id	sid	tid	token	lemma	pos
fcis-3173	1	1	frontiers	frontier	NOUN
fcis-3173	1	2	in	in	ADP
fcis-3173	1	3	computing	computing	NOUN
fcis-3173	1	4	and	and	CCONJ
fcis-3173	1	5	intelligent	intelligent	ADJ
fcis-3173	1	6	systems	system	NOUN
fcis-3173	1	7	issn	issn	VERB
fcis-3173	1	8	:	:	PUNCT
fcis-3173	1	9	2832	2832	NUM
fcis-3173	1	10	-	-	SYM
fcis-3173	1	11	6024	6024	NUM
fcis-3173	1	12	|	|	NOUN
fcis-3173	1	13	vol	vol	NOUN
fcis-3173	1	14	.	.	PROPN
fcis-3173	2	1	2	2	NUM
fcis-3173	2	2	,	,	PUNCT
fcis-3173	2	3	no	no	INTJ
fcis-3173	2	4	.	.	NOUN
fcis-3173	2	5	1	1	NUM
fcis-3173	2	6	,	,	PUNCT
fcis-3173	2	7	2022	2022	NUM
fcis-3173	2	8	101	101	NUM
fcis-3173	2	9	research	research	NOUN
fcis-3173	2	10	on	on	ADP
fcis-3173	2	11	aluminum	aluminum	NOUN
fcis-3173	2	12	defect	defect	NOUN
fcis-3173	2	13	classification	classification	NOUN
fcis-3173	2	14	algorithm	algorithm	NOUN
fcis-3173	2	15	based	base	VERB
fcis-3173	2	16	on	on	ADP
fcis-3173	2	17	deep	deep	ADJ
fcis-3173	2	18	learning	learning	NOUN
fcis-3173	2	19	with	with	ADP
fcis-3173	2	20	attention	attention	NOUN
fcis-3173	2	21	mechanism	mechanism	NOUN
fcis-3173	2	22	wen	wen	PROPN
fcis-3173	2	23	zhang	zhang	PROPN
fcis-3173	2	24	,	,	PUNCT
fcis-3173	2	25	shibao	shibao	PROPN
fcis-3173	2	26	sun	sun	NOUN
fcis-3173	2	27	,	,	PUNCT
fcis-3173	2	28	huanjing	huanje	VERB
fcis-3173	2	29	yang	yang	PROPN
fcis-3173	2	30	college	college	PROPN
fcis-3173	2	31	of	of	ADP
fcis-3173	2	32	information	information	NOUN
fcis-3173	2	33	engineering	engineering	PROPN
fcis-3173	2	34	,	,	PUNCT
fcis-3173	2	35	henan	henan	PROPN
fcis-3173	2	36	university	university	PROPN
fcis-3173	2	37	of	of	ADP
fcis-3173	2	38	science	science	NOUN
fcis-3173	2	39	and	and	CCONJ
fcis-3173	2	40	technology	technology	NOUN
fcis-3173	2	41	,	,	PUNCT
fcis-3173	2	42	luoyang	luoyang	PROPN
fcis-3173	2	43	471000	471000	NUM
fcis-3173	2	44	,	,	PUNCT
fcis-3173	2	45	china	china	PROPN
fcis-3173	2	46	abstract	abstract	NOUN
fcis-3173	2	47	:	:	PUNCT
fcis-3173	2	48	product	product	NOUN
fcis-3173	2	49	quality	quality	NOUN
fcis-3173	2	50	is	be	AUX
fcis-3173	2	51	an	an	DET
fcis-3173	2	52	important	important	ADJ
fcis-3173	2	53	indicator	indicator	NOUN
fcis-3173	2	54	for	for	ADP
fcis-3173	2	55	determining	determine	VERB
fcis-3173	2	56	the	the	DET
fcis-3173	2	57	quality	quality	NOUN
fcis-3173	2	58	of	of	ADP
fcis-3173	2	59	industrial	industrial	ADJ
fcis-3173	2	60	products	product	NOUN
fcis-3173	2	61	.	.	PUNCT
fcis-3173	3	1	defects	defect	NOUN
fcis-3173	3	2	on	on	ADP
fcis-3173	3	3	the	the	DET
fcis-3173	3	4	surface	surface	NOUN
fcis-3173	3	5	of	of	ADP
fcis-3173	3	6	aluminum	aluminum	NOUN
fcis-3173	3	7	profiles	profile	NOUN
fcis-3173	3	8	are	be	AUX
fcis-3173	3	9	inevitably	inevitably	ADV
fcis-3173	3	10	caused	cause	VERB
fcis-3173	3	11	in	in	ADP
fcis-3173	3	12	the	the	DET
fcis-3173	3	13	actual	actual	ADJ
fcis-3173	3	14	production	production	NOUN
fcis-3173	3	15	process	process	NOUN
fcis-3173	3	16	due	due	ADP
fcis-3173	3	17	to	to	ADP
fcis-3173	3	18	the	the	DET
fcis-3173	3	19	influence	influence	NOUN
fcis-3173	3	20	of	of	ADP
fcis-3173	3	21	various	various	ADJ
fcis-3173	3	22	factors	factor	NOUN
fcis-3173	3	23	such	such	ADJ
fcis-3173	3	24	as	as	ADP
fcis-3173	3	25	environment	environment	NOUN
fcis-3173	3	26	and	and	CCONJ
fcis-3173	3	27	equipment	equipment	NOUN
fcis-3173	3	28	,	,	PUNCT
fcis-3173	3	29	and	and	CCONJ
fcis-3173	3	30	these	these	DET
fcis-3173	3	31	defects	defect	NOUN
fcis-3173	3	32	seriously	seriously	ADV
fcis-3173	3	33	affect	affect	VERB
fcis-3173	3	34	the	the	DET
fcis-3173	3	35	quality	quality	NOUN
fcis-3173	3	36	of	of	ADP
fcis-3173	3	37	aluminum	aluminum	NOUN
fcis-3173	3	38	profiles	profile	NOUN
fcis-3173	3	39	.	.	PUNCT
fcis-3173	4	1	the	the	DET
fcis-3173	4	2	focus	focus	NOUN
fcis-3173	4	3	and	and	CCONJ
fcis-3173	4	4	difficulty	difficulty	NOUN
fcis-3173	4	5	of	of	ADP
fcis-3173	4	6	research	research	NOUN
fcis-3173	4	7	have	have	AUX
fcis-3173	4	8	shifted	shift	VERB
fcis-3173	4	9	to	to	ADP
fcis-3173	4	10	how	how	SCONJ
fcis-3173	4	11	to	to	PART
fcis-3173	4	12	quickly	quickly	ADV
fcis-3173	4	13	and	and	CCONJ
fcis-3173	4	14	accurately	accurately	ADV
fcis-3173	4	15	identify	identify	VERB
fcis-3173	4	16	and	and	CCONJ
fcis-3173	4	17	classify	classify	VERB
fcis-3173	4	18	surface	surface	NOUN
fcis-3173	4	19	defects	defect	NOUN
fcis-3173	4	20	in	in	ADP
fcis-3173	4	21	aluminum	aluminum	NOUN
fcis-3173	4	22	profiles	profile	NOUN
fcis-3173	4	23	.	.	PUNCT
fcis-3173	5	1	to	to	PART
fcis-3173	5	2	address	address	VERB
fcis-3173	5	3	this	this	DET
fcis-3173	5	4	issue	issue	NOUN
fcis-3173	5	5	,	,	PUNCT
fcis-3173	5	6	this	this	DET
fcis-3173	5	7	paper	paper	NOUN
fcis-3173	5	8	proposes	propose	VERB
fcis-3173	5	9	an	an	DET
fcis-3173	5	10	aluminum	aluminum	NOUN
fcis-3173	5	11	defect	defect	NOUN
fcis-3173	5	12	classification	classification	NOUN
fcis-3173	5	13	algorithm	algorithm	NOUN
fcis-3173	5	14	that	that	PRON
fcis-3173	5	15	uses	use	VERB
fcis-3173	5	16	an	an	DET
fcis-3173	5	17	attention	attention	NOUN
fcis-3173	5	18	mechanism	mechanism	NOUN
fcis-3173	5	19	in	in	ADP
fcis-3173	5	20	conjunction	conjunction	NOUN
fcis-3173	5	21	with	with	ADP
fcis-3173	5	22	the	the	DET
fcis-3173	5	23	traditional	traditional	ADJ
fcis-3173	5	24	inception	inception	NOUN
fcis-3173	5	25	v4	v4	NOUN
fcis-3173	5	26	network	network	NOUN
fcis-3173	5	27	model	model	NOUN
fcis-3173	5	28	for	for	ADP
fcis-3173	5	29	deep	deep	ADJ
fcis-3173	5	30	learning	learn	VERB
fcis-3173	5	31	image	image	NOUN
fcis-3173	5	32	classification	classification	NOUN
fcis-3173	5	33	,	,	PUNCT
fcis-3173	5	34	to	to	PART
fcis-3173	5	35	accurately	accurately	ADV
fcis-3173	5	36	identify	identify	VERB
fcis-3173	5	37	and	and	CCONJ
fcis-3173	5	38	classify	classify	VERB
fcis-3173	5	39	aluminum	aluminum	NOUN
fcis-3173	5	40	defect	defect	NOUN
fcis-3173	5	41	areas	area	NOUN
fcis-3173	5	42	.	.	PUNCT
fcis-3173	6	1	experiments	experiment	NOUN
fcis-3173	6	2	and	and	CCONJ
fcis-3173	6	3	comparative	comparative	ADJ
fcis-3173	6	4	analysis	analysis	NOUN
fcis-3173	6	5	are	be	AUX
fcis-3173	6	6	performed	perform	VERB
fcis-3173	6	7	on	on	ADP
fcis-3173	6	8	the	the	DET
fcis-3173	6	9	aluminum	aluminum	NOUN
fcis-3173	6	10	defect	defect	NOUN
fcis-3173	6	11	recognition	recognition	NOUN
fcis-3173	6	12	dataset	dataset	VERB
fcis-3173	6	13	from	from	ADP
fcis-3173	6	14	the	the	DET
fcis-3173	6	15	alias	alia	NOUN
fcis-3173	6	16	tianchi	tianchi	ADJ
fcis-3173	6	17	platform	platform	NOUN
fcis-3173	6	18	,	,	PUNCT
fcis-3173	6	19	and	and	CCONJ
fcis-3173	6	20	the	the	DET
fcis-3173	6	21	results	result	NOUN
fcis-3173	6	22	show	show	VERB
fcis-3173	6	23	that	that	SCONJ
fcis-3173	6	24	the	the	DET
fcis-3173	6	25	algorithm	algorithm	NOUN
fcis-3173	6	26	with	with	ADP
fcis-3173	6	27	the	the	DET
fcis-3173	6	28	addition	addition	NOUN
fcis-3173	6	29	of	of	ADP
fcis-3173	6	30	the	the	DET
fcis-3173	6	31	attention	attention	NOUN
fcis-3173	6	32	mechanism	mechanism	NOUN
fcis-3173	6	33	improves	improve	VERB
fcis-3173	6	34	accuracy	accuracy	NOUN
fcis-3173	6	35	by	by	ADP
fcis-3173	6	36	1.24	1.24	NUM
fcis-3173	6	37	%	%	NOUN
fcis-3173	6	38	over	over	ADP
fcis-3173	6	39	the	the	DET
fcis-3173	6	40	original	original	ADJ
fcis-3173	6	41	model	model	NOUN
fcis-3173	6	42	.	.	PUNCT
fcis-3173	7	1	keywords	keyword	NOUN
fcis-3173	7	2	:	:	PUNCT
fcis-3173	7	3	deep	deep	ADJ
fcis-3173	7	4	learning	learning	NOUN
fcis-3173	7	5	;	;	PUNCT
fcis-3173	7	6	defect	defect	VERB
fcis-3173	7	7	classification	classification	NOUN
fcis-3173	7	8	;	;	PUNCT
fcis-3173	7	9	attention	attention	NOUN
fcis-3173	7	10	mechanism	mechanism	NOUN
fcis-3173	7	11	;	;	PUNCT
fcis-3173	7	12	inception	inception	NOUN
fcis-3173	7	13	v4	v4	NOUN
fcis-3173	7	14	.	.	PUNCT
fcis-3173	8	1	1	1	X
fcis-3173	8	2	.	.	X
fcis-3173	8	3	introduction	introduction	NOUN
fcis-3173	8	4	with	with	ADP
fcis-3173	8	5	the	the	DET
fcis-3173	8	6	development	development	NOUN
fcis-3173	8	7	of	of	ADP
fcis-3173	8	8	the	the	DET
fcis-3173	8	9	industrial	industrial	ADJ
fcis-3173	8	10	manufacturing	manufacturing	NOUN
fcis-3173	8	11	field	field	NOUN
fcis-3173	8	12	in	in	ADP
fcis-3173	8	13	the	the	DET
fcis-3173	8	14	world	world	NOUN
fcis-3173	8	15	,	,	PUNCT
fcis-3173	8	16	china	china	PROPN
fcis-3173	8	17	has	have	AUX
fcis-3173	8	18	become	become	VERB
fcis-3173	8	19	a	a	DET
fcis-3173	8	20	major	major	ADJ
fcis-3173	8	21	manufacturing	manufacturing	NOUN
fcis-3173	8	22	country	country	NOUN
fcis-3173	8	23	with	with	ADP
fcis-3173	8	24	a	a	DET
fcis-3173	8	25	large	large	ADJ
fcis-3173	8	26	number	number	NOUN
fcis-3173	8	27	of	of	ADP
fcis-3173	8	28	industrial	industrial	ADJ
fcis-3173	8	29	products	product	NOUN
fcis-3173	8	30	to	to	PART
fcis-3173	8	31	be	be	AUX
fcis-3173	8	32	produced	produce	VERB
fcis-3173	8	33	every	every	DET
fcis-3173	8	34	day	day	NOUN
fcis-3173	8	35	.	.	PUNCT
fcis-3173	9	1	with	with	ADP
fcis-3173	9	2	the	the	DET
fcis-3173	9	3	increasing	increase	VERB
fcis-3173	9	4	demand	demand	NOUN
fcis-3173	9	5	,	,	PUNCT
fcis-3173	9	6	the	the	DET
fcis-3173	9	7	quality	quality	NOUN
fcis-3173	9	8	of	of	ADP
fcis-3173	9	9	industrial	industrial	ADJ
fcis-3173	9	10	products	product	NOUN
fcis-3173	9	11	is	be	AUX
fcis-3173	9	12	becoming	become	VERB
fcis-3173	9	13	more	more	ADV
fcis-3173	9	14	and	and	CCONJ
fcis-3173	9	15	more	more	ADV
fcis-3173	9	16	demanding	demanding	ADJ
fcis-3173	9	17	,	,	PUNCT
fcis-3173	9	18	however	however	ADV
fcis-3173	9	19	,	,	PUNCT
fcis-3173	9	20	no	no	ADV
fcis-3173	9	21	matter	matter	ADV
fcis-3173	9	22	the	the	DET
fcis-3173	9	23	process	process	NOUN
fcis-3173	9	24	of	of	ADP
fcis-3173	9	25	production	production	NOUN
fcis-3173	9	26	or	or	CCONJ
fcis-3173	9	27	transportation	transportation	NOUN
fcis-3173	9	28	,	,	PUNCT
fcis-3173	9	29	there	there	PRON
fcis-3173	9	30	is	be	VERB
fcis-3173	9	31	no	no	DET
fcis-3173	9	32	way	way	NOUN
fcis-3173	9	33	to	to	PART
fcis-3173	9	34	avoid	avoid	VERB
fcis-3173	9	35	the	the	DET
fcis-3173	9	36	defects	defect	NOUN
fcis-3173	9	37	on	on	ADP
fcis-3173	9	38	the	the	DET
fcis-3173	9	39	surface	surface	NOUN
fcis-3173	9	40	of	of	ADP
fcis-3173	9	41	the	the	DET
fcis-3173	9	42	products	product	NOUN
fcis-3173	9	43	[	[	X
fcis-3173	9	44	1	1	NUM
fcis-3173	9	45	]	]	PUNCT
fcis-3173	9	46	.	.	PUNCT
fcis-3173	10	1	for	for	ADP
fcis-3173	10	2	example	example	NOUN
fcis-3173	10	3	,	,	PUNCT
fcis-3173	10	4	due	due	ADP
fcis-3173	10	5	to	to	ADP
fcis-3173	10	6	the	the	DET
fcis-3173	10	7	complexity	complexity	NOUN
fcis-3173	10	8	of	of	ADP
fcis-3173	10	9	the	the	DET
fcis-3173	10	10	production	production	NOUN
fcis-3173	10	11	environment	environment	NOUN
fcis-3173	10	12	and	and	CCONJ
fcis-3173	10	13	the	the	DET
fcis-3173	10	14	limitations	limitation	NOUN
fcis-3173	10	15	of	of	ADP
fcis-3173	10	16	the	the	DET
fcis-3173	10	17	processing	processing	NOUN
fcis-3173	10	18	equipment	equipment	NOUN
fcis-3173	10	19	,	,	PUNCT
fcis-3173	10	20	the	the	DET
fcis-3173	10	21	surface	surface	NOUN
fcis-3173	10	22	of	of	ADP
fcis-3173	10	23	aluminum	aluminum	NOUN
fcis-3173	10	24	profiles	profile	NOUN
fcis-3173	10	25	,	,	PUNCT
fcis-3173	10	26	as	as	ADP
fcis-3173	10	27	a	a	DET
fcis-3173	10	28	basic	basic	ADJ
fcis-3173	10	29	material	material	NOUN
fcis-3173	10	30	in	in	ADP
fcis-3173	10	31	industrial	industrial	ADJ
fcis-3173	10	32	manufacturing	manufacturing	NOUN
fcis-3173	10	33	,	,	PUNCT
fcis-3173	10	34	is	be	AUX
fcis-3173	10	35	prone	prone	ADJ
fcis-3173	10	36	to	to	ADP
fcis-3173	10	37	cracks	crack	NOUN
fcis-3173	10	38	,	,	PUNCT
fcis-3173	10	39	abrasions	abrasion	NOUN
fcis-3173	10	40	,	,	PUNCT
fcis-3173	10	41	peeling	peel	VERB
fcis-3173	10	42	,	,	PUNCT
fcis-3173	10	43	pits	pit	NOUN
fcis-3173	10	44	,	,	PUNCT
fcis-3173	10	45	scratches	scratch	NOUN
fcis-3173	10	46	,	,	PUNCT
fcis-3173	10	47	miscellaneous	miscellaneous	ADJ
fcis-3173	10	48	colors	color	NOUN
fcis-3173	10	49	,	,	PUNCT
fcis-3173	10	50	dirty	dirty	ADJ
fcis-3173	10	51	spots	spot	NOUN
fcis-3173	10	52	,	,	PUNCT
fcis-3173	10	53	and	and	CCONJ
fcis-3173	10	54	other	other	ADJ
fcis-3173	10	55	defects	defect	NOUN
fcis-3173	10	56	during	during	ADP
fcis-3173	10	57	production	production	NOUN
fcis-3173	10	58	and	and	CCONJ
fcis-3173	10	59	transportation	transportation	NOUN
fcis-3173	10	60	,	,	PUNCT
fcis-3173	10	61	which	which	PRON
fcis-3173	10	62	can	can	AUX
fcis-3173	10	63	seriously	seriously	ADV
fcis-3173	10	64	affect	affect	VERB
fcis-3173	10	65	the	the	DET
fcis-3173	10	66	quality	quality	NOUN
fcis-3173	10	67	of	of	ADP
fcis-3173	10	68	aluminum	aluminum	NOUN
fcis-3173	10	69	profiles	profile	NOUN
fcis-3173	10	70	[	[	X
fcis-3173	10	71	2	2	NUM
fcis-3173	10	72	]	]	PUNCT
fcis-3173	10	73	.	.	PUNCT
fcis-3173	11	1	therefore	therefore	ADV
fcis-3173	11	2	,	,	PUNCT
fcis-3173	11	3	it	it	PRON
fcis-3173	11	4	is	be	AUX
fcis-3173	11	5	important	important	ADJ
fcis-3173	11	6	to	to	PART
fcis-3173	11	7	automate	automate	VERB
fcis-3173	11	8	the	the	DET
fcis-3173	11	9	classification	classification	NOUN
fcis-3173	11	10	and	and	CCONJ
fcis-3173	11	11	identification	identification	NOUN
fcis-3173	11	12	of	of	ADP
fcis-3173	11	13	defects	defect	NOUN
fcis-3173	11	14	in	in	ADP
fcis-3173	11	15	aluminum	aluminum	NOUN
fcis-3173	11	16	profiles	profile	NOUN
fcis-3173	11	17	.	.	PUNCT
fcis-3173	12	1	the	the	DET
fcis-3173	12	2	existing	exist	VERB
fcis-3173	12	3	defect	defect	NOUN
fcis-3173	12	4	recognition	recognition	NOUN
fcis-3173	12	5	classification	classification	NOUN
fcis-3173	12	6	methods	method	NOUN
fcis-3173	12	7	are	be	AUX
fcis-3173	12	8	the	the	DET
fcis-3173	12	9	manual	manual	ADJ
fcis-3173	12	10	recognition	recognition	NOUN
fcis-3173	12	11	method	method	NOUN
fcis-3173	12	12	,	,	PUNCT
fcis-3173	12	13	single	single	ADJ
fcis-3173	12	14	mechanism	mechanism	NOUN
fcis-3173	12	15	recognition	recognition	NOUN
fcis-3173	12	16	method	method	NOUN
fcis-3173	12	17	,	,	PUNCT
fcis-3173	12	18	infrared	infrared	ADJ
fcis-3173	12	19	recognition	recognition	NOUN
fcis-3173	12	20	method	method	NOUN
fcis-3173	12	21	,	,	PUNCT
fcis-3173	12	22	magnetic	magnetic	ADJ
fcis-3173	12	23	particle	particle	NOUN
fcis-3173	12	24	recognition	recognition	NOUN
fcis-3173	12	25	method	method	NOUN
fcis-3173	12	26	,	,	PUNCT
fcis-3173	12	27	eddy	eddy	PROPN
fcis-3173	12	28	current	current	ADJ
fcis-3173	12	29	recognition	recognition	NOUN
fcis-3173	12	30	method	method	NOUN
fcis-3173	12	31	,	,	PUNCT
fcis-3173	12	32	magnetic	magnetic	ADJ
fcis-3173	12	33	leakage	leakage	NOUN
fcis-3173	12	34	recognition	recognition	NOUN
fcis-3173	12	35	method	method	NOUN
fcis-3173	12	36	,	,	PUNCT
fcis-3173	12	37	machine	machine	NOUN
fcis-3173	12	38	vision	vision	NOUN
fcis-3173	12	39	recognition	recognition	PROPN
fcis-3173	12	40	method	method	NOUN
fcis-3173	12	41	[	[	X
fcis-3173	12	42	3	3	NUM
fcis-3173	12	43	-	-	SYM
fcis-3173	12	44	7	7	NUM
fcis-3173	12	45	]	]	PUNCT
fcis-3173	12	46	,	,	PUNCT
fcis-3173	12	47	and	and	CCONJ
fcis-3173	12	48	other	other	ADJ
fcis-3173	12	49	seven	seven	NUM
fcis-3173	12	50	methods	method	NOUN
fcis-3173	12	51	.	.	PUNCT
fcis-3173	13	1	however	however	ADV
fcis-3173	13	2	,	,	PUNCT
fcis-3173	13	3	the	the	DET
fcis-3173	13	4	first	first	ADJ
fcis-3173	13	5	six	six	NUM
fcis-3173	13	6	methods	method	NOUN
fcis-3173	13	7	mentioned	mention	VERB
fcis-3173	13	8	above	above	ADV
fcis-3173	13	9	have	have	VERB
fcis-3173	13	10	the	the	DET
fcis-3173	13	11	shortcomings	shortcoming	NOUN
fcis-3173	13	12	of	of	ADP
fcis-3173	13	13	low	low	ADJ
fcis-3173	13	14	efficiency	efficiency	NOUN
fcis-3173	13	15	and	and	CCONJ
fcis-3173	13	16	accuracy	accuracy	NOUN
fcis-3173	13	17	due	due	ADP
fcis-3173	13	18	to	to	ADP
fcis-3173	13	19	the	the	DET
fcis-3173	13	20	limitation	limitation	NOUN
fcis-3173	13	21	of	of	ADP
fcis-3173	13	22	the	the	DET
fcis-3173	13	23	principle	principle	NOUN
fcis-3173	13	24	.	.	PUNCT
fcis-3173	14	1	among	among	ADP
fcis-3173	14	2	the	the	DET
fcis-3173	14	3	methods	method	NOUN
fcis-3173	14	4	of	of	ADP
fcis-3173	14	5	machine	machine	NOUN
fcis-3173	14	6	learning	learn	VERB
fcis-3173	14	7	recognition	recognition	NOUN
fcis-3173	14	8	,	,	PUNCT
fcis-3173	14	9	such	such	ADJ
fcis-3173	14	10	as	as	ADP
fcis-3173	14	11	support	support	NOUN
fcis-3173	14	12	vector	vector	NOUN
fcis-3173	14	13	machines	machine	NOUN
fcis-3173	14	14	and	and	CCONJ
fcis-3173	14	15	decision	decision	NOUN
fcis-3173	14	16	trees	tree	NOUN
fcis-3173	14	17	are	be	AUX
fcis-3173	14	18	based	base	VERB
fcis-3173	14	19	on	on	ADP
fcis-3173	14	20	manual	manual	NOUN
fcis-3173	14	21	to	to	PART
fcis-3173	14	22	feature	feature	VERB
fcis-3173	14	23	extraction	extraction	NOUN
fcis-3173	14	24	and	and	CCONJ
fcis-3173	14	25	classification	classification	NOUN
fcis-3173	14	26	of	of	ADP
fcis-3173	14	27	defects	defect	NOUN
fcis-3173	14	28	,	,	PUNCT
fcis-3173	14	29	which	which	PRON
fcis-3173	14	30	can	can	AUX
fcis-3173	14	31	only	only	ADV
fcis-3173	14	32	learn	learn	VERB
fcis-3173	14	33	some	some	DET
fcis-3173	14	34	low	low	ADJ
fcis-3173	14	35	-	-	PUNCT
fcis-3173	14	36	level	level	NOUN
fcis-3173	14	37	features	feature	NOUN
fcis-3173	14	38	and	and	CCONJ
fcis-3173	14	39	can	can	AUX
fcis-3173	14	40	not	not	PART
fcis-3173	14	41	learn	learn	VERB
fcis-3173	14	42	detailed	detailed	ADJ
fcis-3173	14	43	abstract	abstract	ADJ
fcis-3173	14	44	high	high	ADJ
fcis-3173	14	45	-	-	PUNCT
fcis-3173	14	46	level	level	NOUN
fcis-3173	14	47	features	feature	NOUN
fcis-3173	14	48	[	[	X
fcis-3173	14	49	8	8	NUM
fcis-3173	14	50	]	]	PUNCT
fcis-3173	14	51	,	,	PUNCT
fcis-3173	14	52	and	and	CCONJ
fcis-3173	14	53	the	the	DET
fcis-3173	14	54	accuracy	accuracy	NOUN
fcis-3173	14	55	rate	rate	NOUN
fcis-3173	14	56	is	be	AUX
fcis-3173	14	57	low	low	ADJ
fcis-3173	14	58	in	in	ADP
fcis-3173	14	59	the	the	DET
fcis-3173	14	60	recognition	recognition	NOUN
fcis-3173	14	61	and	and	CCONJ
fcis-3173	14	62	classification	classification	NOUN
fcis-3173	14	63	of	of	ADP
fcis-3173	14	64	subtle	subtle	ADJ
fcis-3173	14	65	defects	defect	NOUN
fcis-3173	14	66	.	.	PUNCT
fcis-3173	15	1	with	with	ADP
fcis-3173	15	2	the	the	DET
fcis-3173	15	3	development	development	NOUN
fcis-3173	15	4	of	of	ADP
fcis-3173	15	5	deep	deep	ADJ
fcis-3173	15	6	learning	learning	NOUN
fcis-3173	15	7	,	,	PUNCT
fcis-3173	15	8	convolutional	convolutional	ADJ
fcis-3173	15	9	neural	neural	ADJ
fcis-3173	15	10	networks	network	NOUN
fcis-3173	15	11	(	(	PUNCT
fcis-3173	15	12	cnns	cnns	PROPN
fcis-3173	15	13	)	)	PUNCT
fcis-3173	15	14	are	be	AUX
fcis-3173	15	15	widely	widely	ADV
fcis-3173	15	16	used	use	VERB
fcis-3173	15	17	in	in	ADP
fcis-3173	15	18	image	image	NOUN
fcis-3173	15	19	classification	classification	NOUN
fcis-3173	15	20	,	,	PUNCT
fcis-3173	15	21	target	target	NOUN
fcis-3173	15	22	detection	detection	NOUN
fcis-3173	15	23	[	[	X
fcis-3173	15	24	9	9	NUM
fcis-3173	15	25	-	-	SYM
fcis-3173	15	26	10	10	NUM
fcis-3173	15	27	]	]	PUNCT
fcis-3173	15	28	,	,	PUNCT
fcis-3173	15	29	speech	speech	NOUN
fcis-3173	15	30	recognition	recognition	NOUN
fcis-3173	15	31	,	,	PUNCT
fcis-3173	15	32	and	and	CCONJ
fcis-3173	15	33	intelligent	intelligent	ADJ
fcis-3173	15	34	robotics	robotic	NOUN
fcis-3173	15	35	[	[	X
fcis-3173	15	36	11	11	NUM
fcis-3173	15	37	]	]	PUNCT
fcis-3173	15	38	,	,	PUNCT
fcis-3173	15	39	etc	etc	X
fcis-3173	15	40	.	.	X
fcis-3173	15	41	convolutional	convolutional	ADJ
fcis-3173	15	42	neural	neural	ADJ
fcis-3173	15	43	networks	network	NOUN
fcis-3173	15	44	(	(	PUNCT
fcis-3173	15	45	cnns	cnns	PROPN
fcis-3173	15	46	)	)	PUNCT
fcis-3173	15	47	extract	extract	NOUN
fcis-3173	15	48	features	feature	VERB
fcis-3173	15	49	differently	differently	ADV
fcis-3173	15	50	from	from	ADP
fcis-3173	15	51	manual	manual	ADJ
fcis-3173	15	52	extraction	extraction	NOUN
fcis-3173	15	53	,	,	PUNCT
fcis-3173	15	54	but	but	CCONJ
fcis-3173	15	55	learn	learn	VERB
fcis-3173	15	56	features	feature	NOUN
fcis-3173	15	57	and	and	CCONJ
fcis-3173	15	58	extract	extract	VERB
fcis-3173	15	59	them	they	PRON
fcis-3173	15	60	from	from	ADP
fcis-3173	15	61	the	the	DET
fcis-3173	15	62	data	datum	NOUN
fcis-3173	15	63	itself	itself	PRON
fcis-3173	15	64	,	,	PUNCT
fcis-3173	15	65	with	with	ADP
fcis-3173	15	66	better	well	ADJ
fcis-3173	15	67	results	result	NOUN
fcis-3173	15	68	and	and	CCONJ
fcis-3173	15	69	greater	great	ADJ
fcis-3173	15	70	robustness	robustness	NOUN
fcis-3173	15	71	.	.	PUNCT
fcis-3173	16	1	shanshan	shanshan	PROPN
fcis-3173	16	2	xu	xu	PROPN
fcis-3173	16	3	et	et	PROPN
fcis-3173	17	1	al	al	PROPN
fcis-3173	18	1	[	[	X
fcis-3173	18	2	12	12	NUM
fcis-3173	18	3	]	]	PUNCT
fcis-3173	18	4	implemented	implement	VERB
fcis-3173	18	5	wood	wood	NOUN
fcis-3173	18	6	defect	defect	NOUN
fcis-3173	18	7	recognition	recognition	NOUN
fcis-3173	18	8	classification	classification	NOUN
fcis-3173	18	9	by	by	ADP
fcis-3173	18	10	convolutional	convolutional	ADJ
fcis-3173	18	11	neural	neural	ADJ
fcis-3173	18	12	network	network	NOUN
fcis-3173	18	13	and	and	CCONJ
fcis-3173	18	14	this	this	DET
fcis-3173	18	15	method	method	NOUN
fcis-3173	18	16	does	do	AUX
fcis-3173	18	17	not	not	PART
fcis-3173	18	18	require	require	VERB
fcis-3173	18	19	complex	complex	ADJ
fcis-3173	18	20	pre	pre	ADJ
fcis-3173	18	21	-	-	ADJ
fcis-3173	18	22	processing	processing	NOUN
fcis-3173	18	23	of	of	ADP
fcis-3173	18	24	images	image	NOUN
fcis-3173	18	25	and	and	CCONJ
fcis-3173	18	26	can	can	AUX
fcis-3173	18	27	recognize	recognize	VERB
fcis-3173	18	28	many	many	ADJ
fcis-3173	18	29	kinds	kind	NOUN
fcis-3173	18	30	of	of	ADP
fcis-3173	18	31	wood	wood	NOUN
fcis-3173	18	32	defects	defect	NOUN
fcis-3173	18	33	with	with	ADP
fcis-3173	18	34	high	high	ADJ
fcis-3173	18	35	correct	correct	ADJ
fcis-3173	18	36	rate	rate	NOUN
fcis-3173	18	37	and	and	CCONJ
fcis-3173	18	38	efficiency	efficiency	NOUN
fcis-3173	18	39	.	.	PUNCT
fcis-3173	19	1	liu	liu	PROPN
fcis-3173	19	2	,	,	PUNCT
fcis-3173	19	3	meng	meng	PROPN
fcis-3173	19	4	ke	ke	PROPN
fcis-3173	19	5	et	et	PROPN
fcis-3173	19	6	al	al	PROPN
fcis-3173	20	1	[	[	X
fcis-3173	20	2	13	13	NUM
fcis-3173	20	3	]	]	PUNCT
fcis-3173	20	4	achieved	achieve	VERB
fcis-3173	20	5	the	the	DET
fcis-3173	20	6	classification	classification	NOUN
fcis-3173	20	7	of	of	ADP
fcis-3173	20	8	track	track	NOUN
fcis-3173	20	9	surface	surface	NOUN
fcis-3173	20	10	defects	defect	NOUN
fcis-3173	20	11	recognition	recognition	NOUN
fcis-3173	20	12	by	by	ADP
fcis-3173	20	13	the	the	DET
fcis-3173	20	14	convolutional	convolutional	ADJ
fcis-3173	20	15	neural	neural	ADJ
fcis-3173	20	16	network	network	NOUN
fcis-3173	20	17	,	,	PUNCT
fcis-3173	20	18	which	which	PRON
fcis-3173	20	19	solved	solve	VERB
fcis-3173	20	20	the	the	DET
fcis-3173	20	21	problem	problem	NOUN
fcis-3173	20	22	that	that	SCONJ
fcis-3173	20	23	traditional	traditional	ADJ
fcis-3173	20	24	machine	machine	NOUN
fcis-3173	20	25	vision	vision	NOUN
fcis-3173	20	26	recognition	recognition	NOUN
fcis-3173	20	27	technology	technology	NOUN
fcis-3173	20	28	relies	rely	VERB
fcis-3173	20	29	on	on	ADP
fcis-3173	20	30	manual	manual	ADJ
fcis-3173	20	31	experience	experience	NOUN
fcis-3173	20	32	.	.	PUNCT
fcis-3173	21	1	zhiyang	zhiyang	PROPN
fcis-3173	21	2	wu	wu	PROPN
fcis-3173	21	3	et	et	PROPN
fcis-3173	21	4	al	al	PROPN
fcis-3173	22	1	[	[	X
fcis-3173	22	2	14	14	NUM
fcis-3173	22	3	]	]	PUNCT
fcis-3173	22	4	proposed	propose	VERB
fcis-3173	22	5	a	a	DET
fcis-3173	22	6	convolutional	convolutional	ADJ
fcis-3173	22	7	neural	neural	ADJ
fcis-3173	22	8	network	network	NOUN
fcis-3173	22	9	-	-	PUNCT
fcis-3173	22	10	based	base	VERB
fcis-3173	22	11	monochrome	monochrome	NOUN
fcis-3173	22	12	fabric	fabric	NOUN
fcis-3173	22	13	defect	defect	NOUN
fcis-3173	22	14	recognition	recognition	NOUN
fcis-3173	22	15	algorithm	algorithm	NOUN
fcis-3173	22	16	for	for	ADP
fcis-3173	22	17	the	the	DET
fcis-3173	22	18	problem	problem	NOUN
fcis-3173	22	19	of	of	ADP
fcis-3173	22	20	high	high	ADJ
fcis-3173	22	21	leakage	leakage	NOUN
fcis-3173	22	22	rate	rate	NOUN
fcis-3173	22	23	and	and	CCONJ
fcis-3173	22	24	low	low	ADJ
fcis-3173	22	25	efficiency	efficiency	NOUN
fcis-3173	22	26	of	of	ADP
fcis-3173	22	27	manual	manual	ADJ
fcis-3173	22	28	recognition	recognition	NOUN
fcis-3173	22	29	of	of	ADP
fcis-3173	22	30	fabric	fabric	NOUN
fcis-3173	22	31	defects	defect	NOUN
fcis-3173	22	32	in	in	ADP
fcis-3173	22	33	fabric	fabric	NOUN
fcis-3173	22	34	production	production	NOUN
fcis-3173	22	35	enterprises	enterprise	NOUN
fcis-3173	22	36	,	,	PUNCT
fcis-3173	22	37	and	and	CCONJ
fcis-3173	22	38	the	the	DET
fcis-3173	22	39	experimental	experimental	ADJ
fcis-3173	22	40	results	result	NOUN
fcis-3173	22	41	showed	show	VERB
fcis-3173	22	42	that	that	SCONJ
fcis-3173	22	43	this	this	DET
fcis-3173	22	44	method	method	NOUN
fcis-3173	22	45	could	could	AUX
fcis-3173	22	46	achieve	achieve	VERB
fcis-3173	22	47	high	high	ADJ
fcis-3173	22	48	accuracy	accuracy	NOUN
fcis-3173	22	49	and	and	CCONJ
fcis-3173	22	50	speed	speed	NOUN
fcis-3173	22	51	.	.	PUNCT
fcis-3173	23	1	tian	tian	ADJ
fcis-3173	23	2	wang	wang	PROPN
fcis-3173	23	3	et	et	PROPN
fcis-3173	23	4	al	al	PROPN
fcis-3173	23	5	.	.	PUNCT
fcis-3173	24	1	[	[	X
fcis-3173	24	2	15	15	NUM
fcis-3173	24	3	]	]	PUNCT
fcis-3173	24	4	proposed	propose	VERB
fcis-3173	24	5	a	a	DET
fcis-3173	24	6	convolutional	convolutional	ADJ
fcis-3173	24	7	neural	neural	ADJ
fcis-3173	24	8	network	network	NOUN
fcis-3173	24	9	to	to	PART
fcis-3173	24	10	automatically	automatically	ADV
fcis-3173	24	11	extract	extract	VERB
fcis-3173	24	12	features	feature	NOUN
fcis-3173	24	13	to	to	PART
fcis-3173	24	14	distinguish	distinguish	VERB
fcis-3173	24	15	between	between	ADP
fcis-3173	24	16	defectfree	defectfree	NOUN
fcis-3173	24	17	and	and	CCONJ
fcis-3173	24	18	defective	defective	ADJ
fcis-3173	24	19	images	image	NOUN
fcis-3173	24	20	for	for	ADP
fcis-3173	24	21	product	product	NOUN
fcis-3173	24	22	quality	quality	NOUN
fcis-3173	24	23	control	control	NOUN
fcis-3173	24	24	.	.	PUNCT
fcis-3173	25	1	gui	gui	PROPN
fcis-3173	25	2	zhong	zhong	PROPN
fcis-3173	25	3	fu	fu	PROPN
fcis-3173	25	4	et	et	PROPN
fcis-3173	25	5	al	al	PROPN
fcis-3173	25	6	.	.	PUNCT
fcis-3173	26	1	[	[	X
fcis-3173	26	2	16	16	NUM
fcis-3173	26	3	]	]	PUNCT
fcis-3173	26	4	proposed	propose	VERB
fcis-3173	26	5	a	a	DET
fcis-3173	26	6	deep	deep	ADJ
fcis-3173	26	7	learning	learning	NOUN
fcis-3173	26	8	-	-	PUNCT
fcis-3173	26	9	based	base	VERB
fcis-3173	26	10	approach	approach	NOUN
fcis-3173	26	11	through	through	ADP
fcis-3173	26	12	a	a	DET
fcis-3173	26	13	compact	compact	ADJ
fcis-3173	26	14	and	and	CCONJ
fcis-3173	26	15	effective	effective	ADJ
fcis-3173	26	16	convolutional	convolutional	ADJ
fcis-3173	26	17	neural	neural	ADJ
fcis-3173	26	18	network	network	NOUN
fcis-3173	26	19	model	model	NOUN
fcis-3173	26	20	that	that	PRON
fcis-3173	26	21	emphasizes	emphasize	VERB
fcis-3173	26	22	the	the	DET
fcis-3173	26	23	training	training	NOUN
fcis-3173	26	24	of	of	ADP
fcis-3173	26	25	low	low	ADJ
fcis-3173	26	26	-	-	PUNCT
fcis-3173	26	27	level	level	NOUN
fcis-3173	26	28	features	feature	NOUN
fcis-3173	26	29	and	and	CCONJ
fcis-3173	26	30	incorporates	incorporate	VERB
fcis-3173	26	31	multiple	multiple	ADJ
fcis-3173	26	32	sensory	sensory	ADJ
fcis-3173	26	33	fields	field	NOUN
fcis-3173	26	34	.	.	PUNCT
fcis-3173	27	1	however	however	ADV
fcis-3173	27	2	,	,	PUNCT
fcis-3173	27	3	due	due	ADP
fcis-3173	27	4	to	to	ADP
fcis-3173	27	5	the	the	DET
fcis-3173	27	6	uneven	uneven	ADJ
fcis-3173	27	7	distribution	distribution	NOUN
fcis-3173	27	8	of	of	ADP
fcis-3173	27	9	surface	surface	NOUN
fcis-3173	27	10	defects	defect	NOUN
fcis-3173	27	11	of	of	ADP
fcis-3173	27	12	aluminum	aluminum	NOUN
fcis-3173	27	13	profiles	profile	NOUN
fcis-3173	27	14	and	and	CCONJ
fcis-3173	27	15	the	the	DET
fcis-3173	27	16	fact	fact	NOUN
fcis-3173	27	17	that	that	SCONJ
fcis-3173	27	18	aluminum	aluminum	NOUN
fcis-3173	27	19	profile	profile	NOUN
fcis-3173	27	20	defects	defect	NOUN
fcis-3173	27	21	belong	belong	VERB
fcis-3173	27	22	to	to	ADP
fcis-3173	27	23	small	small	ADJ
fcis-3173	27	24	target	target	NOUN
fcis-3173	27	25	information	information	NOUN
fcis-3173	27	26	,	,	PUNCT
fcis-3173	27	27	the	the	DET
fcis-3173	27	28	defect	defect	NOUN
fcis-3173	27	29	classification	classification	NOUN
fcis-3173	27	30	using	use	VERB
fcis-3173	27	31	only	only	ADV
fcis-3173	27	32	the	the	DET
fcis-3173	27	33	above	above	ADV
fcis-3173	27	34	-	-	PUNCT
fcis-3173	27	35	mentioned	mention	VERB
fcis-3173	27	36	deep	deep	ADJ
fcis-3173	27	37	convolutional	convolutional	ADJ
fcis-3173	27	38	neural	neural	ADJ
fcis-3173	27	39	network	network	NOUN
fcis-3173	27	40	method	method	NOUN
fcis-3173	27	41	is	be	AUX
fcis-3173	27	42	easy	easy	ADJ
fcis-3173	27	43	to	to	PART
fcis-3173	27	44	ignore	ignore	VERB
fcis-3173	27	45	some	some	DET
fcis-3173	27	46	detailed	detailed	ADJ
fcis-3173	27	47	information	information	NOUN
fcis-3173	27	48	,	,	PUNCT
fcis-3173	27	49	especially	especially	ADV
fcis-3173	27	50	when	when	SCONJ
fcis-3173	27	51	some	some	DET
fcis-3173	27	52	defects	defect	NOUN
fcis-3173	27	53	on	on	ADP
fcis-3173	27	54	the	the	DET
fcis-3173	27	55	surface	surface	NOUN
fcis-3173	27	56	of	of	ADP
fcis-3173	27	57	aluminum	aluminum	NOUN
fcis-3173	27	58	profiles	profile	NOUN
fcis-3173	27	59	are	be	AUX
fcis-3173	27	60	small	small	ADJ
fcis-3173	27	61	and	and	CCONJ
fcis-3173	27	62	easy	easy	ADJ
fcis-3173	27	63	to	to	PART
fcis-3173	27	64	classify	classify	VERB
fcis-3173	27	65	inaccurately	inaccurately	ADV
fcis-3173	27	66	,	,	PUNCT
fcis-3173	27	67	which	which	PRON
fcis-3173	27	68	leads	lead	VERB
fcis-3173	27	69	to	to	ADP
fcis-3173	27	70	poor	poor	ADJ
fcis-3173	27	71	results	result	NOUN
fcis-3173	27	72	.	.	PUNCT
fcis-3173	28	1	therefore	therefore	ADV
fcis-3173	28	2	,	,	PUNCT
fcis-3173	28	3	this	this	DET
fcis-3173	28	4	paper	paper	NOUN
fcis-3173	28	5	focuses	focus	VERB
fcis-3173	28	6	on	on	ADP
fcis-3173	28	7	the	the	DET
fcis-3173	28	8	current	current	ADJ
fcis-3173	28	9	practical	practical	ADJ
fcis-3173	28	10	problem	problem	NOUN
fcis-3173	28	11	of	of	ADP
fcis-3173	28	12	classifying	classify	VERB
fcis-3173	28	13	aluminum	aluminum	NOUN
fcis-3173	28	14	surface	surface	NOUN
fcis-3173	28	15	defects	defect	NOUN
fcis-3173	28	16	by	by	ADP
fcis-3173	28	17	selecting	select	VERB
fcis-3173	28	18	the	the	DET
fcis-3173	28	19	classical	classical	ADJ
fcis-3173	28	20	deep	deep	ADJ
fcis-3173	28	21	learning	learning	NOUN
fcis-3173	28	22	network	network	NOUN
fcis-3173	28	23	classification	classification	NOUN
fcis-3173	28	24	model	model	NOUN
fcis-3173	28	25	inception	inception	NOUN
fcis-3173	28	26	v4	v4	NOUN
fcis-3173	28	27	and	and	CCONJ
fcis-3173	28	28	adding	add	VERB
fcis-3173	28	29	the	the	DET
fcis-3173	28	30	attention	attention	NOUN
fcis-3173	28	31	mechanism	mechanism	NOUN
fcis-3173	28	32	scse	scse	NOUN
fcis-3173	28	33	module	module	NOUN
fcis-3173	28	34	to	to	ADP
fcis-3173	28	35	its	its	PRON
fcis-3173	28	36	model	model	NOUN
fcis-3173	28	37	for	for	ADP
fcis-3173	28	38	fusion	fusion	NOUN
fcis-3173	28	39	,	,	PUNCT
fcis-3173	28	40	which	which	PRON
fcis-3173	28	41	makes	make	VERB
fcis-3173	28	42	the	the	DET
fcis-3173	28	43	network	network	NOUN
fcis-3173	28	44	model	model	NOUN
fcis-3173	28	45	learning	learn	VERB
fcis-3173	28	46	easier	easy	ADJ
fcis-3173	28	47	to	to	PART
fcis-3173	28	48	capture	capture	VERB
fcis-3173	28	49	the	the	DET
fcis-3173	28	50	local	local	ADJ
fcis-3173	28	51	key	key	ADJ
fcis-3173	28	52	information	information	NOUN
fcis-3173	28	53	on	on	ADP
fcis-3173	28	54	feature	feature	NOUN
fcis-3173	28	55	map	map	NOUN
fcis-3173	28	56	channels	channel	NOUN
fcis-3173	28	57	and	and	CCONJ
fcis-3173	28	58	space	space	NOUN
fcis-3173	28	59	and	and	CCONJ
fcis-3173	28	60	suppress	suppress	VERB
fcis-3173	28	61	irrelevant	irrelevant	ADJ
fcis-3173	28	62	information	information	NOUN
fcis-3173	28	63	,	,	PUNCT
fcis-3173	28	64	to	to	PART
fcis-3173	28	65	further	far	ADV
fcis-3173	28	66	improve	improve	VERB
fcis-3173	28	67	the	the	DET
fcis-3173	28	68	accuracy	accuracy	NOUN
fcis-3173	28	69	of	of	ADP
fcis-3173	28	70	the	the	DET
fcis-3173	28	71	model	model	NOUN
fcis-3173	28	72	in	in	ADP
fcis-3173	28	73	classifying	classify	VERB
fcis-3173	28	74	aluminum	aluminum	NOUN
fcis-3173	28	75	defects	defect	NOUN
fcis-3173	28	76	.	.	PUNCT
fcis-3173	29	1	102	102	NUM
fcis-3173	29	2	2	2	NUM
fcis-3173	29	3	.	.	PUNCT
fcis-3173	29	4	related	relate	VERB
fcis-3173	29	5	work	work	NOUN
fcis-3173	29	6	2.1	2.1	NUM
fcis-3173	29	7	.	.	PUNCT
fcis-3173	30	1	inception	inception	ADJ
fcis-3173	30	2	v4	v4	NOUN
fcis-3173	30	3	network	network	NOUN
fcis-3173	30	4	for	for	ADP
fcis-3173	30	5	convolutional	convolutional	ADJ
fcis-3173	30	6	neural	neural	ADJ
fcis-3173	30	7	networks	network	NOUN
fcis-3173	30	8	,	,	PUNCT
fcis-3173	30	9	an	an	DET
fcis-3173	30	10	effective	effective	ADJ
fcis-3173	30	11	way	way	NOUN
fcis-3173	30	12	to	to	PART
fcis-3173	30	13	obtain	obtain	VERB
fcis-3173	30	14	efficient	efficient	ADJ
fcis-3173	30	15	network	network	NOUN
fcis-3173	30	16	performance	performance	NOUN
fcis-3173	30	17	is	be	AUX
fcis-3173	30	18	to	to	PART
fcis-3173	30	19	increase	increase	VERB
fcis-3173	30	20	the	the	DET
fcis-3173	30	21	breadth	breadth	NOUN
fcis-3173	30	22	and	and	CCONJ
fcis-3173	30	23	depth	depth	NOUN
fcis-3173	30	24	of	of	ADP
fcis-3173	30	25	the	the	DET
fcis-3173	30	26	network	network	NOUN
fcis-3173	30	27	,	,	PUNCT
fcis-3173	30	28	but	but	CCONJ
fcis-3173	30	29	the	the	DET
fcis-3173	30	30	blind	blind	ADJ
fcis-3173	30	31	increase	increase	NOUN
fcis-3173	30	32	will	will	AUX
fcis-3173	30	33	lead	lead	VERB
fcis-3173	30	34	to	to	ADP
fcis-3173	30	35	a	a	DET
fcis-3173	30	36	dramatic	dramatic	ADJ
fcis-3173	30	37	increase	increase	NOUN
fcis-3173	30	38	in	in	ADP
fcis-3173	30	39	network	network	NOUN
fcis-3173	30	40	parameters	parameter	NOUN
fcis-3173	30	41	,	,	PUNCT
fcis-3173	30	42	which	which	PRON
fcis-3173	30	43	can	can	AUX
fcis-3173	30	44	easily	easily	ADV
fcis-3173	30	45	cause	cause	VERB
fcis-3173	30	46	overfitting	overfitte	VERB
fcis-3173	30	47	[	[	AUX
fcis-3173	30	48	17].googlenet	17].googlenet	PROPN
fcis-3173	30	49	[	[	X
fcis-3173	30	50	18	18	NUM
fcis-3173	30	51	]	]	X
fcis-3173	30	52	inception	inception	NOUN
fcis-3173	30	53	v1	v1	NOUN
fcis-3173	30	54	was	be	AUX
fcis-3173	30	55	proposed	propose	VERB
fcis-3173	30	56	by	by	ADP
fcis-3173	30	57	the	the	DET
fcis-3173	30	58	google	google	PROPN
fcis-3173	30	59	team	team	NOUN
fcis-3173	30	60	in	in	ADP
fcis-3173	30	61	2014	2014	NUM
fcis-3173	30	62	and	and	CCONJ
fcis-3173	30	63	won	win	VERB
fcis-3173	30	64	first	first	ADJ
fcis-3173	30	65	place	place	NOUN
fcis-3173	30	66	in	in	ADP
fcis-3173	30	67	that	that	DET
fcis-3173	30	68	year	year	NOUN
fcis-3173	30	69	's	's	PART
fcis-3173	30	70	imagenet	imagenet	NOUN
fcis-3173	30	71	competition	competition	NOUN
fcis-3173	30	72	classification	classification	NOUN
fcis-3173	30	73	task	task	NOUN
fcis-3173	30	74	won	win	VERB
fcis-3173	30	75	the	the	DET
fcis-3173	30	76	first	first	ADJ
fcis-3173	30	77	place	place	NOUN
fcis-3173	30	78	.	.	PUNCT
fcis-3173	31	1	he	he	PRON
fcis-3173	31	2	was	be	AUX
fcis-3173	31	3	the	the	DET
fcis-3173	31	4	first	first	ADJ
fcis-3173	31	5	to	to	PART
fcis-3173	31	6	propose	propose	VERB
fcis-3173	31	7	the	the	DET
fcis-3173	31	8	inception	inception	ADJ
fcis-3173	31	9	structure	structure	NOUN
fcis-3173	31	10	based	base	VERB
fcis-3173	31	11	on	on	ADP
fcis-3173	31	12	lenet-5	lenet-5	PROPN
fcis-3173	31	13	.	.	PUNCT
fcis-3173	32	1	the	the	DET
fcis-3173	32	2	core	core	NOUN
fcis-3173	32	3	of	of	ADP
fcis-3173	32	4	inception	inception	NOUN
fcis-3173	32	5	is	be	AUX
fcis-3173	32	6	to	to	PART
fcis-3173	32	7	use	use	VERB
fcis-3173	32	8	convolutional	convolutional	ADJ
fcis-3173	32	9	kernels	kernel	NOUN
fcis-3173	32	10	of	of	ADP
fcis-3173	32	11	different	different	ADJ
fcis-3173	32	12	sizes	size	NOUN
fcis-3173	32	13	,	,	PUNCT
fcis-3173	32	14	which	which	PRON
fcis-3173	32	15	makes	make	VERB
fcis-3173	32	16	the	the	DET
fcis-3173	32	17	existence	existence	NOUN
fcis-3173	32	18	of	of	ADP
fcis-3173	32	19	different	different	ADJ
fcis-3173	32	20	sizes	size	NOUN
fcis-3173	32	21	of	of	ADP
fcis-3173	32	22	perceptual	perceptual	ADJ
fcis-3173	32	23	fields	field	NOUN
fcis-3173	32	24	,	,	PUNCT
fcis-3173	32	25	and	and	CCONJ
fcis-3173	32	26	finally	finally	ADV
fcis-3173	32	27	,	,	PUNCT
fcis-3173	32	28	achieve	achieve	VERB
fcis-3173	32	29	the	the	DET
fcis-3173	32	30	fusion	fusion	NOUN
fcis-3173	32	31	of	of	ADP
fcis-3173	32	32	different	different	ADJ
fcis-3173	32	33	scale	scale	NOUN
fcis-3173	32	34	features	feature	NOUN
fcis-3173	32	35	by	by	ADP
fcis-3173	32	36	stitching	stitch	VERB
fcis-3173	32	37	.	.	PUNCT
fcis-3173	33	1	the	the	DET
fcis-3173	33	2	effect	effect	NOUN
fcis-3173	33	3	is	be	AUX
fcis-3173	33	4	to	to	PART
fcis-3173	33	5	increase	increase	VERB
fcis-3173	33	6	the	the	DET
fcis-3173	33	7	depth	depth	NOUN
fcis-3173	33	8	and	and	CCONJ
fcis-3173	33	9	width	width	NOUN
fcis-3173	33	10	of	of	ADP
fcis-3173	33	11	the	the	DET
fcis-3173	33	12	network	network	NOUN
fcis-3173	33	13	while	while	SCONJ
fcis-3173	33	14	also	also	ADV
fcis-3173	33	15	reducing	reduce	VERB
fcis-3173	33	16	the	the	DET
fcis-3173	33	17	number	number	NOUN
fcis-3173	33	18	of	of	ADP
fcis-3173	33	19	parameters	parameter	NOUN
fcis-3173	33	20	.	.	PUNCT
fcis-3173	34	1	different	different	ADJ
fcis-3173	34	2	versions	version	NOUN
fcis-3173	34	3	of	of	ADP
fcis-3173	34	4	inception	inception	ADJ
fcis-3173	34	5	v2	v2	PROPN
fcis-3173	34	6	-	-	PUNCT
fcis-3173	34	7	v4	v4	NOUN
fcis-3173	34	8	[	[	X
fcis-3173	34	9	19	19	NUM
fcis-3173	34	10	-	-	SYM
fcis-3173	34	11	21	21	NUM
fcis-3173	34	12	]	]	PUNCT
fcis-3173	34	13	have	have	AUX
fcis-3173	34	14	emerged	emerge	VERB
fcis-3173	34	15	based	base	VERB
fcis-3173	34	16	on	on	ADP
fcis-3173	34	17	this	this	DET
fcis-3173	34	18	network	network	NOUN
fcis-3173	34	19	with	with	ADP
fcis-3173	34	20	continuous	continuous	ADJ
fcis-3173	34	21	optimization	optimization	NOUN
fcis-3173	34	22	.	.	PUNCT
fcis-3173	35	1	in	in	ADP
fcis-3173	35	2	this	this	DET
fcis-3173	35	3	paper	paper	NOUN
fcis-3173	35	4	,	,	PUNCT
fcis-3173	35	5	we	we	PRON
fcis-3173	35	6	use	use	VERB
fcis-3173	35	7	the	the	DET
fcis-3173	35	8	inception	inception	ADJ
fcis-3173	35	9	v4	v4	NOUN
fcis-3173	35	10	network	network	NOUN
fcis-3173	35	11	as	as	ADP
fcis-3173	35	12	the	the	DET
fcis-3173	35	13	main	main	ADJ
fcis-3173	35	14	architecture	architecture	NOUN
fcis-3173	35	15	,	,	PUNCT
fcis-3173	35	16	and	and	CCONJ
fcis-3173	35	17	the	the	DET
fcis-3173	35	18	network	network	NOUN
fcis-3173	35	19	principle	principle	NOUN
fcis-3173	35	20	of	of	ADP
fcis-3173	35	21	inception	inception	PROPN
fcis-3173	35	22	v4	v4	NOUN
fcis-3173	35	23	is	be	AUX
fcis-3173	35	24	described	describe	VERB
fcis-3173	35	25	in	in	ADP
fcis-3173	35	26	detail	detail	NOUN
fcis-3173	35	27	below	below	ADV
fcis-3173	35	28	.	.	PUNCT
fcis-3173	36	1	the	the	DET
fcis-3173	36	2	network	network	NOUN
fcis-3173	36	3	is	be	AUX
fcis-3173	36	4	composed	compose	VERB
fcis-3173	36	5	of	of	ADP
fcis-3173	36	6	a	a	DET
fcis-3173	36	7	stem	stem	NOUN
fcis-3173	36	8	module	module	NOUN
fcis-3173	36	9	,	,	PUNCT
fcis-3173	36	10	inception	inception	NOUN
fcis-3173	36	11	-	-	PUNCT
fcis-3173	36	12	a	a	DET
fcis-3173	36	13	module	module	NOUN
fcis-3173	36	14	,	,	PUNCT
fcis-3173	36	15	inception	inception	NOUN
fcis-3173	36	16	-	-	PUNCT
fcis-3173	36	17	b	b	NOUN
fcis-3173	36	18	module	module	NOUN
fcis-3173	36	19	,	,	PUNCT
fcis-3173	36	20	inception	inception	NOUN
fcis-3173	36	21	-	-	PUNCT
fcis-3173	36	22	c	c	NOUN
fcis-3173	36	23	module	module	NOUN
fcis-3173	36	24	,	,	PUNCT
fcis-3173	36	25	reduction	reduction	NOUN
fcis-3173	36	26	-	-	PUNCT
fcis-3173	36	27	a	a	DET
fcis-3173	36	28	module	module	NOUN
fcis-3173	36	29	,	,	PUNCT
fcis-3173	36	30	and	and	CCONJ
fcis-3173	36	31	reduction	reduction	NOUN
fcis-3173	36	32	-	-	PUNCT
fcis-3173	36	33	b	b	NOUN
fcis-3173	36	34	module	module	NOUN
fcis-3173	36	35	.	.	PUNCT
fcis-3173	37	1	each	each	DET
fcis-3173	37	2	module	module	NOUN
fcis-3173	37	3	is	be	AUX
fcis-3173	37	4	highly	highly	ADV
fcis-3173	37	5	tunable	tunable	ADJ
fcis-3173	37	6	.	.	PUNCT
fcis-3173	38	1	the	the	DET
fcis-3173	38	2	width	width	ADJ
fcis-3173	38	3	and	and	CCONJ
fcis-3173	38	4	depth	depth	NOUN
fcis-3173	38	5	of	of	ADP
fcis-3173	38	6	the	the	DET
fcis-3173	38	7	network	network	NOUN
fcis-3173	38	8	are	be	AUX
fcis-3173	38	9	increased	increase	VERB
fcis-3173	38	10	without	without	ADP
fcis-3173	38	11	increasing	increase	VERB
fcis-3173	38	12	the	the	DET
fcis-3173	38	13	network	network	NOUN
fcis-3173	38	14	parameters	parameter	NOUN
fcis-3173	38	15	,	,	PUNCT
fcis-3173	38	16	thus	thus	ADV
fcis-3173	38	17	increasing	increase	VERB
fcis-3173	38	18	the	the	DET
fcis-3173	38	19	accuracy	accuracy	NOUN
fcis-3173	38	20	and	and	CCONJ
fcis-3173	38	21	not	not	PART
fcis-3173	38	22	overfitting	overfitte	VERB
fcis-3173	38	23	.	.	PUNCT
fcis-3173	39	1	the	the	DET
fcis-3173	39	2	network	network	NOUN
fcis-3173	39	3	structure	structure	NOUN
fcis-3173	39	4	is	be	AUX
fcis-3173	39	5	shown	show	VERB
fcis-3173	39	6	in	in	ADP
fcis-3173	39	7	figure	figure	NOUN
fcis-3173	39	8	1	1	NUM
fcis-3173	39	9	.	.	PUNCT
fcis-3173	39	10	input(229×229×3	input(229×229×3	PROPN
fcis-3173	39	11	)	)	PUNCT
fcis-3173	39	12	stem	stem	VERB
fcis-3173	39	13	4×inception	4×inception	NUM
fcis-3173	39	14	-	-	PUNCT
fcis-3173	39	15	a	a	DET
fcis-3173	39	16	reduction	reduction	NOUN
fcis-3173	39	17	-	-	PUNCT
fcis-3173	39	18	a	a	DET
fcis-3173	39	19	7×inception	7×inception	PROPN
fcis-3173	39	20	-	-	PUNCT
fcis-3173	39	21	b	b	NOUN
fcis-3173	39	22	reduction	reduction	NOUN
fcis-3173	39	23	-	-	PUNCT
fcis-3173	39	24	b	b	NOUN
fcis-3173	39	25	3×inception	3×inception	PROPN
fcis-3173	39	26	-	-	PUNCT
fcis-3173	39	27	c	c	NOUN
fcis-3173	39	28	average	average	ADJ
fcis-3173	39	29	pooling	pool	VERB
fcis-3173	39	30	dropout	dropout	NOUN
fcis-3173	39	31	softmax	softmax	NOUN
fcis-3173	39	32	figure	figure	NOUN
fcis-3173	39	33	1	1	NUM
fcis-3173	39	34	.	.	PUNCT
fcis-3173	40	1	inception	inception	ADJ
fcis-3173	40	2	v4	v4	PROPN
fcis-3173	40	3	structure	structure	NOUN
fcis-3173	40	4	diagram	diagram	NOUN
fcis-3173	40	5	2.2	2.2	NUM
fcis-3173	40	6	.	.	PUNCT
fcis-3173	41	1	scse	scse	NOUN
fcis-3173	41	2	attention	attention	NOUN
fcis-3173	41	3	mechanism	mechanism	NOUN
fcis-3173	41	4	the	the	DET
fcis-3173	41	5	attention	attention	NOUN
fcis-3173	41	6	mechanism	mechanism	NOUN
fcis-3173	41	7	mimics	mimic	VERB
fcis-3173	41	8	the	the	DET
fcis-3173	41	9	study	study	NOUN
fcis-3173	41	10	of	of	ADP
fcis-3173	41	11	human	human	ADJ
fcis-3173	41	12	brain	brain	NOUN
fcis-3173	41	13	vision	vision	NOUN
fcis-3173	41	14	,	,	PUNCT
fcis-3173	41	15	which	which	PRON
fcis-3173	41	16	has	have	VERB
fcis-3173	41	17	access	access	NOUN
fcis-3173	41	18	to	to	ADP
fcis-3173	41	19	a	a	DET
fcis-3173	41	20	large	large	ADJ
fcis-3173	41	21	amount	amount	NOUN
fcis-3173	41	22	of	of	ADP
fcis-3173	41	23	information	information	NOUN
fcis-3173	41	24	from	from	ADP
fcis-3173	41	25	the	the	DET
fcis-3173	41	26	outside	outside	ADJ
fcis-3173	41	27	world	world	NOUN
fcis-3173	41	28	at	at	ADP
fcis-3173	41	29	anytime	anytime	ADV
fcis-3173	41	30	and	and	CCONJ
fcis-3173	41	31	anywhere	anywhere	ADV
fcis-3173	41	32	,	,	PUNCT
fcis-3173	41	33	and	and	CCONJ
fcis-3173	41	34	from	from	ADP
fcis-3173	41	35	a	a	DET
fcis-3173	41	36	large	large	ADJ
fcis-3173	41	37	amount	amount	NOUN
fcis-3173	41	38	of	of	ADP
fcis-3173	41	39	information	information	NOUN
fcis-3173	41	40	,	,	PUNCT
fcis-3173	41	41	quickly	quickly	ADV
fcis-3173	41	42	locates	locate	VERB
fcis-3173	41	43	relatively	relatively	ADV
fcis-3173	41	44	important	important	ADJ
fcis-3173	41	45	information	information	NOUN
fcis-3173	41	46	and	and	CCONJ
fcis-3173	41	47	ignores	ignore	VERB
fcis-3173	41	48	irrelevant	irrelevant	ADJ
fcis-3173	41	49	information	information	NOUN
fcis-3173	41	50	[	[	X
fcis-3173	41	51	22	22	NUM
fcis-3173	41	52	]	]	PUNCT
fcis-3173	41	53	.	.	PUNCT
fcis-3173	42	1	therefore	therefore	ADV
fcis-3173	42	2	,	,	PUNCT
fcis-3173	42	3	it	it	PRON
fcis-3173	42	4	is	be	AUX
fcis-3173	42	5	important	important	ADJ
fcis-3173	42	6	to	to	PART
fcis-3173	42	7	introduce	introduce	VERB
fcis-3173	42	8	the	the	DET
fcis-3173	42	9	attention	attention	NOUN
fcis-3173	42	10	mechanism	mechanism	NOUN
fcis-3173	42	11	into	into	ADP
fcis-3173	42	12	deep	deep	ADJ
fcis-3173	42	13	learning	learning	NOUN
fcis-3173	42	14	networks	network	NOUN
fcis-3173	42	15	to	to	PART
fcis-3173	42	16	achieve	achieve	VERB
fcis-3173	42	17	similar	similar	ADJ
fcis-3173	42	18	functions	function	NOUN
fcis-3173	42	19	in	in	ADP
fcis-3173	42	20	defect	defect	NOUN
fcis-3173	42	21	recognition	recognition	NOUN
fcis-3173	42	22	classification	classification	NOUN
fcis-3173	42	23	.	.	PUNCT
fcis-3173	43	1	the	the	DET
fcis-3173	43	2	scse	scse	NOUN
fcis-3173	43	3	module	module	NOUN
fcis-3173	43	4	is	be	AUX
fcis-3173	43	5	based	base	VERB
fcis-3173	43	6	on	on	ADP
fcis-3173	43	7	the	the	DET
fcis-3173	43	8	evolution	evolution	NOUN
fcis-3173	43	9	of	of	ADP
fcis-3173	43	10	the	the	DET
fcis-3173	43	11	se	se	PROPN
fcis-3173	43	12	module	module	NOUN
fcis-3173	43	13	in	in	ADP
fcis-3173	43	14	senet	senet	NOUN
fcis-3173	43	15	[	[	X
fcis-3173	43	16	23	23	NUM
fcis-3173	43	17	]	]	PUNCT
fcis-3173	43	18	.	.	PUNCT
fcis-3173	44	1	three	three	NUM
fcis-3173	44	2	variants	variant	NOUN
fcis-3173	44	3	of	of	ADP
fcis-3173	44	4	the	the	DET
fcis-3173	44	5	se	se	PROPN
fcis-3173	44	6	module	module	NOUN
fcis-3173	44	7	of	of	ADP
fcis-3173	44	8	royag	royag	PROPN
fcis-3173	44	9	et	et	PROPN
fcis-3173	44	10	al	al	PROPN
fcis-3173	45	1	[	[	X
fcis-3173	45	2	24	24	NUM
fcis-3173	45	3	]	]	PUNCT
fcis-3173	45	4	,	,	PUNCT
fcis-3173	45	5	namely	namely	ADV
fcis-3173	45	6	,	,	PUNCT
fcis-3173	45	7	the	the	DET
fcis-3173	45	8	cse	cse	PROPN
fcis-3173	45	9	channel	channel	PROPN
fcis-3173	45	10	compression	compression	NOUN
fcis-3173	45	11	spatial	spatial	ADJ
fcis-3173	45	12	excitation	excitation	NOUN
fcis-3173	45	13	module	module	NOUN
fcis-3173	45	14	,	,	PUNCT
fcis-3173	45	15	the	the	DET
fcis-3173	45	16	sse	sse	PROPN
fcis-3173	45	17	spatial	spatial	ADJ
fcis-3173	45	18	compression	compression	NOUN
fcis-3173	45	19	channel	channel	NOUN
fcis-3173	45	20	excitation	excitation	NOUN
fcis-3173	45	21	module	module	NOUN
fcis-3173	45	22	,	,	PUNCT
fcis-3173	45	23	and	and	CCONJ
fcis-3173	45	24	the	the	DET
fcis-3173	45	25	scse	scse	NOUN
fcis-3173	45	26	module	module	NOUN
fcis-3173	45	27	formed	form	VERB
fcis-3173	45	28	by	by	ADP
fcis-3173	45	29	combining	combine	VERB
fcis-3173	45	30	the	the	DET
fcis-3173	45	31	cse	cse	NOUN
fcis-3173	45	32	and	and	CCONJ
fcis-3173	45	33	sse	sse	PROPN
fcis-3173	45	34	modules	module	NOUN
fcis-3173	45	35	in	in	ADP
fcis-3173	45	36	parallel	parallel	NOUN
fcis-3173	45	37	.	.	PUNCT
fcis-3173	46	1	and	and	CCONJ
fcis-3173	46	2	it	it	PRON
fcis-3173	46	3	is	be	AUX
fcis-3173	46	4	experimentally	experimentally	ADV
fcis-3173	46	5	demonstrated	demonstrate	VERB
fcis-3173	46	6	that	that	SCONJ
fcis-3173	46	7	such	such	DET
fcis-3173	46	8	a	a	DET
fcis-3173	46	9	module	module	NOUN
fcis-3173	46	10	can	can	AUX
fcis-3173	46	11	enhance	enhance	VERB
fcis-3173	46	12	meaningful	meaningful	ADJ
fcis-3173	46	13	features	feature	NOUN
fcis-3173	46	14	and	and	CCONJ
fcis-3173	46	15	suppress	suppress	VERB
fcis-3173	46	16	useless	useless	ADJ
fcis-3173	46	17	features	feature	NOUN
fcis-3173	46	18	.	.	PUNCT
fcis-3173	47	1	the	the	DET
fcis-3173	47	2	main	main	ADJ
fcis-3173	47	3	idea	idea	NOUN
fcis-3173	47	4	proposed	propose	VERB
fcis-3173	47	5	by	by	ADP
fcis-3173	47	6	the	the	DET
fcis-3173	47	7	cse	cse	NOUN
fcis-3173	47	8	module	module	NOUN
fcis-3173	47	9	is	be	AUX
fcis-3173	47	10	shown	show	VERB
fcis-3173	47	11	in	in	ADP
fcis-3173	47	12	figure	figure	NOUN
fcis-3173	47	13	2	2	NUM
fcis-3173	47	14	,	,	PUNCT
fcis-3173	47	15	firstly	firstly	ADV
fcis-3173	47	16	the	the	DET
fcis-3173	47	17	input	input	NOUN
fcis-3173	47	18	feature	feature	NOUN
fcis-3173	47	19	mapu=[u1	mapu=[u1	NOUN
fcis-3173	47	20	,	,	PUNCT
fcis-3173	47	21	u2,	u2,	NOUN
fcis-3173	47	22	...	...	PUNCT
fcis-3173	47	23	,uc	,uc	PROPN
fcis-3173	47	24	]	]	X
fcis-3173	47	25	,	,	PUNCT
fcis-3173	47	26	each	each	DET
fcis-3173	47	27	channel	channel	NOUN
fcis-3173	47	28	ui	ui	PROPN
fcis-3173	47	29	(	(	PUNCT
fcis-3173	47	30	𝑢𝑖	𝑢𝑖	PRON
fcis-3173	47	31	∈	∈	PROPN
fcis-3173	47	32	ℝ𝐻×𝑊	ℝ𝐻×𝑊	PROPN
fcis-3173	47	33	)	)	PUNCT
fcis-3173	48	1	，	，	PUNCT
fcis-3173	48	2	u	u	NOUN
fcis-3173	48	3	obtains	obtain	VERB
fcis-3173	48	4	the	the	DET
fcis-3173	48	5	vector	vector	NOUN
fcis-3173	48	6	z	z	PROPN
fcis-3173	48	7	(	(	PUNCT
fcis-3173	48	8	𝑧	𝑧	PROPN
fcis-3173	48	9	∈	∈	PROPN
fcis-3173	48	10	𝑅𝐻×𝑊	𝑅𝐻×𝑊	PROPN
fcis-3173	48	11	)	)	PUNCT
fcis-3173	48	12	after	after	ADP
fcis-3173	48	13	passing	pass	VERB
fcis-3173	48	14	through	through	ADP
fcis-3173	48	15	the	the	DET
fcis-3173	48	16	global	global	ADJ
fcis-3173	48	17	pooling	pool	VERB
fcis-3173	48	18	layer	layer	NOUN
fcis-3173	48	19	(	(	PUNCT
fcis-3173	48	20	gap).the	gap).the	DET
fcis-3173	48	21	value	value	NOUN
fcis-3173	48	22	at	at	ADP
fcis-3173	48	23	the	the	DET
fcis-3173	48	24	k	k	PROPN
fcis-3173	48	25	-	-	PUNCT
fcis-3173	48	26	th	th	VERB
fcis-3173	48	27	channel	channel	NOUN
fcis-3173	48	28	can	can	AUX
fcis-3173	48	29	be	be	AUX
fcis-3173	48	30	expressed	express	VERB
fcis-3173	48	31	as	as	ADP
fcis-3173	48	32	:	:	PUNCT
fcis-3173	48	33	𝑧𝑘	𝑧𝑘	NUM
fcis-3173	48	34	=	=	SYM
fcis-3173	48	35	1	1	NUM
fcis-3173	48	36	𝐻×𝑊	𝐻×𝑊	NOUN
fcis-3173	48	37	∑	∑	PUNCT
fcis-3173	48	38	∑	∑	PROPN
fcis-3173	48	39	𝑢𝑘	𝑢𝑘	ADP
fcis-3173	48	40	𝑊	𝑊	PROPN
fcis-3173	48	41	𝑗	𝑗	NOUN
fcis-3173	48	42	𝐻	𝐻	NOUN
fcis-3173	48	43	𝑖	𝑖	SYM
fcis-3173	48	44	(	(	PUNCT
fcis-3173	48	45	𝑖	𝑖	SYM
fcis-3173	48	46	,	,	PUNCT
fcis-3173	48	47	𝑗	𝑗	NOUN
fcis-3173	48	48	)	)	PUNCT
fcis-3173	48	49	(	(	PUNCT
fcis-3173	48	50	1	1	X
fcis-3173	48	51	)	)	PUNCT
fcis-3173	48	52	where	where	SCONJ
fcis-3173	48	53	h	h	NOUN
fcis-3173	48	54	,	,	PUNCT
fcis-3173	48	55	w	w	PROPN
fcis-3173	48	56	is	be	AUX
fcis-3173	48	57	the	the	DET
fcis-3173	48	58	size	size	NOUN
fcis-3173	48	59	of	of	ADP
fcis-3173	48	60	the	the	DET
fcis-3173	48	61	feature	feature	NOUN
fcis-3173	48	62	map	map	NOUN
fcis-3173	48	63	,	,	PUNCT
fcis-3173	48	64	c	c	PROPN
fcis-3173	48	65	is	be	AUX
fcis-3173	48	66	the	the	DET
fcis-3173	48	67	number	number	NOUN
fcis-3173	48	68	of	of	ADP
fcis-3173	48	69	channels	channel	NOUN
fcis-3173	48	70	,	,	PUNCT
fcis-3173	48	71	(	(	PUNCT
fcis-3173	48	72	i	i	PROPN
fcis-3173	48	73	,	,	PUNCT
fcis-3173	48	74	j	j	PROPN
fcis-3173	48	75	)	)	PUNCT
fcis-3173	48	76	is	be	AUX
fcis-3173	48	77	the	the	DET
fcis-3173	48	78	coordinate	coordinate	NOUN
fcis-3173	48	79	on	on	ADP
fcis-3173	48	80	the	the	DET
fcis-3173	48	81	feature	feature	NOUN
fcis-3173	48	82	map.then	map.then	NOUN
fcis-3173	48	83	the	the	DET
fcis-3173	48	84	vector	vector	NOUN
fcis-3173	48	85	is	be	AUX
fcis-3173	48	86	fully	fully	ADV
fcis-3173	48	87	connected	connect	VERB
fcis-3173	48	88	twice	twice	ADV
fcis-3173	48	89	,	,	PUNCT
fcis-3173	48	90	w1,w2	w1,w2	PROPN
fcis-3173	48	91	are	be	AUX
fcis-3173	48	92	the	the	DET
fcis-3173	48	93	weights	weight	NOUN
fcis-3173	48	94	of	of	ADP
fcis-3173	48	95	the	the	DET
fcis-3173	48	96	fully	fully	ADV
fcis-3173	48	97	connected	connect	VERB
fcis-3173	48	98	layer	layer	NOUN
fcis-3173	48	99	,	,	PUNCT
fcis-3173	48	100	and	and	CCONJ
fcis-3173	48	101	then	then	ADV
fcis-3173	48	102	the	the	DET
fcis-3173	48	103	process	process	NOUN
fcis-3173	48	104	of	of	ADP
fcis-3173	48	105	relu	relu	NOUN
fcis-3173	48	106	function	function	NOUN
fcis-3173	48	107	enhances	enhance	VERB
fcis-3173	48	108	the	the	DET
fcis-3173	48	109	independence	independence	NOUN
fcis-3173	48	110	between	between	ADP
fcis-3173	48	111	each	each	DET
fcis-3173	48	112	channel	channel	NOUN
fcis-3173	48	113	and	and	CCONJ
fcis-3173	48	114	sigmoid	sigmoid	NOUN
fcis-3173	48	115	normalization	normalization	NOUN
fcis-3173	48	116	to	to	PART
fcis-3173	48	117	obtain𝜎(𝑧	obtain𝜎(𝑧	VERB
fcis-3173	48	118	∧	∧	PROPN
fcis-3173	48	119	)	)	PUNCT
fcis-3173	48	120	,	,	PUNCT
fcis-3173	48	121	where	where	SCONJ
fcis-3173	48	122	the	the	DET
fcis-3173	48	123	𝑧	𝑧	PROPN
fcis-3173	48	124	∧	∧	NOUN
fcis-3173	48	125	value	value	NOUN
fcis-3173	48	126	can	can	AUX
fcis-3173	48	127	be	be	AUX
fcis-3173	48	128	expressed	express	VERB
fcis-3173	48	129	as	as	ADP
fcis-3173	48	130	:	:	PUNCT
fcis-3173	48	131	𝑧	𝑧	PROPN
fcis-3173	48	132	∧	∧	PROPN
fcis-3173	48	133	=	=	PUNCT
fcis-3173	48	134	𝑊1(𝛿(𝑊2𝑧	𝑊1(𝛿(𝑊2𝑧	PROPN
fcis-3173	48	135	)	)	PUNCT
fcis-3173	48	136	)	)	PUNCT
fcis-3173	49	1	(	(	PUNCT
fcis-3173	49	2	2	2	X
fcis-3173	49	3	)	)	PUNCT
fcis-3173	49	4	finally	finally	ADV
fcis-3173	49	5	,	,	PUNCT
fcis-3173	49	6	it	it	PRON
fcis-3173	49	7	is	be	AUX
fcis-3173	49	8	multiplied	multiply	VERB
fcis-3173	49	9	with	with	ADP
fcis-3173	49	10	the	the	DET
fcis-3173	49	11	unprocessed	unprocesse	VERB
fcis-3173	49	12	feature	feature	NOUN
fcis-3173	49	13	information	information	NOUN
fcis-3173	49	14	of	of	ADP
fcis-3173	49	15	the	the	DET
fcis-3173	49	16	original	original	ADJ
fcis-3173	49	17	channel	channel	NOUN
fcis-3173	49	18	to	to	PART
fcis-3173	49	19	obtain	obtain	VERB
fcis-3173	49	20	the	the	DET
fcis-3173	49	21	calibrated	calibrate	VERB
fcis-3173	49	22	feature	feature	NOUN
fcis-3173	49	23	map	map	NOUN
fcis-3173	49	24	.	.	PUNCT
fcis-3173	50	1	in	in	ADP
fcis-3173	50	2	this	this	DET
fcis-3173	50	3	way	way	NOUN
fcis-3173	50	4	,	,	PUNCT
fcis-3173	50	5	the	the	DET
fcis-3173	50	6	information	information	NOUN
fcis-3173	50	7	within	within	ADP
fcis-3173	50	8	the	the	DET
fcis-3173	50	9	unimportant	unimportant	ADJ
fcis-3173	50	10	channels	channel	NOUN
fcis-3173	50	11	will	will	AUX
fcis-3173	50	12	be	be	AUX
fcis-3173	50	13	reduced	reduce	VERB
fcis-3173	50	14	and	and	CCONJ
fcis-3173	50	15	suppressed	suppress	VERB
fcis-3173	50	16	,	,	PUNCT
fcis-3173	50	17	while	while	SCONJ
fcis-3173	50	18	the	the	DET
fcis-3173	50	19	information	information	NOUN
fcis-3173	50	20	within	within	ADP
fcis-3173	50	21	the	the	DET
fcis-3173	50	22	important	important	ADJ
fcis-3173	50	23	channels	channel	NOUN
fcis-3173	50	24	will	will	AUX
fcis-3173	50	25	remain	remain	VERB
fcis-3173	50	26	almost	almost	ADV
fcis-3173	50	27	unchanged	unchanged	ADJ
fcis-3173	50	28	and	and	CCONJ
fcis-3173	50	29	the	the	DET
fcis-3173	50	30	disguised	disguise	VERB
fcis-3173	50	31	phase	phase	NOUN
fcis-3173	50	32	is	be	AUX
fcis-3173	50	33	enhanced	enhance	VERB
fcis-3173	50	34	.	.	PUNCT
fcis-3173	51	1	the	the	DET
fcis-3173	51	2	whole	whole	ADJ
fcis-3173	51	3	process	process	NOUN
fcis-3173	51	4	can	can	AUX
fcis-3173	51	5	be	be	AUX
fcis-3173	51	6	expressed	express	VERB
fcis-3173	51	7	as	as	ADP
fcis-3173	51	8	:	:	PUNCT
fcis-3173	51	9	𝑈	𝑈	PROPN
fcis-3173	51	10	∧	∧	PROPN
fcis-3173	51	11	cse	cse	NOUN
fcis-3173	51	12	=	=	NOUN
fcis-3173	51	13	𝐹𝑐𝑆𝐸(𝑈	𝐹𝑐𝑆𝐸(𝑈	NUM
fcis-3173	51	14	)	)	PUNCT
fcis-3173	51	15	=	=	NOUN
fcis-3173	52	1	[	[	X
fcis-3173	52	2	𝜎(𝑧1	𝜎(𝑧1	ADJ
fcis-3173	52	3	∧	∧	NOUN
fcis-3173	52	4	)	)	PUNCT
fcis-3173	52	5	𝑢1	𝑢1	PROPN
fcis-3173	52	6	,	,	PUNCT
fcis-3173	52	7	𝜎(𝑧	𝜎(𝑧	PROPN
fcis-3173	52	8	∧	∧	PROPN
fcis-3173	52	9	2)𝑢2	2)𝑢2	PROPN
fcis-3173	52	10	,	,	PUNCT
fcis-3173	52	11	.	.	PUNCT
fcis-3173	52	12	.	.	PUNCT
fcis-3173	53	1	.	.	PUNCT
fcis-3173	54	1	,	,	PUNCT
fcis-3173	54	2	𝜎(𝑧	𝜎(𝑧	PROPN
fcis-3173	54	3	∧	∧	PROPN
fcis-3173	54	4	𝐶)𝑢𝐶	𝐶)𝑢𝐶	NOUN
fcis-3173	54	5	]	]	PUNCT
fcis-3173	54	6	(	(	PUNCT
fcis-3173	54	7	3	3	X
fcis-3173	54	8	)	)	PUNCT
fcis-3173	54	9	w	w	NOUN
fcis-3173	54	10	h	h	NOUN
fcis-3173	54	11	c	c	NOUN
fcis-3173	54	12	u	u	NOUN
fcis-3173	54	13	z	z	PROPN
fcis-3173	54	14	11	11	NUM
fcis-3173	54	15	2	2	NUM
fcis-3173	54	16	c	c	NOUN
fcis-3173	54	17	11	11	NUM
fcis-3173	54	18	c	c	NOUN
fcis-3173	54	19	2	2	NUM
fcis-3173	54	20	c	c	NOUN
fcis-3173	54	21	(	(	PUNCT
fcis-3173	54	22	)	)	PUNCT
fcis-3173	54	23	•	•	PROPN
fcis-3173	55	1	c	c	PROPN
fcis-3173	55	2	c	c	PROPN
fcis-3173	55	3			PROPN
fcis-3173	55	4	z	z	PROPN
fcis-3173	55	5	(	(	PUNCT
fcis-3173	55	6	)	)	PUNCT
fcis-3173	55	7			PROPN
fcis-3173	55	8	u	u	PROPN
fcis-3173	55	9	cse	cse	PROPN
fcis-3173	55	10	channel	channel	PROPN
fcis-3173	55	11	-	-	PUNCT
fcis-3173	55	12	wise	wise	ADV
fcis-3173	55	13	recalibrate	recalibrate	NOUN
fcis-3173	55	14			PROPN
fcis-3173	55	15	*	*	PUNCT
fcis-3173	55	16	*	*	PUNCT
fcis-3173	55	17	relu	relu	NOUN
fcis-3173	55	18	figure	figure	NOUN
fcis-3173	55	19	2	2	NUM
fcis-3173	55	20	.	.	NOUN
fcis-3173	55	21	channel	channel	NOUN
fcis-3173	55	22	attention	attention	NOUN
fcis-3173	55	23	mechanism	mechanism	NOUN
fcis-3173	55	24	(	(	PUNCT
fcis-3173	55	25	2	2	X
fcis-3173	55	26	)	)	PUNCT
fcis-3173	55	27	the	the	DET
fcis-3173	55	28	main	main	ADJ
fcis-3173	55	29	idea	idea	NOUN
fcis-3173	55	30	of	of	ADP
fcis-3173	55	31	sse	sse	NOUN
fcis-3173	55	32	module	module	NOUN
fcis-3173	55	33	is	be	AUX
fcis-3173	55	34	shown	show	VERB
fcis-3173	55	35	in	in	ADP
fcis-3173	55	36	figure	figure	NOUN
fcis-3173	55	37	3	3	NUM
fcis-3173	55	38	,	,	PUNCT
fcis-3173	55	39	sse	sse	PROPN
fcis-3173	55	40	is	be	AUX
fcis-3173	55	41	a	a	DET
fcis-3173	55	42	variant	variant	NOUN
fcis-3173	55	43	on	on	ADP
fcis-3173	55	44	the	the	DET
fcis-3173	55	45	basis	basis	NOUN
fcis-3173	55	46	of	of	ADP
fcis-3173	55	47	cse	cse	PROPN
fcis-3173	55	48	,	,	PUNCT
fcis-3173	55	49	cse	cse	NOUN
fcis-3173	55	50	to	to	PART
fcis-3173	55	51	improve	improve	VERB
fcis-3173	55	52	the	the	DET
fcis-3173	55	53	ability	ability	NOUN
fcis-3173	55	54	of	of	ADP
fcis-3173	55	55	important	important	ADJ
fcis-3173	55	56	channel	channel	NOUN
fcis-3173	55	57	feature	feature	NOUN
fcis-3173	55	58	information	information	NOUN
fcis-3173	55	59	of	of	ADP
fcis-3173	55	60	the	the	DET
fcis-3173	55	61	network	network	NOUN
fcis-3173	55	62	by	by	ADP
fcis-3173	55	63	compressing	compress	VERB
fcis-3173	55	64	spatial	spatial	ADJ
fcis-3173	55	65	information	information	NOUN
fcis-3173	55	66	,	,	PUNCT
fcis-3173	55	67	then	then	ADV
fcis-3173	55	68	,	,	PUNCT
fcis-3173	55	69	in	in	ADP
fcis-3173	55	70	turn	turn	NOUN
fcis-3173	55	71	,	,	PUNCT
fcis-3173	55	72	it	it	PRON
fcis-3173	55	73	can	can	AUX
fcis-3173	55	74	also	also	ADV
fcis-3173	55	75	compress	compress	VERB
fcis-3173	55	76	channel	channel	NOUN
fcis-3173	55	77	information	information	NOUN
fcis-3173	55	78	to	to	PART
fcis-3173	55	79	improve	improve	VERB
fcis-3173	55	80	the	the	DET
fcis-3173	55	81	ability	ability	NOUN
fcis-3173	55	82	of	of	ADP
fcis-3173	55	83	important	important	ADJ
fcis-3173	55	84	spatial	spatial	ADJ
fcis-3173	55	85	feature	feature	NOUN
fcis-3173	55	86	information	information	NOUN
fcis-3173	55	87	of	of	ADP
fcis-3173	55	88	the	the	DET
fcis-3173	55	89	network	network	NOUN
fcis-3173	55	90	,	,	PUNCT
fcis-3173	55	91	so	so	ADV
fcis-3173	55	92	sse	sse	PROPN
fcis-3173	55	93	was	be	AUX
fcis-3173	55	94	born	bear	VERB
fcis-3173	55	95	.	.	PUNCT
fcis-3173	56	1	for	for	ADP
fcis-3173	56	2	the	the	DET
fcis-3173	56	3	input	input	NOUN
fcis-3173	56	4	feature	feature	NOUN
fcis-3173	56	5	map	map	NOUN
fcis-3173	56	6	u=[u1	u=[u1	PROPN
fcis-3173	56	7	,	,	PUNCT
fcis-3173	56	8	1	1	NUM
fcis-3173	56	9	,	,	PUNCT
fcis-3173	56	10	u1	u1	NOUN
fcis-3173	56	11	,	,	PUNCT
fcis-3173	56	12	2	2	NUM
fcis-3173	56	13	,	,	PUNCT
fcis-3173	56	14	...	...	PUNCT
fcis-3173	56	15	,	,	PUNCT
fcis-3173	56	16	ui	ui	PROPN
fcis-3173	56	17	,	,	PUNCT
fcis-3173	56	18	j	j	PROPN
fcis-3173	56	19	,	,	PUNCT
fcis-3173	56	20	...	...	PUNCT
fcis-3173	56	21	,	,	PUNCT
fcis-3173	56	22	uh	uh	INTJ
fcis-3173	56	23	,	,	PUNCT
fcis-3173	56	24	w	w	PROPN
fcis-3173	56	25	]	]	X
fcis-3173	56	26	(	(	PUNCT
fcis-3173	56	27	ui	ui	PROPN
fcis-3173	56	28	,	,	PUNCT
fcis-3173	56	29	j∈	j∈	PROPN
fcis-3173	56	30	ℝ1×1×𝑐)，h	ℝ1×1×𝑐)，h	PROPN
fcis-3173	56	31	,	,	PUNCT
fcis-3173	56	32	w	w	PROPN
fcis-3173	56	33	are	be	AUX
fcis-3173	56	34	the	the	DET
fcis-3173	56	35	dimensions	dimension	NOUN
fcis-3173	56	36	of	of	ADP
fcis-3173	56	37	the	the	DET
fcis-3173	56	38	feature	feature	NOUN
fcis-3173	56	39	map	map	NOUN
fcis-3173	56	40	respectively,(i	respectively,(i	PROPN
fcis-3173	56	41	,	,	PUNCT
fcis-3173	56	42	j	j	NOUN
fcis-3173	56	43	)	)	PUNCT
fcis-3173	56	44	is	be	AUX
fcis-3173	56	45	the	the	DET
fcis-3173	56	46	spatial	spatial	ADJ
fcis-3173	56	47	location	location	NOUN
fcis-3173	56	48	of	of	ADP
fcis-3173	56	49	the	the	DET
fcis-3173	56	50	feature	feature	NOUN
fcis-3173	56	51	map	map	NOUN
fcis-3173	56	52	,	,	PUNCT
fcis-3173	56	53	the	the	DET
fcis-3173	56	54	vector	vector	NOUN
fcis-3173	56	55	q	q	NOUN
fcis-3173	56	56	is	be	AUX
fcis-3173	56	57	obtained	obtain	VERB
fcis-3173	56	58	by	by	ADP
fcis-3173	56	59	squeezing	squeeze	VERB
fcis-3173	56	60	the	the	DET
fcis-3173	56	61	feature	feature	NOUN
fcis-3173	56	62	information	information	NOUN
fcis-3173	56	63	of	of	ADP
fcis-3173	56	64	the	the	DET
fcis-3173	56	65	space	space	NOUN
fcis-3173	56	66	through	through	ADP
fcis-3173	56	67	a	a	DET
fcis-3173	56	68	1×1	1×1	NUM
fcis-3173	56	69	convolution	convolution	NOUN
fcis-3173	56	70	with	with	ADP
fcis-3173	56	71	a	a	DET
fcis-3173	56	72	channel	channel	NOUN
fcis-3173	56	73	number	number	NOUN
fcis-3173	56	74	of	of	ADP
fcis-3173	56	75	1	1	NUM
fcis-3173	56	76	.	.	PUNCT
fcis-3173	57	1	the	the	DET
fcis-3173	57	2	vector	vector	NOUN
fcis-3173	57	3	q	q	NOUN
fcis-3173	57	4	can	can	AUX
fcis-3173	57	5	be	be	AUX
fcis-3173	57	6	expressed	express	VERB
fcis-3173	57	7	as	as	ADP
fcis-3173	57	8	:	:	PUNCT
fcis-3173	57	9	103	103	NUM
fcis-3173	57	10	q=𝑊sq	q=𝑊sq	PUNCT
fcis-3173	57	11	∗	∗	VERB
fcis-3173	57	12	𝑈	𝑈	PROPN
fcis-3173	57	13	(	(	PUNCT
fcis-3173	57	14	4	4	NUM
fcis-3173	57	15	)	)	PUNCT
fcis-3173	57	16	where	where	SCONJ
fcis-3173	57	17	𝑊sq	𝑊sq	PROPN
fcis-3173	57	18	∈	∈	PROPN
fcis-3173	57	19	ℝ1∗1∗c*1	ℝ1∗1∗c*1	PROPN
fcis-3173	57	20	,	,	PUNCT
fcis-3173	57	21	the	the	DET
fcis-3173	57	22	obtained	obtain	VERB
fcis-3173	57	23	feature	feature	NOUN
fcis-3173	57	24	map	map	NOUN
fcis-3173	57	25	q	q	NOUN
fcis-3173	57	26	is	be	AUX
fcis-3173	57	27	then	then	ADV
fcis-3173	57	28	normalized	normalize	VERB
fcis-3173	57	29	by	by	ADP
fcis-3173	57	30	the	the	DET
fcis-3173	57	31	sigmod	sigmod	PROPN
fcis-3173	57	32	function	function	NOUN
fcis-3173	57	33	to	to	PART
fcis-3173	57	34	obtain	obtain	VERB
fcis-3173	57	35	(	(	PUNCT
fcis-3173	57	36	(	(	PUNCT
fcis-3173	57	37	·	·	PUNCT
fcis-3173	57	38	)	)	PUNCT
fcis-3173	57	39	)	)	PUNCT
fcis-3173	57	40	,	,	PUNCT
fcis-3173	57	41	which	which	PRON
fcis-3173	57	42	corresponds	correspond	VERB
fcis-3173	57	43	to	to	ADP
fcis-3173	57	44	the	the	DET
fcis-3173	57	45	spatial	spatial	ADJ
fcis-3173	57	46	feature	feature	NOUN
fcis-3173	57	47	information	information	NOUN
fcis-3173	57	48	of	of	ADP
fcis-3173	57	49	the	the	DET
fcis-3173	57	50	(	(	PUNCT
fcis-3173	57	51	i	i	PROPN
fcis-3173	57	52	,	,	PUNCT
fcis-3173	57	53	j	j	PROPN
fcis-3173	57	54	)	)	PUNCT
fcis-3173	57	55	pixel	pixel	PROPN
fcis-3173	57	56	points	point	NOUN
fcis-3173	57	57	in	in	ADP
fcis-3173	57	58	the	the	DET
fcis-3173	57	59	feature	feature	NOUN
fcis-3173	57	60	map	map	NOUN
fcis-3173	57	61	respectively	respectively	ADV
fcis-3173	57	62	,	,	PUNCT
fcis-3173	57	63	and	and	CCONJ
fcis-3173	57	64	finally	finally	ADV
fcis-3173	57	65	weighted	weight	VERB
fcis-3173	57	66	with	with	ADP
fcis-3173	57	67	the	the	DET
fcis-3173	57	68	input	input	NOUN
fcis-3173	57	69	original	original	ADJ
fcis-3173	57	70	feature	feature	NOUN
fcis-3173	57	71	map.the	map.the	DET
fcis-3173	57	72	whole	whole	ADJ
fcis-3173	57	73	process	process	NOUN
fcis-3173	57	74	can	can	AUX
fcis-3173	57	75	be	be	AUX
fcis-3173	57	76	expressed	express	VERB
fcis-3173	57	77	as	as	ADP
fcis-3173	57	78	:	:	PUNCT
fcis-3173	57	79	𝑈	𝑈	PROPN
fcis-3173	57	80	∧	∧	PROPN
fcis-3173	57	81	sse	sse	NOUN
fcis-3173	57	82	=	=	PUNCT
fcis-3173	57	83	𝐹𝑠𝑆𝐸(𝑈	𝐹𝑠𝑆𝐸(𝑈	NOUN
fcis-3173	57	84	)	)	PUNCT
fcis-3173	57	85	=	=	PUNCT
fcis-3173	58	1	[	[	X
fcis-3173	58	2	𝜎(𝑞1,1)𝑢	𝜎(𝑞1,1)𝑢	NOUN
fcis-3173	58	3	1,1	1,1	NUM
fcis-3173	58	4	,	,	PUNCT
fcis-3173	58	5	.	.	PUNCT
fcis-3173	58	6	.	.	PUNCT
fcis-3173	58	7	.	.	PUNCT
fcis-3173	59	1	,	,	PUNCT
fcis-3173	59	2	𝜎(𝑞𝑖,𝑗)𝑢	𝜎(𝑞𝑖,𝑗)𝑢	NOUN
fcis-3173	59	3	𝑖,𝑗	𝑖,𝑗	X
fcis-3173	59	4	,	,	PUNCT
fcis-3173	59	5	.	.	PUNCT
fcis-3173	59	6	.	.	PUNCT
fcis-3173	59	7	.	.	PUNCT
fcis-3173	60	1	,	,	PUNCT
fcis-3173	60	2	𝜎(𝑞𝐻,𝑊)𝑢	𝜎(𝑞𝐻,𝑊)𝑢	NUM
fcis-3173	60	3	𝐻,𝑊	𝐻,𝑊	NOUN
fcis-3173	60	4	]	]	PUNCT
fcis-3173	60	5	(	(	PUNCT
fcis-3173	60	6	5	5	NUM
fcis-3173	60	7	)	)	PUNCT
fcis-3173	60	8	w	w	NOUN
fcis-3173	60	9	c	c	NOUN
fcis-3173	60	10	u	u	NOUN
fcis-3173	60	11			PROPN
fcis-3173	60	12	u	u	PROPN
fcis-3173	60	13	sse	sse	X
fcis-3173	60	14	spatially	spatially	ADV
fcis-3173	60	15	recalibrate	recalibrate	VERB
fcis-3173	60	16			PROPN
fcis-3173	60	17	(	(	PUNCT
fcis-3173	60	18	q	q	X
fcis-3173	60	19	)	)	PUNCT
fcis-3173	60	20	11	11	NUM
fcis-3173	60	21	1	1	NUM
fcis-3173	60	22	h	h	NOUN
fcis-3173	60	23	w	w	NOUN
fcis-3173	60	24	h	h	NOUN
fcis-3173	60	25	*	*	PUNCT
fcis-3173	60	26	(	(	PUNCT
fcis-3173	60	27	)	)	PUNCT
fcis-3173	60	28	•	•	ADJ
fcis-3173	60	29	figure	figure	NOUN
fcis-3173	60	30	3	3	NUM
fcis-3173	60	31	.	.	PUNCT
fcis-3173	60	32	spatial	spatial	ADJ
fcis-3173	60	33	attention	attention	NOUN
fcis-3173	60	34	mechanism	mechanism	NOUN
fcis-3173	60	35	(	(	PUNCT
fcis-3173	60	36	sse	sse	NOUN
fcis-3173	60	37	)	)	PUNCT
fcis-3173	60	38	(	(	PUNCT
fcis-3173	60	39	3	3	X
fcis-3173	60	40	)	)	PUNCT
fcis-3173	60	41	the	the	DET
fcis-3173	60	42	main	main	ADJ
fcis-3173	60	43	idea	idea	NOUN
fcis-3173	60	44	proposed	propose	VERB
fcis-3173	60	45	by	by	ADP
fcis-3173	60	46	the	the	DET
fcis-3173	60	47	scse	scse	NOUN
fcis-3173	60	48	module	module	NOUN
fcis-3173	60	49	is	be	AUX
fcis-3173	60	50	shown	show	VERB
fcis-3173	60	51	in	in	ADP
fcis-3173	60	52	figure	figure	NOUN
fcis-3173	60	53	4	4	NUM
fcis-3173	60	54	,	,	PUNCT
fcis-3173	60	55	which	which	PRON
fcis-3173	60	56	is	be	AUX
fcis-3173	60	57	a	a	DET
fcis-3173	60	58	tandem	tandem	NOUN
fcis-3173	60	59	of	of	ADP
fcis-3173	60	60	the	the	DET
fcis-3173	60	61	cse	cse	NOUN
fcis-3173	60	62	and	and	CCONJ
fcis-3173	60	63	sse	sse	PROPN
fcis-3173	60	64	modules	module	NOUN
fcis-3173	60	65	,	,	PUNCT
fcis-3173	60	66	and	and	CCONJ
fcis-3173	60	67	the	the	DET
fcis-3173	60	68	feature	feature	NOUN
fcis-3173	60	69	extraction	extraction	NOUN
fcis-3173	60	70	is	be	AUX
fcis-3173	60	71	carried	carry	VERB
fcis-3173	60	72	out	out	ADP
fcis-3173	60	73	separately	separately	ADV
fcis-3173	60	74	by	by	ADP
fcis-3173	60	75	both	both	CCONJ
fcis-3173	60	76	the	the	DET
fcis-3173	60	77	channel	channel	NOUN
fcis-3173	60	78	and	and	CCONJ
fcis-3173	60	79	space	space	NOUN
fcis-3173	60	80	of	of	ADP
fcis-3173	60	81	the	the	DET
fcis-3173	60	82	feature	feature	NOUN
fcis-3173	60	83	map	map	NOUN
fcis-3173	60	84	,	,	PUNCT
fcis-3173	60	85	and	and	CCONJ
fcis-3173	60	86	then	then	ADV
fcis-3173	60	87	the	the	DET
fcis-3173	60	88	weighted	weighted	ADJ
fcis-3173	60	89	summation	summation	NOUN
fcis-3173	60	90	is	be	AUX
fcis-3173	60	91	obtained	obtain	VERB
fcis-3173	60	92	after	after	SCONJ
fcis-3173	60	93	the	the	DET
fcis-3173	60	94	feature	feature	NOUN
fcis-3173	60	95	map	map	NOUN
fcis-3173	60	96	information	information	NOUN
fcis-3173	60	97	obtained	obtain	VERB
fcis-3173	60	98	is	be	AUX
fcis-3173	60	99	more	more	ADV
fcis-3173	60	100	specific	specific	ADJ
fcis-3173	60	101	and	and	CCONJ
fcis-3173	60	102	targeted	target	VERB
fcis-3173	60	103	,	,	PUNCT
fcis-3173	60	104	making	make	VERB
fcis-3173	60	105	the	the	DET
fcis-3173	60	106	final	final	ADJ
fcis-3173	60	107	extracted	extract	VERB
fcis-3173	60	108	feature	feature	NOUN
fcis-3173	60	109	information	information	NOUN
fcis-3173	60	110	also	also	ADV
fcis-3173	60	111	more	more	ADV
fcis-3173	60	112	focused	focused	ADJ
fcis-3173	60	113	.	.	PUNCT
fcis-3173	61	1	its	its	PRON
fcis-3173	61	2	formula	formula	NOUN
fcis-3173	61	3	can	can	AUX
fcis-3173	61	4	be	be	AUX
fcis-3173	61	5	expressed	express	VERB
fcis-3173	61	6	as	as	ADP
fcis-3173	61	7	:	:	PUNCT
fcis-3173	61	8	𝑈𝑠𝑐𝑆𝐸	𝑈𝑠𝑐𝑆𝐸	PROPN
fcis-3173	61	9	=	=	PUNCT
fcis-3173	61	10	𝑈𝑐𝑆𝐸	𝑈𝑐𝑆𝐸	PROPN
fcis-3173	61	11	+	+	CCONJ
fcis-3173	61	12	𝑈𝑠𝑆𝐸	𝑈𝑠𝑆𝐸	PROPN
fcis-3173	61	13	(	(	PUNCT
fcis-3173	61	14	6	6	NUM
fcis-3173	61	15	)	)	PUNCT
fcis-3173	61	16	w	w	NOUN
fcis-3173	61	17	c	c	NOUN
fcis-3173	61	18	u	u	NOUN
fcis-3173	61	19	h	h	NOUN
fcis-3173	61	20			PROPN
fcis-3173	61	21	u	u	PROPN
fcis-3173	61	22	cse	cse	PROPN
fcis-3173	61	23			PROPN
fcis-3173	61	24	usse	usse	PROPN
fcis-3173	61	25			PROPN
fcis-3173	61	26	u	u	PROPN
fcis-3173	61	27	scse	scse	NOUN
fcis-3173	61	28	figure	figure	NOUN
fcis-3173	61	29	4	4	NUM
fcis-3173	61	30	.	.	PUNCT
fcis-3173	61	31	channel	channel	NOUN
fcis-3173	61	32	-	-	PUNCT
fcis-3173	61	33	space	space	NOUN
fcis-3173	61	34	attention	attention	NOUN
fcis-3173	61	35	mechanism	mechanism	NOUN
fcis-3173	61	36	(	(	PUNCT
fcis-3173	61	37	scse	scse	NOUN
fcis-3173	61	38	)	)	PUNCT
fcis-3173	61	39	3	3	NUM
fcis-3173	61	40	.	.	X
fcis-3173	61	41	method	method	VERB
fcis-3173	61	42	the	the	DET
fcis-3173	61	43	difficulty	difficulty	NOUN
fcis-3173	61	44	of	of	ADP
fcis-3173	61	45	classifying	classify	VERB
fcis-3173	61	46	aluminum	aluminum	NOUN
fcis-3173	61	47	defects	defect	NOUN
fcis-3173	61	48	lies	lie	VERB
fcis-3173	61	49	in	in	ADP
fcis-3173	61	50	the	the	DET
fcis-3173	61	51	inconsistent	inconsistent	ADJ
fcis-3173	61	52	shape	shape	NOUN
fcis-3173	61	53	of	of	ADP
fcis-3173	61	54	the	the	DET
fcis-3173	61	55	surface	surface	NOUN
fcis-3173	61	56	defects	defect	NOUN
fcis-3173	61	57	and	and	CCONJ
fcis-3173	61	58	the	the	DET
fcis-3173	61	59	difference	difference	NOUN
fcis-3173	61	60	in	in	ADP
fcis-3173	61	61	size	size	NOUN
fcis-3173	61	62	.	.	PUNCT
fcis-3173	62	1	for	for	ADP
fcis-3173	62	2	the	the	DET
fcis-3173	62	3	surface	surface	NOUN
fcis-3173	62	4	defects	defect	NOUN
fcis-3173	62	5	of	of	ADP
fcis-3173	62	6	aluminum	aluminum	NOUN
fcis-3173	62	7	are	be	AUX
fcis-3173	62	8	very	very	ADV
fcis-3173	62	9	small	small	ADJ
fcis-3173	62	10	and	and	CCONJ
fcis-3173	62	11	some	some	DET
fcis-3173	62	12	small	small	ADJ
fcis-3173	62	13	scratches	scratch	NOUN
fcis-3173	62	14	,	,	PUNCT
fcis-3173	62	15	this	this	DET
fcis-3173	62	16	paper	paper	NOUN
fcis-3173	62	17	incorporates	incorporate	VERB
fcis-3173	62	18	the	the	DET
fcis-3173	62	19	attention	attention	NOUN
fcis-3173	62	20	mechanism	mechanism	NOUN
fcis-3173	62	21	scse	scse	NOUN
fcis-3173	62	22	module	module	NOUN
fcis-3173	62	23	in	in	ADP
fcis-3173	62	24	the	the	DET
fcis-3173	62	25	module	module	NOUN
fcis-3173	62	26	of	of	ADP
fcis-3173	62	27	inception	inception	PROPN
fcis-3173	62	28	v4	v4	PROPN
fcis-3173	62	29	network	network	NOUN
fcis-3173	62	30	model	model	NOUN
fcis-3173	62	31	to	to	PART
fcis-3173	62	32	pay	pay	VERB
fcis-3173	62	33	more	more	ADJ
fcis-3173	62	34	attention	attention	NOUN
fcis-3173	62	35	to	to	ADP
fcis-3173	62	36	these	these	DET
fcis-3173	62	37	regions	region	NOUN
fcis-3173	62	38	of	of	ADP
fcis-3173	62	39	interest	interest	NOUN
fcis-3173	62	40	,	,	PUNCT
fcis-3173	62	41	extract	extract	VERB
fcis-3173	62	42	more	more	ADV
fcis-3173	62	43	important	important	ADJ
fcis-3173	62	44	feature	feature	NOUN
fcis-3173	62	45	information	information	NOUN
fcis-3173	62	46	for	for	ADP
fcis-3173	62	47	learning	learning	NOUN
fcis-3173	62	48	and	and	CCONJ
fcis-3173	62	49	then	then	ADV
fcis-3173	62	50	improve	improve	VERB
fcis-3173	62	51	the	the	DET
fcis-3173	62	52	accuracy	accuracy	NOUN
fcis-3173	62	53	of	of	ADP
fcis-3173	62	54	the	the	DET
fcis-3173	62	55	model	model	NOUN
fcis-3173	62	56	classification	classification	NOUN
fcis-3173	62	57	.	.	PUNCT
fcis-3173	63	1	the	the	DET
fcis-3173	63	2	structure	structure	NOUN
fcis-3173	63	3	diagram	diagram	NOUN
fcis-3173	63	4	of	of	ADP
fcis-3173	63	5	the	the	DET
fcis-3173	63	6	improved	improved	ADJ
fcis-3173	63	7	algorithm	algorithm	NOUN
fcis-3173	63	8	is	be	AUX
fcis-3173	63	9	shown	show	VERB
fcis-3173	63	10	in	in	ADP
fcis-3173	63	11	figure	figure	NOUN
fcis-3173	63	12	5	5	NUM
fcis-3173	63	13	.	.	PUNCT
fcis-3173	63	14	input(229×229×3	input(229×229×3	PROPN
fcis-3173	63	15	)	)	PUNCT
fcis-3173	63	16	stem	stem	VERB
fcis-3173	63	17	4×inception	4×inception	NUM
fcis-3173	63	18	-	-	PUNCT
fcis-3173	63	19	a	a	DET
fcis-3173	63	20	reduction	reduction	NOUN
fcis-3173	63	21	-	-	PUNCT
fcis-3173	63	22	a	a	DET
fcis-3173	63	23	7×inception	7×inception	PROPN
fcis-3173	63	24	-	-	PUNCT
fcis-3173	63	25	b	b	NOUN
fcis-3173	63	26	reduction	reduction	NOUN
fcis-3173	63	27	-	-	PUNCT
fcis-3173	63	28	b	b	NOUN
fcis-3173	63	29	3×inception	3×inception	PROPN
fcis-3173	63	30	-	-	PUNCT
fcis-3173	63	31	c	c	NOUN
fcis-3173	63	32	average	average	ADJ
fcis-3173	63	33	pooling	pool	VERB
fcis-3173	63	34	dropout	dropout	NOUN
fcis-3173	63	35	softmax	softmax	NOUN
fcis-3173	63	36	scse模块	scse模块	PROPN
fcis-3173	63	37	scse模块	scse模块	PROPN
fcis-3173	63	38	scse模块	scse模块	PROPN
fcis-3173	63	39	figure	figure	NOUN
fcis-3173	63	40	5	5	NUM
fcis-3173	63	41	.	.	PUNCT
fcis-3173	64	1	inception	inception	ADJ
fcis-3173	64	2	v4	v4	PROPN
fcis-3173	64	3	-	-	PUNCT
fcis-3173	64	4	scse	scse	NOUN
fcis-3173	64	5	structure	structure	NOUN
fcis-3173	64	6	diagram	diagram	PROPN
fcis-3173	64	7	inception	inception	NOUN
fcis-3173	64	8	v4	v4	PROPN
fcis-3173	64	9	investigates	investigate	VERB
fcis-3173	64	10	two	two	NUM
fcis-3173	64	11	modules	module	NOUN
fcis-3173	64	12	,	,	PUNCT
fcis-3173	64	13	the	the	DET
fcis-3173	64	14	inception	inception	NOUN
fcis-3173	64	15	module	module	NOUN
fcis-3173	64	16	and	and	CCONJ
fcis-3173	64	17	reduction	reduction	NOUN
fcis-3173	64	18	module	module	NOUN
fcis-3173	64	19	,	,	PUNCT
fcis-3173	64	20	the	the	DET
fcis-3173	64	21	attention	attention	NOUN
fcis-3173	64	22	module	module	NOUN
fcis-3173	64	23	scse	scse	NOUN
fcis-3173	64	24	is	be	AUX
fcis-3173	64	25	added	add	VERB
fcis-3173	64	26	to	to	ADP
fcis-3173	64	27	inception	inception	NOUN
fcis-3173	64	28	-	-	PUNCT
fcis-3173	64	29	a	a	NOUN
fcis-3173	64	30	,	,	PUNCT
fcis-3173	64	31	inception	inception	NOUN
fcis-3173	64	32	-	-	PUNCT
fcis-3173	64	33	b	b	NOUN
fcis-3173	64	34	,	,	PUNCT
fcis-3173	64	35	and	and	CCONJ
fcis-3173	64	36	inception	inception	NOUN
fcis-3173	64	37	-	-	PUNCT
fcis-3173	64	38	c	c	NOUN
fcis-3173	64	39	modules	module	NOUN
fcis-3173	64	40	respectively	respectively	ADV
fcis-3173	64	41	for	for	ADP
fcis-3173	64	42	fusion	fusion	NOUN
fcis-3173	64	43	,	,	PUNCT
fcis-3173	64	44	but	but	CCONJ
fcis-3173	64	45	the	the	DET
fcis-3173	64	46	number	number	NOUN
fcis-3173	64	47	of	of	ADP
fcis-3173	64	48	parameters	parameter	NOUN
fcis-3173	64	49	thus	thus	ADV
fcis-3173	64	50	generated	generate	VERB
fcis-3173	64	51	does	do	AUX
fcis-3173	64	52	not	not	PART
fcis-3173	64	53	affect	affect	VERB
fcis-3173	64	54	the	the	DET
fcis-3173	64	55	network	network	NOUN
fcis-3173	64	56	training	training	NOUN
fcis-3173	64	57	time	time	NOUN
fcis-3173	64	58	and	and	CCONJ
fcis-3173	64	59	the	the	DET
fcis-3173	64	60	real	real	ADJ
fcis-3173	64	61	-	-	PUNCT
fcis-3173	64	62	time	time	NOUN
fcis-3173	64	63	demand	demand	NOUN
fcis-3173	64	64	for	for	ADP
fcis-3173	64	65	classification	classification	NOUN
fcis-3173	64	66	of	of	ADP
fcis-3173	64	67	aluminum	aluminum	NOUN
fcis-3173	64	68	defects	defect	NOUN
fcis-3173	64	69	.	.	PUNCT
fcis-3173	65	1	the	the	DET
fcis-3173	65	2	inception	inception	NOUN
fcis-3173	65	3	module	module	NOUN
fcis-3173	65	4	is	be	AUX
fcis-3173	65	5	added	add	VERB
fcis-3173	65	6	to	to	PART
fcis-3173	65	7	increase	increase	VERB
fcis-3173	65	8	the	the	DET
fcis-3173	65	9	width	width	ADJ
fcis-3173	65	10	and	and	CCONJ
fcis-3173	65	11	depth	depth	NOUN
fcis-3173	65	12	of	of	ADP
fcis-3173	65	13	the	the	DET
fcis-3173	65	14	network	network	NOUN
fcis-3173	65	15	to	to	PART
fcis-3173	65	16	obtain	obtain	VERB
fcis-3173	65	17	more	more	ADJ
fcis-3173	65	18	feature	feature	NOUN
fcis-3173	65	19	information	information	NOUN
fcis-3173	65	20	,	,	PUNCT
fcis-3173	65	21	and	and	CCONJ
fcis-3173	65	22	the	the	DET
fcis-3173	65	23	reduction	reduction	NOUN
fcis-3173	65	24	module	module	NOUN
fcis-3173	65	25	is	be	AUX
fcis-3173	65	26	added	add	VERB
fcis-3173	65	27	to	to	PART
fcis-3173	65	28	reduce	reduce	VERB
fcis-3173	65	29	the	the	DET
fcis-3173	65	30	computational	computational	ADJ
fcis-3173	65	31	effort	effort	NOUN
fcis-3173	65	32	.	.	PUNCT
fcis-3173	66	1	the	the	DET
fcis-3173	66	2	reduction	reduction	NOUN
fcis-3173	66	3	module	module	NOUN
fcis-3173	66	4	was	be	AUX
fcis-3173	66	5	added	add	VERB
fcis-3173	66	6	to	to	PART
fcis-3173	66	7	reduce	reduce	VERB
fcis-3173	66	8	the	the	DET
fcis-3173	66	9	computational	computational	ADJ
fcis-3173	66	10	effort	effort	NOUN
fcis-3173	66	11	.	.	PUNCT
fcis-3173	67	1	the	the	DET
fcis-3173	67	2	algorithm	algorithm	NOUN
fcis-3173	67	3	in	in	ADP
fcis-3173	67	4	this	this	DET
fcis-3173	67	5	paper	paper	NOUN
fcis-3173	67	6	uses	use	VERB
fcis-3173	67	7	the	the	DET
fcis-3173	67	8	cross	cross	ADJ
fcis-3173	67	9	-	-	ADJ
fcis-3173	67	10	entropy	entropy	ADJ
fcis-3173	67	11	loss	loss	NOUN
fcis-3173	67	12	function	function	NOUN
fcis-3173	67	13	commonly	commonly	ADV
fcis-3173	67	14	used	use	VERB
fcis-3173	67	15	in	in	ADP
fcis-3173	67	16	classification	classification	NOUN
fcis-3173	67	17	problems	problem	NOUN
fcis-3173	67	18	in	in	ADP
fcis-3173	67	19	the	the	DET
fcis-3173	67	20	network	network	NOUN
fcis-3173	67	21	training	training	NOUN
fcis-3173	67	22	process	process	NOUN
fcis-3173	67	23	,	,	PUNCT
fcis-3173	67	24	its	its	PRON
fcis-3173	67	25	formula	formula	NOUN
fcis-3173	67	26	can	can	AUX
fcis-3173	67	27	be	be	AUX
fcis-3173	67	28	expressed	express	VERB
fcis-3173	67	29	as	as	ADP
fcis-3173	67	30	:	:	PUNCT
fcis-3173	67	31	𝐿	𝐿	PROPN
fcis-3173	67	32	=	=	NOUN
fcis-3173	67	33	−	−	PROPN
fcis-3173	67	34	1	1	NUM
fcis-3173	67	35	𝑁	𝑁	PROPN
fcis-3173	67	36	∑	∑	PROPN
fcis-3173	67	37	(	(	PUNCT
fcis-3173	67	38	𝑦(𝑖	𝑦(𝑖	NOUN
fcis-3173	67	39	)	)	PUNCT
fcis-3173	67	40	𝑙𝑜𝑔	𝑙𝑜𝑔	ADP
fcis-3173	67	41	𝑦	𝑦	PROPN
fcis-3173	67	42	∧	∧	NOUN
fcis-3173	67	43	(	(	PUNCT
fcis-3173	67	44	𝑖	𝑖	NOUN
fcis-3173	67	45	)	)	PUNCT
fcis-3173	68	1	+	+	CCONJ
fcis-3173	68	2	(	(	PUNCT
fcis-3173	68	3	1	1	NUM
fcis-3173	68	4	−	−	NUM
fcis-3173	68	5	𝑦(𝑖	𝑦(𝑖	NOUN
fcis-3173	68	6	)	)	PUNCT
fcis-3173	68	7	)	)	PUNCT
fcis-3173	69	1	𝑙𝑜𝑔	𝑙𝑜𝑔	PROPN
fcis-3173	69	2	(	(	PUNCT
fcis-3173	69	3	1	1	NUM
fcis-3173	69	4	−	−	PROPN
fcis-3173	69	5	𝑦(𝑖	𝑦(𝑖	NOUN
fcis-3173	69	6	)	)	PUNCT
fcis-3173	69	7	)	)	PUNCT
fcis-3173	69	8	)	)	PUNCT
fcis-3173	70	1	𝑁	𝑁	PROPN
fcis-3173	70	2	𝑖=1	𝑖=1	PROPN
fcis-3173	70	3	(	(	PUNCT
fcis-3173	70	4	7	7	NUM
fcis-3173	70	5	)	)	PUNCT
fcis-3173	70	6	where	where	SCONJ
fcis-3173	70	7	n	n	PRON
fcis-3173	70	8	denotes	denote	VERB
fcis-3173	70	9	the	the	DET
fcis-3173	70	10	size	size	NOUN
fcis-3173	70	11	of	of	ADP
fcis-3173	70	12	the	the	DET
fcis-3173	70	13	number	number	NOUN
fcis-3173	70	14	of	of	ADP
fcis-3173	70	15	samples	sample	NOUN
fcis-3173	70	16	,	,	PUNCT
fcis-3173	70	17	and	and	CCONJ
fcis-3173	70	18	i	i	PRON
fcis-3173	70	19	denotes	denote	VERB
fcis-3173	70	20	the	the	DET
fcis-3173	70	21	i	i	PROPN
fcis-3173	70	22	-	-	PUNCT
fcis-3173	70	23	th	th	X
fcis-3173	70	24	sample,𝑦(𝑖	sample,𝑦(𝑖	PROPN
fcis-3173	70	25	)	)	PUNCT
fcis-3173	70	26	is	be	AUX
fcis-3173	70	27	the	the	DET
fcis-3173	70	28	true	true	ADJ
fcis-3173	70	29	value,𝑦	value,𝑦	NUM
fcis-3173	70	30	∧	∧	PROPN
fcis-3173	70	31	(	(	PUNCT
fcis-3173	70	32	𝑖	𝑖	X
fcis-3173	70	33	)	)	PUNCT
fcis-3173	70	34	is	be	AUX
fcis-3173	70	35	the	the	DET
fcis-3173	70	36	predicted	predict	VERB
fcis-3173	70	37	value	value	NOUN
fcis-3173	70	38	.	.	PUNCT
fcis-3173	71	1	4	4	X
fcis-3173	71	2	.	.	X
fcis-3173	71	3	experimental	experimental	ADJ
fcis-3173	71	4	analysis	analysis	NOUN
fcis-3173	71	5	4.1	4.1	NUM
fcis-3173	71	6	.	.	PUNCT
fcis-3173	72	1	datasets	dataset	NOUN
fcis-3173	72	2	this	this	DET
fcis-3173	72	3	paper	paper	NOUN
fcis-3173	72	4	uses	use	VERB
fcis-3173	72	5	a	a	DET
fcis-3173	72	6	dataset	dataset	NOUN
fcis-3173	72	7	from	from	ADP
fcis-3173	72	8	the	the	DET
fcis-3173	72	9	2018	2018	NUM
fcis-3173	72	10	guangdong	guangdong	PROPN
fcis-3173	72	11	industrial	industrial	ADJ
fcis-3173	72	12	smart	smart	ADJ
fcis-3173	72	13	manufacturing	manufacturing	NOUN
fcis-3173	72	14	big	big	ADJ
fcis-3173	72	15	data	datum	NOUN
fcis-3173	72	16	innovation	innovation	NOUN
fcis-3173	72	17	competition	competition	NOUN
fcis-3173	72	18	aluminum	aluminum	NOUN
fcis-3173	72	19	profile	profile	NOUN
fcis-3173	72	20	surface	surface	NOUN
fcis-3173	72	21	defect	defect	NOUN
fcis-3173	72	22	recognition	recognition	NOUN
fcis-3173	72	23	dataset	dataset	NOUN
fcis-3173	72	24	,	,	PUNCT
fcis-3173	72	25	which	which	PRON
fcis-3173	72	26	is	be	AUX
fcis-3173	72	27	hosted	host	VERB
fcis-3173	72	28	by	by	ADP
fcis-3173	72	29	alicloud	alicloud	PROPN
fcis-3173	72	30	tianchi	tianchi	PROPN
fcis-3173	72	31	.	.	PUNCT
fcis-3173	73	1	the	the	DET
fcis-3173	73	2	dataset	dataset	NOUN
fcis-3173	73	3	contains	contain	VERB
fcis-3173	73	4	no	no	DET
fcis-3173	73	5	defect	defect	NOUN
fcis-3173	73	6	samples	sample	NOUN
fcis-3173	73	7	and	and	CCONJ
fcis-3173	73	8	ten	ten	NUM
fcis-3173	73	9	categories	category	NOUN
fcis-3173	73	10	of	of	ADP
fcis-3173	73	11	defect	defect	ADJ
fcis-3173	73	12	samples	sample	NOUN
fcis-3173	73	13	(	(	PUNCT
fcis-3173	73	14	divided	divide	VERB
fcis-3173	73	15	into	into	ADP
fcis-3173	73	16	non	non	ADJ
fcis-3173	73	17	-	-	ADJ
fcis-3173	73	18	conductive	conductive	ADJ
fcis-3173	73	19	,	,	PUNCT
fcis-3173	73	20	scuffed	scuff	VERB
fcis-3173	73	21	,	,	PUNCT
fcis-3173	73	22	cross	cross	ADJ
fcis-3173	73	23	-	-	ADJ
fcis-3173	73	24	strip	strip	ADJ
fcis-3173	73	25	pressure	pressure	NOUN
fcis-3173	73	26	dent	dent	NOUN
fcis-3173	73	27	,	,	PUNCT
fcis-3173	73	28	orange	orange	NOUN
fcis-3173	73	29	peel	peel	PROPN
fcis-3173	73	30	,	,	PUNCT
fcis-3173	73	31	bottom	bottom	ADJ
fcis-3173	73	32	leakage	leakage	NOUN
fcis-3173	73	33	,	,	PUNCT
fcis-3173	73	34	bruise	bruise	NOUN
fcis-3173	73	35	,	,	PUNCT
fcis-3173	73	36	pit	pit	NOUN
fcis-3173	73	37	,	,	PUNCT
fcis-3173	73	38	convex	convex	NOUN
fcis-3173	73	39	powder	powder	NOUN
fcis-3173	73	40	,	,	PUNCT
fcis-3173	73	41	cracked	crack	VERB
fcis-3173	73	42	coating	coating	NOUN
fcis-3173	73	43	,	,	PUNCT
fcis-3173	73	44	and	and	CCONJ
fcis-3173	73	45	dirty	dirty	ADJ
fcis-3173	73	46	spot	spot	NOUN
fcis-3173	73	47	)	)	PUNCT
fcis-3173	73	48	.	.	PUNCT
fcis-3173	74	1	the	the	DET
fcis-3173	74	2	104	104	NUM
fcis-3173	74	3	following	follow	VERB
fcis-3173	74	4	are	be	AUX
fcis-3173	74	5	images	image	NOUN
fcis-3173	74	6	of	of	ADP
fcis-3173	74	7	the	the	DET
fcis-3173	74	8	ten	ten	NUM
fcis-3173	74	9	categories	category	NOUN
fcis-3173	74	10	of	of	ADP
fcis-3173	74	11	defect	defect	NOUN
fcis-3173	74	12	samples	sample	NOUN
fcis-3173	74	13	,	,	PUNCT
fcis-3173	74	14	respectively	respectively	ADV
fcis-3173	74	15	,	,	PUNCT
fcis-3173	74	16	as	as	SCONJ
fcis-3173	74	17	shown	show	VERB
fcis-3173	74	18	in	in	ADP
fcis-3173	74	19	figure	figure	NOUN
fcis-3173	74	20	6	6	NUM
fcis-3173	74	21	.	.	PUNCT
fcis-3173	75	1	figure	figure	VERB
fcis-3173	75	2	6	6	NUM
fcis-3173	75	3	.	.	PUNCT
fcis-3173	76	1	pictures	picture	NOUN
fcis-3173	76	2	of	of	ADP
fcis-3173	76	3	aluminum	aluminum	NOUN
fcis-3173	76	4	defect	defect	NOUN
fcis-3173	76	5	categories	category	NOUN
fcis-3173	76	6	4.2	4.2	NUM
fcis-3173	76	7	.	.	PUNCT
fcis-3173	77	1	evaluation	evaluation	NOUN
fcis-3173	77	2	criteria	criterion	NOUN
fcis-3173	77	3	the	the	DET
fcis-3173	77	4	experiments	experiment	NOUN
fcis-3173	77	5	in	in	ADP
fcis-3173	77	6	this	this	DET
fcis-3173	77	7	paper	paper	NOUN
fcis-3173	77	8	belong	belong	VERB
fcis-3173	77	9	to	to	ADP
fcis-3173	77	10	a	a	DET
fcis-3173	77	11	classification	classification	NOUN
fcis-3173	77	12	task	task	NOUN
fcis-3173	77	13	,	,	PUNCT
fcis-3173	77	14	so	so	CCONJ
fcis-3173	77	15	the	the	DET
fcis-3173	77	16	common	common	ADJ
fcis-3173	77	17	criteria	criterion	NOUN
fcis-3173	77	18	of	of	ADP
fcis-3173	77	19	classification	classification	NOUN
fcis-3173	77	20	,	,	PUNCT
fcis-3173	77	21	accuracy	accuracy	NOUN
fcis-3173	77	22	,	,	PUNCT
fcis-3173	77	23	precision	precision	NOUN
fcis-3173	77	24	and	and	CCONJ
fcis-3173	77	25	recall	recall	NOUN
fcis-3173	77	26	,	,	PUNCT
fcis-3173	77	27	are	be	AUX
fcis-3173	77	28	used	use	VERB
fcis-3173	77	29	to	to	PART
fcis-3173	77	30	evaluate	evaluate	VERB
fcis-3173	77	31	the	the	DET
fcis-3173	77	32	performance	performance	NOUN
fcis-3173	77	33	of	of	ADP
fcis-3173	77	34	the	the	DET
fcis-3173	77	35	algorithm	algorithm	NOUN
fcis-3173	77	36	.	.	PUNCT
fcis-3173	78	1	first	first	ADV
fcis-3173	78	2	of	of	ADP
fcis-3173	78	3	all	all	PRON
fcis-3173	78	4	,	,	PUNCT
fcis-3173	78	5	the	the	DET
fcis-3173	78	6	concepts	concept	NOUN
fcis-3173	78	7	of	of	ADP
fcis-3173	78	8	tp	tp	PROPN
fcis-3173	78	9	,	,	PUNCT
fcis-3173	78	10	tn	tn	PROPN
fcis-3173	78	11	,	,	PUNCT
fcis-3173	78	12	fp	fp	NOUN
fcis-3173	78	13	,	,	PUNCT
fcis-3173	78	14	and	and	CCONJ
fcis-3173	78	15	fn	fn	NOUN
fcis-3173	78	16	are	be	AUX
fcis-3173	78	17	concerned	concerned	ADJ
fcis-3173	78	18	.	.	PUNCT
fcis-3173	79	1	tp	tp	NOUN
fcis-3173	79	2	is	be	AUX
fcis-3173	79	3	predicted	predict	VERB
fcis-3173	79	4	as	as	ADP
fcis-3173	79	5	a	a	DET
fcis-3173	79	6	positive	positive	ADJ
fcis-3173	79	7	sample	sample	NOUN
fcis-3173	79	8	and	and	CCONJ
fcis-3173	79	9	predicted	predict	VERB
fcis-3173	79	10	correctly	correctly	ADV
fcis-3173	79	11	,	,	PUNCT
fcis-3173	79	12	tn	tn	PROPN
fcis-3173	79	13	is	be	AUX
fcis-3173	79	14	predicted	predict	VERB
fcis-3173	79	15	as	as	ADP
fcis-3173	79	16	a	a	DET
fcis-3173	79	17	negative	negative	ADJ
fcis-3173	79	18	sample	sample	NOUN
fcis-3173	79	19	and	and	CCONJ
fcis-3173	79	20	predicted	predict	VERB
fcis-3173	79	21	correctly	correctly	ADV
fcis-3173	79	22	,	,	PUNCT
fcis-3173	79	23	fp	fp	X
fcis-3173	79	24	is	be	AUX
fcis-3173	79	25	classified	classify	VERB
fcis-3173	79	26	(	(	PUNCT
fcis-3173	79	27	predicted	predict	VERB
fcis-3173	79	28	)	)	PUNCT
fcis-3173	79	29	as	as	ADP
fcis-3173	79	30	the	the	DET
fcis-3173	79	31	actual	actual	ADJ
fcis-3173	79	32	negative	negative	ADJ
fcis-3173	79	33	sample	sample	NOUN
fcis-3173	79	34	as	as	ADP
fcis-3173	79	35	a	a	DET
fcis-3173	79	36	positive	positive	ADJ
fcis-3173	79	37	sample	sample	NOUN
fcis-3173	79	38	,	,	PUNCT
fcis-3173	79	39	and	and	CCONJ
fcis-3173	79	40	fn	fn	NOUN
fcis-3173	79	41	is	be	AUX
fcis-3173	79	42	classified	classify	VERB
fcis-3173	79	43	(	(	PUNCT
fcis-3173	79	44	predicted	predict	VERB
fcis-3173	79	45	)	)	PUNCT
fcis-3173	79	46	as	as	ADP
fcis-3173	79	47	the	the	DET
fcis-3173	79	48	actual	actual	ADJ
fcis-3173	79	49	positive	positive	ADJ
fcis-3173	79	50	sample	sample	NOUN
fcis-3173	79	51	as	as	ADP
fcis-3173	79	52	a	a	DET
fcis-3173	79	53	negative	negative	ADJ
fcis-3173	79	54	sample	sample	NOUN
fcis-3173	79	55	.	.	PUNCT
fcis-3173	80	1	table	table	NOUN
fcis-3173	80	2	1	1	NUM
fcis-3173	80	3	.	.	PUNCT
fcis-3173	81	1	classification	classification	NOUN
fcis-3173	81	2	confusion	confusion	NOUN
fcis-3173	81	3	prediction	prediction	NOUN
fcis-3173	81	4	sample	sample	NOUN
fcis-3173	81	5	actual	actual	ADJ
fcis-3173	81	6	sample	sample	NOUN
fcis-3173	81	7	positive	positive	ADJ
fcis-3173	81	8	sample	sample	NOUN
fcis-3173	81	9	negative	negative	ADJ
fcis-3173	81	10	sample	sample	NOUN
fcis-3173	81	11	positive	positive	ADJ
fcis-3173	81	12	sample	sample	NOUN
fcis-3173	81	13	negative	negative	ADJ
fcis-3173	81	14	sample	sample	NOUN
fcis-3173	81	15	tp(true	tp(true	ADJ
fcis-3173	81	16	positives	positive	NOUN
fcis-3173	81	17	)	)	PUNCT
fcis-3173	81	18	fn(false	fn(false	ADV
fcis-3173	81	19	negatives	negative	NOUN
fcis-3173	81	20	)	)	PUNCT
fcis-3173	81	21	fp(false	fp(false	ADJ
fcis-3173	81	22	positives	positive	NOUN
fcis-3173	81	23	)	)	PUNCT
fcis-3173	81	24	tn(true	tn(true	NOUN
fcis-3173	81	25	negatives	negative	NOUN
fcis-3173	81	26	)	)	PUNCT
fcis-3173	81	27	1)accuracy	1)accuracy	NUM
fcis-3173	81	28	accuracy	accuracy	NOUN
fcis-3173	81	29	is	be	AUX
fcis-3173	81	30	the	the	DET
fcis-3173	81	31	most	most	ADV
fcis-3173	81	32	commonly	commonly	ADV
fcis-3173	81	33	used	use	VERB
fcis-3173	81	34	evaluation	evaluation	NOUN
fcis-3173	81	35	metric	metric	ADJ
fcis-3173	81	36	in	in	ADP
fcis-3173	81	37	classification	classification	NOUN
fcis-3173	81	38	,	,	PUNCT
fcis-3173	81	39	and	and	CCONJ
fcis-3173	81	40	it	it	PRON
fcis-3173	81	41	is	be	AUX
fcis-3173	81	42	the	the	DET
fcis-3173	81	43	proportion	proportion	NOUN
fcis-3173	81	44	of	of	ADP
fcis-3173	81	45	all	all	DET
fcis-3173	81	46	correctly	correctly	ADV
fcis-3173	81	47	predicted	predict	VERB
fcis-3173	81	48	samples	sample	NOUN
fcis-3173	81	49	(	(	PUNCT
fcis-3173	81	50	both	both	CCONJ
fcis-3173	81	51	positive	positive	ADJ
fcis-3173	81	52	and	and	CCONJ
fcis-3173	81	53	negative	negative	ADJ
fcis-3173	81	54	classes	class	NOUN
fcis-3173	81	55	)	)	PUNCT
fcis-3173	81	56	to	to	ADP
fcis-3173	81	57	the	the	DET
fcis-3173	81	58	total	total	NOUN
fcis-3173	81	59	.	.	PUNCT
fcis-3173	82	1	(	(	PUNCT
fcis-3173	82	2	8)	8)	NUM
fcis-3173	82	3	2)precision	2)precision	NUM
fcis-3173	82	4	the	the	DET
fcis-3173	82	5	precision	precision	NOUN
fcis-3173	82	6	rate	rate	NOUN
fcis-3173	82	7	is	be	AUX
fcis-3173	82	8	the	the	DET
fcis-3173	82	9	percentage	percentage	NOUN
fcis-3173	82	10	of	of	ADP
fcis-3173	82	11	all	all	PRON
fcis-3173	82	12	predicted	predict	VERB
fcis-3173	82	13	positive	positive	ADJ
fcis-3173	82	14	classes	class	NOUN
fcis-3173	82	15	that	that	PRON
fcis-3173	82	16	are	be	AUX
fcis-3173	82	17	truly	truly	ADV
fcis-3173	82	18	positive	positive	ADJ
fcis-3173	82	19	out	out	ADP
fcis-3173	82	20	of	of	ADP
fcis-3173	82	21	all	all	PRON
fcis-3173	82	22	predicted	predict	VERB
fcis-3173	82	23	positive	positive	ADJ
fcis-3173	82	24	classes	class	NOUN
fcis-3173	82	25	,	,	PUNCT
fcis-3173	82	26	and	and	CCONJ
fcis-3173	82	27	is	be	AUX
fcis-3173	82	28	specific	specific	ADJ
fcis-3173	82	29	to	to	ADP
fcis-3173	82	30	our	our	PRON
fcis-3173	82	31	prediction	prediction	NOUN
fcis-3173	82	32	results	result	NOUN
fcis-3173	82	33	.	.	PUNCT
fcis-3173	83	1	𝑃recision	𝑃recision	NOUN
fcis-3173	83	2	=	=	SYM
fcis-3173	83	3	𝑇𝑃	𝑇𝑃	PROPN
fcis-3173	83	4	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NUM
fcis-3173	83	5	(	(	PUNCT
fcis-3173	83	6	9	9	NUM
fcis-3173	83	7	)	)	PUNCT
fcis-3173	83	8	3)recall	3)recall	NOUN
fcis-3173	83	9	the	the	DET
fcis-3173	83	10	recall	recall	NOUN
fcis-3173	83	11	is	be	AUX
fcis-3173	83	12	the	the	DET
fcis-3173	83	13	proportion	proportion	NOUN
fcis-3173	83	14	of	of	ADP
fcis-3173	83	15	all	all	PRON
fcis-3173	83	16	predicted	predict	VERB
fcis-3173	83	17	positive	positive	ADJ
fcis-3173	83	18	classes	class	NOUN
fcis-3173	83	19	that	that	PRON
fcis-3173	83	20	are	be	AUX
fcis-3173	83	21	actually	actually	ADV
fcis-3173	83	22	positive	positive	ADJ
fcis-3173	83	23	out	out	ADP
fcis-3173	83	24	of	of	ADP
fcis-3173	83	25	the	the	DET
fcis-3173	83	26	total	total	ADJ
fcis-3173	83	27	number	number	NOUN
fcis-3173	83	28	of	of	ADP
fcis-3173	83	29	positive	positive	ADJ
fcis-3173	83	30	classes	class	NOUN
fcis-3173	83	31	.	.	PUNCT
fcis-3173	84	1	𝑅ecall	𝑅ecall	NOUN
fcis-3173	84	2	=	=	SYM
fcis-3173	84	3	𝑇𝑃	𝑇𝑃	PROPN
fcis-3173	84	4	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
fcis-3173	84	5	(	(	PUNCT
fcis-3173	84	6	10	10	NUM
fcis-3173	84	7	)	)	PUNCT
fcis-3173	84	8	4.3	4.3	NUM
fcis-3173	84	9	.	.	PUNCT
fcis-3173	84	10	results	result	NOUN
fcis-3173	84	11	and	and	CCONJ
fcis-3173	84	12	analysis	analysis	NOUN
fcis-3173	84	13	this	this	DET
fcis-3173	84	14	experiment	experiment	NOUN
fcis-3173	84	15	is	be	AUX
fcis-3173	84	16	based	base	VERB
fcis-3173	84	17	on	on	ADP
fcis-3173	84	18	windows	window	NOUN
fcis-3173	84	19	operating	operating	NOUN
fcis-3173	84	20	system	system	NOUN
fcis-3173	84	21	,	,	PUNCT
fcis-3173	84	22	trained	train	VERB
fcis-3173	84	23	on	on	ADP
fcis-3173	84	24	pytorch	pytorch	NOUN
fcis-3173	84	25	deep	deep	ADJ
fcis-3173	84	26	learning	learning	NOUN
fcis-3173	84	27	framework	framework	NOUN
fcis-3173	84	28	,	,	PUNCT
fcis-3173	84	29	using	use	VERB
fcis-3173	84	30	geforce	geforce	NOUN
fcis-3173	84	31	gtx1650ti	gtx1650ti	NOUN
fcis-3173	84	32	gpu	gpu	PROPN
fcis-3173	84	33	.	.	PUNCT
fcis-3173	85	1	the	the	DET
fcis-3173	85	2	size	size	NOUN
fcis-3173	85	3	of	of	ADP
fcis-3173	85	4	all	all	DET
fcis-3173	85	5	images	image	NOUN
fcis-3173	85	6	in	in	ADP
fcis-3173	85	7	the	the	DET
fcis-3173	85	8	experiment	experiment	NOUN
fcis-3173	85	9	is	be	AUX
fcis-3173	85	10	set	set	VERB
fcis-3173	85	11	to	to	ADP
fcis-3173	85	12	400×400	400×400	PRON
fcis-3173	85	13	,	,	PUNCT
fcis-3173	85	14	the	the	DET
fcis-3173	85	15	epoch	epoch	NOUN
fcis-3173	85	16	in	in	ADP
fcis-3173	85	17	the	the	DET
fcis-3173	85	18	experiment	experiment	NOUN
fcis-3173	85	19	is	be	AUX
fcis-3173	85	20	set	set	VERB
fcis-3173	85	21	to	to	ADP
fcis-3173	85	22	50	50	NUM
fcis-3173	85	23	,	,	PUNCT
fcis-3173	85	24	the	the	DET
fcis-3173	85	25	stochastic	stochastic	ADJ
fcis-3173	85	26	gradient	gradient	ADJ
fcis-3173	85	27	descent	descent	NOUN
fcis-3173	85	28	algorithm	algorithm	NOUN
fcis-3173	85	29	is	be	AUX
fcis-3173	85	30	used	use	VERB
fcis-3173	85	31	,	,	PUNCT
fcis-3173	85	32	and	and	CCONJ
fcis-3173	85	33	the	the	DET
fcis-3173	85	34	initial	initial	ADJ
fcis-3173	85	35	learning	learning	NOUN
fcis-3173	85	36	rate	rate	NOUN
fcis-3173	85	37	is	be	AUX
fcis-3173	85	38	0.001	0.001	NUM
fcis-3173	85	39	.	.	PUNCT
fcis-3173	86	1	to	to	PART
fcis-3173	86	2	verify	verify	VERB
fcis-3173	86	3	the	the	DET
fcis-3173	86	4	performance	performance	NOUN
fcis-3173	86	5	of	of	ADP
fcis-3173	86	6	the	the	DET
fcis-3173	86	7	algorithm	algorithm	NOUN
fcis-3173	86	8	proposed	propose	VERB
fcis-3173	86	9	in	in	ADP
fcis-3173	86	10	this	this	DET
fcis-3173	86	11	paper	paper	NOUN
fcis-3173	86	12	,	,	PUNCT
fcis-3173	86	13	the	the	DET
fcis-3173	86	14	improved	improved	ADJ
fcis-3173	86	15	algorithm	algorithm	NOUN
fcis-3173	86	16	inception	inception	NOUN
fcis-3173	86	17	v4	v4	PROPN
fcis-3173	86	18	-	-	PUNCT
fcis-3173	86	19	scse	scse	NOUN
fcis-3173	86	20	is	be	AUX
fcis-3173	86	21	compared	compare	VERB
fcis-3173	86	22	and	and	CCONJ
fcis-3173	86	23	analyzed	analyze	VERB
fcis-3173	86	24	with	with	ADP
fcis-3173	86	25	the	the	DET
fcis-3173	86	26	network	network	NOUN
fcis-3173	86	27	model	model	NOUN
fcis-3173	86	28	inceptionv4	inceptionv4	PROPN
fcis-3173	86	29	and	and	CCONJ
fcis-3173	86	30	the	the	DET
fcis-3173	86	31	two	two	NUM
fcis-3173	86	32	most	most	ADV
fcis-3173	86	33	commonly	commonly	ADV
fcis-3173	86	34	used	use	VERB
fcis-3173	86	35	deep	deep	ADJ
fcis-3173	86	36	learning	learning	NOUN
fcis-3173	86	37	network	network	NOUN
fcis-3173	86	38	models	model	NOUN
fcis-3173	86	39	xception	xception	NOUN
fcis-3173	87	1	[	[	X
fcis-3173	87	2	25	25	NUM
fcis-3173	87	3	]	]	PUNCT
fcis-3173	87	4	and	and	CCONJ
fcis-3173	87	5	shufflenet	shufflenet	NOUN
fcis-3173	87	6	-	-	PUNCT
fcis-3173	87	7	v2	v2	NOUN
fcis-3173	88	1	[	[	X
fcis-3173	88	2	26	26	NUM
fcis-3173	88	3	]	]	PUNCT
fcis-3173	88	4	on	on	ADP
fcis-3173	88	5	the	the	DET
fcis-3173	88	6	aluminum	aluminum	NOUN
fcis-3173	88	7	defect	defect	NOUN
fcis-3173	88	8	recognition	recognition	NOUN
fcis-3173	88	9	dataset	dataset	VERB
fcis-3173	88	10	by	by	ADP
fcis-3173	88	11	these	these	DET
fcis-3173	88	12	three	three	NUM
fcis-3173	88	13	criterias	criteria	NOUN
fcis-3173	88	14	accuracy	accuracy	NOUN
fcis-3173	88	15	,	,	PUNCT
fcis-3173	88	16	precision	precision	NOUN
fcis-3173	88	17	and	and	CCONJ
fcis-3173	88	18	recall	recall	NOUN
fcis-3173	88	19	.	.	PUNCT
fcis-3173	89	1	figure	figure	VERB
fcis-3173	89	2	7	7	NUM
fcis-3173	89	3	.	.	PUNCT
fcis-3173	89	4	comparison	comparison	NOUN
fcis-3173	89	5	of	of	ADP
fcis-3173	89	6	loss	loss	NOUN
fcis-3173	89	7	curves	curve	VERB
fcis-3173	89	8	different	different	ADJ
fcis-3173	89	9	model	model	NOUN
fcis-3173	89	10	validation	validation	NOUN
fcis-3173	89	11	loss	loss	NOUN
fcis-3173	89	12	curves	curve	NOUN
fcis-3173	89	13	figure	figure	NOUN
fcis-3173	89	14	8	8	NUM
fcis-3173	89	15	.	.	PUNCT
fcis-3173	90	1	comparison	comparison	NOUN
fcis-3173	90	2	of	of	ADP
fcis-3173	90	3	acc	acc	PROPN
fcis-3173	90	4	curves	curve	NOUN
fcis-3173	90	5	for	for	ADP
fcis-3173	90	6	different	different	ADJ
fcis-3173	90	7	model	model	NOUN
fcis-3173	90	8	validation	validation	NOUN
fcis-3173	90	9	as	as	SCONJ
fcis-3173	90	10	shown	show	VERB
fcis-3173	90	11	in	in	ADP
fcis-3173	90	12	figure	figure	NOUN
fcis-3173	90	13	7	7	NUM
fcis-3173	90	14	and	and	CCONJ
fcis-3173	90	15	figure	figure	VERB
fcis-3173	90	16	8	8	NUM
fcis-3173	90	17	,	,	PUNCT
fcis-3173	90	18	the	the	DET
fcis-3173	90	19	curves	curve	NOUN
fcis-3173	90	20	of	of	ADP
fcis-3173	90	21	loss	loss	NOUN
fcis-3173	90	22	function	function	NOUN
fcis-3173	90	23	and	and	CCONJ
fcis-3173	90	24	accuracy	accuracy	NOUN
fcis-3173	90	25	of	of	ADP
fcis-3173	90	26	the	the	DET
fcis-3173	90	27	proposed	propose	VERB
fcis-3173	90	28	algorithm	algorithm	NOUN
fcis-3173	90	29	model	model	NOUN
fcis-3173	90	30	in	in	ADP
fcis-3173	90	31	this	this	DET
fcis-3173	90	32	paper	paper	NOUN
fcis-3173	90	33	are	be	AUX
fcis-3173	90	34	compared	compare	VERB
fcis-3173	90	35	with	with	ADP
fcis-3173	90	36	other	other	ADJ
fcis-3173	90	37	network	network	NOUN
fcis-3173	90	38	models	model	NOUN
fcis-3173	90	39	at	at	ADP
fcis-3173	90	40	the	the	DET
fcis-3173	90	41	time	time	NOUN
fcis-3173	90	42	of	of	ADP
fcis-3173	90	43	validation	validation	NOUN
fcis-3173	90	44	.	.	PUNCT
fcis-3173	91	1	from	from	ADP
fcis-3173	91	2	the	the	DET
fcis-3173	91	3	graphs	graph	NOUN
fcis-3173	91	4	,	,	PUNCT
fcis-3173	91	5	it	it	PRON
fcis-3173	91	6	can	can	AUX
fcis-3173	91	7	be	be	AUX
fcis-3173	91	8	seen	see	VERB
fcis-3173	91	9	that	that	SCONJ
fcis-3173	91	10	the	the	DET
fcis-3173	91	11	lowest	low	ADJ
fcis-3173	91	12	loss	loss	NOUN
fcis-3173	91	13	value	value	NOUN
fcis-3173	91	14	of	of	ADP
fcis-3173	91	15	the	the	DET
fcis-3173	91	16	proposed	propose	VERB
fcis-3173	91	17	algorithm	algorithm	NOUN
fcis-3173	91	18	inception	inception	NOUN
fcis-3173	91	19	v4	v4	PROPN
fcis-3173	91	20	-	-	PUNCT
fcis-3173	91	21	scse	scse	NOUN
fcis-3173	91	22	is	be	AUX
fcis-3173	91	23	around	around	ADP
fcis-3173	91	24	0.1	0.1	NUM
fcis-3173	91	25	,	,	PUNCT
fcis-3173	91	26	and	and	CCONJ
fcis-3173	91	27	the	the	DET
fcis-3173	91	28	accuracy	accuracy	NOUN
fcis-3173	91	29	rate	rate	NOUN
fcis-3173	91	30	is	be	AUX
fcis-3173	91	31	around	around	ADV
fcis-3173	91	32	98	98	NUM
fcis-3173	91	33	%	%	NOUN
fcis-3173	91	34	.	.	PUNCT
fcis-3173	92	1	the	the	DET
fcis-3173	92	2	next	next	ADJ
fcis-3173	92	3	lowest	low	ADJ
fcis-3173	92	4	loss	loss	NOUN
fcis-3173	92	5	value	value	NOUN
fcis-3173	92	6	of	of	ADP
fcis-3173	92	7	inception	inception	NOUN
fcis-3173	92	8	is	be	AUX
fcis-3173	92	9	around	around	ADV
fcis-3173	92	10	0.15	0.15	NUM
fcis-3173	92	11	,	,	PUNCT
fcis-3173	92	12	and	and	CCONJ
fcis-3173	92	13	the	the	DET
fcis-3173	92	14	accuracy	accuracy	NOUN
fcis-3173	92	15	is	be	AUX
fcis-3173	92	16	around	around	ADV
fcis-3173	92	17	96	96	NUM
fcis-3173	92	18	%	%	NOUN
fcis-3173	92	19	.	.	PUNCT
fcis-3173	93	1	it	it	PRON
fcis-3173	93	2	can	can	AUX
fcis-3173	93	3	also	also	ADV
fcis-3173	93	4	be	be	AUX
fcis-3173	93	5	seen	see	VERB
fcis-3173	93	6	from	from	ADP
fcis-3173	93	7	the	the	DET
fcis-3173	93	8	figure	figure	NOUN
fcis-3173	93	9	that	that	SCONJ
fcis-3173	93	10	the	the	DET
fcis-3173	93	11	loss	loss	NOUN
fcis-3173	93	12	values	value	NOUN
fcis-3173	93	13	and	and	CCONJ
fcis-3173	93	14	accuracy	accuracy	NOUN
fcis-3173	93	15	rates	rate	NOUN
fcis-3173	93	16	of	of	ADP
fcis-3173	93	17	these	these	DET
fcis-3173	93	18	four	four	NUM
fcis-3173	93	19	algorithms	algorithm	NOUN
fcis-3173	93	20	gradually	gradually	ADV
fcis-3173	93	21	level	level	VERB
fcis-3173	93	22	off	off	ADP
fcis-3173	93	23	after	after	ADP
fcis-3173	93	24	the	the	DET
fcis-3173	93	25	25th	25th	ADJ
fcis-3173	93	26	epoch	epoch	NOUN
fcis-3173	93	27	.	.	PUNCT
fcis-3173	94	1	table	table	NOUN
fcis-3173	94	2	2	2	NUM
fcis-3173	94	3	.	.	PUNCT
fcis-3173	94	4	comparison	comparison	NOUN
fcis-3173	94	5	of	of	ADP
fcis-3173	94	6	different	different	ADJ
fcis-3173	94	7	evaluation	evaluation	NOUN
fcis-3173	94	8	criterias	criteria	NOUN
fcis-3173	94	9	under	under	ADP
fcis-3173	94	10	different	different	ADJ
fcis-3173	94	11	network	network	NOUN
fcis-3173	94	12	models	model	NOUN
fcis-3173	94	13	method	method	NOUN
fcis-3173	94	14	accuracy	accuracy	NOUN
fcis-3173	94	15	xception	xception	PROPN
fcis-3173	94	16	shufflenet	shufflenet	NOUN
fcis-3173	94	17	-	-	PUNCT
fcis-3173	94	18	v2	v2	PROPN
fcis-3173	94	19	inception	inception	NOUN
fcis-3173	94	20	v4	v4	NOUN
fcis-3173	94	21	-	-	PUNCT
fcis-3173	94	22	scse	scse	NOUN
fcis-3173	94	23	inception	inception	PROPN
fcis-3173	94	24	v4	v4	PROPN
fcis-3173	94	25	precision	precision	NOUN
fcis-3173	94	26	recall	recall	VERB
fcis-3173	94	27	92.5	92.5	NUM
fcis-3173	94	28	94.4	94.4	NUM
fcis-3173	94	29	98.00	98.00	NUM
fcis-3173	94	30	93.75	93.75	NUM
fcis-3173	94	31	94.25	94.25	NUM
fcis-3173	94	32	85.7	85.7	NUM
fcis-3173	94	33	85.4	85.4	NUM
fcis-3173	94	34	96.76	96.76	NUM
fcis-3173	94	35	97.8	97.8	NUM
fcis-3173	94	36	91.7	91.7	NUM
fcis-3173	94	37	92.0	92.0	NUM
fcis-3173	94	38	96.0	96.0	NUM
fcis-3173	94	39	as	as	SCONJ
fcis-3173	94	40	can	can	AUX
fcis-3173	94	41	be	be	AUX
fcis-3173	94	42	seen	see	VERB
fcis-3173	94	43	from	from	ADP
fcis-3173	94	44	table	table	NOUN
fcis-3173	94	45	2	2	NUM
fcis-3173	94	46	,	,	PUNCT
fcis-3173	94	47	the	the	DET
fcis-3173	94	48	inception	inception	ADJ
fcis-3173	94	49	v4	v4	NOUN
fcis-3173	94	50	-	-	PUNCT
fcis-3173	94	51	scse	scse	NOUN
fcis-3173	94	52	network	network	NOUN
fcis-3173	94	53	model	model	NOUN
fcis-3173	94	54	proposed	propose	VERB
fcis-3173	94	55	in	in	ADP
fcis-3173	94	56	this	this	DET
fcis-3173	94	57	paper	paper	NOUN
fcis-3173	94	58	has	have	VERB
fcis-3173	94	59	1.24	1.24	NUM
fcis-3173	94	60	%	%	NOUN
fcis-3173	94	61	higher	high	ADJ
fcis-3173	94	62	accuracy	accuracy	NOUN
fcis-3173	94	63	,	,	PUNCT
fcis-3173	94	64	5.8	5.8	NUM
fcis-3173	94	65	%	%	NOUN
fcis-3173	94	66	higher	high	ADJ
fcis-3173	94	67	precision	precision	NOUN
fcis-3173	94	68	,	,	PUNCT
fcis-3173	94	69	and	and	CCONJ
fcis-3173	94	70	1.6	1.6	NUM
fcis-3173	94	71	%	%	NOUN
fcis-3173	94	72	higher	high	ADJ
fcis-3173	94	73	recall	recall	NOUN
fcis-3173	94	74	than	than	ADP
fcis-3173	94	75	the	the	DET
fcis-3173	94	76	original	original	ADJ
fcis-3173	94	77	model	model	NOUN
fcis-3173	94	78	inception	inception	PROPN
fcis-3173	94	79	v4	v4	PROPN
fcis-3173	94	80	.	.	PUNCT
fcis-3173	95	1	and	and	CCONJ
fcis-3173	95	2	it	it	PRON
fcis-3173	95	3	is	be	AUX
fcis-3173	95	4	also	also	ADV
fcis-3173	95	5	compared	compare	VERB
fcis-3173	95	6	with	with	ADP
fcis-3173	95	7	other	other	ADJ
fcis-3173	95	8	deep	deep	ADJ
fcis-3173	95	9	learning	learn	VERB
fcis-3173	95	10	network	network	NOUN
fcis-3173	95	11	models	model	NOUN
fcis-3173	95	12	xception	xception	NOUN
fcis-3173	95	13	and	and	CCONJ
fcis-3173	95	14	shufflenetv2	shufflenetv2	NOUN
fcis-3173	95	15	,	,	PUNCT
fcis-3173	95	16	and	and	CCONJ
fcis-3173	95	17	both	both	PRON
fcis-3173	95	18	the	the	DET
fcis-3173	95	19	accuracy	accuracy	NOUN
fcis-3173	95	20	,	,	PUNCT
fcis-3173	95	21	precision	precision	NOUN
fcis-3173	95	22	and	and	CCONJ
fcis-3173	95	23	recall	recall	NOUN
fcis-3173	95	24	rates	rate	NOUN
fcis-3173	95	25	are	be	AUX
fcis-3173	95	26	significantly	significantly	ADV
fcis-3173	95	27	better	well	ADJ
fcis-3173	95	28	than	than	ADP
fcis-3173	95	29	the	the	DET
fcis-3173	95	30	performance	performance	NOUN
fcis-3173	95	31	of	of	ADP
fcis-3173	95	32	these	these	DET
fcis-3173	95	33	network	network	NOUN
fcis-3173	95	34	models	model	NOUN
fcis-3173	95	35	.	.	PUNCT
fcis-3173	96	1	5	5	X
fcis-3173	96	2	.	.	X
fcis-3173	96	3	conclusion	conclusion	NOUN
fcis-3173	96	4	in	in	ADP
fcis-3173	96	5	this	this	DET
fcis-3173	96	6	paper	paper	NOUN
fcis-3173	96	7	,	,	PUNCT
fcis-3173	96	8	we	we	PRON
fcis-3173	96	9	propose	propose	VERB
fcis-3173	96	10	a	a	DET
fcis-3173	96	11	deep	deep	ADJ
fcis-3173	96	12	learning	learning	NOUN
fcis-3173	96	13	aluminum	aluminum	NOUN
fcis-3173	96	14	defect	defect	NOUN
fcis-3173	96	15	classification	classification	NOUN
fcis-3173	96	16	algorithm	algorithm	NOUN
fcis-3173	96	17	incorporating	incorporate	VERB
fcis-3173	96	18	an	an	DET
fcis-3173	96	19	attention	attention	NOUN
fcis-3173	96	20	mechanism	mechanism	NOUN
fcis-3173	96	21	to	to	PART
fcis-3173	96	22	address	address	VERB
fcis-3173	96	23	the	the	DET
fcis-3173	96	24	practical	practical	ADJ
fcis-3173	96	25	problem	problem	NOUN
fcis-3173	96	26	of	of	ADP
fcis-3173	96	27	identifying	identify	VERB
fcis-3173	96	28	and	and	CCONJ
fcis-3173	96	29	classifying	classify	VERB
fcis-3173	96	30	defects	defect	NOUN
fcis-3173	96	31	generated	generate	VERB
fcis-3173	96	32	by	by	ADP
fcis-3173	96	33	aluminum	aluminum	NOUN
fcis-3173	96	34	in	in	ADP
fcis-3173	96	35	the	the	DET
fcis-3173	96	36	production	production	NOUN
fcis-3173	96	37	process	process	NOUN
fcis-3173	96	38	and	and	CCONJ
fcis-3173	96	39	various	various	ADJ
fcis-3173	96	40	factors	factor	NOUN
fcis-3173	96	41	.	.	PUNCT
fcis-3173	97	1	the	the	DET
fcis-3173	97	2	accuracy	accuracy	NOUN
fcis-3173	97	3	of	of	ADP
fcis-3173	97	4	classifying	classify	VERB
fcis-3173	97	5	aluminum	aluminum	NOUN
fcis-3173	97	6	defects	defect	NOUN
fcis-3173	97	7	is	be	AUX
fcis-3173	97	8	improved	improve	VERB
fcis-3173	97	9	by	by	ADP
fcis-3173	97	10	adding	add	VERB
fcis-3173	97	11	an	an	DET
fcis-3173	97	12	attention	attention	NOUN
fcis-3173	97	13	mechanism	mechanism	NOUN
fcis-3173	97	14	module	module	NOUN
fcis-3173	97	15	to	to	ADP
fcis-3173	97	16	the	the	DET
fcis-3173	97	17	deep	deep	ADJ
fcis-3173	97	18	neural	neural	ADJ
fcis-3173	97	19	network	network	NOUN
fcis-3173	97	20	model	model	PROPN
fcis-3173	97	21	inception	inception	PROPN
fcis-3173	97	22	v4	v4	PROPN
fcis-3173	97	23	to	to	PART
fcis-3173	97	24	enhance	enhance	VERB
fcis-3173	97	25	the	the	DET
fcis-3173	97	26	sensitivity	sensitivity	NOUN
fcis-3173	97	27	of	of	ADP
fcis-3173	97	28	the	the	DET
fcis-3173	97	29	network	network	NOUN
fcis-3173	97	30	to	to	ADP
fcis-3173	97	31	small	small	ADJ
fcis-3173	97	32	defects	defect	NOUN
fcis-3173	97	33	.	.	PUNCT
fcis-3173	98	1	experiments	experiment	NOUN
fcis-3173	98	2	are	be	AUX
fcis-3173	98	3	also	also	ADV
fcis-3173	98	4	conducted	conduct	VERB
fcis-3173	98	5	on	on	ADP
fcis-3173	98	6	the	the	DET
fcis-3173	98	7	aluminum	aluminum	NOUN
fcis-3173	98	8	defect	defect	NOUN
fcis-3173	98	9	recognition	recognition	NOUN
fcis-3173	98	10	dataset	dataset	VERB
fcis-3173	98	11	from	from	ADP
fcis-3173	98	12	ali	ali	PROPN
fcis-3173	98	13	tianchi	tianchi	PROPN
fcis-3173	98	14	to	to	PART
fcis-3173	98	15	fnfptntp	fnfptntp	VERB
fcis-3173	98	16	tntp	tntp	NOUN
fcis-3173	98	17	accuracy	accuracy	NOUN
fcis-3173	98	18	+	+	PROPN
fcis-3173	98	19	+	+	ADJ
fcis-3173	98	20	+	+	ADJ
fcis-3173	98	21	+	+	CCONJ
fcis-3173	98	22	=	=	SYM
fcis-3173	98	23	105	105	NUM
fcis-3173	98	24	demonstrate	demonstrate	VERB
fcis-3173	98	25	the	the	DET
fcis-3173	98	26	effectiveness	effectiveness	NOUN
fcis-3173	98	27	of	of	ADP
fcis-3173	98	28	the	the	DET
fcis-3173	98	29	algorithm	algorithm	NOUN
fcis-3173	98	30	.	.	PUNCT
fcis-3173	99	1	however	however	ADV
fcis-3173	99	2	,	,	PUNCT
fcis-3173	99	3	this	this	DET
fcis-3173	99	4	algorithm	algorithm	NOUN
fcis-3173	99	5	only	only	ADV
fcis-3173	99	6	classifies	classify	VERB
fcis-3173	99	7	a	a	DET
fcis-3173	99	8	single	single	ADJ
fcis-3173	99	9	defect	defect	NOUN
fcis-3173	99	10	on	on	ADP
fcis-3173	99	11	an	an	DET
fcis-3173	99	12	image	image	NOUN
fcis-3173	99	13	,	,	PUNCT
fcis-3173	99	14	and	and	CCONJ
fcis-3173	99	15	in	in	ADP
fcis-3173	99	16	reality	reality	NOUN
fcis-3173	99	17	,	,	PUNCT
fcis-3173	99	18	there	there	PRON
fcis-3173	99	19	are	be	VERB
fcis-3173	99	20	often	often	ADV
fcis-3173	99	21	multiple	multiple	ADJ
fcis-3173	99	22	defects	defect	NOUN
fcis-3173	99	23	on	on	ADP
fcis-3173	99	24	a	a	DET
fcis-3173	99	25	single	single	ADJ
fcis-3173	99	26	image	image	NOUN
fcis-3173	99	27	,	,	PUNCT
fcis-3173	99	28	so	so	SCONJ
fcis-3173	99	29	in	in	ADP
fcis-3173	99	30	-	-	PUNCT
fcis-3173	99	31	depth	depth	NOUN
fcis-3173	99	32	research	research	NOUN
fcis-3173	99	33	is	be	AUX
fcis-3173	99	34	needed	need	VERB
fcis-3173	99	35	for	for	SCONJ
fcis-3173	99	36	this	this	DET
fcis-3173	99	37	multilabel	multilabel	NOUN
fcis-3173	99	38	defect	defect	VERB
fcis-3173	99	39	classification	classification	NOUN
fcis-3173	99	40	task	task	NOUN
fcis-3173	99	41	in	in	ADP
fcis-3173	99	42	the	the	DET
fcis-3173	99	43	future	future	NOUN
fcis-3173	99	44	,	,	PUNCT
fcis-3173	99	45	making	make	VERB
fcis-3173	99	46	it	it	PRON
fcis-3173	99	47	possible	possible	ADJ
fcis-3173	99	48	to	to	PART
fcis-3173	99	49	truly	truly	ADV
fcis-3173	99	50	solve	solve	VERB
fcis-3173	99	51	the	the	DET
fcis-3173	99	52	practical	practical	ADJ
fcis-3173	99	53	problems	problem	NOUN
fcis-3173	99	54	that	that	PRON
fcis-3173	99	55	exist	exist	VERB
fcis-3173	99	56	in	in	ADP
fcis-3173	99	57	reality	reality	NOUN
fcis-3173	99	58	and	and	CCONJ
fcis-3173	99	59	improve	improve	VERB
fcis-3173	99	60	the	the	DET
fcis-3173	99	61	practicality	practicality	NOUN
fcis-3173	99	62	and	and	CCONJ
fcis-3173	99	63	robustness	robustness	NOUN
fcis-3173	99	64	of	of	ADP
fcis-3173	99	65	the	the	DET
fcis-3173	99	66	algorithm	algorithm	NOUN
fcis-3173	99	67	.	.	PUNCT
fcis-3173	100	1	references	reference	NOUN
fcis-3173	100	2	[	[	X
fcis-3173	100	3	1	1	NUM
fcis-3173	100	4	]	]	PUNCT
fcis-3173	100	5	xu	xu	PROPN
fcis-3173	100	6	qianxin	qianxin	PROPN
fcis-3173	100	7	,	,	PUNCT
fcis-3173	100	8	et	et	PROPN
fcis-3173	100	9	al	al	PROPN
fcis-3173	100	10	.	.	PUNCT
fcis-3173	101	1	"	"	PUNCT
fcis-3173	101	2	improved	improved	ADJ
fcis-3173	101	3	yolov3	yolov3	PROPN
fcis-3173	101	4	network	network	NOUN
fcis-3173	101	5	for	for	ADP
fcis-3173	101	6	surface	surface	NOUN
fcis-3173	101	7	defect	defect	NOUN
fcis-3173	101	8	detection	detection	NOUN
fcis-3173	101	9	of	of	ADP
fcis-3173	101	10	steel	steel	NOUN
fcis-3173	101	11	plates	plate	NOUN
fcis-3173	101	12	.	.	PUNCT
fcis-3173	101	13	"	"	PUNCT
fcis-3173	101	14	computer	computer	NOUN
fcis-3173	101	15	engineering	engineering	NOUN
fcis-3173	101	16	and	and	CCONJ
fcis-3173	101	17	applications	application	NOUN
fcis-3173	101	18	56.16(2020):265	56.16(2020):265	NUM
fcis-3173	101	19	-	-	SYM
fcis-3173	101	20	272	272	NUM
fcis-3173	101	21	.	.	PUNCT
fcis-3173	102	1	[	[	X
fcis-3173	102	2	2	2	NUM
fcis-3173	102	3	]	]	X
fcis-3173	102	4	duan	duan	PROPN
fcis-3173	102	5	,	,	PUNCT
fcis-3173	102	6	chunmei	chunmei	PROPN
fcis-3173	102	7	,	,	PUNCT
fcis-3173	102	8	and	and	CCONJ
fcis-3173	102	9	taochuan	taochuan	PROPN
fcis-3173	102	10	zhang	zhang	PROPN
fcis-3173	102	11	.	.	PUNCT
fcis-3173	103	1	"	"	PUNCT
fcis-3173	103	2	two	two	NUM
fcis-3173	103	3	-	-	PUNCT
fcis-3173	103	4	stream	stream	NOUN
fcis-3173	103	5	convolutional	convolutional	ADJ
fcis-3173	103	6	neural	neural	ADJ
fcis-3173	103	7	network	network	NOUN
fcis-3173	103	8	based	base	VERB
fcis-3173	103	9	on	on	ADP
fcis-3173	103	10	gradient	gradient	ADJ
fcis-3173	103	11	image	image	NOUN
fcis-3173	103	12	for	for	ADP
fcis-3173	103	13	aluminum	aluminum	NOUN
fcis-3173	103	14	profile	profile	NOUN
fcis-3173	103	15	surface	surface	NOUN
fcis-3173	103	16	defects	defect	VERB
fcis-3173	103	17	classification	classification	NOUN
fcis-3173	103	18	and	and	CCONJ
fcis-3173	103	19	recognition	recognition	NOUN
fcis-3173	103	20	.	.	PUNCT
fcis-3173	103	21	"	"	PUNCT
fcis-3173	104	1	ieee	ieee	NOUN
fcis-3173	104	2	access	access	NOUN
fcis-3173	104	3	8	8	NUM
fcis-3173	104	4	(	(	PUNCT
fcis-3173	104	5	2020	2020	NUM
fcis-3173	104	6	):	):	PUNCT
fcis-3173	104	7	172152	172152	NUM
fcis-3173	104	8	-	-	SYM
fcis-3173	104	9	172165	172165	NUM
fcis-3173	104	10	.	.	PUNCT
fcis-3173	105	1	[	[	X
fcis-3173	105	2	3	3	NUM
fcis-3173	105	3	]	]	X
fcis-3173	105	4	gupta	gupta	PROPN
fcis-3173	105	5	,	,	PUNCT
fcis-3173	105	6	ranjeetkumar	ranjeetkumar	PROPN
fcis-3173	105	7	,	,	PUNCT
fcis-3173	105	8	et	et	PROPN
fcis-3173	105	9	al	al	PROPN
fcis-3173	105	10	.	.	PUNCT
fcis-3173	106	1	"	"	PUNCT
fcis-3173	106	2	a	a	DET
fcis-3173	106	3	review	review	NOUN
fcis-3173	106	4	of	of	ADP
fcis-3173	106	5	sensing	sense	VERB
fcis-3173	106	6	technologies	technology	NOUN
fcis-3173	106	7	for	for	ADP
fcis-3173	106	8	non	non	ADJ
fcis-3173	106	9	-	-	ADJ
fcis-3173	106	10	destructive	destructive	ADJ
fcis-3173	106	11	evaluation	evaluation	NOUN
fcis-3173	106	12	of	of	ADP
fcis-3173	106	13	structural	structural	ADJ
fcis-3173	106	14	composite	composite	ADJ
fcis-3173	106	15	materials	material	NOUN
fcis-3173	106	16	.	.	PUNCT
fcis-3173	106	17	"	"	PUNCT
fcis-3173	107	1	journal	journal	NOUN
fcis-3173	107	2	of	of	ADP
fcis-3173	107	3	composites	composite	NOUN
fcis-3173	107	4	science	science	NOUN
fcis-3173	107	5	5.12	5.12	NUM
fcis-3173	107	6	(	(	PUNCT
fcis-3173	107	7	2021	2021	NUM
fcis-3173	107	8	):	):	PUNCT
fcis-3173	107	9	319	319	NUM
fcis-3173	107	10	.	.	PUNCT
fcis-3173	108	1	[	[	X
fcis-3173	108	2	4	4	X
fcis-3173	108	3	]	]	PUNCT
fcis-3173	108	4	tu	tu	PROPN
fcis-3173	108	5	zhenyue	zhenyue	PROPN
fcis-3173	108	6	.	.	PUNCT
fcis-3173	109	1	"	"	PUNCT
fcis-3173	109	2	application	application	NOUN
fcis-3173	109	3	of	of	ADP
fcis-3173	109	4	magnetic	magnetic	ADJ
fcis-3173	109	5	particle	particle	NOUN
fcis-3173	109	6	inspection	inspection	NOUN
fcis-3173	109	7	in	in	ADP
fcis-3173	109	8	boiler	boiler	NOUN
fcis-3173	109	9	inspection	inspection	NOUN
fcis-3173	109	10	.	.	PUNCT
fcis-3173	109	11	"	"	PUNCT
fcis-3173	109	12	applied	apply	VERB
fcis-3173	109	13	energy	energy	NOUN
fcis-3173	109	14	technology	technology	NOUN
fcis-3173	109	15	11(2019):3	11(2019):3	NUM
fcis-3173	109	16	.	.	PUNCT
fcis-3173	110	1	[	[	X
fcis-3173	110	2	5	5	NUM
fcis-3173	110	3	]	]	X
fcis-3173	110	4	huang	huang	PROPN
fcis-3173	110	5	,	,	PUNCT
fcis-3173	110	6	feng	feng	PROPN
fcis-3173	110	7	-	-	PUNCT
fcis-3173	110	8	ying	ying	PROPN
fcis-3173	110	9	.	.	PUNCT
fcis-3173	111	1	"	"	PUNCT
fcis-3173	111	2	quantitative	quantitative	ADJ
fcis-3173	111	3	assessment	assessment	NOUN
fcis-3173	111	4	method	method	NOUN
fcis-3173	111	5	for	for	ADP
fcis-3173	111	6	eddy	eddy	PROPN
fcis-3173	111	7	current	current	ADJ
fcis-3173	111	8	detection	detection	NOUN
fcis-3173	111	9	of	of	ADP
fcis-3173	111	10	cracks	crack	NOUN
fcis-3173	111	11	on	on	ADP
fcis-3173	111	12	rail	rail	NOUN
fcis-3173	111	13	surface	surface	NOUN
fcis-3173	111	14	.	.	PUNCT
fcis-3173	111	15	"	"	PUNCT
fcis-3173	112	1	china	china	PROPN
fcis-3173	112	2	railway	railway	PROPN
fcis-3173	112	3	science	science	NOUN
fcis-3173	112	4	38.2	38.2	NUM
fcis-3173	112	5	(	(	PUNCT
fcis-3173	112	6	2017	2017	NUM
fcis-3173	112	7	):	):	PUNCT
fcis-3173	112	8	6	6	NUM
fcis-3173	112	9	.	.	PUNCT
fcis-3173	113	1	[	[	X
fcis-3173	113	2	6	6	NUM
fcis-3173	113	3	]	]	X
fcis-3173	113	4	ege	ege	PROPN
fcis-3173	113	5	,	,	PUNCT
fcis-3173	113	6	yavuz	yavuz	PROPN
fcis-3173	113	7	,	,	PUNCT
fcis-3173	113	8	and	and	CCONJ
fcis-3173	113	9	mustafa	mustafa	PROPN
fcis-3173	113	10	coramik	coramik	PROPN
fcis-3173	113	11	.	.	PUNCT
fcis-3173	114	1	"	"	PUNCT
fcis-3173	114	2	a	a	DET
fcis-3173	114	3	new	new	ADJ
fcis-3173	114	4	measurement	measurement	NOUN
fcis-3173	114	5	system	system	NOUN
fcis-3173	114	6	using	use	VERB
fcis-3173	114	7	magnetic	magnetic	ADJ
fcis-3173	114	8	flux	flux	NOUN
fcis-3173	114	9	leakage	leakage	NOUN
fcis-3173	114	10	method	method	NOUN
fcis-3173	114	11	in	in	ADP
fcis-3173	114	12	pipeline	pipeline	NOUN
fcis-3173	114	13	inspection	inspection	NOUN
fcis-3173	114	14	.	.	PUNCT
fcis-3173	114	15	"	"	PUNCT
fcis-3173	115	1	measurement	measurement	NOUN
fcis-3173	115	2	123	123	NUM
fcis-3173	115	3	(	(	PUNCT
fcis-3173	115	4	2018	2018	NUM
fcis-3173	115	5	):	):	PUNCT
fcis-3173	115	6	163	163	NUM
fcis-3173	115	7	-	-	SYM
fcis-3173	115	8	174	174	NUM
fcis-3173	115	9	.	.	PUNCT
fcis-3173	116	1	[	[	X
fcis-3173	116	2	7	7	NUM
fcis-3173	116	3	]	]	X
fcis-3173	116	4	duan	duan	PROPN
fcis-3173	116	5	,	,	PUNCT
fcis-3173	116	6	chunmei	chunmei	PROPN
fcis-3173	116	7	,	,	PUNCT
fcis-3173	116	8	and	and	CCONJ
fcis-3173	116	9	taochuan	taochuan	PROPN
fcis-3173	116	10	zhang	zhang	PROPN
fcis-3173	116	11	.	.	PUNCT
fcis-3173	117	1	"	"	PUNCT
fcis-3173	117	2	two	two	NUM
fcis-3173	117	3	-	-	PUNCT
fcis-3173	117	4	stream	stream	NOUN
fcis-3173	117	5	convolutional	convolutional	ADJ
fcis-3173	117	6	neural	neural	ADJ
fcis-3173	117	7	network	network	NOUN
fcis-3173	117	8	based	base	VERB
fcis-3173	117	9	on	on	ADP
fcis-3173	117	10	gradient	gradient	ADJ
fcis-3173	117	11	image	image	NOUN
fcis-3173	117	12	for	for	ADP
fcis-3173	117	13	aluminum	aluminum	NOUN
fcis-3173	117	14	profile	profile	NOUN
fcis-3173	117	15	surface	surface	NOUN
fcis-3173	117	16	defects	defect	VERB
fcis-3173	117	17	classification	classification	NOUN
fcis-3173	117	18	and	and	CCONJ
fcis-3173	117	19	recognition	recognition	NOUN
fcis-3173	117	20	.	.	PUNCT
fcis-3173	117	21	"	"	PUNCT
fcis-3173	118	1	ieee	ieee	NOUN
fcis-3173	118	2	access	access	NOUN
fcis-3173	118	3	8	8	NUM
fcis-3173	118	4	(	(	PUNCT
fcis-3173	118	5	2020	2020	NUM
fcis-3173	118	6	):	):	PUNCT
fcis-3173	118	7	172152	172152	NUM
fcis-3173	118	8	-	-	SYM
fcis-3173	118	9	172165	172165	NUM
fcis-3173	118	10	.	.	PUNCT
fcis-3173	119	1	[	[	X
fcis-3173	119	2	8	8	NUM
fcis-3173	119	3	]	]	X
fcis-3173	119	4	saeed	saeed	PROPN
fcis-3173	119	5	,	,	PUNCT
fcis-3173	119	6	faisal	faisal	PROPN
fcis-3173	119	7	,	,	PUNCT
fcis-3173	119	8	et	et	PROPN
fcis-3173	119	9	al	al	PROPN
fcis-3173	119	10	.	.	PUNCT
fcis-3173	120	1	"	"	PUNCT
fcis-3173	120	2	a	a	DET
fcis-3173	120	3	robust	robust	ADJ
fcis-3173	120	4	approach	approach	NOUN
fcis-3173	120	5	for	for	ADP
fcis-3173	120	6	industrial	industrial	ADJ
fcis-3173	120	7	smallobject	smallobject	NOUN
fcis-3173	120	8	detection	detection	NOUN
fcis-3173	120	9	using	use	VERB
fcis-3173	120	10	an	an	DET
fcis-3173	120	11	improved	improve	VERB
fcis-3173	120	12	faster	fast	ADV
fcis-3173	120	13	regional	regional	ADJ
fcis-3173	120	14	convolutional	convolutional	ADJ
fcis-3173	120	15	neural	neural	ADJ
fcis-3173	120	16	network	network	NOUN
fcis-3173	120	17	.	.	PUNCT
fcis-3173	120	18	"	"	PUNCT
fcis-3173	121	1	scientific	scientific	ADJ
fcis-3173	121	2	reports	report	NOUN
fcis-3173	121	3	11.1	11.1	NUM
fcis-3173	121	4	(	(	PUNCT
fcis-3173	121	5	2021	2021	NUM
fcis-3173	121	6	):	):	PUNCT
fcis-3173	121	7	1	1	NUM
fcis-3173	121	8	-	-	SYM
fcis-3173	121	9	13	13	NUM
fcis-3173	121	10	.	.	PUNCT
fcis-3173	122	1	[	[	X
fcis-3173	122	2	9	9	NUM
fcis-3173	122	3	]	]	SYM
fcis-3173	122	4	li	li	PROPN
fcis-3173	122	5	,	,	PUNCT
fcis-3173	122	6	zewen	zewen	PROPN
fcis-3173	122	7	,	,	PUNCT
fcis-3173	122	8	et	et	PROPN
fcis-3173	122	9	al	al	PROPN
fcis-3173	122	10	.	.	PUNCT
fcis-3173	123	1	"	"	PUNCT
fcis-3173	123	2	a	a	DET
fcis-3173	123	3	survey	survey	NOUN
fcis-3173	123	4	of	of	ADP
fcis-3173	123	5	convolutional	convolutional	ADJ
fcis-3173	123	6	neural	neural	ADJ
fcis-3173	123	7	networks	network	NOUN
fcis-3173	123	8	:	:	PUNCT
fcis-3173	123	9	analysis	analysis	NOUN
fcis-3173	123	10	,	,	PUNCT
fcis-3173	123	11	applications	application	NOUN
fcis-3173	123	12	,	,	PUNCT
fcis-3173	123	13	and	and	CCONJ
fcis-3173	123	14	prospects	prospect	NOUN
fcis-3173	123	15	.	.	PUNCT
fcis-3173	123	16	"	"	PUNCT
fcis-3173	123	17	ieee	ieee	NOUN
fcis-3173	123	18	transactions	transaction	NOUN
fcis-3173	123	19	on	on	ADP
fcis-3173	123	20	neural	neural	ADJ
fcis-3173	123	21	networks	network	NOUN
fcis-3173	123	22	and	and	CCONJ
fcis-3173	123	23	learning	learn	VERB
fcis-3173	123	24	systems	system	NOUN
fcis-3173	123	25	(	(	PUNCT
fcis-3173	123	26	2021	2021	NUM
fcis-3173	123	27	)	)	PUNCT
fcis-3173	123	28	.	.	PUNCT
fcis-3173	124	1	[	[	X
fcis-3173	124	2	10	10	NUM
fcis-3173	124	3	]	]	SYM
fcis-3173	124	4	yang	yang	PROPN
fcis-3173	124	5	,	,	PUNCT
fcis-3173	124	6	jing	jing	PROPN
fcis-3173	124	7	,	,	PUNCT
fcis-3173	124	8	et	et	PROPN
fcis-3173	124	9	al	al	PROPN
fcis-3173	124	10	.	.	PUNCT
fcis-3173	124	11	"	"	PUNCT
fcis-3173	124	12	real	real	ADJ
fcis-3173	124	13	-	-	PUNCT
fcis-3173	124	14	time	time	NOUN
fcis-3173	124	15	recognition	recognition	NOUN
fcis-3173	124	16	method	method	NOUN
fcis-3173	124	17	for	for	ADP
fcis-3173	124	18	0.8	0.8	NUM
fcis-3173	124	19	cm	cm	NOUN
fcis-3173	124	20	darning	darning	NOUN
fcis-3173	124	21	needles	needle	NOUN
fcis-3173	124	22	and	and	CCONJ
fcis-3173	124	23	kr22	kr22	PROPN
fcis-3173	124	24	bearings	bearing	NOUN
fcis-3173	124	25	based	base	VERB
fcis-3173	124	26	on	on	ADP
fcis-3173	124	27	convolution	convolution	NOUN
fcis-3173	124	28	neural	neural	ADJ
fcis-3173	124	29	networks	network	NOUN
fcis-3173	124	30	and	and	CCONJ
fcis-3173	124	31	data	datum	NOUN
fcis-3173	124	32	increase	increase	NOUN
fcis-3173	124	33	.	.	PUNCT
fcis-3173	124	34	"	"	PUNCT
fcis-3173	124	35	applied	apply	VERB
fcis-3173	124	36	sciences	science	NOUN
fcis-3173	124	37	8.10	8.10	NUM
fcis-3173	124	38	(	(	PUNCT
fcis-3173	124	39	2018	2018	NUM
fcis-3173	124	40	):	):	PUNCT
fcis-3173	124	41	1857	1857	NUM
fcis-3173	124	42	.	.	PUNCT
fcis-3173	125	1	[	[	X
fcis-3173	125	2	11	11	NUM
fcis-3173	125	3	]	]	SYM
fcis-3173	125	4	yang	yang	PROPN
fcis-3173	125	5	,	,	PUNCT
fcis-3173	125	6	guanci	guanci	PROPN
fcis-3173	125	7	,	,	PUNCT
fcis-3173	125	8	et	et	PROPN
fcis-3173	125	9	al	al	PROPN
fcis-3173	125	10	.	.	PUNCT
fcis-3173	126	1	"	"	PUNCT
fcis-3173	126	2	convolutional	convolutional	ADJ
fcis-3173	126	3	neural	neural	ADJ
fcis-3173	126	4	network	network	NOUN
fcis-3173	126	5	-	-	PUNCT
fcis-3173	126	6	based	base	VERB
fcis-3173	126	7	embarrassing	embarrassing	ADJ
fcis-3173	126	8	situation	situation	NOUN
fcis-3173	126	9	detection	detection	NOUN
fcis-3173	126	10	under	under	ADP
fcis-3173	126	11	camera	camera	NOUN
fcis-3173	126	12	for	for	ADP
fcis-3173	126	13	social	social	ADJ
fcis-3173	126	14	robot	robot	NOUN
fcis-3173	126	15	in	in	ADP
fcis-3173	126	16	smart	smart	ADJ
fcis-3173	126	17	homes	home	NOUN
fcis-3173	126	18	.	.	PUNCT
fcis-3173	126	19	"	"	PUNCT
fcis-3173	127	1	sensors	sensor	NOUN
fcis-3173	127	2	18.5	18.5	NUM
fcis-3173	127	3	(	(	PUNCT
fcis-3173	127	4	2018	2018	NUM
fcis-3173	127	5	):	):	PUNCT
fcis-3173	127	6	1530	1530	NUM
fcis-3173	127	7	.	.	PUNCT
fcis-3173	128	1	[	[	X
fcis-3173	128	2	12	12	NUM
fcis-3173	128	3	]	]	SYM
fcis-3173	128	4	shanshan	shanshan	PROPN
fcis-3173	128	5	xu	xu	PROPN
fcis-3173	128	6	,	,	PUNCT
fcis-3173	128	7	yingan	yingan	PROPN
fcis-3173	128	8	liu	liu	PROPN
fcis-3173	128	9	,	,	PUNCT
fcis-3173	128	10	and	and	CCONJ
fcis-3173	128	11	sheng	sheng	PROPN
fcis-3173	128	12	xu	xu	PROPN
fcis-3173	128	13	.	.	PUNCT
fcis-3173	129	1	"	"	PUNCT
fcis-3173	129	2	wood	wood	NOUN
fcis-3173	129	3	defect	defect	NOUN
fcis-3173	129	4	recognition	recognition	NOUN
fcis-3173	129	5	based	base	VERB
fcis-3173	129	6	on	on	ADP
fcis-3173	129	7	convolutional	convolutional	ADJ
fcis-3173	129	8	neural	neural	ADJ
fcis-3173	129	9	network	network	NOUN
fcis-3173	129	10	.	.	PUNCT
fcis-3173	129	11	"	"	PUNCT
fcis-3173	130	1	journal	journal	PROPN
fcis-3173	130	2	of	of	ADP
fcis-3173	130	3	shandong	shandong	PROPN
fcis-3173	130	4	university	university	PROPN
fcis-3173	130	5	(	(	PUNCT
fcis-3173	130	6	engineering	engineering	NOUN
fcis-3173	130	7	edition	edition	NOUN
fcis-3173	130	8	)	)	PUNCT
fcis-3173	130	9	43.2	43.2	NUM
fcis-3173	130	10	(	(	PUNCT
fcis-3173	130	11	2013	2013	NUM
fcis-3173	130	12	):	):	PUNCT
fcis-3173	130	13	23	23	NUM
fcis-3173	130	14	-	-	SYM
fcis-3173	130	15	28	28	NUM
fcis-3173	130	16	.	.	PUNCT
fcis-3173	131	1	[	[	X
fcis-3173	131	2	13	13	NUM
fcis-3173	131	3	]	]	X
fcis-3173	131	4	liu	liu	PROPN
fcis-3173	131	5	,	,	PUNCT
fcis-3173	131	6	meng	meng	PROPN
fcis-3173	131	7	ke	ke	PROPN
fcis-3173	131	8	,	,	PUNCT
fcis-3173	131	9	wu	wu	PROPN
fcis-3173	131	10	,	,	PUNCT
fcis-3173	131	11	yang	yang	PROPN
fcis-3173	131	12	,	,	PUNCT
fcis-3173	131	13	and	and	CCONJ
fcis-3173	131	14	wang	wang	PROPN
fcis-3173	131	15	,	,	PUNCT
fcis-3173	131	16	xun	xun	PROPN
fcis-3173	131	17	.	.	PUNCT
fcis-3173	132	1	"	"	PUNCT
fcis-3173	132	2	implementation	implementation	NOUN
fcis-3173	132	3	of	of	ADP
fcis-3173	132	4	convolutional	convolutional	ADJ
fcis-3173	132	5	neural	neural	ADJ
fcis-3173	132	6	network	network	NOUN
fcis-3173	132	7	based	base	VERB
fcis-3173	132	8	track	track	NOUN
fcis-3173	132	9	surface	surface	NOUN
fcis-3173	132	10	defect	defect	NOUN
fcis-3173	132	11	detection	detection	NOUN
fcis-3173	132	12	technology	technology	NOUN
fcis-3173	132	13	.	.	PUNCT
fcis-3173	132	14	"	"	PUNCT
fcis-3173	133	1	modern	modern	ADJ
fcis-3173	133	2	computer	computer	NOUN
fcis-3173	133	3	:	:	PUNCT
fcis-3173	133	4	professional	professional	PROPN
fcis-3173	133	5	edition	edition	PROPN
fcis-3173	133	6	000.029(2017):65	000.029(2017):65	PROPN
fcis-3173	133	7	-	-	PUNCT
fcis-3173	133	8	69	69	NUM
fcis-3173	133	9	.	.	PUNCT
fcis-3173	134	1	[	[	X
fcis-3173	134	2	14	14	NUM
fcis-3173	134	3	]	]	SYM
fcis-3173	134	4	wu	wu	PROPN
fcis-3173	134	5	,	,	PUNCT
fcis-3173	134	6	zhiyang	zhiyang	PROPN
fcis-3173	134	7	et	et	PROPN
fcis-3173	134	8	al	al	PROPN
fcis-3173	134	9	.	.	PUNCT
fcis-3173	135	1	"	"	PUNCT
fcis-3173	135	2	a	a	DET
fcis-3173	135	3	fast	fast	ADJ
fcis-3173	135	4	detection	detection	NOUN
fcis-3173	135	5	algorithm	algorithm	NOUN
fcis-3173	135	6	for	for	ADP
fcis-3173	135	7	monochrome	monochrome	NOUN
fcis-3173	135	8	fabric	fabric	NOUN
fcis-3173	135	9	defects	defect	NOUN
fcis-3173	135	10	based	base	VERB
fcis-3173	135	11	on	on	ADP
fcis-3173	135	12	convolutional	convolutional	ADJ
fcis-3173	135	13	neural	neural	ADJ
fcis-3173	135	14	networks	network	NOUN
fcis-3173	135	15	.	.	PUNCT
fcis-3173	135	16	"	"	PUNCT
fcis-3173	136	1	journal	journal	NOUN
fcis-3173	136	2	of	of	ADP
fcis-3173	136	3	computer	computer	NOUN
fcis-3173	136	4	-	-	PUNCT
fcis-3173	136	5	aided	aid	VERB
fcis-3173	136	6	design	design	NOUN
fcis-3173	136	7	and	and	CCONJ
fcis-3173	136	8	graphics	graphic	NOUN
fcis-3173	136	9	30.12	30.12	NUM
fcis-3173	136	10	(	(	PUNCT
fcis-3173	136	11	2018	2018	NUM
fcis-3173	136	12	):	):	PUNCT
fcis-3173	136	13	9	9	NUM
fcis-3173	136	14	.	.	PUNCT
fcis-3173	137	1	[	[	X
fcis-3173	137	2	15	15	NUM
fcis-3173	137	3	]	]	X
fcis-3173	137	4	wang	wang	PROPN
fcis-3173	137	5	,	,	PUNCT
fcis-3173	137	6	tian	tian	PROPN
fcis-3173	137	7	,	,	PUNCT
fcis-3173	137	8	et	et	PROPN
fcis-3173	137	9	al	al	PROPN
fcis-3173	137	10	.	.	PUNCT
fcis-3173	138	1	"	"	PUNCT
fcis-3173	138	2	a	a	DET
fcis-3173	138	3	fast	fast	ADJ
fcis-3173	138	4	and	and	CCONJ
fcis-3173	138	5	robust	robust	ADJ
fcis-3173	138	6	convolutional	convolutional	ADJ
fcis-3173	138	7	neural	neural	ADJ
fcis-3173	138	8	network	network	NOUN
fcis-3173	138	9	-	-	PUNCT
fcis-3173	138	10	based	base	VERB
fcis-3173	138	11	defect	defect	NOUN
fcis-3173	138	12	detection	detection	NOUN
fcis-3173	138	13	model	model	NOUN
fcis-3173	138	14	in	in	ADP
fcis-3173	138	15	product	product	NOUN
fcis-3173	138	16	quality	quality	NOUN
fcis-3173	138	17	control	control	NOUN
fcis-3173	138	18	.	.	PUNCT
fcis-3173	138	19	"	"	PUNCT
fcis-3173	139	1	the	the	DET
fcis-3173	139	2	international	international	ADJ
fcis-3173	139	3	journal	journal	NOUN
fcis-3173	139	4	of	of	ADP
fcis-3173	139	5	advanced	advanced	ADJ
fcis-3173	139	6	manufacturing	manufacturing	NOUN
fcis-3173	139	7	technology	technology	NOUN
fcis-3173	139	8	94.9	94.9	NUM
fcis-3173	139	9	(	(	PUNCT
fcis-3173	139	10	2018	2018	NUM
fcis-3173	139	11	):	):	PUNCT
fcis-3173	139	12	3465	3465	NUM
fcis-3173	139	13	-	-	SYM
fcis-3173	139	14	3471	3471	NUM
fcis-3173	139	15	.	.	PUNCT
fcis-3173	140	1	[	[	X
fcis-3173	140	2	16	16	NUM
fcis-3173	140	3	]	]	X
fcis-3173	140	4	fu	fu	ADJ
fcis-3173	140	5	,	,	PUNCT
fcis-3173	140	6	guizhong	guizhong	PROPN
fcis-3173	140	7	,	,	PUNCT
fcis-3173	140	8	et	et	PROPN
fcis-3173	140	9	al	al	PROPN
fcis-3173	140	10	.	.	PUNCT
fcis-3173	141	1	"	"	PUNCT
fcis-3173	141	2	a	a	DET
fcis-3173	141	3	deep	deep	ADJ
fcis-3173	141	4	-	-	PUNCT
fcis-3173	141	5	learning	learning	NOUN
fcis-3173	141	6	-	-	PUNCT
fcis-3173	141	7	based	base	VERB
fcis-3173	141	8	approach	approach	NOUN
fcis-3173	141	9	for	for	ADP
fcis-3173	141	10	fast	fast	ADJ
fcis-3173	141	11	and	and	CCONJ
fcis-3173	141	12	robust	robust	ADJ
fcis-3173	141	13	steel	steel	NOUN
fcis-3173	141	14	surface	surface	NOUN
fcis-3173	141	15	defects	defect	VERB
fcis-3173	141	16	classification	classification	NOUN
fcis-3173	141	17	.	.	PUNCT
fcis-3173	141	18	"	"	PUNCT
fcis-3173	142	1	optics	optic	NOUN
fcis-3173	142	2	and	and	CCONJ
fcis-3173	142	3	lasers	laser	NOUN
fcis-3173	142	4	in	in	ADP
fcis-3173	142	5	engineering	engineer	VERB
fcis-3173	142	6	121	121	NUM
fcis-3173	142	7	(	(	PUNCT
fcis-3173	142	8	2019	2019	NUM
fcis-3173	142	9	):	):	PUNCT
fcis-3173	142	10	397	397	NUM
fcis-3173	142	11	-	-	SYM
fcis-3173	142	12	405	405	NUM
fcis-3173	142	13	.	.	PUNCT
fcis-3173	143	1	[	[	X
fcis-3173	143	2	17	17	NUM
fcis-3173	143	3	]	]	X
fcis-3173	143	4	wang	wang	PROPN
fcis-3173	143	5	d	d	PROPN
fcis-3173	143	6	,	,	PUNCT
fcis-3173	143	7	cai	cai	PROPN
fcis-3173	143	8	b	b	X
fcis-3173	143	9	-	-	PUNCT
fcis-3173	143	10	b	b	NOUN
fcis-3173	143	11	,	,	PUNCT
fcis-3173	143	12	and	and	CCONJ
fcis-3173	143	13	zai	zai	NOUN
fcis-3173	143	14	c	c	X
fcis-3173	143	15	-	-	PUNCT
fcis-3173	143	16	f.	f.	PROPN
fcis-3173	143	17	"	"	PUNCT
fcis-3173	143	18	an	an	DET
fcis-3173	143	19	inception	inception	NOUN
fcis-3173	143	20	-	-	PUNCT
fcis-3173	143	21	v4	v4	NOUN
fcis-3173	143	22	-	-	PUNCT
fcis-3173	143	23	based	base	VERB
fcis-3173	143	24	method	method	NOUN
fcis-3173	143	25	for	for	ADP
fcis-3173	143	26	car	car	NOUN
fcis-3173	143	27	parking	parking	NOUN
fcis-3173	143	28	status	status	NOUN
fcis-3173	143	29	detection	detection	NOUN
fcis-3173	143	30	.	.	PUNCT
fcis-3173	143	31	"	"	PUNCT
fcis-3173	143	32	computer	computer	NOUN
fcis-3173	143	33	age	age	NOUN
fcis-3173	143	34	003(2022):000	003(2022):000	PROPN
fcis-3173	143	35	.	.	PUNCT
fcis-3173	144	1	[	[	X
fcis-3173	144	2	18	18	NUM
fcis-3173	144	3	]	]	PUNCT
fcis-3173	144	4	szegedy	szegedy	PROPN
fcis-3173	144	5	,	,	PUNCT
fcis-3173	144	6	christian	christian	PROPN
fcis-3173	144	7	,	,	PUNCT
fcis-3173	144	8	et	et	PROPN
fcis-3173	144	9	al	al	PROPN
fcis-3173	144	10	.	.	PUNCT
fcis-3173	145	1	"	"	PUNCT
fcis-3173	145	2	going	go	VERB
fcis-3173	145	3	deeper	deeply	ADV
fcis-3173	145	4	with	with	ADP
fcis-3173	145	5	convolutions	convolution	NOUN
fcis-3173	145	6	.	.	PUNCT
fcis-3173	145	7	"	"	PUNCT
fcis-3173	146	1	proceedings	proceeding	NOUN
fcis-3173	146	2	of	of	ADP
fcis-3173	146	3	the	the	DET
fcis-3173	146	4	ieee	ieee	NOUN
fcis-3173	146	5	conference	conference	NOUN
fcis-3173	146	6	on	on	ADP
fcis-3173	146	7	computer	computer	NOUN
fcis-3173	146	8	vision	vision	NOUN
fcis-3173	146	9	and	and	CCONJ
fcis-3173	146	10	pattern	pattern	NOUN
fcis-3173	146	11	recognition	recognition	NOUN
fcis-3173	146	12	.	.	PUNCT
fcis-3173	147	1	2015	2015	NUM
fcis-3173	147	2	.	.	PUNCT
fcis-3173	148	1	[	[	X
fcis-3173	148	2	19	19	NUM
fcis-3173	148	3	]	]	X
fcis-3173	148	4	ioffe	ioffe	PROPN
fcis-3173	148	5	,	,	PUNCT
fcis-3173	148	6	sergey	sergey	PROPN
fcis-3173	148	7	,	,	PUNCT
fcis-3173	148	8	and	and	CCONJ
fcis-3173	148	9	christian	christian	PROPN
fcis-3173	148	10	szegedy	szegedy	NOUN
fcis-3173	148	11	.	.	PUNCT
fcis-3173	149	1	"	"	PUNCT
fcis-3173	149	2	batch	batch	VERB
fcis-3173	149	3	normalization	normalization	NOUN
fcis-3173	149	4	:	:	PUNCT
fcis-3173	149	5	accelerating	accelerate	VERB
fcis-3173	149	6	deep	deep	ADJ
fcis-3173	149	7	network	network	NOUN
fcis-3173	149	8	training	training	NOUN
fcis-3173	149	9	by	by	ADP
fcis-3173	149	10	reducing	reduce	VERB
fcis-3173	149	11	internal	internal	ADJ
fcis-3173	149	12	covariate	covariate	ADJ
fcis-3173	149	13	shift	shift	NOUN
fcis-3173	149	14	.	.	PUNCT
fcis-3173	149	15	"	"	PUNCT
fcis-3173	150	1	international	international	ADJ
fcis-3173	150	2	conference	conference	NOUN
fcis-3173	150	3	on	on	ADP
fcis-3173	150	4	machine	machine	NOUN
fcis-3173	150	5	learning	learning	NOUN
fcis-3173	150	6	.	.	PUNCT
fcis-3173	151	1	pmlr	pmlr	NOUN
fcis-3173	151	2	,	,	PUNCT
fcis-3173	151	3	2015	2015	NUM
fcis-3173	151	4	.	.	PUNCT
fcis-3173	152	1	[	[	X
fcis-3173	152	2	20	20	NUM
fcis-3173	152	3	]	]	PUNCT
fcis-3173	152	4	szegedy	szegedy	PROPN
fcis-3173	152	5	,	,	PUNCT
fcis-3173	152	6	christian	christian	PROPN
fcis-3173	152	7	,	,	PUNCT
fcis-3173	152	8	et	et	PROPN
fcis-3173	152	9	al	al	PROPN
fcis-3173	152	10	.	.	PUNCT
fcis-3173	153	1	"	"	PUNCT
fcis-3173	153	2	rethinking	rethink	VERB
fcis-3173	153	3	the	the	DET
fcis-3173	153	4	inception	inception	ADJ
fcis-3173	153	5	architecture	architecture	NOUN
fcis-3173	153	6	for	for	ADP
fcis-3173	153	7	computer	computer	NOUN
fcis-3173	153	8	vision	vision	NOUN
fcis-3173	153	9	.	.	PUNCT
fcis-3173	153	10	"	"	PUNCT
fcis-3173	154	1	proceedings	proceeding	NOUN
fcis-3173	154	2	of	of	ADP
fcis-3173	154	3	the	the	DET
fcis-3173	154	4	ieee	ieee	NOUN
fcis-3173	154	5	conference	conference	NOUN
fcis-3173	154	6	on	on	ADP
fcis-3173	154	7	computer	computer	NOUN
fcis-3173	154	8	vision	vision	NOUN
fcis-3173	154	9	and	and	CCONJ
fcis-3173	154	10	pattern	pattern	NOUN
fcis-3173	154	11	recognition	recognition	NOUN
fcis-3173	154	12	.	.	PUNCT
fcis-3173	155	1	2016	2016	NUM
fcis-3173	155	2	.	.	PUNCT
fcis-3173	156	1	[	[	X
fcis-3173	156	2	21	21	NUM
fcis-3173	156	3	]	]	X
fcis-3173	156	4	szegedy	szegedy	PROPN
fcis-3173	156	5	,	,	PUNCT
fcis-3173	156	6	christian	christian	PROPN
fcis-3173	156	7	,	,	PUNCT
fcis-3173	156	8	et	et	PROPN
fcis-3173	156	9	al	al	PROPN
fcis-3173	156	10	.	.	PUNCT
fcis-3173	157	1	"	"	PUNCT
fcis-3173	157	2	inception	inception	NOUN
fcis-3173	157	3	-	-	PUNCT
fcis-3173	157	4	v4	v4	NOUN
fcis-3173	157	5	,	,	PUNCT
fcis-3173	157	6	inception	inception	NOUN
fcis-3173	157	7	-	-	PUNCT
fcis-3173	157	8	resnet	resnet	NOUN
fcis-3173	157	9	and	and	CCONJ
fcis-3173	157	10	the	the	DET
fcis-3173	157	11	impact	impact	NOUN
fcis-3173	157	12	of	of	ADP
fcis-3173	157	13	residual	residual	ADJ
fcis-3173	157	14	connections	connection	NOUN
fcis-3173	157	15	on	on	ADP
fcis-3173	157	16	learning	learn	VERB
fcis-3173	157	17	.	.	PUNCT
fcis-3173	157	18	"	"	PUNCT
fcis-3173	158	1	thirty	thirty	NUM
fcis-3173	158	2	-	-	PUNCT
fcis-3173	158	3	first	first	ADV
fcis-3173	158	4	aaai	aaai	PROPN
fcis-3173	158	5	conference	conference	NOUN
fcis-3173	158	6	on	on	ADP
fcis-3173	158	7	artificial	artificial	ADJ
fcis-3173	158	8	intelligence	intelligence	NOUN
fcis-3173	158	9	.	.	PUNCT
fcis-3173	159	1	2017	2017	NUM
fcis-3173	159	2	.	.	PUNCT
fcis-3173	160	1	[	[	X
fcis-3173	160	2	22	22	NUM
fcis-3173	160	3	]	]	SYM
fcis-3173	160	4	li	li	PROPN
fcis-3173	160	5	,	,	PUNCT
fcis-3173	160	6	yubo	yubo	ADJ
fcis-3173	160	7	,	,	PUNCT
fcis-3173	160	8	et	et	PROPN
fcis-3173	160	9	al	al	PROPN
fcis-3173	160	10	.	.	PUNCT
fcis-3173	160	11	"	"	PUNCT
fcis-3173	160	12	application	application	NOUN
fcis-3173	160	13	of	of	ADP
fcis-3173	160	14	yolov5	yolov5	NOUN
fcis-3173	160	15	based	base	VERB
fcis-3173	160	16	on	on	ADP
fcis-3173	160	17	attention	attention	NOUN
fcis-3173	160	18	mechanism	mechanism	NOUN
fcis-3173	160	19	and	and	CCONJ
fcis-3173	160	20	receptive	receptive	ADJ
fcis-3173	160	21	field	field	NOUN
fcis-3173	160	22	in	in	ADP
fcis-3173	160	23	identifying	identify	VERB
fcis-3173	160	24	defects	defect	NOUN
fcis-3173	160	25	of	of	ADP
fcis-3173	160	26	thangka	thangka	PROPN
fcis-3173	160	27	images	image	NOUN
fcis-3173	160	28	.	.	PUNCT
fcis-3173	160	29	"	"	PUNCT
fcis-3173	160	30	ieee	ieee	NOUN
fcis-3173	160	31	access	access	NOUN
fcis-3173	160	32	10	10	NUM
fcis-3173	160	33	(	(	PUNCT
fcis-3173	160	34	2022	2022	NUM
fcis-3173	160	35	):	):	PUNCT
fcis-3173	160	36	81597	81597	NUM
fcis-3173	160	37	-	-	SYM
fcis-3173	160	38	81611	81611	NUM
fcis-3173	160	39	.	.	PUNCT
fcis-3173	161	1	[	[	X
fcis-3173	161	2	23	23	NUM
fcis-3173	161	3	]	]	SYM
fcis-3173	161	4	hu	hu	PROPN
fcis-3173	161	5	,	,	PUNCT
fcis-3173	161	6	jie	jie	PROPN
fcis-3173	161	7	,	,	PUNCT
fcis-3173	161	8	li	li	PROPN
fcis-3173	161	9	shen	shen	PROPN
fcis-3173	161	10	,	,	PUNCT
fcis-3173	161	11	and	and	CCONJ
fcis-3173	161	12	gang	gang	NOUN
fcis-3173	161	13	sun	sun	NOUN
fcis-3173	161	14	.	.	PUNCT
fcis-3173	162	1	"	"	PUNCT
fcis-3173	162	2	squeeze	squeeze	NOUN
fcis-3173	162	3	-	-	PUNCT
fcis-3173	162	4	and	and	CCONJ
fcis-3173	162	5	-	-	PUNCT
fcis-3173	162	6	excitation	excitation	NOUN
fcis-3173	162	7	networks	network	NOUN
fcis-3173	162	8	.	.	PUNCT
fcis-3173	162	9	"	"	PUNCT
fcis-3173	163	1	proceedings	proceeding	NOUN
fcis-3173	163	2	of	of	ADP
fcis-3173	163	3	the	the	DET
fcis-3173	163	4	ieee	ieee	NOUN
fcis-3173	163	5	conference	conference	NOUN
fcis-3173	163	6	on	on	ADP
fcis-3173	163	7	computer	computer	NOUN
fcis-3173	163	8	vision	vision	NOUN
fcis-3173	163	9	and	and	CCONJ
fcis-3173	163	10	pattern	pattern	NOUN
fcis-3173	163	11	recognition	recognition	NOUN
fcis-3173	163	12	.	.	PUNCT
fcis-3173	164	1	2018	2018	NUM
fcis-3173	164	2	.	.	PUNCT
fcis-3173	165	1	[	[	X
fcis-3173	165	2	24	24	NUM
fcis-3173	165	3	]	]	X
fcis-3173	165	4	roy	roy	PROPN
fcis-3173	165	5	,	,	PUNCT
fcis-3173	165	6	abhijit	abhijit	PROPN
fcis-3173	165	7	guha	guha	PROPN
fcis-3173	165	8	,	,	PUNCT
fcis-3173	165	9	nassir	nassir	PROPN
fcis-3173	165	10	navab	navab	PROPN
fcis-3173	165	11	,	,	PUNCT
fcis-3173	165	12	and	and	CCONJ
fcis-3173	165	13	christian	christian	ADJ
fcis-3173	165	14	wachinger	wachinger	NOUN
fcis-3173	165	15	.	.	PUNCT
fcis-3173	166	1	"	"	PUNCT
fcis-3173	166	2	concurrent	concurrent	ADJ
fcis-3173	166	3	spatial	spatial	ADJ
fcis-3173	166	4	and	and	CCONJ
fcis-3173	166	5	channel	channel	NOUN
fcis-3173	166	6	‘	'	PUNCT
fcis-3173	166	7	squeeze	squeeze	NOUN
fcis-3173	166	8	&	&	CCONJ
fcis-3173	166	9	excitation’in	excitation’in	VERB
fcis-3173	166	10	fully	fully	ADV
fcis-3173	166	11	convolutional	convolutional	ADJ
fcis-3173	166	12	networks	network	NOUN
fcis-3173	166	13	.	.	PUNCT
fcis-3173	166	14	"	"	PUNCT
fcis-3173	167	1	international	international	ADJ
fcis-3173	167	2	conference	conference	NOUN
fcis-3173	167	3	on	on	ADP
fcis-3173	167	4	medical	medical	ADJ
fcis-3173	167	5	image	image	NOUN
fcis-3173	167	6	computing	computing	NOUN
fcis-3173	167	7	and	and	CCONJ
fcis-3173	167	8	computer	computer	NOUN
fcis-3173	167	9	-	-	PUNCT
fcis-3173	167	10	assisted	assist	VERB
fcis-3173	167	11	intervention	intervention	NOUN
fcis-3173	167	12	.	.	PUNCT
fcis-3173	168	1	springer	springer	NOUN
fcis-3173	168	2	,	,	PUNCT
fcis-3173	168	3	cham	cham	PROPN
fcis-3173	168	4	,	,	PUNCT
fcis-3173	168	5	2018	2018	NUM
fcis-3173	168	6	.	.	PUNCT
fcis-3173	169	1	[	[	X
fcis-3173	169	2	25	25	NUM
fcis-3173	169	3	]	]	X
fcis-3173	169	4	chollet	chollet	NOUN
fcis-3173	169	5	,	,	PUNCT
fcis-3173	169	6	françois	françois	PROPN
fcis-3173	169	7	.	.	PUNCT
fcis-3173	170	1	"	"	PUNCT
fcis-3173	170	2	xception	xception	NOUN
fcis-3173	170	3	:	:	PUNCT
fcis-3173	170	4	deep	deep	ADJ
fcis-3173	170	5	learning	learn	VERB
fcis-3173	170	6	with	with	ADP
fcis-3173	170	7	depthwise	depthwise	NOUN
fcis-3173	170	8	separable	separable	ADJ
fcis-3173	170	9	convolutions	convolution	NOUN
fcis-3173	170	10	.	.	PUNCT
fcis-3173	170	11	"	"	PUNCT
fcis-3173	171	1	proceedings	proceeding	NOUN
fcis-3173	171	2	of	of	ADP
fcis-3173	171	3	the	the	DET
fcis-3173	171	4	ieee	ieee	NOUN
fcis-3173	171	5	conference	conference	NOUN
fcis-3173	171	6	on	on	ADP
fcis-3173	171	7	computer	computer	NOUN
fcis-3173	171	8	vision	vision	NOUN
fcis-3173	171	9	and	and	CCONJ
fcis-3173	171	10	pattern	pattern	NOUN
fcis-3173	171	11	recognition	recognition	NOUN
fcis-3173	171	12	.	.	PUNCT
fcis-3173	172	1	2017	2017	NUM
fcis-3173	172	2	.	.	PUNCT
fcis-3173	173	1	[	[	X
fcis-3173	173	2	26	26	NUM
fcis-3173	173	3	]	]	X
fcis-3173	173	4	ma	ma	PROPN
fcis-3173	173	5	,	,	PUNCT
fcis-3173	173	6	ningning	ningning	NOUN
fcis-3173	173	7	,	,	PUNCT
fcis-3173	173	8	et	et	PROPN
fcis-3173	173	9	al	al	PROPN
fcis-3173	173	10	.	.	PUNCT
fcis-3173	174	1	"	"	PUNCT
fcis-3173	174	2	shufflenet	shufflenet	NOUN
fcis-3173	174	3	v2	v2	NOUN
fcis-3173	174	4	:	:	PUNCT
fcis-3173	174	5	practical	practical	ADJ
fcis-3173	174	6	guidelines	guideline	NOUN
fcis-3173	174	7	for	for	ADP
fcis-3173	174	8	efficient	efficient	ADJ
fcis-3173	174	9	cnn	cnn	PROPN
fcis-3173	174	10	architecture	architecture	NOUN
fcis-3173	174	11	design	design	NOUN
fcis-3173	174	12	.	.	PUNCT
fcis-3173	174	13	"	"	PUNCT
fcis-3173	175	1	proceedings	proceeding	NOUN
fcis-3173	175	2	of	of	ADP
fcis-3173	175	3	the	the	DET
fcis-3173	175	4	european	european	PROPN
fcis-3173	175	5	conference	conference	PROPN
fcis-3173	175	6	on	on	ADP
fcis-3173	175	7	computer	computer	NOUN
fcis-3173	175	8	vision	vision	NOUN
fcis-3173	175	9	(	(	PUNCT
fcis-3173	175	10	eccv	eccv	ADV
fcis-3173	175	11	)	)	PUNCT
fcis-3173	175	12	.	.	PUNCT
fcis-3173	176	1	2018	2018	NUM
fcis-3173	176	2	.	.	PUNCT
fcis-3173	177	1	javascript	javascript	NOUN
fcis-3173	177	2	:	:	PUNCT
fcis-3173	177	3	;	;	PUNCT