id	sid	tid	token	lemma	pos
ajst-11778	1	1	academic	academic	ADJ
ajst-11778	1	2	journal	journal	NOUN
ajst-11778	1	3	of	of	ADP
ajst-11778	1	4	science	science	NOUN
ajst-11778	1	5	and	and	CCONJ
ajst-11778	1	6	technology	technology	NOUN
ajst-11778	1	7	issn	issn	NOUN
ajst-11778	1	8	:	:	PUNCT
ajst-11778	1	9	2771	2771	NUM
ajst-11778	1	10	-	-	SYM
ajst-11778	1	11	3032	3032	NUM
ajst-11778	1	12	|	|	NOUN
ajst-11778	1	13	vol	vol	NOUN
ajst-11778	1	14	.	.	PROPN
ajst-11778	2	1	7	7	NUM
ajst-11778	2	2	,	,	PUNCT
ajst-11778	2	3	no	no	INTJ
ajst-11778	2	4	.	.	NOUN
ajst-11778	2	5	2	2	NUM
ajst-11778	2	6	,	,	PUNCT
ajst-11778	2	7	2023	2023	NUM
ajst-11778	2	8	66	66	NUM
ajst-11778	2	9	surface	surface	NOUN
ajst-11778	2	10	defect	defect	NOUN
ajst-11778	2	11	detection	detection	NOUN
ajst-11778	2	12	of	of	ADP
ajst-11778	2	13	strip	strip	NOUN
ajst-11778	2	14	steel	steel	NOUN
ajst-11778	2	15	based	base	VERB
ajst-11778	2	16	on	on	ADP
ajst-11778	2	17	yolov5	yolov5	PROPN
ajst-11778	2	18	dewei	dewei	PROPN
ajst-11778	2	19	wang	wang	PROPN
ajst-11778	2	20	,	,	PUNCT
ajst-11778	2	21	xiaofang	xiaofang	PROPN
ajst-11778	2	22	liu	liu	PROPN
ajst-11778	2	23	*	*	PROPN
ajst-11778	2	24	school	school	NOUN
ajst-11778	2	25	of	of	ADP
ajst-11778	2	26	computer	computer	NOUN
ajst-11778	2	27	science	science	NOUN
ajst-11778	2	28	and	and	CCONJ
ajst-11778	2	29	engineering	engineering	NOUN
ajst-11778	2	30	,	,	PUNCT
ajst-11778	2	31	sichuan	sichuan	PROPN
ajst-11778	2	32	university	university	PROPN
ajst-11778	2	33	of	of	ADP
ajst-11778	2	34	computer	computer	NOUN
ajst-11778	2	35	science	science	NOUN
ajst-11778	2	36	and	and	CCONJ
ajst-11778	2	37	engineering	engineering	NOUN
ajst-11778	2	38	,	,	PUNCT
ajst-11778	2	39	yibin	yibin	PROPN
ajst-11778	2	40	,	,	PUNCT
ajst-11778	2	41	644002	644002	NUM
ajst-11778	2	42	,	,	PUNCT
ajst-11778	2	43	china	china	PROPN
ajst-11778	2	44	*	*	PUNCT
ajst-11778	2	45	corresponding	correspond	VERB
ajst-11778	2	46	author	author	NOUN
ajst-11778	2	47	abstract	abstract	NOUN
ajst-11778	2	48	:	:	PUNCT
ajst-11778	2	49	given	give	VERB
ajst-11778	2	50	the	the	DET
ajst-11778	2	51	various	various	ADJ
ajst-11778	2	52	kinds	kind	NOUN
ajst-11778	2	53	of	of	ADP
ajst-11778	2	54	surface	surface	NOUN
ajst-11778	2	55	defects	defect	NOUN
ajst-11778	2	56	and	and	CCONJ
ajst-11778	2	57	the	the	DET
ajst-11778	2	58	lack	lack	NOUN
ajst-11778	2	59	of	of	ADP
ajst-11778	2	60	obvious	obvious	ADJ
ajst-11778	2	61	features	feature	NOUN
ajst-11778	2	62	,	,	PUNCT
ajst-11778	2	63	which	which	PRON
ajst-11778	2	64	lead	lead	VERB
ajst-11778	2	65	to	to	ADP
ajst-11778	2	66	detection	detection	NOUN
ajst-11778	2	67	error	error	NOUN
ajst-11778	2	68	and	and	CCONJ
ajst-11778	2	69	leakage	leakage	NOUN
ajst-11778	2	70	detection	detection	NOUN
ajst-11778	2	71	,	,	PUNCT
ajst-11778	2	72	a	a	DET
ajst-11778	2	73	method	method	NOUN
ajst-11778	2	74	of	of	ADP
ajst-11778	2	75	strip	strip	NOUN
ajst-11778	2	76	surface	surface	NOUN
ajst-11778	2	77	defects	defect	NOUN
ajst-11778	2	78	detection	detection	NOUN
ajst-11778	2	79	with	with	ADP
ajst-11778	2	80	a	a	DET
ajst-11778	2	81	decoupling	decouple	VERB
ajst-11778	2	82	head	head	NOUN
ajst-11778	2	83	,	,	PUNCT
ajst-11778	2	84	yolov5s	yolov5s	PROPN
ajst-11778	2	85	,	,	PUNCT
ajst-11778	2	86	is	be	AUX
ajst-11778	2	87	proposed	propose	VERB
ajst-11778	2	88	.	.	PUNCT
ajst-11778	3	1	firstly	firstly	ADV
ajst-11778	3	2	,	,	PUNCT
ajst-11778	3	3	the	the	DET
ajst-11778	3	4	k	k	NOUN
ajst-11778	3	5	-	-	PUNCT
ajst-11778	3	6	means	mean	VERB
ajst-11778	3	7	+	+	SYM
ajst-11778	3	8	+	+	NUM
ajst-11778	3	9	method	method	NOUN
ajst-11778	3	10	was	be	AUX
ajst-11778	3	11	utilized	utilize	VERB
ajst-11778	3	12	to	to	PART
ajst-11778	3	13	relocate	relocate	VERB
ajst-11778	3	14	the	the	DET
ajst-11778	3	15	anchor	anchor	NOUN
ajst-11778	3	16	frames	frame	NOUN
ajst-11778	3	17	to	to	PART
ajst-11778	3	18	produce	produce	VERB
ajst-11778	3	19	an	an	DET
ajst-11778	3	20	optimised	optimise	VERB
ajst-11778	3	21	coupling	coupling	NOUN
ajst-11778	3	22	between	between	ADP
ajst-11778	3	23	the	the	DET
ajst-11778	3	24	transcendental	transcendental	NOUN
ajst-11778	3	25	and	and	CCONJ
ajst-11778	3	26	the	the	DET
ajst-11778	3	27	real	real	ADJ
ajst-11778	3	28	frame	frame	NOUN
ajst-11778	3	29	.	.	PUNCT
ajst-11778	4	1	simam	simam	PROPN
ajst-11778	4	2	's	's	PART
ajst-11778	4	3	attention	attention	NOUN
ajst-11778	4	4	free	free	ADJ
ajst-11778	4	5	mechanism	mechanism	NOUN
ajst-11778	4	6	was	be	AUX
ajst-11778	4	7	incorporated	incorporate	VERB
ajst-11778	4	8	into	into	ADP
ajst-11778	4	9	the	the	DET
ajst-11778	4	10	neck	neck	NOUN
ajst-11778	4	11	network	network	NOUN
ajst-11778	4	12	to	to	PART
ajst-11778	4	13	improve	improve	VERB
ajst-11778	4	14	the	the	DET
ajst-11778	4	15	performance	performance	NOUN
ajst-11778	4	16	of	of	ADP
ajst-11778	4	17	the	the	DET
ajst-11778	4	18	model	model	NOUN
ajst-11778	4	19	.	.	PUNCT
ajst-11778	5	1	finally	finally	ADV
ajst-11778	5	2	,	,	PUNCT
ajst-11778	5	3	the	the	DET
ajst-11778	5	4	head	head	NOUN
ajst-11778	5	5	of	of	ADP
ajst-11778	5	6	the	the	DET
ajst-11778	5	7	network	network	NOUN
ajst-11778	5	8	is	be	AUX
ajst-11778	5	9	replaced	replace	VERB
ajst-11778	5	10	by	by	ADP
ajst-11778	5	11	the	the	DET
ajst-11778	5	12	decoupling	decouple	VERB
ajst-11778	5	13	head	head	NOUN
ajst-11778	5	14	,	,	PUNCT
ajst-11778	5	15	which	which	PRON
ajst-11778	5	16	separates	separate	VERB
ajst-11778	5	17	classified	classified	ADJ
ajst-11778	5	18	tasks	task	NOUN
ajst-11778	5	19	from	from	ADP
ajst-11778	5	20	regressive	regressive	ADJ
ajst-11778	5	21	tasks	task	NOUN
ajst-11778	5	22	,	,	PUNCT
ajst-11778	5	23	thus	thus	ADV
ajst-11778	5	24	enhancing	enhance	VERB
ajst-11778	5	25	convergence	convergence	NOUN
ajst-11778	5	26	and	and	CCONJ
ajst-11778	5	27	recognition	recognition	NOUN
ajst-11778	5	28	accuracy	accuracy	NOUN
ajst-11778	5	29	.	.	PUNCT
ajst-11778	6	1	the	the	DET
ajst-11778	6	2	results	result	NOUN
ajst-11778	6	3	indicate	indicate	VERB
ajst-11778	6	4	that	that	SCONJ
ajst-11778	6	5	the	the	DET
ajst-11778	6	6	average	average	ADJ
ajst-11778	6	7	precision	precision	NOUN
ajst-11778	6	8	of	of	ADP
ajst-11778	6	9	the	the	DET
ajst-11778	6	10	proposed	propose	VERB
ajst-11778	6	11	algorithm	algorithm	NOUN
ajst-11778	6	12	is	be	AUX
ajst-11778	6	13	80.7	80.7	NUM
ajst-11778	6	14	%	%	NOUN
ajst-11778	6	15	on	on	ADP
ajst-11778	6	16	the	the	DET
ajst-11778	6	17	neu	neu	PROPN
ajst-11778	6	18	-	-	PUNCT
ajst-11778	6	19	det	det	PROPN
ajst-11778	6	20	dataset	dataset	PROPN
ajst-11778	6	21	,	,	PUNCT
ajst-11778	6	22	an	an	DET
ajst-11778	6	23	increase	increase	NOUN
ajst-11778	6	24	of	of	ADP
ajst-11778	6	25	3.2	3.2	NUM
ajst-11778	6	26	%	%	NOUN
ajst-11778	6	27	compared	compare	VERB
ajst-11778	6	28	to	to	ADP
ajst-11778	6	29	yolov5s	yolov5s	PROPN
ajst-11778	6	30	,	,	PUNCT
ajst-11778	6	31	and	and	CCONJ
ajst-11778	6	32	a	a	DET
ajst-11778	6	33	transfer	transfer	NOUN
ajst-11778	6	34	frame	frame	NOUN
ajst-11778	6	35	number	number	NOUN
ajst-11778	6	36	of	of	ADP
ajst-11778	6	37	fps	fps	NOUN
ajst-11778	6	38	of	of	ADP
ajst-11778	6	39	50	50	NUM
ajst-11778	6	40	per	per	ADP
ajst-11778	6	41	second	second	NOUN
ajst-11778	6	42	,	,	PUNCT
ajst-11778	6	43	which	which	PRON
ajst-11778	6	44	balanced	balance	VERB
ajst-11778	6	45	detection	detection	NOUN
ajst-11778	6	46	accuracy	accuracy	NOUN
ajst-11778	6	47	and	and	CCONJ
ajst-11778	6	48	operational	operational	ADJ
ajst-11778	6	49	efficiency	efficiency	NOUN
ajst-11778	6	50	.	.	PUNCT
ajst-11778	7	1	the	the	DET
ajst-11778	7	2	increased	increase	VERB
ajst-11778	7	3	accuracy	accuracy	NOUN
ajst-11778	7	4	of	of	ADP
ajst-11778	7	5	detection	detection	NOUN
ajst-11778	7	6	as	as	SCONJ
ajst-11778	7	7	compared	compare	VERB
ajst-11778	7	8	to	to	ADP
ajst-11778	7	9	other	other	ADJ
ajst-11778	7	10	techniques	technique	NOUN
ajst-11778	7	11	meets	meet	VERB
ajst-11778	7	12	the	the	DET
ajst-11778	7	13	requirements	requirement	NOUN
ajst-11778	7	14	of	of	ADP
ajst-11778	7	15	precision	precision	NOUN
ajst-11778	7	16	and	and	CCONJ
ajst-11778	7	17	timeliness	timeliness	NOUN
ajst-11778	7	18	.	.	PUNCT
ajst-11778	8	1	keywords	keyword	NOUN
ajst-11778	8	2	:	:	PUNCT
ajst-11778	8	3	surface	surface	NOUN
ajst-11778	8	4	defect	defect	NOUN
ajst-11778	8	5	detection	detection	NOUN
ajst-11778	8	6	,	,	PUNCT
ajst-11778	8	7	simam	simam	NOUN
ajst-11778	8	8	without	without	ADP
ajst-11778	8	9	attention	attention	NOUN
ajst-11778	8	10	span	span	NOUN
ajst-11778	8	11	,	,	PUNCT
ajst-11778	8	12	yolov5	yolov5	NOUN
ajst-11778	8	13	.	.	PROPN
ajst-11778	9	1	1	1	X
ajst-11778	9	2	.	.	X
ajst-11778	9	3	introduction	introduction	NOUN
ajst-11778	9	4	in	in	ADP
ajst-11778	9	5	the	the	DET
ajst-11778	9	6	field	field	NOUN
ajst-11778	9	7	of	of	ADP
ajst-11778	9	8	computer	computer	NOUN
ajst-11778	9	9	vision	vision	NOUN
ajst-11778	9	10	,	,	PUNCT
ajst-11778	9	11	target	target	NOUN
ajst-11778	9	12	detection	detection	NOUN
ajst-11778	9	13	is	be	AUX
ajst-11778	9	14	a	a	DET
ajst-11778	9	15	challenging	challenging	ADJ
ajst-11778	9	16	task	task	NOUN
ajst-11778	9	17	that	that	PRON
ajst-11778	9	18	aims	aim	VERB
ajst-11778	9	19	to	to	PART
ajst-11778	9	20	determine	determine	VERB
ajst-11778	9	21	the	the	DET
ajst-11778	9	22	location	location	NOUN
ajst-11778	9	23	of	of	ADP
ajst-11778	9	24	a	a	DET
ajst-11778	9	25	specific	specific	ADJ
ajst-11778	9	26	target	target	NOUN
ajst-11778	9	27	in	in	ADP
ajst-11778	9	28	a	a	DET
ajst-11778	9	29	given	give	VERB
ajst-11778	9	30	image	image	NOUN
ajst-11778	9	31	and	and	CCONJ
ajst-11778	9	32	to	to	PART
ajst-11778	9	33	correctly	correctly	ADV
ajst-11778	9	34	identify	identify	VERB
ajst-11778	9	35	and	and	CCONJ
ajst-11778	9	36	categorize	categorize	VERB
ajst-11778	9	37	it	it	PRON
ajst-11778	9	38	for	for	ADP
ajst-11778	9	39	in	in	ADP
ajst-11778	9	40	-	-	PUNCT
ajst-11778	9	41	depth	depth	NOUN
ajst-11778	9	42	analysis	analysis	NOUN
ajst-11778	9	43	and	and	CCONJ
ajst-11778	9	44	understanding	understanding	NOUN
ajst-11778	9	45	of	of	ADP
ajst-11778	9	46	the	the	DET
ajst-11778	9	47	image	image	NOUN
ajst-11778	9	48	content	content	NOUN
ajst-11778	9	49	by	by	ADP
ajst-11778	9	50	using	use	VERB
ajst-11778	9	51	specific	specific	ADJ
ajst-11778	9	52	algorithmic	algorithmic	ADJ
ajst-11778	9	53	techniques	technique	NOUN
ajst-11778	9	54	.	.	PUNCT
ajst-11778	10	1	at	at	ADP
ajst-11778	10	2	present	present	ADJ
ajst-11778	10	3	,	,	PUNCT
ajst-11778	10	4	the	the	DET
ajst-11778	10	5	dimensional	dimensional	ADJ
ajst-11778	10	6	accuracy	accuracy	NOUN
ajst-11778	10	7	of	of	ADP
ajst-11778	10	8	strip	strip	NOUN
ajst-11778	10	9	steel	steel	NOUN
ajst-11778	10	10	products	product	NOUN
ajst-11778	10	11	has	have	AUX
ajst-11778	10	12	basically	basically	ADV
ajst-11778	10	13	reached	reach	VERB
ajst-11778	10	14	the	the	DET
ajst-11778	10	15	needs	need	NOUN
ajst-11778	10	16	of	of	ADP
ajst-11778	10	17	industrial	industrial	ADJ
ajst-11778	10	18	production	production	NOUN
ajst-11778	10	19	,	,	PUNCT
ajst-11778	10	20	but	but	CCONJ
ajst-11778	10	21	the	the	DET
ajst-11778	10	22	issue	issue	NOUN
ajst-11778	10	23	of	of	ADP
ajst-11778	10	24	strip	strip	PROPN
ajst-11778	10	25	steel	steel	NOUN
ajst-11778	10	26	surface	surface	NOUN
ajst-11778	10	27	quality	quality	NOUN
ajst-11778	10	28	still	still	ADV
ajst-11778	10	29	needs	need	VERB
ajst-11778	10	30	further	further	ADJ
ajst-11778	10	31	improvement	improvement	NOUN
ajst-11778	10	32	.	.	PUNCT
ajst-11778	11	1	strip	strip	NOUN
ajst-11778	11	2	steel	steel	NOUN
ajst-11778	11	3	in	in	ADP
ajst-11778	11	4	the	the	DET
ajst-11778	11	5	manufacturing	manufacturing	NOUN
ajst-11778	11	6	process	process	NOUN
ajst-11778	11	7	,	,	PUNCT
ajst-11778	11	8	subject	subject	ADJ
ajst-11778	11	9	to	to	ADP
ajst-11778	11	10	the	the	DET
ajst-11778	11	11	influence	influence	NOUN
ajst-11778	11	12	of	of	ADP
ajst-11778	11	13	raw	raw	ADJ
ajst-11778	11	14	material	material	NOUN
ajst-11778	11	15	impurities	impurity	NOUN
ajst-11778	11	16	factors	factor	NOUN
ajst-11778	11	17	,	,	PUNCT
ajst-11778	11	18	its	its	PRON
ajst-11778	11	19	surface	surface	NOUN
ajst-11778	11	20	generated	generate	VERB
ajst-11778	11	21	cracking	cracking	NOUN
ajst-11778	11	22	,	,	PUNCT
ajst-11778	11	23	plaque	plaque	NOUN
ajst-11778	11	24	,	,	PUNCT
ajst-11778	11	25	pitting	pitting	NOUN
ajst-11778	11	26	surface	surface	NOUN
ajst-11778	11	27	,	,	PUNCT
ajst-11778	11	28	rolling	roll	VERB
ajst-11778	11	29	oxide	oxide	NOUN
ajst-11778	11	30	,	,	PUNCT
ajst-11778	11	31	scratch	scratch	NOUN
ajst-11778	11	32	,	,	PUNCT
ajst-11778	11	33	and	and	CCONJ
ajst-11778	11	34	other	other	ADJ
ajst-11778	11	35	defects	defect	NOUN
ajst-11778	11	36	,	,	PUNCT
ajst-11778	11	37	will	will	AUX
ajst-11778	11	38	not	not	PART
ajst-11778	11	39	only	only	ADV
ajst-11778	11	40	affect	affect	VERB
ajst-11778	11	41	the	the	DET
ajst-11778	11	42	appearance	appearance	NOUN
ajst-11778	11	43	of	of	ADP
ajst-11778	11	44	the	the	DET
ajst-11778	11	45	steel	steel	NOUN
ajst-11778	11	46	surface	surface	NOUN
ajst-11778	11	47	but	but	CCONJ
ajst-11778	11	48	also	also	ADV
ajst-11778	11	49	seriously	seriously	ADV
ajst-11778	11	50	reduce	reduce	VERB
ajst-11778	11	51	the	the	DET
ajst-11778	11	52	corrosion	corrosion	NOUN
ajst-11778	11	53	resistance	resistance	NOUN
ajst-11778	11	54	,	,	PUNCT
ajst-11778	11	55	high	high	ADJ
ajst-11778	11	56	-	-	PUNCT
ajst-11778	11	57	temperature	temperature	NOUN
ajst-11778	11	58	resistance	resistance	NOUN
ajst-11778	11	59	and	and	CCONJ
ajst-11778	11	60	fatigue	fatigue	NOUN
ajst-11778	11	61	strength	strength	NOUN
ajst-11778	11	62	of	of	ADP
ajst-11778	11	63	steel	steel	NOUN
ajst-11778	11	64	.	.	PUNCT
ajst-11778	12	1	therefore	therefore	ADV
ajst-11778	12	2	,	,	PUNCT
ajst-11778	12	3	the	the	DET
ajst-11778	12	4	detection	detection	NOUN
ajst-11778	12	5	of	of	ADP
ajst-11778	12	6	strip	strip	PROPN
ajst-11778	12	7	steel	steel	NOUN
ajst-11778	12	8	surface	surface	NOUN
ajst-11778	12	9	defects	defect	NOUN
ajst-11778	12	10	is	be	AUX
ajst-11778	12	11	important	important	ADJ
ajst-11778	12	12	to	to	PART
ajst-11778	12	13	improve	improve	VERB
ajst-11778	12	14	the	the	DET
ajst-11778	12	15	quality	quality	NOUN
ajst-11778	12	16	of	of	ADP
ajst-11778	12	17	product	product	NOUN
ajst-11778	12	18	production	production	NOUN
ajst-11778	12	19	.	.	PUNCT
ajst-11778	13	1	in	in	ADP
ajst-11778	13	2	recent	recent	ADJ
ajst-11778	13	3	years	year	NOUN
ajst-11778	13	4	,	,	PUNCT
ajst-11778	13	5	target	target	NOUN
ajst-11778	13	6	detection	detection	NOUN
ajst-11778	13	7	and	and	CCONJ
ajst-11778	13	8	image	image	NOUN
ajst-11778	13	9	segmentation	segmentation	NOUN
ajst-11778	13	10	based	base	VERB
ajst-11778	13	11	on	on	ADP
ajst-11778	13	12	deep	deep	ADJ
ajst-11778	13	13	learning	learning	NOUN
ajst-11778	13	14	have	have	AUX
ajst-11778	13	15	gradually	gradually	ADV
ajst-11778	13	16	emerged	emerge	VERB
ajst-11778	13	17	.	.	PUNCT
ajst-11778	14	1	target	target	NOUN
ajst-11778	14	2	detection	detection	NOUN
ajst-11778	14	3	algorithms	algorithm	NOUN
ajst-11778	14	4	can	can	AUX
ajst-11778	14	5	be	be	AUX
ajst-11778	14	6	divided	divide	VERB
ajst-11778	14	7	into	into	ADP
ajst-11778	14	8	two	two	NUM
ajst-11778	14	9	categories	category	NOUN
ajst-11778	14	10	:	:	PUNCT
ajst-11778	14	11	one	one	NUM
ajst-11778	14	12	is	be	AUX
ajst-11778	14	13	based	base	VERB
ajst-11778	14	14	on	on	ADP
ajst-11778	14	15	a	a	DET
ajst-11778	14	16	region	region	NOUN
ajst-11778	14	17	-	-	PUNCT
ajst-11778	14	18	based	base	VERB
ajst-11778	14	19	convolutional	convolutional	ADJ
ajst-11778	14	20	neural	neural	ADJ
ajst-11778	14	21	network	network	NOUN
ajst-11778	14	22	(	(	PUNCT
ajst-11778	14	23	rcnn	rcnn	PROPN
ajst-11778	14	24	)	)	PUNCT
ajst-11778	14	25	,	,	PUNCT
ajst-11778	14	26	and	and	CCONJ
ajst-11778	14	27	the	the	DET
ajst-11778	14	28	other	other	ADJ
ajst-11778	14	29	is	be	AUX
ajst-11778	14	30	based	base	VERB
ajst-11778	14	31	on	on	ADP
ajst-11778	14	32	a	a	DET
ajst-11778	14	33	single	single	ADJ
ajst-11778	14	34	stage	stage	NOUN
ajst-11778	14	35	.	.	PUNCT
ajst-11778	15	1	r	r	X
ajst-11778	15	2	-	-	PUNCT
ajst-11778	15	3	cnn	cnn	PROPN
ajst-11778	15	4	uses	use	VERB
ajst-11778	15	5	a	a	DET
ajst-11778	15	6	two	two	NUM
ajst-11778	15	7	-	-	PUNCT
ajst-11778	15	8	stage	stage	NOUN
ajst-11778	15	9	approach	approach	NOUN
ajst-11778	15	10	for	for	ADP
ajst-11778	15	11	target	target	NOUN
ajst-11778	15	12	detection	detection	NOUN
ajst-11778	15	13	.	.	PUNCT
ajst-11778	16	1	specifically	specifically	ADV
ajst-11778	16	2	,	,	PUNCT
ajst-11778	16	3	the	the	DET
ajst-11778	16	4	first	first	ADJ
ajst-11778	16	5	step	step	NOUN
ajst-11778	16	6	is	be	AUX
ajst-11778	16	7	the	the	DET
ajst-11778	16	8	extraction	extraction	NOUN
ajst-11778	16	9	of	of	ADP
ajst-11778	16	10	candidate	candidate	NOUN
ajst-11778	16	11	frames	frame	NOUN
ajst-11778	16	12	in	in	ADP
ajst-11778	16	13	the	the	DET
ajst-11778	16	14	image	image	NOUN
ajst-11778	16	15	,	,	PUNCT
ajst-11778	16	16	which	which	PRON
ajst-11778	16	17	cover	cover	VERB
ajst-11778	16	18	all	all	DET
ajst-11778	16	19	possible	possible	ADJ
ajst-11778	16	20	objects	object	NOUN
ajst-11778	16	21	in	in	ADP
ajst-11778	16	22	the	the	DET
ajst-11778	16	23	image	image	NOUN
ajst-11778	16	24	.	.	PUNCT
ajst-11778	17	1	the	the	DET
ajst-11778	17	2	second	second	ADJ
ajst-11778	17	3	step	step	NOUN
ajst-11778	17	4	is	be	AUX
ajst-11778	17	5	to	to	PART
ajst-11778	17	6	classify	classify	VERB
ajst-11778	17	7	and	and	CCONJ
ajst-11778	17	8	regress	regress	NOUN
ajst-11778	17	9	the	the	DET
ajst-11778	17	10	candidate	candidate	NOUN
ajst-11778	17	11	frames	frame	NOUN
ajst-11778	17	12	before	before	ADP
ajst-11778	17	13	finally	finally	ADV
ajst-11778	17	14	producing	produce	VERB
ajst-11778	17	15	results	result	NOUN
ajst-11778	17	16	.	.	PUNCT
ajst-11778	18	1	commonly	commonly	ADV
ajst-11778	18	2	used	use	VERB
ajst-11778	18	3	models	model	NOUN
ajst-11778	18	4	for	for	ADP
ajst-11778	18	5	r	r	NOUN
ajst-11778	18	6	-	-	PUNCT
ajst-11778	18	7	cnn	cnn	PROPN
ajst-11778	18	8	include	include	VERB
ajst-11778	18	9	faster	fast	ADJ
ajst-11778	18	10	r	r	NOUN
ajst-11778	18	11	-	-	PUNCT
ajst-11778	18	12	cnn	cnn	PROPN
ajst-11778	18	13	and	and	CCONJ
ajst-11778	18	14	mask	mask	VERB
ajst-11778	18	15	r	r	PROPN
ajst-11778	18	16	-	-	PUNCT
ajst-11778	18	17	cnn	cnn	PROPN
ajst-11778	18	18	,	,	PUNCT
ajst-11778	18	19	among	among	ADP
ajst-11778	18	20	others	other	NOUN
ajst-11778	18	21	.	.	PUNCT
ajst-11778	19	1	in	in	ADP
ajst-11778	19	2	contrast	contrast	NOUN
ajst-11778	19	3	,	,	PUNCT
ajst-11778	19	4	single	single	ADJ
ajst-11778	19	5	-	-	PUNCT
ajst-11778	19	6	stage	stage	NOUN
ajst-11778	19	7	target	target	NOUN
ajst-11778	19	8	detection	detection	NOUN
ajst-11778	19	9	algorithms	algorithm	NOUN
ajst-11778	19	10	process	process	NOUN
ajst-11778	19	11	candidate	candidate	NOUN
ajst-11778	19	12	frames	frame	NOUN
ajst-11778	19	13	directly	directly	ADV
ajst-11778	19	14	,	,	PUNCT
ajst-11778	19	15	without	without	ADP
ajst-11778	19	16	dealing	deal	VERB
ajst-11778	19	17	with	with	ADP
ajst-11778	19	18	complex	complex	ADJ
ajst-11778	19	19	upstream	upstream	ADJ
ajst-11778	19	20	and	and	CCONJ
ajst-11778	19	21	downstream	downstream	ADJ
ajst-11778	19	22	work	work	NOUN
ajst-11778	19	23	,	,	PUNCT
ajst-11778	19	24	and	and	CCONJ
ajst-11778	19	25	get	get	VERB
ajst-11778	19	26	the	the	DET
ajst-11778	19	27	aim	aim	NOUN
ajst-11778	19	28	frame	frame	NOUN
ajst-11778	19	29	classification	classification	NOUN
ajst-11778	19	30	and	and	CCONJ
ajst-11778	19	31	regression	regression	NOUN
ajst-11778	19	32	results	result	NOUN
ajst-11778	19	33	in	in	ADP
ajst-11778	19	34	a	a	DET
ajst-11778	19	35	single	single	ADJ
ajst-11778	19	36	forward	forward	ADJ
ajst-11778	19	37	inference	inference	NOUN
ajst-11778	19	38	,	,	PUNCT
ajst-11778	19	39	thus	thus	ADV
ajst-11778	19	40	,	,	PUNCT
ajst-11778	19	41	such	such	ADJ
ajst-11778	19	42	algorithms	algorithm	NOUN
ajst-11778	19	43	are	be	AUX
ajst-11778	19	44	usually	usually	ADV
ajst-11778	19	45	simpler	simple	ADJ
ajst-11778	19	46	and	and	CCONJ
ajst-11778	19	47	more	more	ADV
ajst-11778	19	48	efficient	efficient	ADJ
ajst-11778	19	49	than	than	ADP
ajst-11778	19	50	r	r	NOUN
ajst-11778	19	51	-	-	PUNCT
ajst-11778	19	52	cnns	cnns	ADJ
ajst-11778	19	53	.	.	PUNCT
ajst-11778	20	1	commonly	commonly	ADV
ajst-11778	20	2	used	use	VERB
ajst-11778	20	3	models	model	NOUN
ajst-11778	20	4	for	for	ADP
ajst-11778	20	5	singlestage	singlestage	NOUN
ajst-11778	20	6	target	target	NOUN
ajst-11778	20	7	detection	detection	NOUN
ajst-11778	20	8	algorithms	algorithm	NOUN
ajst-11778	20	9	include	include	VERB
ajst-11778	20	10	the	the	DET
ajst-11778	20	11	yolo	yolo	ADJ
ajst-11778	20	12	family	family	NOUN
ajst-11778	20	13	and	and	CCONJ
ajst-11778	20	14	ssd	ssd	PROPN
ajst-11778	20	15	(	(	PUNCT
ajst-11778	20	16	single	single	ADJ
ajst-11778	20	17	shot	shot	ADJ
ajst-11778	20	18	multi	multi	ADJ
ajst-11778	20	19	-	-	ADJ
ajst-11778	20	20	box	box	NOUN
ajst-11778	20	21	detector	detector	NOUN
ajst-11778	20	22	)	)	PUNCT
ajst-11778	20	23	,	,	PUNCT
ajst-11778	20	24	among	among	ADP
ajst-11778	20	25	others	other	NOUN
ajst-11778	20	26	.	.	PUNCT
ajst-11778	21	1	in	in	ADP
ajst-11778	21	2	general	general	ADJ
ajst-11778	21	3	,	,	PUNCT
ajst-11778	21	4	different	different	ADJ
ajst-11778	21	5	target	target	NOUN
ajst-11778	21	6	detection	detection	NOUN
ajst-11778	21	7	algorithms	algorithm	NOUN
ajst-11778	21	8	are	be	AUX
ajst-11778	21	9	suitable	suitable	ADJ
ajst-11778	21	10	for	for	ADP
ajst-11778	21	11	different	different	ADJ
ajst-11778	21	12	scenarios	scenario	NOUN
ajst-11778	21	13	and	and	CCONJ
ajst-11778	21	14	tasks	task	NOUN
ajst-11778	21	15	.	.	PUNCT
ajst-11778	22	1	the	the	DET
ajst-11778	22	2	single	single	ADJ
ajst-11778	22	3	-	-	PUNCT
ajst-11778	22	4	stage	stage	NOUN
ajst-11778	22	5	approach	approach	NOUN
ajst-11778	22	6	has	have	VERB
ajst-11778	22	7	the	the	DET
ajst-11778	22	8	advantage	advantage	NOUN
ajst-11778	22	9	of	of	ADP
ajst-11778	22	10	faster	fast	ADJ
ajst-11778	22	11	model	model	NOUN
ajst-11778	22	12	inference	inference	NOUN
ajst-11778	22	13	and	and	CCONJ
ajst-11778	22	14	easy	easy	ADJ
ajst-11778	22	15	model	model	NOUN
ajst-11778	22	16	deployment	deployment	NOUN
ajst-11778	22	17	,	,	PUNCT
ajst-11778	22	18	while	while	SCONJ
ajst-11778	22	19	the	the	DET
ajst-11778	22	20	two	two	NUM
ajst-11778	22	21	-	-	PUNCT
ajst-11778	22	22	stage	stage	NOUN
ajst-11778	22	23	approach	approach	NOUN
ajst-11778	22	24	simply	simply	ADV
ajst-11778	22	25	lies	lie	VERB
ajst-11778	22	26	in	in	ADP
ajst-11778	22	27	the	the	DET
ajst-11778	22	28	relatively	relatively	ADV
ajst-11778	22	29	high	high	ADJ
ajst-11778	22	30	detection	detection	NOUN
ajst-11778	22	31	accuracy	accuracy	NOUN
ajst-11778	22	32	.	.	PUNCT
ajst-11778	23	1	2	2	X
ajst-11778	23	2	.	.	X
ajst-11778	23	3	yolov5	yolov5	NOUN
ajst-11778	23	4	algorithm	algorithm	PROPN
ajst-11778	23	5	the	the	DET
ajst-11778	23	6	yolo	yolo	ADJ
ajst-11778	23	7	family	family	NOUN
ajst-11778	23	8	of	of	ADP
ajst-11778	23	9	algorithms	algorithm	NOUN
ajst-11778	23	10	is	be	AUX
ajst-11778	23	11	a	a	DET
ajst-11778	23	12	single	single	ADJ
ajst-11778	23	13	-	-	PUNCT
ajst-11778	23	14	stage	stage	NOUN
ajst-11778	23	15	based	base	VERB
ajst-11778	23	16	target	target	NOUN
ajst-11778	23	17	detection	detection	NOUN
ajst-11778	23	18	algorithm	algorithm	NOUN
ajst-11778	23	19	.	.	PUNCT
ajst-11778	24	1	the	the	DET
ajst-11778	24	2	algorithm	algorithm	NOUN
ajst-11778	24	3	uses	use	VERB
ajst-11778	24	4	a	a	DET
ajst-11778	24	5	regression	regression	NOUN
ajst-11778	24	6	model	model	NOUN
ajst-11778	24	7	to	to	PART
ajst-11778	24	8	tackle	tackle	VERB
ajst-11778	24	9	the	the	DET
ajst-11778	24	10	target	target	NOUN
ajst-11778	24	11	detection	detection	NOUN
ajst-11778	24	12	problem	problem	NOUN
ajst-11778	24	13	and	and	CCONJ
ajst-11778	24	14	aims	aim	VERB
ajst-11778	24	15	to	to	PART
ajst-11778	24	16	achieve	achieve	VERB
ajst-11778	24	17	accurate	accurate	ADJ
ajst-11778	24	18	and	and	CCONJ
ajst-11778	24	19	fast	fast	ADJ
ajst-11778	24	20	determination	determination	NOUN
ajst-11778	24	21	of	of	ADP
ajst-11778	24	22	the	the	DET
ajst-11778	24	23	location	location	NOUN
ajst-11778	24	24	and	and	CCONJ
ajst-11778	24	25	class	class	NOUN
ajst-11778	24	26	of	of	ADP
ajst-11778	24	27	targets	target	NOUN
ajst-11778	24	28	through	through	ADP
ajst-11778	24	29	hierarchical	hierarchical	ADJ
ajst-11778	24	30	feature	feature	NOUN
ajst-11778	24	31	extraction	extraction	NOUN
ajst-11778	24	32	and	and	CCONJ
ajst-11778	24	33	fusion	fusion	NOUN
ajst-11778	24	34	of	of	ADP
ajst-11778	24	35	feature	feature	NOUN
ajst-11778	24	36	maps	map	NOUN
ajst-11778	24	37	.	.	PUNCT
ajst-11778	25	1	through	through	ADP
ajst-11778	25	2	continuous	continuous	ADJ
ajst-11778	25	3	innovation	innovation	NOUN
ajst-11778	25	4	and	and	CCONJ
ajst-11778	25	5	improvement	improvement	NOUN
ajst-11778	25	6	,	,	PUNCT
ajst-11778	25	7	the	the	DET
ajst-11778	25	8	algorithm	algorithm	NOUN
ajst-11778	25	9	has	have	AUX
ajst-11778	25	10	evolved	evolve	VERB
ajst-11778	25	11	to	to	ADP
ajst-11778	25	12	the	the	DET
ajst-11778	25	13	yolov5	yolov5	NOUN
ajst-11778	25	14	version	version	NOUN
ajst-11778	25	15	,	,	PUNCT
ajst-11778	25	16	which	which	PRON
ajst-11778	25	17	is	be	AUX
ajst-11778	25	18	the	the	DET
ajst-11778	25	19	most	most	ADV
ajst-11778	25	20	widely	widely	ADV
ajst-11778	25	21	used	use	VERB
ajst-11778	25	22	and	and	CCONJ
ajst-11778	25	23	has	have	AUX
ajst-11778	25	24	achieved	achieve	VERB
ajst-11778	25	25	dynamic	dynamic	ADJ
ajst-11778	25	26	equilibrium	equilibrium	NOUN
ajst-11778	25	27	in	in	ADP
ajst-11778	25	28	both	both	DET
ajst-11778	25	29	detection	detection	NOUN
ajst-11778	25	30	speed	speed	NOUN
ajst-11778	25	31	and	and	CCONJ
ajst-11778	25	32	accuracy	accuracy	NOUN
ajst-11778	25	33	.	.	PUNCT
ajst-11778	26	1	yolov5s	yolov5s	NOUN
ajst-11778	26	2	is	be	AUX
ajst-11778	26	3	a	a	DET
ajst-11778	26	4	target	target	NOUN
ajst-11778	26	5	detection	detection	NOUN
ajst-11778	26	6	model	model	NOUN
ajst-11778	26	7	whose	whose	DET
ajst-11778	26	8	structure	structure	NOUN
ajst-11778	26	9	consists	consist	VERB
ajst-11778	26	10	of	of	ADP
ajst-11778	26	11	four	four	NUM
ajst-11778	26	12	main	main	ADJ
ajst-11778	26	13	parts	part	NOUN
ajst-11778	26	14	:	:	PUNCT
ajst-11778	26	15	the	the	DET
ajst-11778	26	16	input	input	NOUN
ajst-11778	26	17	,	,	PUNCT
ajst-11778	26	18	backbone	backbone	NOUN
ajst-11778	26	19	network	network	NOUN
ajst-11778	26	20	,	,	PUNCT
ajst-11778	26	21	neck	neck	NOUN
ajst-11778	26	22	feature	feature	NOUN
ajst-11778	26	23	fusion	fusion	NOUN
ajst-11778	26	24	network	network	NOUN
ajst-11778	26	25	,	,	PUNCT
ajst-11778	26	26	and	and	CCONJ
ajst-11778	26	27	head	head	NOUN
ajst-11778	26	28	detection	detection	NOUN
ajst-11778	26	29	head	head	NOUN
ajst-11778	26	30	,	,	PUNCT
ajst-11778	26	31	where	where	SCONJ
ajst-11778	26	32	the	the	DET
ajst-11778	26	33	input	input	NOUN
ajst-11778	26	34	requires	require	VERB
ajst-11778	26	35	pre	pre	ADJ
ajst-11778	26	36	-	-	ADJ
ajst-11778	26	37	processing	processing	NOUN
ajst-11778	26	38	of	of	ADP
ajst-11778	26	39	the	the	DET
ajst-11778	26	40	image	image	NOUN
ajst-11778	26	41	and	and	CCONJ
ajst-11778	26	42	scaling	scale	VERB
ajst-11778	26	43	the	the	DET
ajst-11778	26	44	image	image	NOUN
ajst-11778	26	45	according	accord	VERB
ajst-11778	26	46	to	to	ADP
ajst-11778	26	47	the	the	DET
ajst-11778	26	48	size	size	NOUN
ajst-11778	26	49	of	of	ADP
ajst-11778	26	50	the	the	DET
ajst-11778	26	51	network	network	NOUN
ajst-11778	26	52	input	input	NOUN
ajst-11778	26	53	,	,	PUNCT
ajst-11778	26	54	before	before	ADP
ajst-11778	26	55	finally	finally	ADV
ajst-11778	26	56	performing	perform	VERB
ajst-11778	26	57	the	the	DET
ajst-11778	26	58	normalization	normalization	NOUN
ajst-11778	26	59	operation	operation	NOUN
ajst-11778	26	60	.	.	PUNCT
ajst-11778	27	1	the	the	DET
ajst-11778	27	2	structure	structure	NOUN
ajst-11778	27	3	of	of	ADP
ajst-11778	27	4	the	the	DET
ajst-11778	27	5	yolov5s	yolov5s	PROPN
ajst-11778	27	6	network	network	NOUN
ajst-11778	27	7	is	be	AUX
ajst-11778	27	8	shown	show	VERB
ajst-11778	27	9	in	in	ADP
ajst-11778	27	10	figure	figure	NOUN
ajst-11778	27	11	1	1	NUM
ajst-11778	27	12	.	.	PUNCT
ajst-11778	28	1	the	the	DET
ajst-11778	28	2	algorithm	algorithm	NOUN
ajst-11778	28	3	uses	use	VERB
ajst-11778	28	4	mosaic	mosaic	PROPN
ajst-11778	28	5	to	to	PART
ajst-11778	28	6	enhance	enhance	VERB
ajst-11778	28	7	the	the	DET
ajst-11778	28	8	images	image	NOUN
ajst-11778	28	9	in	in	ADP
ajst-11778	28	10	the	the	DET
ajst-11778	28	11	training	training	NOUN
ajst-11778	28	12	phase	phase	NOUN
ajst-11778	28	13	,	,	PUNCT
ajst-11778	28	14	by	by	ADP
ajst-11778	28	15	cropping	crop	VERB
ajst-11778	28	16	and	and	CCONJ
ajst-11778	28	17	rotating	rotate	VERB
ajst-11778	28	18	four	four	NUM
ajst-11778	28	19	randomly	randomly	ADV
ajst-11778	28	20	selected	select	VERB
ajst-11778	28	21	images	image	NOUN
ajst-11778	28	22	and	and	CCONJ
ajst-11778	28	23	stitching	stitch	VERB
ajst-11778	28	24	them	they	PRON
ajst-11778	28	25	together	together	ADV
ajst-11778	28	26	to	to	ADP
ajst-11778	28	27	a	a	DET
ajst-11778	28	28	specified	specify	VERB
ajst-11778	28	29	resolution	resolution	NOUN
ajst-11778	28	30	size	size	NOUN
ajst-11778	28	31	to	to	PART
ajst-11778	28	32	achieve	achieve	VERB
ajst-11778	28	33	data	datum	NOUN
ajst-11778	28	34	enhancement	enhancement	NOUN
ajst-11778	28	35	;	;	PUNCT
ajst-11778	28	36	in	in	ADP
ajst-11778	28	37	addition	addition	NOUN
ajst-11778	28	38	,	,	PUNCT
ajst-11778	28	39	before	before	SCONJ
ajst-11778	28	40	the	the	DET
ajst-11778	28	41	images	image	NOUN
ajst-11778	28	42	are	be	AUX
ajst-11778	28	43	input	input	NOUN
ajst-11778	28	44	to	to	ADP
ajst-11778	28	45	the	the	DET
ajst-11778	28	46	network	network	NOUN
ajst-11778	28	47	in	in	ADP
ajst-11778	28	48	bulk	bulk	NOUN
ajst-11778	28	49	,	,	PUNCT
ajst-11778	28	50	the	the	DET
ajst-11778	28	51	real	real	ADJ
ajst-11778	28	52	frames	frame	NOUN
ajst-11778	28	53	to	to	PART
ajst-11778	28	54	which	which	PRON
ajst-11778	28	55	the	the	DET
ajst-11778	28	56	dataset	dataset	NOUN
ajst-11778	28	57	belongs	belong	VERB
ajst-11778	28	58	are	be	AUX
ajst-11778	28	59	clustered	cluster	VERB
ajst-11778	28	60	,	,	PUNCT
ajst-11778	28	61	and	and	CCONJ
ajst-11778	28	62	then	then	ADV
ajst-11778	28	63	the	the	DET
ajst-11778	28	64	anchor	anchor	NOUN
ajst-11778	28	65	frames	frame	NOUN
ajst-11778	28	66	are	be	AUX
ajst-11778	28	67	clustered	cluster	VERB
ajst-11778	28	68	using	use	VERB
ajst-11778	28	69	k	k	NOUN
ajst-11778	28	70	-	-	PUNCT
ajst-11778	28	71	means	means	NOUN
ajst-11778	28	72	to	to	PART
ajst-11778	28	73	achieve	achieve	VERB
ajst-11778	28	74	backpropagation	backpropagation	NOUN
ajst-11778	28	75	by	by	ADP
ajst-11778	28	76	adaptively	adaptively	ADV
ajst-11778	28	77	calculating	calculate	VERB
ajst-11778	28	78	the	the	DET
ajst-11778	28	79	anchor	anchor	NOUN
ajst-11778	28	80	frame	frame	NOUN
ajst-11778	28	81	and	and	CCONJ
ajst-11778	28	82	the	the	DET
ajst-11778	28	83	real	real	ADJ
ajst-11778	28	84	frame	frame	NOUN
ajst-11778	28	85	difference	difference	NOUN
ajst-11778	28	86	between	between	ADP
ajst-11778	28	87	the	the	DET
ajst-11778	28	88	anchor	anchor	NOUN
ajst-11778	28	89	frame	frame	NOUN
ajst-11778	28	90	and	and	CCONJ
ajst-11778	28	91	the	the	DET
ajst-11778	28	92	rear	rear	ADJ
ajst-11778	28	93	frame	frame	NOUN
ajst-11778	28	94	to	to	PART
ajst-11778	28	95	achieve	achieve	VERB
ajst-11778	28	96	backpropagation	backpropagation	NOUN
ajst-11778	28	97	.	.	PUNCT
ajst-11778	29	1	67	67	NUM
ajst-11778	29	2	csp2_1	csp2_1	NUM
ajst-11778	29	3	cbs	cbs	PROPN
ajst-11778	29	4	sppf	sppf	PROPN
ajst-11778	29	5	cbs	cbs	PROPN
ajst-11778	29	6	csp1_1	csp1_1	X
ajst-11778	30	1	cbs	cbs	INTJ
ajst-11778	30	2	csp1_3	csp1_3	PUNCT
ajst-11778	30	3	csp1_2	csp1_2	X
ajst-11778	31	1	cbs	cbs	PROPN
ajst-11778	32	1	cbs	cbs	PROPN
ajst-11778	32	2	cbs	cbs	PROPN
ajst-11778	32	3	csp1_1	csp1_1	X
ajst-11778	33	1	cbs	cbs	PROPN
ajst-11778	33	2	unsample	unsample	PROPN
ajst-11778	33	3	unsample	unsample	NOUN
ajst-11778	33	4	csp2_1	csp2_1	PUNCT
ajst-11778	33	5	cbs	cbs	PROPN
ajst-11778	33	6	csp2_1	csp2_1	NUM
ajst-11778	33	7	10	10	NUM
ajst-11778	33	8	11	11	NUM
ajst-11778	33	9	12	12	NUM
ajst-11778	33	10	13	13	NUM
ajst-11778	33	11	14	14	NUM
ajst-11778	33	12	15	15	NUM
ajst-11778	33	13	18	18	NUM
ajst-11778	33	14	19	19	NUM
ajst-11778	33	15	20	20	NUM
ajst-11778	33	16	16	16	NUM
ajst-11778	33	17	17	17	NUM
ajst-11778	33	18	cbs21	cbs21	NOUN
ajst-11778	33	19	22	22	NUM
ajst-11778	33	20	23	23	NUM
ajst-11778	33	21	csp2_1	csp2_1	NUM
ajst-11778	33	22	conv	conv	ADJ
ajst-11778	33	23	conv	conv	PROPN
ajst-11778	33	24	conv	conv	PROPN
ajst-11778	33	25	concat	concat	PROPN
ajst-11778	33	26	concat	concat	PROPN
ajst-11778	33	27	concat	concat	NOUN
ajst-11778	33	28	concat	concat	NOUN
ajst-11778	33	29	640	640	NUM
ajst-11778	33	30	*	*	SYM
ajst-11778	33	31	640	640	NUM
ajst-11778	33	32	*	*	SYM
ajst-11778	33	33	3	3	NUM
ajst-11778	33	34	80	80	NUM
ajst-11778	33	35	*	*	NUM
ajst-11778	33	36	80	80	NUM
ajst-11778	33	37	*	*	NUM
ajst-11778	33	38	255	255	NUM
ajst-11778	33	39	40	40	NUM
ajst-11778	33	40	*	*	NUM
ajst-11778	33	41	40	40	NUM
ajst-11778	33	42	*	*	SYM
ajst-11778	33	43	255	255	NUM
ajst-11778	33	44	20	20	NUM
ajst-11778	33	45	*	*	SYM
ajst-11778	33	46	20	20	NUM
ajst-11778	33	47	*	*	SYM
ajst-11778	33	48	255	255	NUM
ajst-11778	33	49	input	input	NOUN
ajst-11778	33	50	backbone	backbone	NOUN
ajst-11778	33	51	neck	neck	NOUN
ajst-11778	33	52	prediction	prediction	NOUN
ajst-11778	33	53	cbs	cbs	PROPN
ajst-11778	33	54	conv	conv	PROPN
ajst-11778	33	55	silu	silu	PROPN
ajst-11778	33	56	b	b	PROPN
ajst-11778	33	57	n	n	CCONJ
ajst-11778	33	58	bottle	bottle	NOUN
ajst-11778	33	59	neck	neck	NOUN
ajst-11778	33	60	cbs	cbs	PROPN
ajst-11778	33	61	cbs	cbs	PROPN
ajst-11778	33	62	add	add	VERB
ajst-11778	33	63	bottle	bottle	NOUN
ajst-11778	33	64	neck_f	neck_f	NOUN
ajst-11778	33	65	cbs	cbs	PROPN
ajst-11778	34	1	cbs	cbs	PROPN
ajst-11778	34	2	add	add	VERB
ajst-11778	34	3	cbs	cbs	PROPN
ajst-11778	34	4	csp2_x	csp2_x	PROPN
ajst-11778	34	5	sppf	sppf	PROPN
ajst-11778	34	6	cbs	cbs	PROPN
ajst-11778	34	7	max	max	PROPN
ajst-11778	34	8	pool	pool	PROPN
ajst-11778	34	9	max	max	PROPN
ajst-11778	34	10	pool	pool	NOUN
ajst-11778	34	11	max	max	PROPN
ajst-11778	34	12	pool	pool	NOUN
ajst-11778	34	13	concat	concat	NOUN
ajst-11778	34	14	cbs	cbs	NOUN
ajst-11778	34	15	*	*	PUNCT
ajst-11778	34	16	x	x	PUNCT
ajst-11778	34	17	*	*	PUNCT
ajst-11778	34	18	x	x	SYM
ajst-11778	34	19	0	0	NUM
ajst-11778	34	20	1	1	NUM
ajst-11778	34	21	2	2	NUM
ajst-11778	34	22	3	3	NUM
ajst-11778	34	23	4	4	NUM
ajst-11778	34	24	5	5	NUM
ajst-11778	34	25	6	6	NUM
ajst-11778	34	26	7	7	NUM
ajst-11778	34	27	8	8	NUM
ajst-11778	34	28	9	9	NUM
ajst-11778	34	29	csp1_x	csp1_x	ADJ
ajst-11778	34	30	cbs	cbs	PROPN
ajst-11778	34	31	bottle	bottle	NOUN
ajst-11778	34	32	neck	neck	PROPN
ajst-11778	34	33	cbs	cbs	PROPN
ajst-11778	34	34	concat	concat	PROPN
ajst-11778	35	1	cbs	cbs	PROPN
ajst-11778	35	2	cbs	cbs	PROPN
ajst-11778	35	3	bottle	bottle	PROPN
ajst-11778	35	4	neck_f	neck_f	PROPN
ajst-11778	35	5	concat	concat	PROPN
ajst-11778	35	6	cbs	cbs	PROPN
ajst-11778	35	7	figure	figure	NOUN
ajst-11778	35	8	1	1	NUM
ajst-11778	35	9	.	.	PUNCT
ajst-11778	36	1	yolov5	yolov5	NOUN
ajst-11778	36	2	network	network	NOUN
ajst-11778	36	3	structure	structure	NOUN
ajst-11778	36	4	diagram	diagram	VERB
ajst-11778	36	5	the	the	DET
ajst-11778	36	6	backbone	backbone	NOUN
ajst-11778	36	7	network	network	NOUN
ajst-11778	36	8	consists	consist	VERB
ajst-11778	36	9	of	of	ADP
ajst-11778	36	10	a	a	DET
ajst-11778	36	11	series	series	NOUN
ajst-11778	36	12	of	of	ADP
ajst-11778	36	13	convolutional	convolutional	ADJ
ajst-11778	36	14	neural	neural	ADJ
ajst-11778	36	15	networks	network	NOUN
ajst-11778	36	16	used	use	VERB
ajst-11778	36	17	to	to	PART
ajst-11778	36	18	extract	extract	VERB
ajst-11778	36	19	image	image	NOUN
ajst-11778	36	20	features	feature	NOUN
ajst-11778	36	21	,	,	PUNCT
ajst-11778	36	22	mainly	mainly	ADV
ajst-11778	36	23	consisting	consist	VERB
ajst-11778	36	24	of	of	ADP
ajst-11778	36	25	cbs	cbs	PROPN
ajst-11778	36	26	,	,	PUNCT
ajst-11778	36	27	c3	c3	PROPN
ajst-11778	36	28	,	,	PUNCT
ajst-11778	36	29	and	and	CCONJ
ajst-11778	36	30	sppf	sppf	ADJ
ajst-11778	36	31	structures	structure	NOUN
ajst-11778	36	32	.	.	PUNCT
ajst-11778	37	1	yolov5	yolov5	NOUN
ajst-11778	37	2	in	in	ADP
ajst-11778	37	3	version	version	NOUN
ajst-11778	37	4	7.0	7.0	NUM
ajst-11778	37	5	replaces	replace	VERB
ajst-11778	37	6	the	the	DET
ajst-11778	37	7	focus	focus	NOUN
ajst-11778	37	8	module	module	NOUN
ajst-11778	37	9	with	with	ADP
ajst-11778	37	10	a	a	DET
ajst-11778	37	11	convolutional	convolutional	ADJ
ajst-11778	37	12	layer	layer	NOUN
ajst-11778	37	13	of	of	ADP
ajst-11778	37	14	size	size	NOUN
ajst-11778	37	15	66	66	NUM
ajst-11778	37	16	for	for	ADP
ajst-11778	37	17	the	the	DET
ajst-11778	37	18	first	first	ADJ
ajst-11778	37	19	layer	layer	NOUN
ajst-11778	37	20	of	of	ADP
ajst-11778	37	21	the	the	DET
ajst-11778	37	22	network	network	NOUN
ajst-11778	37	23	,	,	PUNCT
ajst-11778	37	24	and	and	CCONJ
ajst-11778	37	25	is	be	AUX
ajst-11778	37	26	an	an	DET
ajst-11778	37	27	optimization	optimization	NOUN
ajst-11778	37	28	of	of	ADP
ajst-11778	37	29	the	the	DET
ajst-11778	37	30	existing	exist	VERB
ajst-11778	37	31	algorithm	algorithm	NOUN
ajst-11778	37	32	,	,	PUNCT
ajst-11778	37	33	using	use	VERB
ajst-11778	37	34	a	a	DET
ajst-11778	37	35	convolutional	convolutional	ADJ
ajst-11778	37	36	layer	layer	NOUN
ajst-11778	37	37	of	of	ADP
ajst-11778	37	38	size	size	NOUN
ajst-11778	37	39	the	the	DET
ajst-11778	37	40	use	use	NOUN
ajst-11778	37	41	of	of	ADP
ajst-11778	37	42	a	a	DET
ajst-11778	37	43	6x6	6x6	NUM
ajst-11778	37	44	convolutional	convolutional	ADJ
ajst-11778	37	45	layer	layer	NOUN
ajst-11778	37	46	is	be	AUX
ajst-11778	37	47	more	more	ADV
ajst-11778	37	48	efficient	efficient	ADJ
ajst-11778	37	49	than	than	ADP
ajst-11778	37	50	using	use	VERB
ajst-11778	37	51	the	the	DET
ajst-11778	37	52	focus	focus	NOUN
ajst-11778	37	53	module	module	NOUN
ajst-11778	37	54	.	.	PUNCT
ajst-11778	38	1	the	the	DET
ajst-11778	38	2	neck	neck	NOUN
ajst-11778	38	3	section	section	NOUN
ajst-11778	38	4	uses	use	VERB
ajst-11778	38	5	a	a	DET
ajst-11778	38	6	"	"	PUNCT
ajst-11778	38	7	double	double	ADJ
ajst-11778	38	8	tower	tower	NOUN
ajst-11778	38	9	structure	structure	NOUN
ajst-11778	38	10	"	"	PUNCT
ajst-11778	38	11	.	.	PUNCT
ajst-11778	39	1	it	it	PRON
ajst-11778	39	2	consists	consist	VERB
ajst-11778	39	3	of	of	ADP
ajst-11778	39	4	a	a	DET
ajst-11778	39	5	feature	feature	NOUN
ajst-11778	39	6	pyramid	pyramid	NOUN
ajst-11778	39	7	network	network	NOUN
ajst-11778	39	8	(	(	PUNCT
ajst-11778	39	9	fpn	fpn	PROPN
ajst-11778	39	10	)	)	PUNCT
ajst-11778	39	11	and	and	CCONJ
ajst-11778	39	12	a	a	DET
ajst-11778	39	13	path	path	NOUN
ajst-11778	39	14	aggregation	aggregation	NOUN
ajst-11778	39	15	network	network	NOUN
ajst-11778	39	16	(	(	PUNCT
ajst-11778	39	17	pan	pan	NOUN
ajst-11778	39	18	)	)	PUNCT
ajst-11778	39	19	.	.	PUNCT
ajst-11778	40	1	localization	localization	NOUN
ajst-11778	40	2	features	feature	VERB
ajst-11778	40	3	to	to	ADP
ajst-11778	40	4	a	a	DET
ajst-11778	40	5	higher	high	ADJ
ajst-11778	40	6	level	level	NOUN
ajst-11778	40	7	.	.	PUNCT
ajst-11778	41	1	the	the	DET
ajst-11778	41	2	combination	combination	NOUN
ajst-11778	41	3	of	of	ADP
ajst-11778	41	4	the	the	DET
ajst-11778	41	5	two	two	NUM
ajst-11778	41	6	further	further	ADJ
ajst-11778	41	7	enhances	enhance	VERB
ajst-11778	41	8	the	the	DET
ajst-11778	41	9	network	network	NOUN
ajst-11778	41	10	's	's	PART
ajst-11778	41	11	ability	ability	NOUN
ajst-11778	41	12	to	to	PART
ajst-11778	41	13	fuse	fuse	VERB
ajst-11778	41	14	features	feature	NOUN
ajst-11778	41	15	and	and	CCONJ
ajst-11778	41	16	obtain	obtain	VERB
ajst-11778	41	17	richer	rich	ADJ
ajst-11778	41	18	feature	feature	NOUN
ajst-11778	41	19	information	information	NOUN
ajst-11778	41	20	.	.	PUNCT
ajst-11778	42	1	the	the	DET
ajst-11778	42	2	head	head	NOUN
ajst-11778	42	3	is	be	AUX
ajst-11778	42	4	the	the	DET
ajst-11778	42	5	network	network	NOUN
ajst-11778	42	6	detection	detection	NOUN
ajst-11778	42	7	head	head	NOUN
ajst-11778	42	8	section	section	NOUN
ajst-11778	42	9	of	of	ADP
ajst-11778	42	10	yolov5	yolov5	NOUN
ajst-11778	42	11	,	,	PUNCT
ajst-11778	42	12	which	which	PRON
ajst-11778	42	13	contains	contain	VERB
ajst-11778	42	14	three	three	NUM
ajst-11778	42	15	detect	detect	NOUN
ajst-11778	42	16	detectors	detector	NOUN
ajst-11778	42	17	.	.	PUNCT
ajst-11778	43	1	firstly	firstly	ADV
ajst-11778	43	2	,	,	PUNCT
ajst-11778	43	3	for	for	ADP
ajst-11778	43	4	the	the	DET
ajst-11778	43	5	input	input	NOUN
ajst-11778	43	6	image	image	NOUN
ajst-11778	43	7	with	with	ADP
ajst-11778	43	8	a	a	DET
ajst-11778	43	9	resolution	resolution	NOUN
ajst-11778	43	10	of	of	ADP
ajst-11778	43	11	640640	640640	NUM
ajst-11778	43	12	,	,	PUNCT
ajst-11778	43	13	the	the	DET
ajst-11778	43	14	feature	feature	NOUN
ajst-11778	43	15	extraction	extraction	NOUN
ajst-11778	43	16	network	network	NOUN
ajst-11778	43	17	is	be	AUX
ajst-11778	43	18	used	use	VERB
ajst-11778	43	19	to	to	PART
ajst-11778	43	20	extract	extract	VERB
ajst-11778	43	21	features	feature	NOUN
ajst-11778	43	22	from	from	ADP
ajst-11778	43	23	the	the	DET
ajst-11778	43	24	input	input	NOUN
ajst-11778	43	25	image	image	NOUN
ajst-11778	43	26	to	to	PART
ajst-11778	43	27	obtain	obtain	VERB
ajst-11778	43	28	three	three	NUM
ajst-11778	43	29	types	type	NOUN
ajst-11778	43	30	of	of	ADP
ajst-11778	43	31	feature	feature	NOUN
ajst-11778	43	32	maps	map	NOUN
ajst-11778	43	33	of	of	ADP
ajst-11778	43	34	different	different	ADJ
ajst-11778	43	35	sizes	size	NOUN
ajst-11778	43	36	,	,	PUNCT
ajst-11778	43	37	which	which	PRON
ajst-11778	43	38	are	be	AUX
ajst-11778	43	39	used	use	VERB
ajst-11778	43	40	to	to	PART
ajst-11778	43	41	predict	predict	VERB
ajst-11778	43	42	large	large	ADJ
ajst-11778	43	43	,	,	PUNCT
ajst-11778	43	44	medium	medium	ADJ
ajst-11778	43	45	,	,	PUNCT
ajst-11778	43	46	and	and	CCONJ
ajst-11778	43	47	small	small	ADJ
ajst-11778	43	48	targets	target	NOUN
ajst-11778	43	49	respectively	respectively	ADV
ajst-11778	43	50	.	.	PUNCT
ajst-11778	44	1	a	a	DET
ajst-11778	44	2	diagram	diagram	NOUN
ajst-11778	44	3	of	of	ADP
ajst-11778	44	4	the	the	DET
ajst-11778	44	5	prediction	prediction	NOUN
ajst-11778	44	6	box	box	NOUN
ajst-11778	44	7	is	be	AUX
ajst-11778	44	8	shown	show	VERB
ajst-11778	44	9	in	in	ADP
ajst-11778	44	10	figure	figure	NOUN
ajst-11778	44	11	2	2	NUM
ajst-11778	44	12	.	.	PUNCT
ajst-11778	45	1	the	the	DET
ajst-11778	45	2	small	small	ADJ
ajst-11778	45	3	black	black	ADJ
ajst-11778	45	4	grid	grid	NOUN
ajst-11778	45	5	(	(	PUNCT
ajst-11778	45	6	c	c	NOUN
ajst-11778	45	7	x	x	PROPN
ajst-11778	45	8	,	,	PUNCT
ajst-11778	45	9	cy	cy	PROPN
ajst-11778	45	10	)	)	PUNCT
ajst-11778	45	11	is	be	AUX
ajst-11778	45	12	the	the	DET
ajst-11778	45	13	standard	standard	ADJ
ajst-11778	45	14	aiming	aim	VERB
ajst-11778	45	15	frame	frame	NOUN
ajst-11778	45	16	,	,	PUNCT
ajst-11778	45	17	the	the	DET
ajst-11778	45	18	blue	blue	ADJ
ajst-11778	45	19	part	part	NOUN
ajst-11778	45	20	is	be	AUX
ajst-11778	45	21	the	the	DET
ajst-11778	45	22	prediction	prediction	NOUN
ajst-11778	45	23	frame	frame	NOUN
ajst-11778	45	24	,	,	PUNCT
ajst-11778	45	25	and	and	CCONJ
ajst-11778	45	26	the	the	DET
ajst-11778	45	27	dotted	dotted	ADJ
ajst-11778	45	28	line	line	NOUN
ajst-11778	45	29	is	be	AUX
ajst-11778	45	30	the	the	DET
ajst-11778	45	31	a	a	DET
ajst-11778	45	32	priori	priori	ADJ
ajst-11778	45	33	frame	frame	NOUN
ajst-11778	45	34	to	to	PART
ajst-11778	45	35	be	be	AUX
ajst-11778	45	36	adjusted	adjust	VERB
ajst-11778	45	37	,	,	PUNCT
ajst-11778	45	38	whose	whose	DET
ajst-11778	45	39	height	height	NOUN
ajst-11778	45	40	and	and	CCONJ
ajst-11778	45	41	width	width	NOUN
ajst-11778	45	42	are	be	AUX
ajst-11778	45	43	h	h	NOUN
ajst-11778	45	44	p	p	NOUN
ajst-11778	45	45	and	and	CCONJ
ajst-11778	45	46	w	w	PROPN
ajst-11778	45	47	p	p	NOUN
ajst-11778	45	48	,	,	PUNCT
ajst-11778	45	49	and	and	CCONJ
ajst-11778	45	50	the	the	DET
ajst-11778	45	51	offset	offset	NOUN
ajst-11778	45	52	of	of	ADP
ajst-11778	45	53	the	the	DET
ajst-11778	45	54	prediction	prediction	NOUN
ajst-11778	45	55	with	with	ADP
ajst-11778	45	56	respect	respect	NOUN
ajst-11778	45	57	to	to	ADP
ajst-11778	45	58	the	the	DET
ajst-11778	45	59	anchor	anchor	NOUN
ajst-11778	45	60	frame	frame	NOUN
ajst-11778	45	61	is	be	AUX
ajst-11778	45	62	w	w	PROPN
ajst-11778	45	63	t	t	PROPN
ajst-11778	45	64	and	and	CCONJ
ajst-11778	45	65	h	h	PROPN
ajst-11778	45	66	t	t	PROPN
ajst-11778	45	67	,	,	PUNCT
ajst-11778	45	68	from	from	ADP
ajst-11778	45	69	equation	equation	NOUN
ajst-11778	45	70	(	(	PUNCT
ajst-11778	45	71	1	1	NUM
ajst-11778	45	72	)	)	PUNCT
ajst-11778	45	73	,	,	PUNCT
ajst-11778	45	74	we	we	PRON
ajst-11778	45	75	can	can	AUX
ajst-11778	45	76	obtain	obtain	VERB
ajst-11778	45	77	the	the	DET
ajst-11778	45	78	prediction	prediction	NOUN
ajst-11778	45	79	frame	frame	NOUN
ajst-11778	45	80	,	,	PUNCT
ajst-11778	45	81	w	w	PROPN
ajst-11778	45	82	b	b	PROPN
ajst-11778	45	83	,	,	PUNCT
ajst-11778	45	84	h	h	PROPN
ajst-11778	45	85	b	b	PROPN
ajst-11778	45	86	,	,	PUNCT
ajst-11778	45	87	y	y	PROPN
ajst-11778	45	88	b	b	PROPN
ajst-11778	45	89	and	and	CCONJ
ajst-11778	45	90	x	x	PROPN
ajst-11778	45	91	b	b	PROPN
ajst-11778	45	92	.	.	PUNCT
ajst-11778	46	1	h	h	NOUN
ajst-11778	47	1	x	x	PUNCT
ajst-11778	47	2	y	y	NOUN
ajst-11778	47	3	y	y	PROPN
ajst-11778	47	4	2	2	NUM
ajst-11778	47	5	w	w	PROPN
ajst-11778	47	6	w	w	PROPN
ajst-11778	47	7	w	w	PROPN
ajst-11778	47	8	2	2	NUM
ajst-11778	47	9	x	x	SYM
ajst-11778	47	10	h	h	NOUN
ajst-11778	47	11	h	h	NOUN
ajst-11778	47	12	2	2	NUM
ajst-11778	47	13	(	(	PUNCT
ajst-11778	47	14	)	)	PUNCT
ajst-11778	47	15	0.5	0.5	NUM
ajst-11778	47	16	2	2	NUM
ajst-11778	47	17	(	(	PUNCT
ajst-11778	47	18	)	)	PUNCT
ajst-11778	47	19	0.5	0.5	NUM
ajst-11778	47	20	4	4	NUM
ajst-11778	47	21	(	(	PUNCT
ajst-11778	47	22	)	)	PUNCT
ajst-11778	47	23	4	4	NUM
ajst-11778	47	24	(	(	PUNCT
ajst-11778	47	25	)	)	PUNCT
ajst-11778	47	26	b	b	NOUN
ajst-11778	48	1	t	t	NOUN
ajst-11778	48	2	p	p	X
ajst-11778	48	3	t	t	PROPN
ajst-11778	48	4	b	b	PROPN
ajst-11778	48	5	t	t	PROPN
ajst-11778	48	6	b	b	PROPN
ajst-11778	48	7	t	t	PROPN
ajst-11778	48	8	b	b	PROPN
ajst-11778	48	9	p	p	PRON
ajst-11778	48	10			PROPN
ajst-11778	48	11			X
ajst-11778	48	12			X
ajst-11778	48	13			X
ajst-11778	48	14			PROPN
ajst-11778	48	15			PROPN
ajst-11778	48	16			PROPN
ajst-11778	48	17			PROPN
ajst-11778	48	18			VERB
ajst-11778	48	19			NOUN
ajst-11778	48	20			PROPN
ajst-11778	48	21			PROPN
ajst-11778	48	22			PROPN
ajst-11778	48	23			PROPN
ajst-11778	48	24			PROPN
ajst-11778	48	25			PROPN
ajst-11778	48	26			ADP
ajst-11778	48	27			NUM
ajst-11778	48	28			NUM
ajst-11778	48	29			NUM
ajst-11778	48	30			NUM
ajst-11778	48	31			NUM
ajst-11778	48	32			PROPN
ajst-11778	48	33	(	(	PUNCT
ajst-11778	48	34	1	1	NUM
ajst-11778	48	35	)	)	PUNCT
ajst-11778	48	36	cx	cx	NOUN
ajst-11778	48	37	cy	cy	INTJ
ajst-11778	48	38	ph	ph	PROPN
ajst-11778	48	39	bw	bw	PROPN
ajst-11778	48	40	bh	bh	PROPN
ajst-11778	48	41	σ(tx	σ(tx	PROPN
ajst-11778	48	42	)	)	PUNCT
ajst-11778	48	43	σ(ty	σ(ty	NOUN
ajst-11778	48	44	)	)	PUNCT
ajst-11778	48	45	pw	pw	PROPN
ajst-11778	48	46	figure	figure	NOUN
ajst-11778	48	47	2	2	NUM
ajst-11778	48	48	.	.	NOUN
ajst-11778	48	49	prediction	prediction	NOUN
ajst-11778	48	50	box	box	PROPN
ajst-11778	48	51	diagram	diagram	PROPN
ajst-11778	48	52	3	3	NUM
ajst-11778	48	53	.	.	PUNCT
ajst-11778	48	54	improved	improve	VERB
ajst-11778	48	55	to	to	ADP
ajst-11778	48	56	the	the	DET
ajst-11778	48	57	yolov5	yolov5	NOUN
ajst-11778	48	58	algorithm	algorithm	NOUN
ajst-11778	48	59	(	(	PUNCT
ajst-11778	48	60	1	1	NUM
ajst-11778	48	61	)	)	PUNCT
ajst-11778	48	62	optimal	optimal	ADJ
ajst-11778	48	63	design	design	NOUN
ajst-11778	48	64	of	of	ADP
ajst-11778	48	65	the	the	DET
ajst-11778	48	66	a	a	DET
ajst-11778	48	67	priori	priori	ADJ
ajst-11778	48	68	frame	frame	NOUN
ajst-11778	48	69	the	the	DET
ajst-11778	48	70	selection	selection	NOUN
ajst-11778	48	71	of	of	ADP
ajst-11778	48	72	suitable	suitable	ADJ
ajst-11778	48	73	prior	prior	ADJ
ajst-11778	48	74	frames	frame	NOUN
ajst-11778	48	75	plays	play	VERB
ajst-11778	48	76	a	a	DET
ajst-11778	48	77	key	key	ADJ
ajst-11778	48	78	role	role	NOUN
ajst-11778	48	79	in	in	ADP
ajst-11778	48	80	improving	improve	VERB
ajst-11778	48	81	the	the	DET
ajst-11778	48	82	training	training	NOUN
ajst-11778	48	83	effect	effect	NOUN
ajst-11778	48	84	of	of	ADP
ajst-11778	48	85	the	the	DET
ajst-11778	48	86	network	network	NOUN
ajst-11778	48	87	.	.	PUNCT
ajst-11778	49	1	the	the	DET
ajst-11778	49	2	yolov5s	yolov5s	PROPN
ajst-11778	49	3	network	network	NOUN
ajst-11778	49	4	model	model	NOUN
ajst-11778	49	5	uses	use	VERB
ajst-11778	49	6	k	k	NOUN
ajst-11778	49	7	-	-	PUNCT
ajst-11778	49	8	means	mean	VERB
ajst-11778	49	9	clustering	clustering	NOUN
ajst-11778	49	10	and	and	CCONJ
ajst-11778	49	11	uses	use	VERB
ajst-11778	49	12	three	three	NUM
ajst-11778	49	13	types	type	NOUN
ajst-11778	49	14	of	of	ADP
ajst-11778	49	15	aiming	aim	VERB
ajst-11778	49	16	frames	frame	NOUN
ajst-11778	49	17	to	to	PART
ajst-11778	49	18	predict	predict	VERB
ajst-11778	49	19	three	three	NUM
ajst-11778	49	20	types	type	NOUN
ajst-11778	49	21	of	of	ADP
ajst-11778	49	22	detection	detection	NOUN
ajst-11778	49	23	heads	head	NOUN
ajst-11778	49	24	according	accord	VERB
ajst-11778	49	25	to	to	ADP
ajst-11778	49	26	three	three	NUM
ajst-11778	49	27	sizes	size	NOUN
ajst-11778	49	28	:	:	PUNCT
ajst-11778	49	29	large	large	ADJ
ajst-11778	49	30	,	,	PUNCT
ajst-11778	49	31	medium	medium	ADJ
ajst-11778	49	32	,	,	PUNCT
ajst-11778	49	33	and	and	CCONJ
ajst-11778	49	34	small	small	ADJ
ajst-11778	49	35	.	.	PUNCT
ajst-11778	50	1	the	the	DET
ajst-11778	50	2	method	method	NOUN
ajst-11778	50	3	is	be	AUX
ajst-11778	50	4	based	base	VERB
ajst-11778	50	5	on	on	ADP
ajst-11778	50	6	the	the	DET
ajst-11778	50	7	coco	coco	PROPN
ajst-11778	50	8	dataset	dataset	NOUN
ajst-11778	50	9	for	for	ADP
ajst-11778	50	10	clustering	clustering	NOUN
ajst-11778	50	11	,	,	PUNCT
ajst-11778	50	12	and	and	CCONJ
ajst-11778	50	13	although	although	SCONJ
ajst-11778	50	14	there	there	PRON
ajst-11778	50	15	is	be	VERB
ajst-11778	50	16	good	good	ADJ
ajst-11778	50	17	generalisability	generalisability	NOUN
ajst-11778	50	18	in	in	ADP
ajst-11778	50	19	common	common	ADJ
ajst-11778	50	20	target	target	NOUN
ajst-11778	50	21	detection	detection	NOUN
ajst-11778	50	22	tasks	task	NOUN
ajst-11778	50	23	,	,	PUNCT
ajst-11778	50	24	there	there	PRON
ajst-11778	50	25	is	be	VERB
ajst-11778	50	26	still	still	ADV
ajst-11778	50	27	some	some	DET
ajst-11778	50	28	error	error	NOUN
ajst-11778	50	29	in	in	ADP
ajst-11778	50	30	using	use	VERB
ajst-11778	50	31	the	the	DET
ajst-11778	50	32	method	method	NOUN
ajst-11778	50	33	for	for	ADP
ajst-11778	50	34	small	small	ADJ
ajst-11778	50	35	target	target	NOUN
ajst-11778	50	36	identification	identification	NOUN
ajst-11778	50	37	in	in	ADP
ajst-11778	50	38	complex	complex	ADJ
ajst-11778	50	39	scenarios	scenario	NOUN
ajst-11778	50	40	,	,	PUNCT
ajst-11778	50	41	which	which	PRON
ajst-11778	50	42	affects	affect	VERB
ajst-11778	50	43	the	the	DET
ajst-11778	50	44	detection	detection	NOUN
ajst-11778	50	45	accuracy	accuracy	NOUN
ajst-11778	50	46	of	of	ADP
ajst-11778	50	47	the	the	DET
ajst-11778	50	48	model	model	NOUN
ajst-11778	50	49	.	.	PUNCT
ajst-11778	51	1	therefore	therefore	ADV
ajst-11778	51	2	,	,	PUNCT
ajst-11778	51	3	in	in	ADP
ajst-11778	51	4	order	order	NOUN
ajst-11778	51	5	to	to	PART
ajst-11778	51	6	improve	improve	VERB
ajst-11778	51	7	the	the	DET
ajst-11778	51	8	accuracy	accuracy	NOUN
ajst-11778	51	9	of	of	ADP
ajst-11778	51	10	small	small	ADJ
ajst-11778	51	11	target	target	NOUN
ajst-11778	51	12	detection	detection	NOUN
ajst-11778	51	13	of	of	ADP
ajst-11778	51	14	strip	strip	NOUN
ajst-11778	51	15	defects	defect	NOUN
ajst-11778	51	16	and	and	CCONJ
ajst-11778	51	17	reduce	reduce	VERB
ajst-11778	51	18	the	the	DET
ajst-11778	51	19	error	error	NOUN
ajst-11778	51	20	caused	cause	VERB
ajst-11778	51	21	by	by	ADP
ajst-11778	51	22	the	the	DET
ajst-11778	51	23	a	a	DET
ajst-11778	51	24	priori	priori	ADJ
ajst-11778	51	25	frame	frame	NOUN
ajst-11778	51	26	size	size	NOUN
ajst-11778	51	27	,	,	PUNCT
ajst-11778	51	28	the	the	DET
ajst-11778	51	29	dataset	dataset	NOUN
ajst-11778	51	30	was	be	AUX
ajst-11778	51	31	re	re	VERB
ajst-11778	51	32	-	-	VERB
ajst-11778	51	33	clustered	clustered	ADJ
ajst-11778	51	34	using	use	VERB
ajst-11778	51	35	k	k	NOUN
ajst-11778	51	36	-	-	PUNCT
ajst-11778	51	37	means++	means++	PROPN
ajst-11778	51	38	,	,	PUNCT
ajst-11778	51	39	and	and	CCONJ
ajst-11778	51	40	the	the	DET
ajst-11778	51	41	clustering	clustering	ADJ
ajst-11778	51	42	results	result	NOUN
ajst-11778	51	43	are	be	AUX
ajst-11778	51	44	shown	show	VERB
ajst-11778	51	45	in	in	ADP
ajst-11778	51	46	table	table	NOUN
ajst-11778	51	47	1	1	NUM
ajst-11778	51	48	.	.	PUNCT
ajst-11778	51	49	table	table	NOUN
ajst-11778	51	50	1	1	NUM
ajst-11778	51	51	.	.	PUNCT
ajst-11778	52	1	k	k	X
ajst-11778	52	2	-	-	PUNCT
ajst-11778	52	3	means++	means++	NOUN
ajst-11778	52	4	generate	generate	VERB
ajst-11778	52	5	a	a	DET
ajst-11778	52	6	priori	priori	ADJ
ajst-11778	52	7	boxes	box	NOUN
ajst-11778	52	8	特征图	特征图	VERB
ajst-11778	52	9	感受野	感受野	X
ajst-11778	52	10	achor	achor	PROPN
ajst-11778	52	11	88	88	PROPN
ajst-11778	52	12	大	大	PROPN
ajst-11778	52	13	(	(	PUNCT
ajst-11778	52	14	165,85	165,85	NUM
ajst-11778	52	15	)	)	PUNCT
ajst-11778	52	16	,	,	PUNCT
ajst-11778	52	17	(	(	PUNCT
ajst-11778	52	18	91,189	91,189	NUM
ajst-11778	52	19	)	)	PUNCT
ajst-11778	52	20	,	,	PUNCT
ajst-11778	52	21	(	(	PUNCT
ajst-11778	52	22	217,221	217,221	NUM
ajst-11778	52	23	)	)	PUNCT
ajst-11778	52	24	1616	1616	NUM
ajst-11778	52	25	中	中	NOUN
ajst-11778	52	26	(	(	PUNCT
ajst-11778	52	27	59,51	59,51	NOUN
ajst-11778	52	28	)	)	PUNCT
ajst-11778	52	29	,	,	PUNCT
ajst-11778	52	30	(	(	PUNCT
ajst-11778	52	31	72,95	72,95	NUM
ajst-11778	52	32	)	)	PUNCT
ajst-11778	52	33	,	,	PUNCT
ajst-11778	52	34	(	(	PUNCT
ajst-11778	52	35	209,33	209,33	NUM
ajst-11778	52	36	)	)	PUNCT
ajst-11778	53	1	3232	3232	NUM
ajst-11778	53	2	小	小	SYM
ajst-11778	53	3	(	(	PUNCT
ajst-11778	53	4	23,49	23,49	NOUN
ajst-11778	53	5	)	)	PUNCT
ajst-11778	53	6	,	,	PUNCT
ajst-11778	53	7	(	(	PUNCT
ajst-11778	53	8	30,95	30,95	NOUN
ajst-11778	53	9	)	)	PUNCT
ajst-11778	53	10	,	,	PUNCT
ajst-11778	53	11	(	(	PUNCT
ajst-11778	53	12	59,51	59,51	NOUN
ajst-11778	53	13	)	)	PUNCT
ajst-11778	53	14	the	the	DET
ajst-11778	53	15	initial	initial	ADJ
ajst-11778	53	16	points	point	NOUN
ajst-11778	53	17	of	of	ADP
ajst-11778	53	18	the	the	DET
ajst-11778	53	19	k	k	ADJ
ajst-11778	53	20	-	-	PUNCT
ajst-11778	53	21	means++	means++	ADJ
ajst-11778	53	22	algorithm	algorithm	NOUN
ajst-11778	53	23	are	be	AUX
ajst-11778	53	24	selected	select	VERB
ajst-11778	53	25	randomly	randomly	ADV
ajst-11778	53	26	from	from	ADP
ajst-11778	53	27	the	the	DET
ajst-11778	53	28	whole	whole	ADJ
ajst-11778	53	29	data	datum	NOUN
ajst-11778	53	30	set	set	VERB
ajst-11778	53	31	,	,	PUNCT
ajst-11778	53	32	thus	thus	ADV
ajst-11778	53	33	jumping	jump	VERB
ajst-11778	53	34	out	out	ADP
ajst-11778	53	35	of	of	ADP
ajst-11778	53	36	the	the	DET
ajst-11778	53	37	initial	initial	ADJ
ajst-11778	53	38	clusters	cluster	NOUN
ajst-11778	53	39	,	,	PUNCT
ajst-11778	53	40	which	which	PRON
ajst-11778	53	41	makes	make	VERB
ajst-11778	53	42	the	the	DET
ajst-11778	53	43	algorithm	algorithm	NOUN
ajst-11778	53	44	have	have	VERB
ajst-11778	53	45	a	a	DET
ajst-11778	53	46	high	high	ADJ
ajst-11778	53	47	probability	probability	NOUN
ajst-11778	53	48	of	of	ADP
ajst-11778	53	49	jumping	jump	VERB
ajst-11778	53	50	out	out	ADP
ajst-11778	53	51	of	of	ADP
ajst-11778	53	52	the	the	DET
ajst-11778	53	53	local	local	ADJ
ajst-11778	53	54	optimal	optimal	ADJ
ajst-11778	53	55	solution	solution	NOUN
ajst-11778	53	56	,	,	PUNCT
ajst-11778	53	57	thus	thus	ADV
ajst-11778	53	58	obtaining	obtain	VERB
ajst-11778	53	59	the	the	DET
ajst-11778	53	60	global	global	ADJ
ajst-11778	53	61	optimal	optimal	ADJ
ajst-11778	53	62	solution	solution	NOUN
ajst-11778	53	63	during	during	ADP
ajst-11778	53	64	the	the	DET
ajst-11778	53	65	iterative	iterative	NOUN
ajst-11778	53	66	process	process	NOUN
ajst-11778	53	67	,	,	PUNCT
ajst-11778	53	68	and	and	CCONJ
ajst-11778	53	69	its	its	PRON
ajst-11778	53	70	specific	specific	ADJ
ajst-11778	53	71	computational	computational	ADJ
ajst-11778	53	72	steps	step	NOUN
ajst-11778	53	73	are	be	AUX
ajst-11778	53	74	as	as	SCONJ
ajst-11778	53	75	follows	follow	VERB
ajst-11778	53	76	:	:	PUNCT
ajst-11778	53	77	1	1	X
ajst-11778	53	78	)	)	PUNCT
ajst-11778	53	79	given	give	VERB
ajst-11778	53	80	p	p	NOUN
ajst-11778	53	81	candidate	candidate	NOUN
ajst-11778	53	82	frames	frame	NOUN
ajst-11778	53	83	;	;	PUNCT
ajst-11778	53	84	2	2	X
ajst-11778	53	85	)	)	PUNCT
ajst-11778	53	86	randomly	randomly	ADV
ajst-11778	53	87	pick	pick	VERB
ajst-11778	53	88	a	a	DET
ajst-11778	53	89	candidate	candidate	NOUN
ajst-11778	53	90	frame	frame	NOUN
ajst-11778	53	91	as	as	ADP
ajst-11778	53	92	the	the	DET
ajst-11778	53	93	center	center	NOUN
ajst-11778	53	94	of	of	ADP
ajst-11778	53	95	the	the	DET
ajst-11778	53	96	first	first	ADJ
ajst-11778	53	97	cluster	cluster	NOUN
ajst-11778	53	98	;	;	PUNCT
ajst-11778	53	99	3	3	X
ajst-11778	53	100	)	)	PUNCT
ajst-11778	53	101	from	from	ADP
ajst-11778	53	102	the	the	DET
ajst-11778	53	103	remaining	remain	VERB
ajst-11778	53	104	p	p	NOUN
ajst-11778	53	105	　	　	SPACE
ajst-11778	53	106	1	1	NUM
ajst-11778	53	107	candidate	candidate	NOUN
ajst-11778	53	108	boxes	box	NOUN
ajst-11778	53	109	,	,	PUNCT
ajst-11778	53	110	select	select	VERB
ajst-11778	53	111	the	the	DET
ajst-11778	53	112	candidate	candidate	NOUN
ajst-11778	53	113	box	box	NOUN
ajst-11778	53	114	that	that	PRON
ajst-11778	53	115	is	be	AUX
ajst-11778	53	116	farthest	farth	ADJ
ajst-11778	53	117	from	from	ADP
ajst-11778	53	118	the	the	DET
ajst-11778	53	119	first	first	ADJ
ajst-11778	53	120	cluster	cluster	NOUN
ajst-11778	53	121	center	center	NOUN
ajst-11778	53	122	d	d	X
ajst-11778	53	123	68	68	NUM
ajst-11778	53	124	(	(	PUNCT
ajst-11778	53	125	a	a	DET
ajst-11778	53	126	,	,	PUNCT
ajst-11778	53	127	b	b	NOUN
ajst-11778	53	128	)	)	PUNCT
ajst-11778	53	129	is	be	AUX
ajst-11778	53	130	the	the	DET
ajst-11778	53	131	largest	large	ADJ
ajst-11778	53	132	)	)	PUNCT
ajst-11778	53	133	,	,	PUNCT
ajst-11778	53	134	and	and	CCONJ
ajst-11778	53	135	this	this	DET
ajst-11778	53	136	candidate	candidate	NOUN
ajst-11778	53	137	box	box	NOUN
ajst-11778	53	138	is	be	AUX
ajst-11778	53	139	used	use	VERB
ajst-11778	53	140	as	as	ADP
ajst-11778	53	141	the	the	DET
ajst-11778	53	142	second	second	ADJ
ajst-11778	53	143	cluster	cluster	NOUN
ajst-11778	53	144	center	center	NOUN
ajst-11778	53	145	.	.	PUNCT
ajst-11778	54	1	4	4	X
ajst-11778	54	2	)	)	PUNCT
ajst-11778	54	3	repeat	repeat	VERB
ajst-11778	54	4	the	the	DET
ajst-11778	54	5	above	above	ADJ
ajst-11778	54	6	steps	step	NOUN
ajst-11778	54	7	,	,	PUNCT
ajst-11778	54	8	and	and	CCONJ
ajst-11778	54	9	finally	finally	ADV
ajst-11778	54	10	select	select	VERB
ajst-11778	54	11	q	q	NOUN
ajst-11778	54	12	cluster	cluster	NOUN
ajst-11778	54	13	centers	center	NOUN
ajst-11778	54	14	from	from	ADP
ajst-11778	54	15	p	p	NOUN
ajst-11778	54	16	candidate	candidate	NOUN
ajst-11778	54	17	boxes	box	NOUN
ajst-11778	54	18	;	;	PUNCT
ajst-11778	54	19	5	5	X
ajst-11778	54	20	)	)	PUNCT
ajst-11778	54	21	replace	replace	VERB
ajst-11778	54	22	the	the	DET
ajst-11778	54	23	initial	initial	ADJ
ajst-11778	54	24	point	point	NOUN
ajst-11778	54	25	of	of	ADP
ajst-11778	54	26	the	the	DET
ajst-11778	54	27	k	k	NOUN
ajst-11778	54	28	-	-	PUNCT
ajst-11778	54	29	means	means	NOUN
ajst-11778	54	30	algorithm	algorithm	NOUN
ajst-11778	54	31	with	with	ADP
ajst-11778	54	32	the	the	DET
ajst-11778	54	33	selected	select	VERB
ajst-11778	54	34	q	q	NOUN
ajst-11778	54	35	cluster	cluster	NOUN
ajst-11778	54	36	cores	core	NOUN
ajst-11778	54	37	,	,	PUNCT
ajst-11778	54	38	and	and	CCONJ
ajst-11778	54	39	select	select	VERB
ajst-11778	54	40	q	q	ADJ
ajst-11778	54	41	anchor	anchor	NOUN
ajst-11778	54	42	boxes	box	NOUN
ajst-11778	54	43	according	accord	VERB
ajst-11778	54	44	to	to	ADP
ajst-11778	54	45	the	the	DET
ajst-11778	54	46	algorithm	algorithm	NOUN
ajst-11778	54	47	process	process	NOUN
ajst-11778	54	48	.	.	PUNCT
ajst-11778	55	1	where	where	SCONJ
ajst-11778	55	2	the	the	DET
ajst-11778	55	3	expression	expression	NOUN
ajst-11778	55	4	of	of	ADP
ajst-11778	55	5	d(a	d(a	PROPN
ajst-11778	55	6	,	,	PUNCT
ajst-11778	55	7	b	b	NOUN
ajst-11778	55	8	)	)	PUNCT
ajst-11778	55	9	is	be	AUX
ajst-11778	55	10	:	:	PUNCT
ajst-11778	55	11	(	(	PUNCT
ajst-11778	55	12	,	,	PUNCT
ajst-11778	55	13	)	)	PUNCT
ajst-11778	55	14	(	(	PUNCT
ajst-11778	55	15	,	,	PUNCT
ajst-11778	55	16	)	)	PUNCT
ajst-11778	55	17	1	1	NUM
ajst-11778	55	18	1	1	NUM
ajst-11778	55	19	a	a	DET
ajst-11778	55	20	b	b	NOUN
ajst-11778	55	21	a	a	DET
ajst-11778	55	22	b	b	NOUN
ajst-11778	55	23	d	d	PROPN
ajst-11778	55	24	d	d	PROPN
ajst-11778	55	25	a	a	DET
ajst-11778	55	26	b	b	NOUN
ajst-11778	55	27	iou	iou	VERB
ajst-11778	55	28	a	a	DET
ajst-11778	55	29	b	b	PROPN
ajst-11778	55	30			PUNCT
ajst-11778	55	31			PROPN
ajst-11778	55	32			PROPN
ajst-11778	55	33			PROPN
ajst-11778	55	34			PROPN
ajst-11778	55	35			PROPN
ajst-11778	55	36			NOUN
ajst-11778	55	37	(	(	PUNCT
ajst-11778	55	38	2	2	NUM
ajst-11778	55	39	)	)	PUNCT
ajst-11778	55	40	(	(	PUNCT
ajst-11778	55	41	2	2	X
ajst-11778	55	42	)	)	PUNCT
ajst-11778	55	43	introduction	introduction	NOUN
ajst-11778	55	44	of	of	ADP
ajst-11778	55	45	simam	simam	NOUN
ajst-11778	55	46	's	's	PART
ajst-11778	55	47	non	non	ADJ
ajst-11778	55	48	-	-	ADJ
ajst-11778	55	49	referential	referential	ADJ
ajst-11778	55	50	attention	attention	NOUN
ajst-11778	55	51	mechanism	mechanism	NOUN
ajst-11778	55	52	since	since	SCONJ
ajst-11778	55	53	strip	strip	NOUN
ajst-11778	55	54	defects	defect	NOUN
ajst-11778	55	55	exist	exist	VERB
ajst-11778	55	56	in	in	ADP
ajst-11778	55	57	images	image	NOUN
ajst-11778	55	58	with	with	ADP
ajst-11778	55	59	low	low	ADJ
ajst-11778	55	60	pixels	pixel	NOUN
ajst-11778	55	61	and	and	CCONJ
ajst-11778	55	62	are	be	AUX
ajst-11778	55	63	prone	prone	ADJ
ajst-11778	55	64	to	to	ADP
ajst-11778	55	65	information	information	NOUN
ajst-11778	55	66	loss	loss	NOUN
ajst-11778	55	67	,	,	PUNCT
ajst-11778	55	68	the	the	DET
ajst-11778	55	69	simam	simam	ADJ
ajst-11778	55	70	non	non	ADJ
ajst-11778	55	71	-	-	ADJ
ajst-11778	55	72	parametric	parametric	ADJ
ajst-11778	55	73	attention	attention	NOUN
ajst-11778	55	74	mechanism	mechanism	NOUN
ajst-11778	55	75	is	be	AUX
ajst-11778	55	76	introduced	introduce	VERB
ajst-11778	55	77	into	into	ADP
ajst-11778	55	78	the	the	DET
ajst-11778	55	79	yolov5s	yolov5s	PROPN
ajst-11778	55	80	neural	neural	ADJ
ajst-11778	55	81	network	network	NOUN
ajst-11778	55	82	model	model	NOUN
ajst-11778	55	83	to	to	PART
ajst-11778	55	84	enhance	enhance	VERB
ajst-11778	55	85	the	the	DET
ajst-11778	55	86	attention	attention	NOUN
ajst-11778	55	87	of	of	ADP
ajst-11778	55	88	the	the	DET
ajst-11778	55	89	detected	detect	VERB
ajst-11778	55	90	object	object	NOUN
ajst-11778	55	91	and	and	CCONJ
ajst-11778	55	92	improve	improve	VERB
ajst-11778	55	93	the	the	DET
ajst-11778	55	94	target	target	NOUN
ajst-11778	55	95	detection	detection	NOUN
ajst-11778	55	96	accuracy	accuracy	NOUN
ajst-11778	55	97	by	by	ADP
ajst-11778	55	98	extracting	extract	VERB
ajst-11778	55	99	feature	feature	NOUN
ajst-11778	55	100	information	information	NOUN
ajst-11778	55	101	in	in	ADP
ajst-11778	55	102	the	the	DET
ajst-11778	55	103	image	image	NOUN
ajst-11778	55	104	.	.	PUNCT
ajst-11778	56	1	simam	simam	PROPN
ajst-11778	56	2	is	be	AUX
ajst-11778	56	3	a	a	DET
ajst-11778	56	4	novel	novel	ADJ
ajst-11778	56	5	nonparametric	nonparametric	NOUN
ajst-11778	56	6	3d	3d	NUM
ajst-11778	56	7	attention	attention	NOUN
ajst-11778	56	8	module	module	NOUN
ajst-11778	56	9	,	,	PUNCT
ajst-11778	56	10	and	and	CCONJ
ajst-11778	56	11	its	its	PRON
ajst-11778	56	12	research	research	NOUN
ajst-11778	56	13	is	be	AUX
ajst-11778	56	14	based	base	VERB
ajst-11778	56	15	on	on	ADP
ajst-11778	56	16	the	the	DET
ajst-11778	56	17	human	human	ADJ
ajst-11778	56	18	brain	brain	NOUN
ajst-11778	56	19	attention	attention	NOUN
ajst-11778	56	20	mechanism	mechanism	NOUN
ajst-11778	56	21	characteristics	characteristic	NOUN
ajst-11778	56	22	.	.	PUNCT
ajst-11778	57	1	the	the	DET
ajst-11778	57	2	module	module	NOUN
ajst-11778	57	3	adopts	adopt	VERB
ajst-11778	57	4	feature	feature	NOUN
ajst-11778	57	5	maps	map	NOUN
ajst-11778	57	6	as	as	ADP
ajst-11778	57	7	an	an	DET
ajst-11778	57	8	important	important	ADJ
ajst-11778	57	9	means	mean	NOUN
ajst-11778	57	10	to	to	PART
ajst-11778	57	11	assign	assign	VERB
ajst-11778	57	12	uniform	uniform	ADJ
ajst-11778	57	13	weights	weight	NOUN
ajst-11778	57	14	to	to	ADP
ajst-11778	57	15	3d	3d	NOUN
ajst-11778	57	16	attention	attention	NOUN
ajst-11778	57	17	and	and	CCONJ
ajst-11778	57	18	aims	aim	VERB
ajst-11778	57	19	to	to	PART
ajst-11778	57	20	enhance	enhance	VERB
ajst-11778	57	21	the	the	DET
ajst-11778	57	22	feature	feature	NOUN
ajst-11778	57	23	extraction	extraction	NOUN
ajst-11778	57	24	capability	capability	NOUN
ajst-11778	57	25	of	of	ADP
ajst-11778	57	26	the	the	DET
ajst-11778	57	27	model	model	NOUN
ajst-11778	57	28	.	.	PUNCT
ajst-11778	58	1	unlike	unlike	ADP
ajst-11778	58	2	traditional	traditional	ADJ
ajst-11778	58	3	attention	attention	NOUN
ajst-11778	58	4	modules	module	NOUN
ajst-11778	58	5	,	,	PUNCT
ajst-11778	58	6	simam	simam	NOUN
ajst-11778	58	7	does	do	AUX
ajst-11778	58	8	not	not	PART
ajst-11778	58	9	introduce	introduce	VERB
ajst-11778	58	10	additional	additional	ADJ
ajst-11778	58	11	parameters	parameter	NOUN
ajst-11778	58	12	and	and	CCONJ
ajst-11778	58	13	has	have	VERB
ajst-11778	58	14	the	the	DET
ajst-11778	58	15	advantage	advantage	NOUN
ajst-11778	58	16	of	of	ADP
ajst-11778	58	17	being	be	AUX
ajst-11778	58	18	lightweight	lightweight	ADJ
ajst-11778	58	19	compared	compare	VERB
ajst-11778	58	20	to	to	ADP
ajst-11778	58	21	existing	exist	VERB
ajst-11778	58	22	channel	channel	NOUN
ajst-11778	58	23	and	and	CCONJ
ajst-11778	58	24	null	null	ADJ
ajst-11778	58	25	-	-	PUNCT
ajst-11778	58	26	field	field	NOUN
ajst-11778	58	27	attention	attention	NOUN
ajst-11778	58	28	modules	module	NOUN
ajst-11778	58	29	.	.	PUNCT
ajst-11778	59	1	the	the	DET
ajst-11778	59	2	distribution	distribution	NOUN
ajst-11778	59	3	of	of	ADP
ajst-11778	59	4	assigned	assign	VERB
ajst-11778	59	5	3d	3d	NUM
ajst-11778	59	6	attention	attention	NOUN
ajst-11778	59	7	weights	weight	NOUN
ajst-11778	59	8	is	be	AUX
ajst-11778	59	9	shown	show	VERB
ajst-11778	59	10	in	in	ADP
ajst-11778	59	11	figure	figure	NOUN
ajst-11778	59	12	3	3	NUM
ajst-11778	59	13	.	.	PUNCT
ajst-11778	59	14	simam	simam	PROPN
ajst-11778	59	15	gives	give	VERB
ajst-11778	59	16	a	a	DET
ajst-11778	59	17	measure	measure	NOUN
ajst-11778	59	18	of	of	ADP
ajst-11778	59	19	linear	linear	PROPN
ajst-11778	59	20	differentiability	differentiability	NOUN
ajst-11778	59	21	among	among	ADP
ajst-11778	59	22	neurons	neuron	NOUN
ajst-11778	59	23	to	to	PART
ajst-11778	59	24	evaluate	evaluate	VERB
ajst-11778	59	25	the	the	DET
ajst-11778	59	26	importance	importance	NOUN
ajst-11778	59	27	of	of	ADP
ajst-11778	59	28	each	each	DET
ajst-11778	59	29	neuron	neuron	NOUN
ajst-11778	59	30	.	.	PUNCT
ajst-11778	60	1	x	x	X
ajst-11778	60	2	c	c	NOUN
ajst-11778	60	3	h	h	NOUN
ajst-11778	60	4	w	w	NOUN
ajst-11778	60	5	fusion	fusion	NOUN
ajst-11778	60	6	3	3	NUM
ajst-11778	60	7	-	-	SYM
ajst-11778	60	8	d	d	NOUN
ajst-11778	60	9	weights	weight	NOUN
ajst-11778	60	10	c	c	PROPN
ajst-11778	60	11	w	w	PROPN
ajst-11778	60	12	h	h	NOUN
ajst-11778	60	13	figure	figure	NOUN
ajst-11778	60	14	3	3	NUM
ajst-11778	60	15	.	.	X
ajst-11778	60	16	3d	3d	NUM
ajst-11778	60	17	attention	attention	NOUN
ajst-11778	60	18	weighting	weighting	NOUN
ajst-11778	60	19	simulating	simulate	VERB
ajst-11778	60	20	the	the	DET
ajst-11778	60	21	neuronal	neuronal	ADJ
ajst-11778	60	22	properties	property	NOUN
ajst-11778	60	23	,	,	PUNCT
ajst-11778	60	24	the	the	DET
ajst-11778	60	25	final	final	ADJ
ajst-11778	60	26	energy	energy	NOUN
ajst-11778	60	27	function	function	NOUN
ajst-11778	60	28	is	be	AUX
ajst-11778	60	29	defined	define	VERB
ajst-11778	60	30	as	as	ADP
ajst-11778	60	31	:	:	SYM
ajst-11778	60	32	1	1	NUM
ajst-11778	60	33	2	2	NUM
ajst-11778	60	34	2	2	NUM
ajst-11778	60	35	2	2	NUM
ajst-11778	60	36	1	1	NUM
ajst-11778	60	37	1	1	NUM
ajst-11778	60	38	(	(	PUNCT
ajst-11778	60	39	,	,	PUNCT
ajst-11778	60	40	,	,	PUNCT
ajst-11778	60	41	,	,	PUNCT
ajst-11778	60	42	)	)	PUNCT
ajst-11778	60	43	(	(	PUNCT
ajst-11778	60	44	1	1	NUM
ajst-11778	60	45	(	(	PUNCT
ajst-11778	60	46	)	)	PUNCT
ajst-11778	60	47	)	)	PUNCT
ajst-11778	60	48	(	(	PUNCT
ajst-11778	60	49	1	1	NUM
ajst-11778	60	50	(	(	PUNCT
ajst-11778	60	51	)	)	PUNCT
ajst-11778	60	52	)	)	PUNCT
ajst-11778	61	1	1	1	NUM
ajst-11778	61	2	m	m	NOUN
ajst-11778	61	3	t	t	NOUN
ajst-11778	61	4	t	t	PROPN
ajst-11778	61	5	t	t	PROPN
ajst-11778	61	6	t	t	PROPN
ajst-11778	62	1	i	i	PRON
ajst-11778	62	2	t	t	PROPN
ajst-11778	62	3	t	t	X
ajst-11778	62	4	t	t	X
ajst-11778	62	5	ti	ti	INTJ
ajst-11778	63	1	i	i	NOUN
ajst-11778	63	2	e	e	PROPN
ajst-11778	63	3	w	w	PROPN
ajst-11778	63	4	b	b	PROPN
ajst-11778	63	5	y	y	PROPN
ajst-11778	63	6	x	x	SYM
ajst-11778	63	7	wx	wx	PROPN
ajst-11778	63	8	b	b	PROPN
ajst-11778	63	9	w	w	PROPN
ajst-11778	63	10	b	b	PROPN
ajst-11778	63	11	w	w	PROPN
ajst-11778	63	12	m	m	PROPN
ajst-11778	63	13	t	t	PROPN
ajst-11778	63	14			ADJ
ajst-11778	63	15			PROPN
ajst-11778	63	16			PROPN
ajst-11778	63	17			PROPN
ajst-11778	63	18			PROPN
ajst-11778	63	19			PROPN
ajst-11778	63	20			ADV
ajst-11778	63	21			PUNCT
ajst-11778	63	22			PROPN
ajst-11778	63	23			ADV
ajst-11778	63	24			PUNCT
ajst-11778	63	25			X
ajst-11778	63	26			X
ajst-11778	63	27	(	(	PUNCT
ajst-11778	63	28	3	3	X
ajst-11778	63	29	)	)	PUNCT
ajst-11778	63	30	t	t	NOUN
ajst-11778	63	31	and	and	CCONJ
ajst-11778	63	32	ix	ix	PROPN
ajst-11778	63	33	are	be	AUX
ajst-11778	63	34	the	the	DET
ajst-11778	63	35	target	target	NOUN
ajst-11778	63	36	neurons	neuron	NOUN
ajst-11778	63	37	and	and	CCONJ
ajst-11778	63	38	other	other	ADJ
ajst-11778	63	39	neurons	neuron	NOUN
ajst-11778	63	40	of	of	ADP
ajst-11778	63	41	the	the	DET
ajst-11778	63	42	input	input	NOUN
ajst-11778	63	43	features	feature	NOUN
ajst-11778	63	44	,	,	PUNCT
ajst-11778	63	45	respectively	respectively	ADV
ajst-11778	63	46	,	,	PUNCT
ajst-11778	63	47	tw	tw	PRON
ajst-11778	63	48	and	and	CCONJ
ajst-11778	63	49	tb	tb	X
ajst-11778	63	50	are	be	AUX
ajst-11778	63	51	the	the	DET
ajst-11778	63	52	weights	weight	NOUN
ajst-11778	63	53	and	and	CCONJ
ajst-11778	63	54	deviations	deviation	NOUN
ajst-11778	63	55	of	of	ADP
ajst-11778	63	56	the	the	DET
ajst-11778	63	57	linear	linear	ADJ
ajst-11778	63	58	variation	variation	NOUN
ajst-11778	63	59	of	of	ADP
ajst-11778	63	60	a	a	DET
ajst-11778	63	61	neuron	neuron	NOUN
ajst-11778	63	62	,	,	PUNCT
ajst-11778	63	63	and	and	CCONJ
ajst-11778	63	64	i	i	PRON
ajst-11778	63	65	are	be	AUX
ajst-11778	63	66	indexed	index	VERB
ajst-11778	63	67	in	in	ADP
ajst-11778	63	68	the	the	DET
ajst-11778	63	69	spatial	spatial	ADJ
ajst-11778	63	70	dimension	dimension	NOUN
ajst-11778	63	71	.	.	PUNCT
ajst-11778	64	1	m	m	VERB
ajst-11778	65	1	=	=	NOUN
ajst-11778	65	2	h	h	NOUN
ajst-11778	66	1	w	w	NOUN
ajst-11778	66	2	is	be	AUX
ajst-11778	66	3	the	the	DET
ajst-11778	66	4	number	number	NOUN
ajst-11778	66	5	of	of	ADP
ajst-11778	66	6	all	all	DET
ajst-11778	66	7	neurons	neuron	NOUN
ajst-11778	66	8	on	on	ADP
ajst-11778	66	9	a	a	DET
ajst-11778	66	10	channel	channel	NOUN
ajst-11778	66	11	.	.	PUNCT
ajst-11778	67	1	the	the	DET
ajst-11778	67	2	minimum	minimum	ADJ
ajst-11778	67	3	energy	energy	NOUN
ajst-11778	67	4	equation	equation	NOUN
ajst-11778	67	5	can	can	AUX
ajst-11778	67	6	be	be	AUX
ajst-11778	67	7	found	find	VERB
ajst-11778	67	8	by	by	ADP
ajst-11778	67	9	taking	take	VERB
ajst-11778	67	10	the	the	DET
ajst-11778	67	11	partial	partial	ADJ
ajst-11778	67	12	derivatives	derivative	NOUN
ajst-11778	67	13	of	of	ADP
ajst-11778	67	14	tw	tw	NOUN
ajst-11778	67	15	and	and	CCONJ
ajst-11778	67	16	tb	tb	ADP
ajst-11778	67	17	substituting	substitute	VERB
ajst-11778	67	18	the	the	DET
ajst-11778	67	19	original	original	ADJ
ajst-11778	67	20	energy	energy	NOUN
ajst-11778	67	21	function	function	NOUN
ajst-11778	67	22	.	.	PUNCT
ajst-11778	68	1	2	2	NUM
ajst-11778	68	2	2	2	NUM
ajst-11778	68	3	*	*	SYM
ajst-11778	68	4	2	2	NUM
ajst-11778	68	5	ˆ4	ˆ4	NOUN
ajst-11778	68	6	(	(	PUNCT
ajst-11778	68	7	)	)	PUNCT
ajst-11778	68	8	ˆ	ˆ	PRON
ajst-11778	68	9	ˆ	ˆ	PROPN
ajst-11778	68	10	(	(	PUNCT
ajst-11778	68	11	)	)	PUNCT
ajst-11778	68	12	2	2	NUM
ajst-11778	68	13	2	2	NUM
ajst-11778	68	14	t	t	NOUN
ajst-11778	68	15	e	e	X
ajst-11778	68	16	t	t	PROPN
ajst-11778	68	17			PROPN
ajst-11778	68	18			ADJ
ajst-11778	68	19			NOUN
ajst-11778	68	20			PROPN
ajst-11778	68	21			X
ajst-11778	68	22			X
ajst-11778	68	23			PROPN
ajst-11778	68	24			PROPN
ajst-11778	68	25			PUNCT
ajst-11778	68	26			X
ajst-11778	68	27	(	(	PUNCT
ajst-11778	68	28	4	4	NUM
ajst-11778	68	29	)	)	PUNCT
ajst-11778	68	30	as	as	SCONJ
ajst-11778	68	31	its	its	PRON
ajst-11778	68	32	energy	energy	NOUN
ajst-11778	68	33	decreases	decrease	VERB
ajst-11778	68	34	,	,	PUNCT
ajst-11778	68	35	the	the	DET
ajst-11778	68	36	linear	linear	ADJ
ajst-11778	68	37	separation	separation	NOUN
ajst-11778	68	38	between	between	ADP
ajst-11778	68	39	the	the	DET
ajst-11778	68	40	neuron	neuron	NOUN
ajst-11778	68	41	t	t	PROPN
ajst-11778	68	42	and	and	CCONJ
ajst-11778	68	43	other	other	ADJ
ajst-11778	68	44	neurons	neuron	NOUN
ajst-11778	68	45	increases	increase	NOUN
ajst-11778	68	46	,	,	PUNCT
ajst-11778	68	47	and	and	CCONJ
ajst-11778	68	48	its	its	PRON
ajst-11778	68	49	importance	importance	NOUN
ajst-11778	68	50	increases	increase	NOUN
ajst-11778	68	51	.	.	PUNCT
ajst-11778	69	1	therefore	therefore	ADV
ajst-11778	69	2	,	,	PUNCT
ajst-11778	69	3	the	the	DET
ajst-11778	69	4	importance	importance	NOUN
ajst-11778	69	5	of	of	ADP
ajst-11778	69	6	a	a	DET
ajst-11778	69	7	neuron	neuron	NOUN
ajst-11778	69	8	can	can	AUX
ajst-11778	69	9	be	be	AUX
ajst-11778	69	10	expressed	express	VERB
ajst-11778	69	11	by	by	ADP
ajst-11778	69	12	1	1	NUM
ajst-11778	69	13	te	te	PROPN
ajst-11778	69	14			PROPN
ajst-11778	69	15	and	and	CCONJ
ajst-11778	69	16	finally	finally	ADV
ajst-11778	69	17	its	its	PRON
ajst-11778	69	18	features	feature	NOUN
ajst-11778	69	19	are	be	AUX
ajst-11778	69	20	augmented	augment	VERB
ajst-11778	69	21	to	to	PART
ajst-11778	69	22	obtain	obtain	VERB
ajst-11778	69	23	the	the	DET
ajst-11778	69	24	processed	process	VERB
ajst-11778	69	25	feature	feature	NOUN
ajst-11778	69	26	tensor	tensor	NOUN
ajst-11778	69	27	x	x	NOUN
ajst-11778	69	28	�	�	PROPN
ajst-11778	69	29	.	.	PUNCT
ajst-11778	70	1	1	1	NUM
ajst-11778	70	2	x=	x=	NUM
ajst-11778	70	3	(	(	PUNCT
ajst-11778	70	4	)	)	PUNCT
ajst-11778	70	5	sigmoid	sigmoid	NOUN
ajst-11778	70	6	x	x	PART
ajst-11778	70	7	e	e	X
ajst-11778	70	8	�	�	PROPN
ajst-11778	70	9	�	�	PROPN
ajst-11778	70	10	(	(	PUNCT
ajst-11778	70	11	5	5	NUM
ajst-11778	70	12	)	)	PUNCT
ajst-11778	70	13	where	where	SCONJ
ajst-11778	70	14	e	e	NOUN
ajst-11778	70	15	is	be	AUX
ajst-11778	70	16	the	the	DET
ajst-11778	70	17	set	set	NOUN
ajst-11778	70	18	of	of	ADP
ajst-11778	70	19	all	all	DET
ajst-11778	70	20	channels	channel	NOUN
ajst-11778	70	21	and	and	CCONJ
ajst-11778	70	22	spatial	spatial	ADJ
ajst-11778	70	23	dimensions	dimension	NOUN
ajst-11778	70	24	in	in	ADP
ajst-11778	70	25	te	te	PROPN
ajst-11778	70	26			PROPN
ajst-11778	70	27	.	.	PUNCT
ajst-11778	71	1	(	(	PUNCT
ajst-11778	71	2	3	3	X
ajst-11778	71	3	)	)	PUNCT
ajst-11778	71	4	decoupling	decouple	VERB
ajst-11778	71	5	headers	header	NOUN
ajst-11778	71	6	design	design	NOUN
ajst-11778	71	7	from	from	ADP
ajst-11778	71	8	yolov6	yolov6	PROPN
ajst-11778	71	9	,	,	PUNCT
ajst-11778	71	10	it	it	PRON
ajst-11778	71	11	is	be	AUX
ajst-11778	71	12	known	know	VERB
ajst-11778	71	13	that	that	SCONJ
ajst-11778	71	14	the	the	DET
ajst-11778	71	15	decoupled	decouple	VERB
ajst-11778	71	16	head	head	NOUN
ajst-11778	71	17	structure	structure	NOUN
ajst-11778	71	18	can	can	AUX
ajst-11778	71	19	take	take	VERB
ajst-11778	71	20	into	into	ADP
ajst-11778	71	21	account	account	NOUN
ajst-11778	71	22	the	the	DET
ajst-11778	71	23	difference	difference	NOUN
ajst-11778	71	24	in	in	ADP
ajst-11778	71	25	the	the	DET
ajst-11778	71	26	content	content	NOUN
ajst-11778	71	27	of	of	ADP
ajst-11778	71	28	target	target	NOUN
ajst-11778	71	29	detection	detection	NOUN
ajst-11778	71	30	and	and	CCONJ
ajst-11778	71	31	semantic	semantic	ADJ
ajst-11778	71	32	segmentation	segmentation	NOUN
ajst-11778	71	33	,	,	PUNCT
ajst-11778	71	34	where	where	SCONJ
ajst-11778	71	35	target	target	NOUN
ajst-11778	71	36	detection	detection	NOUN
ajst-11778	71	37	focuses	focus	VERB
ajst-11778	71	38	on	on	ADP
ajst-11778	71	39	the	the	DET
ajst-11778	71	40	edge	edge	NOUN
ajst-11778	71	41	information	information	NOUN
ajst-11778	71	42	of	of	ADP
ajst-11778	71	43	the	the	DET
ajst-11778	71	44	target	target	NOUN
ajst-11778	71	45	,	,	PUNCT
ajst-11778	71	46	while	while	SCONJ
ajst-11778	71	47	semantic	semantic	ADJ
ajst-11778	71	48	segmentation	segmentation	NOUN
ajst-11778	71	49	focuses	focus	VERB
ajst-11778	71	50	on	on	ADP
ajst-11778	71	51	understanding	understand	VERB
ajst-11778	71	52	the	the	DET
ajst-11778	71	53	pixel	pixel	PROPN
ajst-11778	71	54	content	content	NOUN
ajst-11778	71	55	of	of	ADP
ajst-11778	71	56	the	the	DET
ajst-11778	71	57	object	object	NOUN
ajst-11778	71	58	.	.	PUNCT
ajst-11778	72	1	therefore	therefore	ADV
ajst-11778	72	2	,	,	PUNCT
ajst-11778	72	3	an	an	DET
ajst-11778	72	4	efficient	efficient	ADJ
ajst-11778	72	5	decoupled	decouple	VERB
ajst-11778	72	6	detection	detection	NOUN
ajst-11778	72	7	head	head	NOUN
ajst-11778	72	8	with	with	ADP
ajst-11778	72	9	a	a	DET
ajst-11778	72	10	hybrid	hybrid	ADJ
ajst-11778	72	11	channel	channel	NOUN
ajst-11778	72	12	strategy	strategy	NOUN
ajst-11778	72	13	can	can	AUX
ajst-11778	72	14	further	far	ADV
ajst-11778	72	15	reduce	reduce	VERB
ajst-11778	72	16	the	the	DET
ajst-11778	72	17	computational	computational	ADJ
ajst-11778	72	18	cost	cost	NOUN
ajst-11778	72	19	,	,	PUNCT
ajst-11778	72	20	achieve	achieve	VERB
ajst-11778	72	21	lower	low	ADJ
ajst-11778	72	22	inference	inference	NOUN
ajst-11778	72	23	latency	latency	NOUN
ajst-11778	72	24	,	,	PUNCT
ajst-11778	72	25	train	train	NOUN
ajst-11778	72	26	and	and	CCONJ
ajst-11778	72	27	adjust	adjust	VERB
ajst-11778	72	28	the	the	DET
ajst-11778	72	29	network	network	NOUN
ajst-11778	72	30	model	model	NOUN
ajst-11778	72	31	faster	fast	ADV
ajst-11778	72	32	in	in	ADP
ajst-11778	72	33	order	order	NOUN
ajst-11778	72	34	to	to	PART
ajst-11778	72	35	make	make	VERB
ajst-11778	72	36	it	it	PRON
ajst-11778	72	37	converge	converge	VERB
ajst-11778	72	38	to	to	ADP
ajst-11778	72	39	the	the	DET
ajst-11778	72	40	optimal	optimal	ADJ
ajst-11778	72	41	state	state	NOUN
ajst-11778	72	42	faster	fast	ADV
ajst-11778	72	43	and	and	CCONJ
ajst-11778	72	44	improve	improve	VERB
ajst-11778	72	45	the	the	DET
ajst-11778	72	46	accuracy	accuracy	NOUN
ajst-11778	72	47	and	and	CCONJ
ajst-11778	72	48	precision	precision	NOUN
ajst-11778	72	49	of	of	ADP
ajst-11778	72	50	the	the	DET
ajst-11778	72	51	model	model	NOUN
ajst-11778	72	52	recognition	recognition	NOUN
ajst-11778	72	53	,	,	PUNCT
ajst-11778	72	54	which	which	PRON
ajst-11778	72	55	effectively	effectively	ADV
ajst-11778	72	56	improves	improve	VERB
ajst-11778	72	57	the	the	DET
ajst-11778	72	58	strip	strip	NOUN
ajst-11778	72	59	defect	defect	NOUN
ajst-11778	72	60	recognition	recognition	NOUN
ajst-11778	72	61	.	.	PUNCT
ajst-11778	73	1	the	the	DET
ajst-11778	73	2	structure	structure	NOUN
ajst-11778	73	3	diagram	diagram	NOUN
ajst-11778	73	4	of	of	ADP
ajst-11778	73	5	the	the	DET
ajst-11778	73	6	decoupled	decouple	VERB
ajst-11778	73	7	head	head	NOUN
ajst-11778	73	8	is	be	AUX
ajst-11778	73	9	shown	show	VERB
ajst-11778	73	10	in	in	ADP
ajst-11778	73	11	figure	figure	NOUN
ajst-11778	73	12	4	4	NUM
ajst-11778	73	13	.	.	PUNCT
ajst-11778	74	1	h	h	PROPN
ajst-11778	74	2	w	w	PROPN
ajst-11778	74	3	{128	{128	PROPN
ajst-11778	74	4	,	,	PUNCT
ajst-11778	74	5	256	256	NUM
ajst-11778	74	6	,	,	PUNCT
ajst-11778	74	7	512	512	NUM
ajst-11778	74	8	}	}	PUNCT
ajst-11778	74	9	h	h	NOUN
ajst-11778	74	10	w	w	PROPN
ajst-11778	74	11	c	c	PROPN
ajst-11778	74	12	cls	cls	NOUN
ajst-11778	74	13	.	.	PUNCT
ajst-11778	75	1	h	h	PROPN
ajst-11778	75	2	w	w	PROPN
ajst-11778	76	1	4	4	PROPN
ajst-11778	76	2	h	h	PROPN
ajst-11778	76	3	w	w	PROPN
ajst-11778	76	4	1	1	PROPN
ajst-11778	76	5	obj	obj	PROPN
ajst-11778	76	6	.	.	PUNCT
ajst-11778	77	1	reg	reg	PROPN
ajst-11778	77	2	.	.	PROPN
ajst-11778	78	1	1	1	NUM
ajst-11778	78	2	1	1	PROPN
ajst-11778	78	3	conv	conv	NOUN
ajst-11778	78	4	3	3	NUM
ajst-11778	78	5	3	3	PROPN
ajst-11778	78	6	convfeature	convfeature	ADJ
ajst-11778	78	7	figure	figure	NOUN
ajst-11778	78	8	4	4	NUM
ajst-11778	78	9	.	.	PUNCT
ajst-11778	78	10	decoupled	decouple	VERB
ajst-11778	78	11	head	head	NOUN
ajst-11778	78	12	structure	structure	NOUN
ajst-11778	78	13	diagram	diagram	NOUN
ajst-11778	78	14	4	4	NUM
ajst-11778	78	15	.	.	PUNCT
ajst-11778	78	16	experimental	experimental	ADJ
ajst-11778	78	17	results	result	NOUN
ajst-11778	78	18	and	and	CCONJ
ajst-11778	78	19	analysis	analysis	NOUN
ajst-11778	78	20	(	(	PUNCT
ajst-11778	78	21	1	1	X
ajst-11778	78	22	)	)	PUNCT
ajst-11778	78	23	dataset	dataset	VERB
ajst-11778	78	24	the	the	DET
ajst-11778	78	25	neu	neu	PROPN
ajst-11778	78	26	-	-	PUNCT
ajst-11778	78	27	det	det	PROPN
ajst-11778	78	28	dataset	dataset	NOUN
ajst-11778	78	29	used	use	VERB
ajst-11778	78	30	in	in	ADP
ajst-11778	78	31	this	this	DET
ajst-11778	78	32	study	study	NOUN
ajst-11778	78	33	is	be	AUX
ajst-11778	78	34	published	publish	VERB
ajst-11778	78	35	by	by	ADP
ajst-11778	78	36	northeastern	northeastern	ADJ
ajst-11778	78	37	university	university	NOUN
ajst-11778	78	38	,	,	PUNCT
ajst-11778	78	39	which	which	PRON
ajst-11778	78	40	is	be	AUX
ajst-11778	78	41	a	a	DET
ajst-11778	78	42	classical	classical	ADJ
ajst-11778	78	43	image	image	NOUN
ajst-11778	78	44	data	datum	NOUN
ajst-11778	78	45	set	set	VERB
ajst-11778	78	46	on	on	ADP
ajst-11778	78	47	surface	surface	NOUN
ajst-11778	78	48	defects	defect	NOUN
ajst-11778	78	49	of	of	ADP
ajst-11778	78	50	hot	hot	ADV
ajst-11778	78	51	-	-	PUNCT
ajst-11778	78	52	rolled	roll	VERB
ajst-11778	78	53	strip	strip	NOUN
ajst-11778	78	54	steel	steel	NOUN
ajst-11778	78	55	,	,	PUNCT
ajst-11778	78	56	containing	contain	VERB
ajst-11778	78	57	1800	1800	NUM
ajst-11778	78	58	images	image	NOUN
ajst-11778	78	59	,	,	PUNCT
ajst-11778	78	60	and	and	CCONJ
ajst-11778	78	61	the	the	DET
ajst-11778	78	62	images	image	NOUN
ajst-11778	78	63	are	be	AUX
ajst-11778	78	64	divided	divide	VERB
ajst-11778	78	65	into	into	ADP
ajst-11778	78	66	6	6	NUM
ajst-11778	78	67	broad	broad	ADJ
ajst-11778	78	68	categories	category	NOUN
ajst-11778	78	69	,	,	PUNCT
ajst-11778	78	70	each	each	PRON
ajst-11778	78	71	corresponding	correspond	VERB
ajst-11778	78	72	to	to	ADP
ajst-11778	78	73	one	one	NUM
ajst-11778	78	74	type	type	NOUN
ajst-11778	78	75	of	of	ADP
ajst-11778	78	76	surface	surface	NOUN
ajst-11778	78	77	defects	defect	NOUN
ajst-11778	78	78	,	,	PUNCT
ajst-11778	78	79	and	and	CCONJ
ajst-11778	78	80	each	each	DET
ajst-11778	78	81	broad	broad	ADJ
ajst-11778	78	82	category	category	NOUN
ajst-11778	78	83	includes	include	VERB
ajst-11778	78	84	300	300	NUM
ajst-11778	78	85	images	image	NOUN
ajst-11778	78	86	.	.	PUNCT
ajst-11778	79	1	the	the	DET
ajst-11778	79	2	image	image	NOUN
ajst-11778	79	3	size	size	NOUN
ajst-11778	79	4	is	be	AUX
ajst-11778	79	5	200	200	NUM
ajst-11778	79	6	×	×	NOUN
ajst-11778	79	7	200	200	NUM
ajst-11778	79	8	to	to	PART
ajst-11778	79	9	ensure	ensure	VERB
ajst-11778	79	10	the	the	DET
ajst-11778	79	11	consistency	consistency	NOUN
ajst-11778	79	12	and	and	CCONJ
ajst-11778	79	13	comparability	comparability	NOUN
ajst-11778	79	14	of	of	ADP
ajst-11778	79	15	the	the	DET
ajst-11778	79	16	data	datum	NOUN
ajst-11778	79	17	.	.	PUNCT
ajst-11778	80	1	in	in	ADP
ajst-11778	80	2	this	this	DET
ajst-11778	80	3	study	study	NOUN
ajst-11778	80	4	,	,	PUNCT
ajst-11778	80	5	we	we	PRON
ajst-11778	80	6	used	use	VERB
ajst-11778	80	7	the	the	DET
ajst-11778	80	8	following	follow	VERB
ajst-11778	80	9	six	six	NUM
ajst-11778	80	10	surface	surface	NOUN
ajst-11778	80	11	defect	defect	NOUN
ajst-11778	80	12	types	type	NOUN
ajst-11778	80	13	:	:	PUNCT
ajst-11778	80	14	crazing	crazing	NOUN
ajst-11778	80	15	(	(	PUNCT
ajst-11778	80	16	cr	cr	NOUN
ajst-11778	80	17	)	)	PUNCT
ajst-11778	80	18	,	,	PUNCT
ajst-11778	80	19	inclusion	inclusion	NOUN
ajst-11778	80	20	(	(	PUNCT
ajst-11778	80	21	in	in	ADP
ajst-11778	80	22	)	)	PUNCT
ajst-11778	80	23	,	,	PUNCT
ajst-11778	80	24	patches	patch	NOUN
ajst-11778	80	25	(	(	PUNCT
ajst-11778	80	26	pa	pa	PROPN
ajst-11778	80	27	)	)	PUNCT
ajst-11778	80	28	,	,	PUNCT
ajst-11778	80	29	pitted	pit	VERB
ajst-11778	80	30	surface	surface	NOUN
ajst-11778	80	31	(	(	PUNCT
ajst-11778	80	32	ps	ps	NOUN
ajst-11778	80	33	)	)	PUNCT
ajst-11778	80	34	,	,	PUNCT
ajst-11778	80	35	69	69	NUM
ajst-11778	80	36	rolled	roll	VERB
ajst-11778	80	37	-	-	PUNCT
ajst-11778	80	38	in	in	ADP
ajst-11778	80	39	scale	scale	NOUN
ajst-11778	80	40	(	(	PUNCT
ajst-11778	80	41	rs	rs	NOUN
ajst-11778	80	42	)	)	PUNCT
ajst-11778	80	43	,	,	PUNCT
ajst-11778	80	44	and	and	CCONJ
ajst-11778	80	45	scratches	scratch	NOUN
ajst-11778	80	46	(	(	PUNCT
ajst-11778	80	47	sc	sc	PROPN
ajst-11778	80	48	)	)	PUNCT
ajst-11778	80	49	.	.	PUNCT
ajst-11778	81	1	the	the	DET
ajst-11778	81	2	1800	1800	NUM
ajst-11778	81	3	defect	defect	NOUN
ajst-11778	81	4	images	image	NOUN
ajst-11778	81	5	were	be	AUX
ajst-11778	81	6	randomly	randomly	ADV
ajst-11778	81	7	divided	divide	VERB
ajst-11778	81	8	in	in	ADP
ajst-11778	81	9	the	the	DET
ajst-11778	81	10	ratio	ratio	NOUN
ajst-11778	81	11	of	of	ADP
ajst-11778	81	12	8:2	8:2	NUM
ajst-11778	81	13	,	,	PUNCT
ajst-11778	81	14	the	the	DET
ajst-11778	81	15	training	training	NOUN
ajst-11778	81	16	set	set	NOUN
ajst-11778	81	17	reached	reach	VERB
ajst-11778	81	18	1440	1440	NUM
ajst-11778	81	19	,	,	PUNCT
ajst-11778	81	20	and	and	CCONJ
ajst-11778	81	21	the	the	DET
ajst-11778	81	22	remaining	remain	VERB
ajst-11778	81	23	360	360	NUM
ajst-11778	81	24	were	be	AUX
ajst-11778	81	25	used	use	VERB
ajst-11778	81	26	as	as	ADP
ajst-11778	81	27	the	the	DET
ajst-11778	81	28	test	test	NOUN
ajst-11778	81	29	set	set	VERB
ajst-11778	81	30	.	.	PUNCT
ajst-11778	82	1	each	each	DET
ajst-11778	82	2	class	class	NOUN
ajst-11778	82	3	of	of	ADP
ajst-11778	82	4	strip	strip	NOUN
ajst-11778	82	5	defects	defect	NOUN
ajst-11778	82	6	is	be	AUX
ajst-11778	82	7	shown	show	VERB
ajst-11778	82	8	in	in	ADP
ajst-11778	82	9	figure	figure	NOUN
ajst-11778	82	10	5	5	NUM
ajst-11778	82	11	.	.	PUNCT
ajst-11778	83	1	(	(	PUNCT
ajst-11778	83	2	a	a	X
ajst-11778	83	3	)	)	PUNCT
ajst-11778	83	4	crazing	crazing	NOUN
ajst-11778	83	5	(	(	PUNCT
ajst-11778	83	6	b	b	NOUN
ajst-11778	83	7	)	)	PUNCT
ajst-11778	83	8	inclusion	inclusion	NOUN
ajst-11778	83	9	(	(	PUNCT
ajst-11778	83	10	c	c	NOUN
ajst-11778	83	11	)	)	PUNCT
ajst-11778	83	12	patches	patch	NOUN
ajst-11778	83	13	(	(	PUNCT
ajst-11778	83	14	d	d	NOUN
ajst-11778	83	15	)	)	PUNCT
ajst-11778	83	16	pitted	pit	VERB
ajst-11778	83	17	-	-	PUNCT
ajst-11778	83	18	surface	surface	NOUN
ajst-11778	83	19	(	(	PUNCT
ajst-11778	83	20	e	e	NOUN
ajst-11778	83	21	)	)	PUNCT
ajst-11778	83	22	rolled	roll	VERB
ajst-11778	83	23	-	-	PUNCT
ajst-11778	83	24	in	in	ADP
ajst-11778	83	25	-	-	PUNCT
ajst-11778	83	26	scale	scale	NOUN
ajst-11778	83	27	(	(	PUNCT
ajst-11778	83	28	f	f	X
ajst-11778	83	29	)	)	PUNCT
ajst-11778	83	30	scratches	scratch	NOUN
ajst-11778	83	31	figure	figure	NOUN
ajst-11778	83	32	5	5	NUM
ajst-11778	83	33	.	.	PUNCT
ajst-11778	83	34	various	various	ADJ
ajst-11778	83	35	strip	strip	NOUN
ajst-11778	83	36	defects	defect	NOUN
ajst-11778	83	37	diagrams	diagram	NOUN
ajst-11778	83	38	(	(	PUNCT
ajst-11778	83	39	2	2	X
ajst-11778	83	40	)	)	PUNCT
ajst-11778	83	41	experimental	experimental	ADJ
ajst-11778	83	42	configuration	configuration	NOUN
ajst-11778	83	43	the	the	DET
ajst-11778	83	44	experiments	experiment	NOUN
ajst-11778	83	45	use	use	VERB
ajst-11778	83	46	windows	window	NOUN
ajst-11778	83	47	10	10	NUM
ajst-11778	83	48	operating	operating	NOUN
ajst-11778	83	49	system	system	NOUN
ajst-11778	83	50	with	with	ADP
ajst-11778	83	51	nvidia	nvidia	PROPN
ajst-11778	83	52	geforce	geforce	PROPN
ajst-11778	83	53	rtx3080	rtx3080	PROPN
ajst-11778	83	54	gpu	gpu	PROPN
ajst-11778	83	55	and	and	CCONJ
ajst-11778	83	56	python	python	NOUN
ajst-11778	83	57	3.8	3.8	NUM
ajst-11778	83	58	programming	programming	NOUN
ajst-11778	83	59	language	language	NOUN
ajst-11778	83	60	,	,	PUNCT
ajst-11778	83	61	based	base	VERB
ajst-11778	83	62	on	on	ADP
ajst-11778	83	63	pytorch	pytorch	NOUN
ajst-11778	83	64	1.13.1	1.13.1	NUM
ajst-11778	83	65	and	and	CCONJ
ajst-11778	83	66	cuda	cuda	NOUN
ajst-11778	83	67	11.6	11.6	NUM
ajst-11778	83	68	deep	deep	ADJ
ajst-11778	83	69	learning	learning	NOUN
ajst-11778	83	70	framework	framework	NOUN
ajst-11778	83	71	.	.	PUNCT
ajst-11778	84	1	the	the	DET
ajst-11778	84	2	experimental	experimental	ADJ
ajst-11778	84	3	parameters	parameter	NOUN
ajst-11778	84	4	are	be	AUX
ajst-11778	84	5	configured	configure	VERB
ajst-11778	84	6	as	as	SCONJ
ajst-11778	84	7	shown	show	VERB
ajst-11778	84	8	in	in	ADP
ajst-11778	84	9	table	table	NOUN
ajst-11778	84	10	2	2	NUM
ajst-11778	84	11	.	.	PUNCT
ajst-11778	84	12	table	table	NOUN
ajst-11778	84	13	2	2	NUM
ajst-11778	84	14	.	.	PUNCT
ajst-11778	84	15	experimental	experimental	ADJ
ajst-11778	84	16	parameter	parameter	NOUN
ajst-11778	84	17	configurations	configuration	NOUN
ajst-11778	84	18	parameter	parameter	PROPN
ajst-11778	84	19	name	name	NOUN
ajst-11778	84	20	parameter	parameter	NOUN
ajst-11778	84	21	value	value	NOUN
ajst-11778	84	22	image	image	NOUN
ajst-11778	84	23	size	size	NOUN
ajst-11778	84	24	256x256	256x256	PROPN
ajst-11778	84	25	initial	initial	ADJ
ajst-11778	84	26	learning	learning	NOUN
ajst-11778	84	27	rate	rate	NOUN
ajst-11778	84	28	0.001	0.001	NUM
ajst-11778	84	29	momentum	momentum	NOUN
ajst-11778	84	30	0.937	0.937	NUM
ajst-11778	84	31	weight	weight	NOUN
ajst-11778	84	32	decay	decay	NOUN
ajst-11778	84	33	0.0005	0.0005	NUM
ajst-11778	84	34	batch	batch	NOUN
ajst-11778	84	35	processing	process	VERB
ajst-11778	84	36	32	32	NUM
ajst-11778	84	37	iterations	iteration	NOUN
ajst-11778	84	38	300	300	NUM
ajst-11778	84	39	(	(	PUNCT
ajst-11778	84	40	3	3	NUM
ajst-11778	84	41	)	)	PUNCT
ajst-11778	84	42	experimental	experimental	ADJ
ajst-11778	84	43	procedure	procedure	NOUN
ajst-11778	84	44	to	to	PART
ajst-11778	84	45	detect	detect	VERB
ajst-11778	84	46	the	the	DET
ajst-11778	84	47	accuracy	accuracy	NOUN
ajst-11778	84	48	of	of	ADP
ajst-11778	84	49	strip	strip	NOUN
ajst-11778	84	50	defects	defect	NOUN
ajst-11778	84	51	,	,	PUNCT
ajst-11778	84	52	yolov5s	yolov5s	PROPN
ajst-11778	84	53	is	be	AUX
ajst-11778	84	54	improved	improve	VERB
ajst-11778	84	55	.	.	PUNCT
ajst-11778	85	1	by	by	ADP
ajst-11778	85	2	using	use	VERB
ajst-11778	85	3	the	the	DET
ajst-11778	85	4	k	k	ADJ
ajst-11778	85	5	-	-	ADJ
ajst-11778	85	6	means++	means++	ADJ
ajst-11778	85	7	algorithm	algorithm	NOUN
ajst-11778	85	8	to	to	ADP
ajst-11778	85	9	cluster	cluster	NOUN
ajst-11778	85	10	anchor	anchor	NOUN
ajst-11778	85	11	frames	frame	NOUN
ajst-11778	85	12	(	(	PUNCT
ajst-11778	85	13	anchor	anchor	PROPN
ajst-11778	85	14	)	)	PUNCT
ajst-11778	85	15	,	,	PUNCT
ajst-11778	85	16	the	the	DET
ajst-11778	85	17	affiliation	affiliation	NOUN
ajst-11778	85	18	of	of	ADP
ajst-11778	85	19	the	the	DET
ajst-11778	85	20	anchor	anchor	NOUN
ajst-11778	85	21	frames	frame	NOUN
ajst-11778	85	22	clustered	cluster	VERB
ajst-11778	85	23	by	by	ADP
ajst-11778	85	24	each	each	DET
ajst-11778	85	25	real	real	ADJ
ajst-11778	85	26	frame	frame	NOUN
ajst-11778	85	27	is	be	AUX
ajst-11778	85	28	regained	regain	VERB
ajst-11778	85	29	,	,	PUNCT
ajst-11778	85	30	and	and	CCONJ
ajst-11778	85	31	then	then	ADV
ajst-11778	85	32	the	the	DET
ajst-11778	85	33	category	category	NOUN
ajst-11778	85	34	of	of	ADP
ajst-11778	85	35	these	these	DET
ajst-11778	85	36	real	real	ADJ
ajst-11778	85	37	frames	frame	NOUN
ajst-11778	85	38	is	be	AUX
ajst-11778	85	39	determined	determine	VERB
ajst-11778	85	40	to	to	PART
ajst-11778	85	41	achieve	achieve	VERB
ajst-11778	85	42	the	the	DET
ajst-11778	85	43	purpose	purpose	NOUN
ajst-11778	85	44	of	of	ADP
ajst-11778	85	45	clustering	cluster	VERB
ajst-11778	85	46	aiming	aiming	NOUN
ajst-11778	85	47	frames	frame	NOUN
ajst-11778	85	48	;	;	PUNCT
ajst-11778	85	49	in	in	ADP
ajst-11778	85	50	addition	addition	NOUN
ajst-11778	85	51	,	,	PUNCT
ajst-11778	85	52	in	in	ADP
ajst-11778	85	53	order	order	NOUN
ajst-11778	85	54	to	to	PART
ajst-11778	85	55	further	far	ADV
ajst-11778	85	56	improve	improve	VERB
ajst-11778	85	57	the	the	DET
ajst-11778	85	58	accuracy	accuracy	NOUN
ajst-11778	85	59	of	of	ADP
ajst-11778	85	60	defect	defect	ADJ
ajst-11778	85	61	detection	detection	NOUN
ajst-11778	85	62	,	,	PUNCT
ajst-11778	85	63	for	for	ADP
ajst-11778	85	64	the	the	DET
ajst-11778	85	65	situation	situation	NOUN
ajst-11778	85	66	that	that	SCONJ
ajst-11778	85	67	the	the	DET
ajst-11778	85	68	target	target	NOUN
ajst-11778	85	69	pixels	pixel	NOUN
ajst-11778	85	70	of	of	ADP
ajst-11778	85	71	strip	strip	NOUN
ajst-11778	85	72	steel	steel	NOUN
ajst-11778	85	73	defects	defect	NOUN
ajst-11778	85	74	are	be	AUX
ajst-11778	85	75	low	low	ADJ
ajst-11778	85	76	and	and	CCONJ
ajst-11778	85	77	the	the	DET
ajst-11778	85	78	information	information	NOUN
ajst-11778	85	79	is	be	AUX
ajst-11778	85	80	easily	easily	ADV
ajst-11778	85	81	lost	lose	VERB
ajst-11778	85	82	,	,	PUNCT
ajst-11778	85	83	the	the	DET
ajst-11778	85	84	simam	simam	ADJ
ajst-11778	85	85	non	non	ADJ
ajst-11778	85	86	-	-	ADJ
ajst-11778	85	87	parametric	parametric	ADJ
ajst-11778	85	88	attention	attention	NOUN
ajst-11778	85	89	is	be	AUX
ajst-11778	85	90	embedded	embed	VERB
ajst-11778	85	91	in	in	ADP
ajst-11778	85	92	the	the	DET
ajst-11778	85	93	neck	neck	NOUN
ajst-11778	85	94	network	network	NOUN
ajst-11778	85	95	part	part	NOUN
ajst-11778	85	96	mechanism	mechanism	NOUN
ajst-11778	85	97	to	to	PART
ajst-11778	85	98	capture	capture	VERB
ajst-11778	85	99	more	more	ADJ
ajst-11778	85	100	local	local	ADJ
ajst-11778	85	101	information	information	NOUN
ajst-11778	85	102	in	in	ADP
ajst-11778	85	103	the	the	DET
ajst-11778	85	104	image	image	NOUN
ajst-11778	85	105	and	and	CCONJ
ajst-11778	85	106	enhance	enhance	VERB
ajst-11778	85	107	the	the	DET
ajst-11778	85	108	extraction	extraction	NOUN
ajst-11778	85	109	of	of	ADP
ajst-11778	85	110	image	image	NOUN
ajst-11778	85	111	features	feature	NOUN
ajst-11778	85	112	without	without	ADP
ajst-11778	85	113	introducing	introduce	VERB
ajst-11778	85	114	an	an	DET
ajst-11778	85	115	additional	additional	ADJ
ajst-11778	85	116	number	number	NOUN
ajst-11778	85	117	of	of	ADP
ajst-11778	85	118	parameters	parameter	NOUN
ajst-11778	85	119	to	to	PART
ajst-11778	85	120	make	make	VERB
ajst-11778	85	121	it	it	PRON
ajst-11778	85	122	more	more	ADV
ajst-11778	85	123	focused	focused	ADJ
ajst-11778	85	124	on	on	ADP
ajst-11778	85	125	the	the	DET
ajst-11778	85	126	identification	identification	NOUN
ajst-11778	85	127	of	of	ADP
ajst-11778	85	128	defects	defect	NOUN
ajst-11778	85	129	,	,	PUNCT
ajst-11778	85	130	thus	thus	ADV
ajst-11778	85	131	improving	improve	VERB
ajst-11778	85	132	the	the	DET
ajst-11778	85	133	detection	detection	NOUN
ajst-11778	85	134	accuracy	accuracy	NOUN
ajst-11778	85	135	.	.	PUNCT
ajst-11778	86	1	finally	finally	ADV
ajst-11778	86	2	,	,	PUNCT
ajst-11778	86	3	in	in	ADP
ajst-11778	86	4	order	order	NOUN
ajst-11778	86	5	to	to	PART
ajst-11778	86	6	overcome	overcome	VERB
ajst-11778	86	7	the	the	DET
ajst-11778	86	8	problem	problem	NOUN
ajst-11778	86	9	of	of	ADP
ajst-11778	86	10	classification	classification	NOUN
ajst-11778	86	11	and	and	CCONJ
ajst-11778	86	12	regression	regression	NOUN
ajst-11778	86	13	conflicts	conflict	NOUN
ajst-11778	86	14	of	of	ADP
ajst-11778	86	15	the	the	DET
ajst-11778	86	16	defect	defect	ADJ
ajst-11778	86	17	image	image	NOUN
ajst-11778	86	18	output	output	NOUN
ajst-11778	86	19	variables	variable	NOUN
ajst-11778	86	20	,	,	PUNCT
ajst-11778	86	21	the	the	DET
ajst-11778	86	22	network	network	NOUN
ajst-11778	86	23	head	head	NOUN
ajst-11778	86	24	is	be	AUX
ajst-11778	86	25	replaced	replace	VERB
ajst-11778	86	26	with	with	ADP
ajst-11778	86	27	a	a	DET
ajst-11778	86	28	decoupled	decouple	VERB
ajst-11778	86	29	detection	detection	NOUN
ajst-11778	86	30	head	head	NOUN
ajst-11778	86	31	,	,	PUNCT
ajst-11778	86	32	which	which	PRON
ajst-11778	86	33	not	not	PART
ajst-11778	86	34	only	only	ADV
ajst-11778	86	35	increases	increase	VERB
ajst-11778	86	36	the	the	DET
ajst-11778	86	37	model	model	NOUN
ajst-11778	86	38	recognition	recognition	NOUN
ajst-11778	86	39	accuracy	accuracy	NOUN
ajst-11778	86	40	but	but	CCONJ
ajst-11778	86	41	also	also	ADV
ajst-11778	86	42	significantly	significantly	ADV
ajst-11778	86	43	speeds	speed	VERB
ajst-11778	86	44	up	up	ADP
ajst-11778	86	45	the	the	DET
ajst-11778	86	46	convergence	convergence	NOUN
ajst-11778	86	47	of	of	ADP
ajst-11778	86	48	the	the	DET
ajst-11778	86	49	model	model	NOUN
ajst-11778	86	50	.	.	PUNCT
ajst-11778	87	1	the	the	DET
ajst-11778	87	2	flow	flow	NOUN
ajst-11778	87	3	chart	chart	NOUN
ajst-11778	87	4	of	of	ADP
ajst-11778	87	5	the	the	DET
ajst-11778	87	6	experimental	experimental	ADJ
ajst-11778	87	7	method	method	NOUN
ajst-11778	87	8	is	be	AUX
ajst-11778	87	9	shown	show	VERB
ajst-11778	87	10	in	in	ADP
ajst-11778	87	11	figure	figure	NOUN
ajst-11778	87	12	6	6	NUM
ajst-11778	87	13	.	.	PUNCT
ajst-11778	87	14	input	input	PROPN
ajst-11778	87	15	dataset	dataset	PROPN
ajst-11778	87	16	mosica	mosica	PROPN
ajst-11778	87	17	data	datum	NOUN
ajst-11778	87	18	enhancement	enhancement	NOUN
ajst-11778	87	19	improved	improve	VERB
ajst-11778	87	20	k	k	ADJ
ajst-11778	87	21	-	-	ADJ
ajst-11778	87	22	means++	means++	ADV
ajst-11778	87	23	clustering	clustering	ADJ
ajst-11778	87	24	aiming	aim	VERB
ajst-11778	87	25	frame	frame	NOUN
ajst-11778	87	26	introduction	introduction	NOUN
ajst-11778	87	27	of	of	ADP
ajst-11778	87	28	simam	simam	ADJ
ajst-11778	87	29	nonreference	nonreference	NOUN
ajst-11778	87	30	attention	attention	NOUN
ajst-11778	87	31	fusion	fusion	NOUN
ajst-11778	87	32	decoupling	decouple	VERB
ajst-11778	87	33	detection	detection	NOUN
ajst-11778	87	34	head	head	NOUN
ajst-11778	87	35	save	save	VERB
ajst-11778	87	36	model	model	NOUN
ajst-11778	87	37	figure	figure	NOUN
ajst-11778	87	38	6	6	NUM
ajst-11778	87	39	.	.	PUNCT
ajst-11778	88	1	flow	flow	NOUN
ajst-11778	88	2	chart	chart	NOUN
ajst-11778	88	3	of	of	ADP
ajst-11778	88	4	the	the	DET
ajst-11778	88	5	proposed	propose	VERB
ajst-11778	88	6	method	method	NOUN
ajst-11778	88	7	(	(	PUNCT
ajst-11778	88	8	4	4	NUM
ajst-11778	88	9	)	)	PUNCT
ajst-11778	88	10	evaluation	evaluation	NOUN
ajst-11778	88	11	index	index	NOUN
ajst-11778	88	12	in	in	ADP
ajst-11778	88	13	order	order	NOUN
ajst-11778	88	14	to	to	PART
ajst-11778	88	15	evaluate	evaluate	VERB
ajst-11778	88	16	and	and	CCONJ
ajst-11778	88	17	improve	improve	VERB
ajst-11778	88	18	the	the	DET
ajst-11778	88	19	effectiveness	effectiveness	NOUN
ajst-11778	88	20	of	of	ADP
ajst-11778	88	21	the	the	DET
ajst-11778	88	22	defect	defect	NOUN
ajst-11778	88	23	detection	detection	NOUN
ajst-11778	88	24	model	model	NOUN
ajst-11778	88	25	,	,	PUNCT
ajst-11778	88	26	this	this	DET
ajst-11778	88	27	paper	paper	NOUN
ajst-11778	88	28	selects	select	VERB
ajst-11778	88	29	the	the	DET
ajst-11778	88	30	intersection	intersection	NOUN
ajst-11778	88	31	ratio	ratio	NOUN
ajst-11778	88	32	between	between	ADP
ajst-11778	88	33	the	the	DET
ajst-11778	88	34	prediction	prediction	NOUN
ajst-11778	88	35	frame	frame	NOUN
ajst-11778	88	36	and	and	CCONJ
ajst-11778	88	37	the	the	DET
ajst-11778	88	38	target	target	NOUN
ajst-11778	88	39	frame	frame	NOUN
ajst-11778	88	40	greater	great	ADJ
ajst-11778	88	41	than	than	ADP
ajst-11778	88	42	0.5	0.5	NUM
ajst-11778	88	43	as	as	ADP
ajst-11778	88	44	the	the	DET
ajst-11778	88	45	criterion	criterion	NOUN
ajst-11778	88	46	for	for	ADP
ajst-11778	88	47	determining	determine	VERB
ajst-11778	88	48	the	the	DET
ajst-11778	88	49	target	target	NOUN
ajst-11778	88	50	detection	detection	NOUN
ajst-11778	88	51	and	and	CCONJ
ajst-11778	88	52	uses	use	VERB
ajst-11778	88	53	average	average	ADJ
ajst-11778	88	54	precision	precision	NOUN
ajst-11778	88	55	(	(	PUNCT
ajst-11778	88	56	ap	ap	PROPN
ajst-11778	88	57	)	)	PUNCT
ajst-11778	88	58	,	,	PUNCT
ajst-11778	88	59	mean	mean	VERB
ajst-11778	88	60	average	average	ADJ
ajst-11778	88	61	precision	precision	NOUN
ajst-11778	88	62	(	(	PUNCT
ajst-11778	88	63	map	map	NOUN
ajst-11778	88	64	)	)	PUNCT
ajst-11778	88	65	and	and	CCONJ
ajst-11778	88	66	frames	frame	NOUN
ajst-11778	88	67	per	per	ADP
ajst-11778	88	68	second	second	ADJ
ajst-11778	88	69	(	(	PUNCT
ajst-11778	88	70	fps	fps	PROPN
ajst-11778	88	71	)	)	PUNCT
ajst-11778	88	72	for	for	ADP
ajst-11778	88	73	a	a	DET
ajst-11778	88	74	comprehensive	comprehensive	ADJ
ajst-11778	88	75	and	and	CCONJ
ajst-11778	88	76	objective	objective	ADJ
ajst-11778	88	77	evaluation	evaluation	NOUN
ajst-11778	88	78	of	of	ADP
ajst-11778	88	79	the	the	DET
ajst-11778	88	80	model	model	NOUN
ajst-11778	88	81	performance	performance	NOUN
ajst-11778	88	82	.	.	PUNCT
ajst-11778	89	1	precision	precision	NOUN
ajst-11778	89	2	(	(	PUNCT
ajst-11778	89	3	p	p	NOUN
ajst-11778	89	4	)	)	PUNCT
ajst-11778	89	5	describes	describe	VERB
ajst-11778	89	6	the	the	DET
ajst-11778	89	7	proportion	proportion	NOUN
ajst-11778	89	8	of	of	ADP
ajst-11778	89	9	all	all	DET
ajst-11778	89	10	detected	detect	VERB
ajst-11778	89	11	targets	target	NOUN
ajst-11778	89	12	that	that	PRON
ajst-11778	89	13	are	be	AUX
ajst-11778	89	14	correctly	correctly	ADV
ajst-11778	89	15	predicted	predict	VERB
ajst-11778	89	16	by	by	ADP
ajst-11778	89	17	the	the	DET
ajst-11778	89	18	model	model	NOUN
ajst-11778	89	19	,	,	PUNCT
ajst-11778	89	20	while	while	SCONJ
ajst-11778	89	21	recall	recall	NOUN
ajst-11778	89	22	(	(	PUNCT
ajst-11778	89	23	r	r	NOUN
ajst-11778	89	24	)	)	PUNCT
ajst-11778	89	25	describes	describe	VERB
ajst-11778	89	26	the	the	DET
ajst-11778	89	27	percentage	percentage	NOUN
ajst-11778	89	28	of	of	ADP
ajst-11778	89	29	all	all	DET
ajst-11778	89	30	targets	target	NOUN
ajst-11778	89	31	that	that	PRON
ajst-11778	89	32	are	be	AUX
ajst-11778	89	33	correctly	correctly	ADV
ajst-11778	89	34	predicted	predict	VERB
ajst-11778	89	35	by	by	ADP
ajst-11778	89	36	the	the	DET
ajst-11778	89	37	model	model	NOUN
ajst-11778	89	38	.	.	PUNCT
ajst-11778	90	1	the	the	DET
ajst-11778	90	2	expressions	expression	NOUN
ajst-11778	90	3	are	be	AUX
ajst-11778	90	4	shown	show	VERB
ajst-11778	90	5	in	in	ADP
ajst-11778	90	6	equations	equation	NOUN
ajst-11778	90	7	(	(	PUNCT
ajst-11778	90	8	6	6	NUM
ajst-11778	90	9	)	)	PUNCT
ajst-11778	90	10	and	and	CCONJ
ajst-11778	90	11	(	(	PUNCT
ajst-11778	90	12	7	7	NUM
ajst-11778	90	13	)	)	PUNCT
ajst-11778	90	14	,	,	PUNCT
ajst-11778	90	15	respectively	respectively	ADV
ajst-11778	90	16	.	.	PUNCT
ajst-11778	91	1	p	p	X
ajst-11778	91	2	t	t	X
ajst-11778	91	3	p	p	X
ajst-11778	91	4	p	p	X
ajst-11778	91	5	=	=	X
ajst-11778	91	6	pt	pt	PROPN
ajst-11778	92	1	+	+	NUM
ajst-11778	92	2	f	f	X
ajst-11778	92	3	(	(	PUNCT
ajst-11778	92	4	6	6	NUM
ajst-11778	92	5	)	)	PUNCT
ajst-11778	92	6	t	t	NOUN
ajst-11778	92	7	r	r	NOUN
ajst-11778	92	8	=	=	SYM
ajst-11778	92	9	t	t	PROPN
ajst-11778	92	10	p	p	NOUN
ajst-11778	92	11	p+	p+	NOUN
ajst-11778	92	12	fn	fn	NOUN
ajst-11778	92	13	(	(	PUNCT
ajst-11778	92	14	7	7	NUM
ajst-11778	92	15	)	)	PUNCT
ajst-11778	92	16	where	where	SCONJ
ajst-11778	92	17	:	:	PUNCT
ajst-11778	92	18	tp	tp	NOUN
ajst-11778	92	19	indicates	indicate	VERB
ajst-11778	92	20	that	that	SCONJ
ajst-11778	92	21	the	the	DET
ajst-11778	92	22	target	target	NOUN
ajst-11778	92	23	was	be	AUX
ajst-11778	92	24	detected	detect	VERB
ajst-11778	92	25	completely	completely	ADV
ajst-11778	92	26	correctly	correctly	ADV
ajst-11778	92	27	;	;	PUNCT
ajst-11778	92	28	fp	fp	X
ajst-11778	92	29	indicates	indicate	VERB
ajst-11778	92	30	that	that	SCONJ
ajst-11778	92	31	the	the	DET
ajst-11778	92	32	target	target	NOUN
ajst-11778	92	33	was	be	AUX
ajst-11778	92	34	originally	originally	ADV
ajst-11778	92	35	the	the	DET
ajst-11778	92	36	wrong	wrong	ADJ
ajst-11778	92	37	sample	sample	NOUN
ajst-11778	92	38	but	but	CCONJ
ajst-11778	92	39	was	be	AUX
ajst-11778	92	40	detected	detect	VERB
ajst-11778	92	41	by	by	ADP
ajst-11778	92	42	the	the	DET
ajst-11778	92	43	model	model	NOUN
ajst-11778	92	44	as	as	ADP
ajst-11778	92	45	the	the	DET
ajst-11778	92	46	correct	correct	ADJ
ajst-11778	92	47	sample	sample	NOUN
ajst-11778	92	48	,	,	PUNCT
ajst-11778	92	49	i.e.	i.e.	X
ajst-11778	92	50	,	,	PUNCT
ajst-11778	92	51	error	error	NOUN
ajst-11778	92	52	detection	detection	NOUN
ajst-11778	92	53	;	;	PUNCT
ajst-11778	92	54	and	and	CCONJ
ajst-11778	92	55	fn	fn	NOUN
ajst-11778	92	56	indicates	indicate	VERB
ajst-11778	92	57	that	that	SCONJ
ajst-11778	92	58	the	the	DET
ajst-11778	92	59	positive	positive	ADJ
ajst-11778	92	60	class	class	NOUN
ajst-11778	92	61	of	of	ADP
ajst-11778	92	62	the	the	DET
ajst-11778	92	63	target	target	NOUN
ajst-11778	92	64	sample	sample	NOUN
ajst-11778	92	65	was	be	AUX
ajst-11778	92	66	predicted	predict	VERB
ajst-11778	92	67	as	as	ADP
ajst-11778	92	68	the	the	DET
ajst-11778	92	69	negative	negative	ADJ
ajst-11778	92	70	class	class	NOUN
ajst-11778	92	71	,	,	PUNCT
ajst-11778	92	72	i.e.	i.e.	X
ajst-11778	92	73	,	,	PUNCT
ajst-11778	92	74	missed	miss	VERB
ajst-11778	92	75	detection	detection	NOUN
ajst-11778	92	76	.	.	PUNCT
ajst-11778	93	1	(	(	PUNCT
ajst-11778	93	2	5	5	X
ajst-11778	93	3	)	)	PUNCT
ajst-11778	93	4	ablation	ablation	NOUN
ajst-11778	93	5	experiment	experiment	NOUN
ajst-11778	93	6	ablation	ablation	NOUN
ajst-11778	93	7	experiment	experiment	NOUN
ajst-11778	93	8	the	the	DET
ajst-11778	93	9	aiming	aiming	NOUN
ajst-11778	93	10	frames	frame	NOUN
ajst-11778	93	11	originally	originally	ADV
ajst-11778	93	12	set	set	VERB
ajst-11778	93	13	by	by	ADP
ajst-11778	93	14	yolo	yolo	ADJ
ajst-11778	93	15	and	and	CCONJ
ajst-11778	93	16	the	the	DET
ajst-11778	93	17	aiming	aim	VERB
ajst-11778	93	18	frames	frame	NOUN
ajst-11778	93	19	reacquired	reacquire	VERB
ajst-11778	93	20	after	after	ADP
ajst-11778	93	21	clustering	cluster	VERB
ajst-11778	93	22	with	with	ADP
ajst-11778	93	23	k	k	NOUN
ajst-11778	93	24	-	-	ADJ
ajst-11778	93	25	means++	means++	PROPN
ajst-11778	93	26	were	be	AUX
ajst-11778	93	27	collated	collate	VERB
ajst-11778	93	28	and	and	CCONJ
ajst-11778	93	29	imported	import	VERB
ajst-11778	93	30	into	into	ADP
ajst-11778	93	31	the	the	DET
ajst-11778	93	32	model	model	NOUN
ajst-11778	93	33	for	for	ADP
ajst-11778	93	34	experiments	experiment	NOUN
ajst-11778	93	35	,	,	PUNCT
ajst-11778	93	36	and	and	CCONJ
ajst-11778	93	37	the	the	DET
ajst-11778	93	38	results	result	NOUN
ajst-11778	93	39	were	be	AUX
ajst-11778	93	40	obtained	obtain	VERB
ajst-11778	93	41	as	as	SCONJ
ajst-11778	93	42	shown	show	VERB
ajst-11778	93	43	in	in	ADP
ajst-11778	93	44	table	table	NOUN
ajst-11778	93	45	3	3	NUM
ajst-11778	93	46	.	.	PUNCT
ajst-11778	94	1	in	in	ADP
ajst-11778	94	2	experiment	experiment	NOUN
ajst-11778	94	3	1	1	NUM
ajst-11778	94	4	,	,	PUNCT
ajst-11778	94	5	the	the	DET
ajst-11778	94	6	k	k	NOUN
ajst-11778	94	7	-	-	PUNCT
ajst-11778	94	8	means	means	NOUN
ajst-11778	94	9	algorithm	algorithm	NOUN
ajst-11778	94	10	was	be	AUX
ajst-11778	94	11	used	use	VERB
ajst-11778	94	12	for	for	ADP
ajst-11778	94	13	clustering	cluster	VERB
ajst-11778	94	14	without	without	ADP
ajst-11778	94	15	any	any	DET
ajst-11778	94	16	modification	modification	NOUN
ajst-11778	94	17	to	to	ADP
ajst-11778	94	18	it	it	PRON
ajst-11778	94	19	.	.	PUNCT
ajst-11778	95	1	in	in	ADP
ajst-11778	95	2	experiment	experiment	NOUN
ajst-11778	95	3	2	2	NUM
ajst-11778	95	4	,	,	PUNCT
ajst-11778	95	5	an	an	DET
ajst-11778	95	6	attempt	attempt	NOUN
ajst-11778	95	7	was	be	AUX
ajst-11778	95	8	made	make	VERB
ajst-11778	95	9	to	to	PART
ajst-11778	95	10	use	use	VERB
ajst-11778	95	11	the	the	DET
ajst-11778	95	12	modified	modified	ADJ
ajst-11778	95	13	clustering	cluster	VERB
ajst-11778	95	14	algorithm	algorithm	NOUN
ajst-11778	95	15	k	k	NOUN
ajst-11778	95	16	-	-	PUNCT
ajst-11778	95	17	means++	means++	NOUN
ajst-11778	95	18	for	for	ADP
ajst-11778	95	19	clustering	clustering	ADJ
ajst-11778	95	20	detection	detection	NOUN
ajst-11778	95	21	of	of	ADP
ajst-11778	95	22	the	the	DET
ajst-11778	95	23	prior	prior	ADJ
ajst-11778	95	24	frames	frame	NOUN
ajst-11778	95	25	.	.	PUNCT
ajst-11778	96	1	however	however	ADV
ajst-11778	96	2	,	,	PUNCT
ajst-11778	96	3	the	the	DET
ajst-11778	96	4	experimental	experimental	ADJ
ajst-11778	96	5	results	result	NOUN
ajst-11778	96	6	showed	show	VERB
ajst-11778	96	7	that	that	SCONJ
ajst-11778	96	8	the	the	DET
ajst-11778	96	9	mapped	map	VERB
ajst-11778	96	10	value	value	NOUN
ajst-11778	96	11	improved	improve	VERB
ajst-11778	96	12	by	by	ADP
ajst-11778	96	13	1.7	1.7	NUM
ajst-11778	96	14	percentage	percentage	NOUN
ajst-11778	96	15	points	point	NOUN
ajst-11778	96	16	compared	compare	VERB
ajst-11778	96	17	with	with	ADP
ajst-11778	96	18	experiment	experiment	NOUN
ajst-11778	96	19	1	1	NUM
ajst-11778	96	20	,	,	PUNCT
ajst-11778	96	21	and	and	CCONJ
ajst-11778	96	22	the	the	DET
ajst-11778	96	23	inference	inference	NOUN
ajst-11778	96	24	speed	speed	NOUN
ajst-11778	96	25	of	of	ADP
ajst-11778	96	26	the	the	DET
ajst-11778	96	27	improved	improved	ADJ
ajst-11778	96	28	algorithm	algorithm	NOUN
ajst-11778	96	29	did	do	AUX
ajst-11778	96	30	not	not	PART
ajst-11778	96	31	decrease	decrease	VERB
ajst-11778	96	32	significantly	significantly	ADV
ajst-11778	96	33	.	.	PUNCT
ajst-11778	97	1	despite	despite	SCONJ
ajst-11778	97	2	the	the	DET
ajst-11778	97	3	change	change	NOUN
ajst-11778	97	4	in	in	ADP
ajst-11778	97	5	the	the	DET
ajst-11778	97	6	clustering	clustering	NOUN
ajst-11778	97	7	of	of	ADP
ajst-11778	97	8	the	the	DET
ajst-11778	97	9	prior	prior	ADJ
ajst-11778	97	10	frame	frame	NOUN
ajst-11778	97	11	,	,	PUNCT
ajst-11778	97	12	the	the	DET
ajst-11778	97	13	network	network	NOUN
ajst-11778	97	14	structure	structure	NOUN
ajst-11778	97	15	itself	itself	PRON
ajst-11778	97	16	did	do	AUX
ajst-11778	97	17	not	not	PART
ajst-11778	97	18	change	change	VERB
ajst-11778	97	19	.	.	PUNCT
ajst-11778	98	1	the	the	DET
ajst-11778	98	2	present	present	ADJ
ajst-11778	98	3	algorithm	algorithm	NOUN
ajst-11778	98	4	shows	show	VERB
ajst-11778	98	5	superior	superior	ADJ
ajst-11778	98	6	results	result	NOUN
ajst-11778	98	7	in	in	ADP
ajst-11778	98	8	clustering	clustering	NOUN
ajst-11778	98	9	and	and	CCONJ
ajst-11778	98	10	the	the	DET
ajst-11778	98	11	resulting	result	VERB
ajst-11778	98	12	prior	prior	ADJ
ajst-11778	98	13	frames	frame	NOUN
ajst-11778	98	14	are	be	AUX
ajst-11778	98	15	closer	close	ADJ
ajst-11778	98	16	to	to	ADP
ajst-11778	98	17	the	the	DET
ajst-11778	98	18	actual	actual	ADJ
ajst-11778	98	19	size	size	NOUN
ajst-11778	98	20	of	of	ADP
ajst-11778	98	21	the	the	DET
ajst-11778	98	22	dataset	dataset	NOUN
ajst-11778	98	23	.	.	PUNCT
ajst-11778	99	1	or	or	CCONJ
ajst-11778	99	2	matching	matching	NOUN
ajst-11778	99	3	are	be	AUX
ajst-11778	99	4	more	more	ADV
ajst-11778	99	5	fashionable	fashionable	ADJ
ajst-11778	99	6	.	.	PUNCT
ajst-11778	100	1	for	for	ADP
ajst-11778	100	2	example	example	NOUN
ajst-11778	100	3	,	,	PUNCT
ajst-11778	100	4	in	in	ADP
ajst-11778	100	5	some	some	DET
ajst-11778	100	6	haute	haute	NOUN
ajst-11778	100	7	couture	couture	NOUN
ajst-11778	100	8	conferences	conference	NOUN
ajst-11778	100	9	,	,	PUNCT
ajst-11778	100	10	many	many	ADJ
ajst-11778	100	11	design	design	NOUN
ajst-11778	100	12	products	product	NOUN
ajst-11778	100	13	will	will	AUX
ajst-11778	100	14	use	use	VERB
ajst-11778	100	15	bead	bead	NOUN
ajst-11778	100	16	embroidery	embroidery	NOUN
ajst-11778	100	17	,	,	PUNCT
ajst-11778	100	18	plate	plate	NOUN
ajst-11778	100	19	gold	gold	NOUN
ajst-11778	100	20	and	and	CCONJ
ajst-11778	100	21	other	other	ADJ
ajst-11778	100	22	three	three	NUM
ajst-11778	100	23	-	-	PUNCT
ajst-11778	100	24	dimensional	dimensional	ADJ
ajst-11778	100	25	embroidery	embroidery	NOUN
ajst-11778	100	26	to	to	PART
ajst-11778	100	27	decorate	decorate	VERB
ajst-11778	100	28	the	the	DET
ajst-11778	100	29	whole	whole	ADJ
ajst-11778	100	30	clothing	clothing	NOUN
ajst-11778	100	31	,	,	PUNCT
ajst-11778	100	32	which	which	PRON
ajst-11778	100	33	makes	make	VERB
ajst-11778	100	34	the	the	DET
ajst-11778	100	35	three	three	NUM
ajst-11778	100	36	-	-	PUNCT
ajst-11778	100	37	dimensional	dimensional	ADJ
ajst-11778	100	38	effect	effect	NOUN
ajst-11778	100	39	of	of	ADP
ajst-11778	100	40	the	the	DET
ajst-11778	100	41	whole	whole	ADJ
ajst-11778	100	42	clothing	clothing	NOUN
ajst-11778	100	43	stronger	strong	ADJ
ajst-11778	100	44	and	and	CCONJ
ajst-11778	100	45	elegant	elegant	ADJ
ajst-11778	100	46	.	.	PUNCT
ajst-11778	101	1	70	70	NUM
ajst-11778	101	2	table	table	NOUN
ajst-11778	101	3	2	2	NUM
ajst-11778	101	4	.	.	PUNCT
ajst-11778	102	1	k	k	X
ajst-11778	102	2	-	-	ADJ
ajst-11778	102	3	means++	means++	ADV
ajst-11778	102	4	clustered	clustered	ADJ
ajst-11778	102	5	results	result	NOUN
ajst-11778	102	6	algorithm	algorithm	NOUN
ajst-11778	102	7	p	p	NOUN
ajst-11778	102	8	r	r	NOUN
ajst-11778	102	9	map.5/%	map.5/%	PROPN
ajst-11778	102	10	map.75/%	map.75/%	NOUN
ajst-11778	102	11	fps	fps	PROPN
ajst-11778	103	1	yolov5s	yolov5s	PROPN
ajst-11778	103	2	70.9	70.9	NUM
ajst-11778	103	3	77.0	77.0	NUM
ajst-11778	103	4	77.5	77.5	NUM
ajst-11778	103	5	42.3	42.3	NUM
ajst-11778	103	6	57.47	57.47	NUM
ajst-11778	103	7	k	k	NOUN
ajst-11778	103	8	-	-	PUNCT
ajst-11778	103	9	means++	means++	NOUN
ajst-11778	103	10	77.0	77.0	NUM
ajst-11778	103	11	75.5	75.5	NUM
ajst-11778	103	12	79.2	79.2	NUM
ajst-11778	103	13	43.8	43.8	NUM
ajst-11778	103	14	55.87	55.87	NUM
ajst-11778	103	15	the	the	DET
ajst-11778	103	16	purpose	purpose	NOUN
ajst-11778	103	17	of	of	ADP
ajst-11778	103	18	this	this	DET
ajst-11778	103	19	paper	paper	NOUN
ajst-11778	103	20	is	be	AUX
ajst-11778	103	21	to	to	PART
ajst-11778	103	22	investigate	investigate	VERB
ajst-11778	103	23	the	the	DET
ajst-11778	103	24	effect	effect	NOUN
ajst-11778	103	25	of	of	ADP
ajst-11778	103	26	different	different	ADJ
ajst-11778	103	27	improved	improved	ADJ
ajst-11778	103	28	modules	module	NOUN
ajst-11778	103	29	in	in	ADP
ajst-11778	103	30	yolov5	yolov5	NOUN
ajst-11778	103	31	on	on	ADP
ajst-11778	103	32	the	the	DET
ajst-11778	103	33	excellence	excellence	PROPN
ajst-11778	103	34	of	of	ADP
ajst-11778	103	35	strip	strip	PROPN
ajst-11778	103	36	steel	steel	NOUN
ajst-11778	103	37	surface	surface	NOUN
ajst-11778	103	38	defect	defect	NOUN
ajst-11778	103	39	detection	detection	NOUN
ajst-11778	103	40	and	and	CCONJ
ajst-11778	103	41	to	to	PART
ajst-11778	103	42	analyze	analyze	VERB
ajst-11778	103	43	it	it	PRON
ajst-11778	103	44	in	in	ADP
ajst-11778	103	45	depth	depth	NOUN
ajst-11778	103	46	by	by	ADP
ajst-11778	103	47	designing	design	VERB
ajst-11778	103	48	ablation	ablation	NOUN
ajst-11778	103	49	experiments	experiment	NOUN
ajst-11778	103	50	.	.	PUNCT
ajst-11778	104	1	in	in	ADP
ajst-11778	104	2	this	this	DET
ajst-11778	104	3	study	study	NOUN
ajst-11778	104	4	,	,	PUNCT
ajst-11778	104	5	the	the	DET
ajst-11778	104	6	original	original	ADJ
ajst-11778	104	7	yolov5s	yolov5s	NOUN
ajst-11778	104	8	were	be	AUX
ajst-11778	104	9	used	use	VERB
ajst-11778	104	10	as	as	ADP
ajst-11778	104	11	the	the	DET
ajst-11778	104	12	illuminated	illuminated	ADJ
ajst-11778	104	13	group	group	NOUN
ajst-11778	104	14	,	,	PUNCT
ajst-11778	104	15	while	while	SCONJ
ajst-11778	104	16	its	its	PRON
ajst-11778	104	17	backbone	backbone	NOUN
ajst-11778	104	18	,	,	PUNCT
ajst-11778	104	19	feature	feature	NOUN
ajst-11778	104	20	fusion	fusion	NOUN
ajst-11778	104	21	network	network	NOUN
ajst-11778	104	22	,	,	PUNCT
ajst-11778	104	23	and	and	CCONJ
ajst-11778	104	24	detection	detection	NOUN
ajst-11778	104	25	head	head	NOUN
ajst-11778	104	26	parts	part	NOUN
ajst-11778	104	27	were	be	AUX
ajst-11778	104	28	fused	fuse	VERB
ajst-11778	104	29	as	as	ADP
ajst-11778	104	30	the	the	DET
ajst-11778	104	31	comparison	comparison	NOUN
ajst-11778	104	32	experimental	experimental	ADJ
ajst-11778	104	33	group	group	NOUN
ajst-11778	104	34	.	.	PUNCT
ajst-11778	105	1	the	the	DET
ajst-11778	105	2	experimental	experimental	ADJ
ajst-11778	105	3	results	result	NOUN
ajst-11778	105	4	are	be	AUX
ajst-11778	105	5	shown	show	VERB
ajst-11778	105	6	in	in	ADP
ajst-11778	105	7	table	table	NOUN
ajst-11778	105	8	3	3	NUM
ajst-11778	105	9	.	.	PUNCT
ajst-11778	106	1	the	the	DET
ajst-11778	106	2	detection	detection	NOUN
ajst-11778	106	3	effect	effect	NOUN
ajst-11778	106	4	was	be	AUX
ajst-11778	106	5	improved	improve	VERB
ajst-11778	106	6	by	by	ADP
ajst-11778	106	7	re	re	VERB
ajst-11778	106	8	-	-	VERB
ajst-11778	106	9	clustering	cluster	VERB
ajst-11778	106	10	the	the	DET
ajst-11778	106	11	aiming	aim	VERB
ajst-11778	106	12	frame	frame	NOUN
ajst-11778	106	13	in	in	ADP
ajst-11778	106	14	the	the	DET
ajst-11778	106	15	yolov5s	yolov5s	PROPN
ajst-11778	106	16	network	network	NOUN
ajst-11778	106	17	,	,	PUNCT
ajst-11778	106	18	and	and	CCONJ
ajst-11778	106	19	the	the	DET
ajst-11778	106	20	map	map	NOUN
ajst-11778	106	21	increased	increase	VERB
ajst-11778	106	22	by	by	ADP
ajst-11778	106	23	1.7	1.7	NUM
ajst-11778	106	24	%	%	NOUN
ajst-11778	106	25	;	;	PUNCT
ajst-11778	106	26	the	the	DET
ajst-11778	106	27	map	map	NOUN
ajst-11778	106	28	was	be	AUX
ajst-11778	106	29	improved	improve	VERB
ajst-11778	106	30	by	by	ADP
ajst-11778	106	31	1.3	1.3	NUM
ajst-11778	106	32	%	%	NOUN
ajst-11778	106	33	by	by	ADP
ajst-11778	106	34	adding	add	VERB
ajst-11778	106	35	the	the	DET
ajst-11778	106	36	simam	simam	ADJ
ajst-11778	106	37	non	non	ADJ
ajst-11778	106	38	-	-	ADJ
ajst-11778	106	39	parametric	parametric	ADJ
ajst-11778	106	40	attention	attention	NOUN
ajst-11778	106	41	mechanism	mechanism	NOUN
ajst-11778	106	42	to	to	ADP
ajst-11778	106	43	the	the	DET
ajst-11778	106	44	neck	neck	NOUN
ajst-11778	106	45	feature	feature	NOUN
ajst-11778	106	46	fusion	fusion	NOUN
ajst-11778	106	47	network	network	NOUN
ajst-11778	106	48	;	;	PUNCT
ajst-11778	106	49	the	the	DET
ajst-11778	106	50	map	map	NOUN
ajst-11778	106	51	was	be	AUX
ajst-11778	106	52	improved	improve	VERB
ajst-11778	106	53	by	by	ADP
ajst-11778	106	54	2.5	2.5	NUM
ajst-11778	106	55	%	%	NOUN
ajst-11778	106	56	by	by	ADP
ajst-11778	106	57	replacing	replace	VERB
ajst-11778	106	58	the	the	DET
ajst-11778	106	59	head	head	NOUN
ajst-11778	106	60	of	of	ADP
ajst-11778	106	61	the	the	DET
ajst-11778	106	62	network	network	NOUN
ajst-11778	106	63	with	with	ADP
ajst-11778	106	64	the	the	DET
ajst-11778	106	65	decoupled	decouple	VERB
ajst-11778	106	66	detection	detection	NOUN
ajst-11778	106	67	head	head	NOUN
ajst-11778	106	68	,	,	PUNCT
ajst-11778	106	69	and	and	CCONJ
ajst-11778	106	70	decoupling	decouple	VERB
ajst-11778	106	71	the	the	DET
ajst-11778	106	72	classification	classification	NOUN
ajst-11778	106	73	task	task	NOUN
ajst-11778	106	74	and	and	CCONJ
ajst-11778	106	75	localization	localization	NOUN
ajst-11778	106	76	task	task	NOUN
ajst-11778	106	77	separately	separately	ADV
ajst-11778	106	78	.	.	PUNCT
ajst-11778	107	1	when	when	SCONJ
ajst-11778	107	2	all	all	DET
ajst-11778	107	3	strategies	strategy	NOUN
ajst-11778	107	4	are	be	AUX
ajst-11778	107	5	used	use	VERB
ajst-11778	107	6	simultaneously	simultaneously	ADV
ajst-11778	107	7	in	in	ADP
ajst-11778	107	8	the	the	DET
ajst-11778	107	9	yolov5s	yolov5s	PROPN
ajst-11778	107	10	model	model	NOUN
ajst-11778	107	11	,	,	PUNCT
ajst-11778	107	12	the	the	DET
ajst-11778	107	13	map	map	NOUN
ajst-11778	107	14	value	value	NOUN
ajst-11778	107	15	of	of	ADP
ajst-11778	107	16	the	the	DET
ajst-11778	107	17	model	model	NOUN
ajst-11778	107	18	is	be	AUX
ajst-11778	107	19	improved	improve	VERB
ajst-11778	107	20	by	by	ADP
ajst-11778	107	21	3.2	3.2	NUM
ajst-11778	107	22	%	%	NOUN
ajst-11778	107	23	,	,	PUNCT
ajst-11778	107	24	and	and	CCONJ
ajst-11778	107	25	the	the	DET
ajst-11778	107	26	final	final	ADJ
ajst-11778	107	27	map	map	NOUN
ajst-11778	107	28	reaches	reach	VERB
ajst-11778	107	29	80.7	80.7	NUM
ajst-11778	107	30	%	%	NOUN
ajst-11778	107	31	,	,	PUNCT
ajst-11778	107	32	which	which	PRON
ajst-11778	107	33	proves	prove	VERB
ajst-11778	107	34	that	that	SCONJ
ajst-11778	107	35	the	the	DET
ajst-11778	107	36	improvement	improvement	NOUN
ajst-11778	107	37	of	of	ADP
ajst-11778	107	38	the	the	DET
ajst-11778	107	39	algorithm	algorithm	NOUN
ajst-11778	107	40	is	be	AUX
ajst-11778	107	41	effective	effective	ADJ
ajst-11778	107	42	.	.	PUNCT
ajst-11778	108	1	table	table	NOUN
ajst-11778	108	2	3	3	NUM
ajst-11778	108	3	.	.	X
ajst-11778	109	1	yolov5s	yolov5s	NOUN
ajst-11778	109	2	ablation	ablation	NOUN
ajst-11778	109	3	experiments	experiment	NOUN
ajst-11778	109	4	results	result	VERB
ajst-11778	109	5	models	model	NOUN
ajst-11778	109	6	k	k	ADJ
ajst-11778	109	7	-	-	ADJ
ajst-11778	109	8	means++	means++	ADV
ajst-11778	109	9	double	double	ADJ
ajst-11778	109	10	head	head	NOUN
ajst-11778	109	11	simam	simam	NOUN
ajst-11778	109	12	accuracy	accuracy	NOUN
ajst-11778	109	13	of	of	ADP
ajst-11778	109	14	each	each	DET
ajst-11778	109	15	type	type	NOUN
ajst-11778	109	16	of	of	ADP
ajst-11778	109	17	defect	defect	ADJ
ajst-11778	109	18	map.5/%	map.5/%	NOUN
ajst-11778	109	19	map.75/%	map.75/%	VERB
ajst-11778	109	20	cr	cr	NOUN
ajst-11778	109	21	in	in	ADP
ajst-11778	109	22	pa	pa	PROPN
ajst-11778	109	23	ps	ps	PROPN
ajst-11778	109	24	rs	rs	PROPN
ajst-11778	109	25	sc	sc	PROPN
ajst-11778	109	26	1	1	NUM
ajst-11778	109	27	42.6	42.6	NUM
ajst-11778	109	28	84.0	84.0	NUM
ajst-11778	109	29	90.3	90.3	NUM
ajst-11778	109	30	84.4	84.4	NUM
ajst-11778	109	31	67.6	67.6	NUM
ajst-11778	109	32	96.4	96.4	NUM
ajst-11778	109	33	77.5	77.5	NUM
ajst-11778	109	34	42.3	42.3	NUM
ajst-11778	109	35	2	2	NUM
ajst-11778	109	36			ADP
ajst-11778	109	37	43.3	43.3	NUM
ajst-11778	109	38	87.0	87.0	NUM
ajst-11778	109	39	89.8	89.8	NUM
ajst-11778	109	40	86.9	86.9	NUM
ajst-11778	109	41	74.7	74.7	NUM
ajst-11778	109	42	93.7	93.7	NUM
ajst-11778	109	43	79.2	79.2	NUM
ajst-11778	109	44	43.8	43.8	NUM
ajst-11778	109	45	3	3	NUM
ajst-11778	109	46			ADP
ajst-11778	109	47	45.5	45.5	NUM
ajst-11778	109	48	88.2	88.2	NUM
ajst-11778	109	49	91.9	91.9	NUM
ajst-11778	109	50	83.9	83.9	NUM
ajst-11778	109	51	75.4	75.4	NUM
ajst-11778	109	52	95	95	NUM
ajst-11778	109	53	80.0	80.0	NUM
ajst-11778	109	54	42.9	42.9	NUM
ajst-11778	109	55	4	4	NUM
ajst-11778	109	56			ADP
ajst-11778	109	57	45.9	45.9	NUM
ajst-11778	109	58	85.1	85.1	NUM
ajst-11778	109	59	89.6	89.6	NUM
ajst-11778	109	60	86.8	86.8	NUM
ajst-11778	109	61	68.8	68.8	NUM
ajst-11778	109	62	96.6	96.6	NUM
ajst-11778	109	63	78.8	78.8	NUM
ajst-11778	109	64	43.1	43.1	NUM
ajst-11778	109	65	5	5	NUM
ajst-11778	109	66			PROPN
ajst-11778	109	67			ADP
ajst-11778	109	68	48.4	48.4	NUM
ajst-11778	109	69	85.8	85.8	NUM
ajst-11778	109	70	92.2	92.2	NUM
ajst-11778	109	71	85.4	85.4	NUM
ajst-11778	109	72	74.9	74.9	NUM
ajst-11778	109	73	95.9	95.9	NUM
ajst-11778	109	74	80.2	80.2	NUM
ajst-11778	109	75	43.2	43.2	NUM
ajst-11778	109	76	6	6	NUM
ajst-11778	109	77			PROPN
ajst-11778	109	78			PROPN
ajst-11778	109	79			ADP
ajst-11778	109	80	53.0	53.0	NUM
ajst-11778	109	81	84.9	84.9	NUM
ajst-11778	109	82	89.7	89.7	NUM
ajst-11778	109	83	85.5	85.5	NUM
ajst-11778	109	84	74.0	74.0	NUM
ajst-11778	109	85	97.1	97.1	NUM
ajst-11778	109	86	80.7	80.7	NUM
ajst-11778	109	87	43.3	43.3	NUM
ajst-11778	109	88	(	(	PUNCT
ajst-11778	109	89	6	6	NUM
ajst-11778	109	90	)	)	PUNCT
ajst-11778	109	91	comparison	comparison	NOUN
ajst-11778	109	92	experiment	experiment	NOUN
ajst-11778	109	93	to	to	PART
ajst-11778	109	94	verify	verify	VERB
ajst-11778	109	95	the	the	DET
ajst-11778	109	96	superiority	superiority	NOUN
ajst-11778	109	97	of	of	ADP
ajst-11778	109	98	the	the	DET
ajst-11778	109	99	yolov5s	yolov5s	PROPN
ajst-11778	109	100	model	model	NOUN
ajst-11778	109	101	after	after	ADP
ajst-11778	109	102	fusing	fuse	VERB
ajst-11778	109	103	decoupling	decouple	VERB
ajst-11778	109	104	heads	head	NOUN
ajst-11778	109	105	in	in	ADP
ajst-11778	109	106	this	this	DET
ajst-11778	109	107	paper	paper	NOUN
ajst-11778	109	108	for	for	ADP
ajst-11778	109	109	strip	strip	NOUN
ajst-11778	109	110	surface	surface	NOUN
ajst-11778	109	111	defect	defect	NOUN
ajst-11778	109	112	detection	detection	NOUN
ajst-11778	109	113	,	,	PUNCT
ajst-11778	109	114	it	it	PRON
ajst-11778	109	115	is	be	AUX
ajst-11778	109	116	compared	compare	VERB
ajst-11778	109	117	with	with	ADP
ajst-11778	109	118	current	current	ADJ
ajst-11778	109	119	algorithms	algorithm	NOUN
ajst-11778	109	120	with	with	ADP
ajst-11778	109	121	good	good	ADJ
ajst-11778	109	122	performance	performance	NOUN
ajst-11778	109	123	,	,	PUNCT
ajst-11778	109	124	such	such	ADJ
ajst-11778	109	125	as	as	ADP
ajst-11778	109	126	the	the	DET
ajst-11778	109	127	same	same	ADJ
ajst-11778	109	128	types	type	NOUN
ajst-11778	109	129	of	of	ADP
ajst-11778	109	130	yolov3	yolov3	PROPN
ajst-11778	109	131	,	,	PUNCT
ajst-11778	109	132	ppyoloe	ppyoloe	NOUN
ajst-11778	109	133	-	-	PUNCT
ajst-11778	109	134	plus	plus	CCONJ
ajst-11778	109	135	and	and	CCONJ
ajst-11778	109	136	yolov7	yolov7	PROPN
ajst-11778	109	137	and	and	CCONJ
ajst-11778	109	138	ssd	ssd	NOUN
ajst-11778	109	139	and	and	CCONJ
ajst-11778	109	140	faster	fast	ADJ
ajst-11778	109	141	-	-	PUNCT
ajst-11778	109	142	rcnn	rcnn	NOUN
ajst-11778	109	143	,	,	PUNCT
ajst-11778	109	144	which	which	PRON
ajst-11778	109	145	perform	perform	VERB
ajst-11778	109	146	well	well	ADV
ajst-11778	109	147	in	in	ADP
ajst-11778	109	148	all	all	DET
ajst-11778	109	149	aspects	aspect	NOUN
ajst-11778	109	150	.	.	PUNCT
ajst-11778	110	1	by	by	ADP
ajst-11778	110	2	training	training	NOUN
ajst-11778	110	3	on	on	ADP
ajst-11778	110	4	the	the	DET
ajst-11778	110	5	neudet	neudet	NOUN
ajst-11778	110	6	dataset	dataset	NOUN
ajst-11778	110	7	and	and	CCONJ
ajst-11778	110	8	testing	testing	NOUN
ajst-11778	110	9	,	,	PUNCT
ajst-11778	110	10	the	the	DET
ajst-11778	110	11	map	map	NOUN
ajst-11778	110	12	and	and	CCONJ
ajst-11778	110	13	inference	inference	NOUN
ajst-11778	110	14	speed	speed	NOUN
ajst-11778	110	15	metrics	metric	NOUN
ajst-11778	110	16	are	be	AUX
ajst-11778	110	17	selected	select	VERB
ajst-11778	110	18	as	as	ADP
ajst-11778	110	19	the	the	DET
ajst-11778	110	20	measures	measure	NOUN
ajst-11778	110	21	,	,	PUNCT
ajst-11778	110	22	and	and	CCONJ
ajst-11778	110	23	the	the	DET
ajst-11778	110	24	experimental	experimental	ADJ
ajst-11778	110	25	results	result	NOUN
ajst-11778	110	26	are	be	AUX
ajst-11778	110	27	shown	show	VERB
ajst-11778	110	28	in	in	ADP
ajst-11778	110	29	table	table	NOUN
ajst-11778	110	30	5	5	NUM
ajst-11778	110	31	.	.	PUNCT
ajst-11778	110	32	table	table	NOUN
ajst-11778	110	33	5	5	NUM
ajst-11778	110	34	.	.	PUNCT
ajst-11778	110	35	experiments	experiment	NOUN
ajst-11778	110	36	with	with	ADP
ajst-11778	110	37	different	different	ADJ
ajst-11778	110	38	algorithms	algorithm	NOUN
ajst-11778	110	39	model	model	NOUN
ajst-11778	110	40	backbone	backbone	NOUN
ajst-11778	110	41	map%	map%	PROPN
ajst-11778	110	42	fps	fps	VERB
ajst-11778	110	43	faster	fast	ADJ
ajst-11778	110	44	-	-	PUNCT
ajst-11778	110	45	rcnn	rcnn	NOUN
ajst-11778	110	46	resnet50	resnet50	NOUN
ajst-11778	110	47	74.45	74.45	NUM
ajst-11778	110	48	%	%	NOUN
ajst-11778	110	49	10.37	10.37	NUM
ajst-11778	110	50	ssd	ssd	NOUN
ajst-11778	110	51	vgg	vgg	NOUN
ajst-11778	110	52	73.96	73.96	NUM
ajst-11778	110	53	%	%	NOUN
ajst-11778	110	54	57.00	57.00	NUM
ajst-11778	110	55	yolov3	yolov3	NOUN
ajst-11778	111	1	darknet-53	darknet-53	PROPN
ajst-11778	111	2	70.69	70.69	NUM
ajst-11778	111	3	%	%	NOUN
ajst-11778	111	4	35.64	35.64	NUM
ajst-11778	111	5	pp	pp	ADJ
ajst-11778	111	6	-	-	PUNCT
ajst-11778	111	7	yoloe	yoloe	NOUN
ajst-11778	111	8	-	-	PUNCT
ajst-11778	111	9	plus	plus	NOUN
ajst-11778	111	10	csprepresnet	csprepresnet	VERB
ajst-11778	111	11	73.60	73.60	NUM
ajst-11778	111	12	%	%	NOUN
ajst-11778	111	13	32.07	32.07	NUM
ajst-11778	111	14	yolov5	yolov5	NOUN
ajst-11778	111	15	cspdarknet53	cspdarknet53	NOUN
ajst-11778	111	16	77.50	77.50	NUM
ajst-11778	111	17	%	%	NOUN
ajst-11778	111	18	57.47	57.47	NUM
ajst-11778	111	19	yolov7	yolov7	NOUN
ajst-11778	111	20	cspdarknet53	cspdarknet53	PROPN
ajst-11778	111	21	76.90	76.90	NUM
ajst-11778	111	22	%	%	NOUN
ajst-11778	111	23	44.88	44.88	NUM
ajst-11778	111	24	improve	improve	VERB
ajst-11778	111	25	yolov5	yolov5	NOUN
ajst-11778	111	26	cspdarknet53	cspdarknet53	NOUN
ajst-11778	111	27	80.70	80.70	NUM
ajst-11778	111	28	%	%	NOUN
ajst-11778	111	29	49.87	49.87	NUM
ajst-11778	111	30	as	as	SCONJ
ajst-11778	111	31	can	can	AUX
ajst-11778	111	32	be	be	AUX
ajst-11778	111	33	seen	see	VERB
ajst-11778	111	34	from	from	ADP
ajst-11778	111	35	table	table	NOUN
ajst-11778	111	36	4	4	NUM
ajst-11778	111	37	,	,	PUNCT
ajst-11778	111	38	although	although	SCONJ
ajst-11778	111	39	the	the	DET
ajst-11778	111	40	faster	fast	ADJ
ajst-11778	111	41	r	r	NOUN
ajst-11778	111	42	-	-	PUNCT
ajst-11778	111	43	cnn	cnn	PROPN
ajst-11778	111	44	has	have	VERB
ajst-11778	111	45	slightly	slightly	ADV
ajst-11778	111	46	higher	high	ADJ
ajst-11778	111	47	accuracy	accuracy	NOUN
ajst-11778	111	48	than	than	ADP
ajst-11778	111	49	the	the	DET
ajst-11778	111	50	ssd	ssd	NOUN
ajst-11778	111	51	and	and	CCONJ
ajst-11778	111	52	yolov3	yolov3	PROPN
ajst-11778	111	53	algorithms	algorithm	NOUN
ajst-11778	111	54	,	,	PUNCT
ajst-11778	111	55	it	it	PRON
ajst-11778	111	56	consumes	consume	VERB
ajst-11778	111	57	a	a	DET
ajst-11778	111	58	lot	lot	NOUN
ajst-11778	111	59	of	of	ADP
ajst-11778	111	60	time	time	NOUN
ajst-11778	111	61	and	and	CCONJ
ajst-11778	111	62	can	can	AUX
ajst-11778	111	63	not	not	PART
ajst-11778	111	64	meet	meet	VERB
ajst-11778	111	65	the	the	DET
ajst-11778	111	66	requirement	requirement	NOUN
ajst-11778	111	67	of	of	ADP
ajst-11778	111	68	real	real	ADJ
ajst-11778	111	69	-	-	PUNCT
ajst-11778	111	70	time	time	NOUN
ajst-11778	111	71	detection	detection	NOUN
ajst-11778	111	72	.	.	PUNCT
ajst-11778	112	1	although	although	SCONJ
ajst-11778	112	2	the	the	DET
ajst-11778	112	3	fps	fps	NOUN
ajst-11778	112	4	of	of	ADP
ajst-11778	112	5	ssd	ssd	PROPN
ajst-11778	112	6	algorithm	algorithm	NOUN
ajst-11778	112	7	reaches	reach	VERB
ajst-11778	112	8	57	57	NUM
ajst-11778	112	9	frames	frame	NOUN
ajst-11778	112	10	/	/	SYM
ajst-11778	112	11	s	s	PROPN
ajst-11778	112	12	,	,	PUNCT
ajst-11778	112	13	its	its	PRON
ajst-11778	112	14	accuracy	accuracy	NOUN
ajst-11778	112	15	is	be	AUX
ajst-11778	112	16	lower	low	ADJ
ajst-11778	112	17	than	than	ADP
ajst-11778	112	18	yolov5s	yolov5s	PROPN
ajst-11778	112	19	algorithm	algorithm	NOUN
ajst-11778	112	20	.	.	PUNCT
ajst-11778	113	1	yolov3	yolov3	PROPN
ajst-11778	113	2	algorithm	algorithm	PROPN
ajst-11778	113	3	is	be	AUX
ajst-11778	113	4	inferior	inferior	ADJ
ajst-11778	113	5	to	to	ADP
ajst-11778	113	6	other	other	ADJ
ajst-11778	113	7	algorithms	algorithm	NOUN
ajst-11778	113	8	in	in	ADP
ajst-11778	113	9	terms	term	NOUN
ajst-11778	113	10	of	of	ADP
ajst-11778	113	11	both	both	DET
ajst-11778	113	12	accuracy	accuracy	NOUN
ajst-11778	113	13	and	and	CCONJ
ajst-11778	113	14	speed	speed	NOUN
ajst-11778	113	15	.	.	PUNCT
ajst-11778	114	1	pp	pp	ADV
ajst-11778	114	2	-	-	PUNCT
ajst-11778	114	3	yoloeplus	yoloeplus	NOUN
ajst-11778	114	4	,	,	PUNCT
ajst-11778	114	5	although	although	SCONJ
ajst-11778	114	6	it	it	PRON
ajst-11778	114	7	is	be	AUX
ajst-11778	114	8	a	a	DET
ajst-11778	114	9	high	high	ADJ
ajst-11778	114	10	-	-	PUNCT
ajst-11778	114	11	precision	precision	NOUN
ajst-11778	114	12	target	target	NOUN
ajst-11778	114	13	detection	detection	NOUN
ajst-11778	114	14	algorithm	algorithm	NOUN
ajst-11778	114	15	model	model	NOUN
ajst-11778	114	16	,	,	PUNCT
ajst-11778	114	17	is	be	AUX
ajst-11778	114	18	not	not	PART
ajst-11778	114	19	obvious	obvious	ADJ
ajst-11778	114	20	for	for	ADP
ajst-11778	114	21	this	this	DET
ajst-11778	114	22	dataset	dataset	VERB
ajst-11778	114	23	crack	crack	NOUN
ajst-11778	114	24	target	target	NOUN
ajst-11778	114	25	,	,	PUNCT
ajst-11778	114	26	and	and	CCONJ
ajst-11778	114	27	the	the	DET
ajst-11778	114	28	effect	effect	NOUN
ajst-11778	114	29	is	be	AUX
ajst-11778	114	30	not	not	PART
ajst-11778	114	31	as	as	ADV
ajst-11778	114	32	good	good	ADJ
ajst-11778	114	33	as	as	SCONJ
ajst-11778	114	34	expected	expect	VERB
ajst-11778	114	35	.	.	PUNCT
ajst-11778	115	1	yolov7	yolov7	PROPN
ajst-11778	115	2	also	also	ADV
ajst-11778	115	3	uses	use	VERB
ajst-11778	115	4	the	the	DET
ajst-11778	115	5	cspdarknet53	cspdarknet53	NOUN
ajst-11778	115	6	structure	structure	NOUN
ajst-11778	115	7	,	,	PUNCT
ajst-11778	115	8	which	which	PRON
ajst-11778	115	9	achieves	achieve	VERB
ajst-11778	115	10	a	a	DET
ajst-11778	115	11	certain	certain	ADJ
ajst-11778	115	12	level	level	NOUN
ajst-11778	115	13	of	of	ADP
ajst-11778	115	14	detection	detection	NOUN
ajst-11778	115	15	accuracy	accuracy	NOUN
ajst-11778	115	16	,	,	PUNCT
ajst-11778	115	17	but	but	CCONJ
ajst-11778	115	18	still	still	ADV
ajst-11778	115	19	suffers	suffer	VERB
ajst-11778	115	20	from	from	ADP
ajst-11778	115	21	a	a	DET
ajst-11778	115	22	less	less	ADJ
ajst-11778	115	23	-	-	PUNCT
ajst-11778	115	24	than	than	ADP
ajst-11778	115	25	-	-	PUNCT
ajst-11778	115	26	ideal	ideal	NOUN
ajst-11778	115	27	state	state	NOUN
ajst-11778	115	28	.	.	PUNCT
ajst-11778	116	1	the	the	DET
ajst-11778	116	2	improved	improved	ADJ
ajst-11778	116	3	yolov5s	yolov5s	PROPN
ajst-11778	116	4	algorithm	algorithm	NOUN
ajst-11778	116	5	has	have	VERB
ajst-11778	116	6	an	an	DET
ajst-11778	116	7	overall	overall	ADJ
ajst-11778	116	8	accuracy	accuracy	NOUN
ajst-11778	116	9	improvement	improvement	NOUN
ajst-11778	116	10	of	of	ADP
ajst-11778	116	11	3.2	3.2	NUM
ajst-11778	116	12	%	%	NOUN
ajst-11778	116	13	,	,	PUNCT
ajst-11778	116	14	and	and	CCONJ
ajst-11778	116	15	despite	despite	SCONJ
ajst-11778	116	16	the	the	DET
ajst-11778	116	17	decrease	decrease	NOUN
ajst-11778	116	18	in	in	ADP
ajst-11778	116	19	speed	speed	NOUN
ajst-11778	116	20	,	,	PUNCT
ajst-11778	116	21	there	there	PRON
ajst-11778	116	22	is	be	VERB
ajst-11778	116	23	little	little	ADJ
ajst-11778	116	24	loss	loss	NOUN
ajst-11778	116	25	of	of	ADP
ajst-11778	116	26	accuracy	accuracy	NOUN
ajst-11778	116	27	to	to	PART
ajst-11778	116	28	meet	meet	VERB
ajst-11778	116	29	the	the	DET
ajst-11778	116	30	requirement	requirement	NOUN
ajst-11778	116	31	of	of	ADP
ajst-11778	116	32	real	real	ADJ
ajst-11778	116	33	-	-	PUNCT
ajst-11778	116	34	time	time	NOUN
ajst-11778	116	35	detection	detection	NOUN
ajst-11778	116	36	(	(	PUNCT
ajst-11778	116	37	fps	fps	VERB
ajst-11778	116	38	higher	high	ADJ
ajst-11778	116	39	than	than	ADP
ajst-11778	116	40	30	30	NUM
ajst-11778	116	41	fps	fps	NOUN
ajst-11778	116	42	)	)	PUNCT
ajst-11778	116	43	to	to	ADP
ajst-11778	116	44	50	50	NUM
ajst-11778	116	45	fps	fps	NOUN
ajst-11778	116	46	.	.	PUNCT
ajst-11778	117	1	5	5	X
ajst-11778	117	2	.	.	X
ajst-11778	117	3	conclusion	conclusion	NOUN
ajst-11778	117	4	in	in	ADP
ajst-11778	117	5	this	this	DET
ajst-11778	117	6	paper	paper	NOUN
ajst-11778	117	7	,	,	PUNCT
ajst-11778	117	8	we	we	PRON
ajst-11778	117	9	propose	propose	VERB
ajst-11778	117	10	an	an	DET
ajst-11778	117	11	improved	improved	ADJ
ajst-11778	117	12	yolov5s	yolov5s	PROPN
ajst-11778	117	13	algorithm	algorithm	NOUN
ajst-11778	117	14	to	to	PART
ajst-11778	117	15	address	address	VERB
ajst-11778	117	16	the	the	DET
ajst-11778	117	17	problems	problem	NOUN
ajst-11778	117	18	of	of	ADP
ajst-11778	117	19	concentrated	concentrated	ADJ
ajst-11778	117	20	strip	strip	PROPN
ajst-11778	117	21	steel	steel	NOUN
ajst-11778	117	22	surface	surface	NOUN
ajst-11778	117	23	defect	defect	NOUN
ajst-11778	117	24	data	datum	NOUN
ajst-11778	117	25	,	,	PUNCT
ajst-11778	117	26	possible	possible	ADJ
ajst-11778	117	27	small	small	ADJ
ajst-11778	117	28	defect	defect	NOUN
ajst-11778	117	29	targets	target	NOUN
ajst-11778	117	30	,	,	PUNCT
ajst-11778	117	31	and	and	CCONJ
ajst-11778	117	32	unclear	unclear	ADJ
ajst-11778	117	33	defect	defect	NOUN
ajst-11778	117	34	features	feature	NOUN
ajst-11778	117	35	,	,	PUNCT
ajst-11778	117	36	which	which	PRON
ajst-11778	117	37	may	may	AUX
ajst-11778	117	38	lead	lead	VERB
ajst-11778	117	39	to	to	ADP
ajst-11778	117	40	missed	miss	VERB
ajst-11778	117	41	detection	detection	NOUN
ajst-11778	117	42	and	and	CCONJ
ajst-11778	117	43	low	low	ADJ
ajst-11778	117	44	accuracy	accuracy	NOUN
ajst-11778	117	45	.	.	PUNCT
ajst-11778	118	1	firstly	firstly	ADV
ajst-11778	118	2	,	,	PUNCT
ajst-11778	118	3	the	the	DET
ajst-11778	118	4	a	a	DET
ajst-11778	118	5	priori	priori	ADJ
ajst-11778	118	6	frame	frame	NOUN
ajst-11778	118	7	is	be	AUX
ajst-11778	118	8	re	re	VERB
ajst-11778	118	9	-	-	VERB
ajst-11778	118	10	clustered	clustered	ADJ
ajst-11778	118	11	to	to	PART
ajst-11778	118	12	locate	locate	VERB
ajst-11778	118	13	small	small	ADJ
ajst-11778	118	14	targets	target	NOUN
ajst-11778	118	15	more	more	ADV
ajst-11778	118	16	precisely	precisely	ADV
ajst-11778	118	17	;	;	PUNCT
ajst-11778	118	18	then	then	ADV
ajst-11778	118	19	the	the	DET
ajst-11778	118	20	lightweight	lightweight	ADJ
ajst-11778	118	21	decoupled	decouple	VERB
ajst-11778	118	22	detection	detection	NOUN
ajst-11778	118	23	head	head	NOUN
ajst-11778	118	24	is	be	AUX
ajst-11778	118	25	replaced	replace	VERB
ajst-11778	118	26	so	so	SCONJ
ajst-11778	118	27	that	that	SCONJ
ajst-11778	118	28	the	the	DET
ajst-11778	118	29	classification	classification	NOUN
ajst-11778	118	30	and	and	CCONJ
ajst-11778	118	31	localization	localization	NOUN
ajst-11778	118	32	tasks	task	NOUN
ajst-11778	118	33	are	be	AUX
ajst-11778	118	34	performed	perform	VERB
ajst-11778	118	35	separately	separately	ADV
ajst-11778	118	36	to	to	PART
ajst-11778	118	37	enhance	enhance	VERB
ajst-11778	118	38	the	the	DET
ajst-11778	118	39	information	information	NOUN
ajst-11778	118	40	of	of	ADP
ajst-11778	118	41	the	the	DET
ajst-11778	118	42	network	network	NOUN
ajst-11778	118	43	extracted	extract	VERB
ajst-11778	118	44	feature	feature	NOUN
ajst-11778	118	45	map	map	NOUN
ajst-11778	118	46	;	;	PUNCT
ajst-11778	118	47	finally	finally	ADV
ajst-11778	118	48	,	,	PUNCT
ajst-11778	118	49	the	the	DET
ajst-11778	118	50	inclusion	inclusion	NOUN
ajst-11778	118	51	of	of	ADP
ajst-11778	118	52	simam	simam	ADJ
ajst-11778	118	53	non	non	ADJ
ajst-11778	118	54	-	-	ADJ
ajst-11778	118	55	parametric	parametric	ADJ
ajst-11778	118	56	attention	attention	NOUN
ajst-11778	118	57	module	module	NOUN
ajst-11778	118	58	is	be	AUX
ajst-11778	118	59	added	add	VERB
ajst-11778	118	60	,	,	PUNCT
ajst-11778	118	61	and	and	CCONJ
ajst-11778	118	62	the	the	DET
ajst-11778	118	63	effect	effect	NOUN
ajst-11778	118	64	is	be	AUX
ajst-11778	118	65	significantly	significantly	ADV
ajst-11778	118	66	improved	improve	VERB
ajst-11778	118	67	without	without	ADP
ajst-11778	118	68	introducing	introduce	VERB
ajst-11778	118	69	additional	additional	ADJ
ajst-11778	118	70	parameters	parameter	NOUN
ajst-11778	118	71	.	.	PUNCT
ajst-11778	119	1	the	the	DET
ajst-11778	119	2	final	final	ADJ
ajst-11778	119	3	experimental	experimental	ADJ
ajst-11778	119	4	results	result	NOUN
ajst-11778	119	5	show	show	VERB
ajst-11778	119	6	that	that	SCONJ
ajst-11778	119	7	the	the	DET
ajst-11778	119	8	detection	detection	NOUN
ajst-11778	119	9	method	method	NOUN
ajst-11778	119	10	of	of	ADP
ajst-11778	119	11	fusing	fuse	VERB
ajst-11778	119	12	decoupled	decouple	VERB
ajst-11778	119	13	detection	detection	NOUN
ajst-11778	119	14	heads	head	NOUN
ajst-11778	119	15	in	in	ADP
ajst-11778	119	16	this	this	DET
ajst-11778	119	17	paper	paper	NOUN
ajst-11778	119	18	can	can	AUX
ajst-11778	119	19	effectively	effectively	ADV
ajst-11778	119	20	improve	improve	VERB
ajst-11778	119	21	the	the	DET
ajst-11778	119	22	problem	problem	NOUN
ajst-11778	119	23	of	of	ADP
ajst-11778	119	24	inaccurate	inaccurate	ADJ
ajst-11778	119	25	detection	detection	NOUN
ajst-11778	119	26	of	of	ADP
ajst-11778	119	27	strip	strip	NOUN
ajst-11778	119	28	images	image	NOUN
ajst-11778	119	29	,	,	PUNCT
ajst-11778	119	30	and	and	CCONJ
ajst-11778	119	31	the	the	DET
ajst-11778	119	32	detection	detection	NOUN
ajst-11778	119	33	speed	speed	NOUN
ajst-11778	119	34	also	also	ADV
ajst-11778	119	35	has	have	VERB
ajst-11778	119	36	a	a	DET
ajst-11778	119	37	good	good	ADJ
ajst-11778	119	38	advantage	advantage	NOUN
ajst-11778	119	39	compared	compare	VERB
ajst-11778	119	40	with	with	ADP
ajst-11778	119	41	other	other	ADJ
ajst-11778	119	42	algorithms	algorithm	NOUN
ajst-11778	119	43	,	,	PUNCT
ajst-11778	119	44	and	and	CCONJ
ajst-11778	119	45	the	the	DET
ajst-11778	119	46	final	final	ADJ
ajst-11778	119	47	map	map	NOUN
ajst-11778	119	48	of	of	ADP
ajst-11778	119	49	the	the	DET
ajst-11778	119	50	model	model	NOUN
ajst-11778	119	51	reaches	reach	VERB
ajst-11778	119	52	0.807	0.807	NUM
ajst-11778	119	53	with	with	ADP
ajst-11778	119	54	a	a	DET
ajst-11778	119	55	speed	speed	NOUN
ajst-11778	119	56	of	of	ADP
ajst-11778	119	57	50	50	NUM
ajst-11778	119	58	frames	frame	NOUN
ajst-11778	119	59	/	/	SYM
ajst-11778	119	60	s.	s.	PROPN
ajst-11778	120	1	the	the	DET
ajst-11778	120	2	detection	detection	NOUN
ajst-11778	120	3	accuracy	accuracy	NOUN
ajst-11778	120	4	is	be	AUX
ajst-11778	120	5	higher	high	ADJ
ajst-11778	120	6	than	than	ADP
ajst-11778	120	7	that	that	PRON
ajst-11778	120	8	of	of	ADP
ajst-11778	120	9	the	the	DET
ajst-11778	120	10	generic	generic	ADJ
ajst-11778	120	11	target	target	NOUN
ajst-11778	120	12	detection	detection	NOUN
ajst-11778	120	13	model	model	NOUN
ajst-11778	120	14	,	,	PUNCT
ajst-11778	120	15	and	and	CCONJ
ajst-11778	120	16	the	the	DET
ajst-11778	120	17	method	method	NOUN
ajst-11778	120	18	provides	provide	VERB
ajst-11778	120	19	useful	useful	ADJ
ajst-11778	120	20	help	help	NOUN
ajst-11778	120	21	for	for	ADP
ajst-11778	120	22	the	the	DET
ajst-11778	120	23	detection	detection	NOUN
ajst-11778	120	24	of	of	ADP
ajst-11778	120	25	defects	defect	NOUN
ajst-11778	120	26	in	in	ADP
ajst-11778	120	27	strips	strip	NOUN
ajst-11778	120	28	.	.	PUNCT
ajst-11778	121	1	however	however	ADV
ajst-11778	121	2	,	,	PUNCT
ajst-11778	121	3	in	in	ADP
ajst-11778	121	4	order	order	NOUN
ajst-11778	121	5	to	to	PART
ajst-11778	121	6	achieve	achieve	VERB
ajst-11778	121	7	real	real	ADJ
ajst-11778	121	8	-	-	PUNCT
ajst-11778	121	9	time	time	NOUN
ajst-11778	121	10	detection	detection	NOUN
ajst-11778	121	11	,	,	PUNCT
ajst-11778	121	12	a	a	DET
ajst-11778	121	13	more	more	ADV
ajst-11778	121	14	lightweight	lightweight	ADJ
ajst-11778	121	15	architecture	architecture	NOUN
ajst-11778	121	16	is	be	AUX
ajst-11778	121	17	needed	need	VERB
ajst-11778	121	18	to	to	PART
ajst-11778	121	19	further	far	ADV
ajst-11778	121	20	optimize	optimize	VERB
ajst-11778	121	21	the	the	DET
ajst-11778	121	22	model	model	NOUN
ajst-11778	121	23	and	and	CCONJ
ajst-11778	121	24	improve	improve	VERB
ajst-11778	121	25	the	the	DET
ajst-11778	121	26	model	model	NOUN
ajst-11778	121	27	generalization	generalization	NOUN
ajst-11778	121	28	in	in	ADP
ajst-11778	121	29	order	order	NOUN
ajst-11778	121	30	to	to	PART
ajst-11778	121	31	implant	implant	VERB
ajst-11778	121	32	the	the	DET
ajst-11778	121	33	model	model	NOUN
ajst-11778	121	34	into	into	ADP
ajst-11778	121	35	the	the	DET
ajst-11778	121	36	mobile	mobile	NOUN
ajst-11778	121	37	for	for	ADP
ajst-11778	121	38	detecting	detect	VERB
ajst-11778	121	39	strip	strip	NOUN
ajst-11778	121	40	defects	defect	NOUN
ajst-11778	121	41	in	in	ADP
ajst-11778	121	42	real	real	ADJ
ajst-11778	121	43	-	-	PUNCT
ajst-11778	121	44	time	time	NOUN
ajst-11778	121	45	.	.	PUNCT
ajst-11778	122	1	references	reference	NOUN
ajst-11778	122	2	[	[	X
ajst-11778	122	3	1	1	X
ajst-11778	122	4	]	]	PUNCT
ajst-11778	122	5	cao	cao	PROPN
ajst-11778	122	6	j	j	PROPN
ajst-11778	122	7	,	,	PUNCT
ajst-11778	122	8	li	li	PROPN
ajst-11778	122	9	y	y	PROPN
ajst-11778	122	10	,	,	PUNCT
ajst-11778	122	11	sun	sun	PROPN
ajst-11778	122	12	h	h	PROPN
ajst-11778	122	13	,	,	PUNCT
ajst-11778	122	14	et	et	PROPN
ajst-11778	122	15	al	al	PROPN
ajst-11778	122	16	.	.	PUNCT
ajst-11778	123	1	a	a	DET
ajst-11778	123	2	survey	survey	NOUN
ajst-11778	123	3	on	on	ADP
ajst-11778	123	4	deep	deep	ADJ
ajst-11778	123	5	learning	learning	NOUN
ajst-11778	123	6	based	base	VERB
ajst-11778	123	7	visual	visual	ADJ
ajst-11778	123	8	object	object	NOUN
ajst-11778	123	9	detection[j	detection[j	PROPN
ajst-11778	123	10	]	]	PUNCT
ajst-11778	123	11	.	.	PUNCT
ajst-11778	124	1	image	image	NOUN
ajst-11778	124	2	graph	graph	NOUN
ajst-11778	124	3	,	,	PUNCT
ajst-11778	124	4	2022	2022	NUM
ajst-11778	124	5	,	,	PUNCT
ajst-11778	124	6	27	27	NUM
ajst-11778	124	7	:	:	SYM
ajst-11778	124	8	1697	1697	NUM
ajst-11778	124	9	-	-	SYM
ajst-11778	124	10	1722	1722	NUM
ajst-11778	124	11	.	.	PUNCT
ajst-11778	125	1	[	[	X
ajst-11778	125	2	2	2	X
ajst-11778	125	3	]	]	X
ajst-11778	125	4	shi	shi	PROPN
ajst-11778	125	5	j	j	PROPN
ajst-11778	125	6	,	,	PUNCT
ajst-11778	125	7	yang	yang	PROPN
ajst-11778	125	8	j	j	PROPN
ajst-11778	125	9	,	,	PUNCT
ajst-11778	125	10	zhang	zhang	PROPN
ajst-11778	125	11	y.	y.	PROPN
ajst-11778	125	12	research	research	PROPN
ajst-11778	125	13	on	on	ADP
ajst-11778	125	14	steel	steel	NOUN
ajst-11778	125	15	surface	surface	NOUN
ajst-11778	125	16	defect	defect	NOUN
ajst-11778	125	17	detection	detection	NOUN
ajst-11778	125	18	based	base	VERB
ajst-11778	125	19	on	on	ADP
ajst-11778	125	20	yolov5	yolov5	NOUN
ajst-11778	125	21	with	with	ADP
ajst-11778	125	22	attention	attention	NOUN
ajst-11778	125	23	mechanism[j	mechanism[j	PROPN
ajst-11778	125	24	]	]	PUNCT
ajst-11778	125	25	.	.	PUNCT
ajst-11778	126	1	electronics	electronic	NOUN
ajst-11778	126	2	,	,	PUNCT
ajst-11778	126	3	2022	2022	NUM
ajst-11778	126	4	,	,	PUNCT
ajst-11778	126	5	11(22	11(22	NUM
ajst-11778	126	6	):	):	PUNCT
ajst-11778	126	7	3735	3735	NUM
ajst-11778	126	8	.	.	PUNCT
ajst-11778	127	1	[	[	X
ajst-11778	127	2	3	3	X
ajst-11778	127	3	]	]	X
ajst-11778	127	4	girshick	girshick	ADJ
ajst-11778	127	5	r	r	PROPN
ajst-11778	127	6	,	,	PUNCT
ajst-11778	127	7	donahue	donahue	PROPN
ajst-11778	127	8	j	j	PROPN
ajst-11778	127	9	,	,	PUNCT
ajst-11778	127	10	darrell	darrell	PROPN
ajst-11778	127	11	t	t	PROPN
ajst-11778	127	12	,	,	PUNCT
ajst-11778	127	13	et	et	PROPN
ajst-11778	127	14	al	al	PROPN
ajst-11778	127	15	.	.	PROPN
ajst-11778	127	16	rich	rich	ADJ
ajst-11778	127	17	feature	feature	NOUN
ajst-11778	127	18	hierarchies	hierarchy	NOUN
ajst-11778	127	19	for	for	ADP
ajst-11778	127	20	accurate	accurate	ADJ
ajst-11778	127	21	object	object	NOUN
ajst-11778	127	22	detection	detection	NOUN
ajst-11778	127	23	and	and	CCONJ
ajst-11778	127	24	semantic	semantic	ADJ
ajst-11778	127	25	segmentation[c]//	segmentation[c]//	PROPN
ajst-11778	127	26	proceedings	proceeding	NOUN
ajst-11778	127	27	of	of	ADP
ajst-11778	127	28	the	the	DET
ajst-11778	127	29	2014	2014	NUM
ajst-11778	127	30	ieee	ieee	NOUN
ajst-11778	127	31	conference	conference	NOUN
ajst-11778	127	32	on	on	ADP
ajst-11778	127	33	computer	computer	NOUN
ajst-11778	127	34	vision	vision	NOUN
ajst-11778	127	35	and	and	CCONJ
ajst-11778	127	36	pattern	pattern	NOUN
ajst-11778	127	37	recognition	recognition	NOUN
ajst-11778	127	38	.	.	PUNCT
ajst-11778	128	1	piscataway	piscataway	PROPN
ajst-11778	128	2	:	:	PUNCT
ajst-11778	128	3	ieee	ieee	NOUN
ajst-11778	128	4	,	,	PUNCT
ajst-11778	128	5	2014	2014	NUM
ajst-11778	128	6	:	:	PUNCT
ajst-11778	128	7	580	580	NUM
ajst-11778	128	8	-	-	SYM
ajst-11778	128	9	587	587	NUM
ajst-11778	128	10	.	.	PUNCT
ajst-11778	128	11	71	71	NUM
ajst-11778	129	1	[	[	SYM
ajst-11778	129	2	4	4	NUM
ajst-11778	129	3	]	]	X
ajst-11778	129	4	ren	ren	PROPN
ajst-11778	129	5	s	s	PROPN
ajst-11778	129	6	q	q	NOUN
ajst-11778	129	7	,	,	PUNCT
ajst-11778	129	8	he	he	PRON
ajst-11778	129	9	k	k	PROPN
ajst-11778	129	10	m	m	PROPN
ajst-11778	129	11	,	,	PUNCT
ajst-11778	129	12	girshick	girshick	ADJ
ajst-11778	129	13	r	r	NOUN
ajst-11778	129	14	,	,	PUNCT
ajst-11778	129	15	et	et	PROPN
ajst-11778	129	16	al	al	PROPN
ajst-11778	129	17	.	.	PUNCT
ajst-11778	130	1	faster	fast	ADJ
ajst-11778	130	2	r	r	NOUN
ajst-11778	130	3	-	-	PUNCT
ajst-11778	130	4	cnn	cnn	NOUN
ajst-11778	130	5	:	:	PUNCT
ajst-11778	130	6	towards	towards	ADP
ajst-11778	130	7	real	real	ADJ
ajst-11778	130	8	-	-	PUNCT
ajst-11778	130	9	time	time	NOUN
ajst-11778	130	10	object	object	NOUN
ajst-11778	130	11	detection	detection	NOUN
ajst-11778	130	12	with	with	ADP
ajst-11778	130	13	region	region	NOUN
ajst-11778	130	14	proposal	proposal	NOUN
ajst-11778	130	15	networks[j	networks[j	PROPN
ajst-11778	130	16	]	]	X
ajst-11778	130	17	.	.	PUNCT
ajst-11778	131	1	ieee	ieee	NOUN
ajst-11778	131	2	transactions	transaction	NOUN
ajst-11778	131	3	on	on	ADP
ajst-11778	131	4	pattern	pattern	NOUN
ajst-11778	131	5	analysis	analysis	NOUN
ajst-11778	131	6	and	and	CCONJ
ajst-11778	131	7	machine	machine	NOUN
ajst-11778	131	8	intelligence，2017	intelligence，2017	NOUN
ajst-11778	131	9	,	,	PUNCT
ajst-11778	131	10	39(6	39(6	NUM
ajst-11778	131	11	):	):	PUNCT
ajst-11778	131	12	1137	1137	NUM
ajst-11778	131	13	-	-	SYM
ajst-11778	131	14	1149	1149	NUM
ajst-11778	131	15	.	.	PUNCT
ajst-11778	132	1	[	[	X
ajst-11778	132	2	5	5	X
ajst-11778	132	3	]	]	PUNCT
ajst-11778	132	4	he	he	PRON
ajst-11778	132	5	k	k	PROPN
ajst-11778	132	6	m	m	PROPN
ajst-11778	132	7	,	,	PUNCT
ajst-11778	132	8	kioxari	kioxari	PROPN
ajst-11778	132	9	g	g	NOUN
ajst-11778	132	10	,	,	PUNCT
ajst-11778	132	11	dollar	dollar	NOUN
ajst-11778	132	12	p	p	NOUN
ajst-11778	132	13	,	,	PUNCT
ajst-11778	132	14	et	et	PROPN
ajst-11778	132	15	al	al	PROPN
ajst-11778	132	16	.	.	PROPN
ajst-11778	132	17	mask	mask	NOUN
ajst-11778	132	18	r	r	PROPN
ajst-11778	132	19	-	-	PUNCT
ajst-11778	132	20	cnn[c]//	cnn[c]//	ADJ
ajst-11778	132	21	proceedings	proceeding	NOUN
ajst-11778	132	22	of	of	ADP
ajst-11778	132	23	the	the	DET
ajst-11778	132	24	2017	2017	NUM
ajst-11778	132	25	ieee	ieee	NOUN
ajst-11778	132	26	international	international	ADJ
ajst-11778	132	27	conference	conference	NOUN
ajst-11778	132	28	on	on	ADP
ajst-11778	132	29	computer	computer	NOUN
ajst-11778	132	30	vision	vision	NOUN
ajst-11778	132	31	.	.	PUNCT
ajst-11778	133	1	piscataway	piscataway	NOUN
ajst-11778	133	2	:	:	PUNCT
ajst-11778	133	3	ieee	ieee	NOUN
ajst-11778	133	4	,	,	PUNCT
ajst-11778	133	5	2017	2017	NUM
ajst-11778	133	6	:	:	PUNCT
ajst-11778	133	7	2961	2961	NUM
ajst-11778	133	8	-	-	SYM
ajst-11778	133	9	2969	2969	NUM
ajst-11778	133	10	.	.	PUNCT
ajst-11778	134	1	[	[	X
ajst-11778	134	2	6	6	NUM
ajst-11778	134	3	]	]	X
ajst-11778	134	4	redmon	redmon	PROPN
ajst-11778	134	5	j	j	PROPN
ajst-11778	134	6	,	,	PUNCT
ajst-11778	134	7	divvala	divvala	PROPN
ajst-11778	134	8	s	s	PROPN
ajst-11778	134	9	,	,	PUNCT
ajst-11778	134	10	girshick	girshick	ADJ
ajst-11778	134	11	r	r	NOUN
ajst-11778	134	12	,	,	PUNCT
ajst-11778	134	13	et	et	PROPN
ajst-11778	134	14	al	al	PROPN
ajst-11778	134	15	.	.	PUNCT
ajst-11778	135	1	you	you	PRON
ajst-11778	135	2	only	only	ADV
ajst-11778	135	3	look	look	VERB
ajst-11778	135	4	once	once	ADV
ajst-11778	135	5	:	:	PUNCT
ajst-11778	135	6	unified	unified	ADJ
ajst-11778	135	7	,	,	PUNCT
ajst-11778	135	8	real	real	ADJ
ajst-11778	135	9	-	-	PUNCT
ajst-11778	135	10	time	time	NOUN
ajst-11778	135	11	object	object	NOUN
ajst-11778	135	12	detection[c]//	detection[c]//	ADJ
ajst-11778	135	13	proceedings	proceeding	NOUN
ajst-11778	135	14	of	of	ADP
ajst-11778	135	15	the	the	DET
ajst-11778	135	16	ieee	ieee	NOUN
ajst-11778	135	17	conference	conference	NOUN
ajst-11778	135	18	on	on	ADP
ajst-11778	135	19	computer	computer	NOUN
ajst-11778	135	20	vision	vision	NOUN
ajst-11778	135	21	and	and	CCONJ
ajst-11778	135	22	pattern	pattern	NOUN
ajst-11778	135	23	recognition	recognition	NOUN
ajst-11778	135	24	.	.	PUNCT
ajst-11778	136	1	2016	2016	NUM
ajst-11778	136	2	:	:	PUNCT
ajst-11778	136	3	779	779	NUM
ajst-11778	136	4	-	-	SYM
ajst-11778	136	5	788	788	NUM
ajst-11778	136	6	.	.	PUNCT
ajst-11778	137	1	[	[	X
ajst-11778	137	2	7	7	X
ajst-11778	137	3	]	]	X
ajst-11778	137	4	redmon	redmon	PROPN
ajst-11778	137	5	j	j	PROPN
ajst-11778	137	6	,	,	PUNCT
ajst-11778	137	7	farhadi	farhadi	PROPN
ajst-11778	137	8	a.	a.	PROPN
ajst-11778	137	9	yolo9000	yolo9000	PROPN
ajst-11778	137	10	:	:	PUNCT
ajst-11778	137	11	better	well	ADJ
ajst-11778	137	12	,	,	PUNCT
ajst-11778	137	13	faster	fast	ADV
ajst-11778	137	14	,	,	PUNCT
ajst-11778	137	15	stronger[c]//	stronger[c]//	PROPN
ajst-11778	137	16	proceedings	proceeding	NOUN
ajst-11778	137	17	of	of	ADP
ajst-11778	137	18	the	the	DET
ajst-11778	137	19	ieee	ieee	NOUN
ajst-11778	137	20	conference	conference	NOUN
ajst-11778	137	21	on	on	ADP
ajst-11778	137	22	computer	computer	NOUN
ajst-11778	137	23	vision	vision	NOUN
ajst-11778	137	24	and	and	CCONJ
ajst-11778	137	25	pattern	pattern	NOUN
ajst-11778	137	26	recognition	recognition	PROPN
ajst-11778	137	27	washington	washington	PROPN
ajst-11778	137	28	d.c	d.c	PROPN
ajst-11778	137	29	.	.	PROPN
ajst-11778	137	30	usa	usa	PROPN
ajst-11778	137	31	:	:	PUNCT
ajst-11778	137	32	ieee	ieee	NOUN
ajst-11778	137	33	,	,	PUNCT
ajst-11778	137	34	computer	computer	NOUN
ajst-11778	137	35	society	society	NOUN
ajst-11778	137	36	,	,	PUNCT
ajst-11778	137	37	2017	2017	NUM
ajst-11778	137	38	:	:	PUNCT
ajst-11778	137	39	6517	6517	NUM
ajst-11778	137	40	-	-	SYM
ajst-11778	137	41	6525	6525	NUM
ajst-11778	137	42	.	.	PUNCT
ajst-11778	138	1	[	[	X
ajst-11778	138	2	8	8	NUM
ajst-11778	138	3	]	]	X
ajst-11778	138	4	redmon	redmon	PROPN
ajst-11778	138	5	j	j	PROPN
ajst-11778	138	6	,	,	PUNCT
ajst-11778	138	7	farhadi	farhadi	PROPN
ajst-11778	138	8	a.	a.	PROPN
ajst-11778	138	9	yolov3	yolov3	PROPN
ajst-11778	138	10	:	:	PUNCT
ajst-11778	138	11	an	an	DET
ajst-11778	138	12	incremental	incremental	ADJ
ajst-11778	138	13	improvement[c]//	improvement[c]//	PROPN
ajst-11778	138	14	computer	computer	NOUN
ajst-11778	138	15	vision	vision	NOUN
ajst-11778	138	16	and	and	CCONJ
ajst-11778	138	17	pattern	pattern	NOUN
ajst-11778	138	18	recognition	recognition	NOUN
ajst-11778	138	19	.	.	PUNCT
ajst-11778	139	1	berlin	berlin	PROPN
ajst-11778	139	2	/	/	SYM
ajst-11778	139	3	heidelberg	heidelberg	PROPN
ajst-11778	139	4	,	,	PUNCT
ajst-11778	139	5	germany	germany	PROPN
ajst-11778	139	6	:	:	PUNCT
ajst-11778	139	7	springer	springer	NOUN
ajst-11778	139	8	,	,	PUNCT
ajst-11778	139	9	2018	2018	NUM
ajst-11778	139	10	,	,	PUNCT
ajst-11778	139	11	1804	1804	NUM
ajst-11778	139	12	:	:	PUNCT
ajst-11778	139	13	1	1	NUM
ajst-11778	139	14	-	-	SYM
ajst-11778	139	15	6	6	NUM
ajst-11778	139	16	.	.	PUNCT
ajst-11778	140	1	[	[	X
ajst-11778	140	2	9	9	NUM
ajst-11778	140	3	]	]	PUNCT
ajst-11778	140	4	wang	wang	PROPN
ajst-11778	140	5	c	c	PROPN
ajst-11778	140	6	y	y	PROPN
ajst-11778	140	7	,	,	PUNCT
ajst-11778	140	8	bochkovskiy	bochkovskiy	VERB
ajst-11778	140	9	a	a	PRON
ajst-11778	140	10	,	,	PUNCT
ajst-11778	140	11	liao	liao	PROPN
ajst-11778	140	12	h	h	PROPN
ajst-11778	140	13	y	y	PROPN
ajst-11778	140	14	m.	m.	NOUN
ajst-11778	140	15	yolov7	yolov7	NOUN
ajst-11778	140	16	:	:	PUNCT
ajst-11778	140	17	trainable	trainable	ADJ
ajst-11778	140	18	bag	bag	NOUN
ajst-11778	140	19	-	-	PUNCT
ajst-11778	140	20	of	of	ADP
ajst-11778	140	21	-	-	PUNCT
ajst-11778	140	22	freebies	freebie	NOUN
ajst-11778	140	23	sets	set	VERB
ajst-11778	140	24	new	new	ADJ
ajst-11778	140	25	state	state	NOUN
ajst-11778	140	26	-	-	PUNCT
ajst-11778	140	27	of	of	ADP
ajst-11778	140	28	-	-	PUNCT
ajst-11778	140	29	the	the	DET
ajst-11778	140	30	-	-	PUNCT
ajst-11778	140	31	art	art	NOUN
ajst-11778	140	32	for	for	ADP
ajst-11778	140	33	real	real	ADJ
ajst-11778	140	34	-	-	PUNCT
ajst-11778	140	35	time	time	NOUN
ajst-11778	140	36	object	object	NOUN
ajst-11778	140	37	detectors[j	detectors[j	PROPN
ajst-11778	140	38	]	]	PUNCT
ajst-11778	140	39	.	.	PUNCT
ajst-11778	141	1	arxiv	arxiv	PROPN
ajst-11778	141	2	2022	2022	NUM
ajst-11778	141	3	,	,	PUNCT
ajst-11778	141	4	arxiv	arxiv	NOUN
ajst-11778	141	5	:	:	PUNCT
ajst-11778	141	6	2207	2207	NUM
ajst-11778	141	7	.	.	PUNCT
ajst-11778	142	1	02696	02696	NUM
ajst-11778	142	2	.	.	PUNCT
ajst-11778	143	1	[	[	X
ajst-11778	143	2	10	10	NUM
ajst-11778	143	3	]	]	X
ajst-11778	143	4	liu	liu	PROPN
ajst-11778	143	5	w	w	PROPN
ajst-11778	143	6	,	,	PUNCT
ajst-11778	143	7	anguelov	anguelov	NOUN
ajst-11778	143	8	d	d	NOUN
ajst-11778	143	9	,	,	PUNCT
ajst-11778	143	10	erhan	erhan	ADP
ajst-11778	143	11	d	d	PROPN
ajst-11778	143	12	,	,	PUNCT
ajst-11778	143	13	et	et	PROPN
ajst-11778	143	14	al	al	PROPN
ajst-11778	143	15	.	.	PROPN
ajst-11778	143	16	ssd	ssd	PROPN
ajst-11778	143	17	:	:	PUNCT
ajst-11778	143	18	single	single	ADJ
ajst-11778	143	19	shot	shot	NOUN
ajst-11778	143	20	multibox	multibox	PROPN
ajst-11778	143	21	detector[c]//	detector[c]//	PROPN
ajst-11778	143	22	proceedings	proceeding	NOUN
ajst-11778	143	23	of	of	ADP
ajst-11778	143	24	the	the	DET
ajst-11778	143	25	2016	2016	NUM
ajst-11778	143	26	european	european	ADJ
ajst-11778	143	27	conference	conference	NOUN
ajst-11778	143	28	on	on	ADP
ajst-11778	143	29	computer	computer	NOUN
ajst-11778	143	30	vision	vision	NOUN
ajst-11778	143	31	,	,	PUNCT
ajst-11778	143	32	lncs	lncs	PROPN
ajst-11778	143	33	9905	9905	NUM
ajst-11778	143	34	.	.	PUNCT
ajst-11778	144	1	cham	cham	PROPN
ajst-11778	144	2	:	:	PUNCT
ajst-11778	144	3	springer	springer	NOUN
ajst-11778	144	4	,	,	PUNCT
ajst-11778	144	5	2016	2016	NUM
ajst-11778	144	6	:	:	PUNCT
ajst-11778	144	7	21	21	NUM
ajst-11778	144	8	-	-	SYM
ajst-11778	144	9	37	37	NUM
ajst-11778	144	10	.	.	PUNCT
ajst-11778	145	1	[	[	X
ajst-11778	145	2	11	11	NUM
ajst-11778	145	3	]	]	X
ajst-11778	145	4	yang	yang	PROPN
ajst-11778	145	5	l	l	PROPN
ajst-11778	145	6	,	,	PUNCT
ajst-11778	145	7	zhang	zhang	PROPN
ajst-11778	145	8	r	r	PROPN
ajst-11778	145	9	y	y	PROPN
ajst-11778	145	10	,	,	PUNCT
ajst-11778	145	11	li	li	PROPN
ajst-11778	145	12	l	l	PROPN
ajst-11778	145	13	,	,	PUNCT
ajst-11778	145	14	et	et	PROPN
ajst-11778	145	15	al	al	PROPN
ajst-11778	145	16	.	.	PUNCT
ajst-11778	145	17	simam	simam	PROPN
ajst-11778	145	18	:	:	PUNCT
ajst-11778	145	19	a	a	DET
ajst-11778	145	20	simple	simple	ADJ
ajst-11778	145	21	,	,	PUNCT
ajst-11778	145	22	parameter	parameter	NOUN
ajst-11778	145	23	-	-	PUNCT
ajst-11778	145	24	free	free	ADJ
ajst-11778	145	25	attention	attention	NOUN
ajst-11778	145	26	module	module	NOUN
ajst-11778	145	27	for	for	ADP
ajst-11778	145	28	convolutional	convolutional	ADJ
ajst-11778	145	29	neural	neural	ADJ
ajst-11778	145	30	networks[c]//	networks[c]//	ADJ
ajst-11778	145	31	international	international	ADJ
ajst-11778	145	32	conference	conference	NOUN
ajst-11778	145	33	on	on	ADP
ajst-11778	145	34	machine	machine	NOUN
ajst-11778	145	35	learning	learning	NOUN
ajst-11778	145	36	.	.	PUNCT
ajst-11778	146	1	pmlr	pmlr	NOUN
ajst-11778	146	2	,	,	PUNCT
ajst-11778	146	3	2021	2021	NUM
ajst-11778	146	4	:	:	PUNCT
ajst-11778	146	5	11863	11863	NUM
ajst-11778	146	6	-	-	SYM
ajst-11778	146	7	11874	11874	NUM
ajst-11778	146	8	.	.	PUNCT
ajst-11778	147	1	[	[	X
ajst-11778	147	2	12	12	NUM
ajst-11778	147	3	]	]	X
ajst-11778	147	4	fang	fang	PROPN
ajst-11778	147	5	b	b	PROPN
ajst-11778	147	6	,	,	PUNCT
ajst-11778	147	7	fang	fang	PROPN
ajst-11778	147	8	l.	l.	PROPN
ajst-11778	147	9	concise	concise	PROPN
ajst-11778	147	10	feature	feature	PROPN
ajst-11778	147	11	pyramid	pyramid	PROPN
ajst-11778	147	12	region	region	NOUN
ajst-11778	147	13	proposal	proposal	NOUN
ajst-11778	147	14	network	network	NOUN
ajst-11778	147	15	for	for	ADP
ajst-11778	147	16	multi	multi	ADJ
ajst-11778	147	17	-	-	ADJ
ajst-11778	147	18	scale	scale	ADJ
ajst-11778	147	19	object	object	NOUN
ajst-11778	147	20	detection[j	detection[j	PROPN
ajst-11778	147	21	]	]	PUNCT
ajst-11778	147	22	.	.	PUNCT
ajst-11778	148	1	the	the	DET
ajst-11778	148	2	journal	journal	NOUN
ajst-11778	148	3	of	of	ADP
ajst-11778	148	4	supercomputing	supercomputing	NOUN
ajst-11778	148	5	,	,	PUNCT
ajst-11778	148	6	2020	2020	NUM
ajst-11778	148	7	,	,	PUNCT
ajst-11778	148	8	765(5):3327	765(5):3327	PROPN
ajst-11778	148	9	-	-	NOUN
ajst-11778	148	10	3337	3337	NUM
ajst-11778	148	11	.	.	PUNCT
ajst-11778	149	1	[	[	X
ajst-11778	149	2	13	13	NUM
ajst-11778	149	3	]	]	X
ajst-11778	149	4	liu	liu	PROPN
ajst-11778	149	5	s	s	PROPN
ajst-11778	149	6	,	,	PUNCT
ajst-11778	149	7	qi	qi	PROPN
ajst-11778	149	8	l	l	NOUN
ajst-11778	149	9	,	,	PUNCT
ajst-11778	149	10	qin	qin	PROPN
ajst-11778	149	11	h	h	PROPN
ajst-11778	149	12	,	,	PUNCT
ajst-11778	149	13	et	et	PROPN
ajst-11778	149	14	al	al	PROPN
ajst-11778	149	15	.	.	PROPN
ajst-11778	149	16	path	path	PROPN
ajst-11778	149	17	aggregation	aggregation	NOUN
ajst-11778	149	18	network	network	NOUN
ajst-11778	149	19	for	for	ADP
ajst-11778	149	20	instance	instance	NOUN
ajst-11778	149	21	segmentation[c]//proceedings	segmentation[c]//proceeding	NOUN
ajst-11778	149	22	of	of	ADP
ajst-11778	149	23	the	the	DET
ajst-11778	149	24	ieee	ieee	NOUN
ajst-11778	149	25	con⁃	con⁃	PROPN
ajst-11778	149	26	ference	ference	NOUN
ajst-11778	149	27	on	on	ADP
ajst-11778	149	28	computer	computer	NOUN
ajst-11778	149	29	vision	vision	NOUN
ajst-11778	149	30	and	and	CCONJ
ajst-11778	149	31	pattern	pattern	NOUN
ajst-11778	149	32	recognition	recognition	NOUN
ajst-11778	149	33	,	,	PUNCT
ajst-11778	149	34	2018	2018	NUM
ajst-11778	149	35	:	:	PUNCT
ajst-11778	149	36	8759	8759	NUM
ajst-11778	149	37	-	-	SYM
ajst-11778	149	38	8768	8768	NUM
ajst-11778	149	39	.	.	PUNCT
ajst-11778	150	1	[	[	X
ajst-11778	150	2	14	14	NUM
ajst-11778	150	3	]	]	PUNCT
ajst-11778	150	4	he	he	PRON
ajst-11778	150	5	y	y	PROPN
ajst-11778	150	6	,	,	PUNCT
ajst-11778	150	7	song	song	NOUN
ajst-11778	150	8	k	k	PROPN
ajst-11778	150	9	c	c	PROPN
ajst-11778	150	10	,	,	PUNCT
ajst-11778	150	11	meng	meng	PROPN
ajst-11778	150	12	q	q	PROPN
ajst-11778	150	13	g	g	PROPN
ajst-11778	150	14	,	,	PUNCT
ajst-11778	150	15	et	et	PROPN
ajst-11778	150	16	al	al	PROPN
ajst-11778	150	17	.	.	PUNCT
ajst-11778	151	1	an	an	DET
ajst-11778	151	2	end	end	NOUN
ajst-11778	151	3	-	-	PUNCT
ajst-11778	151	4	to	to	ADP
ajst-11778	151	5	-	-	PUNCT
ajst-11778	151	6	end	end	NOUN
ajst-11778	151	7	steel	steel	NOUN
ajst-11778	151	8	surface	surface	NOUN
ajst-11778	151	9	defect	defect	NOUN
ajst-11778	151	10	detection	detection	NOUN
ajst-11778	151	11	approach	approach	NOUN
ajst-11778	151	12	via	via	ADP
ajst-11778	151	13	fusing	fuse	VERB
ajst-11778	151	14	multiple	multiple	ADJ
ajst-11778	151	15	hierarchical	hierarchical	ADJ
ajst-11778	151	16	features	feature	NOUN
ajst-11778	151	17	.	.	PUNCT
ajst-11778	152	1	ieee	ieee	NOUN
ajst-11778	152	2	transactions	transaction	NOUN
ajst-11778	152	3	on	on	ADP
ajst-11778	152	4	instrumentation	instrumentation	NOUN
ajst-11778	152	5	and	and	CCONJ
ajst-11778	152	6	measurement	measurement	NOUN
ajst-11778	152	7	,	,	PUNCT
ajst-11778	152	8	2020	2020	NUM
ajst-11778	152	9	,	,	PUNCT
ajst-11778	152	10	69(4	69(4	NUM
ajst-11778	152	11	):	):	PUNCT
ajst-11778	152	12	1493	1493	NUM
ajst-11778	152	13	-	-	SYM
ajst-11778	152	14	1504	1504	NUM
ajst-11778	152	15	.	.	PUNCT
ajst-11778	153	1	[	[	X
ajst-11778	153	2	15	15	NUM
ajst-11778	153	3	]	]	X
ajst-11778	153	4	bao	bao	PROPN
ajst-11778	153	5	y	y	PROPN
ajst-11778	153	6	,	,	PUNCT
ajst-11778	153	7	song	song	NOUN
ajst-11778	153	8	k	k	PROPN
ajst-11778	153	9	,	,	PUNCT
ajst-11778	153	10	liu	liu	PROPN
ajst-11778	153	11	j.	j.	PROPN
ajst-11778	153	12	triplet	triplet	NOUN
ajst-11778	153	13	-	-	PUNCT
ajst-11778	153	14	graph	graph	NOUN
ajst-11778	153	15	reasoning	reasoning	NOUN
ajst-11778	153	16	network	network	NOUN
ajst-11778	153	17	for	for	ADP
ajst-11778	153	18	few	few	ADJ
ajst-11778	153	19	-	-	PUNCT
ajst-11778	153	20	shot	shot	NOUN
ajst-11778	153	21	metal	metal	NOUN
ajst-11778	153	22	generic	generic	ADJ
ajst-11778	153	23	surface	surface	NOUN
ajst-11778	153	24	defect	defect	NOUN
ajst-11778	153	25	segmentation	segmentation	NOUN
ajst-11778	153	26	.	.	PUNCT
ajst-11778	154	1	ieee	ieee	NOUN
ajst-11778	154	2	transactions	transaction	NOUN
ajst-11778	154	3	on	on	ADP
ajst-11778	154	4	instrumentation	instrumentation	NOUN
ajst-11778	154	5	and	and	CCONJ
ajst-11778	154	6	measurement	measurement	NOUN
ajst-11778	154	7	,	,	PUNCT
ajst-11778	154	8	2021	2021	NUM
ajst-11778	154	9	,	,	PUNCT
ajst-11778	154	10	70	70	NUM
ajst-11778	154	11	:	:	SYM
ajst-11778	154	12	1	1	NUM
ajst-11778	154	13	-	-	SYM
ajst-11778	154	14	11	11	NUM
ajst-11778	154	15	.	.	PUNCT