id	sid	tid	token	lemma	pos
fcis-3164	1	1	frontiers	frontier	NOUN
fcis-3164	1	2	in	in	ADP
fcis-3164	1	3	computing	computing	NOUN
fcis-3164	1	4	and	and	CCONJ
fcis-3164	1	5	intelligent	intelligent	ADJ
fcis-3164	1	6	systems	system	NOUN
fcis-3164	1	7	issn	issn	VERB
fcis-3164	1	8	:	:	PUNCT
fcis-3164	1	9	2832	2832	NUM
fcis-3164	1	10	-	-	SYM
fcis-3164	1	11	6024	6024	NUM
fcis-3164	1	12	|	|	NOUN
fcis-3164	1	13	vol	vol	NOUN
fcis-3164	1	14	.	.	PROPN
fcis-3164	2	1	2	2	NUM
fcis-3164	2	2	,	,	PUNCT
fcis-3164	2	3	no	no	INTJ
fcis-3164	2	4	.	.	NOUN
fcis-3164	2	5	1	1	NUM
fcis-3164	2	6	,	,	PUNCT
fcis-3164	2	7	2022	2022	NUM
fcis-3164	2	8	83	83	NUM
fcis-3164	2	9	small	small	ADJ
fcis-3164	2	10	pedestrian	pedestrian	NOUN
fcis-3164	2	11	target	target	NOUN
fcis-3164	2	12	detection	detection	NOUN
fcis-3164	2	13	based	base	VERB
fcis-3164	2	14	on	on	ADP
fcis-3164	2	15	yolov5	yolov5	PROPN
fcis-3164	2	16	ziyi	ziyi	PROPN
fcis-3164	2	17	zhang	zhang	PROPN
fcis-3164	2	18	1	1	NUM
fcis-3164	2	19	,	,	PUNCT
fcis-3164	2	20	xuewen	xuewen	NOUN
fcis-3164	2	21	ding	de	VERB
fcis-3164	2	22	1,2	1,2	NUM
fcis-3164	2	23	,	,	PUNCT
fcis-3164	2	24	*	*	SYM
fcis-3164	2	25	1	1	NUM
fcis-3164	2	26	school	school	NOUN
fcis-3164	2	27	of	of	ADP
fcis-3164	2	28	electronic	electronic	ADJ
fcis-3164	2	29	engineering	engineering	NOUN
fcis-3164	2	30	,	,	PUNCT
fcis-3164	2	31	tianjin	tianjin	PROPN
fcis-3164	2	32	university	university	PROPN
fcis-3164	2	33	of	of	ADP
fcis-3164	2	34	technology	technology	NOUN
fcis-3164	2	35	and	and	CCONJ
fcis-3164	2	36	education	education	NOUN
fcis-3164	2	37	,	,	PUNCT
fcis-3164	2	38	tianjin	tianjin	PROPN
fcis-3164	2	39	300222	300222	NUM
fcis-3164	2	40	,	,	PUNCT
fcis-3164	2	41	china	china	PROPN
fcis-3164	2	42	2	2	NUM
fcis-3164	2	43	tianjin	tianjin	PROPN
fcis-3164	2	44	yunzhitong	yunzhitong	PROPN
fcis-3164	2	45	technology	technology	PROPN
fcis-3164	2	46	co.	co.	PROPN
fcis-3164	2	47	,	,	PUNCT
fcis-3164	2	48	ltd	ltd	PROPN
fcis-3164	2	49	.	.	PROPN
fcis-3164	2	50	,	,	PUNCT
fcis-3164	2	51	tianjin	tianjin	PROPN
fcis-3164	2	52	300350	300350	NUM
fcis-3164	2	53	,	,	PUNCT
fcis-3164	2	54	china	china	PROPN
fcis-3164	2	55	*	*	PUNCT
fcis-3164	2	56	corresponding	correspond	VERB
fcis-3164	2	57	author	author	NOUN
fcis-3164	2	58	:	:	PUNCT
fcis-3164	2	59	xuewen	xuewen	PROPN
fcis-3164	2	60	ding	ding	PROPN
fcis-3164	2	61	(	(	PUNCT
fcis-3164	2	62	email	email	NOUN
fcis-3164	2	63	:	:	PUNCT
fcis-3164	2	64	dingxw1@126.com	dingxw1@126.com	X
fcis-3164	2	65	)	)	PUNCT
fcis-3164	3	1	abstract	abstract	NOUN
fcis-3164	3	2	:	:	PUNCT
fcis-3164	3	3	yolov5s	yolov5s	NOUN
fcis-3164	3	4	is	be	AUX
fcis-3164	3	5	the	the	DET
fcis-3164	3	6	network	network	NOUN
fcis-3164	3	7	with	with	ADP
fcis-3164	3	8	the	the	DET
fcis-3164	3	9	smallest	small	ADJ
fcis-3164	3	10	depth	depth	NOUN
fcis-3164	3	11	and	and	CCONJ
fcis-3164	3	12	feature	feature	NOUN
fcis-3164	3	13	map	map	NOUN
fcis-3164	3	14	width	width	ADJ
fcis-3164	3	15	and	and	CCONJ
fcis-3164	3	16	the	the	DET
fcis-3164	3	17	fastest	fast	ADJ
fcis-3164	3	18	image	image	NOUN
fcis-3164	3	19	inference	inference	NOUN
fcis-3164	3	20	,	,	PUNCT
fcis-3164	3	21	but	but	CCONJ
fcis-3164	3	22	when	when	SCONJ
fcis-3164	3	23	applied	apply	VERB
fcis-3164	3	24	to	to	ADP
fcis-3164	3	25	small	small	ADJ
fcis-3164	3	26	pedestrian	pedestrian	NOUN
fcis-3164	3	27	target	target	NOUN
fcis-3164	3	28	detection	detection	NOUN
fcis-3164	3	29	in	in	ADP
fcis-3164	3	30	complex	complex	ADJ
fcis-3164	3	31	scenes	scene	NOUN
fcis-3164	3	32	,	,	PUNCT
fcis-3164	3	33	the	the	DET
fcis-3164	3	34	detection	detection	NOUN
fcis-3164	3	35	still	still	ADV
fcis-3164	3	36	suffers	suffer	VERB
fcis-3164	3	37	from	from	ADP
fcis-3164	3	38	wrong	wrong	ADJ
fcis-3164	3	39	and	and	CCONJ
fcis-3164	3	40	missed	miss	VERB
fcis-3164	3	41	detections	detection	NOUN
fcis-3164	3	42	.	.	PUNCT
fcis-3164	4	1	to	to	PART
fcis-3164	4	2	address	address	VERB
fcis-3164	4	3	this	this	DET
fcis-3164	4	4	problem	problem	NOUN
fcis-3164	4	5	,	,	PUNCT
fcis-3164	4	6	an	an	DET
fcis-3164	4	7	improved	improved	ADJ
fcis-3164	4	8	model	model	NOUN
fcis-3164	4	9	based	base	VERB
fcis-3164	4	10	on	on	ADP
fcis-3164	4	11	yolov5s	yolov5s	PROPN
fcis-3164	4	12	is	be	AUX
fcis-3164	4	13	proposed	propose	VERB
fcis-3164	4	14	with	with	ADP
fcis-3164	4	15	the	the	DET
fcis-3164	4	16	addition	addition	NOUN
fcis-3164	4	17	of	of	ADP
fcis-3164	4	18	a	a	DET
fcis-3164	4	19	new	new	ADJ
fcis-3164	4	20	convolutional	convolutional	ADJ
fcis-3164	4	21	neural	neural	ADJ
fcis-3164	4	22	module	module	NOUN
fcis-3164	4	23	,	,	PUNCT
fcis-3164	4	24	spd	spd	NOUN
fcis-3164	4	25	-	-	PUNCT
fcis-3164	4	26	conv	conv	ADJ
fcis-3164	4	27	,	,	PUNCT
fcis-3164	4	28	which	which	PRON
fcis-3164	4	29	improves	improve	VERB
fcis-3164	4	30	the	the	DET
fcis-3164	4	31	accuracy	accuracy	NOUN
fcis-3164	4	32	of	of	ADP
fcis-3164	4	33	the	the	DET
fcis-3164	4	34	network	network	NOUN
fcis-3164	4	35	in	in	ADP
fcis-3164	4	36	detection	detection	NOUN
fcis-3164	4	37	tasks	task	NOUN
fcis-3164	4	38	of	of	ADP
fcis-3164	4	39	low	low	ADJ
fcis-3164	4	40	-	-	PUNCT
fcis-3164	4	41	resolution	resolution	NOUN
fcis-3164	4	42	images	image	NOUN
fcis-3164	4	43	or	or	CCONJ
fcis-3164	4	44	smaller	small	ADJ
fcis-3164	4	45	objects	object	NOUN
fcis-3164	4	46	.	.	PUNCT
fcis-3164	5	1	the	the	DET
fcis-3164	5	2	improved	improved	ADJ
fcis-3164	5	3	yolov5s	yolov5s	PROPN
fcis-3164	5	4	-	-	PUNCT
fcis-3164	5	5	spd	spd	PROPN
fcis-3164	5	6	model	model	NOUN
fcis-3164	5	7	obtained	obtain	VERB
fcis-3164	5	8	better	well	ADJ
fcis-3164	5	9	detection	detection	NOUN
fcis-3164	5	10	results	result	NOUN
fcis-3164	5	11	compared	compare	VERB
fcis-3164	5	12	with	with	ADP
fcis-3164	5	13	the	the	DET
fcis-3164	5	14	original	original	ADJ
fcis-3164	5	15	network	network	NOUN
fcis-3164	5	16	model	model	NOUN
fcis-3164	5	17	,	,	PUNCT
fcis-3164	5	18	with	with	ADP
fcis-3164	5	19	an	an	DET
fcis-3164	5	20	average	average	ADJ
fcis-3164	5	21	accuracy	accuracy	NOUN
fcis-3164	5	22	improvement	improvement	NOUN
fcis-3164	5	23	of	of	ADP
fcis-3164	5	24	3.9	3.9	NUM
fcis-3164	5	25	%	%	NOUN
fcis-3164	5	26	and	and	CCONJ
fcis-3164	5	27	an	an	DET
fcis-3164	5	28	increase	increase	NOUN
fcis-3164	5	29	in	in	ADP
fcis-3164	5	30	map	map	NOUN
fcis-3164	5	31	value	value	NOUN
fcis-3164	5	32	of	of	ADP
fcis-3164	5	33	about	about	ADP
fcis-3164	5	34	9.9	9.9	NUM
fcis-3164	5	35	%	%	NOUN
fcis-3164	5	36	.	.	PUNCT
fcis-3164	6	1	keywords	keyword	NOUN
fcis-3164	6	2	:	:	PUNCT
fcis-3164	6	3	yolov5s	yolov5s	NOUN
fcis-3164	6	4	;	;	PUNCT
fcis-3164	6	5	pedestrian	pedestrian	NOUN
fcis-3164	6	6	detection	detection	NOUN
fcis-3164	6	7	;	;	PUNCT
fcis-3164	6	8	yolov5s	yolov5s	NOUN
fcis-3164	6	9	-	-	PUNCT
fcis-3164	6	10	spd	spd	NOUN
fcis-3164	6	11	;	;	PUNCT
fcis-3164	6	12	small	small	ADJ
fcis-3164	6	13	pedestrian	pedestrian	NOUN
fcis-3164	6	14	target	target	NOUN
fcis-3164	6	15	.	.	PUNCT
fcis-3164	7	1	1	1	X
fcis-3164	7	2	.	.	X
fcis-3164	7	3	introduction	introduction	NOUN
fcis-3164	7	4	target	target	NOUN
fcis-3164	7	5	detection	detection	NOUN
fcis-3164	7	6	is	be	AUX
fcis-3164	7	7	a	a	DET
fcis-3164	7	8	hot	hot	ADJ
fcis-3164	7	9	research	research	NOUN
fcis-3164	7	10	topic	topic	NOUN
fcis-3164	7	11	in	in	ADP
fcis-3164	7	12	the	the	DET
fcis-3164	7	13	field	field	NOUN
fcis-3164	7	14	of	of	ADP
fcis-3164	7	15	machine	machine	NOUN
fcis-3164	7	16	vision	vision	NOUN
fcis-3164	7	17	[	[	X
fcis-3164	7	18	1	1	NUM
fcis-3164	7	19	]	]	PUNCT
fcis-3164	7	20	,	,	PUNCT
fcis-3164	7	21	and	and	CCONJ
fcis-3164	7	22	pedestrian	pedestrian	NOUN
fcis-3164	7	23	detection	detection	NOUN
fcis-3164	7	24	[	[	X
fcis-3164	7	25	2	2	NUM
fcis-3164	7	26	]	]	PUNCT
fcis-3164	7	27	,	,	PUNCT
fcis-3164	7	28	which	which	PRON
fcis-3164	7	29	is	be	AUX
fcis-3164	7	30	one	one	NUM
fcis-3164	7	31	of	of	ADP
fcis-3164	7	32	the	the	DET
fcis-3164	7	33	important	important	ADJ
fcis-3164	7	34	components	component	NOUN
fcis-3164	7	35	of	of	ADP
fcis-3164	7	36	the	the	DET
fcis-3164	7	37	target	target	NOUN
fcis-3164	7	38	detection	detection	NOUN
fcis-3164	7	39	task	task	NOUN
fcis-3164	7	40	,	,	PUNCT
fcis-3164	7	41	has	have	AUX
fcis-3164	7	42	a	a	DET
fcis-3164	7	43	higher	high	ADJ
fcis-3164	7	44	research	research	NOUN
fcis-3164	7	45	and	and	CCONJ
fcis-3164	7	46	commercial	commercial	ADJ
fcis-3164	7	47	value	value	NOUN
fcis-3164	7	48	.	.	PUNCT
fcis-3164	8	1	it	it	PRON
fcis-3164	8	2	is	be	AUX
fcis-3164	8	3	a	a	DET
fcis-3164	8	4	prerequisite	prerequisite	NOUN
fcis-3164	8	5	for	for	ADP
fcis-3164	8	6	pedestrian	pedestrian	NOUN
fcis-3164	8	7	segmentation	segmentation	NOUN
fcis-3164	8	8	[	[	X
fcis-3164	8	9	3	3	X
fcis-3164	8	10	]	]	PUNCT
fcis-3164	8	11	and	and	CCONJ
fcis-3164	8	12	pedestrian	pedestrian	NOUN
fcis-3164	8	13	reidentification	reidentification	NOUN
fcis-3164	8	14	[	[	X
fcis-3164	8	15	4	4	NUM
fcis-3164	8	16	]	]	PUNCT
fcis-3164	8	17	,	,	PUNCT
fcis-3164	8	18	and	and	CCONJ
fcis-3164	8	19	drives	drive	VERB
fcis-3164	8	20	the	the	DET
fcis-3164	8	21	development	development	NOUN
fcis-3164	8	22	of	of	ADP
fcis-3164	8	23	other	other	ADJ
fcis-3164	8	24	target	target	NOUN
fcis-3164	8	25	detection	detection	NOUN
fcis-3164	8	26	tasks	task	NOUN
fcis-3164	8	27	.	.	PUNCT
fcis-3164	9	1	as	as	SCONJ
fcis-3164	9	2	pedestrian	pedestrian	NOUN
fcis-3164	9	3	detection	detection	NOUN
fcis-3164	9	4	in	in	ADP
fcis-3164	9	5	realistic	realistic	ADJ
fcis-3164	9	6	environments	environment	NOUN
fcis-3164	9	7	is	be	AUX
fcis-3164	9	8	unavoidably	unavoidably	ADV
fcis-3164	9	9	affected	affect	VERB
fcis-3164	9	10	by	by	ADP
fcis-3164	9	11	the	the	DET
fcis-3164	9	12	environment	environment	NOUN
fcis-3164	9	13	,	,	PUNCT
fcis-3164	9	14	e.g.	e.g.	ADV
fcis-3164	9	15	exposure	exposure	NOUN
fcis-3164	9	16	and	and	CCONJ
fcis-3164	9	17	shadow	shadow	NOUN
fcis-3164	9	18	surfaces	surface	NOUN
fcis-3164	9	19	due	due	ADJ
fcis-3164	9	20	to	to	ADP
fcis-3164	9	21	strong	strong	ADJ
fcis-3164	9	22	daylight	daylight	NOUN
fcis-3164	9	23	exposure	exposure	NOUN
fcis-3164	9	24	;	;	PUNCT
fcis-3164	9	25	blurred	blurred	ADJ
fcis-3164	9	26	pedestrian	pedestrian	NOUN
fcis-3164	9	27	features	feature	NOUN
fcis-3164	9	28	caused	cause	VERB
fcis-3164	9	29	by	by	ADP
fcis-3164	9	30	foggy[5	foggy[5	NOUN
fcis-3164	9	31	]	]	X
fcis-3164	9	32	and	and	CCONJ
fcis-3164	9	33	rainy[6	rainy[6	ADV
fcis-3164	9	34	]	]	X
fcis-3164	9	35	weather	weather	NOUN
fcis-3164	9	36	;	;	PUNCT
fcis-3164	9	37	the	the	DET
fcis-3164	9	38	distance	distance	NOUN
fcis-3164	9	39	of	of	ADP
fcis-3164	9	40	pedestrians	pedestrian	NOUN
fcis-3164	9	41	from	from	ADP
fcis-3164	9	42	the	the	DET
fcis-3164	9	43	camera	camera	NOUN
fcis-3164	9	44	in	in	ADP
fcis-3164	9	45	surveillance	surveillance	NOUN
fcis-3164	9	46	scenes	scene	NOUN
fcis-3164	9	47	,	,	PUNCT
fcis-3164	9	48	which	which	PRON
fcis-3164	9	49	can	can	AUX
fcis-3164	9	50	lead	lead	VERB
fcis-3164	9	51	to	to	ADP
fcis-3164	9	52	differences	difference	NOUN
fcis-3164	9	53	in	in	ADP
fcis-3164	9	54	scale	scale	NOUN
fcis-3164	9	55	spanning	span	VERB
fcis-3164	9	56	;	;	PUNCT
fcis-3164	9	57	and	and	CCONJ
fcis-3164	9	58	the	the	DET
fcis-3164	9	59	small	small	ADJ
fcis-3164	9	60	pedestrian	pedestrian	NOUN
fcis-3164	9	61	problem	problem	NOUN
fcis-3164	9	62	caused	cause	VERB
fcis-3164	9	63	by	by	ADP
fcis-3164	9	64	the	the	DET
fcis-3164	9	65	dense	dense	ADJ
fcis-3164	9	66	pedestrian	pedestrian	NOUN
fcis-3164	9	67	flow	flow	NOUN
fcis-3164	9	68	in	in	ADP
fcis-3164	9	69	special	special	ADJ
fcis-3164	9	70	scenes	scene	NOUN
fcis-3164	9	71	such	such	ADJ
fcis-3164	9	72	as	as	ADP
fcis-3164	9	73	high	high	ADJ
fcis-3164	9	74	-	-	PUNCT
fcis-3164	9	75	speed	speed	NOUN
fcis-3164	9	76	railway	railway	NOUN
fcis-3164	9	77	stations	station	NOUN
fcis-3164	9	78	,	,	PUNCT
fcis-3164	9	79	airports	airport	NOUN
fcis-3164	9	80	,	,	PUNCT
fcis-3164	9	81	and	and	CCONJ
fcis-3164	9	82	public	public	ADJ
fcis-3164	9	83	gathering	gathering	NOUN
fcis-3164	9	84	places[7	places[7	X
fcis-3164	9	85	]	]	PUNCT
fcis-3164	9	86	can	can	AUX
fcis-3164	9	87	all	all	PRON
fcis-3164	9	88	affect	affect	VERB
fcis-3164	9	89	the	the	DET
fcis-3164	9	90	effectiveness	effectiveness	NOUN
fcis-3164	9	91	of	of	ADP
fcis-3164	9	92	detection	detection	NOUN
fcis-3164	9	93	.	.	PUNCT
fcis-3164	10	1	to	to	PART
fcis-3164	10	2	address	address	VERB
fcis-3164	10	3	the	the	DET
fcis-3164	10	4	problems	problem	NOUN
fcis-3164	10	5	of	of	ADP
fcis-3164	10	6	low	low	ADJ
fcis-3164	10	7	detection	detection	NOUN
fcis-3164	10	8	accuracy	accuracy	NOUN
fcis-3164	10	9	of	of	ADP
fcis-3164	10	10	obscured	obscured	ADJ
fcis-3164	10	11	pedestrian	pedestrian	NOUN
fcis-3164	10	12	targets	target	NOUN
fcis-3164	10	13	and	and	CCONJ
fcis-3164	10	14	small	small	ADJ
fcis-3164	10	15	pedestrian	pedestrian	NOUN
fcis-3164	10	16	targets	target	NOUN
fcis-3164	10	17	in	in	ADP
fcis-3164	10	18	real	real	ADJ
fcis-3164	10	19	scenes	scene	NOUN
fcis-3164	10	20	,	,	PUNCT
fcis-3164	10	21	zou	zou	PROPN
fcis-3164	10	22	ziyin	ziyin	PROPN
fcis-3164	10	23	and	and	CCONJ
fcis-3164	10	24	li	li	PROPN
fcis-3164	10	25	jinyu[8	jinyu[8	PROPN
fcis-3164	10	26	]	]	PUNCT
fcis-3164	10	27	proposed	propose	VERB
fcis-3164	10	28	a	a	DET
fcis-3164	10	29	series	series	NOUN
fcis-3164	10	30	of	of	ADP
fcis-3164	10	31	solutions	solution	NOUN
fcis-3164	10	32	to	to	PART
fcis-3164	10	33	further	far	ADV
fcis-3164	10	34	improve	improve	VERB
fcis-3164	10	35	the	the	DET
fcis-3164	10	36	detection	detection	NOUN
fcis-3164	10	37	accuracy	accuracy	NOUN
fcis-3164	10	38	and	and	CCONJ
fcis-3164	10	39	optimise	optimise	VERB
fcis-3164	10	40	the	the	DET
fcis-3164	10	41	performance	performance	NOUN
fcis-3164	10	42	of	of	ADP
fcis-3164	10	43	the	the	DET
fcis-3164	10	44	model	model	NOUN
fcis-3164	10	45	.	.	PUNCT
fcis-3164	11	1	however	however	ADV
fcis-3164	11	2	,	,	PUNCT
fcis-3164	11	3	for	for	ADP
fcis-3164	11	4	small	small	ADJ
fcis-3164	11	5	pedestrian	pedestrian	NOUN
fcis-3164	11	6	targets	target	NOUN
fcis-3164	11	7	,	,	PUNCT
fcis-3164	11	8	the	the	DET
fcis-3164	11	9	features	feature	NOUN
fcis-3164	11	10	extracted	extract	VERB
fcis-3164	11	11	by	by	ADP
fcis-3164	11	12	the	the	DET
fcis-3164	11	13	model	model	NOUN
fcis-3164	11	14	contain	contain	VERB
fcis-3164	11	15	a	a	DET
fcis-3164	11	16	large	large	ADJ
fcis-3164	11	17	amount	amount	NOUN
fcis-3164	11	18	of	of	ADP
fcis-3164	11	19	redundant	redundant	ADJ
fcis-3164	11	20	background	background	NOUN
fcis-3164	11	21	information	information	NOUN
fcis-3164	11	22	,	,	PUNCT
fcis-3164	11	23	and	and	CCONJ
fcis-3164	11	24	the	the	DET
fcis-3164	11	25	detection	detection	NOUN
fcis-3164	11	26	accuracy	accuracy	NOUN
fcis-3164	11	27	still	still	ADV
fcis-3164	11	28	needs	need	VERB
fcis-3164	11	29	to	to	PART
fcis-3164	11	30	be	be	AUX
fcis-3164	11	31	improved	improve	VERB
fcis-3164	11	32	.	.	PUNCT
fcis-3164	12	1	to	to	PART
fcis-3164	12	2	address	address	VERB
fcis-3164	12	3	these	these	DET
fcis-3164	12	4	problems	problem	NOUN
fcis-3164	12	5	,	,	PUNCT
fcis-3164	12	6	this	this	DET
fcis-3164	12	7	paper	paper	NOUN
fcis-3164	12	8	proposes	propose	VERB
fcis-3164	12	9	an	an	DET
fcis-3164	12	10	improved	improved	ADJ
fcis-3164	12	11	spd	spd	NOUN
fcis-3164	12	12	-	-	PUNCT
fcis-3164	12	13	conv	conv	ADJ
fcis-3164	12	14	(	(	PUNCT
fcis-3164	12	15	space	space	NOUN
fcis-3164	12	16	-	-	PUNCT
fcis-3164	12	17	to	to	ADP
fcis-3164	12	18	-	-	PUNCT
fcis-3164	12	19	depth	depth	NOUN
fcis-3164	12	20	layer	layer	NOUN
fcis-3164	12	21	and	and	CCONJ
fcis-3164	12	22	non	non	ADJ
fcis-3164	12	23	-	-	ADJ
fcis-3164	12	24	strided	strided	ADJ
fcis-3164	12	25	convolution	convolution	NOUN
fcis-3164	12	26	layer	layer	NOUN
fcis-3164	12	27	)	)	PUNCT
fcis-3164	12	28	algorithm	algorithm	NOUN
fcis-3164	12	29	based	base	VERB
fcis-3164	12	30	on	on	ADP
fcis-3164	12	31	yolov5s[9	yolov5s[9	PROPN
fcis-3164	12	32	]	]	PUNCT
fcis-3164	12	33	for	for	ADP
fcis-3164	12	34	the	the	DET
fcis-3164	12	35	detection	detection	NOUN
fcis-3164	12	36	of	of	ADP
fcis-3164	12	37	small	small	ADJ
fcis-3164	12	38	pedestrian	pedestrian	NOUN
fcis-3164	12	39	targets	target	NOUN
fcis-3164	12	40	in	in	ADP
fcis-3164	12	41	complex	complex	ADJ
fcis-3164	12	42	scenes[10	scenes[10	NOUN
fcis-3164	12	43	]	]	PUNCT
fcis-3164	12	44	,	,	PUNCT
fcis-3164	12	45	which	which	PRON
fcis-3164	12	46	improves	improve	VERB
fcis-3164	12	47	the	the	DET
fcis-3164	12	48	detection	detection	NOUN
fcis-3164	12	49	capability	capability	NOUN
fcis-3164	12	50	of	of	ADP
fcis-3164	12	51	the	the	DET
fcis-3164	12	52	network	network	NOUN
fcis-3164	12	53	for	for	ADP
fcis-3164	12	54	small	small	ADJ
fcis-3164	12	55	pedestrian	pedestrian	NOUN
fcis-3164	12	56	targets	target	NOUN
fcis-3164	12	57	.	.	PUNCT
fcis-3164	13	1	2	2	X
fcis-3164	13	2	.	.	X
fcis-3164	13	3	yolov5	yolov5	NOUN
fcis-3164	13	4	algorithm	algorithm	NOUN
fcis-3164	13	5	and	and	CCONJ
fcis-3164	13	6	improvements	improvement	NOUN
fcis-3164	13	7	2.1	2.1	NUM
fcis-3164	13	8	.	.	PUNCT
fcis-3164	14	1	yolov5	yolov5	NOUN
fcis-3164	14	2	network	network	NOUN
fcis-3164	14	3	structure	structure	NOUN
fcis-3164	14	4	the	the	DET
fcis-3164	14	5	yolov5	yolov5	NOUN
fcis-3164	14	6	model	model	NOUN
fcis-3164	14	7	was	be	AUX
fcis-3164	14	8	proposed	propose	VERB
fcis-3164	14	9	in	in	ADP
fcis-3164	14	10	june	june	PROPN
fcis-3164	14	11	2020	2020	NUM
fcis-3164	14	12	by	by	ADP
fcis-3164	14	13	glenn	glenn	PROPN
fcis-3164	14	14	jocher	jocher	PROPN
fcis-3164	14	15	from	from	ADP
fcis-3164	14	16	the	the	DET
fcis-3164	14	17	ultralytics	ultralytic	NOUN
fcis-3164	14	18	team	team	NOUN
fcis-3164	14	19	,	,	PUNCT
fcis-3164	14	20	who	who	PRON
fcis-3164	14	21	updated	update	VERB
fcis-3164	14	22	yolov5	yolov5	NOUN
fcis-3164	14	23	after	after	ADP
fcis-3164	14	24	studying	study	VERB
fcis-3164	14	25	yolov3	yolov3	PROPN
fcis-3164	14	26	.	.	PUNCT
fcis-3164	15	1	the	the	DET
fcis-3164	15	2	initial	initial	ADJ
fcis-3164	15	3	version	version	NOUN
fcis-3164	15	4	of	of	ADP
fcis-3164	15	5	yolov5	yolov5	NOUN
fcis-3164	15	6	is	be	AUX
fcis-3164	15	7	very	very	ADV
fcis-3164	15	8	fast	fast	ADJ
fcis-3164	15	9	,	,	PUNCT
fcis-3164	15	10	efficient	efficient	ADJ
fcis-3164	15	11	and	and	CCONJ
fcis-3164	15	12	easy	easy	ADJ
fcis-3164	15	13	to	to	PART
fcis-3164	15	14	use	use	VERB
fcis-3164	15	15	.	.	PUNCT
fcis-3164	16	1	yolov5s	yolov5s	NOUN
fcis-3164	16	2	is	be	AUX
fcis-3164	16	3	the	the	DET
fcis-3164	16	4	smallest	small	ADJ
fcis-3164	16	5	network	network	NOUN
fcis-3164	16	6	in	in	ADP
fcis-3164	16	7	terms	term	NOUN
fcis-3164	16	8	of	of	ADP
fcis-3164	16	9	depth	depth	NOUN
fcis-3164	16	10	and	and	CCONJ
fcis-3164	16	11	width	width	NOUN
fcis-3164	16	12	of	of	ADP
fcis-3164	16	13	the	the	DET
fcis-3164	16	14	feature	feature	NOUN
fcis-3164	16	15	map	map	NOUN
fcis-3164	16	16	,	,	PUNCT
fcis-3164	16	17	and	and	CCONJ
fcis-3164	16	18	the	the	DET
fcis-3164	16	19	fastest	fast	ADJ
fcis-3164	16	20	inference	inference	NOUN
fcis-3164	16	21	speed	speed	NOUN
fcis-3164	16	22	of	of	ADP
fcis-3164	16	23	0.007s	0.007s	PROPN
fcis-3164	16	24	.	.	PUNCT
fcis-3164	17	1	the	the	DET
fcis-3164	17	2	network	network	NOUN
fcis-3164	17	3	structure	structure	NOUN
fcis-3164	17	4	of	of	ADP
fcis-3164	17	5	yolov5s	yolov5s	PROPN
fcis-3164	17	6	consists	consist	VERB
fcis-3164	17	7	of	of	ADP
fcis-3164	17	8	four	four	NUM
fcis-3164	17	9	main	main	ADJ
fcis-3164	17	10	components	component	NOUN
fcis-3164	17	11	,	,	PUNCT
fcis-3164	17	12	namely	namely	ADV
fcis-3164	17	13	the	the	DET
fcis-3164	17	14	input	input	NOUN
fcis-3164	17	15	,	,	PUNCT
fcis-3164	17	16	the	the	DET
fcis-3164	17	17	reference	reference	NOUN
fcis-3164	17	18	network	network	NOUN
fcis-3164	17	19	,	,	PUNCT
fcis-3164	17	20	the	the	DET
fcis-3164	17	21	neck	neck	NOUN
fcis-3164	17	22	network	network	NOUN
fcis-3164	17	23	,	,	PUNCT
fcis-3164	17	24	and	and	CCONJ
fcis-3164	17	25	the	the	DET
fcis-3164	17	26	head	head	NOUN
fcis-3164	17	27	output.the	output.the	DET
fcis-3164	17	28	network	network	NOUN
fcis-3164	17	29	structure	structure	NOUN
fcis-3164	17	30	of	of	ADP
fcis-3164	17	31	yolov5s	yolov5s	PROPN
fcis-3164	17	32	is	be	AUX
fcis-3164	17	33	shown	show	VERB
fcis-3164	17	34	in	in	ADP
fcis-3164	17	35	figure	figure	NOUN
fcis-3164	17	36	1	1	NUM
fcis-3164	17	37	.	.	PUNCT
fcis-3164	17	38	figure	figure	NOUN
fcis-3164	17	39	1	1	NUM
fcis-3164	17	40	.	.	PUNCT
fcis-3164	18	1	yolov5s	yolov5s	NOUN
fcis-3164	18	2	model	model	NOUN
fcis-3164	18	3	structure	structure	NOUN
fcis-3164	18	4	2.2	2.2	NUM
fcis-3164	18	5	.	.	PUNCT
fcis-3164	19	1	yolov5	yolov5	NOUN
fcis-3164	19	2	effect	effect	NOUN
fcis-3164	19	3	demonstration	demonstration	NOUN
fcis-3164	19	4	the	the	DET
fcis-3164	19	5	test	test	NOUN
fcis-3164	19	6	results	result	NOUN
fcis-3164	19	7	of	of	ADP
fcis-3164	19	8	the	the	DET
fcis-3164	19	9	different	different	ADJ
fcis-3164	19	10	versions	version	NOUN
fcis-3164	19	11	of	of	ADP
fcis-3164	19	12	the	the	DET
fcis-3164	19	13	yolov5	yolov5	NOUN
fcis-3164	19	14	detection	detection	NOUN
fcis-3164	19	15	algorithm	algorithm	NOUN
fcis-3164	19	16	on	on	ADP
fcis-3164	19	17	the	the	DET
fcis-3164	19	18	ms	ms	PROPN
fcis-3164	19	19	coco	coco	PROPN
fcis-3164	19	20	dataset	dataset	VERB
fcis-3164	19	21	without	without	ADP
fcis-3164	19	22	using	use	VERB
fcis-3164	19	23	any	any	DET
fcis-3164	19	24	other	other	ADJ
fcis-3164	19	25	datasets	dataset	NOUN
fcis-3164	19	26	or	or	CCONJ
fcis-3164	19	27	pre	pre	VERB
fcis-3164	19	28	-	-	ADJ
fcis-3164	19	29	trained	trained	ADJ
fcis-3164	19	30	weights	weight	NOUN
fcis-3164	19	31	are	be	AUX
fcis-3164	19	32	shown	show	VERB
fcis-3164	19	33	in	in	ADP
fcis-3164	19	34	figure	figure	NOUN
fcis-3164	19	35	2	2	NUM
fcis-3164	19	36	.	.	PUNCT
fcis-3164	19	37	where	where	SCONJ
fcis-3164	19	38	the	the	DET
fcis-3164	19	39	grey	grey	ADJ
fcis-3164	19	40	dash	dash	NOUN
fcis-3164	19	41	is	be	AUX
fcis-3164	19	42	the	the	DET
fcis-3164	19	43	efficientdet	efficientdet	NOUN
fcis-3164	19	44	model	model	NOUN
fcis-3164	19	45	and	and	CCONJ
fcis-3164	19	46	the	the	DET
fcis-3164	19	47	remaining	remain	VERB
fcis-3164	19	48	four	four	NUM
fcis-3164	19	49	are	be	AUX
fcis-3164	19	50	different	different	ADJ
fcis-3164	19	51	network	network	NOUN
fcis-3164	19	52	models	model	NOUN
fcis-3164	19	53	of	of	ADP
fcis-3164	19	54	the	the	DET
fcis-3164	19	55	yolov5	yolov5	NOUN
fcis-3164	19	56	family	family	NOUN
fcis-3164	19	57	.	.	PUNCT
fcis-3164	20	1	figure	figure	NOUN
fcis-3164	20	2	2	2	NUM
fcis-3164	20	3	.	.	PUNCT
fcis-3164	21	1	testing	testing	NOUN
fcis-3164	21	2	of	of	ADP
fcis-3164	21	3	the	the	DET
fcis-3164	21	4	yolov5	yolov5	NOUN
fcis-3164	21	5	weighting	weight	VERB
fcis-3164	21	6	file	file	NOUN
fcis-3164	21	7	84	84	NUM
fcis-3164	21	8	2.3	2.3	NUM
fcis-3164	21	9	.	.	PUNCT
fcis-3164	22	1	improvements	improvement	NOUN
fcis-3164	22	2	to	to	ADP
fcis-3164	22	3	the	the	DET
fcis-3164	22	4	yolov5	yolov5	NOUN
fcis-3164	22	5	algorithm	algorithm	PROPN
fcis-3164	22	6	convolutional	convolutional	ADJ
fcis-3164	22	7	neural	neural	ADJ
fcis-3164	22	8	networks	network	NOUN
fcis-3164	22	9	(	(	PUNCT
fcis-3164	22	10	cnn	cnn	PROPN
fcis-3164	22	11	)	)	PUNCT
fcis-3164	22	12	have	have	AUX
fcis-3164	22	13	achieved	achieve	VERB
fcis-3164	22	14	great	great	ADJ
fcis-3164	22	15	success	success	NOUN
fcis-3164	22	16	in	in	ADP
fcis-3164	22	17	computer	computer	NOUN
fcis-3164	22	18	vision	vision	NOUN
fcis-3164	22	19	tasks	task	NOUN
fcis-3164	22	20	such	such	ADJ
fcis-3164	22	21	as	as	ADP
fcis-3164	22	22	image	image	NOUN
fcis-3164	22	23	classification	classification	NOUN
fcis-3164	22	24	and	and	CCONJ
fcis-3164	22	25	target	target	NOUN
fcis-3164	22	26	detection	detection	NOUN
fcis-3164	22	27	.	.	PUNCT
fcis-3164	23	1	however	however	ADV
fcis-3164	23	2	,	,	PUNCT
fcis-3164	23	3	the	the	DET
fcis-3164	23	4	loss	loss	NOUN
fcis-3164	23	5	of	of	ADP
fcis-3164	23	6	fine	fine	ADV
fcis-3164	23	7	-	-	PUNCT
fcis-3164	23	8	grained	grain	VERB
fcis-3164	23	9	information	information	NOUN
fcis-3164	23	10	caused	cause	VERB
fcis-3164	23	11	by	by	ADP
fcis-3164	23	12	the	the	DET
fcis-3164	23	13	convolutional	convolutional	ADJ
fcis-3164	23	14	and	and	CCONJ
fcis-3164	23	15	pooling	pool	VERB
fcis-3164	23	16	layers	layer	NOUN
fcis-3164	23	17	of	of	ADP
fcis-3164	23	18	the	the	DET
fcis-3164	23	19	convolutional	convolutional	ADJ
fcis-3164	23	20	neural	neural	ADJ
fcis-3164	23	21	network	network	NOUN
fcis-3164	23	22	itself	itself	PRON
fcis-3164	23	23	and	and	CCONJ
fcis-3164	23	24	the	the	DET
fcis-3164	23	25	low	low	ADJ
fcis-3164	23	26	feature	feature	NOUN
fcis-3164	23	27	extraction	extraction	NOUN
fcis-3164	23	28	capability	capability	NOUN
fcis-3164	23	29	lead	lead	NOUN
fcis-3164	23	30	to	to	ADP
fcis-3164	23	31	a	a	DET
fcis-3164	23	32	rapid	rapid	ADJ
fcis-3164	23	33	degradation	degradation	NOUN
fcis-3164	23	34	of	of	ADP
fcis-3164	23	35	the	the	DET
fcis-3164	23	36	network	network	NOUN
fcis-3164	23	37	's	's	PART
fcis-3164	23	38	detection	detection	NOUN
fcis-3164	23	39	accuracy	accuracy	NOUN
fcis-3164	23	40	in	in	ADP
fcis-3164	23	41	low	low	ADJ
fcis-3164	23	42	-	-	PUNCT
fcis-3164	23	43	resolution	resolution	NOUN
fcis-3164	23	44	images	image	NOUN
fcis-3164	23	45	or	or	CCONJ
fcis-3164	23	46	detection	detection	NOUN
fcis-3164	23	47	tasks	task	NOUN
fcis-3164	23	48	of	of	ADP
fcis-3164	23	49	smaller	small	ADJ
fcis-3164	23	50	objects	object	NOUN
fcis-3164	23	51	.	.	PUNCT
fcis-3164	24	1	therefore	therefore	ADV
fcis-3164	24	2	,	,	PUNCT
fcis-3164	24	3	this	this	DET
fcis-3164	24	4	paper	paper	NOUN
fcis-3164	24	5	adds	add	VERB
fcis-3164	24	6	a	a	DET
fcis-3164	24	7	new	new	ADJ
fcis-3164	24	8	convolutional	convolutional	ADJ
fcis-3164	24	9	neural	neural	ADJ
fcis-3164	24	10	module	module	NOUN
fcis-3164	24	11	,	,	PUNCT
fcis-3164	24	12	spd	spd	NOUN
fcis-3164	24	13	-	-	PUNCT
fcis-3164	24	14	conv	conv	ADJ
fcis-3164	24	15	,	,	PUNCT
fcis-3164	24	16	which	which	PRON
fcis-3164	24	17	is	be	AUX
fcis-3164	24	18	composed	compose	VERB
fcis-3164	24	19	of	of	ADP
fcis-3164	24	20	a	a	DET
fcis-3164	24	21	space	space	NOUN
fcis-3164	24	22	-	-	PUNCT
fcis-3164	24	23	to	to	ADP
fcis-3164	24	24	-	-	PUNCT
fcis-3164	24	25	depth	depth	NOUN
fcis-3164	24	26	(	(	PUNCT
fcis-3164	24	27	spd	spd	NOUN
fcis-3164	24	28	)	)	PUNCT
fcis-3164	24	29	layer	layer	NOUN
fcis-3164	24	30	and	and	CCONJ
fcis-3164	24	31	a	a	DET
fcis-3164	24	32	non	non	ADJ
fcis-3164	24	33	-	-	ADJ
fcis-3164	24	34	strided	strided	ADJ
fcis-3164	24	35	convolution	convolution	NOUN
fcis-3164	24	36	(	(	PUNCT
fcis-3164	24	37	conv	conv	ADJ
fcis-3164	24	38	)	)	PUNCT
fcis-3164	24	39	layer	layer	NOUN
fcis-3164	24	40	.	.	PUNCT
fcis-3164	25	1	where	where	SCONJ
fcis-3164	25	2	space_to_depth	space_to_depth	PROPN
fcis-3164	25	3	means	mean	VERB
fcis-3164	25	4	superimposing	superimpose	VERB
fcis-3164	25	5	the	the	DET
fcis-3164	25	6	dimensions	dimension	NOUN
fcis-3164	25	7	on	on	ADP
fcis-3164	25	8	the	the	DET
fcis-3164	25	9	length	length	NOUN
fcis-3164	25	10	and	and	CCONJ
fcis-3164	25	11	width	width	VERB
fcis-3164	25	12	to	to	ADP
fcis-3164	25	13	the	the	DET
fcis-3164	25	14	depth	depth	NOUN
fcis-3164	25	15	,	,	PUNCT
fcis-3164	25	16	which	which	PRON
fcis-3164	25	17	is	be	AUX
fcis-3164	25	18	equivalent	equivalent	ADJ
fcis-3164	25	19	to	to	ADP
fcis-3164	25	20	the	the	DET
fcis-3164	25	21	pooling	pool	VERB
fcis-3164	25	22	layer	layer	NOUN
fcis-3164	25	23	,	,	PUNCT
fcis-3164	25	24	but	but	CCONJ
fcis-3164	25	25	pooling	pooling	NOUN
fcis-3164	25	26	is	be	AUX
fcis-3164	25	27	choosing	choose	VERB
fcis-3164	25	28	one	one	NUM
fcis-3164	25	29	of	of	ADP
fcis-3164	25	30	all	all	DET
fcis-3164	25	31	sizes	size	NOUN
fcis-3164	25	32	,	,	PUNCT
fcis-3164	25	33	whereas	whereas	SCONJ
fcis-3164	25	34	this	this	DET
fcis-3164	25	35	method	method	NOUN
fcis-3164	25	36	takes	take	VERB
fcis-3164	25	37	one	one	NUM
fcis-3164	25	38	of	of	ADP
fcis-3164	25	39	the	the	DET
fcis-3164	25	40	sizes	size	NOUN
fcis-3164	25	41	and	and	CCONJ
fcis-3164	25	42	superimposes	superimpose	VERB
fcis-3164	25	43	the	the	DET
fcis-3164	25	44	rest	rest	NOUN
fcis-3164	25	45	to	to	ADP
fcis-3164	25	46	the	the	DET
fcis-3164	25	47	depth	depth	NOUN
fcis-3164	25	48	direction	direction	NOUN
fcis-3164	25	49	,	,	PUNCT
fcis-3164	25	50	thus	thus	ADV
fcis-3164	25	51	preserving	preserve	VERB
fcis-3164	25	52	the	the	DET
fcis-3164	25	53	low	low	ADJ
fcis-3164	25	54	latitude	latitude	NOUN
fcis-3164	25	55	features	feature	VERB
fcis-3164	25	56	,	,	PUNCT
fcis-3164	25	57	the	the	DET
fcis-3164	25	58	figure	figure	NOUN
fcis-3164	25	59	below	below	ADP
fcis-3164	25	60	shows	show	NOUN
fcis-3164	25	61	when	when	SCONJ
fcis-3164	25	62	block_size=2	block_size=2	NOUN
fcis-3164	25	63	(	(	PUNCT
fcis-3164	25	64	block_size	block_size	VERB
fcis-3164	25	65	is	be	AUX
fcis-3164	25	66	the	the	DET
fcis-3164	25	67	pooled	pooled	ADJ
fcis-3164	25	68	size	size	NOUN
fcis-3164	25	69	of	of	ADP
fcis-3164	25	70	the	the	DET
fcis-3164	25	71	block	block	NOUN
fcis-3164	25	72	in	in	ADP
fcis-3164	25	73	the	the	DET
fcis-3164	25	74	pooling	pooling	NOUN
fcis-3164	25	75	)	)	PUNCT
fcis-3164	25	76	the	the	DET
fcis-3164	25	77	schematic	schematic	ADJ
fcis-3164	25	78	diagram	diagram	NOUN
fcis-3164	25	79	of	of	ADP
fcis-3164	25	80	spd	spd	ADJ
fcis-3164	25	81	-	-	PUNCT
fcis-3164	25	82	conv	conv	ADJ
fcis-3164	26	1	.	.	PUNCT
fcis-3164	26	2	figure	figure	NOUN
fcis-3164	26	3	3	3	NUM
fcis-3164	26	4	.	.	PUNCT
fcis-3164	26	5	schematic	schematic	ADJ
fcis-3164	26	6	diagram	diagram	NOUN
fcis-3164	26	7	of	of	ADP
fcis-3164	26	8	spd	spd	NOUN
fcis-3164	26	9	-	-	PUNCT
fcis-3164	26	10	conv	conv	ADJ
fcis-3164	26	11	with	with	ADP
fcis-3164	26	12	block_size=2	block_size=2	NOUN
fcis-3164	26	13	the	the	DET
fcis-3164	26	14	yolov5s	yolov5s	PROPN
fcis-3164	26	15	-	-	PUNCT
fcis-3164	26	16	spd	spd	PROPN
fcis-3164	26	17	model	model	NOUN
fcis-3164	26	18	after	after	ADP
fcis-3164	26	19	adding	add	VERB
fcis-3164	26	20	spd	spd	ADJ
fcis-3164	26	21	-	-	PUNCT
fcis-3164	26	22	conv	conv	NOUN
fcis-3164	26	23	simply	simply	ADV
fcis-3164	26	24	replaces	replace	VERB
fcis-3164	26	25	the	the	DET
fcis-3164	26	26	yolov5	yolov5	NOUN
fcis-3164	26	27	stride-2	stride-2	NUM
fcis-3164	26	28	convolution	convolution	NOUN
fcis-3164	26	29	with	with	ADP
fcis-3164	26	30	spd	spd	NOUN
fcis-3164	26	31	-	-	PUNCT
fcis-3164	26	32	conv	conv	ADJ
fcis-3164	26	33	,	,	PUNCT
fcis-3164	26	34	which	which	PRON
fcis-3164	26	35	is	be	AUX
fcis-3164	26	36	structured	structure	VERB
fcis-3164	26	37	as	as	SCONJ
fcis-3164	26	38	follows	follow	VERB
fcis-3164	26	39	.	.	PUNCT
fcis-3164	27	1	figure	figure	VERB
fcis-3164	27	2	4	4	NUM
fcis-3164	27	3	.	.	X
fcis-3164	27	4	yolov5s	yolov5s	NOUN
fcis-3164	27	5	-	-	PUNCT
fcis-3164	27	6	spd	spd	PROPN
fcis-3164	27	7	model	model	NOUN
fcis-3164	27	8	structure	structure	NOUN
fcis-3164	27	9	3	3	NUM
fcis-3164	27	10	.	.	PUNCT
fcis-3164	28	1	experiments	experiment	NOUN
fcis-3164	28	2	and	and	CCONJ
fcis-3164	28	3	analysis	analysis	NOUN
fcis-3164	28	4	of	of	ADP
fcis-3164	28	5	results	result	NOUN
fcis-3164	28	6	3.1	3.1	NUM
fcis-3164	28	7	.	.	PUNCT
fcis-3164	29	1	description	description	NOUN
fcis-3164	29	2	of	of	ADP
fcis-3164	29	3	the	the	DET
fcis-3164	29	4	relevant	relevant	ADJ
fcis-3164	29	5	data	data	NOUN
fcis-3164	29	6	sets	set	NOUN
fcis-3164	29	7	in	in	ADP
fcis-3164	29	8	this	this	DET
fcis-3164	29	9	paper	paper	NOUN
fcis-3164	29	10	,	,	PUNCT
fcis-3164	29	11	we	we	PRON
fcis-3164	29	12	use	use	VERB
fcis-3164	29	13	the	the	DET
fcis-3164	29	14	caltech	caltech	PROPN
fcis-3164	29	15	pedestrian	pedestrian	NOUN
fcis-3164	29	16	dataset	dataset	NOUN
fcis-3164	29	17	released	release	VERB
fcis-3164	29	18	by	by	ADP
fcis-3164	29	19	the	the	DET
fcis-3164	29	20	california	california	PROPN
fcis-3164	29	21	institute	institute	PROPN
fcis-3164	29	22	of	of	ADP
fcis-3164	29	23	science	science	NOUN
fcis-3164	29	24	and	and	CCONJ
fcis-3164	29	25	technology	technology	NOUN
fcis-3164	29	26	in	in	ADP
fcis-3164	29	27	2009	2009	NUM
fcis-3164	29	28	,	,	PUNCT
fcis-3164	29	29	which	which	PRON
fcis-3164	29	30	consists	consist	VERB
fcis-3164	29	31	of	of	ADP
fcis-3164	29	32	the	the	DET
fcis-3164	29	33	training	training	NOUN
fcis-3164	29	34	set	set	VERB
fcis-3164	29	35	+	+	CCONJ
fcis-3164	29	36	test	test	NOUN
fcis-3164	29	37	set	set	VERB
fcis-3164	29	38	data	datum	NOUN
fcis-3164	29	39	in	in	ADP
fcis-3164	29	40	.	.	PUNCT
fcis-3164	29	41	seq	seq	NOUN
fcis-3164	29	42	format	format	NOUN
fcis-3164	29	43	and	and	CCONJ
fcis-3164	29	44	the	the	DET
fcis-3164	29	45	pedestrian	pedestrian	NOUN
fcis-3164	29	46	label	label	NOUN
fcis-3164	29	47	data	datum	NOUN
fcis-3164	29	48	in.ebb	in.ebb	PROPN
fcis-3164	29	49	(	(	PUNCT
fcis-3164	29	50	video	video	NOUN
fcis-3164	29	51	bounding	bounding	NOUN
fcis-3164	29	52	box	box	NOUN
fcis-3164	29	53	)	)	PUNCT
fcis-3164	29	54	format	format	NOUN
fcis-3164	29	55	.	.	PUNCT
fcis-3164	30	1	for	for	ADP
fcis-3164	30	2	training	training	NOUN
fcis-3164	30	3	with	with	ADP
fcis-3164	30	4	yolo	yolo	PROPN
fcis-3164	30	5	,	,	PUNCT
fcis-3164	30	6	we	we	PRON
fcis-3164	30	7	need	need	VERB
fcis-3164	30	8	.jpg	.jpg	NOUN
fcis-3164	30	9	images	image	NOUN
fcis-3164	30	10	and	and	CCONJ
fcis-3164	30	11	.txt	.txt	ADJ
fcis-3164	30	12	annotated	annotated	ADJ
fcis-3164	30	13	data	datum	NOUN
fcis-3164	30	14	,	,	PUNCT
fcis-3164	30	15	so	so	SCONJ
fcis-3164	30	16	we	we	PRON
fcis-3164	30	17	need	need	VERB
fcis-3164	30	18	to	to	PART
fcis-3164	30	19	convert	convert	VERB
fcis-3164	30	20	the	the	DET
fcis-3164	30	21	.	.	PROPN
fcis-3164	30	22	seq	seq	PROPN
fcis-3164	30	23	.	.	PROPN
fcis-3164	31	1	vbb	vbb	PROPN
fcis-3164	31	2	data	datum	NOUN
fcis-3164	31	3	into	into	ADP
fcis-3164	31	4	the	the	DET
fcis-3164	31	5	corresponding	corresponding	ADJ
fcis-3164	31	6	images	image	NOUN
fcis-3164	31	7	and	and	CCONJ
fcis-3164	31	8	annotated	annotated	ADJ
fcis-3164	31	9	data	datum	NOUN
fcis-3164	31	10	.	.	PUNCT
fcis-3164	32	1	3.2	3.2	NUM
fcis-3164	32	2	.	.	PUNCT
fcis-3164	33	1	comparison	comparison	NOUN
fcis-3164	33	2	of	of	ADP
fcis-3164	33	3	detection	detection	NOUN
fcis-3164	33	4	accuracy	accuracy	NOUN
fcis-3164	33	5	of	of	ADP
fcis-3164	33	6	different	different	ADJ
fcis-3164	33	7	models	model	NOUN
fcis-3164	33	8	in	in	ADP
fcis-3164	33	9	this	this	DET
fcis-3164	33	10	experiment	experiment	NOUN
fcis-3164	33	11	,	,	PUNCT
fcis-3164	33	12	2000	2000	NUM
fcis-3164	33	13	images	image	NOUN
fcis-3164	33	14	from	from	ADP
fcis-3164	33	15	caltech	caltech	NOUN
fcis-3164	33	16	pedestrian	pedestrian	NOUN
fcis-3164	33	17	were	be	AUX
fcis-3164	33	18	randomly	randomly	ADV
fcis-3164	33	19	selected	select	VERB
fcis-3164	33	20	as	as	ADP
fcis-3164	33	21	the	the	DET
fcis-3164	33	22	training	training	NOUN
fcis-3164	33	23	set	set	NOUN
fcis-3164	33	24	and	and	CCONJ
fcis-3164	33	25	400	400	NUM
fcis-3164	33	26	images	image	NOUN
fcis-3164	33	27	were	be	AUX
fcis-3164	33	28	trained	train	VERB
fcis-3164	33	29	300	300	NUM
fcis-3164	33	30	times	time	NOUN
fcis-3164	33	31	as	as	ADP
fcis-3164	33	32	the	the	DET
fcis-3164	33	33	validation	validation	NOUN
fcis-3164	33	34	set	set	NOUN
fcis-3164	33	35	.	.	PUNCT
fcis-3164	34	1	the	the	DET
fcis-3164	34	2	evaluation	evaluation	NOUN
fcis-3164	34	3	effect	effect	NOUN
fcis-3164	34	4	of	of	ADP
fcis-3164	34	5	the	the	DET
fcis-3164	34	6	datasets	dataset	NOUN
fcis-3164	34	7	of	of	ADP
fcis-3164	34	8	yolov5s	yolov5s	PROPN
fcis-3164	34	9	and	and	CCONJ
fcis-3164	34	10	yolov5s	yolov5s	PROPN
fcis-3164	34	11	-	-	PUNCT
fcis-3164	34	12	spd	spd	NOUN
fcis-3164	34	13	are	be	AUX
fcis-3164	34	14	shown	show	VERB
fcis-3164	34	15	in	in	ADP
fcis-3164	34	16	figure	figure	NOUN
fcis-3164	34	17	5	5	NUM
fcis-3164	34	18	and	and	CCONJ
fcis-3164	34	19	figure	figure	VERB
fcis-3164	34	20	6	6	NUM
fcis-3164	34	21	.	.	PUNCT
fcis-3164	35	1	(	(	PUNCT
fcis-3164	35	2	a	a	X
fcis-3164	35	3	)	)	PUNCT
fcis-3164	35	4	(	(	PUNCT
fcis-3164	35	5	b	b	X
fcis-3164	35	6	)	)	PUNCT
fcis-3164	35	7	(	(	PUNCT
fcis-3164	35	8	c	c	X
fcis-3164	35	9	)	)	PUNCT
fcis-3164	35	10	figure	figure	NOUN
fcis-3164	35	11	5	5	NUM
fcis-3164	35	12	.	.	PUNCT
fcis-3164	36	1	yolov5s	yolov5s	NOUN
fcis-3164	36	2	training	training	NOUN
fcis-3164	36	3	results	result	NOUN
fcis-3164	36	4	(	(	PUNCT
fcis-3164	36	5	a	a	X
fcis-3164	36	6	)	)	PUNCT
fcis-3164	36	7	(	(	PUNCT
fcis-3164	36	8	b	b	X
fcis-3164	36	9	)	)	PUNCT
fcis-3164	36	10	(	(	PUNCT
fcis-3164	36	11	c	c	X
fcis-3164	36	12	)	)	PUNCT
fcis-3164	36	13	figure	figure	NOUN
fcis-3164	36	14	6	6	NUM
fcis-3164	36	15	.	.	PUNCT
fcis-3164	36	16	yolov5s	yolov5s	NOUN
fcis-3164	36	17	-	-	PUNCT
fcis-3164	36	18	spd	spd	NOUN
fcis-3164	36	19	training	training	NOUN
fcis-3164	36	20	results	result	VERB
fcis-3164	36	21	the	the	DET
fcis-3164	36	22	specific	specific	ADJ
fcis-3164	36	23	values	value	NOUN
fcis-3164	36	24	evaluated	evaluate	VERB
fcis-3164	36	25	for	for	ADP
fcis-3164	36	26	the	the	DET
fcis-3164	36	27	yolov5s	yolov5s	PROPN
fcis-3164	36	28	and	and	CCONJ
fcis-3164	36	29	the	the	DET
fcis-3164	36	30	modified	modify	VERB
fcis-3164	36	31	yolov5s	yolov5s	PROPN
fcis-3164	36	32	-	-	PUNCT
fcis-3164	36	33	spd	spd	NOUN
fcis-3164	36	34	datasets	dataset	NOUN
fcis-3164	36	35	are	be	AUX
fcis-3164	36	36	shown	show	VERB
fcis-3164	36	37	in	in	ADP
fcis-3164	36	38	table	table	NOUN
fcis-3164	36	39	1	1	NUM
fcis-3164	36	40	below	below	ADV
fcis-3164	36	41	.	.	PUNCT
fcis-3164	37	1	table	table	NOUN
fcis-3164	37	2	1	1	NUM
fcis-3164	37	3	.	.	PUNCT
fcis-3164	37	4	evaluation	evaluation	NOUN
fcis-3164	37	5	of	of	ADP
fcis-3164	37	6	results	result	NOUN
fcis-3164	37	7	for	for	ADP
fcis-3164	37	8	the	the	DET
fcis-3164	37	9	caltech	caltech	PROPN
fcis-3164	37	10	pedestrian	pedestrian	PROPN
fcis-3164	37	11	dataset	dataset	PROPN
fcis-3164	37	12	model	model	NOUN
fcis-3164	37	13	type	type	NOUN
fcis-3164	37	14	precision	precision	NOUN
fcis-3164	37	15	recall	recall	NOUN
fcis-3164	37	16	map@0.5	map@0.5	PROPN
fcis-3164	37	17	yolov5s	yolov5s	PROPN
fcis-3164	37	18	0.816	0.816	NUM
fcis-3164	37	19	0.564	0.564	NUM
fcis-3164	37	20	0.652	0.652	NUM
fcis-3164	37	21	yolov5s	yolov5s	PROPN
fcis-3164	37	22	-	-	PUNCT
fcis-3164	37	23	spd	spd	NOUN
fcis-3164	37	24	0.855	0.855	NUM
fcis-3164	37	25	0.626	0.626	NUM
fcis-3164	37	26	0.751	0.751	NUM
fcis-3164	37	27	from	from	ADP
fcis-3164	37	28	the	the	DET
fcis-3164	37	29	experimental	experimental	ADJ
fcis-3164	37	30	results	result	NOUN
fcis-3164	37	31	it	it	PRON
fcis-3164	37	32	can	can	AUX
fcis-3164	37	33	be	be	AUX
fcis-3164	37	34	seen	see	VERB
fcis-3164	37	35	that	that	SCONJ
fcis-3164	37	36	the	the	DET
fcis-3164	37	37	improved	improved	ADJ
fcis-3164	37	38	yolov5s	yolov5s	PROPN
fcis-3164	37	39	has	have	AUX
fcis-3164	37	40	improved	improve	VERB
fcis-3164	37	41	accuracy	accuracy	NOUN
fcis-3164	37	42	by	by	ADP
fcis-3164	37	43	3.9	3.9	NUM
fcis-3164	37	44	%	%	NOUN
fcis-3164	37	45	,	,	PUNCT
fcis-3164	37	46	recall	recall	PROPN
fcis-3164	37	47	has	have	AUX
fcis-3164	37	48	improved	improve	VERB
fcis-3164	37	49	by	by	ADP
fcis-3164	37	50	6.2	6.2	NUM
fcis-3164	37	51	%	%	NOUN
fcis-3164	37	52	and	and	CCONJ
fcis-3164	37	53	map	map	VERB
fcis-3164	37	54	values	value	NOUN
fcis-3164	37	55	have	have	AUX
fcis-3164	37	56	improved	improve	VERB
fcis-3164	37	57	by	by	ADP
fcis-3164	37	58	approximately	approximately	ADV
fcis-3164	37	59	10	10	NUM
fcis-3164	37	60	%	%	NOUN
fcis-3164	37	61	.	.	PUNCT
fcis-3164	38	1	this	this	PRON
fcis-3164	38	2	shows	show	VERB
fcis-3164	38	3	that	that	SCONJ
fcis-3164	38	4	the	the	DET
fcis-3164	38	5	improved	improved	ADJ
fcis-3164	38	6	yolov5s	yolov5s	PROPN
fcis-3164	38	7	does	do	AUX
fcis-3164	38	8	provide	provide	VERB
fcis-3164	38	9	a	a	DET
fcis-3164	38	10	good	good	ADJ
fcis-3164	38	11	improvement	improvement	NOUN
fcis-3164	38	12	on	on	ADP
fcis-3164	38	13	the	the	DET
fcis-3164	38	14	small	small	ADJ
fcis-3164	38	15	pedestrian	pedestrian	NOUN
fcis-3164	38	16	target	target	NOUN
fcis-3164	38	17	detection	detection	NOUN
fcis-3164	38	18	problem	problem	NOUN
fcis-3164	38	19	.	.	PUNCT
fcis-3164	39	1	after	after	ADP
fcis-3164	39	2	training	training	NOUN
fcis-3164	39	3	,	,	PUNCT
fcis-3164	39	4	267	267	NUM
fcis-3164	39	5	images	image	NOUN
fcis-3164	39	6	were	be	AUX
fcis-3164	39	7	randomly	randomly	ADV
fcis-3164	39	8	selected	select	VERB
fcis-3164	39	9	from	from	ADP
fcis-3164	39	10	caltech	caltech	PROPN
fcis-3164	39	11	pedestrian	pedestrian	NOUN
fcis-3164	39	12	's	's	PART
fcis-3164	39	13	test	test	NOUN
fcis-3164	39	14	set	set	VERB
fcis-3164	39	15	for	for	ADP
fcis-3164	39	16	testing	testing	NOUN
fcis-3164	39	17	.	.	PUNCT
fcis-3164	40	1	a	a	DET
fcis-3164	40	2	comparison	comparison	NOUN
fcis-3164	40	3	of	of	ADP
fcis-3164	40	4	the	the	DET
fcis-3164	40	5	specific	specific	ADJ
fcis-3164	40	6	test	test	NOUN
fcis-3164	40	7	results	result	NOUN
fcis-3164	40	8	is	be	AUX
fcis-3164	40	9	shown	show	VERB
fcis-3164	40	10	in	in	ADP
fcis-3164	40	11	figure	figure	NOUN
fcis-3164	40	12	7	7	NUM
fcis-3164	40	13	.	.	PUNCT
fcis-3164	41	1	where	where	SCONJ
fcis-3164	41	2	(	(	PUNCT
fcis-3164	41	3	a	a	X
fcis-3164	41	4	)	)	PUNCT
fcis-3164	41	5	is	be	AUX
fcis-3164	41	6	the	the	DET
fcis-3164	41	7	test	test	NOUN
fcis-3164	41	8	result	result	NOUN
fcis-3164	41	9	of	of	ADP
fcis-3164	41	10	the	the	DET
fcis-3164	41	11	yolov5s	yolov5s	PROPN
fcis-3164	41	12	model	model	NOUN
fcis-3164	41	13	and	and	CCONJ
fcis-3164	41	14	(	(	PUNCT
fcis-3164	41	15	b	b	X
fcis-3164	41	16	)	)	PUNCT
fcis-3164	41	17	is	be	AUX
fcis-3164	41	18	the	the	DET
fcis-3164	41	19	test	test	NOUN
fcis-3164	41	20	result	result	NOUN
fcis-3164	41	21	of	of	ADP
fcis-3164	41	22	the	the	DET
fcis-3164	41	23	yolov5s	yolov5s	PROPN
fcis-3164	41	24	-	-	PUNCT
fcis-3164	41	25	spd	spd	PROPN
fcis-3164	41	26	model	model	NOUN
fcis-3164	41	27	.	.	PUNCT
fcis-3164	42	1	(	(	PUNCT
fcis-3164	42	2	a)yolov5s	a)yolov5s	VERB
fcis-3164	42	3	test	test	NOUN
fcis-3164	42	4	results	result	NOUN
fcis-3164	42	5	(	(	PUNCT
fcis-3164	42	6	b)yolov5s	b)yolov5s	NOUN
fcis-3164	42	7	-	-	PUNCT
fcis-3164	42	8	spd	spd	NOUN
fcis-3164	42	9	test	test	NOUN
fcis-3164	42	10	results	result	NOUN
fcis-3164	42	11	figure	figure	VERB
fcis-3164	42	12	7	7	NUM
fcis-3164	42	13	.	.	PUNCT
fcis-3164	42	14	test	test	NOUN
fcis-3164	42	15	results	result	VERB
fcis-3164	42	16	the	the	DET
fcis-3164	42	17	small	small	ADJ
fcis-3164	42	18	pedestrian	pedestrian	NOUN
fcis-3164	42	19	target	target	NOUN
fcis-3164	42	20	person	person	NOUN
fcis-3164	42	21	in	in	ADP
fcis-3164	42	22	fig.7	fig.7	ADJ
fcis-3164	42	23	(	(	PUNCT
fcis-3164	42	24	a	a	NOUN
fcis-3164	42	25	)	)	PUNCT
fcis-3164	42	26	is	be	AUX
fcis-3164	42	27	not	not	PART
fcis-3164	42	28	detected	detect	VERB
fcis-3164	42	29	;	;	PUNCT
fcis-3164	42	30	whereas	whereas	SCONJ
fcis-3164	42	31	the	the	DET
fcis-3164	42	32	small	small	ADJ
fcis-3164	42	33	target	target	NOUN
fcis-3164	42	34	pedestrian	pedestrian	NOUN
fcis-3164	42	35	person	person	NOUN
fcis-3164	42	36	in	in	ADP
fcis-3164	42	37	the	the	DET
fcis-3164	42	38	improved	improved	ADJ
fcis-3164	42	39	test	test	NOUN
fcis-3164	42	40	result	result	NOUN
fcis-3164	42	41	(	(	PUNCT
fcis-3164	42	42	b	b	NOUN
fcis-3164	42	43	)	)	PUNCT
fcis-3164	42	44	is	be	AUX
fcis-3164	42	45	correctly	correctly	ADV
fcis-3164	42	46	detected	detect	VERB
fcis-3164	42	47	with	with	ADP
fcis-3164	42	48	an	an	DET
fcis-3164	42	49	accuracy	accuracy	NOUN
fcis-3164	42	50	of	of	ADP
fcis-3164	42	51	83	83	NUM
fcis-3164	42	52	%	%	NOUN
fcis-3164	42	53	.	.	PUNCT
fcis-3164	43	1	thus	thus	ADV
fcis-3164	43	2	yolov5s	yolov5s	NOUN
fcis-3164	43	3	-	-	PUNCT
fcis-3164	43	4	spd	spd	NOUN
fcis-3164	43	5	improves	improve	VERB
fcis-3164	43	6	the	the	DET
fcis-3164	43	7	problem	problem	NOUN
fcis-3164	43	8	of	of	ADP
fcis-3164	43	9	missed	miss	VERB
fcis-3164	43	10	and	and	CCONJ
fcis-3164	43	11	false	false	ADJ
fcis-3164	43	12	detections	detection	NOUN
fcis-3164	43	13	and	and	CCONJ
fcis-3164	43	14	poor	poor	ADJ
fcis-3164	43	15	detection	detection	NOUN
fcis-3164	43	16	,	,	PUNCT
fcis-3164	43	17	but	but	CCONJ
fcis-3164	43	18	the	the	DET
fcis-3164	43	19	model	model	NOUN
fcis-3164	43	20	85	85	NUM
fcis-3164	43	21	can	can	AUX
fcis-3164	43	22	be	be	AUX
fcis-3164	43	23	optimised	optimise	VERB
fcis-3164	43	24	by	by	ADP
fcis-3164	43	25	expanding	expand	VERB
fcis-3164	43	26	the	the	DET
fcis-3164	43	27	dataset	dataset	NOUN
fcis-3164	43	28	and	and	CCONJ
fcis-3164	43	29	performing	perform	VERB
fcis-3164	43	30	more	more	ADJ
fcis-3164	43	31	training	training	NOUN
fcis-3164	43	32	sessions	session	NOUN
fcis-3164	43	33	.	.	PUNCT
fcis-3164	44	1	in	in	ADP
fcis-3164	44	2	summary	summary	NOUN
fcis-3164	44	3	,	,	PUNCT
fcis-3164	44	4	the	the	DET
fcis-3164	44	5	yolov5s	yolov5s	PROPN
fcis-3164	44	6	-	-	PUNCT
fcis-3164	44	7	spd	spd	NOUN
fcis-3164	44	8	algorithm	algorithm	NOUN
fcis-3164	44	9	improves	improve	VERB
fcis-3164	44	10	the	the	DET
fcis-3164	44	11	network	network	NOUN
fcis-3164	44	12	's	's	PART
fcis-3164	44	13	ability	ability	NOUN
fcis-3164	44	14	to	to	PART
fcis-3164	44	15	detect	detect	VERB
fcis-3164	44	16	small	small	ADJ
fcis-3164	44	17	pedestrian	pedestrian	NOUN
fcis-3164	44	18	targets	target	NOUN
fcis-3164	44	19	with	with	ADP
fcis-3164	44	20	better	well	ADJ
fcis-3164	44	21	accuracy	accuracy	NOUN
fcis-3164	44	22	,	,	PUNCT
fcis-3164	44	23	reduced	reduce	VERB
fcis-3164	44	24	miss	miss	NOUN
fcis-3164	44	25	detection	detection	NOUN
fcis-3164	44	26	rates	rate	NOUN
fcis-3164	44	27	and	and	CCONJ
fcis-3164	44	28	improved	improved	ADJ
fcis-3164	44	29	detection	detection	NOUN
fcis-3164	44	30	accuracy	accuracy	NOUN
fcis-3164	44	31	.	.	PUNCT
fcis-3164	45	1	4	4	X
fcis-3164	45	2	.	.	X
fcis-3164	45	3	conclusion	conclusion	NOUN
fcis-3164	45	4	in	in	ADP
fcis-3164	45	5	this	this	DET
fcis-3164	45	6	paper	paper	NOUN
fcis-3164	45	7	,	,	PUNCT
fcis-3164	45	8	an	an	DET
fcis-3164	45	9	improved	improved	ADJ
fcis-3164	45	10	pedestrian	pedestrian	NOUN
fcis-3164	45	11	detection	detection	NOUN
fcis-3164	45	12	model	model	NOUN
fcis-3164	45	13	for	for	ADP
fcis-3164	45	14	complex	complex	ADJ
fcis-3164	45	15	scenes	scene	NOUN
fcis-3164	45	16	based	base	VERB
fcis-3164	45	17	on	on	ADP
fcis-3164	45	18	the	the	DET
fcis-3164	45	19	yolov5s	yolov5s	PROPN
fcis-3164	45	20	model	model	NOUN
fcis-3164	45	21	is	be	AUX
fcis-3164	45	22	proposed	propose	VERB
fcis-3164	45	23	to	to	PART
fcis-3164	45	24	address	address	VERB
fcis-3164	45	25	the	the	DET
fcis-3164	45	26	problems	problem	NOUN
fcis-3164	45	27	of	of	ADP
fcis-3164	45	28	missed	miss	VERB
fcis-3164	45	29	detection	detection	NOUN
fcis-3164	45	30	,	,	PUNCT
fcis-3164	45	31	false	false	ADJ
fcis-3164	45	32	detection	detection	NOUN
fcis-3164	45	33	and	and	CCONJ
fcis-3164	45	34	poor	poor	ADJ
fcis-3164	45	35	detection	detection	NOUN
fcis-3164	45	36	results	result	NOUN
fcis-3164	45	37	in	in	ADP
fcis-3164	45	38	complex	complex	ADJ
fcis-3164	45	39	scenes	scene	NOUN
fcis-3164	45	40	using	use	VERB
fcis-3164	45	41	yolov5s	yolov5s	PROPN
fcis-3164	45	42	detection	detection	NOUN
fcis-3164	45	43	.	.	PUNCT
fcis-3164	46	1	a	a	DET
fcis-3164	46	2	new	new	ADJ
fcis-3164	46	3	convolutional	convolutional	ADJ
fcis-3164	46	4	neural	neural	ADJ
fcis-3164	46	5	module	module	NOUN
fcis-3164	46	6	,	,	PUNCT
fcis-3164	46	7	spd	spd	NOUN
fcis-3164	46	8	-	-	PUNCT
fcis-3164	46	9	conv	conv	ADJ
fcis-3164	46	10	,	,	PUNCT
fcis-3164	46	11	is	be	AUX
fcis-3164	46	12	added	add	VERB
fcis-3164	46	13	to	to	PART
fcis-3164	46	14	improve	improve	VERB
fcis-3164	46	15	the	the	DET
fcis-3164	46	16	accuracy	accuracy	NOUN
fcis-3164	46	17	of	of	ADP
fcis-3164	46	18	the	the	DET
fcis-3164	46	19	network	network	NOUN
fcis-3164	46	20	in	in	ADP
fcis-3164	46	21	detection	detection	NOUN
fcis-3164	46	22	tasks	task	NOUN
fcis-3164	46	23	with	with	ADP
fcis-3164	46	24	low	low	ADJ
fcis-3164	46	25	-	-	PUNCT
fcis-3164	46	26	resolution	resolution	NOUN
fcis-3164	46	27	images	image	NOUN
fcis-3164	46	28	or	or	CCONJ
fcis-3164	46	29	smaller	small	ADJ
fcis-3164	46	30	objects	object	NOUN
fcis-3164	46	31	.	.	PUNCT
fcis-3164	47	1	the	the	DET
fcis-3164	47	2	improved	improved	ADJ
fcis-3164	47	3	yolov5s	yolov5s	PROPN
fcis-3164	47	4	-	-	PUNCT
fcis-3164	47	5	spd	spd	PROPN
fcis-3164	47	6	model	model	NOUN
fcis-3164	47	7	yields	yield	VERB
fcis-3164	47	8	relatively	relatively	ADV
fcis-3164	47	9	good	good	ADJ
fcis-3164	47	10	detection	detection	NOUN
fcis-3164	47	11	results	result	NOUN
fcis-3164	47	12	compared	compare	VERB
fcis-3164	47	13	to	to	ADP
fcis-3164	47	14	the	the	DET
fcis-3164	47	15	original	original	ADJ
fcis-3164	47	16	network	network	NOUN
fcis-3164	47	17	model	model	NOUN
fcis-3164	47	18	:	:	PUNCT
fcis-3164	47	19	the	the	DET
fcis-3164	47	20	average	average	ADJ
fcis-3164	47	21	accuracy	accuracy	NOUN
fcis-3164	47	22	improvement	improvement	NOUN
fcis-3164	47	23	is	be	AUX
fcis-3164	47	24	increased	increase	VERB
fcis-3164	47	25	by	by	ADP
fcis-3164	47	26	3.9	3.9	NUM
fcis-3164	47	27	%	%	NOUN
fcis-3164	47	28	and	and	CCONJ
fcis-3164	47	29	the	the	DET
fcis-3164	47	30	map	map	NOUN
fcis-3164	47	31	value	value	NOUN
fcis-3164	47	32	is	be	AUX
fcis-3164	47	33	increased	increase	VERB
fcis-3164	47	34	by	by	ADP
fcis-3164	47	35	about	about	ADV
fcis-3164	47	36	9.9	9.9	NUM
fcis-3164	47	37	%	%	NOUN
fcis-3164	47	38	.	.	PUNCT
fcis-3164	48	1	acknowledgment	acknowledgment	NOUN
fcis-3164	48	2	this	this	DET
fcis-3164	48	3	work	work	NOUN
fcis-3164	48	4	was	be	AUX
fcis-3164	48	5	supported	support	VERB
fcis-3164	48	6	by	by	ADP
fcis-3164	48	7	tianjin	tianjin	PROPN
fcis-3164	48	8	science	science	PROPN
fcis-3164	48	9	and	and	CCONJ
fcis-3164	48	10	technology	technology	PROPN
fcis-3164	48	11	commissioner	commissioner	NOUN
fcis-3164	48	12	project	project	NOUN
fcis-3164	48	13	,	,	PUNCT
fcis-3164	48	14	grant	grant	VERB
fcis-3164	48	15	number	number	NOUN
fcis-3164	48	16	20ydtpjc01110	20ydtpjc01110	PROPN
fcis-3164	48	17	.	.	PUNCT
fcis-3164	49	1	references	reference	NOUN
fcis-3164	49	2	[	[	X
fcis-3164	49	3	1	1	NUM
fcis-3164	49	4	]	]	X
fcis-3164	49	5	chollet	chollet	PROPN
fcis-3164	49	6	f.xception	f.xception	NOUN
fcis-3164	49	7	:	:	PUNCT
fcis-3164	49	8	deep	deep	ADJ
fcis-3164	49	9	learning	learning	NOUN
fcis-3164	49	10	with	with	ADP
fcis-3164	49	11	depthwise	depthwise	PROPN
fcis-3164	49	12	separable	separable	PROPN
fcis-3164	49	13	convolutions[c	convolutions[c	PROPN
fcis-3164	49	14	]	]	PUNCT
fcis-3164	49	15	//proceedings	//proceeding	NOUN
fcis-3164	49	16	of	of	ADP
fcis-3164	49	17	the	the	DET
fcis-3164	49	18	ieee	ieee	NOUN
fcis-3164	49	19	conference	conference	NOUN
fcis-3164	49	20	on	on	ADP
fcis-3164	49	21	computer	computer	NOUN
fcis-3164	49	22	vision	vision	NOUN
fcis-3164	49	23	and	and	CCONJ
fcis-3164	49	24	pattern	pattern	NOUN
fcis-3164	49	25	recognition.2017:1251	recognition.2017:1251	NOUN
fcis-3164	49	26	-	-	NOUN
fcis-3164	49	27	1258.w.k	1258.w.k	NOUN
fcis-3164	49	28	.	.	PUNCT
fcis-3164	50	1	chen	chen	PROPN
fcis-3164	50	2	,	,	PUNCT
fcis-3164	50	3	linear	linear	ADJ
fcis-3164	50	4	networks	network	NOUN
fcis-3164	50	5	and	and	CCONJ
fcis-3164	50	6	systems	system	NOUN
fcis-3164	50	7	(	(	PUNCT
fcis-3164	50	8	book	book	NOUN
fcis-3164	50	9	style	style	NOUN
fcis-3164	50	10	)	)	PUNCT
fcis-3164	50	11	.	.	PUNCT
fcis-3164	51	1	belmont	belmont	PROPN
fcis-3164	51	2	,	,	PUNCT
fcis-3164	51	3	ca	can	AUX
fcis-3164	51	4	:	:	PUNCT
fcis-3164	51	5	wadsworth	wadsworth	NOUN
fcis-3164	51	6	,	,	PUNCT
fcis-3164	51	7	1993	1993	NUM
fcis-3164	51	8	,	,	PUNCT
fcis-3164	51	9	pp	pp	ADP
fcis-3164	51	10	.	.	PUNCT
fcis-3164	52	1	123–135	123–135	NUM
fcis-3164	52	2	.	.	PUNCT
fcis-3164	53	1	[	[	X
fcis-3164	53	2	2	2	NUM
fcis-3164	53	3	]	]	PUNCT
fcis-3164	53	4	anides	anide	NOUN
fcis-3164	53	5	esteban	esteban	PROPN
fcis-3164	53	6	,	,	PUNCT
fcis-3164	53	7	garcia	garcia	PROPN
fcis-3164	53	8	luis	luis	PROPN
fcis-3164	53	9	,	,	PUNCT
fcis-3164	53	10	sanchez	sanchez	PROPN
fcis-3164	53	11	giovanny	giovanny	PROPN
fcis-3164	53	12	,	,	PUNCT
fcis-3164	53	13	avalos	avalos	PROPN
fcis-3164	53	14	juan	juan	PROPN
fcis-3164	53	15	gerardo	gerardo	PROPN
fcis-3164	53	16	,	,	PUNCT
fcis-3164	53	17	abarca	abarca	PROPN
fcis-3164	53	18	marco	marco	PROPN
fcis-3164	53	19	,	,	PUNCT
fcis-3164	53	20	frias	frias	PROPN
fcis-3164	53	21	thania	thania	PROPN
fcis-3164	53	22	,	,	PUNCT
fcis-3164	53	23	vazquez	vazquez	PROPN
fcis-3164	53	24	eduardo	eduardo	PROPN
fcis-3164	53	25	,	,	PUNCT
fcis-3164	53	26	juarez	juarez	PROPN
fcis-3164	53	27	emmanuel	emmanuel	PROPN
fcis-3164	53	28	,	,	PUNCT
fcis-3164	53	29	trejo	trejo	PROPN
fcis-3164	53	30	carlos	carlos	PROPN
fcis-3164	53	31	,	,	PUNCT
fcis-3164	53	32	hernandez	hernandez	PROPN
fcis-3164	53	33	derlis	derlis	PROPN
fcis-3164	53	34	.	.	PUNCT
fcis-3164	54	1	a	a	DET
fcis-3164	54	2	biologically	biologically	ADV
fcis-3164	54	3	inspired	inspire	VERB
fcis-3164	54	4	spiking	spike	VERB
fcis-3164	54	5	neural	neural	ADJ
fcis-3164	54	6	p	p	NOUN
fcis-3164	54	7	system	system	NOUN
fcis-3164	54	8	in	in	ADP
fcis-3164	54	9	selective	selective	ADJ
fcis-3164	54	10	visual	visual	ADJ
fcis-3164	54	11	attention	attention	NOUN
fcis-3164	54	12	for	for	ADP
fcis-3164	54	13	efficient	efficient	ADJ
fcis-3164	54	14	feature	feature	NOUN
fcis-3164	54	15	extraction	extraction	NOUN
fcis-3164	54	16	from	from	ADP
fcis-3164	54	17	human	human	PROPN
fcis-3164	54	18	motion[j	motion[j	PROPN
fcis-3164	54	19	]	]	PUNCT
fcis-3164	54	20	.	.	PUNCT
fcis-3164	55	1	frontiers	frontier	NOUN
fcis-3164	55	2	in	in	ADP
fcis-3164	55	3	robotics	robotic	NOUN
fcis-3164	55	4	and	and	CCONJ
fcis-3164	55	5	ai,2022,9.b	ai,2022,9.b	PROPN
fcis-3164	55	6	.	.	PUNCT
fcis-3164	56	1	smith	smith	PROPN
fcis-3164	56	2	,	,	PUNCT
fcis-3164	56	3	“	"	PUNCT
fcis-3164	56	4	an	an	DET
fcis-3164	56	5	approach	approach	NOUN
fcis-3164	56	6	to	to	ADP
fcis-3164	56	7	graphs	graph	NOUN
fcis-3164	56	8	of	of	ADP
fcis-3164	56	9	linear	linear	ADJ
fcis-3164	56	10	forms	form	NOUN
fcis-3164	56	11	(	(	PUNCT
fcis-3164	56	12	unpublished	unpublished	ADJ
fcis-3164	56	13	work	work	NOUN
fcis-3164	56	14	style	style	NOUN
fcis-3164	56	15	)	)	PUNCT
fcis-3164	56	16	,	,	PUNCT
fcis-3164	56	17	”	"	PUNCT
fcis-3164	56	18	unpublished	unpublished	ADJ
fcis-3164	56	19	.	.	PUNCT
fcis-3164	57	1	[	[	X
fcis-3164	57	2	3	3	X
fcis-3164	57	3	]	]	X
fcis-3164	57	4	zhang	zhang	PROPN
fcis-3164	57	5	jianlong	jianlong	PROPN
fcis-3164	57	6	,	,	PUNCT
fcis-3164	57	7	liu	liu	PROPN
fcis-3164	57	8	chishuai	chishuai	PROPN
fcis-3164	57	9	,	,	PUNCT
fcis-3164	57	10	wang	wang	PROPN
fcis-3164	57	11	bin	bin	PROPN
fcis-3164	57	12	,	,	PUNCT
fcis-3164	57	13	chen	chen	PROPN
fcis-3164	57	14	chen	chen	PROPN
fcis-3164	57	15	,	,	PUNCT
fcis-3164	57	16	he	he	PRON
fcis-3164	57	17	jianhui	jianhui	NOUN
fcis-3164	57	18	,	,	PUNCT
fcis-3164	57	19	zhou	zhou	PROPN
fcis-3164	57	20	yang	yang	PROPN
fcis-3164	57	21	,	,	PUNCT
fcis-3164	57	22	li	li	PROPN
fcis-3164	58	1	ji	ji	PROPN
fcis-3164	58	2	.	.	PROPN
fcis-3164	59	1	an	an	DET
fcis-3164	59	2	infrared	infrared	ADJ
fcis-3164	59	3	pedestrian	pedestrian	NOUN
fcis-3164	59	4	detection	detection	NOUN
fcis-3164	59	5	method	method	NOUN
fcis-3164	59	6	based	base	VERB
fcis-3164	59	7	on	on	ADP
fcis-3164	59	8	segmentation	segmentation	NOUN
fcis-3164	59	9	and	and	CCONJ
fcis-3164	59	10	domain	domain	NOUN
fcis-3164	59	11	adaptation	adaptation	NOUN
fcis-3164	59	12	learning[j	learning[j	NOUN
fcis-3164	59	13	]	]	PUNCT
fcis-3164	59	14	.	.	PUNCT
fcis-3164	60	1	computers	computer	NOUN
fcis-3164	60	2	and	and	CCONJ
fcis-3164	60	3	electrical	electrical	ADJ
fcis-3164	60	4	engineering,2022,99.j	engineering,2022,99.j	PROPN
fcis-3164	60	5	.	.	PUNCT
fcis-3164	61	1	wang	wang	PROPN
fcis-3164	61	2	,	,	PUNCT
fcis-3164	61	3	“	"	PUNCT
fcis-3164	61	4	fundamentals	fundamental	NOUN
fcis-3164	61	5	of	of	ADP
fcis-3164	61	6	erbium	erbium	NOUN
fcis-3164	61	7	-	-	PUNCT
fcis-3164	61	8	doped	dope	VERB
fcis-3164	61	9	fiber	fiber	NOUN
fcis-3164	61	10	amplifiers	amplifier	NOUN
fcis-3164	61	11	arrays	array	VERB
fcis-3164	61	12	(	(	PUNCT
fcis-3164	61	13	periodical	periodical	ADJ
fcis-3164	61	14	style	style	NOUN
fcis-3164	61	15	—	—	PUNCT
fcis-3164	61	16	submitted	submit	VERB
fcis-3164	61	17	for	for	ADP
fcis-3164	61	18	publication	publication	NOUN
fcis-3164	61	19	)	)	PUNCT
fcis-3164	61	20	,	,	PUNCT
fcis-3164	61	21	”	"	PUNCT
fcis-3164	61	22	ieee	ieee	PROPN
fcis-3164	61	23	j.	j.	PROPN
fcis-3164	61	24	quantum	quantum	PROPN
fcis-3164	61	25	electron	electron	PROPN
fcis-3164	61	26	.	.	PUNCT
fcis-3164	61	27	,	,	PUNCT
fcis-3164	61	28	submitted	submit	VERB
fcis-3164	61	29	for	for	ADP
fcis-3164	61	30	publication	publication	NOUN
fcis-3164	61	31	.	.	PUNCT
fcis-3164	62	1	[	[	X
fcis-3164	62	2	4	4	X
fcis-3164	62	3	]	]	X
fcis-3164	62	4	zhang	zhang	PROPN
fcis-3164	62	5	renjie	renjie	PROPN
fcis-3164	62	6	,	,	PUNCT
fcis-3164	62	7	fang	fang	PROPN
fcis-3164	62	8	yu	yu	PROPN
fcis-3164	62	9	,	,	PUNCT
fcis-3164	62	10	song	song	NOUN
fcis-3164	62	11	huaxin	huaxin	PROPN
fcis-3164	62	12	,	,	PUNCT
fcis-3164	62	13	wan	wan	PROPN
fcis-3164	62	14	fangbin	fangbin	PROPN
fcis-3164	62	15	,	,	PUNCT
fcis-3164	62	16	fu	fu	PROPN
fcis-3164	62	17	yanwei	yanwei	PROPN
fcis-3164	62	18	,	,	PUNCT
fcis-3164	62	19	kato	kato	PROPN
fcis-3164	62	20	hirokazu	hirokazu	PROPN
fcis-3164	62	21	,	,	PUNCT
fcis-3164	62	22	wu	wu	PROPN
fcis-3164	62	23	yang	yang	PROPN
fcis-3164	62	24	.	.	PUNCT
fcis-3164	63	1	specialized	specialized	PROPN
fcis-3164	63	2	re	re	PROPN
fcis-3164	63	3	-	-	VERB
fcis-3164	63	4	ranking	ranking	ADJ
fcis-3164	63	5	:	:	PUNCT
fcis-3164	63	6	a	a	DET
fcis-3164	63	7	novel	novel	ADJ
fcis-3164	63	8	retrieval	retrieval	NOUN
fcis-3164	63	9	-	-	PUNCT
fcis-3164	63	10	verification	verification	NOUN
fcis-3164	63	11	framework	framework	NOUN
fcis-3164	63	12	for	for	ADP
fcis-3164	63	13	cloth	cloth	NOUN
fcis-3164	63	14	changing	change	VERB
fcis-3164	63	15	person	person	NOUN
fcis-3164	63	16	re	re	NOUN
fcis-3164	63	17	-	-	NOUN
fcis-3164	63	18	identification[j	identification[j	PROPN
fcis-3164	63	19	]	]	PUNCT
fcis-3164	63	20	.	.	PUNCT
fcis-3164	63	21	pattern	pattern	PROPN
fcis-3164	63	22	recognition,2023,134.y	recognition,2023,134.y	PROPN
fcis-3164	64	1	.	.	PUNCT
fcis-3164	64	2	yorozu	yorozu	PROPN
fcis-3164	64	3	,	,	PUNCT
fcis-3164	64	4	m.	m.	PROPN
fcis-3164	64	5	hirano	hirano	PROPN
fcis-3164	64	6	,	,	PUNCT
fcis-3164	64	7	k.	k.	PROPN
fcis-3164	64	8	oka	oka	PROPN
fcis-3164	64	9	,	,	PUNCT
fcis-3164	64	10	and	and	CCONJ
fcis-3164	64	11	y.	y.	PROPN
fcis-3164	64	12	tagawa	tagawa	PROPN
fcis-3164	64	13	,	,	PUNCT
fcis-3164	64	14	“	"	PUNCT
fcis-3164	64	15	electron	electron	NOUN
fcis-3164	64	16	spectroscopy	spectroscopy	NOUN
fcis-3164	64	17	studies	study	NOUN
fcis-3164	64	18	on	on	ADP
fcis-3164	64	19	magneto	magneto	ADJ
fcis-3164	64	20	-	-	PUNCT
fcis-3164	64	21	optical	optical	ADJ
fcis-3164	64	22	media	medium	NOUN
fcis-3164	64	23	and	and	CCONJ
fcis-3164	64	24	plastic	plastic	NOUN
fcis-3164	64	25	substrate	substrate	NOUN
fcis-3164	64	26	interfaces	interface	NOUN
fcis-3164	64	27	(	(	PUNCT
fcis-3164	64	28	translation	translation	NOUN
fcis-3164	64	29	journals	journal	NOUN
fcis-3164	64	30	style	style	NOUN
fcis-3164	64	31	)	)	PUNCT
fcis-3164	64	32	,	,	PUNCT
fcis-3164	64	33	”	"	PUNCT
fcis-3164	64	34	ieee	ieee	NOUN
fcis-3164	64	35	transl	transl	PROPN
fcis-3164	64	36	.	.	PUNCT
fcis-3164	65	1	j.	j.	PROPN
fcis-3164	65	2	magn	magn	PROPN
fcis-3164	65	3	.	.	PUNCT
fcis-3164	66	1	jpn	jpn	PROPN
fcis-3164	66	2	.	.	PROPN
fcis-3164	66	3	,	,	PUNCT
fcis-3164	66	4	vol	vol	NOUN
fcis-3164	66	5	.	.	PROPN
fcis-3164	66	6	2	2	NUM
fcis-3164	66	7	,	,	PUNCT
fcis-3164	66	8	aug	aug	PROPN
fcis-3164	66	9	.	.	PROPN
fcis-3164	66	10	1987	1987	NUM
fcis-3164	66	11	,	,	PUNCT
fcis-3164	66	12	pp	pp	ADP
fcis-3164	66	13	.	.	PUNCT
fcis-3164	67	1	740–741	740–741	NUM
fcis-3164	67	2	[	[	X
fcis-3164	67	3	dig	dig	X
fcis-3164	67	4	.	.	PUNCT
fcis-3164	68	1	9th	9th	ADJ
fcis-3164	68	2	annu	annu	PROPN
fcis-3164	68	3	.	.	PUNCT
fcis-3164	68	4	conf	conf	NOUN
fcis-3164	68	5	.	.	PUNCT
fcis-3164	69	1	magnetics	magnetic	NOUN
fcis-3164	69	2	japan	japan	PROPN
fcis-3164	69	3	,	,	PUNCT
fcis-3164	69	4	1982	1982	NUM
fcis-3164	69	5	,	,	PUNCT
fcis-3164	69	6	p.	p.	NOUN
fcis-3164	69	7	301	301	NUM
fcis-3164	69	8	]	]	PUNCT
fcis-3164	69	9	.	.	PUNCT
fcis-3164	70	1	[	[	X
fcis-3164	70	2	5	5	NUM
fcis-3164	70	3	]	]	X
fcis-3164	70	4	zhang	zhang	PROPN
fcis-3164	70	5	h	h	PROPN
fcis-3164	70	6	,	,	PUNCT
fcis-3164	70	7	patel	patel	PROPN
fcis-3164	70	8	v	v	NUM
fcis-3164	70	9	m.	m.	NOUN
fcis-3164	70	10	densely	densely	ADV
fcis-3164	70	11	connected	connected	ADJ
fcis-3164	70	12	pyramid	pyramid	NOUN
fcis-3164	70	13	dehazing	dehaze	VERB
fcis-3164	70	14	network[c	network[c	PROPN
fcis-3164	70	15	]	]	X
fcis-3164	70	16	.	.	PUNCT
fcis-3164	71	1	//proceedings	//proceeding	NOUN
fcis-3164	72	1	of	of	ADP
fcis-3164	72	2	the	the	DET
fcis-3164	72	3	ieee	ieee	NOUN
fcis-3164	72	4	conference	conference	NOUN
fcis-3164	72	5	on	on	ADP
fcis-3164	72	6	computer	computer	NOUN
fcis-3164	72	7	vision	vision	NOUN
fcis-3164	72	8	and	and	CCONJ
fcis-3164	72	9	pattern	pattern	NOUN
fcis-3164	72	10	recognition	recognition	NOUN
fcis-3164	72	11	.	.	PUNCT
fcis-3164	73	1	2018	2018	NUM
fcis-3164	73	2	:	:	PUNCT
fcis-3164	73	3	3194	3194	NUM
fcis-3164	73	4	-	-	SYM
fcis-3164	73	5	3203.j	3203.j	PROPN
fcis-3164	73	6	.	.	PUNCT
fcis-3164	73	7	u.	u.	PROPN
fcis-3164	73	8	duncombe	duncombe	NOUN
fcis-3164	73	9	,	,	PUNCT
fcis-3164	73	10	“	"	PUNCT
fcis-3164	73	11	infrared	infrared	ADJ
fcis-3164	73	12	navigation	navigation	NOUN
fcis-3164	73	13	—	—	PUNCT
fcis-3164	73	14	part	part	NOUN
fcis-3164	74	1	i	i	NOUN
fcis-3164	74	2	:	:	PUNCT
fcis-3164	74	3	an	an	DET
fcis-3164	74	4	assessment	assessment	NOUN
fcis-3164	74	5	of	of	ADP
fcis-3164	74	6	feasibility	feasibility	NOUN
fcis-3164	74	7	(	(	PUNCT
fcis-3164	74	8	periodical	periodical	ADJ
fcis-3164	74	9	style	style	NOUN
fcis-3164	74	10	)	)	PUNCT
fcis-3164	74	11	,	,	PUNCT
fcis-3164	74	12	”	"	PUNCT
fcis-3164	74	13	ieee	ieee	NOUN
fcis-3164	74	14	trans	trans	PROPN
fcis-3164	74	15	.	.	PROPN
fcis-3164	75	1	electron	electron	PROPN
fcis-3164	75	2	devices	device	NOUN
fcis-3164	75	3	,	,	PUNCT
fcis-3164	75	4	vol	vol	NOUN
fcis-3164	75	5	.	.	PUNCT
fcis-3164	76	1	ed-11	ed-11	ADV
fcis-3164	76	2	,	,	PUNCT
fcis-3164	76	3	pp	pp	ADJ
fcis-3164	76	4	.	.	PUNCT
fcis-3164	77	1	34–39	34–39	NUM
fcis-3164	77	2	,	,	PUNCT
fcis-3164	77	3	jan	jan	PROPN
fcis-3164	77	4	.	.	PROPN
fcis-3164	77	5	1959	1959	NUM
fcis-3164	77	6	.	.	PUNCT
fcis-3164	78	1	[	[	X
fcis-3164	78	2	6	6	NUM
fcis-3164	78	3	]	]	X
fcis-3164	78	4	zhang	zhang	PROPN
fcis-3164	78	5	h	h	PROPN
fcis-3164	78	6	,	,	PUNCT
fcis-3164	78	7	patel	patel	NOUN
fcis-3164	78	8	v	v	ADP
fcis-3164	78	9	m.density	m.density	NOUN
fcis-3164	78	10	-	-	PUNCT
fcis-3164	78	11	aware	aware	ADJ
fcis-3164	78	12	single	single	ADJ
fcis-3164	78	13	image	image	NOUN
fcis-3164	78	14	de	de	NOUN
fcis-3164	78	15	-	-	NOUN
fcis-3164	78	16	raining	raining	NOUN
fcis-3164	78	17	using	use	VERB
fcis-3164	78	18	a	a	DET
fcis-3164	78	19	multi	multi	ADJ
fcis-3164	78	20	-	-	ADJ
fcis-3164	78	21	stream	stream	ADJ
fcis-3164	78	22	dense	dense	ADJ
fcis-3164	78	23	network[c	network[c	PROPN
fcis-3164	78	24	]	]	X
fcis-3164	78	25	.	.	PUNCT
fcis-3164	78	26	//proceedings	//proceeding	NOUN
fcis-3164	79	1	of	of	ADP
fcis-3164	79	2	the	the	DET
fcis-3164	79	3	ieee	ieee	NOUN
fcis-3164	79	4	conference	conference	NOUN
fcis-3164	79	5	on	on	ADP
fcis-3164	79	6	computer	computer	NOUN
fcis-3164	79	7	vision	vision	NOUN
fcis-3164	79	8	and	and	CCONJ
fcis-3164	79	9	pattern	pattern	NOUN
fcis-3164	79	10	recognition	recognition	NOUN
fcis-3164	79	11	.	.	PUNCT
fcis-3164	80	1	2018	2018	NUM
fcis-3164	80	2	:	:	PUNCT
fcis-3164	80	3	695	695	NUM
fcis-3164	80	4	-	-	NUM
fcis-3164	80	5	704.r	704.r	NOUN
fcis-3164	80	6	.	.	PUNCT
fcis-3164	81	1	w.	w.	PROPN
fcis-3164	81	2	lucky	lucky	PROPN
fcis-3164	81	3	,	,	PUNCT
fcis-3164	81	4	“	"	PUNCT
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fcis-3164	81	6	equalization	equalization	NOUN
fcis-3164	81	7	for	for	ADP
fcis-3164	81	8	digital	digital	ADJ
fcis-3164	81	9	communication	communication	NOUN
fcis-3164	81	10	,	,	PUNCT
fcis-3164	81	11	”	"	PUNCT
fcis-3164	81	12	bell	bell	NOUN
fcis-3164	81	13	syst	syst	NOUN
fcis-3164	81	14	.	.	PUNCT
fcis-3164	82	1	tech	tech	PROPN
fcis-3164	82	2	.	.	PUNCT
fcis-3164	83	1	j.	j.	PROPN
fcis-3164	83	2	,	,	PUNCT
fcis-3164	83	3	vol	vol	NOUN
fcis-3164	83	4	.	.	PROPN
fcis-3164	83	5	44	44	NUM
fcis-3164	83	6	,	,	PUNCT
fcis-3164	83	7	no	no	INTJ
fcis-3164	83	8	.	.	NOUN
fcis-3164	83	9	4	4	NUM
fcis-3164	83	10	,	,	PUNCT
fcis-3164	83	11	pp	pp	ADJ
fcis-3164	83	12	.	.	PUNCT
fcis-3164	84	1	547–588	547–588	NUM
fcis-3164	84	2	,	,	PUNCT
fcis-3164	84	3	apr	apr	PROPN
fcis-3164	84	4	.	.	PUNCT
fcis-3164	84	5	1965	1965	NUM
fcis-3164	84	6	.	.	PUNCT
fcis-3164	85	1	[	[	X
fcis-3164	85	2	7	7	NUM
fcis-3164	85	3	]	]	X
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fcis-3164	85	5	x	x	SYM
fcis-3164	85	6	w	w	PROPN
fcis-3164	85	7	,	,	PUNCT
fcis-3164	85	8	han	han	PROPN
fcis-3164	85	9	y	y	PROPN
fcis-3164	85	10	,	,	PUNCT
fcis-3164	86	1	zhang	zhang	PROPN
fcis-3164	86	2	z	z	PROPN
fcis-3164	86	3	,	,	PUNCT
fcis-3164	86	4	et	et	PROPN
fcis-3164	86	5	al	al	PROPN
fcis-3164	86	6	.	.	PROPN
fcis-3164	86	7	metro	metro	PROPN
fcis-3164	86	8	pedestrian	pedestrian	PROPN
fcis-3164	86	9	detection	detection	NOUN
fcis-3164	86	10	algorithm	algorithm	NOUN
fcis-3164	86	11	based	base	VERB
fcis-3164	86	12	on	on	ADP
fcis-3164	86	13	multi	multi	ADJ
fcis-3164	86	14	-	-	ADJ
fcis-3164	86	15	scal	scal	ADJ
fcis-3164	86	16	-e	-e	NOUN
fcis-3164	86	17	weighted	weight	VERB
fcis-3164	86	18	feature	feature	NOUN
fcis-3164	86	19	fusion	fusion	NOUN
fcis-3164	86	20	network[j	network[j	NOUN
fcis-3164	86	21	]	]	PUNCT
fcis-3164	86	22	.	.	PUNCT
fcis-3164	87	1	journal	journal	PROPN
fcis-3164	87	2	of	of	ADP
fcis-3164	87	3	electronics	electronics	PROPN
fcis-3164	87	4	&	&	CCONJ
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fcis-3164	87	6	technology	technology	NOUN
fcis-3164	87	7	,	,	PUNCT
fcis-3164	87	8	2	2	NUM
fcis-3164	87	9	021,43(7	021,43(7	NUM
fcis-3164	87	10	):	):	PUNCT
fcis-3164	87	11	2113	2113	NUM
fcis-3164	87	12	-	-	SYM
fcis-3164	87	13	2120	2120	NUM
fcis-3164	87	14	.	.	PUNCT
fcis-3164	88	1	[	[	X
fcis-3164	88	2	8	8	NUM
fcis-3164	88	3	]	]	X
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fcis-3164	88	5	j	j	PROPN
fcis-3164	88	6	y	y	PROPN
fcis-3164	88	7	,	,	PUNCT
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fcis-3164	88	9	j	j	PROPN
fcis-3164	88	10	,	,	PUNCT
fcis-3164	88	11	kong	kong	PROPN
fcis-3164	88	12	b	b	PROPN
fcis-3164	88	13	,	,	PUNCT
fcis-3164	88	14	et	et	PROPN
fcis-3164	88	15	al	al	PROPN
fcis-3164	88	16	.	.	PUNCT
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fcis-3164	89	2	-	-	ADJ
fcis-3164	89	3	scale	scale	ADJ
fcis-3164	89	4	vehicle	vehicle	NOUN
fcis-3164	89	5	and	and	CCONJ
fcis-3164	89	6	pedestrian	pedestrian	NOUN
fcis-3164	89	7	decection	decection	NOUN
fcis-3164	89	8	algorithm	algorithm	NOUN
fcis-3164	89	9	based	base	VERB
fcis-3164	89	10	on	on	ADP
fcis-3164	89	11	attention	attention	NOUN
fcis-3164	89	12	mechanism	mechanism	NOUN
fcis-3164	90	1	[	[	X
fcis-3164	90	2	j	j	X
fcis-3164	90	3	]	]	X
fcis-3164	90	4	.	.	PUNCT
fcis-3164	91	1	optics	optic	NOUN
fcis-3164	91	2	and	and	CCONJ
fcis-3164	91	3	precision	precision	NOUN
fcis-3164	91	4	engineering,2021	engineering,2021	PROPN
fcis-3164	91	5	,	,	PUNCT
fcis-3164	91	6	29(6	29(6	NUM
fcis-3164	91	7	):	):	PUNCT
fcis-3164	91	8	1448	1448	NUM
fcis-3164	91	9	-	-	SYM
fcis-3164	91	10	1458	1458	NUM
fcis-3164	91	11	.	.	PUNCT
fcis-3164	92	1	[	[	X
fcis-3164	92	2	9	9	NUM
fcis-3164	92	3	]	]	PUNCT
fcis-3164	92	4	j.	j.	PROPN
fcis-3164	92	5	redmon	redmon	PROPN
fcis-3164	92	6	,	,	PUNCT
fcis-3164	92	7	s.	s.	PROPN
fcis-3164	92	8	divvala	divvala	PROPN
fcis-3164	92	9	,	,	PUNCT
fcis-3164	92	10	r.	r.	PROPN
fcis-3164	92	11	girshick	girshick	PROPN
fcis-3164	92	12	,	,	PUNCT
fcis-3164	92	13	a.	a.	NOUN
fcis-3164	92	14	farhadi	farhadi	NOUN
fcis-3164	92	15	,	,	PUNCT
fcis-3164	92	16	you	you	PRON
fcis-3164	92	17	only	only	ADV
fcis-3164	92	18	look	look	VERB
fcis-3164	92	19	once	once	ADV
fcis-3164	92	20	:	:	PUNCT
fcis-3164	92	21	unified	unified	ADJ
fcis-3164	92	22	,	,	PUNCT
fcis-3164	92	23	real	real	ADJ
fcis-3164	92	24	-	-	PUNCT
fcis-3164	92	25	time	time	NOUN
fcis-3164	92	26	object	object	NOUN
fcis-3164	92	27	detection	detection	NOUN
fcis-3164	92	28	,	,	PUNCT
fcis-3164	92	29	ieee	ieee	NOUN
fcis-3164	92	30	conference	conference	NOUN
fcis-3164	92	31	on	on	ADP
fcis-3164	92	32	computer	computer	NOUN
fcis-3164	92	33	vision	vision	NOUN
fcis-3164	92	34	and	and	CCONJ
fcis-3164	92	35	pattern	pattern	NOUN
fcis-3164	92	36	recognition	recognition	NOUN
fcis-3164	92	37	.	.	PUNCT
fcis-3164	93	1	2016	2016	NUM
fcis-3164	93	2	,	,	PUNCT
fcis-3164	93	3	pp	pp	ADJ
fcis-3164	93	4	.	.	PUNCT
fcis-3164	94	1	779–788	779–788	X
fcis-3164	94	2	.	.	PUNCT
fcis-3164	95	1	[	[	X
fcis-3164	95	2	10	10	NUM
fcis-3164	95	3	]	]	PUNCT
fcis-3164	95	4	jingwei	jingwei	NOUN
fcis-3164	95	5	cao	cao	PROPN
fcis-3164	95	6	,	,	PUNCT
fcis-3164	95	7	chuanxue	chuanxue	NOUN
fcis-3164	95	8	song	song	NOUN
fcis-3164	95	9	,	,	PUNCT
fcis-3164	95	10	silun	silun	PROPN
fcis-3164	95	11	peng	peng	PROPN
fcis-3164	95	12	,	,	PUNCT
fcis-3164	95	13	shixin	shixin	ADJ
fcis-3164	95	14	song	song	NOUN
fcis-3164	95	15	,	,	PUNCT
fcis-3164	95	16	xu	xu	PROPN
fcis-3164	95	17	zhang	zhang	PROPN
fcis-3164	95	18	,	,	PUNCT
fcis-3164	95	19	yulong	yulong	PROPN
fcis-3164	95	20	shao	shao	PROPN
fcis-3164	95	21	,	,	PUNCT
fcis-3164	95	22	feng	feng	PROPN
fcis-3164	95	23	xiao	xiao	PROPN
fcis-3164	95	24	.	.	PUNCT
fcis-3164	95	25	pedestrian	pedestrian	NOUN
fcis-3164	95	26	detection	detection	NOUN
fcis-3164	95	27	algorithm	algorithm	NOUN
fcis-3164	95	28	for	for	ADP
fcis-3164	95	29	intelligent	intelligent	ADJ
fcis-3164	95	30	vehicles	vehicle	NOUN
fcis-3164	95	31	in	in	ADP
fcis-3164	95	32	complex	complex	ADJ
fcis-3164	95	33	scenarios[j	scenarios[j	NOUN
fcis-3164	95	34	]	]	PUNCT
fcis-3164	95	35	.	.	PUNCT
fcis-3164	96	1	sensors,2020,20(13).j	sensors,2020,20(13).j	NOUN
fcis-3164	96	2	.	.	PUNCT
fcis-3164	97	1	g.	g.	PROPN
fcis-3164	97	2	kreifeldt	kreifeldt	PROPN
fcis-3164	97	3	,	,	PUNCT
fcis-3164	97	4	“	"	PUNCT
fcis-3164	97	5	an	an	DET
fcis-3164	97	6	analysis	analysis	NOUN
fcis-3164	97	7	of	of	ADP
fcis-3164	97	8	surfacedetected	surfacedetecte	VERB
fcis-3164	97	9	emg	emg	NOUN
fcis-3164	97	10	as	as	ADP
fcis-3164	97	11	an	an	DET
fcis-3164	97	12	amplitude	amplitude	NOUN
fcis-3164	97	13	-	-	PUNCT
fcis-3164	97	14	modulated	modulate	VERB
fcis-3164	97	15	noise	noise	NOUN
fcis-3164	97	16	,	,	PUNCT
fcis-3164	97	17	”	"	PUNCT
fcis-3164	97	18	presented	present	VERB
fcis-3164	97	19	at	at	ADP
fcis-3164	97	20	the	the	DET
fcis-3164	97	21	1989	1989	NUM
fcis-3164	97	22	int	int	NOUN
fcis-3164	97	23	.	.	PUNCT
fcis-3164	98	1	conf	conf	PROPN
fcis-3164	98	2	.	.	PUNCT
fcis-3164	99	1	medicine	medicine	NOUN
fcis-3164	99	2	and	and	CCONJ
fcis-3164	99	3	biological	biological	ADJ
fcis-3164	99	4	engineering	engineering	NOUN
fcis-3164	99	5	,	,	PUNCT
fcis-3164	99	6	chicago	chicago	PROPN
fcis-3164	99	7	,	,	PUNCT
fcis-3164	99	8	il	il	PROPN
fcis-3164	99	9	.	.	PUNCT