id	sid	tid	token	lemma	pos
fcis-14021	1	1	frontiers	frontier	NOUN
fcis-14021	1	2	in	in	ADP
fcis-14021	1	3	computing	computing	NOUN
fcis-14021	1	4	and	and	CCONJ
fcis-14021	1	5	intelligent	intelligent	ADJ
fcis-14021	1	6	systems	system	NOUN
fcis-14021	1	7	issn	issn	VERB
fcis-14021	1	8	:	:	PUNCT
fcis-14021	1	9	2832	2832	NUM
fcis-14021	1	10	-	-	SYM
fcis-14021	1	11	6024	6024	NUM
fcis-14021	1	12	|	|	NOUN
fcis-14021	1	13	vol	vol	NOUN
fcis-14021	1	14	.	.	PROPN
fcis-14021	2	1	5	5	NUM
fcis-14021	2	2	,	,	PUNCT
fcis-14021	2	3	no	no	INTJ
fcis-14021	2	4	.	.	NOUN
fcis-14021	2	5	3	3	NUM
fcis-14021	2	6	,	,	PUNCT
fcis-14021	2	7	2023	2023	NUM
fcis-14021	2	8	128	128	NUM
fcis-14021	2	9	real	real	ADJ
fcis-14021	2	10	time	time	NOUN
fcis-14021	2	11	detection	detection	NOUN
fcis-14021	2	12	algorithm	algorithm	NOUN
fcis-14021	2	13	for	for	ADP
fcis-14021	2	14	escape	escape	NOUN
fcis-14021	2	15	ladders	ladder	NOUN
fcis-14021	2	16	based	base	VERB
fcis-14021	2	17	on	on	ADP
fcis-14021	2	18	yolov5s	yolov5s	PROPN
fcis-14021	2	19	sheng	sheng	PROPN
fcis-14021	2	20	jin	jin	PROPN
fcis-14021	3	1	*	*	PUNCT
fcis-14021	3	2	tianjin	tianjin	PROPN
fcis-14021	3	3	university	university	PROPN
fcis-14021	3	4	of	of	ADP
fcis-14021	3	5	technology	technology	NOUN
fcis-14021	3	6	and	and	CCONJ
fcis-14021	3	7	education	education	NOUN
fcis-14021	3	8	,	,	PUNCT
fcis-14021	3	9	china	china	PROPN
fcis-14021	3	10	*	*	PUNCT
fcis-14021	3	11	corresponding	correspond	VERB
fcis-14021	3	12	author	author	NOUN
fcis-14021	3	13	email	email	NOUN
fcis-14021	3	14	:	:	PUNCT
fcis-14021	3	15	875597141@qq.com	875597141@qq.com	NUM
fcis-14021	3	16	abstract	abstract	NOUN
fcis-14021	3	17	:	:	PUNCT
fcis-14021	3	18	in	in	ADP
fcis-14021	3	19	the	the	DET
fcis-14021	3	20	detection	detection	NOUN
fcis-14021	3	21	of	of	ADP
fcis-14021	3	22	escape	escape	NOUN
fcis-14021	3	23	ladders	ladder	NOUN
fcis-14021	3	24	in	in	ADP
fcis-14021	3	25	the	the	DET
fcis-14021	3	26	context	context	NOUN
fcis-14021	3	27	of	of	ADP
fcis-14021	3	28	smart	smart	ADJ
fcis-14021	3	29	construction	construction	NOUN
fcis-14021	3	30	sites	site	NOUN
fcis-14021	3	31	,	,	PUNCT
fcis-14021	3	32	due	due	ADP
fcis-14021	3	33	to	to	ADP
fcis-14021	3	34	the	the	DET
fcis-14021	3	35	relatively	relatively	ADV
fcis-14021	3	36	small	small	ADJ
fcis-14021	3	37	target	target	NOUN
fcis-14021	3	38	size	size	NOUN
fcis-14021	3	39	of	of	ADP
fcis-14021	3	40	the	the	DET
fcis-14021	3	41	escape	escape	NOUN
fcis-14021	3	42	ladder	ladder	NOUN
fcis-14021	3	43	compared	compare	VERB
fcis-14021	3	44	to	to	ADP
fcis-14021	3	45	the	the	DET
fcis-14021	3	46	entire	entire	ADJ
fcis-14021	3	47	input	input	NOUN
fcis-14021	3	48	image	image	NOUN
fcis-14021	3	49	frame	frame	NOUN
fcis-14021	3	50	,	,	PUNCT
fcis-14021	3	51	significant	significant	ADJ
fcis-14021	3	52	environmental	environmental	ADJ
fcis-14021	3	53	interference	interference	NOUN
fcis-14021	3	54	,	,	PUNCT
fcis-14021	3	55	and	and	CCONJ
fcis-14021	3	56	high	high	ADJ
fcis-14021	3	57	missed	miss	VERB
fcis-14021	3	58	detection	detection	NOUN
fcis-14021	3	59	and	and	CCONJ
fcis-14021	3	60	false	false	ADJ
fcis-14021	3	61	detection	detection	NOUN
fcis-14021	3	62	rates	rate	NOUN
fcis-14021	3	63	,	,	PUNCT
fcis-14021	3	64	an	an	DET
fcis-14021	3	65	improved	improved	ADJ
fcis-14021	3	66	yolov5s	yolov5s	NOUN
fcis-14021	3	67	escape	escape	NOUN
fcis-14021	3	68	ladder	ladder	NOUN
fcis-14021	3	69	real	real	ADJ
fcis-14021	3	70	-	-	PUNCT
fcis-14021	3	71	time	time	NOUN
fcis-14021	3	72	detection	detection	NOUN
fcis-14021	3	73	algorithm	algorithm	NOUN
fcis-14021	3	74	is	be	AUX
fcis-14021	3	75	proposed	propose	VERB
fcis-14021	3	76	by	by	ADP
fcis-14021	3	77	combining	combine	VERB
fcis-14021	3	78	the	the	DET
fcis-14021	3	79	attention	attention	NOUN
fcis-14021	3	80	mechanism	mechanism	NOUN
fcis-14021	3	81	network	network	NOUN
fcis-14021	3	82	.	.	PUNCT
fcis-14021	4	1	the	the	DET
fcis-14021	4	2	model	model	NOUN
fcis-14021	4	3	uses	use	VERB
fcis-14021	4	4	csplocknet53	csplocknet53	NOUN
fcis-14021	4	5	as	as	ADP
fcis-14021	4	6	the	the	DET
fcis-14021	4	7	backbone	backbone	NOUN
fcis-14021	4	8	network	network	NOUN
fcis-14021	4	9	for	for	ADP
fcis-14021	4	10	feature	feature	NOUN
fcis-14021	4	11	extraction	extraction	NOUN
fcis-14021	4	12	,	,	PUNCT
fcis-14021	4	13	introduces	introduce	VERB
fcis-14021	4	14	the	the	DET
fcis-14021	4	15	attention	attention	NOUN
fcis-14021	4	16	module	module	NOUN
fcis-14021	4	17	ca	ca	NOUN
fcis-14021	4	18	,	,	PUNCT
fcis-14021	4	19	and	and	CCONJ
fcis-14021	4	20	integrates	integrate	VERB
fcis-14021	4	21	spatial	spatial	ADJ
fcis-14021	4	22	and	and	CCONJ
fcis-14021	4	23	channel	channel	NOUN
fcis-14021	4	24	information	information	NOUN
fcis-14021	4	25	,	,	PUNCT
fcis-14021	4	26	while	while	SCONJ
fcis-14021	4	27	increasing	increase	VERB
fcis-14021	4	28	a	a	DET
fcis-14021	4	29	small	small	ADJ
fcis-14021	4	30	amount	amount	NOUN
fcis-14021	4	31	of	of	ADP
fcis-14021	4	32	computation	computation	NOUN
fcis-14021	4	33	,	,	PUNCT
fcis-14021	4	34	performance	performance	NOUN
fcis-14021	4	35	has	have	AUX
fcis-14021	4	36	been	be	AUX
fcis-14021	4	37	significantly	significantly	ADV
fcis-14021	4	38	improved	improve	VERB
fcis-14021	4	39	.	.	PUNCT
fcis-14021	5	1	optimize	optimize	VERB
fcis-14021	5	2	the	the	DET
fcis-14021	5	3	network	network	NOUN
fcis-14021	5	4	structure	structure	NOUN
fcis-14021	5	5	of	of	ADP
fcis-14021	5	6	yolov5s	yolov5s	PROPN
fcis-14021	5	7	algorithm	algorithm	NOUN
fcis-14021	5	8	,	,	PUNCT
fcis-14021	5	9	strengthen	strengthen	VERB
fcis-14021	5	10	shallow	shallow	ADJ
fcis-14021	5	11	feature	feature	NOUN
fcis-14021	5	12	weights	weight	NOUN
fcis-14021	5	13	to	to	PART
fcis-14021	5	14	enhance	enhance	VERB
fcis-14021	5	15	small	small	ADJ
fcis-14021	5	16	target	target	NOUN
fcis-14021	5	17	detection	detection	NOUN
fcis-14021	5	18	effectiveness	effectiveness	NOUN
fcis-14021	5	19	,	,	PUNCT
fcis-14021	5	20	add	add	VERB
fcis-14021	5	21	attention	attention	NOUN
fcis-14021	5	22	mechanisms	mechanism	NOUN
fcis-14021	5	23	to	to	PART
fcis-14021	5	24	increase	increase	VERB
fcis-14021	5	25	the	the	DET
fcis-14021	5	26	weight	weight	NOUN
fcis-14021	5	27	of	of	ADP
fcis-14021	5	28	small	small	ADJ
fcis-14021	5	29	targets	target	NOUN
fcis-14021	5	30	and	and	CCONJ
fcis-14021	5	31	their	their	PRON
fcis-14021	5	32	surrounding	surround	VERB
fcis-14021	5	33	features	feature	NOUN
fcis-14021	5	34	,	,	PUNCT
fcis-14021	5	35	and	and	CCONJ
fcis-14021	5	36	use	use	VERB
fcis-14021	5	37	mosaic	mosaic	ADJ
fcis-14021	5	38	methods	method	NOUN
fcis-14021	5	39	for	for	ADP
fcis-14021	5	40	data	datum	NOUN
fcis-14021	5	41	augmentation	augmentation	NOUN
fcis-14021	5	42	to	to	PART
fcis-14021	5	43	improve	improve	VERB
fcis-14021	5	44	detection	detection	NOUN
fcis-14021	5	45	accuracy	accuracy	NOUN
fcis-14021	5	46	and	and	CCONJ
fcis-14021	5	47	recall	recall	NOUN
fcis-14021	5	48	.	.	PUNCT
fcis-14021	6	1	after	after	ADP
fcis-14021	6	2	multiple	multiple	ADJ
fcis-14021	6	3	repeated	repeat	VERB
fcis-14021	6	4	experiments	experiment	NOUN
fcis-14021	6	5	,	,	PUNCT
fcis-14021	6	6	these	these	DET
fcis-14021	6	7	experimental	experimental	ADJ
fcis-14021	6	8	results	result	NOUN
fcis-14021	6	9	have	have	AUX
fcis-14021	6	10	proven	prove	VERB
fcis-14021	6	11	that	that	SCONJ
fcis-14021	6	12	the	the	DET
fcis-14021	6	13	optimized	optimize	VERB
fcis-14021	6	14	yolov5s	yolov5s	PROPN
fcis-14021	6	15	algorithm	algorithm	NOUN
fcis-14021	6	16	for	for	ADP
fcis-14021	6	17	real	real	ADJ
fcis-14021	6	18	-	-	PUNCT
fcis-14021	6	19	time	time	NOUN
fcis-14021	6	20	detection	detection	NOUN
fcis-14021	6	21	of	of	ADP
fcis-14021	6	22	escape	escape	NOUN
fcis-14021	6	23	ladders	ladder	NOUN
fcis-14021	6	24	has	have	VERB
fcis-14021	6	25	an	an	DET
fcis-14021	6	26	average	average	ADJ
fcis-14021	6	27	detection	detection	NOUN
fcis-14021	6	28	accuracy	accuracy	NOUN
fcis-14021	6	29	(	(	PUNCT
fcis-14021	6	30	accuracy	accuracy	NOUN
fcis-14021	6	31	,	,	PUNCT
fcis-14021	6	32	recall	recall	NOUN
fcis-14021	6	33	)	)	PUNCT
fcis-14021	6	34	of	of	ADP
fcis-14021	6	35	(	(	PUNCT
fcis-14021	6	36	81.8	81.8	NUM
fcis-14021	6	37	,	,	PUNCT
fcis-14021	6	38	82.6	82.6	NUM
fcis-14021	6	39	)	)	PUNCT
fcis-14021	6	40	.	.	PUNCT
fcis-14021	7	1	compared	compare	VERB
fcis-14021	7	2	with	with	ADP
fcis-14021	7	3	the	the	DET
fcis-14021	7	4	traditional	traditional	ADJ
fcis-14021	7	5	yolov5s	yolov5s	PROPN
fcis-14021	7	6	algorithm	algorithm	NOUN
fcis-14021	7	7	,	,	PUNCT
fcis-14021	7	8	the	the	DET
fcis-14021	7	9	accuracy	accuracy	NOUN
fcis-14021	7	10	and	and	CCONJ
fcis-14021	7	11	recall	recall	NOUN
fcis-14021	7	12	have	have	AUX
fcis-14021	7	13	been	be	AUX
fcis-14021	7	14	improved	improve	VERB
fcis-14021	7	15	by	by	ADP
fcis-14021	7	16	1.4	1.4	NUM
fcis-14021	7	17	%	%	NOUN
fcis-14021	7	18	and	and	CCONJ
fcis-14021	7	19	1.2	1.2	NUM
fcis-14021	7	20	%	%	NOUN
fcis-14021	7	21	,	,	PUNCT
fcis-14021	7	22	respectively	respectively	ADV
fcis-14021	7	23	.	.	PUNCT
fcis-14021	8	1	the	the	DET
fcis-14021	8	2	optimized	optimize	VERB
fcis-14021	8	3	yolov5s	yolov5s	PROPN
fcis-14021	8	4	algorithm	algorithm	NOUN
fcis-14021	8	5	can	can	AUX
fcis-14021	8	6	effectively	effectively	ADV
fcis-14021	8	7	improve	improve	VERB
fcis-14021	8	8	the	the	DET
fcis-14021	8	9	detection	detection	NOUN
fcis-14021	8	10	accuracy	accuracy	NOUN
fcis-14021	8	11	of	of	ADP
fcis-14021	8	12	real	real	ADJ
fcis-14021	8	13	-	-	PUNCT
fcis-14021	8	14	time	time	NOUN
fcis-14021	8	15	detection	detection	NOUN
fcis-14021	8	16	of	of	ADP
fcis-14021	8	17	escape	escape	NOUN
fcis-14021	8	18	ladders	ladder	NOUN
fcis-14021	8	19	,	,	PUNCT
fcis-14021	8	20	and	and	CCONJ
fcis-14021	8	21	improve	improve	VERB
fcis-14021	8	22	the	the	DET
fcis-14021	8	23	detection	detection	NOUN
fcis-14021	8	24	and	and	CCONJ
fcis-14021	8	25	resolution	resolution	NOUN
fcis-14021	8	26	performance	performance	NOUN
fcis-14021	8	27	of	of	ADP
fcis-14021	8	28	small	small	ADJ
fcis-14021	8	29	escape	escape	NOUN
fcis-14021	8	30	ladder	ladder	NOUN
fcis-14021	8	31	targets	target	NOUN
fcis-14021	8	32	.	.	PUNCT
fcis-14021	9	1	keywords	keyword	NOUN
fcis-14021	9	2	:	:	PUNCT
fcis-14021	9	3	escape	escape	VERB
fcis-14021	9	4	ladder	ladder	NOUN
fcis-14021	9	5	real	real	ADJ
fcis-14021	9	6	-	-	PUNCT
fcis-14021	9	7	time	time	NOUN
fcis-14021	9	8	detection	detection	NOUN
fcis-14021	9	9	;	;	PUNCT
fcis-14021	9	10	deep	deep	ADJ
fcis-14021	9	11	learning	learning	NOUN
fcis-14021	9	12	;	;	PUNCT
fcis-14021	9	13	attention	attention	NOUN
fcis-14021	9	14	mechanism	mechanism	NOUN
fcis-14021	9	15	;	;	PUNCT
fcis-14021	9	16	small	small	ADJ
fcis-14021	9	17	target	target	NOUN
fcis-14021	9	18	detection	detection	NOUN
fcis-14021	9	19	.	.	PUNCT
fcis-14021	10	1	1	1	X
fcis-14021	10	2	.	.	X
fcis-14021	10	3	introduction	introduction	NOUN
fcis-14021	10	4	ensuring	ensure	VERB
fcis-14021	10	5	worker	worker	NOUN
fcis-14021	10	6	safety	safety	NOUN
fcis-14021	10	7	is	be	AUX
fcis-14021	10	8	crucial	crucial	ADJ
fcis-14021	10	9	in	in	ADP
fcis-14021	10	10	complex	complex	ADJ
fcis-14021	10	11	construction	construction	NOUN
fcis-14021	10	12	site	site	NOUN
fcis-14021	10	13	environments	environment	NOUN
fcis-14021	10	14	.	.	PUNCT
fcis-14021	11	1	however	however	ADV
fcis-14021	11	2	,	,	PUNCT
fcis-14021	11	3	due	due	ADP
fcis-14021	11	4	to	to	ADP
fcis-14021	11	5	various	various	ADJ
fcis-14021	11	6	factors	factor	NOUN
fcis-14021	11	7	,	,	PUNCT
fcis-14021	11	8	such	such	ADJ
fcis-14021	11	9	as	as	ADP
fcis-14021	11	10	sudden	sudden	ADJ
fcis-14021	11	11	mechanical	mechanical	ADJ
fcis-14021	11	12	failures	failure	NOUN
fcis-14021	11	13	,	,	PUNCT
fcis-14021	11	14	adverse	adverse	ADJ
fcis-14021	11	15	weather	weather	NOUN
fcis-14021	11	16	conditions	condition	NOUN
fcis-14021	11	17	,	,	PUNCT
fcis-14021	11	18	and	and	CCONJ
fcis-14021	11	19	even	even	ADV
fcis-14021	11	20	worker	worker	NOUN
fcis-14021	11	21	negligence	negligence	NOUN
fcis-14021	11	22	,	,	PUNCT
fcis-14021	11	23	danger	danger	NOUN
fcis-14021	11	24	can	can	AUX
fcis-14021	11	25	occur	occur	VERB
fcis-14021	11	26	.	.	PUNCT
fcis-14021	12	1	among	among	ADP
fcis-14021	12	2	them	they	PRON
fcis-14021	12	3	,	,	PUNCT
fcis-14021	12	4	realtime	realtime	ADJ
fcis-14021	12	5	detection	detection	NOUN
fcis-14021	12	6	of	of	ADP
fcis-14021	12	7	escape	escape	NOUN
fcis-14021	12	8	ladders	ladder	NOUN
fcis-14021	12	9	is	be	AUX
fcis-14021	12	10	a	a	DET
fcis-14021	12	11	crucial	crucial	ADJ
fcis-14021	12	12	step	step	NOUN
fcis-14021	12	13	.	.	PUNCT
fcis-14021	13	1	however	however	ADV
fcis-14021	13	2	,	,	PUNCT
fcis-14021	13	3	traditional	traditional	ADJ
fcis-14021	13	4	escape	escape	NOUN
fcis-14021	13	5	ladder	ladder	NOUN
fcis-14021	13	6	detection	detection	NOUN
fcis-14021	13	7	methods	method	NOUN
fcis-14021	13	8	are	be	AUX
fcis-14021	13	9	often	often	ADV
fcis-14021	13	10	limited	limit	VERB
fcis-14021	13	11	by	by	ADP
fcis-14021	13	12	environmental	environmental	ADJ
fcis-14021	13	13	conditions	condition	NOUN
fcis-14021	13	14	such	such	ADJ
fcis-14021	13	15	as	as	ADP
fcis-14021	13	16	lighting	lighting	NOUN
fcis-14021	13	17	,	,	PUNCT
fcis-14021	13	18	visibility	visibility	NOUN
fcis-14021	13	19	,	,	PUNCT
fcis-14021	13	20	noise	noise	NOUN
fcis-14021	13	21	,	,	PUNCT
fcis-14021	13	22	etc	etc	X
fcis-14021	13	23	.	.	X
fcis-14021	13	24	,	,	PUNCT
fcis-14021	13	25	resulting	result	VERB
fcis-14021	13	26	in	in	ADP
fcis-14021	13	27	insufficient	insufficient	ADJ
fcis-14021	13	28	accuracy	accuracy	NOUN
fcis-14021	13	29	and	and	CCONJ
fcis-14021	13	30	real	real	ADJ
fcis-14021	13	31	-	-	PUNCT
fcis-14021	13	32	time	time	NOUN
fcis-14021	13	33	performance	performance	NOUN
fcis-14021	13	34	.	.	PUNCT
fcis-14021	14	1	with	with	ADP
fcis-14021	14	2	the	the	DET
fcis-14021	14	3	development	development	NOUN
fcis-14021	14	4	of	of	ADP
fcis-14021	14	5	deep	deep	ADJ
fcis-14021	14	6	learning	learning	NOUN
fcis-14021	14	7	and	and	CCONJ
fcis-14021	14	8	computer	computer	NOUN
fcis-14021	14	9	vision	vision	NOUN
fcis-14021	14	10	technology	technology	NOUN
fcis-14021	14	11	,	,	PUNCT
fcis-14021	14	12	escape	escape	VERB
fcis-14021	14	13	ladder	ladder	NOUN
fcis-14021	14	14	detection	detection	NOUN
fcis-14021	14	15	algorithms	algorithm	NOUN
fcis-14021	14	16	based	base	VERB
fcis-14021	14	17	on	on	ADP
fcis-14021	14	18	deep	deep	ADJ
fcis-14021	14	19	learning	learning	NOUN
fcis-14021	14	20	have	have	AUX
fcis-14021	14	21	emerged	emerge	VERB
fcis-14021	14	22	.	.	PUNCT
fcis-14021	15	1	these	these	DET
fcis-14021	15	2	algorithms	algorithm	NOUN
fcis-14021	15	3	can	can	AUX
fcis-14021	15	4	automatically	automatically	ADV
fcis-14021	15	5	and	and	CCONJ
fcis-14021	15	6	real	real	ADJ
fcis-14021	15	7	-	-	PUNCT
fcis-14021	15	8	time	time	NOUN
fcis-14021	15	9	detect	detect	VERB
fcis-14021	15	10	the	the	DET
fcis-14021	15	11	position	position	NOUN
fcis-14021	15	12	and	and	CCONJ
fcis-14021	15	13	status	status	NOUN
fcis-14021	15	14	of	of	ADP
fcis-14021	15	15	escape	escape	NOUN
fcis-14021	15	16	ladders	ladder	NOUN
fcis-14021	15	17	,	,	PUNCT
fcis-14021	15	18	improving	improve	VERB
fcis-14021	15	19	the	the	DET
fcis-14021	15	20	safety	safety	NOUN
fcis-14021	15	21	of	of	ADP
fcis-14021	15	22	construction	construction	NOUN
fcis-14021	15	23	sites	site	NOUN
fcis-14021	15	24	.	.	PUNCT
fcis-14021	16	1	zhang	zhang	PROPN
fcis-14021	16	2	et	et	PROPN
fcis-14021	16	3	al	al	PROPN
fcis-14021	16	4	.	.	PUNCT
fcis-14021	17	1	[	[	X
fcis-14021	17	2	2	2	X
fcis-14021	17	3	]	]	PUNCT
fcis-14021	17	4	used	use	VERB
fcis-14021	17	5	parameter	parameter	NOUN
fcis-14021	17	6	calibration	calibration	NOUN
fcis-14021	17	7	to	to	PART
fcis-14021	17	8	generate	generate	VERB
fcis-14021	17	9	regions	region	NOUN
fcis-14021	17	10	of	of	ADP
fcis-14021	17	11	interest	interest	NOUN
fcis-14021	17	12	(	(	PUNCT
fcis-14021	17	13	ious	ious	ADJ
fcis-14021	17	14	)	)	PUNCT
fcis-14021	17	15	and	and	CCONJ
fcis-14021	17	16	coordinate	coordinate	ADJ
fcis-14021	17	17	transformations	transformation	NOUN
fcis-14021	17	18	,	,	PUNCT
fcis-14021	17	19	combined	combine	VERB
fcis-14021	17	20	with	with	ADP
fcis-14021	17	21	the	the	DET
fcis-14021	17	22	velocity	velocity	NOUN
fcis-14021	17	23	and	and	CCONJ
fcis-14021	17	24	position	position	NOUN
fcis-14021	17	25	information	information	NOUN
fcis-14021	17	26	returned	return	VERB
fcis-14021	17	27	by	by	ADP
fcis-14021	17	28	cameras	camera	NOUN
fcis-14021	17	29	and	and	CCONJ
fcis-14021	17	30	radar	radar	NOUN
fcis-14021	17	31	,	,	PUNCT
fcis-14021	17	32	and	and	CCONJ
fcis-14021	17	33	used	use	VERB
fcis-14021	17	34	the	the	DET
fcis-14021	17	35	mean	mean	ADJ
fcis-14021	17	36	square	square	ADJ
fcis-14021	17	37	error	error	NOUN
fcis-14021	17	38	loss	loss	NOUN
fcis-14021	17	39	function	function	NOUN
fcis-14021	17	40	to	to	PART
fcis-14021	17	41	calculate	calculate	VERB
fcis-14021	17	42	the	the	DET
fcis-14021	17	43	updated	update	VERB
fcis-14021	17	44	model	model	NOUN
fcis-14021	17	45	.	.	PUNCT
fcis-14021	18	1	s	s	VERB
fcis-14021	18	2	proposed	propose	VERB
fcis-14021	18	3	an	an	DET
fcis-14021	18	4	algorithm	algorithm	NOUN
fcis-14021	18	5	for	for	ADP
fcis-14021	18	6	precise	precise	ADJ
fcis-14021	18	7	positioning	positioning	NOUN
fcis-14021	18	8	and	and	CCONJ
fcis-14021	18	9	target	target	NOUN
fcis-14021	18	10	recognition	recognition	NOUN
fcis-14021	18	11	based	base	VERB
fcis-14021	18	12	on	on	ADP
fcis-14021	18	13	boundary	boundary	ADJ
fcis-14021	18	14	box	box	PROPN
fcis-14021	18	15	regression	regression	NOUN
fcis-14021	18	16	,	,	PUNCT
fcis-14021	18	17	but	but	CCONJ
fcis-14021	18	18	this	this	DET
fcis-14021	18	19	algorithm	algorithm	NOUN
fcis-14021	18	20	is	be	AUX
fcis-14021	18	21	limited	limit	VERB
fcis-14021	18	22	by	by	ADP
fcis-14021	18	23	various	various	ADJ
fcis-14021	18	24	indicators	indicator	NOUN
fcis-14021	18	25	of	of	ADP
fcis-14021	18	26	the	the	DET
fcis-14021	18	27	sensor	sensor	NOUN
fcis-14021	18	28	.	.	PUNCT
fcis-14021	19	1	pablo	pablo	PROPN
fcis-14021	19	2	et	et	PROPN
fcis-14021	19	3	al	al	PROPN
fcis-14021	19	4	.	.	PUNCT
fcis-14021	20	1	[	[	X
fcis-14021	20	2	3	3	X
fcis-14021	20	3	]	]	PUNCT
fcis-14021	20	4	assumed	assume	VERB
fcis-14021	20	5	that	that	SCONJ
fcis-14021	20	6	the	the	DET
fcis-14021	20	7	data	data	NOUN
fcis-14021	20	8	follows	follow	VERB
fcis-14021	20	9	a	a	DET
fcis-14021	20	10	gaussian	gaussian	ADJ
fcis-14021	20	11	distribution	distribution	NOUN
fcis-14021	20	12	and	and	CCONJ
fcis-14021	20	13	used	use	VERB
fcis-14021	20	14	a	a	DET
fcis-14021	20	15	gaussian	gaussian	ADJ
fcis-14021	20	16	mixture	mixture	NOUN
fcis-14021	20	17	model	model	NOUN
fcis-14021	20	18	for	for	ADP
fcis-14021	20	19	background	background	NOUN
fcis-14021	20	20	modeling	modeling	NOUN
fcis-14021	20	21	.	.	PUNCT
fcis-14021	21	1	they	they	PRON
fcis-14021	21	2	proposed	propose	VERB
fcis-14021	21	3	a	a	DET
fcis-14021	21	4	new	new	ADJ
fcis-14021	21	5	particle	particle	NOUN
fcis-14021	21	6	filter	filter	NOUN
fcis-14021	21	7	algorithm	algorithm	NOUN
fcis-14021	21	8	for	for	ADP
fcis-14021	21	9	target	target	NOUN
fcis-14021	21	10	tracking	tracking	NOUN
fcis-14021	21	11	,	,	PUNCT
fcis-14021	21	12	which	which	PRON
fcis-14021	21	13	has	have	VERB
fcis-14021	21	14	better	well	ADJ
fcis-14021	21	15	adaptability	adaptability	NOUN
fcis-14021	21	16	to	to	ADP
fcis-14021	21	17	spatial	spatial	ADJ
fcis-14021	21	18	transformations	transformation	NOUN
fcis-14021	21	19	.	.	PUNCT
fcis-14021	22	1	kachach	kachach	PROPN
fcis-14021	22	2	et	et	PROPN
fcis-14021	22	3	al	al	PROPN
fcis-14021	22	4	.	.	PUNCT
fcis-14021	23	1	[	[	X
fcis-14021	23	2	4	4	X
fcis-14021	23	3	]	]	PUNCT
fcis-14021	23	4	proposed	propose	VERB
fcis-14021	23	5	the	the	DET
fcis-14021	23	6	directional	directional	ADJ
fcis-14021	23	7	gradient	gradient	NOUN
fcis-14021	23	8	histogram	histogram	NOUN
fcis-14021	23	9	(	(	PUNCT
fcis-14021	23	10	hog	hog	NOUN
fcis-14021	23	11	)	)	PUNCT
fcis-14021	23	12	algorithm	algorithm	NOUN
fcis-14021	23	13	,	,	PUNCT
fcis-14021	23	14	which	which	PRON
fcis-14021	23	15	uses	use	VERB
fcis-14021	23	16	sliding	slide	VERB
fcis-14021	23	17	windows	window	NOUN
fcis-14021	23	18	to	to	PART
fcis-14021	23	19	extract	extract	VERB
fcis-14021	23	20	directional	directional	ADJ
fcis-14021	23	21	gradient	gradient	NOUN
fcis-14021	23	22	features	feature	NOUN
fcis-14021	23	23	of	of	ADP
fcis-14021	23	24	pixels	pixel	NOUN
fcis-14021	23	25	in	in	ADP
fcis-14021	23	26	each	each	DET
fcis-14021	23	27	window	window	NOUN
fcis-14021	23	28	.	.	PUNCT
fcis-14021	24	1	the	the	DET
fcis-14021	24	2	development	development	NOUN
fcis-14021	24	3	of	of	ADP
fcis-14021	24	4	deep	deep	ADJ
fcis-14021	24	5	learning	learning	NOUN
fcis-14021	24	6	is	be	AUX
fcis-14021	24	7	on	on	ADP
fcis-14021	24	8	the	the	DET
fcis-14021	24	9	rise	rise	NOUN
fcis-14021	24	10	,	,	PUNCT
fcis-14021	24	11	and	and	CCONJ
fcis-14021	24	12	more	more	ADJ
fcis-14021	24	13	and	and	CCONJ
fcis-14021	24	14	more	more	ADV
fcis-14021	24	15	deep	deep	ADJ
fcis-14021	24	16	learning	learning	NOUN
fcis-14021	24	17	technologies	technology	NOUN
fcis-14021	24	18	are	be	AUX
fcis-14021	24	19	being	be	AUX
fcis-14021	24	20	applied	apply	VERB
fcis-14021	24	21	to	to	ADP
fcis-14021	24	22	construction	construction	NOUN
fcis-14021	24	23	site	site	NOUN
fcis-14021	24	24	scenarios	scenario	NOUN
fcis-14021	24	25	.	.	PUNCT
fcis-14021	25	1	for	for	ADP
fcis-14021	25	2	the	the	DET
fcis-14021	25	3	yolo	yolo	NOUN
fcis-14021	25	4	(	(	PUNCT
fcis-14021	25	5	you	you	PRON
fcis-14021	25	6	only	only	ADV
fcis-14021	25	7	look	look	VERB
fcis-14021	25	8	once	once	ADV
fcis-14021	25	9	)	)	PUNCT
fcis-14021	26	1	[	[	X
fcis-14021	26	2	6,7	6,7	NUM
fcis-14021	26	3	]	]	PUNCT
fcis-14021	26	4	series	series	NOUN
fcis-14021	26	5	of	of	ADP
fcis-14021	26	6	algorithms	algorithm	NOUN
fcis-14021	26	7	,	,	PUNCT
fcis-14021	26	8	it	it	PRON
fcis-14021	26	9	is	be	AUX
fcis-14021	26	10	the	the	DET
fcis-14021	26	11	ancestor	ancestor	NOUN
fcis-14021	26	12	of	of	ADP
fcis-14021	26	13	the	the	DET
fcis-14021	26	14	one	one	NUM
fcis-14021	26	15	stage	stage	NOUN
fcis-14021	26	16	[	[	X
fcis-14021	26	17	5	5	NUM
fcis-14021	26	18	]	]	PUNCT
fcis-14021	26	19	algorithm	algorithm	NOUN
fcis-14021	26	20	in	in	ADP
fcis-14021	26	21	object	object	NOUN
fcis-14021	26	22	detection	detection	NOUN
fcis-14021	26	23	algorithms	algorithm	NOUN
fcis-14021	26	24	.	.	PUNCT
fcis-14021	27	1	yolov5	yolov5	NOUN
fcis-14021	27	2	has	have	VERB
fcis-14021	27	3	excellent	excellent	ADJ
fcis-14021	27	4	accuracy	accuracy	NOUN
fcis-14021	27	5	and	and	CCONJ
fcis-14021	27	6	robustness	robustness	NOUN
fcis-14021	27	7	,	,	PUNCT
fcis-14021	27	8	and	and	CCONJ
fcis-14021	27	9	yolov5	yolov5	NOUN
fcis-14021	27	10	uses	use	VERB
fcis-14021	27	11	the	the	DET
fcis-14021	27	12	same	same	ADJ
fcis-14021	27	13	fully	fully	ADV
fcis-14021	27	14	connected	connect	VERB
fcis-14021	27	15	layer	layer	NOUN
fcis-14021	27	16	for	for	ADP
fcis-14021	27	17	object	object	NOUN
fcis-14021	27	18	detection	detection	NOUN
fcis-14021	27	19	and	and	CCONJ
fcis-14021	27	20	image	image	NOUN
fcis-14021	27	21	classification	classification	NOUN
fcis-14021	27	22	.	.	PUNCT
fcis-14021	28	1	unlike	unlike	ADP
fcis-14021	28	2	the	the	DET
fcis-14021	28	3	two	two	NUM
fcis-14021	28	4	stage	stage	NOUN
fcis-14021	28	5	[	[	X
fcis-14021	28	6	9	9	NUM
fcis-14021	28	7	]	]	PUNCT
fcis-14021	28	8	algorithm	algorithm	NOUN
fcis-14021	28	9	represented	represent	VERB
fcis-14021	28	10	by	by	ADP
fcis-14021	28	11	region	region	NOUN
fcis-14021	28	12	convolutional	convolutional	ADJ
fcis-14021	28	13	neural	neural	ADJ
fcis-14021	28	14	network	network	NOUN
fcis-14021	28	15	(	(	PUNCT
fcis-14021	28	16	r	r	NOUN
fcis-14021	28	17	-	-	PUNCT
fcis-14021	28	18	cnn	cnn	NOUN
fcis-14021	28	19	)	)	PUNCT
fcis-14021	29	1	[	[	X
fcis-14021	29	2	10,11,12	10,11,12	NUM
fcis-14021	29	3	]	]	PUNCT
fcis-14021	29	4	,	,	PUNCT
fcis-14021	29	5	it	it	PRON
fcis-14021	29	6	first	first	ADV
fcis-14021	29	7	searches	search	VERB
fcis-14021	29	8	for	for	ADP
fcis-14021	29	9	candidate	candidate	NOUN
fcis-14021	29	10	regions	region	NOUN
fcis-14021	29	11	,	,	PUNCT
fcis-14021	29	12	prunes	prune	VERB
fcis-14021	29	13	them	they	PRON
fcis-14021	29	14	,	,	PUNCT
fcis-14021	29	15	and	and	CCONJ
fcis-14021	29	16	then	then	ADV
fcis-14021	29	17	classifies	classify	VERB
fcis-14021	29	18	them	they	PRON
fcis-14021	29	19	using	use	VERB
fcis-14021	29	20	neural	neural	ADJ
fcis-14021	29	21	networks	network	NOUN
fcis-14021	29	22	and	and	CCONJ
fcis-14021	29	23	support	support	VERB
fcis-14021	29	24	vector	vector	NOUN
fcis-14021	29	25	machines	machine	NOUN
fcis-14021	29	26	(	(	PUNCT
fcis-14021	29	27	svm	svm	PROPN
fcis-14021	29	28	)	)	PUNCT
fcis-14021	29	29	.	.	PUNCT
fcis-14021	30	1	the	the	DET
fcis-14021	30	2	significant	significant	ADJ
fcis-14021	30	3	improvement	improvement	NOUN
fcis-14021	30	4	in	in	ADP
fcis-14021	30	5	detection	detection	NOUN
fcis-14021	30	6	speed	speed	NOUN
fcis-14021	30	7	of	of	ADP
fcis-14021	30	8	yolo	yolo	ADJ
fcis-14021	30	9	series	series	NOUN
fcis-14021	30	10	algorithms	algorithm	NOUN
fcis-14021	30	11	makes	make	VERB
fcis-14021	30	12	them	they	PRON
fcis-14021	30	13	more	more	ADV
fcis-14021	30	14	suitable	suitable	ADJ
fcis-14021	30	15	for	for	ADP
fcis-14021	30	16	applications	application	NOUN
fcis-14021	30	17	in	in	ADP
fcis-14021	30	18	scenarios	scenario	NOUN
fcis-14021	30	19	that	that	PRON
fcis-14021	30	20	require	require	VERB
fcis-14021	30	21	high	high	ADJ
fcis-14021	30	22	realtime	realtime	NOUN
fcis-14021	30	23	detection	detection	NOUN
fcis-14021	30	24	requirements	requirement	NOUN
fcis-14021	30	25	.	.	PUNCT
fcis-14021	31	1	because	because	SCONJ
fcis-14021	31	2	deep	deep	ADJ
fcis-14021	31	3	neural	neural	ADJ
fcis-14021	31	4	networks	network	NOUN
fcis-14021	31	5	extract	extract	VERB
fcis-14021	31	6	features	feature	NOUN
fcis-14021	31	7	through	through	ADP
fcis-14021	31	8	operations	operation	NOUN
fcis-14021	31	9	such	such	ADJ
fcis-14021	31	10	as	as	ADP
fcis-14021	31	11	downsampling	downsample	VERB
fcis-14021	31	12	and	and	CCONJ
fcis-14021	31	13	pooling	pooling	NOUN
fcis-14021	31	14	,	,	PUNCT
fcis-14021	31	15	when	when	SCONJ
fcis-14021	31	16	the	the	DET
fcis-14021	31	17	target	target	NOUN
fcis-14021	31	18	to	to	PART
fcis-14021	31	19	be	be	AUX
fcis-14021	31	20	detected	detect	VERB
fcis-14021	31	21	is	be	AUX
fcis-14021	31	22	small	small	ADJ
fcis-14021	31	23	,	,	PUNCT
fcis-14021	31	24	these	these	DET
fcis-14021	31	25	operations	operation	NOUN
fcis-14021	31	26	will	will	AUX
fcis-14021	31	27	result	result	VERB
fcis-14021	31	28	in	in	ADP
fcis-14021	31	29	very	very	ADV
fcis-14021	31	30	few	few	ADJ
fcis-14021	31	31	small	small	ADJ
fcis-14021	31	32	target	target	NOUN
fcis-14021	31	33	pixels	pixel	NOUN
fcis-14021	31	34	,	,	PUNCT
fcis-14021	31	35	and	and	CCONJ
fcis-14021	31	36	the	the	DET
fcis-14021	31	37	features	feature	NOUN
fcis-14021	31	38	that	that	PRON
fcis-14021	31	39	can	can	AUX
fcis-14021	31	40	be	be	AUX
fcis-14021	31	41	extracted	extract	VERB
fcis-14021	31	42	will	will	AUX
fcis-14021	31	43	become	become	VERB
fcis-14021	31	44	very	very	ADV
fcis-14021	31	45	limited	limited	ADJ
fcis-14021	31	46	,	,	PUNCT
fcis-14021	31	47	resulting	result	VERB
fcis-14021	31	48	in	in	ADP
fcis-14021	31	49	a	a	DET
fcis-14021	31	50	high	high	ADJ
fcis-14021	31	51	missed	miss	VERB
fcis-14021	31	52	detection	detection	NOUN
fcis-14021	31	53	rate	rate	NOUN
fcis-14021	31	54	and	and	CCONJ
fcis-14021	31	55	low	low	ADJ
fcis-14021	31	56	detection	detection	NOUN
fcis-14021	31	57	efficiency	efficiency	NOUN
fcis-14021	31	58	when	when	SCONJ
fcis-14021	31	59	detecting	detect	VERB
fcis-14021	31	60	small	small	ADJ
fcis-14021	31	61	targets	target	NOUN
fcis-14021	31	62	.	.	PUNCT
fcis-14021	32	1	in	in	ADP
fcis-14021	32	2	summary	summary	NOUN
fcis-14021	32	3	,	,	PUNCT
fcis-14021	32	4	based	base	VERB
fcis-14021	32	5	on	on	ADP
fcis-14021	32	6	yolov5s	yolov5s	PROPN
fcis-14021	32	7	,	,	PUNCT
fcis-14021	32	8	this	this	DET
fcis-14021	32	9	article	article	NOUN
fcis-14021	32	10	adds	add	VERB
fcis-14021	32	11	a	a	DET
fcis-14021	32	12	small	small	ADJ
fcis-14021	32	13	target	target	NOUN
fcis-14021	32	14	detection	detection	NOUN
fcis-14021	32	15	layer	layer	NOUN
fcis-14021	33	1	[	[	X
fcis-14021	33	2	14	14	NUM
fcis-14021	33	3	]	]	PUNCT
fcis-14021	33	4	in	in	ADP
fcis-14021	33	5	the	the	DET
fcis-14021	33	6	construction	construction	NOUN
fcis-14021	33	7	of	of	ADP
fcis-14021	33	8	the	the	DET
fcis-14021	33	9	detection	detection	NOUN
fcis-14021	33	10	layer	layer	NOUN
fcis-14021	33	11	,	,	PUNCT
fcis-14021	33	12	and	and	CCONJ
fcis-14021	33	13	adds	add	VERB
fcis-14021	33	14	an	an	DET
fcis-14021	33	15	attention	attention	NOUN
fcis-14021	33	16	mechanism	mechanism	NOUN
fcis-14021	33	17	network	network	NOUN
fcis-14021	33	18	ca	ca	NOUN
fcis-14021	33	19	[	[	X
fcis-14021	33	20	15	15	NUM
fcis-14021	33	21	]	]	PUNCT
fcis-14021	33	22	to	to	PART
fcis-14021	33	23	solve	solve	VERB
fcis-14021	33	24	the	the	DET
fcis-14021	33	25	problem	problem	NOUN
fcis-14021	33	26	of	of	ADP
fcis-14021	33	27	high	high	ADJ
fcis-14021	33	28	detection	detection	NOUN
fcis-14021	33	29	rate	rate	NOUN
fcis-14021	33	30	of	of	ADP
fcis-14021	33	31	small	small	ADJ
fcis-14021	33	32	target	target	NOUN
fcis-14021	33	33	escape	escape	NOUN
fcis-14021	33	34	ladders	ladder	NOUN
fcis-14021	33	35	.	.	PUNCT
fcis-14021	34	1	when	when	SCONJ
fcis-14021	34	2	constructing	construct	VERB
fcis-14021	34	3	the	the	DET
fcis-14021	34	4	dataset	dataset	NOUN
fcis-14021	34	5	,	,	PUNCT
fcis-14021	34	6	a	a	DET
fcis-14021	34	7	dataset	dataset	NOUN
fcis-14021	34	8	containing	contain	VERB
fcis-14021	34	9	multiple	multiple	ADJ
fcis-14021	34	10	escape	escape	NOUN
fcis-14021	34	11	ladder	ladder	NOUN
fcis-14021	34	12	states	state	NOUN
fcis-14021	34	13	and	and	CCONJ
fcis-14021	34	14	environmental	environmental	ADJ
fcis-14021	34	15	conditions	condition	NOUN
fcis-14021	34	16	was	be	AUX
fcis-14021	34	17	constructed	construct	VERB
fcis-14021	34	18	,	,	PUNCT
fcis-14021	34	19	and	and	CCONJ
fcis-14021	34	20	standard	standard	ADJ
fcis-14021	34	21	evaluation	evaluation	NOUN
fcis-14021	34	22	indicators	indicator	NOUN
fcis-14021	34	23	such	such	ADJ
fcis-14021	34	24	as	as	ADP
fcis-14021	34	25	accuracy	accuracy	NOUN
fcis-14021	34	26	,	,	PUNCT
fcis-14021	34	27	recall	recall	NOUN
fcis-14021	34	28	,	,	PUNCT
fcis-14021	34	29	and	and	CCONJ
fcis-14021	34	30	map	map	NOUN
fcis-14021	34	31	were	be	AUX
fcis-14021	34	32	used	use	VERB
fcis-14021	34	33	to	to	PART
fcis-14021	34	34	evaluate	evaluate	VERB
fcis-14021	34	35	the	the	DET
fcis-14021	34	36	algorithm	algorithm	NOUN
fcis-14021	34	37	proposed	propose	VERB
fcis-14021	34	38	in	in	ADP
fcis-14021	34	39	this	this	DET
fcis-14021	34	40	paper	paper	NOUN
fcis-14021	34	41	.	.	PUNCT
fcis-14021	35	1	1.1	1.1	NUM
fcis-14021	35	2	.	.	PUNCT
fcis-14021	36	1	yolov5s	yolov5s	NOUN
fcis-14021	36	2	algorithm	algorithm	PROPN
fcis-14021	36	3	principle	principle	PROPN
fcis-14021	36	4	yolov5s	yolov5s	PROPN
fcis-14021	36	5	algorithm	algorithm	NOUN
fcis-14021	36	6	is	be	AUX
fcis-14021	36	7	an	an	DET
fcis-14021	36	8	object	object	NOUN
fcis-14021	36	9	detection	detection	NOUN
fcis-14021	36	10	algorithm	algorithm	NOUN
fcis-14021	36	11	that	that	PRON
fcis-14021	36	12	can	can	AUX
fcis-14021	36	13	detect	detect	VERB
fcis-14021	36	14	and	and	CCONJ
fcis-14021	36	15	locate	locate	ADJ
fcis-14021	36	16	objects	object	NOUN
fcis-14021	36	17	in	in	ADP
fcis-14021	36	18	images	image	NOUN
fcis-14021	36	19	in	in	ADP
fcis-14021	36	20	real	real	ADJ
fcis-14021	36	21	-	-	PUNCT
fcis-14021	36	22	time	time	NOUN
fcis-14021	36	23	.	.	PUNCT
fcis-14021	37	1	the	the	DET
fcis-14021	37	2	yolov5s	yolov5s	PROPN
fcis-14021	37	3	algorithm	algorithm	NOUN
fcis-14021	37	4	transforms	transform	VERB
fcis-14021	37	5	the	the	DET
fcis-14021	37	6	object	object	NOUN
fcis-14021	37	7	detection	detection	NOUN
fcis-14021	37	8	problem	problem	NOUN
fcis-14021	37	9	into	into	ADP
fcis-14021	37	10	a	a	DET
fcis-14021	37	11	regression	regression	NOUN
fcis-14021	37	12	problem	problem	NOUN
fcis-14021	37	13	,	,	PUNCT
fcis-14021	37	14	dividing	divide	VERB
fcis-14021	37	15	the	the	DET
fcis-14021	37	16	image	image	NOUN
fcis-14021	37	17	into	into	ADP
fcis-14021	37	18	grids	grid	NOUN
fcis-14021	37	19	using	use	VERB
fcis-14021	37	20	a	a	DET
fcis-14021	37	21	neural	neural	ADJ
fcis-14021	37	22	network	network	NOUN
fcis-14021	37	23	cnn	cnn	PROPN
fcis-14021	37	24	and	and	CCONJ
fcis-14021	37	25	predicting	predict	VERB
fcis-14021	37	26	each	each	DET
fcis-14021	37	27	grid	grid	NOUN
fcis-14021	37	28	to	to	PART
fcis-14021	37	29	determine	determine	VERB
fcis-14021	37	30	the	the	DET
fcis-14021	37	31	position	position	NOUN
fcis-14021	37	32	and	and	CCONJ
fcis-14021	37	33	category	category	NOUN
fcis-14021	37	34	of	of	ADP
fcis-14021	37	35	objects	object	NOUN
fcis-14021	37	36	in	in	ADP
fcis-14021	37	37	each	each	DET
fcis-14021	37	38	grid	grid	NOUN
fcis-14021	37	39	.	.	PUNCT
fcis-14021	38	1	then	then	ADV
fcis-14021	38	2	,	,	PUNCT
fcis-14021	38	3	the	the	DET
fcis-14021	38	4	best	good	ADJ
fcis-14021	38	5	candidate	candidate	NOUN
fcis-14021	38	6	box	box	NOUN
fcis-14021	38	7	is	be	AUX
fcis-14021	38	8	matched	match	VERB
fcis-14021	38	9	using	use	VERB
fcis-14021	38	10	non	non	ADJ
fcis-14021	38	11	-	-	ADJ
fcis-14021	38	12	maximum	maximum	ADJ
fcis-14021	38	13	129	129	NUM
fcis-14021	38	14	suppression	suppression	NOUN
fcis-14021	38	15	(	(	PUNCT
fcis-14021	38	16	nms	nms	NOUN
fcis-14021	38	17	)	)	PUNCT
fcis-14021	39	1	[	[	X
fcis-14021	39	2	15	15	NUM
fcis-14021	39	3	]	]	PUNCT
fcis-14021	39	4	.	.	PUNCT
fcis-14021	40	1	at	at	ADP
fcis-14021	40	2	the	the	DET
fcis-14021	40	3	same	same	ADJ
fcis-14021	40	4	time	time	NOUN
fcis-14021	40	5	,	,	PUNCT
fcis-14021	40	6	the	the	DET
fcis-14021	40	7	yolov5s	yolov5s	PROPN
fcis-14021	40	8	algorithm	algorithm	NOUN
fcis-14021	40	9	also	also	ADV
fcis-14021	40	10	adopts	adopt	VERB
fcis-14021	40	11	a	a	DET
fcis-14021	40	12	feature	feature	NOUN
fcis-14021	40	13	pyramid	pyramid	NOUN
fcis-14021	40	14	network	network	NOUN
fcis-14021	40	15	to	to	PART
fcis-14021	40	16	improve	improve	VERB
fcis-14021	40	17	the	the	DET
fcis-14021	40	18	accuracy	accuracy	NOUN
fcis-14021	40	19	and	and	CCONJ
fcis-14021	40	20	speed	speed	NOUN
fcis-14021	40	21	of	of	ADP
fcis-14021	40	22	the	the	DET
fcis-14021	40	23	algorithm	algorithm	NOUN
fcis-14021	40	24	.	.	PUNCT
fcis-14021	41	1	specifically	specifically	ADV
fcis-14021	41	2	,	,	PUNCT
fcis-14021	41	3	the	the	DET
fcis-14021	41	4	yolov5s	yolov5s	PROPN
fcis-14021	41	5	algorithm	algorithm	NOUN
fcis-14021	41	6	first	first	ADV
fcis-14021	41	7	divides	divide	VERB
fcis-14021	41	8	the	the	DET
fcis-14021	41	9	input	input	NOUN
fcis-14021	41	10	image	image	NOUN
fcis-14021	41	11	into	into	ADP
fcis-14021	41	12	sxs	sxs	PROPN
fcis-14021	41	13	grids	grid	NOUN
fcis-14021	41	14	,	,	PUNCT
fcis-14021	41	15	with	with	ADP
fcis-14021	41	16	each	each	DET
fcis-14021	41	17	grid	grid	NOUN
fcis-14021	41	18	predicting	predict	VERB
fcis-14021	41	19	b	b	NOUN
fcis-14021	41	20	bounding	bounding	NOUN
fcis-14021	41	21	boxes	box	NOUN
fcis-14021	41	22	,	,	PUNCT
fcis-14021	41	23	where	where	SCONJ
fcis-14021	41	24	each	each	DET
fcis-14021	41	25	bounding	bounding	NOUN
fcis-14021	41	26	box	box	NOUN
fcis-14021	41	27	predicts	predict	VERB
fcis-14021	41	28	k	k	PROPN
fcis-14021	41	29	coordinates	coordinate	NOUN
fcis-14021	41	30	and	and	CCONJ
fcis-14021	41	31	k	k	PROPN
fcis-14021	41	32	confidence	confidence	NOUN
fcis-14021	41	33	scores	score	NOUN
fcis-14021	41	34	.	.	PUNCT
fcis-14021	42	1	among	among	ADP
fcis-14021	42	2	them	they	PRON
fcis-14021	42	3	,	,	PUNCT
fcis-14021	42	4	s	s	PROPN
fcis-14021	42	5	,	,	PUNCT
fcis-14021	42	6	b	b	NOUN
fcis-14021	42	7	,	,	PUNCT
fcis-14021	42	8	and	and	CCONJ
fcis-14021	42	9	k	k	PROPN
fcis-14021	42	10	are	be	AUX
fcis-14021	42	11	hyperparameters	hyperparameter	NOUN
fcis-14021	42	12	of	of	ADP
fcis-14021	42	13	the	the	DET
fcis-14021	42	14	yolov5s	yolov5s	PROPN
fcis-14021	42	15	algorithm	algorithm	NOUN
fcis-14021	42	16	,	,	PUNCT
fcis-14021	42	17	which	which	PRON
fcis-14021	42	18	can	can	AUX
fcis-14021	42	19	be	be	AUX
fcis-14021	42	20	adjusted	adjust	VERB
fcis-14021	42	21	according	accord	VERB
fcis-14021	42	22	to	to	ADP
fcis-14021	42	23	specific	specific	ADJ
fcis-14021	42	24	problems	problem	NOUN
fcis-14021	42	25	.	.	PUNCT
fcis-14021	43	1	the	the	DET
fcis-14021	43	2	feature	feature	NOUN
fcis-14021	43	3	pyramid	pyramid	NOUN
fcis-14021	43	4	network	network	NOUN
fcis-14021	43	5	is	be	AUX
fcis-14021	43	6	an	an	DET
fcis-14021	43	7	important	important	ADJ
fcis-14021	43	8	component	component	NOUN
fcis-14021	43	9	of	of	ADP
fcis-14021	43	10	the	the	DET
fcis-14021	43	11	yolov5s	yolov5s	PROPN
fcis-14021	43	12	algorithm	algorithm	NOUN
fcis-14021	43	13	,	,	PUNCT
fcis-14021	43	14	which	which	PRON
fcis-14021	43	15	includes	include	VERB
fcis-14021	43	16	multiple	multiple	ADJ
fcis-14021	43	17	convolutional	convolutional	ADJ
fcis-14021	43	18	and	and	CCONJ
fcis-14021	43	19	pooling	pool	VERB
fcis-14021	43	20	layers	layer	NOUN
fcis-14021	43	21	and	and	CCONJ
fcis-14021	43	22	can	can	AUX
fcis-14021	43	23	extract	extract	VERB
fcis-14021	43	24	features	feature	NOUN
fcis-14021	43	25	of	of	ADP
fcis-14021	43	26	different	different	ADJ
fcis-14021	43	27	scales	scale	NOUN
fcis-14021	43	28	from	from	ADP
fcis-14021	43	29	input	input	NOUN
fcis-14021	43	30	images	image	NOUN
fcis-14021	43	31	.	.	PUNCT
fcis-14021	44	1	these	these	DET
fcis-14021	44	2	features	feature	NOUN
fcis-14021	44	3	are	be	AUX
fcis-14021	44	4	transmitted	transmit	VERB
fcis-14021	44	5	to	to	ADP
fcis-14021	44	6	different	different	ADJ
fcis-14021	44	7	detection	detection	NOUN
fcis-14021	44	8	layers	layer	NOUN
fcis-14021	44	9	,	,	PUNCT
fcis-14021	44	10	each	each	PRON
fcis-14021	44	11	of	of	ADP
fcis-14021	44	12	which	which	PRON
fcis-14021	44	13	outputs	output	VERB
fcis-14021	44	14	a	a	DET
fcis-14021	44	15	set	set	NOUN
fcis-14021	44	16	of	of	ADP
fcis-14021	44	17	bounding	bound	VERB
fcis-14021	44	18	boxes	box	NOUN
fcis-14021	44	19	and	and	CCONJ
fcis-14021	44	20	corresponding	correspond	VERB
fcis-14021	44	21	category	category	NOUN
fcis-14021	44	22	probabilities	probability	NOUN
fcis-14021	44	23	.	.	PUNCT
fcis-14021	45	1	yolov5	yolov5	NOUN
fcis-14021	45	2	(	(	PUNCT
fcis-14021	45	3	you	you	PRON
fcis-14021	45	4	only	only	ADV
fcis-14021	45	5	look	look	VERB
fcis-14021	45	6	once	once	ADV
fcis-14021	45	7	version	version	NOUN
fcis-14021	45	8	5	5	NUM
fcis-14021	45	9	)	)	PUNCT
fcis-14021	45	10	is	be	AUX
fcis-14021	45	11	a	a	DET
fcis-14021	45	12	single	single	ADJ
fcis-14021	45	13	stage	stage	NOUN
fcis-14021	45	14	object	object	NOUN
fcis-14021	45	15	detection	detection	NOUN
fcis-14021	45	16	algorithm	algorithm	NOUN
fcis-14021	45	17	.	.	PUNCT
fcis-14021	46	1	it	it	PRON
fcis-14021	46	2	has	have	AUX
fcis-14021	46	3	added	add	VERB
fcis-14021	46	4	some	some	DET
fcis-14021	46	5	new	new	ADJ
fcis-14021	46	6	improvement	improvement	NOUN
fcis-14021	46	7	ideas	idea	NOUN
fcis-14021	46	8	on	on	ADP
fcis-14021	46	9	the	the	DET
fcis-14021	46	10	basis	basis	NOUN
fcis-14021	46	11	of	of	ADP
fcis-14021	46	12	yolov4	yolov4	PROPN
fcis-14021	46	13	,	,	PUNCT
fcis-14021	46	14	which	which	PRON
fcis-14021	46	15	has	have	AUX
fcis-14021	46	16	greatly	greatly	ADV
fcis-14021	46	17	improved	improve	VERB
fcis-14021	46	18	its	its	PRON
fcis-14021	46	19	speed	speed	NOUN
fcis-14021	46	20	and	and	CCONJ
fcis-14021	46	21	accuracy	accuracy	NOUN
fcis-14021	46	22	.	.	PUNCT
fcis-14021	47	1	compared	compare	VERB
fcis-14021	47	2	to	to	ADP
fcis-14021	47	3	the	the	DET
fcis-14021	47	4	previous	previous	ADJ
fcis-14021	47	5	yolo	yolo	ADJ
fcis-14021	47	6	version	version	NOUN
fcis-14021	47	7	,	,	PUNCT
fcis-14021	47	8	yolov5	yolov5	NOUN
fcis-14021	47	9	adopts	adopt	VERB
fcis-14021	47	10	more	more	ADJ
fcis-14021	47	11	data	datum	NOUN
fcis-14021	47	12	augmentation	augmentation	NOUN
fcis-14021	47	13	methods	method	NOUN
fcis-14021	47	14	during	during	ADP
fcis-14021	47	15	the	the	DET
fcis-14021	47	16	training	training	NOUN
fcis-14021	47	17	process	process	NOUN
fcis-14021	47	18	,	,	PUNCT
fcis-14021	47	19	such	such	ADJ
fcis-14021	47	20	as	as	ADP
fcis-14021	47	21	mosaic	mosaic	ADJ
fcis-14021	47	22	data	datum	NOUN
fcis-14021	47	23	augmentation	augmentation	NOUN
fcis-14021	47	24	,	,	PUNCT
fcis-14021	47	25	adaptive	adaptive	ADJ
fcis-14021	47	26	anchor	anchor	NOUN
fcis-14021	47	27	box	box	NOUN
fcis-14021	47	28	calculation	calculation	NOUN
fcis-14021	47	29	,	,	PUNCT
fcis-14021	47	30	adaptive	adaptive	ADJ
fcis-14021	47	31	image	image	NOUN
fcis-14021	47	32	scaling	scaling	NOUN
fcis-14021	47	33	,	,	PUNCT
fcis-14021	47	34	etc	etc	X
fcis-14021	47	35	.	.	X
fcis-14021	48	1	in	in	ADP
fcis-14021	48	2	addition	addition	NOUN
fcis-14021	48	3	,	,	PUNCT
fcis-14021	48	4	yolov5	yolov5	NOUN
fcis-14021	48	5	also	also	ADV
fcis-14021	48	6	incorporates	incorporate	VERB
fcis-14021	48	7	some	some	DET
fcis-14021	48	8	new	new	ADJ
fcis-14021	48	9	ideas	idea	NOUN
fcis-14021	48	10	from	from	ADP
fcis-14021	48	11	other	other	ADJ
fcis-14021	48	12	detection	detection	NOUN
fcis-14021	48	13	algorithms	algorithm	NOUN
fcis-14021	48	14	,	,	PUNCT
fcis-14021	48	15	such	such	ADJ
fcis-14021	48	16	as	as	ADP
fcis-14021	48	17	focus	focus	NOUN
fcis-14021	48	18	structure	structure	NOUN
fcis-14021	48	19	,	,	PUNCT
fcis-14021	48	20	csp	csp	PROPN
fcis-14021	48	21	structure	structure	NOUN
fcis-14021	48	22	,	,	PUNCT
fcis-14021	48	23	fpn+pan	fpn+pan	NOUN
fcis-14021	48	24	structure	structure	NOUN
fcis-14021	48	25	,	,	PUNCT
fcis-14021	48	26	etc	etc	X
fcis-14021	48	27	.	.	X
fcis-14021	48	28	the	the	DET
fcis-14021	48	29	anchor	anchor	NOUN
fcis-14021	48	30	box	box	NOUN
fcis-14021	48	31	mechanism	mechanism	NOUN
fcis-14021	48	32	of	of	ADP
fcis-14021	48	33	its	its	PRON
fcis-14021	48	34	output	output	NOUN
fcis-14021	48	35	layer	layer	NOUN
fcis-14021	48	36	is	be	AUX
fcis-14021	48	37	the	the	DET
fcis-14021	48	38	same	same	ADJ
fcis-14021	48	39	as	as	ADP
fcis-14021	48	40	yolov4	yolov4	PROPN
fcis-14021	48	41	,	,	PUNCT
fcis-14021	48	42	but	but	CCONJ
fcis-14021	48	43	it	it	PRON
fcis-14021	48	44	improves	improve	VERB
fcis-14021	48	45	the	the	DET
fcis-14021	48	46	loss	loss	NOUN
fcis-14021	48	47	function	function	NOUN
fcis-14021	48	48	giou	giou	NOUN
fcis-14021	48	49	during	during	ADP
fcis-14021	48	50	training	training	NOUN
fcis-14021	48	51	_	_	PRON
fcis-14021	48	52	loss	loss	NOUN
fcis-14021	48	53	,	,	PUNCT
fcis-14021	48	54	as	as	ADV
fcis-14021	48	55	well	well	ADV
fcis-14021	48	56	as	as	ADP
fcis-14021	48	57	diou	diou	NOUN
fcis-14021	48	58	for	for	ADP
fcis-14021	48	59	prediction	prediction	NOUN
fcis-14021	48	60	box	box	NOUN
fcis-14021	48	61	filtering	filtering	NOUN
fcis-14021	48	62	_	_	PROPN
fcis-14021	49	1	nms	nms	PROPN
fcis-14021	49	2	.	.	PUNCT
fcis-14021	50	1	during	during	ADP
fcis-14021	50	2	the	the	DET
fcis-14021	50	3	algorithm	algorithm	NOUN
fcis-14021	50	4	's	's	PART
fcis-14021	50	5	detection	detection	NOUN
fcis-14021	50	6	,	,	PUNCT
fcis-14021	50	7	the	the	DET
fcis-14021	50	8	images	image	NOUN
fcis-14021	50	9	input	input	NOUN
fcis-14021	50	10	to	to	ADP
fcis-14021	50	11	the	the	DET
fcis-14021	50	12	yolov5s	yolov5s	PROPN
fcis-14021	50	13	network	network	NOUN
fcis-14021	50	14	are	be	AUX
fcis-14021	50	15	preprocessed	preprocesse	VERB
fcis-14021	50	16	,	,	PUNCT
fcis-14021	50	17	including	include	VERB
fcis-14021	50	18	scaling	scaling	NOUN
fcis-14021	50	19	,	,	PUNCT
fcis-14021	50	20	zeroing	zeroing	NOUN
fcis-14021	50	21	,	,	PUNCT
fcis-14021	50	22	and	and	CCONJ
fcis-14021	50	23	other	other	ADJ
fcis-14021	50	24	operations	operation	NOUN
fcis-14021	50	25	,	,	PUNCT
fcis-14021	50	26	so	so	SCONJ
fcis-14021	50	27	that	that	SCONJ
fcis-14021	50	28	the	the	DET
fcis-14021	50	29	network	network	NOUN
fcis-14021	50	30	can	can	AUX
fcis-14021	50	31	process	process	VERB
fcis-14021	50	32	the	the	DET
fcis-14021	50	33	images	image	NOUN
fcis-14021	50	34	normally	normally	ADV
fcis-14021	50	35	.	.	PUNCT
fcis-14021	51	1	after	after	ADP
fcis-14021	51	2	input	input	NOUN
fcis-14021	51	3	to	to	ADP
fcis-14021	51	4	the	the	DET
fcis-14021	51	5	network	network	NOUN
fcis-14021	51	6	,	,	PUNCT
fcis-14021	51	7	multiple	multiple	ADJ
fcis-14021	51	8	predicted	predict	VERB
fcis-14021	51	9	boundary	boundary	ADJ
fcis-14021	51	10	boxes	box	NOUN
fcis-14021	51	11	and	and	CCONJ
fcis-14021	51	12	type	type	NOUN
fcis-14021	51	13	results	result	NOUN
fcis-14021	51	14	are	be	AUX
fcis-14021	51	15	output	output	ADJ
fcis-14021	51	16	.	.	PUNCT
fcis-14021	52	1	at	at	ADP
fcis-14021	52	2	this	this	DET
fcis-14021	52	3	time	time	NOUN
fcis-14021	52	4	,	,	PUNCT
fcis-14021	52	5	yolov5	yolov5	NOUN
fcis-14021	52	6	performs	perform	VERB
fcis-14021	52	7	nms	nms	PROPN
fcis-14021	52	8	(	(	PUNCT
fcis-14021	52	9	non	non	X
fcis-14021	52	10	maximum	maximum	PROPN
fcis-14021	52	11	suppression	suppression	NOUN
fcis-14021	52	12	)	)	PUNCT
fcis-14021	52	13	post	post	NOUN
fcis-14021	52	14	-	-	ADJ
fcis-14021	52	15	processing	processing	NOUN
fcis-14021	52	16	on	on	ADP
fcis-14021	52	17	the	the	DET
fcis-14021	52	18	output	output	NOUN
fcis-14021	52	19	results	result	NOUN
fcis-14021	52	20	,	,	PUNCT
fcis-14021	52	21	selecting	select	VERB
fcis-14021	52	22	the	the	DET
fcis-14021	52	23	most	most	ADJ
fcis-14021	52	24	matching	matching	NOUN
fcis-14021	52	25	option	option	NOUN
fcis-14021	52	26	box	box	NOUN
fcis-14021	52	27	as	as	SCONJ
fcis-14021	52	28	the	the	DET
fcis-14021	52	29	final	final	ADJ
fcis-14021	52	30	detection	detection	NOUN
fcis-14021	52	31	result	result	VERB
fcis-14021	52	32	output	output	NOUN
fcis-14021	52	33	.	.	PUNCT
fcis-14021	53	1	cbs	cbs	PROPN
fcis-14021	53	2	cbs	cbs	PROPN
fcis-14021	53	3	c3	c3	PROPN
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fcis-14021	53	10	x	x	NOUN
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fcis-14021	53	12	160	160	NUM
fcis-14021	53	13	cbs	cbs	PROPN
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fcis-14021	53	16	x320x	x320x	PROPN
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fcis-14021	53	21	x	x	NOUN
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fcis-14021	53	24	 	 	SPACE
fcis-14021	53	25	x	x	NOUN
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fcis-14021	53	35	x	x	NOUN
fcis-14021	53	36	 	 	SPACE
fcis-14021	53	37	80	80	NUM
fcis-14021	53	38	cbs	cbs	PROPN
fcis-14021	53	39	256	256	NUM
fcis-14021	53	40	 	 	SPACE
fcis-14021	53	41	x	x	NOUN
fcis-14021	53	42	 	 	SPACE
fcis-14021	53	43	80	80	NUM
fcis-14021	53	44	 	 	SPACE
fcis-14021	53	45	x	x	NOUN
fcis-14021	53	46	 	 	SPACE
fcis-14021	53	47	80	80	NUM
fcis-14021	53	48	c3	c3	PROPN
fcis-14021	53	49	512	512	NUM
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fcis-14021	53	51	x	x	NOUN
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fcis-14021	53	53	40	40	NUM
fcis-14021	53	54	 	 	SPACE
fcis-14021	53	55	x40	x40	NOUN
fcis-14021	54	1	cbs	cbs	PROPN
fcis-14021	54	2	c3	c3	PROPN
fcis-14021	54	3	sppf	sppf	PROPN
fcis-14021	54	4	cbs	cbs	PROPN
fcis-14021	54	5	up	up	ADP
fcis-14021	54	6	sample	sample	NOUN
fcis-14021	54	7	concat	concat	NOUN
fcis-14021	54	8	c3	c3	X
fcis-14021	54	9	cbs	cbs	PROPN
fcis-14021	54	10	up	up	ADP
fcis-14021	54	11	sample	sample	NOUN
fcis-14021	54	12	concat	concat	NOUN
fcis-14021	54	13	c3	c3	PROPN
fcis-14021	54	14	cbs	cbs	PROPN
fcis-14021	54	15	concat	concat	PROPN
fcis-14021	54	16	c3	c3	PROPN
fcis-14021	54	17	cbs	cbs	PROPN
fcis-14021	54	18	concat	concat	PROPN
fcis-14021	54	19	c3	c3	PROPN
fcis-14021	54	20	conv	conv	PROPN
fcis-14021	54	21	conv	conv	PROPN
fcis-14021	54	22	conv	conv	PROPN
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fcis-14021	54	24	 	 	SPACE
fcis-14021	54	25	x	x	NOUN
fcis-14021	54	26	 	 	SPACE
fcis-14021	54	27	80	80	NUM
fcis-14021	54	28	 	 	SPACE
fcis-14021	54	29	x	x	NOUN
fcis-14021	54	30	 	 	SPACE
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fcis-14021	54	33	 	 	SPACE
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fcis-14021	54	43	x	x	NOUN
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fcis-14021	54	46	 	 	SPACE
fcis-14021	54	47	x	x	NOUN
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fcis-14021	54	51	x	x	SYM
fcis-14021	54	52	 	 	SPACE
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fcis-14021	54	54	 	 	SPACE
fcis-14021	54	55	x	x	NOUN
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fcis-14021	54	193	x	x	NOUN
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fcis-14021	54	196	 	 	SPACE
fcis-14021	54	197	x40	x40	NOUN
fcis-14021	54	198	cbs	cbs	PROPN
fcis-14021	54	199	=	=	PROPN
fcis-14021	55	1	conv	conv	PROPN
fcis-14021	55	2	bn	bn	PROPN
fcis-14021	55	3	silu	silu	NOUN
fcis-14021	55	4	c3	c3	X
fcis-14021	55	5	=	=	PUNCT
fcis-14021	55	6	cbs	cbs	PROPN
fcis-14021	55	7	bottleneck	bottleneck	NOUN
fcis-14021	55	8	cbs	cbs	PROPN
fcis-14021	55	9	concat	concat	PROPN
fcis-14021	55	10	cbs	cbs	PROPN
fcis-14021	55	11	bottleneck	bottleneck	NOUN
fcis-14021	55	12	cbs=	cbs=	PROPN
fcis-14021	55	13	cbs	cbs	PROPN
fcis-14021	55	14	add	add	VERB
fcis-14021	55	15	concat	concat	NOUN
fcis-14021	55	16	cbssppf	cbssppf	NOUN
fcis-14021	55	17	cbs	cbs	PROPN
fcis-14021	55	18	maxpool	maxpool	PROPN
fcis-14021	55	19	maxpool	maxpool	PROPN
fcis-14021	55	20	maxpool	maxpool	PROPN
fcis-14021	55	21	fig	fig	PROPN
fcis-14021	55	22	1	1	NUM
fcis-14021	55	23	.	.	PUNCT
fcis-14021	56	1	yolov5s	yolov5s	NOUN
fcis-14021	56	2	network	network	NOUN
fcis-14021	56	3	structure	structure	NOUN
fcis-14021	56	4	2	2	NUM
fcis-14021	56	5	.	.	PUNCT
fcis-14021	56	6	algorithm	algorithm	NOUN
fcis-14021	56	7	improvement	improvement	NOUN
fcis-14021	56	8	this	this	DET
fcis-14021	56	9	study	study	NOUN
fcis-14021	56	10	introduced	introduce	VERB
fcis-14021	56	11	the	the	DET
fcis-14021	56	12	ca	ca	NOUN
fcis-14021	56	13	attention	attention	NOUN
fcis-14021	56	14	network	network	NOUN
fcis-14021	56	15	module	module	NOUN
fcis-14021	56	16	and	and	CCONJ
fcis-14021	56	17	made	make	VERB
fcis-14021	56	18	some	some	DET
fcis-14021	56	19	adjustments	adjustment	NOUN
fcis-14021	56	20	to	to	ADP
fcis-14021	56	21	the	the	DET
fcis-14021	56	22	design	design	NOUN
fcis-14021	56	23	and	and	CCONJ
fcis-14021	56	24	output	output	NOUN
fcis-14021	56	25	structure	structure	NOUN
fcis-14021	56	26	of	of	ADP
fcis-14021	56	27	the	the	DET
fcis-14021	56	28	backbone	backbone	NOUN
fcis-14021	56	29	network	network	NOUN
fcis-14021	56	30	,	,	PUNCT
fcis-14021	56	31	making	make	VERB
fcis-14021	56	32	the	the	DET
fcis-14021	56	33	network	network	NOUN
fcis-14021	56	34	's	's	PART
fcis-14021	56	35	perception	perception	NOUN
fcis-14021	56	36	of	of	ADP
fcis-14021	56	37	low	low	ADJ
fcis-14021	56	38	-	-	PUNCT
fcis-14021	56	39	level	level	NOUN
fcis-14021	56	40	features	feature	VERB
fcis-14021	56	41	more	more	ADV
fcis-14021	56	42	apparent	apparent	ADJ
fcis-14021	56	43	without	without	ADP
fcis-14021	56	44	increasing	increase	VERB
fcis-14021	56	45	computational	computational	ADJ
fcis-14021	56	46	parameters	parameter	NOUN
fcis-14021	56	47	.	.	PUNCT
fcis-14021	57	1	the	the	DET
fcis-14021	57	2	overall	overall	ADJ
fcis-14021	57	3	adaptation	adaptation	NOUN
fcis-14021	57	4	improvement	improvement	NOUN
fcis-14021	57	5	is	be	AUX
fcis-14021	57	6	shown	show	VERB
fcis-14021	57	7	in	in	ADP
fcis-14021	57	8	the	the	DET
fcis-14021	57	9	following	follow	VERB
fcis-14021	57	10	figure	figure	NOUN
fcis-14021	57	11	.	.	PUNCT
fcis-14021	58	1	2.1	2.1	NUM
fcis-14021	58	2	.	.	PUNCT
fcis-14021	59	1	network	network	NOUN
fcis-14021	59	2	structure	structure	NOUN
fcis-14021	59	3	optimization	optimization	NOUN
fcis-14021	59	4	the	the	DET
fcis-14021	59	5	yolov5s	yolov5s	PROPN
fcis-14021	59	6	algorithm	algorithm	NOUN
fcis-14021	59	7	performs	perform	VERB
fcis-14021	59	8	multiple	multiple	ADJ
fcis-14021	59	9	downsampling	downsample	VERB
fcis-14021	59	10	during	during	ADP
fcis-14021	59	11	feature	feature	NOUN
fcis-14021	59	12	extraction	extraction	NOUN
fcis-14021	59	13	,	,	PUNCT
fcis-14021	59	14	and	and	CCONJ
fcis-14021	59	15	each	each	PRON
fcis-14021	59	16	downsampling	downsample	VERB
fcis-14021	59	17	results	result	NOUN
fcis-14021	59	18	in	in	ADP
fcis-14021	59	19	the	the	DET
fcis-14021	59	20	loss	loss	NOUN
fcis-14021	59	21	of	of	ADP
fcis-14021	59	22	pixel	pixel	PROPN
fcis-14021	59	23	information	information	NOUN
fcis-14021	59	24	.	.	PUNCT
fcis-14021	60	1	this	this	PRON
fcis-14021	60	2	can	can	AUX
fcis-14021	60	3	lead	lead	VERB
fcis-14021	60	4	to	to	ADP
fcis-14021	60	5	poor	poor	ADJ
fcis-14021	60	6	feature	feature	NOUN
fcis-14021	60	7	extraction	extraction	NOUN
fcis-14021	60	8	performance	performance	NOUN
fcis-14021	60	9	when	when	SCONJ
fcis-14021	60	10	detecting	detect	VERB
fcis-14021	60	11	small	small	ADJ
fcis-14021	60	12	targets	target	NOUN
fcis-14021	60	13	,	,	PUNCT
fcis-14021	60	14	thereby	thereby	ADV
fcis-14021	60	15	affecting	affect	VERB
fcis-14021	60	16	the	the	DET
fcis-14021	60	17	detection	detection	NOUN
fcis-14021	60	18	effect	effect	NOUN
fcis-14021	60	19	.	.	PUNCT
fcis-14021	61	1	learning	learn	VERB
fcis-14021	61	2	small	small	ADJ
fcis-14021	61	3	target	target	NOUN
fcis-14021	61	4	information	information	NOUN
fcis-14021	61	5	becomes	become	VERB
fcis-14021	61	6	more	more	ADV
fcis-14021	61	7	difficult	difficult	ADJ
fcis-14021	61	8	when	when	SCONJ
fcis-14021	61	9	the	the	DET
fcis-14021	61	10	number	number	NOUN
fcis-14021	61	11	of	of	ADP
fcis-14021	61	12	feature	feature	NOUN
fcis-14021	61	13	layers	layer	NOUN
fcis-14021	61	14	is	be	AUX
fcis-14021	61	15	deep	deep	ADJ
fcis-14021	61	16	.	.	PUNCT
fcis-14021	62	1	the	the	DET
fcis-14021	62	2	network	network	NOUN
fcis-14021	62	3	model	model	NOUN
fcis-14021	62	4	designed	design	VERB
fcis-14021	62	5	in	in	ADP
fcis-14021	62	6	this	this	DET
fcis-14021	62	7	article	article	NOUN
fcis-14021	62	8	is	be	AUX
fcis-14021	62	9	based	base	VERB
fcis-14021	62	10	on	on	ADP
fcis-14021	62	11	the	the	DET
fcis-14021	62	12	yolov5s	yolov5s	PROPN
fcis-14021	62	13	algorithm	algorithm	NOUN
fcis-14021	62	14	and	and	CCONJ
fcis-14021	62	15	adds	add	VERB
fcis-14021	62	16	a	a	DET
fcis-14021	62	17	ca	ca	NOUN
fcis-14021	62	18	attention	attention	NOUN
fcis-14021	62	19	mechanism	mechanism	NOUN
fcis-14021	62	20	layer	layer	NOUN
fcis-14021	62	21	.	.	PUNCT
fcis-14021	63	1	this	this	DET
fcis-14021	63	2	attention	attention	NOUN
fcis-14021	63	3	mechanism	mechanism	NOUN
fcis-14021	63	4	layer	layer	NOUN
fcis-14021	63	5	can	can	AUX
fcis-14021	63	6	embed	embed	VERB
fcis-14021	63	7	position	position	NOUN
fcis-14021	63	8	information	information	NOUN
fcis-14021	63	9	into	into	ADP
fcis-14021	63	10	new	new	ADJ
fcis-14021	63	11	attention	attention	NOUN
fcis-14021	63	12	,	,	PUNCT
fcis-14021	63	13	which	which	PRON
fcis-14021	63	14	can	can	AUX
fcis-14021	63	15	expand	expand	VERB
fcis-14021	63	16	the	the	DET
fcis-14021	63	17	network	network	NOUN
fcis-14021	63	18	's	's	PART
fcis-14021	63	19	attention	attention	NOUN
fcis-14021	63	20	range	range	NOUN
fcis-14021	63	21	.	.	PUNCT
fcis-14021	64	1	in	in	ADP
fcis-14021	64	2	addition	addition	NOUN
fcis-14021	64	3	,	,	PUNCT
fcis-14021	64	4	it	it	PRON
fcis-14021	64	5	can	can	AUX
fcis-14021	64	6	also	also	ADV
fcis-14021	64	7	avoid	avoid	VERB
fcis-14021	64	8	the	the	DET
fcis-14021	64	9	increase	increase	NOUN
fcis-14021	64	10	in	in	ADP
fcis-14021	64	11	computational	computational	ADJ
fcis-14021	64	12	complexity	complexity	NOUN
fcis-14021	64	13	caused	cause	VERB
fcis-14021	64	14	by	by	ADP
fcis-14021	64	15	large	large	ADJ
fcis-14021	64	16	-	-	PUNCT
fcis-14021	64	17	scale	scale	NOUN
fcis-14021	64	18	attention	attention	NOUN
fcis-14021	64	19	.	.	PUNCT
fcis-14021	65	1	fig	fig	NOUN
fcis-14021	65	2	2	2	NUM
fcis-14021	65	3	.	.	PUNCT
fcis-14021	66	1	network	network	NOUN
fcis-14021	66	2	architecture	architecture	NOUN
fcis-14021	66	3	flowchart	flowchart	NOUN
fcis-14021	66	4	of	of	ADP
fcis-14021	66	5	ca	ca	NOUN
fcis-14021	66	6	attention	attention	NOUN
fcis-14021	66	7	mechanism	mechanism	NOUN
fcis-14021	66	8	2.2	2.2	NUM
fcis-14021	66	9	.	.	PUNCT
fcis-14021	67	1	dataset	dataset	NOUN
fcis-14021	67	2	engineering	engineering	NOUN
fcis-14021	67	3	we	we	PRON
fcis-14021	67	4	constructed	construct	VERB
fcis-14021	67	5	a	a	DET
fcis-14021	67	6	dataset	dataset	NOUN
fcis-14021	67	7	containing	contain	VERB
fcis-14021	67	8	multiple	multiple	ADJ
fcis-14021	67	9	escape	escape	NOUN
fcis-14021	67	10	ladder	ladder	NOUN
fcis-14021	67	11	states	state	NOUN
fcis-14021	67	12	and	and	CCONJ
fcis-14021	67	13	environmental	environmental	ADJ
fcis-14021	67	14	conditions	condition	NOUN
fcis-14021	67	15	.	.	PUNCT
fcis-14021	68	1	the	the	DET
fcis-14021	68	2	dataset	dataset	NOUN
fcis-14021	68	3	consists	consist	VERB
fcis-14021	68	4	of	of	ADP
fcis-14021	68	5	on	on	ADP
fcis-14021	68	6	-	-	PUNCT
fcis-14021	68	7	site	site	NOUN
fcis-14021	68	8	collected	collect	VERB
fcis-14021	68	9	videos	video	NOUN
fcis-14021	68	10	,	,	PUNCT
fcis-14021	68	11	with	with	ADP
fcis-14021	68	12	1000	1000	NUM
fcis-14021	68	13	sample	sample	NOUN
fcis-14021	68	14	images	image	NOUN
fcis-14021	68	15	extracted	extract	VERB
fcis-14021	68	16	every	every	DET
fcis-14021	68	17	15	15	NUM
fcis-14021	68	18	frames	frame	NOUN
fcis-14021	68	19	.	.	PUNCT
fcis-14021	69	1	the	the	DET
fcis-14021	69	2	environmental	environmental	ADJ
fcis-14021	69	3	conditions	condition	NOUN
fcis-14021	69	4	include	include	VERB
fcis-14021	69	5	dim	dim	ADJ
fcis-14021	69	6	and	and	CCONJ
fcis-14021	69	7	bright	bright	ADJ
fcis-14021	69	8	environments	environment	NOUN
fcis-14021	69	9	.	.	PUNCT
fcis-14021	70	1	when	when	SCONJ
fcis-14021	70	2	annotating	annotate	VERB
fcis-14021	70	3	the	the	DET
fcis-14021	70	4	dataset	dataset	NOUN
fcis-14021	70	5	,	,	PUNCT
fcis-14021	70	6	visible	visible	ADJ
fcis-14021	70	7	escape	escape	NOUN
fcis-14021	70	8	ladder	ladder	NOUN
fcis-14021	70	9	objects	object	NOUN
fcis-14021	70	10	are	be	AUX
fcis-14021	70	11	labeled	label	VERB
fcis-14021	70	12	.	.	PUNCT
fcis-14021	71	1	this	this	DET
fcis-14021	71	2	paper	paper	NOUN
fcis-14021	71	3	uses	use	VERB
fcis-14021	71	4	high	high	ADJ
fcis-14021	71	5	pass	pass	VERB
fcis-14021	71	6	filtering	filtering	NOUN
fcis-14021	71	7	and	and	CCONJ
fcis-14021	71	8	median	median	ADJ
fcis-14021	71	9	filtering	filtering	NOUN
fcis-14021	71	10	to	to	PART
fcis-14021	71	11	optimize	optimize	VERB
fcis-14021	71	12	the	the	DET
fcis-14021	71	13	dataset	dataset	NOUN
fcis-14021	71	14	by	by	ADP
fcis-14021	71	15	removing	remove	VERB
fcis-14021	71	16	image	image	NOUN
fcis-14021	71	17	impurities	impurity	NOUN
fcis-14021	71	18	and	and	CCONJ
fcis-14021	71	19	sharpening	sharpen	VERB
fcis-14021	71	20	image	image	NOUN
fcis-14021	71	21	details	detail	NOUN
fcis-14021	71	22	,	,	PUNCT
fcis-14021	71	23	the	the	DET
fcis-14021	71	24	yolov5s	yolov5s	PROPN
fcis-14021	71	25	algorithm	algorithm	NOUN
fcis-14021	71	26	,	,	PUNCT
fcis-14021	71	27	which	which	PRON
fcis-14021	71	28	added	add	VERB
fcis-14021	71	29	a	a	DET
fcis-14021	71	30	ca	ca	NOUN
fcis-14021	71	31	attention	attention	NOUN
fcis-14021	71	32	detection	detection	NOUN
fcis-14021	71	33	layer	layer	NOUN
fcis-14021	71	34	,	,	PUNCT
fcis-14021	71	35	was	be	AUX
fcis-14021	71	36	trained	train	VERB
fcis-14021	71	37	using	use	VERB
fcis-14021	71	38	the	the	DET
fcis-14021	71	39	optimized	optimize	VERB
fcis-14021	71	40	data	datum	NOUN
fcis-14021	71	41	.	.	PUNCT
fcis-14021	72	1	2.3	2.3	NUM
fcis-14021	72	2	.	.	PUNCT
fcis-14021	73	1	algorithm	algorithm	NOUN
fcis-14021	73	2	process	process	NOUN
fcis-14021	73	3	1	1	NUM
fcis-14021	73	4	.	.	PUNCT
fcis-14021	73	5	prepare	prepare	VERB
fcis-14021	73	6	a	a	DET
fcis-14021	73	7	dataset	dataset	NOUN
fcis-14021	73	8	:	:	PUNCT
fcis-14021	73	9	collect	collect	VERB
fcis-14021	73	10	and	and	CCONJ
fcis-14021	73	11	annotate	annotate	VERB
fcis-14021	73	12	the	the	DET
fcis-14021	73	13	image	image	NOUN
fcis-14021	73	14	data	datum	NOUN
fcis-14021	73	15	of	of	ADP
fcis-14021	73	16	the	the	DET
fcis-14021	73	17	escape	escape	NOUN
fcis-14021	73	18	ladder	ladder	NOUN
fcis-14021	73	19	,	,	PUNCT
fcis-14021	73	20	and	and	CCONJ
fcis-14021	73	21	divide	divide	VERB
fcis-14021	73	22	it	it	PRON
fcis-14021	73	23	into	into	ADP
fcis-14021	73	24	training	training	NOUN
fcis-14021	73	25	,	,	PUNCT
fcis-14021	73	26	validation	validation	NOUN
fcis-14021	73	27	,	,	PUNCT
fcis-14021	73	28	and	and	CCONJ
fcis-14021	73	29	testing	testing	NOUN
fcis-14021	73	30	sets	set	NOUN
fcis-14021	73	31	.	.	PUNCT
fcis-14021	74	1	2	2	X
fcis-14021	74	2	.	.	X
fcis-14021	74	3	build	build	VERB
fcis-14021	74	4	yolov5s	yolov5s	PROPN
fcis-14021	74	5	model	model	NOUN
fcis-14021	74	6	:	:	PUNCT
fcis-14021	74	7	based	base	VERB
fcis-14021	74	8	on	on	ADP
fcis-14021	74	9	the	the	DET
fcis-14021	74	10	yolov5s	yolov5s	PROPN
fcis-14021	74	11	network	network	NOUN
fcis-14021	74	12	structure	structure	NOUN
fcis-14021	74	13	,	,	PUNCT
fcis-14021	74	14	build	build	VERB
fcis-14021	74	15	an	an	DET
fcis-14021	74	16	object	object	NOUN
fcis-14021	74	17	detection	detection	NOUN
fcis-14021	74	18	model	model	NOUN
fcis-14021	74	19	.	.	PUNCT
fcis-14021	75	1	3	3	X
fcis-14021	75	2	.	.	X
fcis-14021	75	3	add	add	VERB
fcis-14021	75	4	attention	attention	NOUN
fcis-14021	75	5	mechanism	mechanism	NOUN
fcis-14021	75	6	ca	can	AUX
fcis-14021	75	7	:	:	PUNCT
fcis-14021	75	8	add	add	VERB
fcis-14021	75	9	a	a	DET
fcis-14021	75	10	ca	ca	NOUN
fcis-14021	75	11	attention	attention	NOUN
fcis-14021	75	12	mechanism	mechanism	NOUN
fcis-14021	75	13	layer	layer	NOUN
fcis-14021	75	14	to	to	ADP
fcis-14021	75	15	the	the	DET
fcis-14021	75	16	yolov5s	yolov5s	PROPN
fcis-14021	75	17	model	model	NOUN
fcis-14021	75	18	to	to	PART
fcis-14021	75	19	improve	improve	VERB
fcis-14021	75	20	the	the	DET
fcis-14021	75	21	model	model	NOUN
fcis-14021	75	22	's	's	PART
fcis-14021	75	23	attention	attention	NOUN
fcis-14021	75	24	to	to	ADP
fcis-14021	75	25	small	small	ADJ
fcis-14021	75	26	targets	target	NOUN
fcis-14021	75	27	and	and	CCONJ
fcis-14021	75	28	optimize	optimize	VERB
fcis-14021	75	29	the	the	DET
fcis-14021	75	30	neural	neural	ADJ
fcis-14021	75	31	network	network	NOUN
fcis-14021	75	32	's	's	PART
fcis-14021	75	33	detection	detection	NOUN
fcis-14021	75	34	ability	ability	NOUN
fcis-14021	75	35	for	for	ADP
fcis-14021	75	36	small	small	ADJ
fcis-14021	75	37	size	size	NOUN
fcis-14021	75	38	escape	escape	NOUN
fcis-14021	75	39	ladder	ladder	NOUN
fcis-14021	75	40	images	image	NOUN
fcis-14021	75	41	.	.	PUNCT
fcis-14021	76	1	4	4	X
fcis-14021	76	2	.	.	X
fcis-14021	76	3	training	training	NOUN
fcis-14021	76	4	model	model	NOUN
fcis-14021	76	5	:	:	PUNCT
fcis-14021	76	6	train	train	VERB
fcis-14021	76	7	the	the	DET
fcis-14021	76	8	model	model	NOUN
fcis-14021	76	9	using	use	VERB
fcis-14021	76	10	a	a	DET
fcis-14021	76	11	training	training	NOUN
fcis-14021	76	12	set	set	NOUN
fcis-14021	76	13	and	and	CCONJ
fcis-14021	76	14	optimize	optimize	VERB
fcis-14021	76	15	the	the	DET
fcis-14021	76	16	model	model	NOUN
fcis-14021	76	17	parameters	parameter	NOUN
fcis-14021	76	18	using	use	VERB
fcis-14021	76	19	a	a	DET
fcis-14021	76	20	backpropagation	backpropagation	NOUN
fcis-14021	76	21	algorithm	algorithm	NOUN
fcis-14021	76	22	.	.	PUNCT
fcis-14021	77	1	5	5	X
fcis-14021	77	2	.	.	X
fcis-14021	77	3	validation	validation	NOUN
fcis-14021	77	4	model	model	NOUN
fcis-14021	77	5	:	:	PUNCT
fcis-14021	77	6	use	use	VERB
fcis-14021	77	7	the	the	DET
fcis-14021	77	8	validation	validation	NOUN
fcis-14021	77	9	set	set	VERB
fcis-14021	77	10	to	to	PART
fcis-14021	77	11	evaluate	evaluate	VERB
fcis-14021	77	12	the	the	DET
fcis-14021	77	13	model	model	NOUN
fcis-14021	77	14	during	during	ADP
fcis-14021	77	15	the	the	DET
fcis-14021	77	16	training	training	NOUN
fcis-14021	77	17	process	process	NOUN
fcis-14021	77	18	and	and	CCONJ
fcis-14021	77	19	select	select	VERB
fcis-14021	77	20	the	the	DET
fcis-14021	77	21	model	model	NOUN
fcis-14021	77	22	with	with	ADP
fcis-14021	77	23	the	the	DET
fcis-14021	77	24	best	good	ADJ
fcis-14021	77	25	performance	performance	NOUN
fcis-14021	77	26	.	.	PUNCT
fcis-14021	78	1	6	6	X
fcis-14021	78	2	.	.	PUNCT
fcis-14021	78	3	test	test	NOUN
fcis-14021	78	4	model	model	NOUN
fcis-14021	78	5	:	:	PUNCT
fcis-14021	78	6	use	use	VERB
fcis-14021	78	7	a	a	DET
fcis-14021	78	8	test	test	NOUN
fcis-14021	78	9	set	set	VERB
fcis-14021	78	10	to	to	PART
fcis-14021	78	11	test	test	VERB
fcis-14021	78	12	the	the	DET
fcis-14021	78	13	final	final	ADJ
fcis-14021	78	14	selected	select	VERB
fcis-14021	78	15	model	model	NOUN
fcis-14021	78	16	and	and	CCONJ
fcis-14021	78	17	evaluate	evaluate	VERB
fcis-14021	78	18	its	its	PRON
fcis-14021	78	19	performance	performance	NOUN
fcis-14021	78	20	in	in	ADP
fcis-14021	78	21	small	small	ADJ
fcis-14021	78	22	object	object	NOUN
fcis-14021	78	23	detection	detection	NOUN
fcis-14021	78	24	tasks	task	NOUN
fcis-14021	78	25	.	.	PUNCT
fcis-14021	79	1	7	7	X
fcis-14021	79	2	.	.	NOUN
fcis-14021	79	3	result	result	NOUN
fcis-14021	79	4	analysis	analysis	NOUN
fcis-14021	79	5	:	:	PUNCT
fcis-14021	79	6	analyze	analyze	VERB
fcis-14021	79	7	the	the	DET
fcis-14021	79	8	test	test	NOUN
fcis-14021	79	9	results	result	NOUN
fcis-14021	79	10	,	,	PUNCT
fcis-14021	79	11	compare	compare	VERB
fcis-14021	79	12	the	the	DET
fcis-14021	79	13	130	130	NUM
fcis-14021	79	14	performance	performance	NOUN
fcis-14021	79	15	differences	difference	NOUN
fcis-14021	79	16	before	before	ADV
fcis-14021	79	17	and	and	CCONJ
fcis-14021	79	18	after	after	ADP
fcis-14021	79	19	adding	add	VERB
fcis-14021	79	20	attention	attention	NOUN
fcis-14021	79	21	mechanism	mechanism	NOUN
fcis-14021	79	22	ca	ca	NOUN
fcis-14021	79	23	,	,	PUNCT
fcis-14021	79	24	and	and	CCONJ
fcis-14021	79	25	prove	prove	VERB
fcis-14021	79	26	the	the	DET
fcis-14021	79	27	effectiveness	effectiveness	NOUN
fcis-14021	79	28	of	of	ADP
fcis-14021	79	29	attention	attention	NOUN
fcis-14021	79	30	mechanism	mechanism	NOUN
fcis-14021	79	31	in	in	ADP
fcis-14021	79	32	improving	improve	VERB
fcis-14021	79	33	the	the	DET
fcis-14021	79	34	detection	detection	NOUN
fcis-14021	79	35	effect	effect	NOUN
fcis-14021	79	36	of	of	ADP
fcis-14021	79	37	small	small	ADJ
fcis-14021	79	38	targets	target	NOUN
fcis-14021	79	39	.	.	PUNCT
fcis-14021	80	1	3	3	X
fcis-14021	80	2	.	.	X
fcis-14021	80	3	experimental	experimental	ADJ
fcis-14021	80	4	analysis	analysis	NOUN
fcis-14021	80	5	and	and	CCONJ
fcis-14021	80	6	validation	validation	NOUN
fcis-14021	80	7	3.1	3.1	NUM
fcis-14021	80	8	.	.	PUNCT
fcis-14021	80	9	experimental	experimental	ADJ
fcis-14021	80	10	environment	environment	NOUN
fcis-14021	80	11	and	and	CCONJ
fcis-14021	80	12	datasets	dataset	VERB
fcis-14021	80	13	the	the	DET
fcis-14021	80	14	experimental	experimental	ADJ
fcis-14021	80	15	software	software	NOUN
fcis-14021	80	16	and	and	CCONJ
fcis-14021	80	17	hardware	hardware	NOUN
fcis-14021	80	18	settings	setting	NOUN
fcis-14021	80	19	in	in	ADP
fcis-14021	80	20	this	this	DET
fcis-14021	80	21	article	article	NOUN
fcis-14021	80	22	are	be	AUX
fcis-14021	80	23	:	:	PUNCT
fcis-14021	80	24	nvidia	nvidia	PROPN
fcis-14021	80	25	geforce	geforce	PROPN
fcis-14021	80	26	gtx	gtx	PROPN
fcis-14021	80	27	3060	3060	NUM
fcis-14021	80	28	gpu	gpu	PROPN
fcis-14021	80	29	,	,	PUNCT
fcis-14021	80	30	16	16	NUM
fcis-14021	80	31	gb	gb	NOUN
fcis-14021	80	32	memory	memory	NOUN
fcis-14021	80	33	,	,	PUNCT
fcis-14021	80	34	inter	inter	PROPN
fcis-14021	80	35	core	core	NOUN
fcis-14021	80	36	i5	i5	NOUN
fcis-14021	80	37	-	-	PUNCT
fcis-14021	80	38	10200h	10200h	NUM
fcis-14021	80	39	cpu	cpu	NOUN
fcis-14021	80	40	,	,	PUNCT
fcis-14021	80	41	trained	train	VERB
fcis-14021	80	42	using	use	VERB
fcis-14021	80	43	python	python	NOUN
fcis-14021	80	44	,	,	PUNCT
fcis-14021	80	45	pytharm	pytharm	NOUN
fcis-14021	80	46	community	community	NOUN
fcis-14021	80	47	ide	ide	NOUN
fcis-14021	80	48	,	,	PUNCT
fcis-14021	80	49	python	python	NOUN
fcis-14021	80	50	framework	framework	NOUN
fcis-14021	80	51	version	version	NOUN
fcis-14021	80	52	1.7.0	1.7.0	NUM
fcis-14021	80	53	,	,	PUNCT
fcis-14021	80	54	which	which	PRON
fcis-14021	80	55	can	can	AUX
fcis-14021	80	56	call	call	VERB
fcis-14021	80	57	gpu	gpu	NOUN
fcis-14021	80	58	for	for	ADP
fcis-14021	80	59	high	high	ADJ
fcis-14021	80	60	-	-	PUNCT
fcis-14021	80	61	speed	speed	NOUN
fcis-14021	80	62	tensor	tensor	NOUN
fcis-14021	80	63	calculation	calculation	NOUN
fcis-14021	80	64	,	,	PUNCT
fcis-14021	80	65	using	use	VERB
fcis-14021	80	66	python	python	NOUN
fcis-14021	80	67	3.9	3.9	NUM
fcis-14021	80	68	that	that	PRON
fcis-14021	80	69	is	be	AUX
fcis-14021	80	70	suitable	suitable	ADJ
fcis-14021	80	71	for	for	ADP
fcis-14021	80	72	python	python	NOUN
fcis-14021	80	73	version	version	NOUN
fcis-14021	80	74	,	,	PUNCT
fcis-14021	80	75	and	and	CCONJ
fcis-14021	80	76	operating	operating	NOUN
fcis-14021	80	77	environment	environment	NOUN
fcis-14021	80	78	is	be	AUX
fcis-14021	80	79	windows	window	NOUN
fcis-14021	80	80	10	10	NUM
fcis-14021	80	81	.	.	PUNCT
fcis-14021	81	1	monitoring	monitor	VERB
fcis-14021	81	2	videos	video	NOUN
fcis-14021	81	3	taken	take	VERB
fcis-14021	81	4	using	use	VERB
fcis-14021	81	5	live	live	ADJ
fcis-14021	81	6	scenes	scene	NOUN
fcis-14021	81	7	on	on	ADP
fcis-14021	81	8	the	the	DET
fcis-14021	81	9	construction	construction	NOUN
fcis-14021	81	10	site	site	NOUN
fcis-14021	81	11	and	and	CCONJ
fcis-14021	81	12	videos	video	NOUN
fcis-14021	81	13	recorded	record	VERB
fcis-14021	81	14	normally	normally	ADV
fcis-14021	81	15	,	,	PUNCT
fcis-14021	81	16	including	include	VERB
fcis-14021	81	17	6	6	NUM
fcis-14021	81	18	types	type	NOUN
fcis-14021	81	19	of	of	ADP
fcis-14021	81	20	data	datum	NOUN
fcis-14021	81	21	scenarios	scenario	NOUN
fcis-14021	81	22	:	:	PUNCT
fcis-14021	81	23	day	day	NOUN
fcis-14021	81	24	(	(	PUNCT
fcis-14021	81	25	strong	strong	ADJ
fcis-14021	81	26	light	light	NOUN
fcis-14021	81	27	)	)	PUNCT
fcis-14021	81	28	,	,	PUNCT
fcis-14021	81	29	day	day	NOUN
fcis-14021	81	30	(	(	PUNCT
fcis-14021	81	31	weak	weak	ADJ
fcis-14021	81	32	light	light	NOUN
fcis-14021	81	33	)	)	PUNCT
fcis-14021	81	34	,	,	PUNCT
fcis-14021	81	35	dusk	dusk	NOUN
fcis-14021	81	36	,	,	PUNCT
fcis-14021	81	37	in	in	ADP
fcis-14021	81	38	the	the	DET
fcis-14021	81	39	ditch	ditch	NOUN
fcis-14021	81	40	,	,	PUNCT
fcis-14021	81	41	and	and	CCONJ
fcis-14021	81	42	outside	outside	ADP
fcis-14021	81	43	the	the	DET
fcis-14021	81	44	ditch	ditch	NOUN
fcis-14021	81	45	.	.	PUNCT
fcis-14021	82	1	image	image	NOUN
fcis-14021	82	2	data	datum	NOUN
fcis-14021	82	3	was	be	AUX
fcis-14021	82	4	obtained	obtain	VERB
fcis-14021	82	5	through	through	ADP
fcis-14021	82	6	video	video	NOUN
fcis-14021	82	7	frame	frame	NOUN
fcis-14021	82	8	extraction	extraction	NOUN
fcis-14021	82	9	.	.	PUNCT
fcis-14021	83	1	after	after	ADP
fcis-14021	83	2	data	data	NOUN
fcis-14021	83	3	analysis	analysis	NOUN
fcis-14021	83	4	,	,	PUNCT
fcis-14021	83	5	invalid	invalid	ADJ
fcis-14021	83	6	data	datum	NOUN
fcis-14021	83	7	was	be	AUX
fcis-14021	83	8	deleted	delete	VERB
fcis-14021	83	9	,	,	PUNCT
fcis-14021	83	10	fuzzy	fuzzy	ADJ
fcis-14021	83	11	data	datum	NOUN
fcis-14021	83	12	was	be	AUX
fcis-14021	83	13	removed	remove	VERB
fcis-14021	83	14	,	,	PUNCT
fcis-14021	83	15	and	and	CCONJ
fcis-14021	83	16	other	other	ADJ
fcis-14021	83	17	operations	operation	NOUN
fcis-14021	83	18	were	be	AUX
fcis-14021	83	19	conducted	conduct	VERB
fcis-14021	83	20	to	to	PART
fcis-14021	83	21	create	create	VERB
fcis-14021	83	22	a	a	DET
fcis-14021	83	23	high	high	ADJ
fcis-14021	83	24	-	-	PUNCT
fcis-14021	83	25	quality	quality	NOUN
fcis-14021	83	26	dataset	dataset	NOUN
fcis-14021	83	27	.	.	PUNCT
fcis-14021	84	1	finally	finally	ADV
fcis-14021	84	2	,	,	PUNCT
fcis-14021	84	3	1500	1500	NUM
fcis-14021	84	4	images	image	NOUN
fcis-14021	84	5	were	be	AUX
fcis-14021	84	6	obtained	obtain	VERB
fcis-14021	84	7	,	,	PUNCT
fcis-14021	84	8	including	include	VERB
fcis-14021	84	9	2000	2000	NUM
fcis-14021	84	10	samples	sample	NOUN
fcis-14021	84	11	of	of	ADP
fcis-14021	84	12	escape	escape	NOUN
fcis-14021	84	13	ladders	ladder	NOUN
fcis-14021	84	14	.	.	PUNCT
fcis-14021	85	1	during	during	ADP
fcis-14021	85	2	data	data	NOUN
fcis-14021	85	3	annotation	annotation	NOUN
fcis-14021	85	4	,	,	PUNCT
fcis-14021	85	5	to	to	PART
fcis-14021	85	6	avoid	avoid	VERB
fcis-14021	85	7	interference	interference	NOUN
fcis-14021	85	8	from	from	ADP
fcis-14021	85	9	other	other	ADJ
fcis-14021	85	10	features	feature	NOUN
fcis-14021	85	11	,	,	PUNCT
fcis-14021	85	12	occluded	occluded	ADJ
fcis-14021	85	13	parts	part	NOUN
fcis-14021	85	14	are	be	AUX
fcis-14021	85	15	removed	remove	VERB
fcis-14021	85	16	to	to	PART
fcis-14021	85	17	purify	purify	VERB
fcis-14021	85	18	the	the	DET
fcis-14021	85	19	features	feature	NOUN
fcis-14021	85	20	.	.	PUNCT
fcis-14021	86	1	3.2	3.2	NUM
fcis-14021	86	2	.	.	PUNCT
fcis-14021	87	1	algorithm	algorithm	NOUN
fcis-14021	87	2	improvement	improvement	NOUN
fcis-14021	87	3	effect	effect	NOUN
fcis-14021	87	4	3.2.1	3.2.1	NUM
fcis-14021	87	5	.	.	PUNCT
fcis-14021	88	1	performance	performance	NOUN
fcis-14021	88	2	test	test	NOUN
fcis-14021	88	3	results	result	NOUN
fcis-14021	88	4	of	of	ADP
fcis-14021	88	5	yolov5s	yolov5s	NOUN
fcis-14021	88	6	detection	detection	NOUN
fcis-14021	88	7	algorithm	algorithm	NOUN
fcis-14021	88	8	on	on	ADP
fcis-14021	88	9	self	self	NOUN
fcis-14021	88	10	built	build	VERB
fcis-14021	88	11	datasets	dataset	NOUN
fcis-14021	88	12	improved	improve	VERB
fcis-14021	88	13	algorithm	algorithm	NOUN
fcis-14021	88	14	structure	structure	NOUN
fcis-14021	88	15	,	,	PUNCT
fcis-14021	88	16	added	add	VERB
fcis-14021	88	17	small	small	ADJ
fcis-14021	88	18	object	object	NOUN
fcis-14021	88	19	detection	detection	NOUN
fcis-14021	88	20	layer	layer	NOUN
fcis-14021	88	21	ca	can	AUX
fcis-14021	88	22	,	,	PUNCT
fcis-14021	88	23	improved	improve	VERB
fcis-14021	88	24	anchor	anchor	NOUN
fcis-14021	88	25	box	box	NOUN
fcis-14021	88	26	,	,	PUNCT
fcis-14021	88	27	selected	select	VERB
fcis-14021	88	28	anchors	anchor	NOUN
fcis-14021	88	29	that	that	PRON
fcis-14021	88	30	are	be	AUX
fcis-14021	88	31	more	more	ADV
fcis-14021	88	32	suitable	suitable	ADJ
fcis-14021	88	33	for	for	ADP
fcis-14021	88	34	small	small	ADJ
fcis-14021	88	35	objects	object	NOUN
fcis-14021	88	36	,	,	PUNCT
fcis-14021	88	37	improved	improve	VERB
fcis-14021	88	38	yolov5s	yolov5s	PROPN
fcis-14021	88	39	algorithm	algorithm	NOUN
fcis-14021	88	40	,	,	PUNCT
fcis-14021	88	41	added	add	VERB
fcis-14021	88	42	small	small	ADJ
fcis-14021	88	43	object	object	NOUN
fcis-14021	88	44	detection	detection	NOUN
fcis-14021	88	45	layer	layer	NOUN
fcis-14021	88	46	,	,	PUNCT
fcis-14021	88	47	easy	easy	ADJ
fcis-14021	88	48	to	to	PART
fcis-14021	88	49	analyze	analyze	VERB
fcis-14021	88	50	charts	chart	NOUN
fcis-14021	88	51	,	,	PUNCT
fcis-14021	88	52	optimized	optimize	VERB
fcis-14021	88	53	and	and	CCONJ
fcis-14021	88	54	adapted	adapt	VERB
fcis-14021	88	55	yolov5s	yolov5s	PROPN
fcis-14021	88	56	algorithm	algorithm	NOUN
fcis-14021	88	57	,	,	PUNCT
fcis-14021	88	58	improved	improved	ADJ
fcis-14021	88	59	accuracy	accuracy	NOUN
fcis-14021	88	60	by	by	ADP
fcis-14021	88	61	1.4	1.4	NUM
fcis-14021	88	62	%	%	NOUN
fcis-14021	88	63	and	and	CCONJ
fcis-14021	88	64	recall	recall	NOUN
fcis-14021	88	65	by	by	ADP
fcis-14021	88	66	1.2	1.2	NUM
fcis-14021	88	67	%	%	NOUN
fcis-14021	88	68	map@0.5	map@0.5	NOUN
fcis-14021	88	69	the	the	DET
fcis-14021	88	70	indicator	indicator	NOUN
fcis-14021	88	71	has	have	AUX
fcis-14021	88	72	increased	increase	VERB
fcis-14021	88	73	by	by	ADP
fcis-14021	88	74	2.9	2.9	NUM
fcis-14021	88	75	%	%	NOUN
fcis-14021	88	76	,	,	PUNCT
fcis-14021	88	77	and	and	CCONJ
fcis-14021	88	78	the	the	DET
fcis-14021	88	79	detection	detection	NOUN
fcis-14021	88	80	speed	speed	NOUN
fcis-14021	88	81	has	have	AUX
fcis-14021	88	82	decreased	decrease	VERB
fcis-14021	88	83	by	by	ADP
fcis-14021	88	84	about	about	ADP
fcis-14021	88	85	0.9ms	0.9ms	PROPN
fcis-14021	88	86	.	.	PUNCT
fcis-14021	88	87	table	table	NOUN
fcis-14021	89	1	1	1	NUM
fcis-14021	89	2	.	.	PUNCT
fcis-14021	89	3	comparison	comparison	NOUN
fcis-14021	89	4	of	of	ADP
fcis-14021	89	5	yolov5s	yolov5s	PROPN
fcis-14021	89	6	and	and	CCONJ
fcis-14021	89	7	yolov5s	yolov5s	PROPN
fcis-14021	89	8	with	with	ADP
fcis-14021	89	9	ca	ca	NOUN
fcis-14021	89	10	added	add	VERB
fcis-14021	89	11	map	map	NOUN
fcis-14021	89	12	.5	.5	NUM
fcis-14021	89	13	map	map	NOUN
fcis-14021	89	14	.5	.5	NUM
fcis-14021	89	15	samll	samll	ADJ
fcis-14021	89	16	inference(ms	inference(ms	NOUN
fcis-14021	89	17	)	)	PUNCT
fcis-14021	89	18	yolov5	yolov5	NOUN
fcis-14021	89	19	0.826	0.826	NUM
fcis-14021	89	20	0.769	0.769	NUM
fcis-14021	89	21	8	8	NUM
fcis-14021	89	22	optimized	optimize	VERB
fcis-14021	89	23	yolov5	yolov5	NOUN
fcis-14021	89	24	0.855	0.855	NUM
fcis-14021	89	25	0.825	0.825	NUM
fcis-14021	89	26	8.9	8.9	NUM
fcis-14021	89	27	difference	difference	NOUN
fcis-14021	89	28	2.9	2.9	NUM
fcis-14021	89	29	%	%	NOUN
fcis-14021	89	30	5.6	5.6	NUM
fcis-14021	89	31	%	%	NOUN
fcis-14021	89	32	0.9	0.9	NUM
fcis-14021	89	33	3.2.2	3.2.2	NUM
fcis-14021	89	34	.	.	PUNCT
fcis-14021	90	1	visualization	visualization	NOUN
fcis-14021	90	2	analysis	analysis	NOUN
fcis-14021	90	3	of	of	ADP
fcis-14021	90	4	iterative	iterative	NOUN
fcis-14021	90	5	process	process	NOUN
fcis-14021	90	6	(	(	PUNCT
fcis-14021	90	7	a	a	X
fcis-14021	90	8	)	)	PUNCT
fcis-14021	90	9	comparison	comparison	NOUN
fcis-14021	90	10	of	of	ADP
fcis-14021	90	11	precision	precision	NOUN
fcis-14021	90	12	rate	rate	NOUN
fcis-14021	90	13	(	(	PUNCT
fcis-14021	90	14	b	b	NOUN
fcis-14021	90	15	)	)	PUNCT
fcis-14021	90	16	comparison	comparison	NOUN
fcis-14021	90	17	of	of	ADP
fcis-14021	90	18	recall	recall	NOUN
fcis-14021	90	19	rate	rate	PROPN
fcis-14021	90	20	fig	fig	PROPN
fcis-14021	90	21	3	3	NUM
fcis-14021	90	22	.	.	PUNCT
fcis-14021	91	1	visualization	visualization	NOUN
fcis-14021	91	2	analysis	analysis	NOUN
fcis-14021	91	3	of	of	ADP
fcis-14021	91	4	iteration	iteration	NOUN
fcis-14021	91	5	times	time	NOUN
fcis-14021	91	6	and	and	CCONJ
fcis-14021	91	7	convergence	convergence	NOUN
fcis-14021	91	8	of	of	ADP
fcis-14021	91	9	yolov5s	yolov5s	PROPN
fcis-14021	91	10	algorithm	algorithm	NOUN
fcis-14021	91	11	before	before	ADP
fcis-14021	91	12	and	and	CCONJ
fcis-14021	91	13	after	after	ADP
fcis-14021	91	14	improvement	improvement	NOUN
fcis-14021	91	15	from	from	ADP
fcis-14021	91	16	the	the	DET
fcis-14021	91	17	comparison	comparison	NOUN
fcis-14021	91	18	of	of	ADP
fcis-14021	91	19	the	the	DET
fcis-14021	91	20	changes	change	NOUN
fcis-14021	91	21	in	in	ADP
fcis-14021	91	22	iteration	iteration	NOUN
fcis-14021	91	23	times	time	NOUN
fcis-14021	91	24	,	,	PUNCT
fcis-14021	91	25	precision	precision	NOUN
fcis-14021	91	26	,	,	PUNCT
fcis-14021	91	27	and	and	CCONJ
fcis-14021	91	28	recall	recall	VERB
fcis-14021	91	29	in	in	ADP
fcis-14021	91	30	the	the	DET
fcis-14021	91	31	figure	figure	NOUN
fcis-14021	92	1	,	,	PUNCT
fcis-14021	92	2	it	it	PRON
fcis-14021	92	3	can	can	AUX
fcis-14021	92	4	be	be	AUX
fcis-14021	92	5	seen	see	VERB
fcis-14021	92	6	that	that	SCONJ
fcis-14021	92	7	the	the	DET
fcis-14021	92	8	improved	improved	ADJ
fcis-14021	92	9	yolov5s	yolov5s	PROPN
fcis-14021	92	10	algorithm	algorithm	NOUN
fcis-14021	92	11	has	have	VERB
fcis-14021	92	12	better	well	ADJ
fcis-14021	92	13	convergence	convergence	NOUN
fcis-14021	92	14	speed	speed	NOUN
fcis-14021	92	15	than	than	ADP
fcis-14021	92	16	the	the	DET
fcis-14021	92	17	original	original	ADJ
fcis-14021	92	18	yolov5s	yolov5s	PROPN
fcis-14021	92	19	algorithm	algorithm	NOUN
fcis-14021	92	20	.	.	PUNCT
fcis-14021	93	1	3.2.3	3.2.3	X
fcis-14021	93	2	.	.	X
fcis-14021	93	3	comparison	comparison	NOUN
fcis-14021	93	4	of	of	ADP
fcis-14021	93	5	effects	effect	NOUN
fcis-14021	93	6	before	before	ADV
fcis-14021	93	7	and	and	CCONJ
fcis-14021	93	8	after	after	ADP
fcis-14021	93	9	optimization	optimization	NOUN
fcis-14021	93	10	(	(	PUNCT
fcis-14021	93	11	a	a	X
fcis-14021	93	12	)	)	PUNCT
fcis-14021	93	13	yolov5s	yolov5s	NOUN
fcis-14021	93	14	algorithm	algorithm	NOUN
fcis-14021	93	15	small	small	ADJ
fcis-14021	93	16	target	target	NOUN
fcis-14021	93	17	detection	detection	NOUN
fcis-14021	93	18	effect	effect	NOUN
fcis-14021	93	19	(	(	PUNCT
fcis-14021	93	20	b	b	NOUN
fcis-14021	93	21	)	)	PUNCT
fcis-14021	93	22	improvement	improvement	NOUN
fcis-14021	93	23	of	of	ADP
fcis-14021	93	24	yolov5s	yolov5s	PROPN
fcis-14021	93	25	algorithm	algorithm	PROPN
fcis-14021	93	26	small	small	ADJ
fcis-14021	93	27	target	target	NOUN
fcis-14021	93	28	detection	detection	NOUN
fcis-14021	93	29	effect	effect	NOUN
fcis-14021	93	30	fig	fig	NOUN
fcis-14021	93	31	4	4	NUM
fcis-14021	93	32	.	.	PUNCT
fcis-14021	93	33	comparison	comparison	NOUN
fcis-14021	93	34	of	of	ADP
fcis-14021	93	35	small	small	ADJ
fcis-14021	93	36	target	target	NOUN
fcis-14021	93	37	detection	detection	NOUN
fcis-14021	93	38	effects	effect	NOUN
fcis-14021	93	39	.	.	PUNCT
fcis-14021	94	1	in	in	ADP
fcis-14021	94	2	order	order	NOUN
fcis-14021	94	3	to	to	PART
fcis-14021	94	4	demonstrate	demonstrate	VERB
fcis-14021	94	5	the	the	DET
fcis-14021	94	6	optimization	optimization	NOUN
fcis-14021	94	7	effect	effect	NOUN
fcis-14021	94	8	,	,	PUNCT
fcis-14021	94	9	the	the	DET
fcis-14021	94	10	yolov5s	yolov5s	PROPN
fcis-14021	94	11	algorithm	algorithm	NOUN
fcis-14021	94	12	before	before	ADP
fcis-14021	94	13	and	and	CCONJ
fcis-14021	94	14	after	after	ADP
fcis-14021	94	15	improvement	improvement	NOUN
fcis-14021	94	16	will	will	AUX
fcis-14021	94	17	be	be	AUX
fcis-14021	94	18	analyzed	analyze	VERB
fcis-14021	94	19	and	and	CCONJ
fcis-14021	94	20	verified	verify	VERB
fcis-14021	94	21	for	for	ADP
fcis-14021	94	22	the	the	DET
fcis-14021	94	23	detection	detection	NOUN
fcis-14021	94	24	effect	effect	NOUN
fcis-14021	94	25	of	of	ADP
fcis-14021	94	26	small	small	ADJ
fcis-14021	94	27	target	target	NOUN
fcis-14021	94	28	escape	escape	NOUN
fcis-14021	94	29	ladders	ladder	NOUN
fcis-14021	94	30	.	.	PUNCT
fcis-14021	95	1	the	the	DET
fcis-14021	95	2	test	test	NOUN
fcis-14021	95	3	video	video	NOUN
fcis-14021	95	4	is	be	AUX
fcis-14021	95	5	the	the	DET
fcis-14021	95	6	actual	actual	ADJ
fcis-14021	95	7	video	video	NOUN
fcis-14021	95	8	collected	collect	VERB
fcis-14021	95	9	on	on	ADP
fcis-14021	95	10	the	the	DET
fcis-14021	95	11	construction	construction	NOUN
fcis-14021	95	12	site	site	NOUN
fcis-14021	95	13	.	.	PUNCT
fcis-14021	96	1	as	as	SCONJ
fcis-14021	96	2	shown	show	VERB
fcis-14021	96	3	in	in	ADP
fcis-14021	96	4	figure	figure	NOUN
fcis-14021	96	5	4	4	NUM
fcis-14021	96	6	,	,	PUNCT
fcis-14021	96	7	in	in	ADP
fcis-14021	96	8	the	the	DET
fcis-14021	96	9	video	video	NOUN
fcis-14021	96	10	collected	collect	VERB
fcis-14021	96	11	in	in	ADP
fcis-14021	96	12	practical	practical	ADJ
fcis-14021	96	13	engineering	engineering	NOUN
fcis-14021	96	14	applications	application	NOUN
fcis-14021	96	15	,	,	PUNCT
fcis-14021	96	16	the	the	DET
fcis-14021	96	17	yolov5s	yolov5s	PROPN
fcis-14021	96	18	algorithm	algorithm	NOUN
fcis-14021	96	19	missed	miss	VERB
fcis-14021	96	20	the	the	DET
fcis-14021	96	21	detection	detection	NOUN
fcis-14021	96	22	of	of	ADP
fcis-14021	96	23	small	small	ADJ
fcis-14021	96	24	target	target	NOUN
fcis-14021	96	25	escape	escape	NOUN
fcis-14021	96	26	ladders	ladder	NOUN
fcis-14021	96	27	when	when	SCONJ
fcis-14021	96	28	detecting	detect	VERB
fcis-14021	96	29	backlight	backlight	ADJ
fcis-14021	96	30	small	small	ADJ
fcis-14021	96	31	targets	target	NOUN
fcis-14021	96	32	,	,	PUNCT
fcis-14021	96	33	as	as	SCONJ
fcis-14021	96	34	shown	show	VERB
fcis-14021	96	35	in	in	ADP
fcis-14021	96	36	figure	figure	NOUN
fcis-14021	96	37	4	4	NUM
fcis-14021	96	38	(	(	PUNCT
fcis-14021	96	39	a	a	NOUN
fcis-14021	96	40	)	)	PUNCT
fcis-14021	96	41	.	.	PUNCT
fcis-14021	97	1	the	the	DET
fcis-14021	97	2	improved	improve	VERB
fcis-14021	97	3	and	and	CCONJ
fcis-14021	97	4	optimized	optimize	VERB
fcis-14021	97	5	yolov5s	yolov5s	PROPN
fcis-14021	97	6	algorithm	algorithm	NOUN
fcis-14021	97	7	performs	perform	VERB
fcis-14021	97	8	better	well	ADV
fcis-14021	97	9	in	in	ADP
fcis-14021	97	10	the	the	DET
fcis-14021	97	11	same	same	ADJ
fcis-14021	97	12	test	test	NOUN
fcis-14021	97	13	,	,	PUNCT
fcis-14021	97	14	and	and	CCONJ
fcis-14021	97	15	its	its	PRON
fcis-14021	97	16	detection	detection	NOUN
fcis-14021	97	17	ability	ability	NOUN
fcis-14021	97	18	is	be	AUX
fcis-14021	97	19	stronger	strong	ADJ
fcis-14021	97	20	compared	compare	VERB
fcis-14021	97	21	to	to	ADP
fcis-14021	97	22	before	before	ADV
fcis-14021	97	23	when	when	SCONJ
fcis-14021	97	24	targeting	target	VERB
fcis-14021	97	25	small	small	ADJ
fcis-14021	97	26	targets	target	NOUN
fcis-14021	97	27	with	with	ADP
fcis-14021	97	28	lower	low	ADJ
fcis-14021	97	29	resolution	resolution	NOUN
fcis-14021	97	30	.	.	PUNCT
fcis-14021	98	1	131	131	NUM
fcis-14021	98	2	4	4	NUM
fcis-14021	98	3	.	.	PUNCT
fcis-14021	98	4	conclusion	conclusion	NOUN
fcis-14021	98	5	this	this	DET
fcis-14021	98	6	article	article	NOUN
fcis-14021	98	7	investigates	investigate	VERB
fcis-14021	98	8	the	the	DET
fcis-14021	98	9	method	method	NOUN
fcis-14021	98	10	of	of	ADP
fcis-14021	98	11	adding	add	VERB
fcis-14021	98	12	ca	can	AUX
fcis-14021	98	13	attention	attention	NOUN
fcis-14021	98	14	mechanism	mechanism	NOUN
fcis-14021	98	15	layer	layer	NOUN
fcis-14021	98	16	to	to	ADP
fcis-14021	98	17	the	the	DET
fcis-14021	98	18	yolov5s	yolov5s	PROPN
fcis-14021	98	19	model	model	NOUN
fcis-14021	98	20	and	and	CCONJ
fcis-14021	98	21	explores	explore	VERB
fcis-14021	98	22	its	its	PRON
fcis-14021	98	23	optimization	optimization	NOUN
fcis-14021	98	24	effect	effect	NOUN
fcis-14021	98	25	on	on	ADP
fcis-14021	98	26	escape	escape	NOUN
fcis-14021	98	27	ladder	ladder	NOUN
fcis-14021	98	28	target	target	NOUN
fcis-14021	98	29	detection	detection	NOUN
fcis-14021	98	30	tasks	task	NOUN
fcis-14021	98	31	.	.	PUNCT
fcis-14021	99	1	through	through	ADP
fcis-14021	99	2	experimental	experimental	ADJ
fcis-14021	99	3	verification	verification	NOUN
fcis-14021	99	4	,	,	PUNCT
fcis-14021	99	5	we	we	PRON
fcis-14021	99	6	have	have	AUX
fcis-14021	99	7	demonstrated	demonstrate	VERB
fcis-14021	99	8	that	that	SCONJ
fcis-14021	99	9	adding	add	VERB
fcis-14021	99	10	a	a	DET
fcis-14021	99	11	ca	ca	NOUN
fcis-14021	99	12	attention	attention	NOUN
fcis-14021	99	13	mechanism	mechanism	NOUN
fcis-14021	99	14	layer	layer	NOUN
fcis-14021	99	15	can	can	AUX
fcis-14021	99	16	improve	improve	VERB
fcis-14021	99	17	the	the	DET
fcis-14021	99	18	performance	performance	NOUN
fcis-14021	99	19	of	of	ADP
fcis-14021	99	20	yolov5s	yolov5s	PROPN
fcis-14021	99	21	,	,	PUNCT
fcis-14021	99	22	enabling	enable	VERB
fcis-14021	99	23	it	it	PRON
fcis-14021	99	24	to	to	PART
fcis-14021	99	25	achieve	achieve	VERB
fcis-14021	99	26	better	well	ADJ
fcis-14021	99	27	accuracy	accuracy	NOUN
fcis-14021	99	28	and	and	CCONJ
fcis-14021	99	29	robustness	robustness	NOUN
fcis-14021	99	30	in	in	ADP
fcis-14021	99	31	object	object	NOUN
fcis-14021	99	32	detection	detection	NOUN
fcis-14021	99	33	tasks	task	NOUN
fcis-14021	99	34	,	,	PUNCT
fcis-14021	99	35	which	which	PRON
fcis-14021	99	36	helps	help	VERB
fcis-14021	99	37	further	far	ADV
fcis-14021	99	38	improve	improve	VERB
fcis-14021	99	39	the	the	DET
fcis-14021	99	40	performance	performance	NOUN
fcis-14021	99	41	and	and	CCONJ
fcis-14021	99	42	practicality	practicality	NOUN
fcis-14021	99	43	of	of	ADP
fcis-14021	99	44	object	object	NOUN
fcis-14021	99	45	detection	detection	NOUN
fcis-14021	99	46	.	.	PUNCT
fcis-14021	100	1	the	the	DET
fcis-14021	100	2	research	research	NOUN
fcis-14021	100	3	results	result	NOUN
fcis-14021	100	4	of	of	ADP
fcis-14021	100	5	this	this	DET
fcis-14021	100	6	article	article	NOUN
fcis-14021	100	7	demonstrate	demonstrate	VERB
fcis-14021	100	8	the	the	DET
fcis-14021	100	9	important	important	ADJ
fcis-14021	100	10	role	role	NOUN
fcis-14021	100	11	of	of	ADP
fcis-14021	100	12	attention	attention	NOUN
fcis-14021	100	13	mechanism	mechanism	NOUN
fcis-14021	100	14	in	in	ADP
fcis-14021	100	15	deep	deep	ADJ
fcis-14021	100	16	learning	learning	NOUN
fcis-14021	100	17	models	model	NOUN
fcis-14021	100	18	,	,	PUNCT
fcis-14021	100	19	providing	provide	VERB
fcis-14021	100	20	valuable	valuable	ADJ
fcis-14021	100	21	reference	reference	NOUN
fcis-14021	100	22	for	for	ADP
fcis-14021	100	23	future	future	ADJ
fcis-14021	100	24	research	research	NOUN
fcis-14021	100	25	.	.	PUNCT
fcis-14021	101	1	we	we	PRON
fcis-14021	101	2	believe	believe	VERB
fcis-14021	101	3	that	that	SCONJ
fcis-14021	101	4	in	in	ADP
fcis-14021	101	5	future	future	ADJ
fcis-14021	101	6	research	research	NOUN
fcis-14021	101	7	,	,	PUNCT
fcis-14021	101	8	we	we	PRON
fcis-14021	101	9	can	can	AUX
fcis-14021	101	10	further	far	ADV
fcis-14021	101	11	explore	explore	VERB
fcis-14021	101	12	and	and	CCONJ
fcis-14021	101	13	optimize	optimize	VERB
fcis-14021	101	14	the	the	DET
fcis-14021	101	15	application	application	NOUN
fcis-14021	101	16	of	of	ADP
fcis-14021	101	17	attention	attention	NOUN
fcis-14021	101	18	mechanisms	mechanism	NOUN
fcis-14021	101	19	in	in	ADP
fcis-14021	101	20	the	the	DET
fcis-14021	101	21	field	field	NOUN
fcis-14021	101	22	of	of	ADP
fcis-14021	101	23	object	object	NOUN
fcis-14021	101	24	detection	detection	NOUN
fcis-14021	101	25	,	,	PUNCT
fcis-14021	101	26	laying	lay	VERB
fcis-14021	101	27	a	a	DET
fcis-14021	101	28	solid	solid	ADJ
fcis-14021	101	29	foundation	foundation	NOUN
fcis-14021	101	30	for	for	ADP
fcis-14021	101	31	achieving	achieve	VERB
fcis-14021	101	32	higher	high	ADJ
fcis-14021	101	33	precision	precision	NOUN
fcis-14021	101	34	object	object	NOUN
fcis-14021	101	35	detection	detection	NOUN
fcis-14021	101	36	.	.	PUNCT
fcis-14021	102	1	references	reference	NOUN
fcis-14021	102	2	[	[	X
fcis-14021	102	3	1	1	NUM
fcis-14021	102	4	]	]	PUNCT
fcis-14021	102	5	wu	wu	PROPN
fcis-14021	102	6	t	t	PROPN
fcis-14021	102	7	,	,	PUNCT
fcis-14021	102	8	zhou	zhou	PROPN
fcis-14021	102	9	p	p	PROPN
fcis-14021	102	10	,	,	PUNCT
fcis-14021	102	11	liu	liu	PROPN
fcis-14021	102	12	k	k	PROPN
fcis-14021	102	13	,	,	PUNCT
fcis-14021	102	14	etal	etal	NOUN
fcis-14021	102	15	.	.	PUNCT
fcis-14021	103	1	multi	multi	ADJ
fcis-14021	103	2	-	-	ADJ
fcis-14021	103	3	agent	agent	ADJ
fcis-14021	103	4	deep	deep	ADJ
fcis-14021	103	5	reinforcement	reinforcement	NOUN
fcis-14021	103	6	learning	learning	NOUN
fcis-14021	103	7	for	for	ADP
fcis-14021	103	8	urban	urban	ADJ
fcis-14021	103	9	traffic	traffic	NOUN
fcis-14021	103	10	light	light	NOUN
fcis-14021	103	11	control	control	NOUN
fcis-14021	103	12	in	in	ADP
fcis-14021	103	13	vehicular	vehicular	ADJ
fcis-14021	103	14	networks	network	NOUN
fcis-14021	104	1	[	[	X
fcis-14021	104	2	j	j	X
fcis-14021	104	3	]	]	X
fcis-14021	104	4	.	.	PUNCT
fcis-14021	105	1	ieee	ieee	NOUN
fcis-14021	105	2	transactions	transaction	NOUN
fcis-14021	105	3	on	on	ADP
fcis-14021	105	4	vehicular	vehicular	ADJ
fcis-14021	105	5	technology	technology	NOUN
fcis-14021	105	6	,	,	PUNCT
fcis-14021	105	7	2020	2020	NUM
fcis-14021	105	8	,	,	PUNCT
fcis-14021	105	9	69(8	69(8	ADV
fcis-14021	105	10	):	):	PUNCT
fcis-14021	105	11	8243	8243	NUM
fcis-14021	105	12	-	-	SYM
fcis-14021	105	13	8256	8256	NUM
fcis-14021	105	14	.	.	PUNCT
fcis-14021	106	1	[	[	X
fcis-14021	106	2	2	2	NUM
fcis-14021	106	3	]	]	X
fcis-14021	106	4	zhang	zhang	PROPN
fcis-14021	106	5	x	x	PROPN
fcis-14021	106	6	,	,	PUNCT
fcis-14021	106	7	zhou	zhou	PROPN
fcis-14021	106	8	m	m	PROPN
fcis-14021	106	9	,	,	PUNCT
fcis-14021	106	10	qiu	qiu	PROPN
fcis-14021	106	11	p	p	PRON
fcis-14021	106	12	,	,	PUNCT
fcis-14021	106	13	etal	etal	NOUN
fcis-14021	106	14	.	.	PUNCT
fcis-14021	107	1	radar	radar	NOUN
fcis-14021	107	2	and	and	CCONJ
fcis-14021	107	3	vision	vision	NOUN
fcis-14021	107	4	fusion	fusion	NOUN
fcis-14021	107	5	for	for	ADP
fcis-14021	107	6	the	the	DET
fcis-14021	107	7	real⁃time	real⁃time	PROPN
fcis-14021	107	8	obstacle	obstacle	NOUN
fcis-14021	107	9	detection	detection	NOUN
fcis-14021	107	10	and	and	CCONJ
fcis-14021	107	11	identification	identification	NOUN
fcis-14021	107	12	[	[	X
fcis-14021	107	13	j	j	X
fcis-14021	107	14	]	]	X
fcis-14021	107	15	.	.	PUNCT
fcis-14021	108	1	industrial	industrial	ADJ
fcis-14021	108	2	robot	robot	PROPN
fcis-14021	108	3	an	an	DET
fcis-14021	108	4	international	international	ADJ
fcis-14021	108	5	journal	journal	NOUN
fcis-14021	108	6	,	,	PUNCT
fcis-14021	108	7	2019	2019	NUM
fcis-14021	108	8	,	,	PUNCT
fcis-14021	108	9	46(3	46(3	NOUN
fcis-14021	108	10	):	):	PUNCT
fcis-14021	108	11	391395	391395	NUM
fcis-14021	108	12	.	.	PUNCT
fcis-14021	109	1	[	[	X
fcis-14021	109	2	3	3	X
fcis-14021	109	3	]	]	X
fcis-14021	109	4	pablo	pablo	PROPN
fcis-14021	109	5	b	b	PROPN
fcis-14021	109	6	,	,	PUNCT
fcis-14021	109	7	christiano	christiano	PROPN
fcis-14021	109	8	b	b	PROPN
fcis-14021	109	9	,	,	PUNCT
fcis-14021	109	10	fabiano	fabiano	PROPN
fcis-14021	109	11	,	,	PUNCT
fcis-14021	109	12	et	et	PROPN
fcis-14021	109	13	al	al	PROPN
fcis-14021	109	14	.	.	PUNCT
fcis-14021	110	1	a	a	DET
fcis-14021	110	2	novel	novel	ADJ
fcis-14021	110	3	video	video	NOUN
fcis-14021	110	4	based	base	VERB
fcis-14021	110	5	system	system	NOUN
fcis-14021	110	6	for	for	ADP
fcis-14021	110	7	detecting	detect	VERB
fcis-14021	110	8	and	and	CCONJ
fcis-14021	110	9	counting	count	VERB
fcis-14021	110	10	vehicles	vehicle	NOUN
fcis-14021	110	11	at	at	ADP
fcis-14021	110	12	userdefined	userdefined	ADJ
fcis-14021	110	13	virtual	virtual	ADJ
fcis-14021	110	14	loops	loop	NOUN
fcis-14021	111	1	[	[	X
fcis-14021	111	2	j	j	X
fcis-14021	111	3	]	]	X
fcis-14021	111	4	.	.	PUNCT
fcis-14021	112	1	expert	expert	NOUN
fcis-14021	112	2	systems	system	NOUN
fcis-14021	112	3	with	with	ADP
fcis-14021	112	4	applications	application	NOUN
fcis-14021	112	5	,	,	PUNCT
fcis-14021	112	6	2015	2015	NUM
fcis-14021	112	7	,	,	PUNCT
fcis-14021	112	8	42(4	42(4	NUM
fcis-14021	112	9	):	):	PUNCT
fcis-14021	112	10	1845	1845	NUM
fcis-14021	112	11	-	-	SYM
fcis-14021	112	12	1856	1856	NUM
fcis-14021	112	13	.	.	PUNCT
fcis-14021	113	1	[	[	X
fcis-14021	113	2	4	4	X
fcis-14021	113	3	]	]	X
fcis-14021	113	4	kachach	kachach	NOUN
fcis-14021	113	5	r	r	NOUN
fcis-14021	113	6	,	,	PUNCT
fcis-14021	113	7	canas	canas	PROPN
fcis-14021	113	8	j	j	PROPN
fcis-14021	113	9	m.	m.	NOUN
fcis-14021	113	10	hybrid	hybrid	NOUN
fcis-14021	113	11	three	three	NUM
fcis-14021	113	12	-	-	PUNCT
fcis-14021	113	13	dimensional	dimensional	ADJ
fcis-14021	113	14	and	and	CCONJ
fcis-14021	113	15	support	support	VERB
fcis-14021	113	16	vector	vector	NOUN
fcis-14021	113	17	machine	machine	NOUN
fcis-14021	113	18	approachfor	approachfor	ADP
fcis-14021	113	19	automatic	automatic	ADJ
fcis-14021	113	20	vehicle	vehicle	NOUN
fcis-14021	113	21	tracking	tracking	NOUN
fcis-14021	113	22	and	and	CCONJ
fcis-14021	113	23	classification	classification	NOUN
fcis-14021	113	24	using	use	VERB
fcis-14021	113	25	a	a	DET
fcis-14021	113	26	single	single	ADJ
fcis-14021	113	27	camera	camera	NOUN
fcis-14021	114	1	[	[	X
fcis-14021	114	2	j	j	X
fcis-14021	114	3	]	]	X
fcis-14021	114	4	.	.	PUNCT
fcis-14021	115	1	journal	journal	PROPN
fcis-14021	115	2	of	of	ADP
fcis-14021	115	3	electronic	electronic	ADJ
fcis-14021	115	4	imaging	imaging	NOUN
fcis-14021	115	5	,	,	PUNCT
fcis-14021	115	6	2016	2016	NUM
fcis-14021	115	7	,	,	PUNCT
fcis-14021	115	8	25(3	25(3	NUM
fcis-14021	115	9	):	):	PUNCT
fcis-14021	115	10	033021	033021	NUM
fcis-14021	115	11	.	.	PUNCT
fcis-14021	116	1	[	[	X
fcis-14021	116	2	5	5	NUM
fcis-14021	116	3	]	]	X
fcis-14021	116	4	tian	tian	PROPN
fcis-14021	116	5	y	y	PROPN
fcis-14021	116	6	n	n	PROPN
fcis-14021	116	7	,	,	PUNCT
fcis-14021	116	8	yang	yang	PROPN
fcis-14021	116	9	g	g	PROPN
fcis-14021	116	10	d	d	PROPN
fcis-14021	116	11	,	,	PUNCT
fcis-14021	116	12	wang	wang	PROPN
fcis-14021	116	13	z	z	PROPN
fcis-14021	116	14	,	,	PUNCT
fcis-14021	116	15	et	et	PROPN
fcis-14021	116	16	al	al	PROPN
fcis-14021	116	17	.	.	PUNCT
fcis-14021	116	18	apple	apple	PROPN
fcis-14021	116	19	detection	detection	NOUN
fcis-14021	116	20	during	during	ADP
fcis-14021	116	21	different	different	ADJ
fcis-14021	116	22	growth	growth	NOUN
fcis-14021	116	23	stages	stage	NOUN
fcis-14021	116	24	in	in	ADP
fcis-14021	116	25	orchards	orchard	NOUN
fcis-14021	116	26	using	use	VERB
fcis-14021	116	27	the	the	DET
fcis-14021	116	28	improved	improved	ADJ
fcis-14021	116	29	yolo	yolo	PROPN
fcis-14021	116	30	-	-	PUNCT
fcis-14021	116	31	v3	v3	PROPN
fcis-14021	116	32	model	model	NOUN
fcis-14021	117	1	[	[	X
fcis-14021	117	2	j	j	X
fcis-14021	117	3	]	]	X
fcis-14021	117	4	.	.	PUNCT
fcis-14021	118	1	computers	computer	NOUN
fcis-14021	118	2	and	and	CCONJ
fcis-14021	118	3	electronics	electronic	NOUN
fcis-14021	118	4	in	in	ADP
fcis-14021	118	5	agriculture	agriculture	NOUN
fcis-14021	118	6	,	,	PUNCT
fcis-14021	118	7	2019,157:417	2019,157:417	NOUN
fcis-14021	118	8	-	-	SYM
fcis-14021	118	9	426	426	NUM
fcis-14021	118	10	.	.	PUNCT
fcis-14021	119	1	[	[	X
fcis-14021	119	2	6	6	NUM
fcis-14021	119	3	]	]	X
fcis-14021	119	4	redmon	redmon	PROPN
fcis-14021	119	5	j	j	PROPN
fcis-14021	119	6	,	,	PUNCT
fcis-14021	119	7	divvala	divvala	PROPN
fcis-14021	119	8	s	s	PROPN
fcis-14021	119	9	,	,	PUNCT
fcis-14021	119	10	girshick	girshick	ADJ
fcis-14021	119	11	r	r	NOUN
fcis-14021	119	12	,	,	PUNCT
fcis-14021	119	13	et	et	PROPN
fcis-14021	119	14	al	al	PROPN
fcis-14021	119	15	.	.	PUNCT
fcis-14021	120	1	you	you	PRON
fcis-14021	120	2	only	only	ADV
fcis-14021	120	3	look	look	VERB
fcis-14021	120	4	once	once	ADV
fcis-14021	120	5	:	:	PUNCT
fcis-14021	120	6	unified	unified	ADJ
fcis-14021	120	7	,	,	PUNCT
fcis-14021	120	8	real	real	ADJ
fcis-14021	120	9	-	-	PUNCT
fcis-14021	120	10	time	time	NOUN
fcis-14021	120	11	object	object	NOUN
fcis-14021	120	12	detection	detection	NOUN
fcis-14021	121	1	[	[	X
fcis-14021	121	2	c	c	X
fcis-14021	121	3	]	]	PUNCT
fcis-14021	121	4	.	.	PUNCT
fcis-14021	122	1	proceedings	proceeding	NOUN
fcis-14021	122	2	of	of	ADP
fcis-14021	122	3	the	the	DET
fcis-14021	122	4	ieee	ieee	NOUN
fcis-14021	122	5	conference	conference	NOUN
fcis-14021	122	6	on	on	ADP
fcis-14021	122	7	computer	computer	NOUN
fcis-14021	122	8	vision	vision	NOUN
fcis-14021	122	9	and	and	CCONJ
fcis-14021	122	10	pattern	pattern	NOUN
fcis-14021	122	11	recognition	recognition	NOUN
fcis-14021	122	12	.	.	PUNCT
fcis-14021	123	1	ieee	ieee	NOUN
fcis-14021	123	2	,	,	PUNCT
fcis-14021	123	3	2016	2016	NUM
fcis-14021	123	4	:	:	PUNCT
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fcis-14021	123	6	-	-	SYM
fcis-14021	123	7	788	788	NUM
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fcis-14021	124	17	c	c	X
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fcis-14021	129	4	.	.	PUNCT
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fcis-14021	130	2	,	,	PUNCT
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fcis-14021	130	4	.	.	PUNCT
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fcis-14021	134	4	.	.	PUNCT
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fcis-14021	136	1	[	[	X
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fcis-14021	137	13	):	):	PUNCT
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fcis-14021	141	1	[	[	X
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fcis-14021	143	1	[	[	X
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fcis-14021	156	3	]	]	X
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fcis-14021	156	13	et	et	PROPN
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fcis-14021	158	1	[	[	X
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fcis-14021	161	1	arxiv	arxiv	PROPN
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fcis-14021	161	4	:	:	PUNCT
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fcis-14021	162	11	xin	xin	PROPN
fcis-14021	162	12	,	,	PUNCT
fcis-14021	162	13	et	et	PROPN
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fcis-14021	162	15	.	.	PUNCT
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fcis-14021	163	2	detection	detection	NOUN
fcis-14021	163	3	method	method	NOUN
fcis-14021	163	4	of	of	ADP
fcis-14021	163	5	lentinus	lentinus	ADJ
fcis-14021	163	6	edodes	edode	NOUN
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fcis-14021	163	8	on	on	ADP
fcis-14021	163	9	improved	improved	ADJ
fcis-14021	163	10	yolov4	yolov4	PROPN
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fcis-14021	163	15	.	.	PUNCT
fcis-14021	164	1	journal	journal	PROPN
fcis-14021	164	2	of	of	ADP
fcis-14021	164	3	harbin	harbin	PROPN
fcis-14021	164	4	university	university	PROPN
fcis-14021	164	5	of	of	ADP
fcis-14021	164	6	science	science	NOUN
fcis-14021	164	7	and	and	CCONJ
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fcis-14021	164	9	,	,	PUNCT
fcis-14021	164	10	2022,27(4):23	2022,27(4):23	NUM
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fcis-14021	164	12	31	31	NUM
fcis-14021	164	13	.	.	PUNCT
fcis-14021	165	1	journal	journal	PROPN
fcis-14021	165	2	of	of	ADP
fcis-14021	165	3	harbin	harbin	PROPN
fcis-14021	165	4	university	university	PROPN
fcis-14021	165	5	of	of	ADP
fcis-14021	165	6	science	science	NOUN
fcis-14021	165	7	and	and	CCONJ
fcis-14021	165	8	technology	technology	NOUN
fcis-14021	165	9	.	.	PUNCT
fcis-14021	166	1	[	[	X
fcis-14021	166	2	19	19	NUM
fcis-14021	166	3	]	]	X
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fcis-14021	166	7	yan	yan	PROPN
fcis-14021	166	8	s	s	PROPN
fcis-14021	166	9	,	,	PUNCT
fcis-14021	166	10	duan	duan	PROPN
fcis-14021	166	11	c.	c.	PROPN
fcis-14021	166	12	a	a	DET
fcis-14021	166	13	lightweight	lightweight	ADJ
fcis-14021	166	14	vehicles	vehicle	NOUN
fcis-14021	166	15	detection	detection	NOUN
fcis-14021	166	16	network	network	NOUN
fcis-14021	166	17	model	model	NOUN
fcis-14021	166	18	based	base	VERB
fcis-14021	166	19	on	on	ADP
fcis-14021	166	20	yolov5	yolov5	NOUN
fcis-14021	167	1	[	[	X
fcis-14021	167	2	j	j	X
fcis-14021	167	3	]	]	X
fcis-14021	167	4	.	.	PUNCT
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fcis-14021	168	2	applications	application	NOUN
fcis-14021	168	3	of	of	ADP
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fcis-14021	168	5	intelligence	intelligence	NOUN
fcis-14021	168	6	,	,	PUNCT
fcis-14021	168	7	2022	2022	NUM
fcis-14021	168	8	,	,	PUNCT
fcis-14021	168	9	113	113	NUM
fcis-14021	168	10	:	:	PUNCT
fcis-14021	168	11	104	104	NUM
fcis-14021	168	12	914	914	NUM
fcis-14021	168	13	.	.	PUNCT
fcis-14021	169	1	[	[	X
fcis-14021	169	2	20	20	NUM
fcis-14021	169	3	]	]	SYM
fcis-14021	169	4	fawzi	fawzi	NOUN
fcis-14021	169	5	a	a	NOUN
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fcis-14021	169	7	samulowitz	samulowitz	NOUN
fcis-14021	169	8	h	h	PROPN
fcis-14021	169	9	,	,	PUNCT
fcis-14021	169	10	turaga	turaga	NOUN
fcis-14021	169	11	d	d	NOUN
fcis-14021	169	12	,	,	PUNCT
fcis-14021	169	13	et	et	PROPN
fcis-14021	169	14	al	al	PROPN
fcis-14021	169	15	.	.	PROPN
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fcis-14021	170	2	data	datum	NOUN
fcis-14021	170	3	augmentation	augmentation	NOUN
fcis-14021	170	4	for	for	ADP
fcis-14021	170	5	image	image	NOUN
fcis-14021	170	6	classification	classification	NOUN
fcis-14021	171	1	[	[	X
fcis-14021	171	2	c	c	X
fcis-14021	171	3	]	]	PUNCT
fcis-14021	171	4	.	.	PUNCT
fcis-14021	172	1	ieee	ieee	PROPN
fcis-14021	172	2	international	international	PROPN
fcis-14021	172	3	conference	conference	NOUN
fcis-14021	172	4	on	on	ADP
fcis-14021	172	5	image	image	NOUN
fcis-14021	172	6	processing	processing	NOUN
fcis-14021	172	7	.	.	PUNCT
fcis-14021	173	1	ieee	ieee	PROPN
fcis-14021	173	2	,	,	PUNCT
fcis-14021	173	3	2016	2016	NUM
fcis-14021	173	4	:	:	PUNCT
fcis-14021	173	5	3688	3688	NUM
fcis-14021	173	6	-	-	SYM
fcis-14021	173	7	3692	3692	NUM
fcis-14021	173	8	.	.	PUNCT
fcis-14021	174	1	[	[	X
fcis-14021	174	2	21	21	NUM
fcis-14021	174	3	]	]	X
fcis-14021	174	4	yin	yin	PROPN
fcis-14021	174	5	zhang	zhang	PROPN
fcis-14021	174	6	,	,	PUNCT
fcis-14021	174	7	guiyi	guiyi	PROPN
fcis-14021	174	8	zhu	zhu	PROPN
fcis-14021	174	9	,	,	PUNCT
fcis-14021	174	10	tianjun	tianjun	PROPN
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fcis-14021	174	12	,	,	PUNCT
fcis-14021	174	13	kun	kun	PROPN
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fcis-14021	174	15	,	,	PUNCT
fcis-14021	174	16	junhua	junhua	PROPN
fcis-14021	174	17	yan	yan	PROPN
fcis-14021	174	18	.	.	PUNCT
fcis-14021	175	1	small	small	ADJ
fcis-14021	175	2	object	object	NOUN
fcis-14021	175	3	detection	detection	NOUN
fcis-14021	175	4	in	in	ADP
fcis-14021	175	5	remote	remote	ADJ
fcis-14021	175	6	sensing	sensing	NOUN
fcis-14021	175	7	images	image	NOUN
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fcis-14021	175	10	feature	feature	NOUN
fcis-14021	175	11	fusion	fusion	NOUN
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fcis-14021	175	15	.	.	PUNCT
fcis-14021	176	1	acta	acta	PROPN
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fcis-14021	176	4	,	,	PUNCT
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fcis-14021	176	6	,	,	PUNCT
fcis-14021	176	7	42(24	42(24	NUM
fcis-14021	176	8	):	):	PUNCT
fcis-14021	176	9	2415001	2415001	NUM
fcis-14021	176	10	.	.	PUNCT
fcis-14021	177	1	[	[	X
fcis-14021	177	2	22	22	NUM
fcis-14021	177	3	]	]	X
fcis-14021	177	4	liu	liu	PROPN
fcis-14021	177	5	w	w	PROPN
fcis-14021	177	6	,	,	PUNCT
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fcis-14021	177	9	,	,	PUNCT
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fcis-14021	177	12	,	,	PUNCT
fcis-14021	177	13	et	et	PROPN
fcis-14021	177	14	al	al	PROPN
fcis-14021	177	15	.	.	PROPN
fcis-14021	177	16	ssd	ssd	PROPN
fcis-14021	177	17	:	:	PUNCT
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fcis-14021	177	19	shot	shot	NOUN
fcis-14021	177	20	multibox	multibox	PROPN
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fcis-14021	177	23	.	.	PUNCT
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fcis-14021	178	3	conference	conference	NOUN
fcis-14021	178	4	on	on	ADP
fcis-14021	178	5	computer	computer	NOUN
fcis-14021	178	6	vision	vision	NOUN
fcis-14021	178	7	(	(	PUNCT
fcis-14021	178	8	eccv	eccv	ADV
fcis-14021	178	9	)	)	PUNCT
fcis-14021	178	10	,	,	PUNCT
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fcis-14021	178	12	:	:	PUNCT
fcis-14021	178	13	21	21	NUM
fcis-14021	178	14	-	-	SYM
fcis-14021	178	15	37	37	NUM
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fcis-14021	179	7	divvala	divvala	PROPN
fcis-14021	179	8	s	s	PROPN
fcis-14021	179	9	,	,	PUNCT
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fcis-14021	179	12	,	,	PUNCT
fcis-14021	179	13	et	et	PROPN
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fcis-14021	180	2	only	only	ADV
fcis-14021	180	3	look	look	VERB
fcis-14021	180	4	once	once	ADV
fcis-14021	180	5	:	:	PUNCT
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fcis-14021	180	7	,	,	PUNCT
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fcis-14021	180	9	-	-	PUNCT
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fcis-14021	181	5	computer	computer	NOUN
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fcis-14021	181	10	(	(	PUNCT
fcis-14021	181	11	cvpr	cvpr	NOUN
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fcis-14021	181	13	,	,	PUNCT
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fcis-14021	181	15	:	:	PUNCT
fcis-14021	181	16	779	779	NUM
fcis-14021	181	17	-	-	SYM
fcis-14021	181	18	788	788	NUM
fcis-14021	181	19	.	.	PUNCT
fcis-14021	182	1	[	[	X
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fcis-14021	182	3	]	]	X
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fcis-14021	182	9	,	,	PUNCT
fcis-14021	182	10	ieee	ieee	PROPN
fcis-14021	182	11	.	.	PUNCT
fcis-14021	183	1	yolo9000	yolo9000	PROPN
fcis-14021	183	2	:	:	PUNCT
fcis-14021	183	3	better	well	ADJ
fcis-14021	183	4	,	,	PUNCT
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fcis-14021	183	6	,	,	PUNCT
fcis-14021	183	7	stronger[c	stronger[c	NOUN
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fcis-14021	183	9	.	.	PUNCT
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fcis-14021	184	2	ieee	ieee	NOUN
fcis-14021	184	3	/	/	SYM
fcis-14021	184	4	cvf	cvf	NOUN
fcis-14021	184	5	conference	conference	NOUN
fcis-14021	184	6	on	on	ADP
fcis-14021	184	7	computer	computer	NOUN
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fcis-14021	184	9	and	and	CCONJ
fcis-14021	184	10	pattern	pattern	NOUN
fcis-14021	184	11	recognition	recognition	NOUN
fcis-14021	184	12	(	(	PUNCT
fcis-14021	184	13	cvpr	cvpr	NOUN
fcis-14021	184	14	)	)	PUNCT
fcis-14021	184	15	,	,	PUNCT
fcis-14021	184	16	2017	2017	NUM
fcis-14021	184	17	:	:	PUNCT
fcis-14021	184	18	6517	6517	NUM
fcis-14021	184	19	-	-	SYM
fcis-14021	184	20	6525	6525	NUM
fcis-14021	184	21	.	.	PUNCT
fcis-14021	185	1	[	[	X
fcis-14021	185	2	25	25	NUM
fcis-14021	185	3	]	]	X
fcis-14021	185	4	ma	ma	PROPN
fcis-14021	185	5	n	n	PROPN
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fcis-14021	185	7	,	,	PUNCT
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fcis-14021	185	9	x	x	SYM
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fcis-14021	185	11	,	,	PUNCT
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fcis-14021	185	13	h	h	PROPN
fcis-14021	185	14	t	t	PROPN
fcis-14021	185	15	,	,	PUNCT
fcis-14021	185	16	et	et	PROPN
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fcis-14021	185	18	.	.	PUNCT
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fcis-14021	186	2	v2	v2	PROPN
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fcis-14021	186	5	guidelines	guideline	NOUN
fcis-14021	186	6	for	for	ADP
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fcis-14021	186	8	cnn	cnn	PROPN
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fcis-14021	186	10	design[c	design[c	PROPN
fcis-14021	186	11	]	]	PUNCT
fcis-14021	186	12	.	.	PUNCT
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fcis-14021	187	2	european	european	ADJ
fcis-14021	187	3	conference	conference	NOUN
fcis-14021	187	4	on	on	ADP
fcis-14021	187	5	computer	computer	NOUN
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fcis-14021	187	7	(	(	PUNCT
fcis-14021	187	8	eccv	eccv	ADV
fcis-14021	187	9	)	)	PUNCT
fcis-14021	187	10	,	,	PUNCT
fcis-14021	187	11	2018	2018	NUM
fcis-14021	187	12	:	:	PUNCT
fcis-14021	187	13	122	122	NUM
fcis-14021	187	14	-	-	SYM
fcis-14021	187	15	138	138	NUM
fcis-14021	187	16	.	.	PUNCT
fcis-14021	188	1	[	[	X
fcis-14021	188	2	26	26	NUM
fcis-14021	188	3	]	]	X
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fcis-14021	188	5	x	x	PROPN
fcis-14021	188	6	,	,	PUNCT
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fcis-14021	188	8	x	x	SYM
fcis-14021	188	9	y	y	PROPN
fcis-14021	188	10	,	,	PUNCT
fcis-14021	188	11	lin	lin	PROPN
fcis-14021	188	12	m	m	PROPN
fcis-14021	188	13	x	x	PROPN
fcis-14021	188	14	,	,	PUNCT
fcis-14021	188	15	et	et	PROPN
fcis-14021	188	16	al	al	PROPN
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fcis-14021	188	19	:	:	PUNCT
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fcis-14021	189	9	and	and	CCONJ
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fcis-14021	189	11	recognition	recognition	NOUN
fcis-14021	189	12	(	(	PUNCT
fcis-14021	189	13	cvpr	cvpr	NOUN
fcis-14021	189	14	)	)	PUNCT
fcis-14021	189	15	,	,	PUNCT
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fcis-14021	189	17	:	:	PUNCT
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fcis-14021	189	19	-	-	SYM
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fcis-14021	189	21	.	.	PUNCT
fcis-14021	190	1	[	[	X
fcis-14021	190	2	27	27	NUM
fcis-14021	190	3	]	]	PUNCT
fcis-14021	190	4	am	be	AUX
fcis-14021	190	5	a	a	PRON
fcis-14021	190	6	,	,	PUNCT
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fcis-14021	190	16	.	.	PUNCT
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fcis-14021	191	3	in	in	ADP
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fcis-14021	191	5	restoration	restoration	NOUN
fcis-14021	191	6	:	:	PUNCT
fcis-14021	191	7	a	a	DET
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fcis-14021	191	9	]	]	PUNCT
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fcis-14021	192	4	)	)	PUNCT
fcis-14021	192	5	.	.	PUNCT
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fcis-14021	193	3	21	21	NUM
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fcis-14021	193	5	23	23	NUM
fcis-14021	193	6	(	(	PUNCT
fcis-14021	193	7	5	5	NUM
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fcis-14021	193	9	2385	2385	NUM
fcis-14021	193	10	.	.	PUNCT
fcis-14021	194	1	[	[	X
fcis-14021	194	2	28	28	NUM
fcis-14021	194	3	]	]	X
fcis-14021	194	4	j	j	PROPN
fcis-14021	194	5	h	h	PROPN
fcis-14021	194	6	,	,	PUNCT
fcis-14021	194	7	l	l	PROPN
fcis-14021	194	8	s	s	X
fcis-14021	194	9	,	,	PUNCT
fcis-14021	194	10	s	s	NOUN
fcis-14021	194	11	a	a	PRON
fcis-14021	194	12	,	,	PUNCT
fcis-14021	194	13	et	et	PROPN
fcis-14021	194	14	al	al	PROPN
fcis-14021	194	15	.	.	PUNCT
fcis-14021	194	16	squeeze	squeeze	NOUN
fcis-14021	194	17	-	-	PUNCT
fcis-14021	194	18	and	and	CCONJ
fcis-14021	194	19	-	-	PUNCT
fcis-14021	194	20	excitation	excitation	NOUN
fcis-14021	194	21	networks[j	networks[j	PROPN
fcis-14021	194	22	]	]	X
fcis-14021	194	23	.	.	PUNCT
fcis-14021	195	1	ieee	ieee	PROPN
fcis-14021	195	2	trans	trans	PROPN
fcis-14021	195	3	pattern	pattern	PROPN
fcis-14021	195	4	anal	anal	PROPN
fcis-14021	195	5	mach	mach	NOUN
fcis-14021	195	6	intell	intell	PROPN
fcis-14021	195	7	.	.	PUNCT
fcis-14021	196	1	2020	2020	NUM
fcis-14021	197	1	aug;42(8):20112023	aug;42(8):20112023	PROPN
fcis-14021	197	2	.	.	PUNCT
fcis-14021	198	1	doi:(1939	doi:(1939	NOUN
fcis-14021	198	2	-	-	PUNCT
fcis-14021	198	3	3539	3539	NUM
fcis-14021	198	4	(	(	PUNCT
fcis-14021	198	5	electronic	electronic	ADJ
fcis-14021	198	6	)	)	PUNCT
fcis-14021	198	7	):	):	PUNCT
fcis-14021	198	8	2011	2011	NUM
fcis-14021	198	9	-	-	SYM
fcis-14021	198	10	2023	2023	NUM
fcis-14021	198	11	.	.	PUNCT