41.8	41.8	NUM
brj-22874	346	63	%	%	NOUN
brj-22874	346	64	and	and	CCONJ
brj-22874	346	65	17.4	17.4	NUM
brj-22874	346	66	%	%	NOUN
brj-22874	346	67	higher	high	ADJ
brj-22874	346	68	than	than	ADP
brj-22874	346	69	ssd	ssd	NOUN
brj-22874	346	70	and	and	CCONJ
brj-22874	346	71	centernet	centernet	NOUN
brj-22874	346	72	.	.	PUNCT
brj-22874	347	1	the	the	DET
brj-22874	347	2	precision	precision	NOUN
brj-22874	347	3	and	and	CCONJ
brj-22874	347	4	recall	recall	NOUN
brj-22874	347	5	of	of	ADP
brj-22874	347	6	the	the	DET
brj-22874	347	7	four	four	NUM
brj-22874	347	8	defects	defect	NOUN
brj-22874	347	9	were	be	AUX
brj-22874	347	10	improved	improve	VERB
brj-22874	347	11	compared	compare	VERB
brj-22874	347	12	with	with	ADP
brj-22874	347	13	ssd	ssd	NOUN
brj-22874	347	14	and	and	CCONJ
brj-22874	347	15	centernet	centernet	NOUN
brj-22874	347	16	.	.	PUNCT
brj-22874	348	1	conclusions	conclusion	NOUN
brj-22874	348	2	1	1	NUM
brj-22874	348	3	.	.	PUNCT
brj-22874	349	1	when	when	SCONJ
brj-22874	349	2	improving	improve	VERB
brj-22874	349	3	the	the	DET
brj-22874	349	4	yolov5	yolov5	NOUN
brj-22874	349	5	model	model	NOUN
brj-22874	349	6	to	to	PART
brj-22874	349	7	identify	identify	VERB
brj-22874	349	8	the	the	DET
brj-22874	349	9	four	four	NUM
brj-22874	349	10	main	main	ADJ
brj-22874	349	11	types	type	NOUN
brj-22874	349	12	of	of	ADP
brj-22874	349	13	defects	defect	NOUN
brj-22874	349	14	(	(	PUNCT
brj-22874	349	15	back	back	NOUN
brj-22874	349	16	cracks	crack	NOUN
brj-22874	349	17	,	,	PUNCT
brj-22874	349	18	black	black	ADJ
brj-22874	349	19	knots	knot	NOUN
brj-22874	349	20	,	,	PUNCT
brj-22874	349	21	mineral	mineral	NOUN
brj-22874	349	22	lines	line	NOUN
brj-22874	349	23	,	,	PUNCT
brj-22874	349	24	and	and	CCONJ
brj-22874	349	25	pollution	pollution	NOUN
brj-22874	349	26	)	)	PUNCT
brj-22874	349	27	in	in	ADP
brj-22874	349	28	wooden	wooden	ADJ
brj-22874	349	29	spoons	spoon	NOUN
brj-22874	349	30	,	,	PUNCT
brj-22874	349	31	the	the	DET
brj-22874	349	32	identification	identification	NOUN
brj-22874	349	33	parameters	parameter	NOUN
brj-22874	349	34	(	(	PUNCT
brj-22874	349	35	precision	precision	NOUN
brj-22874	349	36	,	,	PUNCT
brj-22874	349	37	recall	recall	NOUN
brj-22874	349	38	,	,	PUNCT
brj-22874	349	39	and	and	CCONJ
brj-22874	349	40	average	average	ADJ
brj-22874	349	41	precision	precision	NOUN
brj-22874	349	42	)	)	PUNCT
brj-22874	349	43	of	of	ADP
brj-22874	349	44	black	black	ADJ
brj-22874	349	45	knots	knot	NOUN
brj-22874	349	46	were	be	AUX
brj-22874	349	47	the	the	DET
brj-22874	349	48	highest	high	ADJ
brj-22874	349	49	,	,	PUNCT
brj-22874	349	50	and	and	CCONJ
brj-22874	349	51	the	the	DET
brj-22874	349	52	recognition	recognition	NOUN
brj-22874	349	53	effect	effect	NOUN
brj-22874	349	54	is	be	AUX
brj-22874	349	55	better	well	ADJ
brj-22874	349	56	.	.	PUNCT
brj-22874	350	1	2	2	X
brj-22874	350	2	.	.	X
brj-22874	350	3	the	the	DET
brj-22874	350	4	average	average	ADJ
brj-22874	350	5	accuracy	accuracy	NOUN
brj-22874	350	6	of	of	ADP
brj-22874	350	7	the	the	DET
brj-22874	350	8	improved	improved	ADJ
brj-22874	350	9	yolov5	yolov5	NOUN
brj-22874	350	10	model	model	NOUN
brj-22874	350	11	for	for	ADP
brj-22874	350	12	back	back	ADJ
brj-22874	350	13	crack	crack	NOUN
brj-22874	350	14	,	,	PUNCT
brj-22874	350	15	black	black	ADJ
brj-22874	350	16	knot	knot	NOUN
brj-22874	350	17	,	,	PUNCT
brj-22874	350	18	mineral	mineral	NOUN
brj-22874	350	19	line	line	NOUN
brj-22874	350	20	,	,	PUNCT
brj-22874	350	21	and	and	CCONJ
brj-22874	350	22	pollution	pollution	NOUN
brj-22874	350	23	identification	identification	NOUN
brj-22874	350	24	results	result	NOUN
brj-22874	350	25	were	be	AUX
brj-22874	350	26	89.2	89.2	NUM
brj-22874	350	27	%	%	NOUN
brj-22874	350	28	,	,	PUNCT
brj-22874	350	29	92.2	92.2	NUM
brj-22874	350	30	%	%	NOUN
brj-22874	350	31	,	,	PUNCT
brj-22874	350	32	85.3	85.3	NUM
brj-22874	350	33	%	%	NOUN
brj-22874	350	34	,	,	PUNCT
brj-22874	350	35	and	and	CCONJ
brj-22874	350	36	54.4	54.4	NUM
brj-22874	350	37	%	%	NOUN
brj-22874	350	38	,	,	PUNCT
brj-22874	350	39	respectively	respectively	ADV
brj-22874	350	40	.	.	PUNCT
brj-22874	351	1	3	3	X
brj-22874	351	2	.	.	PUNCT
brj-22874	351	3	compared	compare	VERB
brj-22874	351	4	with	with	ADP
brj-22874	351	5	the	the	DET
brj-22874	351	6	current	current	ADJ
brj-22874	351	7	mainstream	mainstream	NOUN
brj-22874	351	8	detection	detection	NOUN
brj-22874	351	9	models	model	NOUN
brj-22874	351	10	(	(	PUNCT
brj-22874	351	11	ssd	ssd	NOUN
brj-22874	351	12	,	,	PUNCT
brj-22874	351	13	centernet	centernet	NOUN
brj-22874	351	14	)	)	PUNCT
brj-22874	351	15	,	,	PUNCT
brj-22874	351	16	the	the	DET
brj-22874	351	17	improved	improved	ADJ
brj-22874	351	18	model	model	NOUN
brj-22874	351	19	achieved	achieve	VERB
brj-22874	351	20	higher	high	ADJ
brj-22874	351	21	accuracy	accuracy	NOUN
brj-22874	351	22	,	,	PUNCT
brj-22874	351	23	recall	recall	NOUN
brj-22874	351	24	rate	rate	NOUN
brj-22874	351	25	,	,	PUNCT
brj-22874	351	26	and	and	CCONJ
brj-22874	351	27	average	average	ADJ
brj-22874	351	28	precision	precision	NOUN
brj-22874	351	29	rate	rate	NOUN
brj-22874	351	30	.	.	PUNCT
brj-22874	352	1	this	this	PRON
brj-22874	352	2	indicates	indicate	VERB
brj-22874	352	3	that	that	SCONJ
brj-22874	352	4	the	the	DET
brj-22874	352	5	improved	improved	ADJ
brj-22874	352	6	model	model	NOUN
brj-22874	352	7	exhibited	exhibit	VERB
brj-22874	352	8	a	a	DET
brj-22874	352	9	better	well	ADJ
brj-22874	352	10	recognition	recognition	NOUN
brj-22874	352	11	effect	effect	NOUN
brj-22874	352	12	on	on	ADP
brj-22874	352	13	the	the	DET
brj-22874	352	14	surface	surface	NOUN
brj-22874	352	15	defects	defect	NOUN
brj-22874	352	16	of	of	ADP
brj-22874	352	17	wooden	wooden	ADJ
brj-22874	352	18	spoons	spoon	NOUN
brj-22874	352	19	than	than	ADP
brj-22874	352	20	the	the	DET
brj-22874	352	21	other	other	ADJ
brj-22874	352	22	two	two	NUM
brj-22874	352	23	mainstream	mainstream	NOUN
brj-22874	352	24	detection	detection	NOUN
brj-22874	352	25	models	model	NOUN
brj-22874	352	26	.	.	PUNCT
brj-22874	353	1	acknowledgments	acknowledgment	NOUN
brj-22874	353	2	this	this	DET
brj-22874	353	3	research	research	NOUN
brj-22874	353	4	was	be	AUX
brj-22874	353	5	funded	fund	VERB
brj-22874	353	6	by	by	ADP
brj-22874	353	7	the	the	DET
brj-22874	353	8	basic	basic	ADJ
brj-22874	353	9	scientific	scientific	ADJ
brj-22874	353	10	research	research	NOUN
brj-22874	353	11	business	business	NOUN
brj-22874	353	12	fee	fee	NOUN
brj-22874	353	13	of	of	ADP
brj-22874	353	14	heilongjiang	heilongjiang	PROPN
brj-22874	353	15	provincial	provincial	ADJ
brj-22874	353	16	colleges	college	NOUN
brj-22874	353	17	and	and	CCONJ
brj-22874	353	18	universities	university	NOUN
brj-22874	353	19	,	,	PUNCT
brj-22874	353	20	grant	grant	VERB
brj-22874	353	21	number	number	NOUN
brj-22874	353	22	2022	2022	NUM
brj-22874	353	23	-	-	PUNCT
brj-22874	353	24	kyywf-0604	kyywf-0604	NOUN
brj-22874	353	25	.	.	PUNCT
brj-22874	354	1	references	reference	NOUN
brj-22874	354	2	cited	cite	VERB
brj-22874	354	3	abdullah	abdullah	PROPN
brj-22874	354	4	,	,	PUNCT
brj-22874	354	5	n.	n.	PROPN
brj-22874	354	6	d.	d.	PROPN
brj-22874	354	7	,	,	PUNCT
brj-22874	354	8	hashim	hashim	PROPN
brj-22874	354	9	,	,	PUNCT
brj-22874	354	10	u.	u.	PROPN
brj-22874	354	11	r.	r.	PROPN
brj-22874	354	12	a.	a.	PROPN
brj-22874	354	13	,	,	PUNCT
brj-22874	354	14	ahmad	ahmad	PROPN
brj-22874	354	15	,	,	PUNCT
brj-22874	354	16	s.	s.	PROPN
brj-22874	354	17	,	,	PUNCT
brj-22874	354	18	and	and	CCONJ
brj-22874	354	19	salahuddin	salahuddin	NOUN
brj-22874	354	20	,	,	PUNCT
brj-22874	354	21	l.	l.	PROPN
brj-22874	354	22	(	(	PUNCT
brj-22874	354	23	2020	2020	NUM
brj-22874	354	24	)	)	PUNCT
brj-22874	354	25	.	.	PUNCT
brj-22874	355	1	“	"	PUNCT
brj-22874	355	2	analysis	analysis	NOUN
brj-22874	355	3	of	of	ADP
brj-22874	355	4	texture	texture	NOUN
brj-22874	355	5	features	feature	NOUN
brj-22874	355	6	for	for	ADP
brj-22874	355	7	wood	wood	NOUN
brj-22874	355	8	defect	defect	NOUN
brj-22874	355	9	classificationn	classificationn	NOUN
brj-22874	355	10	,	,	PUNCT
brj-22874	355	11	”	"	PUNCT
brj-22874	355	12	bulletin	bulletin	NOUN
brj-22874	355	13	of	of	ADP
brj-22874	355	14	electrical	electrical	ADJ
brj-22874	355	15	engineering	engineering	NOUN
brj-22874	355	16	and	and	CCONJ
brj-22874	355	17	informatics	informatic	NOUN
brj-22874	355	18	9	9	NUM
brj-22874	355	19	,	,	PUNCT
brj-22874	355	20	121	121	NUM
brj-22874	355	21	-	-	SYM
brj-22874	355	22	128	128	NUM
brj-22874	355	23	.	.	PUNCT
brj-22874	356	1	doi	doi	NOUN
brj-22874	356	2	:	:	PUNCT
brj-22874	356	3	10.11591	10.11591	NUM
brj-22874	356	4	/	/	SYM
brj-22874	356	5	eei.v9i1.1553	eei.v9i1.1553	PROPN
brj-22874	356	6	aleksi	aleksi	PROPN
brj-22874	356	7	,	,	PUNCT
brj-22874	356	8	i.	i.	PROPN
brj-22874	356	9	,	,	PUNCT
brj-22874	356	10	sušac	sušac	PROPN
brj-22874	356	11	,	,	PUNCT
brj-22874	356	12	f.	f.	PROPN
brj-22874	356	13	,	,	PUNCT
brj-22874	356	14	and	and	CCONJ
brj-22874	356	15	matić	matić	NOUN
brj-22874	356	16	,	,	PUNCT
brj-22874	356	17	t.	t.	PROPN
brj-22874	356	18	(	(	PUNCT
brj-22874	356	19	2019	2019	NUM
brj-22874	356	20	)	)	PUNCT
brj-22874	356	21	.	.	PUNCT
brj-22874	357	1	“	"	PUNCT
brj-22874	357	2	features	feature	VERB
brj-22874	357	3	extraction	extraction	NOUN
brj-22874	357	4	and	and	CCONJ
brj-22874	357	5	texture	texture	NOUN
brj-22874	357	6	defect	defect	NOUN
brj-22874	357	7	detection	detection	NOUN
brj-22874	357	8	of	of	ADP
brj-22874	357	9	sawn	sawn	ADJ
brj-22874	357	10	wooden	wooden	ADJ
brj-22874	357	11	board	board	NOUN
brj-22874	357	12	images	image	NOUN
brj-22874	357	13	,	,	PUNCT
brj-22874	357	14	”	"	PUNCT
brj-22874	357	15	in	in	ADP
brj-22874	357	16	:	:	PUNCT
brj-22874	357	17	proceedings	proceeding	NOUN
brj-22874	357	18	of	of	ADP
brj-22874	357	19	the	the	DET
brj-22874	357	20	2019	2019	NUM
brj-22874	357	21	27th	27th	ADJ
brj-22874	357	22	telecommunications	telecommunications	NOUN
brj-22874	357	23	forum	forum	NOUN
brj-22874	357	24	(	(	PUNCT
brj-22874	357	25	telfor	telfor	PROPN
brj-22874	357	26	)	)	PUNCT
brj-22874	357	27	,	,	PUNCT
brj-22874	357	28	pp	pp	PROPN
brj-22874	357	29	.	.	PUNCT
brj-22874	358	1	1	1	NUM
brj-22874	358	2	-	-	SYM
brj-22874	358	3	4	4	NUM
brj-22874	358	4	..	..	PUNCT
brj-22874	358	5	doi	doi	NOUN
brj-22874	358	6	:	:	PUNCT
brj-22874	358	7	10.1109	10.1109	NUM
brj-22874	358	8	/	/	SYM
brj-22874	358	9	telfor48224.2019.8971381	telfor48224.2019.8971381	NUM
brj-22874	358	10	peer	peer	NOUN
brj-22874	358	11	-	-	PUNCT
brj-22874	358	12	reviewed	review	VERB
brj-22874	358	13	article	article	NOUN
brj-22874	358	14	bioresources.com	bioresources.com	X
brj-22874	358	15	tian	tian	PROPN
brj-22874	358	16	et	et	PROPN
brj-22874	358	17	al	al	PROPN
brj-22874	358	18	.	.	PROPN
brj-22874	359	1	(	(	PUNCT
brj-22874	359	2	2023	2023	NUM
brj-22874	359	3	)	)	PUNCT
brj-22874	359	4	.	.	PUNCT
brj-22874	360	1	“	"	PUNCT
brj-22874	360	2	defect	defect	VERB
brj-22874	360	3	detection	detection	NOUN
brj-22874	360	4	with	with	ADP
brj-22874	360	5	yolov5	yolov5	NOUN
brj-22874	360	6	,	,	PUNCT
brj-22874	360	7	”	"	PUNCT
brj-22874	360	8	bioresources	bioresource	NOUN
brj-22874	360	9	18(4	18(4	NUM
brj-22874	360	10	)	)	PUNCT
brj-22874	360	11	,	,	PUNCT
brj-22874	360	12	7713	7713	NUM
brj-22874	360	13	-	-	SYM
brj-22874	360	14	7730	7730	NUM
brj-22874	360	15	.	.	PUNCT
brj-22874	361	1	7729	7729	NUM
brj-22874	361	2	chen	chen	PROPN
brj-22874	361	3	,	,	PUNCT
brj-22874	361	4	l.-c	l.-c	PROPN
brj-22874	361	5	.	.	PUNCT
brj-22874	361	6	,	,	PUNCT
brj-22874	361	7	pardeshi	pardeshi	PROPN
brj-22874	361	8	,	,	PUNCT
brj-22874	361	9	m.s	m.s	PROPN
brj-22874	361	10	.	.	PROPN
brj-22874	361	11	,	,	PUNCT
brj-22874	361	12	lo	lo	PROPN
brj-22874	361	13	,	,	PUNCT
brj-22874	361	14	w.-t	w.-t	NOUN
brj-22874	361	15	.	.	PUNCT
brj-22874	361	16	,	,	PUNCT
brj-22874	361	17	sheu	sheu	PROPN
brj-22874	361	18	,	,	PUNCT
brj-22874	361	19	r.-k	r.-k	PROPN
brj-22874	361	20	.	.	PUNCT
brj-22874	361	21	,	,	PUNCT
brj-22874	361	22	pai	pai	PROPN
brj-22874	361	23	,	,	PUNCT
brj-22874	361	24	k.-c	k.-c	PROPN
brj-22874	361	25	.	.	PUNCT
brj-22874	361	26	,	,	PUNCT
brj-22874	361	27	chen	chen	PROPN
brj-22874	361	28	,	,	PUNCT
brj-22874	361	29	c.-y	c.-y	NOUN
brj-22874	361	30	.	.	PUNCT
brj-22874	361	31	,	,	PUNCT
brj-22874	361	32	tsai	tsai	PROPN
brj-22874	361	33	,	,	PUNCT
brj-22874	361	34	p.-y	p.-y	PROPN
brj-22874	361	35	.	.	PUNCT
brj-22874	361	36	,	,	PUNCT
brj-22874	361	37	and	and	CCONJ
brj-22874	361	38	tsai	tsai	PROPN
brj-22874	361	39	,	,	PUNCT
brj-22874	361	40	y.-t	y.-t	NOUN
brj-22874	361	41	.	.	PUNCT
brj-22874	361	42	(	(	PUNCT
brj-22874	361	43	2022	2022	NUM
brj-22874	361	44	)	)	PUNCT
brj-22874	361	45	.	.	PUNCT
brj-22874	362	1	“	"	PUNCT
brj-22874	362	2	edge	edge	NOUN
brj-22874	362	3	-	-	PUNCT
brj-22874	362	4	glued	glue	VERB
brj-22874	362	5	wooden	wooden	ADJ
brj-22874	362	6	panel	panel	NOUN
brj-22874	362	7	defect	defect	NOUN
brj-22874	362	8	detection	detection	NOUN
brj-22874	362	9	using	use	VERB
brj-22874	362	10	deep	deep	ADJ
brj-22874	362	11	learning	learning	NOUN
brj-22874	362	12	,	,	PUNCT
brj-22874	362	13	”	"	PUNCT
brj-22874	362	14	wood	wood	PROPN
brj-22874	362	15	sc	sc	PROPN
brj-22874	362	16	.	.	PUNCT
brj-22874	362	17	technol	technol	PROPN
brj-22874	362	18	.	.	PROPN
brj-22874	362	19	56	56	NUM
brj-22874	362	20	,	,	PUNCT
brj-22874	362	21	477	477	NUM
brj-22874	362	22	-	-	SYM
brj-22874	362	23	507	507	NUM
brj-22874	362	24	.	.	PUNCT
brj-22874	363	1	doi	doi	NOUN
brj-22874	363	2	:	:	PUNCT
brj-22874	363	3	10.1007	10.1007	NUM
brj-22874	363	4	/	/	SYM
brj-22874	363	5	s00226	s00226	PROPN
brj-22874	363	6	-	-	PUNCT
brj-22874	363	7	021	021	NUM
brj-22874	363	8	-	-	PUNCT
brj-22874	363	9	01316	01316	NUM
brj-22874	363	10	-	-	PUNCT
brj-22874	363	11	3	3	NUM
brj-22874	363	12	gu	gu	NOUN
brj-22874	363	13	,	,	PUNCT
brj-22874	363	14	i.	i.	PROPN
brj-22874	363	15	y.-h	y.-h	PROPN
brj-22874	363	16	.	.	PROPN
brj-22874	363	17	,	,	PUNCT
brj-22874	363	18	andersson	andersson	PROPN
brj-22874	363	19	,	,	PUNCT
brj-22874	363	20	h.	h.	PROPN
brj-22874	363	21	,	,	PUNCT
brj-22874	363	22	and	and	CCONJ
brj-22874	363	23	vicen	vicen	PROPN
brj-22874	363	24	,	,	PUNCT
brj-22874	363	25	r.	r.	PROPN
brj-22874	363	26	(	(	PUNCT
brj-22874	363	27	2010	2010	NUM
brj-22874	363	28	)	)	PUNCT
brj-22874	363	29	.	.	PUNCT
brj-22874	364	1	“	"	PUNCT
brj-22874	364	2	wood	wood	NOUN
brj-22874	364	3	defect	defect	NOUN
brj-22874	364	4	classification	classification	NOUN
brj-22874	364	5	based	base	VERB
brj-22874	364	6	on	on	ADP
brj-22874	364	7	image	image	NOUN
brj-22874	364	8	analysis	analysis	NOUN
brj-22874	364	9	and	and	CCONJ
brj-22874	364	10	support	support	VERB
brj-22874	364	11	vector	vector	NOUN
brj-22874	364	12	machines	machine	NOUN
brj-22874	364	13	,	,	PUNCT
brj-22874	364	14	”	"	PUNCT
brj-22874	364	15	wood	wood	NOUN
brj-22874	364	16	science	science	NOUN
brj-22874	364	17	and	and	CCONJ
brj-22874	364	18	technology	technology	NOUN
brj-22874	364	19	44	44	NUM
brj-22874	364	20	,	,	PUNCT
brj-22874	364	21	693704	693704	NUM
brj-22874	364	22	.	.	PUNCT
brj-22874	365	1	doi	doi	NOUN
brj-22874	365	2	:	:	PUNCT
brj-22874	365	3	10.1007	10.1007	NUM
brj-22874	365	4	/	/	SYM
brj-22874	365	5	s00226	s00226	PROPN
brj-22874	365	6	-	-	PUNCT
brj-22874	365	7	009	009	NUM
brj-22874	365	8	-	-	PUNCT
brj-22874	365	9	0287	0287	NUM
brj-22874	365	10	-	-	SYM
brj-22874	365	11	9	9	NUM
brj-22874	365	12	hacıefendioğlu	hacıefendioğlu	PROPN
brj-22874	365	13	,	,	PUNCT
brj-22874	365	14	k.	k.	PROPN
brj-22874	365	15	,	,	PUNCT
brj-22874	365	16	ayas	ayas	PROPN
brj-22874	365	17	,	,	PUNCT
brj-22874	365	18	s.	s.	PROPN
brj-22874	365	19	,	,	PUNCT
brj-22874	365	20	başağa	başağa	PROPN
brj-22874	365	21	,	,	PUNCT
brj-22874	365	22	h.	h.	PROPN
brj-22874	365	23	b.	b.	PROPN
brj-22874	365	24	,	,	PUNCT
brj-22874	365	25	toğan	toğan	PROPN
brj-22874	365	26	,	,	PUNCT
brj-22874	365	27	v.	v.	ADV
brj-22874	365	28	,	,	PUNCT
brj-22874	365	29	mostofi	mostofi	NOUN
brj-22874	365	30	,	,	PUNCT
brj-22874	365	31	f.	f.	PROPN
brj-22874	365	32	,	,	PUNCT
brj-22874	365	33	and	and	CCONJ
brj-22874	365	34	can	can	AUX
brj-22874	365	35	,	,	PUNCT
brj-22874	365	36	a.	a.	NOUN
brj-22874	365	37	(	(	PUNCT
brj-22874	365	38	2022	2022	NUM
brj-22874	365	39	)	)	PUNCT
brj-22874	365	40	.	.	PUNCT
brj-22874	366	1	“	"	PUNCT
brj-22874	366	2	wood	wood	NOUN
brj-22874	366	3	construction	construction	NOUN
brj-22874	366	4	damage	damage	NOUN
brj-22874	366	5	detection	detection	NOUN
brj-22874	366	6	and	and	CCONJ
brj-22874	366	7	localization	localization	NOUN
brj-22874	366	8	using	use	VERB
brj-22874	366	9	deep	deep	ADJ
brj-22874	366	10	convolutional	convolutional	ADJ
brj-22874	366	11	neural	neural	ADJ
brj-22874	366	12	network	network	NOUN
brj-22874	366	13	with	with	ADP
brj-22874	366	14	transfer	transfer	NOUN
brj-22874	366	15	learning	learning	NOUN
brj-22874	366	16	,	,	PUNCT
brj-22874	366	17	”	"	PUNCT
brj-22874	366	18	european	european	PROPN
brj-22874	366	19	journal	journal	PROPN
brj-22874	366	20	of	of	ADP
brj-22874	366	21	wood	wood	NOUN
brj-22874	366	22	and	and	CCONJ
brj-22874	366	23	wood	wood	NOUN
brj-22874	366	24	products	product	NOUN
brj-22874	366	25	80	80	NUM
brj-22874	366	26	,	,	PUNCT
brj-22874	366	27	791	791	NUM
brj-22874	366	28	-	-	SYM
brj-22874	366	29	804	804	NUM
brj-22874	366	30	.	.	PUNCT
brj-22874	367	1	doi	doi	NOUN
brj-22874	367	2	:	:	PUNCT
brj-22874	367	3	10.1007	10.1007	NUM
brj-22874	367	4	/	/	SYM
brj-22874	367	5	s00107	s00107	PROPN
brj-22874	367	6	-	-	PUNCT
brj-22874	367	7	022	022	NUM
brj-22874	367	8	-	-	PUNCT
brj-22874	367	9	01815	01815	NUM
brj-22874	367	10	-	-	SYM
brj-22874	367	11	5	5	NUM
brj-22874	367	12	he	he	PRON
brj-22874	367	13	,	,	PUNCT
brj-22874	367	14	k.	k.	PROPN
brj-22874	367	15	,	,	PUNCT
brj-22874	367	16	zhang	zhang	PROPN
brj-22874	367	17	,	,	PUNCT
brj-22874	367	18	x.	x.	PROPN
brj-22874	367	19	,	,	PUNCT
brj-22874	367	20	ren	ren	PROPN
brj-22874	367	21	,	,	PUNCT
brj-22874	367	22	s.	s.	PROPN
brj-22874	367	23	,	,	PUNCT
brj-22874	367	24	and	and	CCONJ
brj-22874	367	25	sun	sun	NOUN
brj-22874	367	26	,	,	PUNCT
brj-22874	367	27	j.	j.	PROPN
brj-22874	367	28	(	(	PUNCT
brj-22874	367	29	2015	2015	NUM
brj-22874	367	30	)	)	PUNCT
brj-22874	367	31	.	.	PUNCT
brj-22874	368	1	“	"	PUNCT
brj-22874	368	2	spatial	spatial	ADJ
brj-22874	368	3	pyramid	pyramid	NOUN
brj-22874	368	4	pooling	pool	VERB
brj-22874	368	5	in	in	ADP
brj-22874	368	6	deep	deep	ADJ
brj-22874	368	7	convolutional	convolutional	ADJ
brj-22874	368	8	networks	network	NOUN
brj-22874	368	9	for	for	ADP
brj-22874	368	10	visual	visual	ADJ
brj-22874	368	11	recognition	recognition	NOUN
brj-22874	368	12	,	,	PUNCT
brj-22874	368	13	”	"	PUNCT
brj-22874	368	14	ieee	ieee	NOUN
brj-22874	368	15	trans	tran	NOUN
brj-22874	368	16	.	.	PUNCT
brj-22874	369	1	pattern	pattern	NOUN
brj-22874	369	2	analysis	analysis	NOUN
brj-22874	369	3	and	and	CCONJ
brj-22874	369	4	machine	machine	NOUN
brj-22874	369	5	intelligence	intelligence	NOUN
brj-22874	369	6	37	37	NUM
brj-22874	369	7	,	,	PUNCT
brj-22874	369	8	1904	1904	NUM
brj-22874	369	9	-	-	SYM
brj-22874	369	10	1916	1916	NUM
brj-22874	369	11	.	.	PUNCT
brj-22874	370	1	doi	doi	NOUN
brj-22874	370	2	:	:	PUNCT
brj-22874	370	3	10.1109	10.1109	NUM
brj-22874	370	4	/	/	SYM
brj-22874	370	5	tpami.2015.2389824	tpami.2015.2389824	NOUN
brj-22874	370	6	he	he	PRON
brj-22874	370	7	,	,	PUNCT
brj-22874	370	8	t.	t.	PROPN
brj-22874	370	9	,	,	PUNCT
brj-22874	370	10	liu	liu	PROPN
brj-22874	370	11	,	,	PUNCT
brj-22874	370	12	y.	y.	PROPN
brj-22874	370	13	,	,	PUNCT
brj-22874	370	14	xu	xu	PROPN
brj-22874	370	15	,	,	PUNCT
brj-22874	370	16	c.	c.	PROPN
brj-22874	370	17	,	,	PUNCT
brj-22874	370	18	zhou	zhou	PROPN
brj-22874	370	19	,	,	PUNCT
brj-22874	370	20	x.	x.	PROPN
brj-22874	370	21	,	,	PUNCT
brj-22874	370	22	hu	hu	PROPN
brj-22874	370	23	,	,	PUNCT
brj-22874	370	24	z.	z.	PROPN
brj-22874	370	25	,	,	PUNCT
brj-22874	370	26	and	and	CCONJ
brj-22874	370	27	fan	fan	PROPN
brj-22874	370	28	,	,	PUNCT
brj-22874	370	29	j.	j.	PROPN
brj-22874	370	30	(	(	PUNCT
brj-22874	370	31	2019	2019	NUM
brj-22874	370	32	)	)	PUNCT
brj-22874	370	33	.	.	PUNCT
brj-22874	371	1	“	"	PUNCT
brj-22874	371	2	a	a	DET
brj-22874	371	3	fully	fully	ADV
brj-22874	371	4	convolutional	convolutional	ADJ
brj-22874	371	5	neural	neural	ADJ
brj-22874	371	6	network	network	NOUN
brj-22874	371	7	for	for	ADP
brj-22874	371	8	wood	wood	NOUN
brj-22874	371	9	defect	defect	NOUN
brj-22874	371	10	location	location	NOUN
brj-22874	371	11	and	and	CCONJ
brj-22874	371	12	identification	identification	NOUN
brj-22874	371	13	,	,	PUNCT
brj-22874	371	14	”	"	PUNCT
brj-22874	371	15	ieee	ieee	NOUN
brj-22874	371	16	access	access	NOUN
brj-22874	371	17	7	7	NUM
brj-22874	371	18	,	,	PUNCT
brj-22874	371	19	123453123462	123453123462	NUM
brj-22874	371	20	.	.	PUNCT
brj-22874	372	1	doi	doi	NOUN
brj-22874	372	2	:	:	PUNCT
brj-22874	372	3	10.1109	10.1109	NUM
brj-22874	372	4	/	/	SYM
brj-22874	372	5	access.2019.2937461	access.2019.2937461	ADV
brj-22874	372	6	he	he	PRON
brj-22874	372	7	,	,	PUNCT
brj-22874	372	8	t.	t.	PROPN
brj-22874	372	9	,	,	PUNCT
brj-22874	372	10	liu	liu	PROPN
brj-22874	372	11	,	,	PUNCT
brj-22874	372	12	y.	y.	PROPN
brj-22874	372	13	,	,	PUNCT
brj-22874	372	14	yu	yu	PROPN
brj-22874	372	15	,	,	PUNCT
brj-22874	372	16	y.	y.	PROPN
brj-22874	372	17	,	,	PUNCT
brj-22874	372	18	zhao	zhao	PROPN
brj-22874	372	19	,	,	PUNCT
brj-22874	372	20	q.	q.	PROPN
brj-22874	372	21	,	,	PUNCT
brj-22874	372	22	and	and	CCONJ
brj-22874	372	23	hu	hu	PROPN
brj-22874	372	24	,	,	PUNCT
brj-22874	372	25	z.	z.	PROPN
brj-22874	372	26	(	(	PUNCT
brj-22874	372	27	2020	2020	NUM
brj-22874	372	28	)	)	PUNCT
brj-22874	372	29	.	.	PUNCT
brj-22874	373	1	“	"	PUNCT
brj-22874	373	2	application	application	NOUN
brj-22874	373	3	of	of	ADP
brj-22874	373	4	deep	deep	ADJ
brj-22874	373	5	convolutional	convolutional	ADJ
brj-22874	373	6	neural	neural	ADJ
brj-22874	373	7	network	network	NOUN
brj-22874	373	8	on	on	ADP
brj-22874	373	9	feature	feature	NOUN
brj-22874	373	10	extraction	extraction	NOUN
brj-22874	373	11	and	and	CCONJ
brj-22874	373	12	detection	detection	NOUN
brj-22874	373	13	of	of	ADP
brj-22874	373	14	wood	wood	NOUN
brj-22874	373	15	defects	defect	NOUN
brj-22874	373	16	,	,	PUNCT
brj-22874	373	17	”	"	PUNCT
brj-22874	373	18	measurement	measurement	NOUN
brj-22874	373	19	152	152	NUM
brj-22874	373	20	.	.	PUNCT
brj-22874	374	1	doi	doi	NOUN
brj-22874	374	2	:	:	PUNCT
brj-22874	374	3	10.1016	10.1016	NUM
brj-22874	374	4	/	/	SYM
brj-22874	374	5	j.measurement.2019.107357	j.measurement.2019.107357	PROPN
brj-22874	374	6	hou	hou	PROPN
brj-22874	374	7	,	,	PUNCT
brj-22874	374	8	q.	q.	PROPN
brj-22874	374	9	,	,	PUNCT
brj-22874	374	10	zhou	zhou	PROPN
brj-22874	374	11	,	,	PUNCT
brj-22874	374	12	d.	d.	PROPN
brj-22874	374	13	,	,	PUNCT
brj-22874	374	14	and	and	CCONJ
brj-22874	374	15	feng	feng	PROPN
brj-22874	374	16	,	,	PUNCT
brj-22874	374	17	j.	j.	PROPN
brj-22874	374	18	(	(	PUNCT
brj-22874	374	19	2021	2021	NUM
brj-22874	374	20	)	)	PUNCT
brj-22874	374	21	.	.	PUNCT
brj-22874	375	1	“	"	PUNCT
brj-22874	375	2	coordinate	coordinate	VERB
brj-22874	375	3	attention	attention	NOUN
brj-22874	375	4	for	for	ADP
brj-22874	375	5	efficient	efficient	ADJ
brj-22874	375	6	mobile	mobile	ADJ
brj-22874	375	7	network	network	NOUN
brj-22874	375	8	design	design	NOUN
brj-22874	375	9	,	,	PUNCT
brj-22874	375	10	”	"	PUNCT
brj-22874	375	11	in	in	ADP
brj-22874	375	12	:	:	PUNCT
brj-22874	375	13	proc	proc	NOUN
brj-22874	375	14	.	.	PUNCT
brj-22874	376	1	ieee	ieee	NOUN
brj-22874	376	2	/	/	SYM
brj-22874	376	3	cvf	cvf	NOUN
brj-22874	376	4	conference	conference	NOUN
brj-22874	376	5	on	on	ADP
brj-22874	376	6	computer	computer	NOUN
brj-22874	376	7	vision	vision	NOUN
brj-22874	376	8	and	and	CCONJ
brj-22874	376	9	pattern	pattern	NOUN
brj-22874	376	10	recognition	recognition	NOUN
brj-22874	376	11	(	(	PUNCT
brj-22874	376	12	cvpr	cvpr	NOUN
brj-22874	376	13	)	)	PUNCT
brj-22874	376	14	,	,	PUNCT
brj-22874	376	15	pp	pp	PROPN
brj-22874	376	16	.	.	PUNCT
brj-22874	377	1	13708	13708	NUM
brj-22874	377	2	-	-	SYM
brj-22874	377	3	13717	13717	NUM
brj-22874	377	4	.	.	PUNCT
brj-22874	378	1	doi	doi	NOUN
brj-22874	378	2	:	:	PUNCT
brj-22874	378	3	10.1109	10.1109	NUM
brj-22874	378	4	/	/	SYM
brj-22874	378	5	cvpr46437.2021.01350	cvpr46437.2021.01350	PROPN
brj-22874	378	6	hu	hu	PROPN
brj-22874	378	7	,	,	PUNCT
brj-22874	378	8	j.	j.	PROPN
brj-22874	378	9	,	,	PUNCT
brj-22874	378	10	shen	shen	PROPN
brj-22874	378	11	,	,	PUNCT
brj-22874	378	12	l.	l.	PROPN
brj-22874	378	13	,	,	PUNCT
brj-22874	378	14	albanie	albanie	PROPN
brj-22874	378	15	,	,	PUNCT
brj-22874	378	16	s.	s.	PROPN
brj-22874	378	17	,	,	PUNCT
brj-22874	378	18	sun	sun	PROPN
brj-22874	378	19	,	,	PUNCT
brj-22874	378	20	g.	g.	PROPN
brj-22874	378	21	,	,	PUNCT
brj-22874	378	22	and	and	CCONJ
brj-22874	378	23	wu	wu	PROPN
brj-22874	378	24	,	,	PUNCT
brj-22874	378	25	e.	e.	PROPN
brj-22874	378	26	(	(	PUNCT
brj-22874	378	27	2020	2020	NUM
brj-22874	378	28	)	)	PUNCT
brj-22874	378	29	.	.	PUNCT
brj-22874	379	1	“	"	PUNCT
brj-22874	379	2	squeeze	squeeze	NOUN
brj-22874	379	3	-	-	PUNCT
brj-22874	379	4	and	and	CCONJ
brj-22874	379	5	-	-	PUNCT
brj-22874	379	6	excitation	excitation	NOUN
brj-22874	379	7	networks	network	NOUN
brj-22874	379	8	,	,	PUNCT
brj-22874	379	9	”	"	PUNCT
brj-22874	379	10	ieee	ieee	NOUN
brj-22874	379	11	transactions	transaction	NOUN
brj-22874	379	12	on	on	ADP
brj-22874	379	13	pattern	pattern	NOUN
brj-22874	379	14	analysis	analysis	NOUN
brj-22874	379	15	and	and	CCONJ
brj-22874	379	16	machine	machine	NOUN
brj-22874	379	17	intelligence	intelligence	NOUN
brj-22874	379	18	42	42	NUM
brj-22874	379	19	,	,	PUNCT
brj-22874	379	20	2011	2011	NUM
brj-22874	379	21	-	-	SYM
brj-22874	379	22	2023	2023	NUM
brj-22874	379	23	.	.	PUNCT
brj-22874	380	1	doi	doi	NOUN
brj-22874	380	2	:	:	PUNCT
brj-22874	380	3	10.1109	10.1109	NUM
brj-22874	380	4	/	/	SYM
brj-22874	380	5	tpami.2019.2913372	tpami.2019.2913372	VERB
brj-22874	380	6	jocher	jocher	PROPN
brj-22874	380	7	,	,	PUNCT
brj-22874	380	8	g.	g.	PROPN
brj-22874	380	9	,	,	PUNCT
brj-22874	380	10	stoken	stoken	NOUN
brj-22874	380	11	,	,	PUNCT
brj-22874	380	12	a.	a.	NOUN
brj-22874	380	13	,	,	PUNCT
brj-22874	380	14	borovec	borovec	PROPN
brj-22874	380	15	,	,	PUNCT
brj-22874	380	16	j.	j.	PROPN
brj-22874	380	17	,	,	PUNCT
brj-22874	380	18	changyu	changyu	PROPN
brj-22874	380	19	,	,	PUNCT
brj-22874	380	20	l.	l.	PROPN
brj-22874	380	21	,	,	PUNCT
brj-22874	380	22	hogan	hogan	PROPN
brj-22874	380	23	,	,	PUNCT
brj-22874	380	24	a.	a.	PROPN
brj-22874	380	25	,	,	PUNCT
brj-22874	380	26	diaconu	diaconu	NOUN
brj-22874	380	27	,	,	PUNCT
brj-22874	380	28	l.	l.	PROPN
brj-22874	380	29	,	,	PUNCT
brj-22874	380	30	and	and	CCONJ
brj-22874	380	31	yu	yu	PROPN
brj-22874	380	32	,	,	PUNCT
brj-22874	380	33	l.u	l.u	PROPN
brj-22874	380	34	.	.	PROPN
brj-22874	380	35	(	(	PUNCT
brj-22874	380	36	2020	2020	NUM
brj-22874	380	37	)	)	PUNCT
brj-22874	380	38	.	.	PUNCT
brj-22874	381	1	“	"	PUNCT
brj-22874	381	2	yolov5	yolov5	NOUN
brj-22874	381	3	:	:	PUNCT
brj-22874	381	4	v3	v3	PROPN
brj-22874	381	5	.	.	PUNCT
brj-22874	382	1	1	1	NUM
brj-22874	382	2	-	-	PUNCT
brj-22874	382	3	bug	bug	NOUN
brj-22874	382	4	fixes	fix	NOUN
brj-22874	382	5	and	and	CCONJ
brj-22874	382	6	performance	performance	NOUN
brj-22874	382	7	improvements	improvement	NOUN
brj-22874	382	8	,	,	PUNCT
brj-22874	382	9	”	"	PUNCT
brj-22874	382	10	doi	doi	NOUN
brj-22874	382	11	:	:	PUNCT
brj-22874	382	12	10.5281	10.5281	NUM
brj-22874	382	13	/	/	SYM
brj-22874	382	14	zenodo.4154370	zenodo.4154370	PROPN
brj-22874	382	15	li	li	PROPN
brj-22874	382	16	,	,	PUNCT
brj-22874	382	17	z.	z.	PROPN
brj-22874	382	18	,	,	PUNCT
brj-22874	382	19	liu	liu	PROPN
brj-22874	382	20	,	,	PUNCT
brj-22874	382	21	f.	f.	PROPN
brj-22874	382	22	,	,	PUNCT
brj-22874	382	23	yang	yang	PROPN
brj-22874	382	24	,	,	PUNCT
brj-22874	382	25	w.	w.	PROPN
brj-22874	382	26	,	,	PUNCT
brj-22874	382	27	peng	peng	PROPN
brj-22874	382	28	,	,	PUNCT
brj-22874	382	29	s.	s.	PROPN
brj-22874	382	30	,	,	PUNCT
brj-22874	382	31	and	and	CCONJ
brj-22874	382	32	zhou	zhou	PROPN
brj-22874	382	33	,	,	PUNCT
brj-22874	382	34	j.	j.	PROPN
brj-22874	382	35	(	(	PUNCT
brj-22874	382	36	2022	2022	NUM
brj-22874	382	37	)	)	PUNCT
brj-22874	382	38	.	.	PUNCT
brj-22874	383	1	“	"	PUNCT
brj-22874	383	2	a	a	DET
brj-22874	383	3	survey	survey	NOUN
brj-22874	383	4	of	of	ADP
brj-22874	383	5	convolutional	convolutional	ADJ
brj-22874	383	6	neural	neural	ADJ
brj-22874	383	7	networks	network	NOUN
brj-22874	383	8	:	:	PUNCT
brj-22874	383	9	analysis	analysis	NOUN
brj-22874	383	10	,	,	PUNCT
brj-22874	383	11	applications	application	NOUN
brj-22874	383	12	,	,	PUNCT
brj-22874	383	13	and	and	CCONJ
brj-22874	383	14	prospects	prospect	NOUN
brj-22874	383	15	,	,	PUNCT
brj-22874	383	16	”	"	PUNCT
brj-22874	383	17	ieee	ieee	NOUN
brj-22874	383	18	transactions	transaction	NOUN
brj-22874	383	19	on	on	ADP
brj-22874	383	20	neural	neural	ADJ
brj-22874	383	21	networks	network	NOUN
brj-22874	383	22	and	and	CCONJ
brj-22874	383	23	learning	learn	VERB
brj-22874	383	24	systems	system	NOUN
brj-22874	383	25	33	33	NUM
brj-22874	383	26	,	,	PUNCT
brj-22874	383	27	6999	6999	NUM
brj-22874	383	28	-	-	SYM
brj-22874	383	29	7019	7019	NUM
brj-22874	383	30	.	.	PUNCT
brj-22874	384	1	doi	doi	NOUN
brj-22874	384	2	:	:	PUNCT
brj-22874	384	3	10.1109	10.1109	NUM
brj-22874	384	4	/	/	SYM
brj-22874	384	5	tnnls.2021.3084827	tnnls.2021.3084827	PROPN
brj-22874	384	6	likas	likas	PROPN
brj-22874	384	7	,	,	PUNCT
brj-22874	384	8	a.	a.	NOUN
brj-22874	384	9	,	,	PUNCT
brj-22874	384	10	vlassis	vlassis	NOUN
brj-22874	384	11	,	,	PUNCT
brj-22874	384	12	n.	n.	NOUN
brj-22874	384	13	,	,	PUNCT
brj-22874	384	14	and	and	CCONJ
brj-22874	384	15	verbeek	verbeek	NOUN
brj-22874	384	16	,	,	PUNCT
brj-22874	384	17	j.	j.	PROPN
brj-22874	384	18	j.	j.	PROPN
brj-22874	384	19	(	(	PUNCT
brj-22874	384	20	2003	2003	NUM
brj-22874	384	21	)	)	PUNCT
brj-22874	384	22	.	.	PUNCT
brj-22874	385	1	“	"	PUNCT
brj-22874	385	2	the	the	DET
brj-22874	385	3	global	global	ADJ
brj-22874	385	4	k	k	PROPN
brj-22874	385	5	-	-	PUNCT
brj-22874	385	6	means	mean	VERB
brj-22874	385	7	clustering	clustering	ADJ
brj-22874	385	8	algorithm	algorithm	NOUN
brj-22874	385	9	,	,	PUNCT
brj-22874	385	10	”	"	PUNCT
brj-22874	385	11	pattern	pattern	NOUN
brj-22874	385	12	recognition	recognition	NOUN
brj-22874	385	13	36	36	NUM
brj-22874	385	14	,	,	PUNCT
brj-22874	385	15	451	451	NUM
brj-22874	385	16	-	-	SYM
brj-22874	385	17	461	461	NUM
brj-22874	385	18	.	.	PUNCT
brj-22874	386	1	doi	doi	NOUN
brj-22874	386	2	:	:	PUNCT
brj-22874	386	3	10.1016	10.1016	NUM
brj-22874	386	4	/	/	SYM
brj-22874	386	5	s0031	s0031	NOUN
brj-22874	386	6	-	-	PUNCT
brj-22874	386	7	3203(02)00060	3203(02)00060	NUM
brj-22874	386	8	-	-	PUNCT
brj-22874	386	9	2	2	NUM
brj-22874	386	10	liu	liu	PROPN
brj-22874	386	11	,	,	PUNCT
brj-22874	386	12	w.	w.	PROPN
brj-22874	386	13	,	,	PUNCT
brj-22874	386	14	anguelov	anguelov	PROPN
brj-22874	386	15	,	,	PUNCT
brj-22874	386	16	d.	d.	PROPN
brj-22874	386	17	,	,	PUNCT
brj-22874	386	18	erhan	erhan	PROPN
brj-22874	386	19	,	,	PUNCT
brj-22874	386	20	d.	d.	PROPN
brj-22874	386	21	,	,	PUNCT
brj-22874	386	22	szegedy	szegedy	PROPN
brj-22874	386	23	,	,	PUNCT
brj-22874	386	24	c.	c.	NOUN
brj-22874	386	25	,	,	PUNCT
brj-22874	386	26	reed	reed	PROPN
brj-22874	386	27	,	,	PUNCT
brj-22874	386	28	s.	s.	PROPN
brj-22874	386	29	,	,	PUNCT
brj-22874	386	30	fu	fu	PROPN
brj-22874	386	31	,	,	PUNCT
brj-22874	386	32	c.-y	c.-y	NOUN
brj-22874	386	33	.	.	PUNCT
brj-22874	386	34	,	,	PUNCT
brj-22874	386	35	and	and	CCONJ
brj-22874	386	36	berg	berg	PROPN
brj-22874	386	37	,	,	PUNCT
brj-22874	386	38	a.	a.	PROPN
brj-22874	386	39	c.	c.	PROPN
brj-22874	386	40	(	(	PUNCT
brj-22874	386	41	2016	2016	NUM
brj-22874	386	42	)	)	PUNCT
brj-22874	386	43	.	.	PUNCT
brj-22874	387	1	“	"	PUNCT
brj-22874	387	2	ssd	ssd	NOUN
brj-22874	387	3	:	:	PUNCT
brj-22874	387	4	single	single	ADJ
brj-22874	387	5	shot	shot	PROPN
brj-22874	387	6	multibox	multibox	NOUN
brj-22874	387	7	detector	detector	NOUN
brj-22874	387	8	,	,	PUNCT
brj-22874	387	9	”	"	PUNCT
brj-22874	387	10	in	in	ADP
brj-22874	387	11	:	:	PUNCT
brj-22874	387	12	proceedings	proceeding	NOUN
brj-22874	387	13	of	of	ADP
brj-22874	387	14	the	the	DET
brj-22874	387	15	14th	14th	ADJ
brj-22874	387	16	european	european	ADJ
brj-22874	387	17	conf	conf	NOUN
brj-22874	387	18	.	.	PUNCT
brj-22874	388	1	on	on	ADP
brj-22874	388	2	computer	computer	NOUN
brj-22874	388	3	vision	vision	NOUN
brj-22874	388	4	(	(	PUNCT
brj-22874	388	5	eccv	eccv	ADV
brj-22874	388	6	)	)	PUNCT
brj-22874	388	7	,	,	PUNCT
brj-22874	388	8	pp	pp	PROPN
brj-22874	388	9	.	.	PUNCT
brj-22874	388	10	21	21	NUM
brj-22874	388	11	-	-	SYM
brj-22874	388	12	37	37	NUM
brj-22874	388	13	.	.	PUNCT
brj-22874	389	1	doi	doi	NOUN
brj-22874	389	2	:	:	PUNCT
brj-22874	389	3	10.1007/978	10.1007/978	NUM
brj-22874	389	4	-	-	SYM
brj-22874	389	5	3	3	NUM
brj-22874	389	6	-	-	NUM
brj-22874	389	7	319	319	NUM
brj-22874	389	8	-	-	PUNCT
brj-22874	389	9	46448	46448	NUM
brj-22874	389	10	-	-	SYM
brj-22874	389	11	0_2	0_2	NUM
brj-22874	389	12	mu	mu	PROPN
brj-22874	389	13	,	,	PUNCT
brj-22874	389	14	h.	h.	PROPN
brj-22874	389	15	,	,	PUNCT
brj-22874	389	16	zhang	zhang	PROPN
brj-22874	389	17	,	,	PUNCT
brj-22874	389	18	m.	m.	NOUN
brj-22874	389	19	,	,	PUNCT
brj-22874	389	20	qi	qi	PROPN
brj-22874	389	21	,	,	PUNCT
brj-22874	389	22	d.	d.	PROPN
brj-22874	389	23	,	,	PUNCT
brj-22874	389	24	guan	guan	PROPN
brj-22874	389	25	,	,	PUNCT
brj-22874	389	26	s.	s.	PROPN
brj-22874	389	27	,	,	PUNCT
brj-22874	389	28	and	and	CCONJ
brj-22874	389	29	ni	ni	PROPN
brj-22874	389	30	,	,	PUNCT
brj-22874	389	31	h.	h.	PROPN
brj-22874	389	32	(	(	PUNCT
brj-22874	389	33	2015	2015	NUM
brj-22874	389	34	)	)	PUNCT
brj-22874	389	35	.	.	PUNCT
brj-22874	390	1	“	"	PUNCT
brj-22874	390	2	wood	wood	NOUN
brj-22874	390	3	defects	defect	NOUN
brj-22874	390	4	recognition	recognition	NOUN
brj-22874	390	5	based	base	VERB
brj-22874	390	6	on	on	ADP
brj-22874	390	7	fuzzy	fuzzy	ADJ
brj-22874	390	8	bp	bp	PROPN
brj-22874	390	9	neural	neural	ADJ
brj-22874	390	10	network	network	NOUN
brj-22874	390	11	,	,	PUNCT
brj-22874	390	12	”	"	PUNCT
brj-22874	390	13	international	international	ADJ
brj-22874	390	14	journal	journal	NOUN
brj-22874	390	15	smart	smart	ADJ
brj-22874	390	16	home	home	NOUN
brj-22874	390	17	9	9	NUM
brj-22874	390	18	,	,	PUNCT
brj-22874	390	19	143	143	NUM
brj-22874	390	20	-	-	SYM
brj-22874	390	21	152	152	NUM
brj-22874	390	22	.	.	PUNCT
brj-22874	391	1	doi	doi	NOUN
brj-22874	391	2	:	:	PUNCT
brj-22874	391	3	10.14257	10.14257	NUM
brj-22874	391	4	/	/	SYM
brj-22874	391	5	ijsh.2015.9.5.14	ijsh.2015.9.5.14	SYM
brj-22874	391	6	redmon	redmon	PROPN
brj-22874	391	7	,	,	PUNCT
brj-22874	391	8	j.	j.	PROPN
brj-22874	391	9	,	,	PUNCT
brj-22874	391	10	divvala	divvala	PROPN
brj-22874	391	11	,	,	PUNCT
brj-22874	391	12	s.	s.	PROPN
brj-22874	391	13	,	,	PUNCT
brj-22874	391	14	girshick	girshick	PROPN
brj-22874	391	15	,	,	PUNCT
brj-22874	391	16	r.	r.	PROPN
brj-22874	391	17	,	,	PUNCT
brj-22874	391	18	and	and	CCONJ
brj-22874	391	19	farhadi	farhadi	NOUN
brj-22874	391	20	,	,	PUNCT
brj-22874	391	21	a.	a.	NOUN
brj-22874	391	22	(	(	PUNCT
brj-22874	391	23	2016	2016	NUM
brj-22874	391	24	)	)	PUNCT
brj-22874	391	25	.	.	PUNCT
brj-22874	392	1	“	"	PUNCT
brj-22874	392	2	you	you	PRON
brj-22874	392	3	only	only	ADV
brj-22874	392	4	look	look	VERB
brj-22874	392	5	once	once	ADV
brj-22874	392	6	:	:	PUNCT
brj-22874	392	7	unified	unified	ADJ
brj-22874	392	8	,	,	PUNCT
brj-22874	392	9	real	real	ADJ
brj-22874	392	10	-	-	PUNCT
brj-22874	392	11	time	time	NOUN
brj-22874	392	12	object	object	NOUN
brj-22874	392	13	detection	detection	NOUN
brj-22874	392	14	,	,	PUNCT
brj-22874	392	15	”	"	PUNCT
brj-22874	392	16	in	in	ADP
brj-22874	392	17	:	:	PUNCT
brj-22874	392	18	proceedings	proceeding	NOUN
brj-22874	392	19	of	of	ADP
brj-22874	392	20	the	the	DET
brj-22874	392	21	2016	2016	NUM
brj-22874	392	22	ieee	ieee	NOUN
brj-22874	392	23	conference	conference	NOUN
brj-22874	392	24	on	on	ADP
brj-22874	392	25	computer	computer	NOUN
brj-22874	392	26	vision	vision	NOUN
brj-22874	392	27	and	and	CCONJ
brj-22874	392	28	pattern	pattern	NOUN
brj-22874	392	29	recognition	recognition	NOUN
brj-22874	392	30	(	(	PUNCT
brj-22874	392	31	cvpr	cvpr	NOUN
brj-22874	392	32	)	)	PUNCT
brj-22874	392	33	,	,	PUNCT
brj-22874	392	34	pp	pp	ADP
brj-22874	392	35	.	.	PUNCT
brj-22874	393	1	779	779	NUM
brj-22874	393	2	-	-	SYM
brj-22874	393	3	788	788	NUM
brj-22874	393	4	.	.	PUNCT
brj-22874	394	1	doi	doi	NOUN
brj-22874	394	2	:	:	PUNCT
brj-22874	394	3	10.1109	10.1109	NUM
brj-22874	394	4	/	/	SYM
brj-22874	394	5	cvpr.2016.91	cvpr.2016.91	PROPN
brj-22874	394	6	ren	ren	PROPN
brj-22874	394	7	,	,	PUNCT
brj-22874	394	8	s.	s.	PROPN
brj-22874	394	9	,	,	PUNCT
brj-22874	394	10	he	he	PRON
brj-22874	394	11	,	,	PUNCT
brj-22874	394	12	k.	k.	PROPN
brj-22874	394	13	,	,	PUNCT
brj-22874	394	14	girshick	girshick	PROPN
brj-22874	394	15	,	,	PUNCT
brj-22874	394	16	r.	r.	PROPN
brj-22874	394	17	,	,	PUNCT
brj-22874	394	18	and	and	CCONJ
brj-22874	394	19	sun	sun	NOUN
brj-22874	394	20	,	,	PUNCT
brj-22874	394	21	j.	j.	PROPN
brj-22874	394	22	(	(	PUNCT
brj-22874	394	23	2015	2015	NUM
brj-22874	394	24	)	)	PUNCT
brj-22874	394	25	.	.	PUNCT
brj-22874	395	1	“	"	PUNCT
brj-22874	395	2	towards	towards	ADP
brj-22874	395	3	real	real	ADJ
brj-22874	395	4	-	-	PUNCT
brj-22874	395	5	time	time	NOUN
brj-22874	395	6	object	object	NOUN
brj-22874	395	7	detection	detection	NOUN
brj-22874	395	8	with	with	ADP
brj-22874	395	9	region	region	NOUN
brj-22874	395	10	proposal	proposal	NOUN
brj-22874	395	11	networks	network	NOUN
brj-22874	395	12	,	,	PUNCT
brj-22874	395	13	”	"	PUNCT
brj-22874	395	14	advances	advance	NOUN
brj-22874	395	15	in	in	ADP
brj-22874	395	16	neural	neural	ADJ
brj-22874	395	17	information	information	NOUN
brj-22874	395	18	processing	processing	NOUN
brj-22874	395	19	systems	system	NOUN
brj-22874	395	20	9199	9199	NUM
brj-22874	395	21	,	,	PUNCT
brj-22874	395	22	2969239	2969239	NUM
brj-22874	395	23	-	-	SYM
brj-22874	395	24	2969250	2969250	NUM
brj-22874	395	25	.	.	PUNCT
brj-22874	396	1	doi	doi	NOUN
brj-22874	396	2	:	:	PUNCT
brj-22874	396	3	10.1109	10.1109	NUM
brj-22874	396	4	/	/	SYM
brj-22874	396	5	tpami.2016.2577031	tpami.2016.2577031	NUM
brj-22874	396	6	shi	shi	PROPN
brj-22874	396	7	,	,	PUNCT
brj-22874	396	8	j.	j.	PROPN
brj-22874	396	9	,	,	PUNCT
brj-22874	396	10	li	li	PROPN
brj-22874	396	11	,	,	PUNCT
brj-22874	396	12	z.	z.	PROPN
brj-22874	396	13	,	,	PUNCT
brj-22874	396	14	zhu	zhu	PROPN
brj-22874	396	15	,	,	PUNCT
brj-22874	396	16	t.	t.	PROPN
brj-22874	396	17	,	,	PUNCT
brj-22874	396	18	wang	wang	PROPN
brj-22874	396	19	,	,	PUNCT
brj-22874	396	20	d.	d.	PROPN
brj-22874	396	21	,	,	PUNCT
brj-22874	396	22	and	and	CCONJ
brj-22874	396	23	ni	ni	PROPN
brj-22874	396	24	,	,	PUNCT
brj-22874	396	25	c.	c.	PROPN
brj-22874	396	26	(	(	PUNCT
brj-22874	396	27	2020	2020	NUM
brj-22874	396	28	)	)	PUNCT
brj-22874	396	29	.	.	PUNCT
brj-22874	397	1	“	"	PUNCT
brj-22874	397	2	defect	defect	VERB
brj-22874	397	3	detection	detection	NOUN
brj-22874	397	4	of	of	ADP
brj-22874	397	5	industry	industry	NOUN
brj-22874	397	6	wood	wood	NOUN
brj-22874	397	7	peer	peer	NOUN
brj-22874	397	8	-	-	PUNCT
brj-22874	397	9	reviewed	review	VERB
brj-22874	397	10	article	article	NOUN
brj-22874	397	11	bioresources.com	bioresources.com	X
brj-22874	397	12	tian	tian	PROPN
brj-22874	397	13	et	et	PROPN
brj-22874	397	14	al	al	PROPN
brj-22874	397	15	.	.	PROPN
brj-22874	397	16	(	(	PUNCT
brj-22874	397	17	2023	2023	NUM
brj-22874	397	18	)	)	PUNCT
brj-22874	397	19	.	.	PUNCT
brj-22874	398	1	“	"	PUNCT
brj-22874	398	2	defect	defect	VERB
brj-22874	398	3	detection	detection	NOUN
brj-22874	398	4	with	with	ADP
brj-22874	398	5	yolov5	yolov5	NOUN
brj-22874	398	6	,	,	PUNCT
brj-22874	398	7	”	"	PUNCT
brj-22874	398	8	bioresources	bioresource	NOUN
brj-22874	398	9	18(4	18(4	NUM
brj-22874	398	10	)	)	PUNCT
brj-22874	398	11	,	,	PUNCT
brj-22874	398	12	7713	7713	NUM
brj-22874	398	13	-	-	SYM
brj-22874	398	14	7730	7730	NUM
brj-22874	398	15	.	.	PUNCT
brj-22874	399	1	7730	7730	NUM
brj-22874	399	2	veneer	veneer	NOUN
brj-22874	399	3	based	base	VERB
brj-22874	399	4	on	on	ADP
brj-22874	399	5	nas	nas	PROPN
brj-22874	399	6	and	and	CCONJ
brj-22874	399	7	multi	multi	ADJ
brj-22874	399	8	-	-	ADJ
brj-22874	399	9	channel	channel	ADJ
brj-22874	399	10	mask	mask	NOUN
brj-22874	399	11	r	r	NOUN
brj-22874	399	12	-	-	PUNCT
brj-22874	399	13	cnn	cnn	PROPN
brj-22874	399	14	,	,	PUNCT
brj-22874	399	15	”	"	PUNCT
brj-22874	399	16	sensors	sensor	NOUN
brj-22874	399	17	(	(	PUNCT
brj-22874	399	18	basel	basel	PROPN
brj-22874	399	19	)	)	PUNCT
brj-22874	399	20	20	20	NUM
brj-22874	399	21	.	.	PUNCT
brj-22874	400	1	doi	doi	NOUN
brj-22874	400	2	:	:	PUNCT
brj-22874	400	3	10.3390	10.3390	NUM
brj-22874	400	4	/	/	SYM
brj-22874	400	5	s20164398	s20164398	NOUN
brj-22874	400	6	song	song	NOUN
brj-22874	400	7	,	,	PUNCT
brj-22874	400	8	w.	w.	PROPN
brj-22874	400	9	,	,	PUNCT
brj-22874	400	10	chen	chen	PROPN
brj-22874	400	11	,	,	PUNCT
brj-22874	400	12	t.	t.	PROPN
brj-22874	400	13	,	,	PUNCT
brj-22874	400	14	gu	gu	PROPN
brj-22874	400	15	,	,	PUNCT
brj-22874	400	16	z.	z.	PROPN
brj-22874	400	17	,	,	PUNCT
brj-22874	400	18	gai	gai	PROPN
brj-22874	400	19	,	,	PUNCT
brj-22874	400	20	w.	w.	PROPN
brj-22874	400	21	,	,	PUNCT
brj-22874	400	22	huang	huang	PROPN
brj-22874	400	23	,	,	PUNCT
brj-22874	400	24	w.	w.	PROPN
brj-22874	400	25	,	,	PUNCT
brj-22874	400	26	and	and	CCONJ
brj-22874	400	27	wang	wang	PROPN
brj-22874	400	28	,	,	PUNCT
brj-22874	400	29	b.	b.	PROPN
brj-22874	400	30	(	(	PUNCT
brj-22874	400	31	2015	2015	NUM
brj-22874	400	32	)	)	PUNCT
brj-22874	400	33	.	.	PUNCT
brj-22874	401	1	“	"	PUNCT
brj-22874	401	2	wood	wood	NOUN
brj-22874	401	3	materials	material	NOUN
brj-22874	401	4	defects	defect	NOUN
brj-22874	401	5	detection	detection	NOUN
brj-22874	401	6	using	use	VERB
brj-22874	401	7	image	image	NOUN
brj-22874	401	8	block	block	NOUN
brj-22874	401	9	percentile	percentile	ADJ
brj-22874	401	10	color	color	NOUN
brj-22874	401	11	histogram	histogram	NOUN
brj-22874	401	12	and	and	CCONJ
brj-22874	401	13	eigenvector	eigenvector	NOUN
brj-22874	401	14	texture	texture	ADJ
brj-22874	401	15	feature	feature	NOUN
brj-22874	401	16	,	,	PUNCT
brj-22874	401	17	”	"	PUNCT
brj-22874	401	18	in	in	ADP
brj-22874	401	19	:	:	PUNCT
brj-22874	401	20	proc	proc	NOUN
brj-22874	401	21	.	.	PUNCT
brj-22874	402	1	first	first	ADJ
brj-22874	402	2	international	international	ADJ
brj-22874	402	3	conference	conference	NOUN
brj-22874	402	4	on	on	ADP
brj-22874	402	5	information	information	NOUN
brj-22874	402	6	sciences	science	NOUN
brj-22874	402	7	,	,	PUNCT
brj-22874	402	8	machinery	machinery	NOUN
brj-22874	402	9	,	,	PUNCT
brj-22874	402	10	materials	material	NOUN
brj-22874	402	11	and	and	CCONJ
brj-22874	402	12	energy	energy	NOUN
brj-22874	402	13	,	,	PUNCT
brj-22874	402	14	pp	pp	ADJ
brj-22874	402	15	.	.	PUNCT
brj-22874	403	1	779	779	NUM
brj-22874	403	2	-	-	SYM
brj-22874	403	3	783	783	NUM
brj-22874	403	4	.	.	PUNCT
brj-22874	404	1	doi	doi	NOUN
brj-22874	404	2	:	:	PUNCT
brj-22874	404	3	10.2991	10.2991	NUM
brj-22874	404	4	/	/	SYM
brj-22874	404	5	icismme-15.2015.163	icismme-15.2015.163	NOUN
brj-22874	404	6	sun	sun	NOUN
brj-22874	404	7	,	,	PUNCT
brj-22874	404	8	p.a	p.a	PROPN
brj-22874	404	9	.	.	PROPN
brj-22874	405	1	(	(	PUNCT
brj-22874	405	2	2022	2022	NUM
brj-22874	405	3	)	)	PUNCT
brj-22874	405	4	.	.	PUNCT
brj-22874	406	1	“	"	PUNCT
brj-22874	406	2	wood	wood	NOUN
brj-22874	406	3	quality	quality	NOUN
brj-22874	406	4	defect	defect	NOUN
brj-22874	406	5	detection	detection	NOUN
brj-22874	406	6	based	base	VERB
brj-22874	406	7	on	on	ADP
brj-22874	406	8	deep	deep	ADJ
brj-22874	406	9	learning	learning	NOUN
brj-22874	406	10	and	and	CCONJ
brj-22874	406	11	multicriteria	multicriteria	PROPN
brj-22874	406	12	framework	framework	NOUN
brj-22874	406	13	,	,	PUNCT
brj-22874	406	14	”	"	PUNCT
brj-22874	406	15	mathematical	mathematical	ADJ
brj-22874	406	16	problems	problem	NOUN
brj-22874	406	17	in	in	ADP
brj-22874	406	18	engineering	engineer	VERB
brj-22874	406	19	2022	2022	NUM
brj-22874	406	20	,	,	PUNCT
brj-22874	406	21	1	1	NUM
brj-22874	406	22	-	-	SYM
brj-22874	406	23	9	9	NUM
brj-22874	406	24	.	.	PUNCT
brj-22874	406	25	doi	doi	NOUN
brj-22874	406	26	:	:	PUNCT
brj-22874	406	27	10.1155/2022/4878090	10.1155/2022/4878090	NUM
brj-22874	406	28	urbonas	urbona	NOUN
brj-22874	406	29	,	,	PUNCT
brj-22874	406	30	a.	a.	NOUN
brj-22874	406	31	,	,	PUNCT
brj-22874	406	32	raudonis	raudonis	PROPN
brj-22874	406	33	,	,	PUNCT
brj-22874	406	34	v.	v.	ADV
brj-22874	406	35	,	,	PUNCT
brj-22874	406	36	maskeliunas	maskeliunas	PROPN
brj-22874	406	37	,	,	PUNCT
brj-22874	406	38	r.	r.	PROPN
brj-22874	406	39	,	,	PUNCT
brj-22874	406	40	and	and	CCONJ
brj-22874	406	41	damasevicius	damasevicius	PROPN
brj-22874	406	42	,	,	PUNCT
brj-22874	406	43	r.	r.	PROPN
brj-22874	406	44	(	(	PUNCT
brj-22874	406	45	2019	2019	NUM
brj-22874	406	46	)	)	PUNCT
brj-22874	406	47	.	.	PUNCT
brj-22874	407	1	“	"	PUNCT
brj-22874	407	2	automated	automate	VERB
brj-22874	407	3	identification	identification	NOUN
brj-22874	407	4	of	of	ADP
brj-22874	407	5	wood	wood	NOUN
brj-22874	407	6	veneer	veneer	NOUN
brj-22874	407	7	surface	surface	NOUN
brj-22874	407	8	defects	defect	NOUN
brj-22874	407	9	using	use	VERB
brj-22874	407	10	faster	fast	ADJ
brj-22874	407	11	region	region	NOUN
brj-22874	407	12	-	-	PUNCT
brj-22874	407	13	based	base	VERB
brj-22874	407	14	convolutional	convolutional	ADJ
brj-22874	407	15	neural	neural	ADJ
brj-22874	407	16	network	network	NOUN
brj-22874	407	17	with	with	ADP
brj-22874	407	18	data	datum	NOUN
brj-22874	407	19	augmentation	augmentation	NOUN
brj-22874	407	20	and	and	CCONJ
brj-22874	407	21	transfer	transfer	NOUN
brj-22874	407	22	learning	learning	NOUN
brj-22874	407	23	,	,	PUNCT
brj-22874	407	24	”	"	PUNCT
brj-22874	407	25	applied	apply	VERB
brj-22874	407	26	sciences	sciences	PROPN
brj-22874	407	27	-	-	PUNCT
brj-22874	407	28	basel	basel	PROPN
brj-22874	407	29	9	9	NUM
brj-22874	407	30	.	.	PUNCT
brj-22874	408	1	doi	doi	NOUN
brj-22874	408	2	:	:	PUNCT
brj-22874	408	3	10.3390	10.3390	NUM
brj-22874	408	4	/	/	SYM
brj-22874	408	5	app9224898	app9224898	PROPN
brj-22874	408	6	wang	wang	PROPN
brj-22874	408	7	,	,	PUNCT
brj-22874	408	8	c.	c.	PROPN
brj-22874	408	9	,	,	PUNCT
brj-22874	408	10	liu	liu	PROPN
brj-22874	408	11	,	,	PUNCT
brj-22874	408	12	y.	y.	PROPN
brj-22874	408	13	,	,	PUNCT
brj-22874	408	14	and	and	CCONJ
brj-22874	408	15	wang	wang	PROPN
brj-22874	408	16	,	,	PUNCT
brj-22874	408	17	p.	p.	NOUN
brj-22874	408	18	(	(	PUNCT
brj-22874	408	19	2018	2018	NUM
brj-22874	408	20	)	)	PUNCT
brj-22874	408	21	.	.	PUNCT
brj-22874	409	1	“	"	PUNCT
brj-22874	409	2	extraction	extraction	NOUN
brj-22874	409	3	and	and	CCONJ
brj-22874	409	4	detection	detection	NOUN
brj-22874	409	5	of	of	ADP
brj-22874	409	6	surface	surface	NOUN
brj-22874	409	7	defects	defect	NOUN
brj-22874	409	8	in	in	ADP
brj-22874	409	9	particleboards	particleboard	NOUN
brj-22874	409	10	by	by	ADP
brj-22874	409	11	tracking	track	VERB
brj-22874	409	12	moving	move	VERB
brj-22874	409	13	targets	target	NOUN
brj-22874	409	14	,	,	PUNCT
brj-22874	409	15	”	"	PUNCT
brj-22874	409	16	algorithms	algorithm	NOUN
brj-22874	409	17	12	12	NUM
brj-22874	409	18	.	.	PUNCT
brj-22874	410	1	doi	doi	NOUN
brj-22874	410	2	:	:	PUNCT
brj-22874	410	3	10.3390	10.3390	NUM
brj-22874	410	4	/	/	SYM
brj-22874	410	5	a12010006	a12010006	ADJ
brj-22874	410	6	woo	woo	NOUN
brj-22874	410	7	,	,	PUNCT
brj-22874	410	8	s.	s.	PROPN
brj-22874	410	9	,	,	PUNCT
brj-22874	410	10	park	park	PROPN
brj-22874	410	11	,	,	PUNCT
brj-22874	410	12	j.	j.	PROPN
brj-22874	410	13	,	,	PUNCT
brj-22874	410	14	lee	lee	PROPN
brj-22874	410	15	,	,	PUNCT
brj-22874	410	16	j.-y	j.-y	PROPN
brj-22874	410	17	.	.	PUNCT
brj-22874	410	18	,	,	PUNCT
brj-22874	410	19	and	and	CCONJ
brj-22874	410	20	kweon	kweon	PROPN
brj-22874	410	21	,	,	PUNCT
brj-22874	410	22	i.	i.	PROPN
brj-22874	410	23	s.	s.	PROPN
brj-22874	410	24	(	(	PUNCT
brj-22874	410	25	2018	2018	NUM
brj-22874	410	26	)	)	PUNCT
brj-22874	410	27	.	.	PUNCT
brj-22874	411	1	“	"	PUNCT
brj-22874	411	2	cbam	cbam	NOUN
brj-22874	411	3	:	:	PUNCT
brj-22874	411	4	convolutional	convolutional	ADJ
brj-22874	411	5	block	block	NOUN
brj-22874	411	6	attention	attention	NOUN
brj-22874	411	7	module	module	NOUN
brj-22874	411	8	,	,	PUNCT
brj-22874	411	9	”	"	PUNCT
brj-22874	411	10	in	in	ADP
brj-22874	411	11	:	:	PUNCT
brj-22874	411	12	proceedings	proceeding	NOUN
brj-22874	411	13	of	of	ADP
brj-22874	411	14	the	the	DET
brj-22874	411	15	15th	15th	ADJ
brj-22874	411	16	european	european	PROPN
brj-22874	411	17	conference	conference	NOUN
brj-22874	411	18	on	on	ADP
brj-22874	411	19	computer	computer	NOUN
brj-22874	411	20	vision	vision	NOUN
brj-22874	411	21	(	(	PUNCT
brj-22874	411	22	eccv	eccv	ADV
brj-22874	411	23	)	)	PUNCT
brj-22874	411	24	,	,	PUNCT
brj-22874	411	25	pp	pp	ADP
brj-22874	411	26	.	.	PUNCT
brj-22874	412	1	3	3	NUM
brj-22874	412	2	-	-	SYM
brj-22874	412	3	19	19	NUM
brj-22874	412	4	.	.	PUNCT
brj-22874	412	5	doi	doi	NOUN
brj-22874	412	6	:	:	PUNCT
brj-22874	412	7	10.1007/978	10.1007/978	NUM
brj-22874	412	8	-	-	SYM
brj-22874	412	9	3	3	NUM
brj-22874	412	10	-	-	PUNCT
brj-22874	412	11	030	030	NUM
brj-22874	412	12	-	-	PUNCT
brj-22874	412	13	01234	01234	NUM
brj-22874	412	14	-	-	PUNCT
brj-22874	412	15	2_1	2_1	NUM
brj-22874	412	16	xia	xia	PROPN
brj-22874	412	17	,	,	PUNCT
brj-22874	412	18	b.	b.	PROPN
brj-22874	412	19	,	,	PUNCT
brj-22874	412	20	luo	luo	PROPN
brj-22874	412	21	,	,	PUNCT
brj-22874	412	22	h.	h.	PROPN
brj-22874	412	23	,	,	PUNCT
brj-22874	412	24	and	and	CCONJ
brj-22874	412	25	shi	shi	PROPN
brj-22874	412	26	,	,	PUNCT
brj-22874	412	27	s.	s.	PROPN
brj-22874	412	28	(	(	PUNCT
brj-22874	412	29	2022	2022	NUM
brj-22874	412	30	)	)	PUNCT
brj-22874	412	31	.	.	PUNCT
brj-22874	413	1	“	"	PUNCT
brj-22874	413	2	improved	improve	VERB
brj-22874	413	3	faster	fast	ADV
brj-22874	413	4	r	r	NOUN
brj-22874	413	5	-	-	PUNCT
brj-22874	413	6	cnn	cnn	PROPN
brj-22874	413	7	based	base	VERB
brj-22874	413	8	surface	surface	NOUN
brj-22874	413	9	defect	defect	NOUN
brj-22874	413	10	detection	detection	NOUN
brj-22874	413	11	algorithm	algorithm	NOUN
brj-22874	413	12	for	for	ADP
brj-22874	413	13	plates	plate	NOUN
brj-22874	413	14	,	,	PUNCT
brj-22874	413	15	”	"	PUNCT
brj-22874	413	16	computational	computational	ADJ
brj-22874	413	17	intelligence	intelligence	NOUN
brj-22874	413	18	and	and	CCONJ
brj-22874	413	19	neuroscience	neuroscience	NOUN
brj-22874	413	20	2022	2022	NUM
brj-22874	413	21	.	.	PUNCT
brj-22874	414	1	doi	doi	NOUN
brj-22874	414	2	:	:	PUNCT
brj-22874	414	3	10.1155/2022/3248722	10.1155/2022/3248722	NUM
brj-22874	414	4	yang	yang	PROPN
brj-22874	414	5	,	,	PUNCT
brj-22874	414	6	h.	h.	PROPN
brj-22874	414	7	,	,	PUNCT
brj-22874	414	8	and	and	CCONJ
brj-22874	414	9	yu	yu	PROPN
brj-22874	414	10	,	,	PUNCT
brj-22874	414	11	l.	l.	PROPN
brj-22874	414	12	(	(	PUNCT
brj-22874	414	13	2017	2017	NUM
brj-22874	414	14	)	)	PUNCT
brj-22874	414	15	.	.	PUNCT
brj-22874	415	1	“	"	PUNCT
brj-22874	415	2	feature	feature	NOUN
brj-22874	415	3	extraction	extraction	NOUN
brj-22874	415	4	of	of	ADP
brj-22874	415	5	wood	wood	NOUN
brj-22874	415	6	-	-	PUNCT
brj-22874	415	7	hole	hole	NOUN
brj-22874	415	8	defects	defect	NOUN
brj-22874	415	9	using	use	VERB
brj-22874	415	10	waveletbased	waveletbase	VERB
brj-22874	415	11	ultrasonic	ultrasonic	ADJ
brj-22874	415	12	testing	testing	NOUN
brj-22874	415	13	,	,	PUNCT
brj-22874	415	14	”	"	PUNCT
brj-22874	415	15	journal	journal	NOUN
brj-22874	415	16	of	of	ADP
brj-22874	415	17	forestry	forestry	PROPN
brj-22874	415	18	research	research	PROPN
brj-22874	415	19	28	28	NUM
brj-22874	415	20	,	,	PUNCT
brj-22874	415	21	395	395	NUM
brj-22874	415	22	-	-	SYM
brj-22874	415	23	402	402	NUM
brj-22874	415	24	.	.	PUNCT
brj-22874	416	1	doi	doi	NOUN
brj-22874	416	2	:	:	PUNCT
brj-22874	416	3	10.1007	10.1007	NUM
brj-22874	416	4	/	/	SYM
brj-22874	416	5	s11676	s11676	NUM
brj-22874	416	6	-	-	PUNCT
brj-22874	416	7	016	016	NUM
brj-22874	416	8	-	-	PUNCT
brj-22874	416	9	0297	0297	NUM
brj-22874	416	10	-	-	PUNCT
brj-22874	416	11	z	z	PROPN
brj-22874	416	12	yang	yang	PROPN
brj-22874	416	13	,	,	PUNCT
brj-22874	416	14	j.	j.	PROPN
brj-22874	416	15	,	,	PUNCT
brj-22874	416	16	zheng	zheng	PROPN
brj-22874	416	17	,	,	PUNCT
brj-22874	416	18	x.	x.	PROPN
brj-22874	416	19	,	,	PUNCT
brj-22874	416	20	yao	yao	PROPN
brj-22874	416	21	,	,	PUNCT
brj-22874	416	22	j.	j.	PROPN
brj-22874	416	23	,	,	PUNCT
brj-22874	416	24	xiao	xiao	PROPN
brj-22874	416	25	,	,	PUNCT
brj-22874	416	26	j.	j.	PROPN
brj-22874	416	27	,	,	PUNCT
brj-22874	416	28	and	and	CCONJ
brj-22874	416	29	yan	yan	PROPN
brj-22874	416	30	,	,	PUNCT
brj-22874	416	31	l.	l.	PROPN
brj-22874	416	32	(	(	PUNCT
brj-22874	416	33	2019	2019	NUM
brj-22874	416	34	)	)	PUNCT
brj-22874	416	35	.	.	PUNCT
brj-22874	417	1	“	"	PUNCT
brj-22874	417	2	3d	3d	NUM
brj-22874	417	3	surface	surface	NOUN
brj-22874	417	4	defects	defect	NOUN
brj-22874	417	5	recognition	recognition	NOUN
brj-22874	417	6	of	of	ADP
brj-22874	417	7	lumber	lumber	NOUN
brj-22874	417	8	and	and	CCONJ
brj-22874	417	9	straw	straw	NOUN
brj-22874	417	10	-	-	PUNCT
brj-22874	417	11	based	base	VERB
brj-22874	417	12	panels	panel	NOUN
brj-22874	417	13	based	base	VERB
brj-22874	417	14	on	on	ADP
brj-22874	417	15	structure	structure	NOUN
brj-22874	417	16	laser	laser	NOUN
brj-22874	417	17	sensor	sensor	NOUN
brj-22874	417	18	scanning	scan	VERB
brj-22874	417	19	technology	technology	NOUN
brj-22874	417	20	,	,	PUNCT
brj-22874	417	21	”	"	PUNCT
brj-22874	417	22	inmateh	inmateh	NOUN
brj-22874	417	23	-	-	PUNCT
brj-22874	417	24	agricultural	agricultural	ADJ
brj-22874	417	25	engineering	engineering	NOUN
brj-22874	417	26	57	57	NUM
brj-22874	417	27	,	,	PUNCT
brj-22874	417	28	225	225	NUM
brj-22874	417	29	-	-	SYM
brj-22874	417	30	232	232	NUM
brj-22874	417	31	.	.	PUNCT
brj-22874	418	1	doi	doi	NOUN
brj-22874	418	2	:	:	PUNCT
brj-22874	418	3	10.35633	10.35633	NUM
brj-22874	418	4	/	/	SYM
brj-22874	418	5	inmateh_57_25	inmateh_57_25	NUM
brj-22874	418	6	ye	ye	PROPN
brj-22874	418	7	,	,	PUNCT
brj-22874	418	8	r.	r.	PROPN
brj-22874	418	9	,	,	PUNCT
brj-22874	418	10	pei	pei	PROPN
brj-22874	418	11	,	,	PUNCT
brj-22874	418	12	y.	y.	PROPN
brj-22874	418	13	,	,	PUNCT
brj-22874	418	14	wang	wang	PROPN
brj-22874	418	15	,	,	PUNCT
brj-22874	418	16	w.	w.	PROPN
brj-22874	418	17	,	,	PUNCT
brj-22874	418	18	and	and	CCONJ
brj-22874	418	19	zhou	zhou	PROPN
brj-22874	418	20	,	,	PUNCT
brj-22874	418	21	h.	h.	PROPN
brj-22874	418	22	(	(	PUNCT
brj-22874	418	23	2022	2022	NUM
brj-22874	418	24	)	)	PUNCT
brj-22874	418	25	.	.	PUNCT
brj-22874	419	1	“	"	PUNCT
brj-22874	419	2	scientific	scientific	ADJ
brj-22874	419	3	computational	computational	ADJ
brj-22874	419	4	visual	visual	ADJ
brj-22874	419	5	analysis	analysis	NOUN
brj-22874	419	6	of	of	ADP
brj-22874	419	7	wood	wood	NOUN
brj-22874	419	8	internal	internal	ADJ
brj-22874	419	9	defects	defect	NOUN
brj-22874	419	10	detection	detection	NOUN
brj-22874	419	11	in	in	ADP
brj-22874	419	12	view	view	NOUN
brj-22874	419	13	of	of	ADP
brj-22874	419	14	tomography	tomography	NOUN
brj-22874	419	15	image	image	NOUN
brj-22874	419	16	reconstruction	reconstruction	NOUN
brj-22874	419	17	algorithm	algorithm	NOUN
brj-22874	419	18	,	,	PUNCT
brj-22874	419	19	”	"	PUNCT
brj-22874	419	20	mobile	mobile	ADJ
brj-22874	419	21	information	information	NOUN
brj-22874	419	22	systems	system	NOUN
brj-22874	419	23	2022	2022	NUM
brj-22874	419	24	.	.	PUNCT
brj-22874	420	1	doi	doi	NOUN
brj-22874	420	2	:	:	PUNCT
brj-22874	420	3	10.1155/2022/6091352	10.1155/2022/6091352	NUM
brj-22874	420	4	yonghua	yonghua	NOUN
brj-22874	420	5	,	,	PUNCT
brj-22874	420	6	x.	x.	NOUN
brj-22874	420	7	,	,	PUNCT
brj-22874	420	8	and	and	CCONJ
brj-22874	420	9	jin	jin	NOUN
brj-22874	420	10	-	-	PUNCT
brj-22874	420	11	cong	cong	NOUN
brj-22874	420	12	,	,	PUNCT
brj-22874	420	13	w.	w.	PROPN
brj-22874	420	14	(	(	PUNCT
brj-22874	420	15	2015	2015	NUM
brj-22874	420	16	)	)	PUNCT
brj-22874	420	17	.	.	PUNCT
brj-22874	421	1	“	"	PUNCT
brj-22874	421	2	study	study	VERB
brj-22874	421	3	on	on	ADP
brj-22874	421	4	the	the	DET
brj-22874	421	5	identification	identification	NOUN
brj-22874	421	6	of	of	ADP
brj-22874	421	7	the	the	DET
brj-22874	421	8	wood	wood	NOUN
brj-22874	421	9	surface	surface	NOUN
brj-22874	421	10	defects	defect	NOUN
brj-22874	421	11	based	base	VERB
brj-22874	421	12	on	on	ADP
brj-22874	421	13	texture	texture	ADJ
brj-22874	421	14	features	feature	NOUN
brj-22874	421	15	,	,	PUNCT
brj-22874	421	16	”	"	PUNCT
brj-22874	421	17	optik	optik	PROPN
brj-22874	421	18	-	-	PUNCT
brj-22874	421	19	international	international	ADJ
brj-22874	421	20	journal	journal	NOUN
brj-22874	421	21	for	for	ADP
brj-22874	421	22	light	light	NOUN
brj-22874	421	23	and	and	CCONJ
brj-22874	421	24	electron	electron	NOUN
brj-22874	421	25	optics	optic	NOUN
brj-22874	421	26	126	126	NUM
brj-22874	421	27	,	,	PUNCT
brj-22874	421	28	2231	2231	NUM
brj-22874	421	29	-	-	SYM
brj-22874	421	30	2235	2235	NUM
brj-22874	421	31	.	.	PUNCT
brj-22874	422	1	doi	doi	NOUN
brj-22874	422	2	:	:	PUNCT
brj-22874	422	3	10.1016	10.1016	NUM
brj-22874	422	4	/	/	SYM
brj-22874	422	5	j.ijleo.2015.05.101	j.ijleo.2015.05.101	PROPN
brj-22874	422	6	zhang	zhang	PROPN
brj-22874	422	7	,	,	PUNCT
brj-22874	422	8	y.	y.	PROPN
brj-22874	422	9	,	,	PUNCT
brj-22874	422	10	xu	xu	PROPN
brj-22874	422	11	,	,	PUNCT
brj-22874	422	12	c.	c.	PROPN
brj-22874	422	13	,	,	PUNCT
brj-22874	422	14	li	li	PROPN
brj-22874	422	15	,	,	PUNCT
brj-22874	422	16	c.	c.	PROPN
brj-22874	422	17	,	,	PUNCT
brj-22874	422	18	yu	yu	PROPN
brj-22874	422	19	,	,	PUNCT
brj-22874	422	20	h.	h.	PROPN
brj-22874	422	21	,	,	PUNCT
brj-22874	422	22	and	and	CCONJ
brj-22874	422	23	cao	cao	PROPN
brj-22874	422	24	,	,	PUNCT
brj-22874	422	25	j.	j.	PROPN
brj-22874	422	26	(	(	PUNCT
brj-22874	422	27	2015	2015	NUM
brj-22874	422	28	)	)	PUNCT
brj-22874	422	29	.	.	PUNCT
brj-22874	423	1	“	"	PUNCT
brj-22874	423	2	wood	wood	NOUN
brj-22874	423	3	defect	defect	NOUN
brj-22874	423	4	detection	detection	NOUN
brj-22874	423	5	method	method	NOUN
brj-22874	423	6	with	with	ADP
brj-22874	423	7	pca	pca	NOUN
brj-22874	423	8	feature	feature	NOUN
brj-22874	423	9	fusion	fusion	NOUN
brj-22874	423	10	and	and	CCONJ
brj-22874	423	11	compressed	compressed	ADJ
brj-22874	423	12	sensing	sensing	NOUN
brj-22874	423	13	,	,	PUNCT
brj-22874	423	14	”	"	PUNCT
brj-22874	423	15	journal	journal	NOUN
brj-22874	423	16	of	of	ADP
brj-22874	423	17	forestry	forestry	PROPN
brj-22874	423	18	research	research	PROPN
brj-22874	423	19	26	26	NUM
brj-22874	423	20	,	,	PUNCT
brj-22874	423	21	745	745	NUM
brj-22874	423	22	-	-	SYM
brj-22874	423	23	751	751	NUM
brj-22874	423	24	.	.	PUNCT
brj-22874	424	1	doi	doi	NOUN
brj-22874	424	2	:	:	PUNCT
brj-22874	424	3	10.1007	10.1007	NUM
brj-22874	424	4	/	/	SYM
brj-22874	424	5	s11676	s11676	NUM
brj-22874	424	6	-	-	NUM
brj-22874	424	7	015	015	NUM
brj-22874	424	8	-	-	PUNCT
brj-22874	424	9	0066	0066	NUM
brj-22874	424	10	-	-	SYM
brj-22874	424	11	4	4	NUM
brj-22874	424	12	zhao	zhao	NOUN
brj-22874	424	13	,	,	PUNCT
brj-22874	424	14	j.	j.	PROPN
brj-22874	424	15	q.	q.	PROPN
brj-22874	424	16	,	,	PUNCT
brj-22874	424	17	zhang	zhang	PROPN
brj-22874	424	18	,	,	PUNCT
brj-22874	424	19	x.	x.	PROPN
brj-22874	424	20	h.	h.	PROPN
brj-22874	424	21	,	,	PUNCT
brj-22874	424	22	yan	yan	PROPN
brj-22874	424	23	,	,	PUNCT
brj-22874	424	24	j.	j.	PROPN
brj-22874	424	25	w.	w.	PROPN
brj-22874	424	26	,	,	PUNCT
brj-22874	424	27	qiu	qiu	PROPN
brj-22874	424	28	,	,	PUNCT
brj-22874	424	29	x.	x.	PROPN
brj-22874	424	30	l.	l.	PROPN
brj-22874	424	31	,	,	PUNCT
brj-22874	424	32	yao	yao	PROPN
brj-22874	424	33	,	,	PUNCT
brj-22874	424	34	x.	x.	PROPN
brj-22874	424	35	,	,	PUNCT
brj-22874	424	36	tian	tian	PROPN
brj-22874	424	37	,	,	PUNCT
brj-22874	424	38	y.	y.	PROPN
brj-22874	424	39	c.	c.	PROPN
brj-22874	424	40	,	,	PUNCT
brj-22874	424	41	and	and	CCONJ
brj-22874	424	42	cao	cao	PROPN
brj-22874	424	43	,	,	PUNCT
brj-22874	424	44	w.	w.	PROPN
brj-22874	424	45	x.	x.	PROPN
brj-22874	424	46	(	(	PUNCT
brj-22874	424	47	2021a	2021a	NUM
brj-22874	424	48	)	)	PUNCT
brj-22874	424	49	.	.	PUNCT
brj-22874	425	1	“	"	PUNCT
brj-22874	425	2	a	a	DET
brj-22874	425	3	wheat	wheat	NOUN
brj-22874	425	4	spike	spike	NOUN
brj-22874	425	5	detection	detection	NOUN
brj-22874	425	6	method	method	NOUN
brj-22874	425	7	in	in	ADP
brj-22874	425	8	uav	uav	PROPN
brj-22874	425	9	images	image	NOUN
brj-22874	425	10	based	base	VERB
brj-22874	425	11	on	on	ADP
brj-22874	425	12	improved	improved	ADJ
brj-22874	425	13	yolov5	yolov5	NOUN
brj-22874	425	14	,	,	PUNCT
brj-22874	425	15	”	"	PUNCT
brj-22874	425	16	remote	remote	ADJ
brj-22874	425	17	sensing	sense	VERB
brj-22874	425	18	13	13	NUM
brj-22874	425	19	.	.	PUNCT
brj-22874	426	1	doi	doi	NOUN
brj-22874	426	2	:	:	PUNCT
brj-22874	426	3	10.3390	10.3390	NUM
brj-22874	426	4	/	/	SYM
brj-22874	426	5	rs13163095	rs13163095	NOUN
brj-22874	426	6	zhao	zhao	PROPN
brj-22874	426	7	,	,	PUNCT
brj-22874	426	8	z.	z.	PROPN
brj-22874	426	9	,	,	PUNCT
brj-22874	426	10	yang	yang	PROPN
brj-22874	426	11	,	,	PUNCT
brj-22874	426	12	x.	x.	PROPN
brj-22874	426	13	,	,	PUNCT
brj-22874	426	14	zhou	zhou	PROPN
brj-22874	426	15	,	,	PUNCT
brj-22874	426	16	y.	y.	PROPN
brj-22874	426	17	,	,	PUNCT
brj-22874	426	18	sun	sun	PROPN
brj-22874	426	19	,	,	PUNCT
brj-22874	426	20	q.	q.	PROPN
brj-22874	426	21	,	,	PUNCT
brj-22874	426	22	ge	ge	PROPN
brj-22874	426	23	,	,	PUNCT
brj-22874	426	24	z.	z.	PROPN
brj-22874	426	25	,	,	PUNCT
brj-22874	426	26	and	and	CCONJ
brj-22874	426	27	liu	liu	PROPN
brj-22874	426	28	,	,	PUNCT
brj-22874	426	29	d.	d.	PROPN
brj-22874	426	30	(	(	PUNCT
brj-22874	426	31	2021b	2021b	NUM
brj-22874	426	32	)	)	PUNCT
brj-22874	426	33	.	.	PUNCT
brj-22874	427	1	“	"	PUNCT
brj-22874	427	2	real	real	ADJ
brj-22874	427	3	-	-	PUNCT
brj-22874	427	4	time	time	NOUN
brj-22874	427	5	detection	detection	NOUN
brj-22874	427	6	of	of	ADP
brj-22874	427	7	particleboard	particleboard	NOUN
brj-22874	427	8	surface	surface	NOUN
brj-22874	427	9	defects	defect	NOUN
brj-22874	427	10	based	base	VERB
brj-22874	427	11	on	on	ADP
brj-22874	427	12	improved	improved	ADJ
brj-22874	427	13	yolov5	yolov5	NOUN
brj-22874	427	14	target	target	NOUN
brj-22874	427	15	detection	detection	NOUN
brj-22874	427	16	,	,	PUNCT
brj-22874	427	17	”	"	PUNCT
brj-22874	427	18	scientific	scientific	ADJ
brj-22874	427	19	reports	report	NOUN
brj-22874	427	20	11	11	NUM
brj-22874	427	21	.	.	PUNCT
brj-22874	428	1	doi	doi	NOUN
brj-22874	428	2	:	:	PUNCT
brj-22874	428	3	10.1038	10.1038	NUM
brj-22874	428	4	/	/	SYM
brj-22874	428	5	s41598	s41598	NOUN
brj-22874	428	6	-	-	PUNCT
brj-22874	428	7	021	021	NUM
brj-22874	428	8	-	-	PUNCT
brj-22874	428	9	01084	01084	NUM
brj-22874	428	10	-	-	PUNCT
brj-22874	428	11	x	x	NOUN
brj-22874	428	12	article	article	NOUN
brj-22874	428	13	submitted	submit	VERB
brj-22874	428	14	:	:	PUNCT
brj-22874	428	15	august	august	PROPN
brj-22874	428	16	5	5	NUM
brj-22874	428	17	,	,	PUNCT
brj-22874	428	18	2023	2023	NUM
brj-22874	428	19	;	;	PUNCT
brj-22874	428	20	peer	peer	NOUN
brj-22874	428	21	review	review	NOUN
brj-22874	428	22	completed	complete	VERB
brj-22874	428	23	:	:	PUNCT
brj-22874	428	24	september	september	PROPN
brj-22874	428	25	9	9	NUM
brj-22874	428	26	,	,	PUNCT
brj-22874	428	27	2023	2023	NUM
brj-22874	428	28	;	;	PUNCT
brj-22874	428	29	revised	revise	VERB
brj-22874	428	30	version	version	NOUN
brj-22874	428	31	received	receive	VERB
brj-22874	428	32	:	:	PUNCT
brj-22874	428	33	september	september	PROPN
brj-22874	428	34	14	14	NUM
brj-22874	428	35	,	,	PUNCT
brj-22874	428	36	2023	2023	NUM
brj-22874	428	37	;	;	PUNCT
brj-22874	428	38	accepted	accept	VERB
brj-22874	428	39	:	:	PUNCT
brj-22874	428	40	september	september	PROPN
brj-22874	428	41	17	17	NUM
brj-22874	428	42	,	,	PUNCT
brj-22874	428	43	2023	2023	NUM
brj-22874	428	44	;	;	PUNCT
brj-22874	428	45	published	publish	VERB
brj-22874	428	46	:	:	PUNCT
brj-22874	428	47	september	september	PROPN
brj-22874	428	48	28	28	NUM
brj-22874	428	49	,	,	PUNCT
brj-22874	428	50	2023	2023	NUM
brj-22874	428	51	.	.	PUNCT
brj-22874	429	1	doi	doi	NOUN
brj-22874	429	2	:	:	PUNCT
brj-22874	429	3	10.15376	10.15376	NUM
brj-22874	429	4	/	/	SYM
brj-22874	429	5	biores.18.4.7713	biores.18.4.7713	PROPN
brj-22874	429	6	-	-	PUNCT
brj-22874	429	7	7730	7730	NUM