id	sid	tid	token	lemma	pos
fcis-10736	1	1	frontiers	frontier	NOUN
fcis-10736	1	2	in	in	ADP
fcis-10736	1	3	computing	computing	NOUN
fcis-10736	1	4	and	and	CCONJ
fcis-10736	1	5	intelligent	intelligent	ADJ
fcis-10736	1	6	systems	system	NOUN
fcis-10736	1	7	issn	issn	VERB
fcis-10736	1	8	:	:	PUNCT
fcis-10736	1	9	2832	2832	NUM
fcis-10736	1	10	-	-	SYM
fcis-10736	1	11	6024	6024	NUM
fcis-10736	1	12	|	|	NOUN
fcis-10736	1	13	vol	vol	NOUN
fcis-10736	1	14	.	.	PROPN
fcis-10736	2	1	4	4	NUM
fcis-10736	2	2	,	,	PUNCT
fcis-10736	2	3	no	no	INTJ
fcis-10736	2	4	.	.	NOUN
fcis-10736	2	5	3	3	NUM
fcis-10736	2	6	,	,	PUNCT
fcis-10736	2	7	2023	2023	NUM
fcis-10736	2	8	17	17	NUM
fcis-10736	2	9	review	review	NOUN
fcis-10736	2	10	of	of	ADP
fcis-10736	2	11	target	target	NOUN
fcis-10736	2	12	detection	detection	NOUN
fcis-10736	2	13	algorithms	algorithm	NOUN
fcis-10736	2	14	xu	xu	PROPN
fcis-10736	2	15	zhou	zhou	PROPN
fcis-10736	2	16	,	,	PUNCT
fcis-10736	2	17	guojun	guojun	PROPN
fcis-10736	2	18	lin	lin	PROPN
fcis-10736	2	19	*	*	PROPN
fcis-10736	2	20	school	school	NOUN
fcis-10736	2	21	of	of	ADP
fcis-10736	2	22	automation	automation	NOUN
fcis-10736	2	23	and	and	CCONJ
fcis-10736	2	24	information	information	NOUN
fcis-10736	2	25	engineering	engineering	NOUN
fcis-10736	2	26	,	,	PUNCT
fcis-10736	2	27	sichuan	sichuan	PROPN
fcis-10736	2	28	university	university	PROPN
fcis-10736	2	29	of	of	ADP
fcis-10736	2	30	technology	technology	NOUN
fcis-10736	2	31	,	,	PUNCT
fcis-10736	2	32	zigong	zigong	PROPN
fcis-10736	2	33	643000	643000	NUM
fcis-10736	2	34	,	,	PUNCT
fcis-10736	2	35	china	china	PROPN
fcis-10736	2	36	*	*	PUNCT
fcis-10736	2	37	corresponding	correspond	VERB
fcis-10736	2	38	author	author	NOUN
fcis-10736	2	39	:	:	PUNCT
fcis-10736	2	40	guojun	guojun	PROPN
fcis-10736	2	41	lin	lin	PROPN
fcis-10736	2	42	(	(	PUNCT
fcis-10736	2	43	email	email	NOUN
fcis-10736	2	44	:	:	PUNCT
fcis-10736	2	45	386988463@qq.com	386988463@qq.com	NUM
fcis-10736	2	46	)	)	PUNCT
fcis-10736	2	47	abstract	abstract	NOUN
fcis-10736	2	48	:	:	PUNCT
fcis-10736	2	49	object	object	NOUN
fcis-10736	2	50	detection	detection	NOUN
fcis-10736	2	51	is	be	AUX
fcis-10736	2	52	a	a	DET
fcis-10736	2	53	popular	popular	ADJ
fcis-10736	2	54	direction	direction	NOUN
fcis-10736	2	55	of	of	ADP
fcis-10736	2	56	computer	computer	NOUN
fcis-10736	2	57	vision	vision	NOUN
fcis-10736	2	58	and	and	CCONJ
fcis-10736	2	59	digital	digital	ADJ
fcis-10736	2	60	image	image	NOUN
fcis-10736	2	61	processing	processing	NOUN
fcis-10736	2	62	,	,	PUNCT
fcis-10736	2	63	which	which	PRON
fcis-10736	2	64	is	be	AUX
fcis-10736	2	65	widely	widely	ADV
fcis-10736	2	66	used	use	VERB
fcis-10736	2	67	in	in	ADP
fcis-10736	2	68	robot	robot	NOUN
fcis-10736	2	69	navigation	navigation	NOUN
fcis-10736	2	70	,	,	PUNCT
fcis-10736	2	71	intelligent	intelligent	ADJ
fcis-10736	2	72	video	video	NOUN
fcis-10736	2	73	surveillance	surveillance	NOUN
fcis-10736	2	74	,	,	PUNCT
fcis-10736	2	75	industrial	industrial	ADJ
fcis-10736	2	76	detection	detection	NOUN
fcis-10736	2	77	,	,	PUNCT
fcis-10736	2	78	aerospace	aerospace	NOUN
fcis-10736	2	79	and	and	CCONJ
fcis-10736	2	80	other	other	ADJ
fcis-10736	2	81	fields	field	NOUN
fcis-10736	2	82	,	,	PUNCT
fcis-10736	2	83	using	use	VERB
fcis-10736	2	84	computer	computer	NOUN
fcis-10736	2	85	vision	vision	NOUN
fcis-10736	2	86	to	to	PART
fcis-10736	2	87	reduce	reduce	VERB
fcis-10736	2	88	human	human	ADJ
fcis-10736	2	89	capital	capital	NOUN
fcis-10736	2	90	consumption	consumption	NOUN
fcis-10736	2	91	has	have	VERB
fcis-10736	2	92	important	important	ADJ
fcis-10736	2	93	practical	practical	ADJ
fcis-10736	2	94	significance	significance	NOUN
fcis-10736	2	95	.	.	PUNCT
fcis-10736	3	1	because	because	SCONJ
fcis-10736	3	2	of	of	ADP
fcis-10736	3	3	the	the	DET
fcis-10736	3	4	wide	wide	ADJ
fcis-10736	3	5	application	application	NOUN
fcis-10736	3	6	of	of	ADP
fcis-10736	3	7	deep	deep	ADJ
fcis-10736	3	8	learning	learning	NOUN
fcis-10736	3	9	,	,	PUNCT
fcis-10736	3	10	the	the	DET
fcis-10736	3	11	algorithm	algorithm	NOUN
fcis-10736	3	12	of	of	ADP
fcis-10736	3	13	target	target	NOUN
fcis-10736	3	14	detection	detection	NOUN
fcis-10736	3	15	has	have	AUX
fcis-10736	3	16	been	be	AUX
fcis-10736	3	17	developed	develop	VERB
fcis-10736	3	18	rapidly	rapidly	ADV
fcis-10736	3	19	.	.	PUNCT
fcis-10736	4	1	this	this	DET
fcis-10736	4	2	paper	paper	NOUN
fcis-10736	4	3	mainly	mainly	ADV
fcis-10736	4	4	introduces	introduce	VERB
fcis-10736	4	5	the	the	DET
fcis-10736	4	6	traditional	traditional	ADJ
fcis-10736	4	7	target	target	NOUN
fcis-10736	4	8	detection	detection	NOUN
fcis-10736	4	9	algorithm	algorithm	NOUN
fcis-10736	4	10	and	and	CCONJ
fcis-10736	4	11	two	two	NUM
fcis-10736	4	12	kinds	kind	NOUN
fcis-10736	4	13	of	of	ADP
fcis-10736	4	14	target	target	NOUN
fcis-10736	4	15	detection	detection	NOUN
fcis-10736	4	16	algorithm	algorithm	NOUN
fcis-10736	4	17	based	base	VERB
fcis-10736	4	18	on	on	ADP
fcis-10736	4	19	depth	depth	NOUN
fcis-10736	4	20	learning	learning	NOUN
fcis-10736	4	21	and	and	CCONJ
fcis-10736	4	22	the	the	DET
fcis-10736	4	23	data	datum	NOUN
fcis-10736	4	24	set	set	VERB
fcis-10736	4	25	commonly	commonly	ADV
fcis-10736	4	26	used	use	VERB
fcis-10736	4	27	in	in	ADP
fcis-10736	4	28	target	target	NOUN
fcis-10736	4	29	detection	detection	NOUN
fcis-10736	4	30	.	.	PUNCT
fcis-10736	5	1	keywords	keyword	NOUN
fcis-10736	5	2	:	:	PUNCT
fcis-10736	5	3	object	object	VERB
fcis-10736	5	4	detection	detection	NOUN
fcis-10736	5	5	;	;	PUNCT
fcis-10736	5	6	deep	deep	ADJ
fcis-10736	5	7	learning	learning	NOUN
fcis-10736	5	8	;	;	PUNCT
fcis-10736	5	9	computer	computer	NOUN
fcis-10736	5	10	vision	vision	NOUN
fcis-10736	5	11	.	.	PUNCT
fcis-10736	6	1	1	1	X
fcis-10736	6	2	.	.	X
fcis-10736	6	3	introduction	introduction	NOUN
fcis-10736	6	4	object	object	NOUN
fcis-10736	6	5	detection	detection	NOUN
fcis-10736	6	6	is	be	AUX
fcis-10736	6	7	one	one	NUM
fcis-10736	6	8	of	of	ADP
fcis-10736	6	9	the	the	DET
fcis-10736	6	10	important	important	ADJ
fcis-10736	6	11	branches	branch	NOUN
fcis-10736	6	12	of	of	ADP
fcis-10736	6	13	computer	computer	NOUN
fcis-10736	6	14	vision	vision	NOUN
fcis-10736	6	15	,	,	PUNCT
fcis-10736	6	16	whose	whose	DET
fcis-10736	6	17	task	task	NOUN
fcis-10736	6	18	is	be	AUX
fcis-10736	6	19	to	to	PART
fcis-10736	6	20	find	find	VERB
fcis-10736	6	21	out	out	ADP
fcis-10736	6	22	some	some	DET
fcis-10736	6	23	specific	specific	ADJ
fcis-10736	6	24	objects	object	NOUN
fcis-10736	6	25	in	in	ADP
fcis-10736	6	26	images	image	NOUN
fcis-10736	6	27	,	,	PUNCT
fcis-10736	6	28	and	and	CCONJ
fcis-10736	6	29	give	give	VERB
fcis-10736	6	30	their	their	PRON
fcis-10736	6	31	categories	category	NOUN
fcis-10736	6	32	and	and	CCONJ
fcis-10736	6	33	positions	position	NOUN
fcis-10736	6	34	.	.	PUNCT
fcis-10736	7	1	in	in	ADP
fcis-10736	7	2	recent	recent	ADJ
fcis-10736	7	3	years	year	NOUN
fcis-10736	7	4	,	,	PUNCT
fcis-10736	7	5	object	object	NOUN
fcis-10736	7	6	detection	detection	NOUN
fcis-10736	7	7	algorithm	algorithm	NOUN
fcis-10736	7	8	has	have	AUX
fcis-10736	7	9	been	be	AUX
fcis-10736	7	10	a	a	DET
fcis-10736	7	11	hot	hot	ADJ
fcis-10736	7	12	research	research	NOUN
fcis-10736	7	13	field	field	NOUN
fcis-10736	7	14	in	in	ADP
fcis-10736	7	15	computer	computer	NOUN
fcis-10736	7	16	vision	vision	NOUN
fcis-10736	7	17	and	and	CCONJ
fcis-10736	7	18	image	image	NOUN
fcis-10736	7	19	processing	processing	NOUN
fcis-10736	7	20	.	.	PUNCT
fcis-10736	8	1	firstly	firstly	ADV
fcis-10736	8	2	,	,	PUNCT
fcis-10736	8	3	it	it	PRON
fcis-10736	8	4	is	be	AUX
fcis-10736	8	5	the	the	DET
fcis-10736	8	6	foundation	foundation	NOUN
fcis-10736	8	7	of	of	ADP
fcis-10736	8	8	more	more	ADV
fcis-10736	8	9	complex	complex	ADJ
fcis-10736	8	10	visual	visual	ADJ
fcis-10736	8	11	task	task	NOUN
fcis-10736	8	12	processing	processing	NOUN
fcis-10736	8	13	such	such	ADJ
fcis-10736	8	14	as	as	ADP
fcis-10736	8	15	image	image	NOUN
fcis-10736	8	16	semantic	semantic	ADJ
fcis-10736	8	17	segmentation	segmentation	NOUN
fcis-10736	8	18	[	[	X
fcis-10736	8	19	1	1	X
fcis-10736	8	20	]	]	PUNCT
fcis-10736	8	21	and	and	CCONJ
fcis-10736	8	22	instance	instance	NOUN
fcis-10736	8	23	segmentation	segmentation	NOUN
fcis-10736	8	24	[	[	X
fcis-10736	8	25	2	2	NUM
fcis-10736	8	26	]	]	PUNCT
fcis-10736	8	27	,	,	PUNCT
fcis-10736	8	28	secondly	secondly	ADV
fcis-10736	8	29	,	,	PUNCT
fcis-10736	8	30	it	it	PRON
fcis-10736	8	31	has	have	VERB
fcis-10736	8	32	great	great	ADJ
fcis-10736	8	33	application	application	NOUN
fcis-10736	8	34	prospect	prospect	NOUN
fcis-10736	8	35	in	in	ADP
fcis-10736	8	36	many	many	ADJ
fcis-10736	8	37	fields	field	NOUN
fcis-10736	8	38	such	such	ADJ
fcis-10736	8	39	as	as	ADP
fcis-10736	8	40	robot	robot	NOUN
fcis-10736	8	41	navigation	navigation	NOUN
fcis-10736	8	42	,	,	PUNCT
fcis-10736	8	43	intelligent	intelligent	ADJ
fcis-10736	8	44	monitoring	monitoring	NOUN
fcis-10736	8	45	and	and	CCONJ
fcis-10736	8	46	industrial	industrial	ADJ
fcis-10736	8	47	detection	detection	NOUN
fcis-10736	8	48	.	.	PUNCT
fcis-10736	9	1	the	the	DET
fcis-10736	9	2	development	development	NOUN
fcis-10736	9	3	of	of	ADP
fcis-10736	9	4	target	target	NOUN
fcis-10736	9	5	detection	detection	NOUN
fcis-10736	9	6	can	can	AUX
fcis-10736	9	7	be	be	AUX
fcis-10736	9	8	divided	divide	VERB
fcis-10736	9	9	into	into	ADP
fcis-10736	9	10	two	two	NUM
fcis-10736	9	11	stages	stage	NOUN
fcis-10736	9	12	:	:	PUNCT
fcis-10736	9	13	the	the	DET
fcis-10736	9	14	traditional	traditional	ADJ
fcis-10736	9	15	stage	stage	NOUN
fcis-10736	9	16	based	base	VERB
fcis-10736	9	17	on	on	ADP
fcis-10736	9	18	artificial	artificial	ADJ
fcis-10736	9	19	feature	feature	NOUN
fcis-10736	9	20	extraction	extraction	NOUN
fcis-10736	9	21	and	and	CCONJ
fcis-10736	9	22	the	the	DET
fcis-10736	9	23	new	new	ADJ
fcis-10736	9	24	stage	stage	NOUN
fcis-10736	9	25	based	base	VERB
fcis-10736	9	26	on	on	ADP
fcis-10736	9	27	deep	deep	ADJ
fcis-10736	9	28	learning	learning	NOUN
fcis-10736	9	29	.	.	PUNCT
fcis-10736	10	1	traditional	traditional	ADJ
fcis-10736	10	2	target	target	NOUN
fcis-10736	10	3	detection	detection	NOUN
fcis-10736	10	4	usually	usually	ADV
fcis-10736	10	5	adopts	adopt	VERB
fcis-10736	10	6	the	the	DET
fcis-10736	10	7	method	method	NOUN
fcis-10736	10	8	of	of	ADP
fcis-10736	10	9	sliding	slide	VERB
fcis-10736	10	10	window	window	NOUN
fcis-10736	10	11	combined	combine	VERB
fcis-10736	10	12	with	with	ADP
fcis-10736	10	13	feature	feature	NOUN
fcis-10736	10	14	extraction	extraction	NOUN
fcis-10736	10	15	by	by	ADP
fcis-10736	10	16	hand	hand	NOUN
fcis-10736	10	17	,	,	PUNCT
fcis-10736	10	18	and	and	CCONJ
fcis-10736	10	19	finally	finally	ADV
fcis-10736	10	20	combines	combine	VERB
fcis-10736	10	21	with	with	ADP
fcis-10736	10	22	special	special	ADJ
fcis-10736	10	23	classifier	classifier	NOUN
fcis-10736	10	24	to	to	PART
fcis-10736	10	25	classify	classify	VERB
fcis-10736	10	26	.	.	PUNCT
fcis-10736	11	1	typical	typical	ADJ
fcis-10736	11	2	algorithms	algorithm	NOUN
fcis-10736	11	3	are	be	AUX
fcis-10736	11	4	viola	viola	PROPN
fcis-10736	11	5	jones	jones	PROPN
fcis-10736	12	1	[	[	X
fcis-10736	12	2	3	3	NUM
fcis-10736	12	3	]	]	PUNCT
fcis-10736	12	4	,	,	PUNCT
fcis-10736	12	5	hog	hog	X
fcis-10736	12	6	[	[	X
fcis-10736	12	7	4	4	NUM
fcis-10736	12	8	]	]	PUNCT
fcis-10736	12	9	,	,	PUNCT
fcis-10736	12	10	dpm	dpm	PROPN
fcis-10736	13	1	[	[	X
fcis-10736	13	2	5	5	NUM
fcis-10736	13	3	]	]	PUNCT
fcis-10736	13	4	and	and	CCONJ
fcis-10736	13	5	so	so	ADV
fcis-10736	13	6	on	on	ADV
fcis-10736	13	7	.	.	PUNCT
fcis-10736	14	1	until	until	ADP
fcis-10736	14	2	2014	2014	NUM
fcis-10736	14	3	,	,	PUNCT
fcis-10736	14	4	r.	r.	PROPN
fcis-10736	14	5	girshick	girshick	PROPN
fcis-10736	14	6	et	et	PROPN
fcis-10736	14	7	al	al	PROPN
fcis-10736	14	8	applied	apply	VERB
fcis-10736	14	9	cnn	cnn	PROPN
fcis-10736	14	10	(	(	PUNCT
fcis-10736	14	11	convolutional	convolutional	ADJ
fcis-10736	14	12	neural	neural	ADJ
fcis-10736	14	13	networks	network	NOUN
fcis-10736	14	14	)	)	PUNCT
fcis-10736	15	1	[	[	X
fcis-10736	15	2	6	6	NUM
fcis-10736	15	3	]	]	PUNCT
fcis-10736	15	4	to	to	PART
fcis-10736	15	5	target	target	VERB
fcis-10736	15	6	detection	detection	NOUN
fcis-10736	15	7	,	,	PUNCT
fcis-10736	15	8	trying	try	VERB
fcis-10736	15	9	to	to	PART
fcis-10736	15	10	extract	extract	VERB
fcis-10736	15	11	features	feature	NOUN
fcis-10736	15	12	using	use	VERB
fcis-10736	15	13	convolutional	convolutional	ADJ
fcis-10736	15	14	neural	neural	ADJ
fcis-10736	15	15	network	network	NOUN
fcis-10736	15	16	,	,	PUNCT
fcis-10736	15	17	improving	improve	VERB
fcis-10736	15	18	the	the	DET
fcis-10736	15	19	average	average	ADJ
fcis-10736	15	20	detection	detection	NOUN
fcis-10736	15	21	accuracy	accuracy	NOUN
fcis-10736	15	22	by	by	ADP
fcis-10736	15	23	about	about	ADV
fcis-10736	15	24	30	30	NUM
fcis-10736	15	25	%	%	NOUN
fcis-10736	15	26	compared	compare	VERB
fcis-10736	15	27	with	with	ADP
fcis-10736	15	28	traditional	traditional	ADJ
fcis-10736	15	29	methods	method	NOUN
fcis-10736	15	30	,	,	PUNCT
fcis-10736	15	31	to	to	ADP
fcis-10736	15	32	the	the	DET
fcis-10736	15	33	detection	detection	NOUN
fcis-10736	15	34	effect	effect	NOUN
fcis-10736	15	35	has	have	AUX
fcis-10736	15	36	brought	bring	VERB
fcis-10736	15	37	a	a	DET
fcis-10736	15	38	qualitative	qualitative	ADJ
fcis-10736	15	39	leap	leap	NOUN
fcis-10736	15	40	.	.	PUNCT
fcis-10736	16	1	this	this	DET
fcis-10736	16	2	paper	paper	NOUN
fcis-10736	16	3	mainly	mainly	ADV
fcis-10736	16	4	introduces	introduce	VERB
fcis-10736	16	5	the	the	DET
fcis-10736	16	6	traditional	traditional	ADJ
fcis-10736	16	7	target	target	NOUN
fcis-10736	16	8	detection	detection	NOUN
fcis-10736	16	9	algorithm	algorithm	NOUN
fcis-10736	16	10	,	,	PUNCT
fcis-10736	16	11	the	the	DET
fcis-10736	16	12	target	target	NOUN
fcis-10736	16	13	detection	detection	NOUN
fcis-10736	16	14	algorithm	algorithm	NOUN
fcis-10736	16	15	based	base	VERB
fcis-10736	16	16	on	on	ADP
fcis-10736	16	17	depth	depth	NOUN
fcis-10736	16	18	learning	learning	NOUN
fcis-10736	16	19	and	and	CCONJ
fcis-10736	16	20	the	the	DET
fcis-10736	16	21	commonly	commonly	ADV
fcis-10736	16	22	used	use	VERB
fcis-10736	16	23	data	datum	NOUN
fcis-10736	16	24	set	set	VERB
fcis-10736	16	25	of	of	ADP
fcis-10736	16	26	target	target	NOUN
fcis-10736	16	27	detection	detection	NOUN
fcis-10736	16	28	.	.	PUNCT
fcis-10736	17	1	2	2	X
fcis-10736	17	2	.	.	X
fcis-10736	17	3	traditional	traditional	ADJ
fcis-10736	17	4	target	target	NOUN
fcis-10736	17	5	detection	detection	NOUN
fcis-10736	17	6	algorithm	algorithm	NOUN
fcis-10736	17	7	taking	take	VERB
fcis-10736	17	8	2012	2012	NUM
fcis-10736	17	9	as	as	ADP
fcis-10736	17	10	a	a	DET
fcis-10736	17	11	watershed	watershed	NOUN
fcis-10736	17	12	,	,	PUNCT
fcis-10736	17	13	target	target	NOUN
fcis-10736	17	14	detection	detection	NOUN
fcis-10736	17	15	in	in	ADP
fcis-10736	17	16	the	the	DET
fcis-10736	17	17	past	past	ADJ
fcis-10736	17	18	two	two	NUM
fcis-10736	17	19	decades	decade	NOUN
fcis-10736	17	20	can	can	AUX
fcis-10736	17	21	be	be	AUX
fcis-10736	17	22	roughly	roughly	ADV
fcis-10736	17	23	divided	divide	VERB
fcis-10736	17	24	into	into	ADP
fcis-10736	17	25	two	two	NUM
fcis-10736	17	26	periods	period	NOUN
fcis-10736	17	27	:	:	PUNCT
fcis-10736	17	28	the	the	DET
fcis-10736	17	29	“	"	PUNCT
fcis-10736	17	30	traditional	traditional	ADJ
fcis-10736	17	31	target	target	NOUN
fcis-10736	17	32	detection	detection	NOUN
fcis-10736	17	33	period	period	NOUN
fcis-10736	17	34	”	"	PUNCT
fcis-10736	17	35	before	before	ADP
fcis-10736	17	36	2012	2012	NUM
fcis-10736	17	37	and	and	CCONJ
fcis-10736	17	38	the	the	DET
fcis-10736	17	39	“	"	PUNCT
fcis-10736	17	40	depth	depth	NOUN
fcis-10736	17	41	-	-	PUNCT
fcis-10736	17	42	learning	learning	NOUN
fcis-10736	17	43	-	-	PUNCT
fcis-10736	17	44	based	base	VERB
fcis-10736	17	45	target	target	NOUN
fcis-10736	17	46	detection	detection	NOUN
fcis-10736	17	47	period	period	NOUN
fcis-10736	17	48	”	"	PUNCT
fcis-10736	17	49	after	after	ADP
fcis-10736	17	50	that	that	PRON
fcis-10736	17	51	.	.	PUNCT
fcis-10736	18	1	most	most	ADJ
fcis-10736	18	2	of	of	ADP
fcis-10736	18	3	the	the	DET
fcis-10736	18	4	early	early	ADJ
fcis-10736	18	5	target	target	NOUN
fcis-10736	18	6	detection	detection	NOUN
fcis-10736	18	7	algorithms	algorithm	NOUN
fcis-10736	18	8	are	be	AUX
fcis-10736	18	9	based	base	VERB
fcis-10736	18	10	on	on	ADP
fcis-10736	18	11	manual	manual	ADJ
fcis-10736	18	12	features	feature	NOUN
fcis-10736	18	13	.	.	PUNCT
fcis-10736	19	1	due	due	ADP
fcis-10736	19	2	to	to	ADP
fcis-10736	19	3	the	the	DET
fcis-10736	19	4	lack	lack	NOUN
fcis-10736	19	5	of	of	ADP
fcis-10736	19	6	effective	effective	ADJ
fcis-10736	19	7	image	image	NOUN
fcis-10736	19	8	representation	representation	NOUN
fcis-10736	19	9	,	,	PUNCT
fcis-10736	19	10	people	people	NOUN
fcis-10736	19	11	had	have	VERB
fcis-10736	19	12	no	no	DET
fcis-10736	19	13	choice	choice	NOUN
fcis-10736	19	14	but	but	SCONJ
fcis-10736	19	15	to	to	PART
fcis-10736	19	16	design	design	VERB
fcis-10736	19	17	complex	complex	ADJ
fcis-10736	19	18	feature	feature	NOUN
fcis-10736	19	19	representation	representation	NOUN
fcis-10736	19	20	and	and	CCONJ
fcis-10736	19	21	various	various	ADJ
fcis-10736	19	22	acceleration	acceleration	NOUN
fcis-10736	19	23	techniques	technique	NOUN
fcis-10736	19	24	to	to	PART
fcis-10736	19	25	make	make	VERB
fcis-10736	19	26	the	the	DET
fcis-10736	19	27	most	most	ADJ
fcis-10736	19	28	of	of	ADP
fcis-10736	19	29	the	the	DET
fcis-10736	19	30	limited	limited	ADJ
fcis-10736	19	31	computing	computing	NOUN
fcis-10736	19	32	resources	resource	NOUN
fcis-10736	19	33	.	.	PUNCT
fcis-10736	20	1	figure	figure	NOUN
fcis-10736	20	2	1	1	NUM
fcis-10736	20	3	.	.	PUNCT
fcis-10736	20	4	traditional	traditional	ADJ
fcis-10736	20	5	target	target	NOUN
fcis-10736	20	6	detection	detection	NOUN
fcis-10736	20	7	model	model	NOUN
fcis-10736	20	8	and	and	CCONJ
fcis-10736	20	9	its	its	PRON
fcis-10736	20	10	development	development	NOUN
fcis-10736	20	11	as	as	SCONJ
fcis-10736	20	12	shown	show	VERB
fcis-10736	20	13	in	in	ADP
fcis-10736	20	14	figure	figure	NOUN
fcis-10736	20	15	1	1	NUM
fcis-10736	20	16	above	above	ADV
fcis-10736	20	17	,	,	PUNCT
fcis-10736	20	18	the	the	DET
fcis-10736	20	19	traditional	traditional	ADJ
fcis-10736	20	20	target	target	NOUN
fcis-10736	20	21	detection	detection	NOUN
fcis-10736	20	22	algorithm	algorithm	NOUN
fcis-10736	20	23	mainly	mainly	ADV
fcis-10736	20	24	relies	rely	VERB
fcis-10736	20	25	on	on	ADP
fcis-10736	20	26	the	the	DET
fcis-10736	20	27	traditional	traditional	ADJ
fcis-10736	20	28	feature	feature	NOUN
fcis-10736	20	29	extractor	extractor	NOUN
fcis-10736	20	30	to	to	PART
fcis-10736	20	31	extract	extract	VERB
fcis-10736	20	32	the	the	DET
fcis-10736	20	33	image	image	NOUN
fcis-10736	20	34	features	feature	NOUN
fcis-10736	20	35	,	,	PUNCT
fcis-10736	20	36	and	and	CCONJ
fcis-10736	20	37	uses	use	VERB
fcis-10736	20	38	the	the	DET
fcis-10736	20	39	sliding	slide	VERB
fcis-10736	20	40	window	window	NOUN
fcis-10736	20	41	to	to	PART
fcis-10736	20	42	generate	generate	VERB
fcis-10736	20	43	a	a	DET
fcis-10736	20	44	large	large	ADJ
fcis-10736	20	45	number	number	NOUN
fcis-10736	20	46	of	of	ADP
fcis-10736	20	47	target	target	NOUN
fcis-10736	20	48	candidate	candidate	NOUN
fcis-10736	20	49	regions	region	NOUN
fcis-10736	20	50	,	,	PUNCT
fcis-10736	20	51	at	at	ADP
fcis-10736	20	52	the	the	DET
fcis-10736	20	53	same	same	ADJ
fcis-10736	20	54	time	time	NOUN
fcis-10736	20	55	,	,	PUNCT
fcis-10736	20	56	there	there	PRON
fcis-10736	20	57	are	be	VERB
fcis-10736	20	58	some	some	DET
fcis-10736	20	59	problems	problem	NOUN
fcis-10736	20	60	such	such	ADJ
fcis-10736	20	61	as	as	ADP
fcis-10736	20	62	low	low	ADJ
fcis-10736	20	63	detection	detection	NOUN
fcis-10736	20	64	speed	speed	NOUN
fcis-10736	20	65	and	and	CCONJ
fcis-10736	20	66	low	low	ADJ
fcis-10736	20	67	detection	detection	NOUN
fcis-10736	20	68	precision	precision	NOUN
fcis-10736	20	69	.	.	PUNCT
fcis-10736	21	1	the	the	DET
fcis-10736	21	2	basic	basic	ADJ
fcis-10736	21	3	operational	operational	ADJ
fcis-10736	21	4	steps	step	NOUN
fcis-10736	21	5	of	of	ADP
fcis-10736	21	6	target	target	NOUN
fcis-10736	21	7	detection	detection	NOUN
fcis-10736	21	8	include	include	VERB
fcis-10736	21	9	the	the	DET
fcis-10736	21	10	following	follow	VERB
fcis-10736	21	11	steps	step	NOUN
fcis-10736	21	12	:	:	PUNCT
fcis-10736	21	13	1	1	X
fcis-10736	21	14	.	.	PUNCT
fcis-10736	21	15	data	datum	NOUN
fcis-10736	21	16	collection	collection	NOUN
fcis-10736	21	17	and	and	CCONJ
fcis-10736	21	18	tagging	tagging	NOUN
fcis-10736	21	19	:	:	PUNCT
fcis-10736	21	20	collect	collect	VERB
fcis-10736	21	21	image	image	NOUN
fcis-10736	21	22	or	or	CCONJ
fcis-10736	21	23	video	video	NOUN
fcis-10736	21	24	data	datum	NOUN
fcis-10736	21	25	containing	contain	VERB
fcis-10736	21	26	the	the	DET
fcis-10736	21	27	target	target	NOUN
fcis-10736	21	28	,	,	PUNCT
fcis-10736	21	29	and	and	CCONJ
fcis-10736	21	30	tagging	tagging	NOUN
fcis-10736	21	31	,	,	PUNCT
fcis-10736	21	32	tagging	tag	VERB
fcis-10736	21	33	the	the	DET
fcis-10736	21	34	target	target	NOUN
fcis-10736	21	35	's	's	PART
fcis-10736	21	36	boundary	boundary	ADJ
fcis-10736	21	37	box	box	NOUN
fcis-10736	21	38	and	and	CCONJ
fcis-10736	21	39	category	category	NOUN
fcis-10736	21	40	information	information	NOUN
fcis-10736	21	41	.	.	PUNCT
fcis-10736	22	1	2	2	X
fcis-10736	22	2	.	.	X
fcis-10736	22	3	feature	feature	NOUN
fcis-10736	22	4	extraction	extraction	NOUN
fcis-10736	22	5	:	:	PUNCT
fcis-10736	22	6	to	to	PART
fcis-10736	22	7	extract	extract	VERB
fcis-10736	22	8	features	feature	NOUN
fcis-10736	22	9	from	from	ADP
fcis-10736	22	10	an	an	DET
fcis-10736	22	11	image	image	NOUN
fcis-10736	22	12	to	to	PART
fcis-10736	22	13	represent	represent	VERB
fcis-10736	22	14	a	a	DET
fcis-10736	22	15	target	target	NOUN
fcis-10736	22	16	,	,	PUNCT
fcis-10736	22	17	common	common	ADJ
fcis-10736	22	18	methods	method	NOUN
fcis-10736	22	19	include	include	VERB
fcis-10736	22	20	feature	feature	NOUN
fcis-10736	22	21	extraction	extraction	NOUN
fcis-10736	22	22	using	use	VERB
fcis-10736	22	23	convolutional	convolutional	ADJ
fcis-10736	22	24	neural	neural	ADJ
fcis-10736	22	25	network	network	NOUN
fcis-10736	22	26	(	(	PUNCT
fcis-10736	22	27	cnn	cnn	PROPN
fcis-10736	22	28	)	)	PUNCT
fcis-10736	22	29	.	.	PUNCT
fcis-10736	23	1	3	3	X
fcis-10736	23	2	.	.	NOUN
fcis-10736	23	3	candidate	candidate	NOUN
fcis-10736	23	4	region	region	NOUN
fcis-10736	23	5	generation	generation	NOUN
fcis-10736	23	6	:	:	PUNCT
fcis-10736	23	7	a	a	DET
fcis-10736	23	8	candidate	candidate	NOUN
fcis-10736	23	9	region	region	NOUN
fcis-10736	23	10	generation	generation	NOUN
fcis-10736	23	11	algorithm	algorithm	NOUN
fcis-10736	23	12	(	(	PUNCT
fcis-10736	23	13	such	such	ADJ
fcis-10736	23	14	as	as	ADP
fcis-10736	23	15	selective	selective	ADJ
fcis-10736	23	16	search	search	NOUN
fcis-10736	23	17	,	,	PUNCT
fcis-10736	23	18	edgeboxes	edgeboxe	NOUN
fcis-10736	23	19	,	,	PUNCT
fcis-10736	23	20	etc	etc	X
fcis-10736	23	21	.	.	X
fcis-10736	23	22	)	)	PUNCT
fcis-10736	23	23	is	be	AUX
fcis-10736	23	24	used	use	VERB
fcis-10736	23	25	to	to	PART
fcis-10736	23	26	generate	generate	VERB
fcis-10736	23	27	candidate	candidate	NOUN
fcis-10736	23	28	regions	region	NOUN
fcis-10736	23	29	that	that	PRON
fcis-10736	23	30	may	may	AUX
fcis-10736	23	31	contain	contain	VERB
fcis-10736	23	32	the	the	DET
fcis-10736	23	33	target	target	NOUN
fcis-10736	23	34	.	.	PUNCT
fcis-10736	24	1	4	4	X
fcis-10736	24	2	.	.	X
fcis-10736	24	3	feature	feature	NOUN
fcis-10736	24	4	matching	matching	NOUN
fcis-10736	24	5	:	:	PUNCT
fcis-10736	24	6	to	to	PART
fcis-10736	24	7	match	match	VERB
fcis-10736	24	8	the	the	DET
fcis-10736	24	9	extracted	extract	VERB
fcis-10736	24	10	features	feature	NOUN
fcis-10736	24	11	with	with	ADP
fcis-10736	24	12	the	the	DET
fcis-10736	24	13	features	feature	NOUN
fcis-10736	24	14	in	in	ADP
fcis-10736	24	15	the	the	DET
fcis-10736	24	16	candidate	candidate	NOUN
fcis-10736	24	17	area	area	NOUN
fcis-10736	24	18	,	,	PUNCT
fcis-10736	24	19	the	the	DET
fcis-10736	24	20	common	common	ADJ
fcis-10736	24	21	method	method	NOUN
fcis-10736	24	22	is	be	AUX
fcis-10736	24	23	to	to	PART
fcis-10736	24	24	calculate	calculate	VERB
fcis-10736	24	25	the	the	DET
fcis-10736	24	26	similarity	similarity	NOUN
fcis-10736	24	27	between	between	ADP
fcis-10736	24	28	the	the	DET
fcis-10736	24	29	two	two	NUM
fcis-10736	24	30	.	.	PUNCT
fcis-10736	25	1	5	5	NUM
fcis-10736	25	2	.	.	X
fcis-10736	25	3	target	target	VERB
fcis-10736	25	4	classification	classification	NOUN
fcis-10736	25	5	:	:	PUNCT
fcis-10736	25	6	according	accord	VERB
fcis-10736	25	7	to	to	ADP
fcis-10736	25	8	the	the	DET
fcis-10736	25	9	features	feature	NOUN
fcis-10736	25	10	obtained	obtain	VERB
fcis-10736	25	11	by	by	ADP
fcis-10736	25	12	matching	matching	NOUN
fcis-10736	25	13	,	,	PUNCT
fcis-10736	25	14	targets	target	NOUN
fcis-10736	25	15	are	be	AUX
fcis-10736	25	16	classified	classify	VERB
fcis-10736	25	17	by	by	ADP
fcis-10736	25	18	using	use	VERB
fcis-10736	25	19	classifiers	classifier	NOUN
fcis-10736	25	20	,	,	PUNCT
fcis-10736	25	21	such	such	ADJ
fcis-10736	25	22	as	as	ADP
fcis-10736	25	23	support	support	NOUN
fcis-10736	25	24	vector	vector	NOUN
fcis-10736	25	25	machine	machine	NOUN
fcis-10736	25	26	support	support	NOUN
fcis-10736	25	27	vector	vector	NOUN
fcis-10736	25	28	machine	machine	NOUN
fcis-10736	25	29	(	(	PUNCT
fcis-10736	25	30	svm	svm	PROPN
fcis-10736	25	31	)	)	PUNCT
fcis-10736	25	32	and	and	CCONJ
fcis-10736	25	33	logistic	logistic	ADJ
fcis-10736	25	34	regression	regression	NOUN
fcis-10736	25	35	.	.	PUNCT
fcis-10736	26	1	6	6	X
fcis-10736	26	2	.	.	X
fcis-10736	26	3	bounding	bound	VERB
fcis-10736	26	4	box	box	NOUN
fcis-10736	26	5	regression	regression	NOUN
fcis-10736	26	6	:	:	PUNCT
fcis-10736	26	7	a	a	DET
fcis-10736	26	8	regression	regression	NOUN
fcis-10736	26	9	algorithm	algorithm	NOUN
fcis-10736	26	10	is	be	AUX
fcis-10736	26	11	used	use	VERB
fcis-10736	26	12	to	to	ADP
fcis-10736	26	13	fine	fine	ADJ
fcis-10736	26	14	-	-	PUNCT
fcis-10736	26	15	tune	tune	NOUN
fcis-10736	26	16	the	the	DET
fcis-10736	26	17	bounding	bounding	NOUN
fcis-10736	26	18	box	box	NOUN
fcis-10736	26	19	of	of	ADP
fcis-10736	26	20	a	a	DET
fcis-10736	26	21	candidate	candidate	NOUN
fcis-10736	26	22	area	area	NOUN
fcis-10736	26	23	to	to	PART
fcis-10736	26	24	frame	frame	VERB
fcis-10736	26	25	the	the	DET
fcis-10736	26	26	location	location	NOUN
fcis-10736	26	27	of	of	ADP
fcis-10736	26	28	the	the	DET
fcis-10736	26	29	target	target	NOUN
fcis-10736	26	30	more	more	ADV
fcis-10736	26	31	accurately	accurately	ADV
fcis-10736	26	32	.	.	PUNCT
fcis-10736	27	1	18	18	NUM
fcis-10736	27	2	3	3	NUM
fcis-10736	27	3	.	.	PUNCT
fcis-10736	27	4	target	target	NOUN
fcis-10736	27	5	detection	detection	NOUN
fcis-10736	27	6	algorithm	algorithm	NOUN
fcis-10736	27	7	based	base	VERB
fcis-10736	27	8	on	on	ADP
fcis-10736	27	9	depth	depth	NOUN
fcis-10736	27	10	learning	learn	VERB
fcis-10736	27	11	3.1	3.1	NUM
fcis-10736	27	12	one	one	NUM
fcis-10736	27	13	-	-	PUNCT
fcis-10736	27	14	stage	stage	NOUN
fcis-10736	27	15	target	target	NOUN
fcis-10736	27	16	detection	detection	NOUN
fcis-10736	27	17	algorithm	algorithm	NOUN
fcis-10736	27	18	one	one	NUM
fcis-10736	27	19	-	-	PUNCT
fcis-10736	27	20	stage	stage	NOUN
fcis-10736	27	21	target	target	NOUN
fcis-10736	27	22	detection	detection	NOUN
fcis-10736	27	23	algorithm	algorithm	NOUN
fcis-10736	27	24	can	can	AUX
fcis-10736	27	25	directly	directly	ADV
fcis-10736	27	26	generate	generate	VERB
fcis-10736	27	27	the	the	DET
fcis-10736	27	28	class	class	NOUN
fcis-10736	27	29	probability	probability	NOUN
fcis-10736	27	30	and	and	CCONJ
fcis-10736	27	31	position	position	NOUN
fcis-10736	27	32	coordinate	coordinate	NOUN
fcis-10736	27	33	values	value	NOUN
fcis-10736	27	34	of	of	ADP
fcis-10736	27	35	objects	object	NOUN
fcis-10736	27	36	in	in	ADP
fcis-10736	27	37	one	one	NUM
fcis-10736	27	38	stage	stage	NOUN
fcis-10736	27	39	,	,	PUNCT
fcis-10736	27	40	and	and	CCONJ
fcis-10736	27	41	the	the	DET
fcis-10736	27	42	overall	overall	ADJ
fcis-10736	27	43	process	process	NOUN
fcis-10736	27	44	is	be	AUX
fcis-10736	27	45	simpler	simple	ADJ
fcis-10736	27	46	than	than	ADP
fcis-10736	27	47	the	the	DET
fcis-10736	27	48	twostage	twostage	NOUN
fcis-10736	27	49	target	target	NOUN
fcis-10736	27	50	detection	detection	NOUN
fcis-10736	27	51	algorithm	algorithm	NOUN
fcis-10736	27	52	,	,	PUNCT
fcis-10736	27	53	which	which	PRON
fcis-10736	27	54	does	do	AUX
fcis-10736	27	55	not	not	PART
fcis-10736	27	56	need	need	VERB
fcis-10736	27	57	the	the	DET
fcis-10736	27	58	region	region	NOUN
fcis-10736	27	59	proposal	proposal	NOUN
fcis-10736	27	60	stage	stage	NOUN
fcis-10736	27	61	.	.	PUNCT
fcis-10736	28	1	the	the	DET
fcis-10736	28	2	following	follow	VERB
fcis-10736	28	3	is	be	AUX
fcis-10736	28	4	a	a	DET
fcis-10736	28	5	model	model	NOUN
fcis-10736	28	6	of	of	ADP
fcis-10736	28	7	one	one	NUM
fcis-10736	28	8	-	-	PUNCT
fcis-10736	28	9	stage	stage	NOUN
fcis-10736	28	10	target	target	NOUN
fcis-10736	28	11	detection	detection	NOUN
fcis-10736	28	12	algorithm	algorithm	NOUN
fcis-10736	28	13	.	.	PUNCT
fcis-10736	29	1	ssd	ssd	PROPN
fcis-10736	29	2	:	:	PUNCT
fcis-10736	29	3	the	the	DET
fcis-10736	29	4	backbone	backbone	NOUN
fcis-10736	29	5	of	of	ADP
fcis-10736	29	6	the	the	DET
fcis-10736	29	7	ssd	ssd	NOUN
fcis-10736	29	8	detection	detection	NOUN
fcis-10736	29	9	algorithm	algorithm	NOUN
fcis-10736	29	10	is	be	AUX
fcis-10736	29	11	a	a	DET
fcis-10736	29	12	vgg	vgg	ADJ
fcis-10736	29	13	network	network	NOUN
fcis-10736	29	14	,	,	PUNCT
fcis-10736	29	15	using	use	VERB
fcis-10736	29	16	feature	feature	NOUN
fcis-10736	29	17	maps	map	NOUN
fcis-10736	29	18	with	with	ADP
fcis-10736	29	19	different	different	ADJ
fcis-10736	29	20	resolutions	resolution	NOUN
fcis-10736	29	21	at	at	ADP
fcis-10736	29	22	different	different	ADJ
fcis-10736	29	23	stages	stage	NOUN
fcis-10736	29	24	to	to	PART
fcis-10736	29	25	make	make	VERB
fcis-10736	29	26	predictions	prediction	NOUN
fcis-10736	29	27	.	.	PUNCT
fcis-10736	30	1	yolo	yolo	ADJ
fcis-10736	30	2	:	:	PUNCT
fcis-10736	31	1	yolo	yolo	PROPN
fcis-10736	31	2	[	[	X
fcis-10736	31	3	7	7	X
fcis-10736	31	4	]	]	PUNCT
fcis-10736	31	5	is	be	AUX
fcis-10736	31	6	an	an	DET
fcis-10736	31	7	end	end	NOUN
fcis-10736	31	8	-	-	PUNCT
fcis-10736	31	9	to	to	ADP
fcis-10736	31	10	-	-	PUNCT
fcis-10736	31	11	end	end	NOUN
fcis-10736	31	12	neural	neural	ADJ
fcis-10736	31	13	network	network	NOUN
fcis-10736	31	14	.	.	PUNCT
fcis-10736	32	1	yolo	yolo	PROPN
fcis-10736	32	2	is	be	AUX
fcis-10736	32	3	an	an	DET
fcis-10736	32	4	algorithm	algorithm	NOUN
fcis-10736	32	5	for	for	ADP
fcis-10736	32	6	object	object	NOUN
fcis-10736	32	7	prediction	prediction	NOUN
fcis-10736	32	8	based	base	VERB
fcis-10736	32	9	on	on	ADP
fcis-10736	32	10	global	global	ADJ
fcis-10736	32	11	image	image	NOUN
fcis-10736	32	12	information	information	NOUN
fcis-10736	32	13	.	.	PUNCT
fcis-10736	33	1	yolov2	yolov2	PROPN
fcis-10736	33	2	:	:	PUNCT
fcis-10736	34	1	yolov2	yolov2	PROPN
fcis-10736	35	1	[	[	X
fcis-10736	35	2	8	8	NUM
fcis-10736	35	3	]	]	PUNCT
fcis-10736	35	4	improves	improve	VERB
fcis-10736	35	5	the	the	DET
fcis-10736	35	6	precision	precision	NOUN
fcis-10736	35	7	of	of	ADP
fcis-10736	35	8	yolov1	yolov1	NOUN
fcis-10736	35	9	.	.	PUNCT
fcis-10736	36	1	by	by	ADP
fcis-10736	36	2	adding	add	VERB
fcis-10736	36	3	the	the	DET
fcis-10736	36	4	bn	bn	NOUN
fcis-10736	36	5	layer	layer	NOUN
fcis-10736	36	6	,	,	PUNCT
fcis-10736	36	7	small	small	ADJ
fcis-10736	36	8	objects	object	NOUN
fcis-10736	36	9	can	can	AUX
fcis-10736	36	10	be	be	AUX
fcis-10736	36	11	detected	detect	VERB
fcis-10736	36	12	better	well	ADV
fcis-10736	36	13	,	,	PUNCT
fcis-10736	36	14	and	and	CCONJ
fcis-10736	36	15	following	follow	VERB
fcis-10736	36	16	the	the	DET
fcis-10736	36	17	practice	practice	NOUN
fcis-10736	36	18	of	of	ADP
fcis-10736	36	19	faster	fast	ADJ
fcis-10736	36	20	r	r	NOUN
fcis-10736	36	21	-	-	PUNCT
fcis-10736	36	22	cnn	cnn	PROPN
fcis-10736	36	23	,	,	PUNCT
fcis-10736	36	24	yolov2	yolov2	PROPN
fcis-10736	36	25	removes	remove	VERB
fcis-10736	36	26	the	the	DET
fcis-10736	36	27	fully	fully	ADV
fcis-10736	36	28	connected	connect	VERB
fcis-10736	36	29	layer	layer	NOUN
fcis-10736	36	30	in	in	ADP
fcis-10736	36	31	yolov1	yolov1	NOUN
fcis-10736	36	32	and	and	CCONJ
fcis-10736	36	33	adopts	adopt	VERB
fcis-10736	36	34	the	the	DET
fcis-10736	36	35	convolutional	convolutional	ADJ
fcis-10736	36	36	kernel	kernel	NOUN
fcis-10736	36	37	anchor	anchor	NOUN
fcis-10736	36	38	boxes	box	NOUN
fcis-10736	36	39	to	to	PART
fcis-10736	36	40	predict	predict	VERB
fcis-10736	36	41	the	the	DET
fcis-10736	36	42	boundary	boundary	ADJ
fcis-10736	36	43	boxes	box	NOUN
fcis-10736	36	44	,	,	PUNCT
fcis-10736	36	45	the	the	DET
fcis-10736	36	46	detection	detection	NOUN
fcis-10736	36	47	accuracy	accuracy	NOUN
fcis-10736	36	48	is	be	AUX
fcis-10736	36	49	improved	improve	VERB
fcis-10736	36	50	.	.	PUNCT
fcis-10736	37	1	yolov3	yolov3	PROPN
fcis-10736	37	2	:	:	PUNCT
fcis-10736	38	1	yolov3	yolov3	PROPN
fcis-10736	39	1	[	[	X
fcis-10736	39	2	9	9	NUM
fcis-10736	39	3	]	]	PUNCT
fcis-10736	39	4	improves	improve	VERB
fcis-10736	39	5	the	the	DET
fcis-10736	39	6	previous	previous	ADJ
fcis-10736	39	7	yolo	yolo	ADJ
fcis-10736	39	8	algorithm	algorithm	NOUN
fcis-10736	39	9	,	,	PUNCT
fcis-10736	39	10	improves	improve	VERB
fcis-10736	39	11	the	the	DET
fcis-10736	39	12	backbone	backbone	NOUN
fcis-10736	39	13	of	of	ADP
fcis-10736	39	14	the	the	DET
fcis-10736	39	15	network	network	NOUN
fcis-10736	39	16	,	,	PUNCT
fcis-10736	39	17	uses	use	VERB
fcis-10736	39	18	the	the	DET
fcis-10736	39	19	multi	multi	ADJ
fcis-10736	39	20	-	-	ADJ
fcis-10736	39	21	scale	scale	ADJ
fcis-10736	39	22	feature	feature	NOUN
fcis-10736	39	23	graph	graph	NOUN
fcis-10736	39	24	to	to	PART
fcis-10736	39	25	detect	detect	VERB
fcis-10736	39	26	the	the	DET
fcis-10736	39	27	target	target	NOUN
fcis-10736	39	28	,	,	PUNCT
fcis-10736	39	29	uses	use	VERB
fcis-10736	39	30	the	the	DET
fcis-10736	39	31	single	single	ADJ
fcis-10736	39	32	neural	neural	ADJ
fcis-10736	39	33	network	network	NOUN
fcis-10736	39	34	to	to	PART
fcis-10736	39	35	process	process	VERB
fcis-10736	39	36	the	the	DET
fcis-10736	39	37	image	image	NOUN
fcis-10736	39	38	,	,	PUNCT
fcis-10736	39	39	and	and	CCONJ
fcis-10736	39	40	divides	divide	VERB
fcis-10736	39	41	the	the	DET
fcis-10736	39	42	image	image	NOUN
fcis-10736	39	43	into	into	ADP
fcis-10736	39	44	several	several	ADJ
fcis-10736	39	45	regions	region	NOUN
fcis-10736	39	46	,	,	PUNCT
fcis-10736	39	47	the	the	DET
fcis-10736	39	48	probability	probability	NOUN
fcis-10736	39	49	of	of	ADP
fcis-10736	39	50	each	each	DET
fcis-10736	39	51	region	region	NOUN
fcis-10736	39	52	and	and	CCONJ
fcis-10736	39	53	the	the	DET
fcis-10736	39	54	information	information	NOUN
fcis-10736	39	55	of	of	ADP
fcis-10736	39	56	boundary	boundary	ADJ
fcis-10736	39	57	box	box	PROPN
fcis-10736	39	58	are	be	AUX
fcis-10736	39	59	predicted	predict	VERB
fcis-10736	39	60	,	,	PUNCT
fcis-10736	39	61	so	so	ADV
fcis-10736	39	62	the	the	DET
fcis-10736	39	63	global	global	ADJ
fcis-10736	39	64	perception	perception	NOUN
fcis-10736	39	65	and	and	CCONJ
fcis-10736	39	66	local	local	ADJ
fcis-10736	39	67	precision	precision	NOUN
fcis-10736	39	68	of	of	ADP
fcis-10736	39	69	target	target	NOUN
fcis-10736	39	70	detection	detection	NOUN
fcis-10736	39	71	are	be	AUX
fcis-10736	39	72	combined	combine	VERB
fcis-10736	39	73	,	,	PUNCT
fcis-10736	39	74	and	and	CCONJ
fcis-10736	39	75	good	good	ADJ
fcis-10736	39	76	detection	detection	NOUN
fcis-10736	39	77	results	result	NOUN
fcis-10736	39	78	are	be	AUX
fcis-10736	39	79	obtained	obtain	VERB
fcis-10736	39	80	.	.	PUNCT
fcis-10736	40	1	yolov4	yolov4	NOUN
fcis-10736	40	2	:	:	PUNCT
fcis-10736	40	3	developed	develop	VERB
fcis-10736	40	4	by	by	ADP
fcis-10736	40	5	alexey	alexey	PROPN
fcis-10736	40	6	bochkovskiy	bochkovskiy	PROPN
fcis-10736	40	7	,	,	PUNCT
fcis-10736	40	8	chien	chien	PROPN
fcis-10736	40	9	yao	yao	PROPN
fcis-10736	40	10	wang	wang	PROPN
fcis-10736	40	11	,	,	PUNCT
fcis-10736	40	12	and	and	CCONJ
fcis-10736	40	13	hong	hong	PROPN
fcis-10736	40	14	-	-	PUNCT
fcis-10736	40	15	yuan	yuan	PROPN
fcis-10736	40	16	mark	mark	PROPN
fcis-10736	40	17	liao	liao	PROPN
fcis-10736	40	18	and	and	CCONJ
fcis-10736	40	19	released	release	VERB
fcis-10736	40	20	in	in	ADP
fcis-10736	40	21	2020	2020	NUM
fcis-10736	40	22	,	,	PUNCT
fcis-10736	40	23	the	the	DET
fcis-10736	40	24	algorithm	algorithm	NOUN
fcis-10736	40	25	uses	use	VERB
fcis-10736	40	26	a	a	DET
fcis-10736	40	27	number	number	NOUN
fcis-10736	40	28	of	of	ADP
fcis-10736	40	29	optimization	optimization	NOUN
fcis-10736	40	30	strategies	strategy	NOUN
fcis-10736	40	31	,	,	PUNCT
fcis-10736	40	32	such	such	ADJ
fcis-10736	40	33	as	as	ADP
fcis-10736	40	34	rapid	rapid	ADJ
fcis-10736	40	35	testing	testing	NOUN
fcis-10736	40	36	,	,	PUNCT
fcis-10736	40	37	cross	cross	ADJ
fcis-10736	40	38	-	-	ADJ
fcis-10736	40	39	layer	layer	ADJ
fcis-10736	40	40	connectivity	connectivity	NOUN
fcis-10736	40	41	,	,	PUNCT
fcis-10736	40	42	and	and	CCONJ
fcis-10736	40	43	convolution	convolution	NOUN
fcis-10736	40	44	operations	operation	NOUN
fcis-10736	40	45	,	,	PUNCT
fcis-10736	40	46	so	so	SCONJ
fcis-10736	40	47	that	that	SCONJ
fcis-10736	40	48	it	it	PRON
fcis-10736	40	49	can	can	AUX
fcis-10736	40	50	achieve	achieve	VERB
fcis-10736	40	51	faster	fast	ADJ
fcis-10736	40	52	detection	detection	NOUN
fcis-10736	40	53	speed	speed	NOUN
fcis-10736	40	54	while	while	SCONJ
fcis-10736	40	55	maintaining	maintain	VERB
fcis-10736	40	56	high	high	ADJ
fcis-10736	40	57	accuracy	accuracy	NOUN
fcis-10736	40	58	.	.	PUNCT
fcis-10736	41	1	yolov5	yolov5	NOUN
fcis-10736	41	2	:	:	PUNCT
fcis-10736	41	3	june	june	PROPN
fcis-10736	41	4	2020	2020	NUM
fcis-10736	41	5	,	,	PUNCT
fcis-10736	41	6	jocher	jocher	PROPN
fcis-10736	41	7	presents	present	VERB
fcis-10736	41	8	yolov5	yolov5	NOUN
fcis-10736	42	1	[	[	X
fcis-10736	42	2	10	10	NUM
fcis-10736	42	3	]	]	PUNCT
fcis-10736	42	4	.	.	PUNCT
fcis-10736	43	1	in	in	ADP
fcis-10736	43	2	the	the	DET
fcis-10736	43	3	input	input	NOUN
fcis-10736	43	4	side	side	NOUN
fcis-10736	43	5	,	,	PUNCT
fcis-10736	43	6	the	the	DET
fcis-10736	43	7	adaptive	adaptive	ADJ
fcis-10736	43	8	anchor	anchor	NOUN
fcis-10736	43	9	frame	frame	NOUN
fcis-10736	43	10	calculation	calculation	NOUN
fcis-10736	43	11	and	and	CCONJ
fcis-10736	43	12	image	image	NOUN
fcis-10736	43	13	zooming	zooming	NOUN
fcis-10736	43	14	technology	technology	NOUN
fcis-10736	43	15	are	be	AUX
fcis-10736	43	16	adopted	adopt	VERB
fcis-10736	43	17	to	to	PART
fcis-10736	43	18	obtain	obtain	VERB
fcis-10736	43	19	the	the	DET
fcis-10736	43	20	suitable	suitable	ADJ
fcis-10736	43	21	anchor	anchor	NOUN
fcis-10736	43	22	frame	frame	NOUN
fcis-10736	43	23	and	and	CCONJ
fcis-10736	43	24	reduce	reduce	VERB
fcis-10736	43	25	the	the	DET
fcis-10736	43	26	computation	computation	NOUN
fcis-10736	43	27	of	of	ADP
fcis-10736	43	28	model	model	NOUN
fcis-10736	43	29	,	,	PUNCT
fcis-10736	43	30	and	and	CCONJ
fcis-10736	43	31	improve	improve	VERB
fcis-10736	43	32	the	the	DET
fcis-10736	43	33	performance	performance	NOUN
fcis-10736	43	34	and	and	CCONJ
fcis-10736	43	35	effect	effect	VERB
fcis-10736	43	36	quality	quality	NOUN
fcis-10736	43	37	of	of	ADP
fcis-10736	43	38	the	the	DET
fcis-10736	43	39	whole	whole	ADJ
fcis-10736	43	40	target	target	NOUN
fcis-10736	43	41	detection	detection	NOUN
fcis-10736	43	42	algorithm	algorithm	NOUN
fcis-10736	43	43	.	.	PUNCT
fcis-10736	44	1	yolov7	yolov7	NOUN
fcis-10736	44	2	:	:	PUNCT
fcis-10736	44	3	yolov7	yolov7	NOUN
fcis-10736	45	1	[	[	X
fcis-10736	45	2	11	11	NUM
fcis-10736	45	3	]	]	PUNCT
fcis-10736	45	4	target	target	NOUN
fcis-10736	45	5	detection	detection	NOUN
fcis-10736	45	6	algorithm	algorithm	NOUN
fcis-10736	45	7	has	have	VERB
fcis-10736	45	8	higher	high	ADJ
fcis-10736	45	9	detection	detection	NOUN
fcis-10736	45	10	accuracy	accuracy	NOUN
fcis-10736	45	11	and	and	CCONJ
fcis-10736	45	12	faster	fast	ADJ
fcis-10736	45	13	detection	detection	NOUN
fcis-10736	45	14	speed	speed	NOUN
fcis-10736	45	15	than	than	ADP
fcis-10736	45	16	previous	previous	ADJ
fcis-10736	45	17	models	model	NOUN
fcis-10736	45	18	.	.	PUNCT
fcis-10736	46	1	and	and	CCONJ
fcis-10736	46	2	for	for	ADP
fcis-10736	46	3	different	different	ADJ
fcis-10736	46	4	target	target	NOUN
fcis-10736	46	5	detection	detection	NOUN
fcis-10736	46	6	tasks	task	NOUN
fcis-10736	46	7	,	,	PUNCT
fcis-10736	46	8	there	there	PRON
fcis-10736	46	9	are	be	VERB
fcis-10736	46	10	7	7	NUM
fcis-10736	46	11	different	different	ADJ
fcis-10736	46	12	size	size	NOUN
fcis-10736	46	13	models	model	NOUN
fcis-10736	46	14	,	,	PUNCT
fcis-10736	46	15	such	such	ADJ
fcis-10736	46	16	as	as	ADP
fcis-10736	46	17	yolov7	yolov7	NOUN
fcis-10736	46	18	-	-	PUNCT
fcis-10736	46	19	w6	w6	NOUN
fcis-10736	46	20	,	,	PUNCT
fcis-10736	46	21	yolov7	yolov7	NOUN
fcis-10736	46	22	-	-	PUNCT
fcis-10736	46	23	d6	d6	NOUN
fcis-10736	46	24	,	,	PUNCT
fcis-10736	46	25	etc	etc	X
fcis-10736	46	26	.	.	X
fcis-10736	47	1	yolov8	yolov8	PROPN
fcis-10736	47	2	is	be	AUX
fcis-10736	47	3	an	an	DET
fcis-10736	47	4	improved	improved	ADJ
fcis-10736	47	5	version	version	NOUN
fcis-10736	47	6	of	of	ADP
fcis-10736	47	7	yolo	yolo	NOUN
fcis-10736	47	8	(	(	PUNCT
fcis-10736	47	9	you	you	PRON
fcis-10736	47	10	only	only	ADV
fcis-10736	47	11	look	look	VERB
fcis-10736	47	12	once	once	ADV
fcis-10736	47	13	)	)	PUNCT
fcis-10736	47	14	,	,	PUNCT
fcis-10736	47	15	which	which	PRON
fcis-10736	47	16	is	be	AUX
fcis-10736	47	17	known	know	VERB
fcis-10736	47	18	for	for	ADP
fcis-10736	47	19	fast	fast	ADJ
fcis-10736	47	20	and	and	CCONJ
fcis-10736	47	21	accurate	accurate	ADJ
fcis-10736	47	22	real	real	ADJ
fcis-10736	47	23	-	-	PUNCT
fcis-10736	47	24	time	time	NOUN
fcis-10736	47	25	object	object	NOUN
fcis-10736	47	26	detection	detection	NOUN
fcis-10736	47	27	capabilities	capability	NOUN
fcis-10736	47	28	.	.	PUNCT
fcis-10736	48	1	yolov8	yolov8	NOUN
fcis-10736	48	2	incorporates	incorporate	VERB
fcis-10736	48	3	several	several	ADJ
fcis-10736	48	4	improvements	improvement	NOUN
fcis-10736	48	5	over	over	ADP
fcis-10736	48	6	its	its	PRON
fcis-10736	48	7	predecessors	predecessor	NOUN
fcis-10736	48	8	,	,	PUNCT
fcis-10736	48	9	such	such	ADJ
fcis-10736	48	10	as	as	ADP
fcis-10736	48	11	a	a	DET
fcis-10736	48	12	powerful	powerful	ADJ
fcis-10736	48	13	backbone	backbone	NOUN
fcis-10736	48	14	network	network	NOUN
fcis-10736	48	15	(	(	PUNCT
fcis-10736	48	16	dark-53	dark-53	PROPN
fcis-10736	48	17	)	)	PUNCT
fcis-10736	48	18	,	,	PUNCT
fcis-10736	48	19	advanced	advanced	ADJ
fcis-10736	48	20	data	datum	NOUN
fcis-10736	48	21	augmentation	augmentation	NOUN
fcis-10736	48	22	techniques	technique	NOUN
fcis-10736	48	23	,	,	PUNCT
fcis-10736	48	24	and	and	CCONJ
fcis-10736	48	25	better	well	ADJ
fcis-10736	48	26	anchor	anchor	NOUN
fcis-10736	48	27	box	box	NOUN
fcis-10736	48	28	clustering	clustering	NOUN
fcis-10736	48	29	.	.	PUNCT
fcis-10736	49	1	these	these	DET
fcis-10736	49	2	improvements	improvement	NOUN
fcis-10736	49	3	help	help	VERB
fcis-10736	49	4	to	to	PART
fcis-10736	49	5	enhance	enhance	VERB
fcis-10736	49	6	the	the	DET
fcis-10736	49	7	accuracy	accuracy	NOUN
fcis-10736	49	8	and	and	CCONJ
fcis-10736	49	9	efficiency	efficiency	NOUN
fcis-10736	49	10	of	of	ADP
fcis-10736	49	11	object	object	NOUN
fcis-10736	49	12	detection	detection	NOUN
fcis-10736	49	13	tasks	task	NOUN
fcis-10736	49	14	in	in	ADP
fcis-10736	49	15	various	various	ADJ
fcis-10736	49	16	applications	application	NOUN
fcis-10736	49	17	.	.	PUNCT
fcis-10736	50	1	3.2	3.2	NUM
fcis-10736	50	2	two	two	NUM
fcis-10736	50	3	-	-	PUNCT
fcis-10736	50	4	stage	stage	NOUN
fcis-10736	50	5	target	target	NOUN
fcis-10736	50	6	detection	detection	NOUN
fcis-10736	50	7	algorithm	algorithm	NOUN
fcis-10736	50	8	the	the	DET
fcis-10736	50	9	two	two	NUM
fcis-10736	50	10	-	-	PUNCT
fcis-10736	50	11	stage	stage	NOUN
fcis-10736	50	12	object	object	NOUN
fcis-10736	50	13	detection	detection	NOUN
fcis-10736	50	14	algorithm	algorithm	NOUN
fcis-10736	50	15	can	can	AUX
fcis-10736	50	16	be	be	AUX
fcis-10736	50	17	regarded	regard	VERB
fcis-10736	50	18	as	as	ADP
fcis-10736	50	19	two	two	NUM
fcis-10736	50	20	one	one	NUM
fcis-10736	50	21	-	-	PUNCT
fcis-10736	50	22	stage	stage	NOUN
fcis-10736	50	23	detection	detection	NOUN
fcis-10736	50	24	,	,	PUNCT
fcis-10736	50	25	the	the	DET
fcis-10736	50	26	first	first	ADJ
fcis-10736	50	27	stage	stage	NOUN
fcis-10736	50	28	can	can	AUX
fcis-10736	50	29	detect	detect	VERB
fcis-10736	50	30	the	the	DET
fcis-10736	50	31	location	location	NOUN
fcis-10736	50	32	of	of	ADP
fcis-10736	50	33	objects	object	NOUN
fcis-10736	50	34	,	,	PUNCT
fcis-10736	50	35	the	the	DET
fcis-10736	50	36	second	second	ADJ
fcis-10736	50	37	stage	stage	NOUN
fcis-10736	50	38	can	can	AUX
fcis-10736	50	39	refine	refine	VERB
fcis-10736	50	40	the	the	DET
fcis-10736	50	41	results	result	NOUN
fcis-10736	50	42	of	of	ADP
fcis-10736	50	43	the	the	DET
fcis-10736	50	44	first	first	ADJ
fcis-10736	50	45	stage	stage	NOUN
fcis-10736	50	46	,	,	PUNCT
fcis-10736	50	47	one	one	NUM
fcis-10736	50	48	-	-	PUNCT
fcis-10736	50	49	stage	stage	NOUN
fcis-10736	50	50	detection	detection	NOUN
fcis-10736	50	51	is	be	AUX
fcis-10736	50	52	performed	perform	VERB
fcis-10736	50	53	for	for	ADP
fcis-10736	50	54	each	each	DET
fcis-10736	50	55	candidate	candidate	NOUN
fcis-10736	50	56	region	region	NOUN
fcis-10736	50	57	.	.	PUNCT
fcis-10736	51	1	the	the	DET
fcis-10736	51	2	overall	overall	ADJ
fcis-10736	51	3	process	process	NOUN
fcis-10736	51	4	is	be	AUX
fcis-10736	51	5	shown	show	VERB
fcis-10736	51	6	in	in	ADP
fcis-10736	51	7	figure	figure	NOUN
fcis-10736	51	8	2	2	NUM
fcis-10736	51	9	below	below	ADV
fcis-10736	51	10	.	.	PUNCT
fcis-10736	52	1	while	while	SCONJ
fcis-10736	52	2	testing	testing	NOUN
fcis-10736	52	3	,	,	PUNCT
fcis-10736	52	4	the	the	DET
fcis-10736	52	5	input	input	NOUN
fcis-10736	52	6	image	image	NOUN
fcis-10736	52	7	is	be	AUX
fcis-10736	52	8	convolutional	convolutional	ADJ
fcis-10736	52	9	neural	neural	ADJ
fcis-10736	52	10	network	network	NOUN
fcis-10736	52	11	to	to	PART
fcis-10736	52	12	produce	produce	VERB
fcis-10736	52	13	the	the	DET
fcis-10736	52	14	first	first	ADJ
fcis-10736	52	15	stage	stage	NOUN
fcis-10736	52	16	output	output	NOUN
fcis-10736	52	17	.	.	PUNCT
fcis-10736	53	1	the	the	DET
fcis-10736	53	2	output	output	NOUN
fcis-10736	53	3	is	be	AUX
fcis-10736	53	4	decoded	decode	VERB
fcis-10736	53	5	to	to	PART
fcis-10736	53	6	generate	generate	VERB
fcis-10736	53	7	candidate	candidate	NOUN
fcis-10736	53	8	regions	region	NOUN
fcis-10736	53	9	,	,	PUNCT
fcis-10736	53	10	then	then	ADV
fcis-10736	53	11	the	the	DET
fcis-10736	53	12	feature	feature	NOUN
fcis-10736	53	13	representation	representation	NOUN
fcis-10736	53	14	(	(	PUNCT
fcis-10736	53	15	rois	rois	PROPN
fcis-10736	53	16	)	)	PUNCT
fcis-10736	53	17	of	of	ADP
fcis-10736	53	18	the	the	DET
fcis-10736	53	19	corresponding	corresponding	ADJ
fcis-10736	53	20	candidate	candidate	NOUN
fcis-10736	53	21	region	region	NOUN
fcis-10736	53	22	is	be	AUX
fcis-10736	53	23	obtained	obtain	VERB
fcis-10736	53	24	,	,	PUNCT
fcis-10736	53	25	and	and	CCONJ
fcis-10736	53	26	the	the	DET
fcis-10736	53	27	second	second	ADJ
fcis-10736	53	28	stage	stage	NOUN
fcis-10736	53	29	output	output	NOUN
fcis-10736	53	30	is	be	AUX
fcis-10736	53	31	generated	generate	VERB
fcis-10736	53	32	by	by	ADP
fcis-10736	53	33	refining	refine	VERB
fcis-10736	53	34	rois	rois	NOUN
fcis-10736	53	35	,	,	PUNCT
fcis-10736	53	36	and	and	CCONJ
fcis-10736	53	37	the	the	DET
fcis-10736	53	38	final	final	ADJ
fcis-10736	53	39	result	result	NOUN
fcis-10736	53	40	is	be	AUX
fcis-10736	53	41	generated	generate	VERB
fcis-10736	53	42	by	by	ADP
fcis-10736	53	43	decoding	decode	VERB
fcis-10736	53	44	(	(	PUNCT
fcis-10736	53	45	postprocessing	postprocesse	VERB
fcis-10736	53	46	)	)	PUNCT
fcis-10736	53	47	,	,	PUNCT
fcis-10736	53	48	and	and	CCONJ
fcis-10736	53	49	the	the	DET
fcis-10736	53	50	corresponding	correspond	VERB
fcis-10736	53	51	detection	detection	NOUN
fcis-10736	53	52	box	box	NOUN
fcis-10736	53	53	is	be	AUX
fcis-10736	53	54	generated	generate	VERB
fcis-10736	53	55	by	by	ADP
fcis-10736	53	56	decoding	decode	VERB
fcis-10736	53	57	while	while	SCONJ
fcis-10736	53	58	training	training	NOUN
fcis-10736	53	59	,	,	PUNCT
fcis-10736	53	60	you	you	PRON
fcis-10736	53	61	need	need	VERB
fcis-10736	53	62	to	to	PART
fcis-10736	53	63	encode	encode	VERB
fcis-10736	53	64	ground	ground	NOUN
fcis-10736	53	65	truth	truth	NOUN
fcis-10736	53	66	into	into	ADP
fcis-10736	53	67	a	a	DET
fcis-10736	53	68	format	format	NOUN
fcis-10736	53	69	corresponding	correspond	VERB
fcis-10736	53	70	to	to	ADP
fcis-10736	53	71	the	the	DET
fcis-10736	53	72	cnn	cnn	PROPN
fcis-10736	53	73	output	output	NOUN
fcis-10736	53	74	in	in	ADP
fcis-10736	53	75	order	order	NOUN
fcis-10736	53	76	to	to	PART
fcis-10736	53	77	calculate	calculate	VERB
fcis-10736	53	78	the	the	DET
fcis-10736	53	79	corresponding	corresponding	ADJ
fcis-10736	53	80	loss	loss	NOUN
fcis-10736	53	81	.	.	PUNCT
fcis-10736	54	1	figure	figure	NOUN
fcis-10736	54	2	2	2	NUM
fcis-10736	54	3	.	.	X
fcis-10736	54	4	two	two	NUM
fcis-10736	54	5	-	-	PUNCT
fcis-10736	54	6	stage	stage	NOUN
fcis-10736	54	7	detection	detection	NOUN
fcis-10736	54	8	algorithm	algorithm	NOUN
fcis-10736	54	9	schematic	schematic	ADJ
fcis-10736	54	10	r	r	NOUN
fcis-10736	54	11	-	-	PUNCT
fcis-10736	54	12	cnn	cnn	NOUN
fcis-10736	54	13	:	:	PUNCT
fcis-10736	54	14	r	r	X
fcis-10736	54	15	-	-	PUNCT
fcis-10736	54	16	cnn	cnn	PROPN
fcis-10736	55	1	[	[	X
fcis-10736	55	2	12	12	NUM
fcis-10736	55	3	]	]	PUNCT
fcis-10736	55	4	(	(	PUNCT
fcis-10736	55	5	regions	region	NOUN
fcis-10736	55	6	with	with	ADP
fcis-10736	55	7	cnn	cnn	PROPN
fcis-10736	55	8	features	feature	NOUN
fcis-10736	55	9	)	)	PUNCT
fcis-10736	55	10	uses	use	VERB
fcis-10736	55	11	dcnn	dcnn	PROPN
fcis-10736	55	12	as	as	ADP
fcis-10736	55	13	the	the	DET
fcis-10736	55	14	backbone	backbone	NOUN
fcis-10736	55	15	network	network	NOUN
fcis-10736	55	16	for	for	ADP
fcis-10736	55	17	feature	feature	NOUN
fcis-10736	55	18	extraction	extraction	NOUN
fcis-10736	55	19	,	,	PUNCT
fcis-10736	55	20	but	but	CCONJ
fcis-10736	55	21	the	the	DET
fcis-10736	55	22	feature	feature	NOUN
fcis-10736	55	23	-	-	PUNCT
fcis-10736	55	24	sharing	share	VERB
fcis-10736	55	25	ability	ability	NOUN
fcis-10736	55	26	of	of	ADP
fcis-10736	55	27	dcnn	dcnn	PROPN
fcis-10736	55	28	is	be	AUX
fcis-10736	55	29	not	not	PART
fcis-10736	55	30	utilized	utilize	VERB
fcis-10736	55	31	when	when	SCONJ
fcis-10736	55	32	extracting	extract	VERB
fcis-10736	55	33	the	the	DET
fcis-10736	55	34	features	feature	NOUN
fcis-10736	55	35	of	of	ADP
fcis-10736	55	36	each	each	DET
fcis-10736	55	37	candidate	candidate	NOUN
fcis-10736	55	38	region	region	NOUN
fcis-10736	55	39	separately	separately	ADV
fcis-10736	55	40	,	,	PUNCT
fcis-10736	55	41	resulting	result	VERB
fcis-10736	55	42	in	in	ADP
fcis-10736	55	43	a	a	DET
fcis-10736	55	44	large	large	ADJ
fcis-10736	55	45	amount	amount	NOUN
fcis-10736	55	46	of	of	ADP
fcis-10736	55	47	waste	waste	NOUN
fcis-10736	55	48	of	of	ADP
fcis-10736	55	49	computing	compute	VERB
fcis-10736	55	50	resources	resource	NOUN
fcis-10736	55	51	,	,	PUNCT
fcis-10736	55	52	and	and	CCONJ
fcis-10736	55	53	it	it	PRON
fcis-10736	55	54	consumes	consume	VERB
fcis-10736	55	55	a	a	DET
fcis-10736	55	56	lot	lot	NOUN
fcis-10736	55	57	of	of	ADP
fcis-10736	55	58	storage	storage	NOUN
fcis-10736	55	59	space	space	NOUN
fcis-10736	55	60	.	.	PUNCT
fcis-10736	56	1	fast	fast	ADJ
fcis-10736	56	2	r	r	NOUN
fcis-10736	56	3	-	-	PUNCT
fcis-10736	56	4	cnn	cnn	NOUN
fcis-10736	56	5	:	:	PUNCT
fcis-10736	56	6	fast	fast	ADJ
fcis-10736	56	7	r	r	X
fcis-10736	56	8	-	-	PUNCT
fcis-10736	56	9	cnn	cnn	PROPN
fcis-10736	56	10	is	be	AUX
fcis-10736	56	11	improved	improve	VERB
fcis-10736	56	12	on	on	ADP
fcis-10736	56	13	the	the	DET
fcis-10736	56	14	basis	basis	NOUN
fcis-10736	56	15	of	of	ADP
fcis-10736	56	16	rcnn	rcnn	PROPN
fcis-10736	56	17	algorithm	algorithm	PROPN
fcis-10736	56	18	.	.	PUNCT
fcis-10736	57	1	compared	compare	VERB
fcis-10736	57	2	with	with	ADP
fcis-10736	57	3	r	r	NOUN
fcis-10736	57	4	-	-	PUNCT
fcis-10736	57	5	cnn	cnn	NOUN
fcis-10736	57	6	/	/	SYM
fcis-10736	57	7	sppnet	sppnet	PROPN
fcis-10736	57	8	,	,	PUNCT
fcis-10736	57	9	fast	fast	ADJ
fcis-10736	57	10	rcnn	rcnn	PROPN
fcis-10736	57	11	has	have	VERB
fcis-10736	57	12	less	less	ADJ
fcis-10736	57	13	memory	memory	NOUN
fcis-10736	57	14	space	space	NOUN
fcis-10736	57	15	and	and	CCONJ
fcis-10736	57	16	higher	high	ADJ
fcis-10736	57	17	accuracy	accuracy	NOUN
fcis-10736	57	18	.	.	PUNCT
fcis-10736	58	1	faster	fast	ADJ
fcis-10736	58	2	r	r	NOUN
fcis-10736	58	3	-	-	PUNCT
fcis-10736	58	4	cnn	cnn	NOUN
fcis-10736	58	5	:	:	PUNCT
fcis-10736	58	6	faster	fast	ADV
fcis-10736	58	7	r	r	X
fcis-10736	58	8	-	-	PUNCT
fcis-10736	58	9	cnn	cnn	PROPN
fcis-10736	58	10	proposes	propose	VERB
fcis-10736	58	11	a	a	DET
fcis-10736	58	12	candidate	candidate	NOUN
fcis-10736	58	13	area	area	NOUN
fcis-10736	58	14	network	network	NOUN
fcis-10736	58	15	(	(	PUNCT
fcis-10736	58	16	rpn	rpn	PROPN
fcis-10736	58	17	)	)	PUNCT
fcis-10736	58	18	to	to	PART
fcis-10736	58	19	replace	replace	VERB
fcis-10736	58	20	the	the	DET
fcis-10736	58	21	selective	selective	ADJ
fcis-10736	58	22	search	search	NOUN
fcis-10736	58	23	algorithm	algorithm	NOUN
fcis-10736	58	24	for	for	ADP
fcis-10736	58	25	generating	generate	VERB
fcis-10736	58	26	candidate	candidate	NOUN
fcis-10736	58	27	areas	area	NOUN
fcis-10736	58	28	,	,	PUNCT
fcis-10736	58	29	which	which	PRON
fcis-10736	58	30	improves	improve	VERB
fcis-10736	58	31	both	both	DET
fcis-10736	58	32	accuracy	accuracy	NOUN
fcis-10736	58	33	and	and	CCONJ
fcis-10736	58	34	speed	speed	NOUN
fcis-10736	58	35	.	.	PUNCT
fcis-10736	59	1	mask	mask	NOUN
fcis-10736	59	2	r	r	NOUN
fcis-10736	59	3	-	-	PUNCT
fcis-10736	59	4	cnn	cnn	PROPN
fcis-10736	59	5	:	:	PUNCT
fcis-10736	59	6	mask	mask	NOUN
fcis-10736	59	7	r	r	NOUN
fcis-10736	59	8	-	-	PUNCT
fcis-10736	59	9	cnn	cnn	PROPN
fcis-10736	59	10	is	be	AUX
fcis-10736	59	11	composed	compose	VERB
fcis-10736	59	12	of	of	ADP
fcis-10736	59	13	faster	fast	ADJ
fcis-10736	59	14	rcnn	rcnn	NOUN
fcis-10736	59	15	and	and	CCONJ
fcis-10736	59	16	semantic	semantic	ADJ
fcis-10736	59	17	segmentation	segmentation	NOUN
fcis-10736	59	18	algorithm	algorithm	PROPN
fcis-10736	59	19	fcn	fcn	PROPN
fcis-10736	59	20	,	,	PUNCT
fcis-10736	59	21	which	which	PRON
fcis-10736	59	22	performs	perform	VERB
fcis-10736	59	23	target	target	VERB
fcis-10736	59	24	classification	classification	NOUN
fcis-10736	59	25	and	and	CCONJ
fcis-10736	59	26	bounding	bound	VERB
fcis-10736	59	27	box	box	NOUN
fcis-10736	59	28	regression	regression	NOUN
fcis-10736	59	29	parallel	parallel	ADJ
fcis-10736	59	30	roi	roi	NOUN
fcis-10736	59	31	prediction	prediction	NOUN
fcis-10736	59	32	segmentation	segmentation	NOUN
fcis-10736	59	33	,	,	PUNCT
fcis-10736	59	34	while	while	SCONJ
fcis-10736	59	35	completing	complete	VERB
fcis-10736	59	36	target	target	NOUN
fcis-10736	59	37	detection	detection	NOUN
fcis-10736	59	38	and	and	CCONJ
fcis-10736	59	39	instance	instance	NOUN
fcis-10736	59	40	segmentation	segmentation	NOUN
fcis-10736	59	41	.	.	PUNCT
fcis-10736	60	1	3.3	3.3	NUM
fcis-10736	60	2	common	common	ADJ
fcis-10736	60	3	data	data	NOUN
fcis-10736	60	4	sets	set	NOUN
fcis-10736	60	5	for	for	ADP
fcis-10736	60	6	target	target	NOUN
fcis-10736	60	7	detection	detection	NOUN
fcis-10736	60	8	pascal	pascal	ADJ
fcis-10736	60	9	voc	voc	NOUN
fcis-10736	60	10	:	:	PUNCT
fcis-10736	60	11	the	the	DET
fcis-10736	60	12	pascal	pascal	PROPN
fcis-10736	60	13	voc	voc	PROPN
fcis-10736	60	14	(	(	PUNCT
fcis-10736	60	15	visual	visual	ADJ
fcis-10736	60	16	object	object	NOUN
fcis-10736	60	17	classes	class	NOUN
fcis-10736	60	18	)	)	PUNCT
fcis-10736	60	19	challenge	challenge	NOUN
fcis-10736	60	20	is	be	AUX
fcis-10736	60	21	one	one	NUM
fcis-10736	60	22	of	of	ADP
fcis-10736	60	23	the	the	DET
fcis-10736	60	24	early	early	ADJ
fcis-10736	60	25	and	and	CCONJ
fcis-10736	60	26	important	important	ADJ
fcis-10736	60	27	competitions	competition	NOUN
fcis-10736	60	28	in	in	ADP
fcis-10736	60	29	the	the	DET
fcis-10736	60	30	field	field	NOUN
fcis-10736	60	31	of	of	ADP
fcis-10736	60	32	computer	computer	NOUN
fcis-10736	60	33	vision	vision	NOUN
fcis-10736	60	34	,	,	PUNCT
fcis-10736	60	35	including	include	VERB
fcis-10736	60	36	image	image	NOUN
fcis-10736	60	37	classification	classification	NOUN
fcis-10736	60	38	,	,	PUNCT
fcis-10736	60	39	object	object	NOUN
fcis-10736	60	40	detection	detection	NOUN
fcis-10736	60	41	,	,	PUNCT
fcis-10736	60	42	scene	scene	NOUN
fcis-10736	60	43	segmentation	segmentation	NOUN
fcis-10736	60	44	,	,	PUNCT
fcis-10736	60	45	event	event	NOUN
fcis-10736	60	46	detection	detection	NOUN
fcis-10736	60	47	and	and	CCONJ
fcis-10736	60	48	other	other	ADJ
fcis-10736	60	49	tasks	task	NOUN
fcis-10736	60	50	.	.	PUNCT
fcis-10736	61	1	voc2007	voc2007	NOUN
fcis-10736	61	2	and	and	CCONJ
fcis-10736	61	3	voc2012	voc2012	NOUN
fcis-10736	61	4	datasets	dataset	NOUN
fcis-10736	61	5	are	be	AUX
fcis-10736	61	6	commonly	commonly	ADV
fcis-10736	61	7	used	use	VERB
fcis-10736	61	8	for	for	ADP
fcis-10736	61	9	target	target	NOUN
fcis-10736	61	10	detection	detection	NOUN
fcis-10736	61	11	.	.	PUNCT
fcis-10736	62	1	voc2007	voc2007	PROPN
fcis-10736	62	2	included	include	VERB
fcis-10736	62	3	5,000	5,000	NUM
fcis-10736	62	4	training	training	NOUN
fcis-10736	62	5	images	image	NOUN
fcis-10736	62	6	and	and	CCONJ
fcis-10736	62	7	12,000	12,000	NUM
fcis-10736	62	8	labeled	label	VERB
fcis-10736	62	9	targets	target	NOUN
fcis-10736	62	10	,	,	PUNCT
fcis-10736	62	11	and	and	CCONJ
fcis-10736	62	12	voc2012	voc2012	NOUN
fcis-10736	62	13	included	include	VERB
fcis-10736	62	14	11,000	11,000	NUM
fcis-10736	62	15	training	training	NOUN
fcis-10736	62	16	images	image	NOUN
fcis-10736	62	17	and	and	CCONJ
fcis-10736	62	18	27,000	27,000	NUM
fcis-10736	62	19	labeled	label	VERB
fcis-10736	62	20	targets	target	NOUN
fcis-10736	62	21	.	.	PUNCT
fcis-10736	63	1	imagenet	imagenet	NOUN
fcis-10736	63	2	:	:	PUNCT
fcis-10736	63	3	imagenet	imagenet	PROPN
fcis-10736	63	4	is	be	AUX
fcis-10736	63	5	a	a	DET
fcis-10736	63	6	large	large	ADJ
fcis-10736	63	7	labeled	label	VERB
fcis-10736	63	8	image	image	NOUN
fcis-10736	63	9	data	datum	NOUN
fcis-10736	63	10	set	set	VERB
fcis-10736	63	11	.	.	PUNCT
fcis-10736	64	1	the	the	DET
fcis-10736	64	2	dataset	dataset	NOUN
fcis-10736	64	3	contains	contain	VERB
fcis-10736	64	4	more	more	ADJ
fcis-10736	64	5	than	than	ADP
fcis-10736	64	6	14	14	NUM
fcis-10736	64	7	million	million	NUM
fcis-10736	64	8	images	image	NOUN
fcis-10736	64	9	and	and	CCONJ
fcis-10736	64	10	about	about	ADV
fcis-10736	64	11	22,000	22,000	NUM
fcis-10736	64	12	categories	category	NOUN
fcis-10736	64	13	.	.	PUNCT
fcis-10736	65	1	more	more	ADJ
fcis-10736	65	2	than	than	ADP
fcis-10736	65	3	a	a	DET
fcis-10736	65	4	million	million	NUM
fcis-10736	65	5	of	of	ADP
fcis-10736	65	6	these	these	DET
fcis-10736	65	7	images	image	NOUN
fcis-10736	65	8	are	be	AUX
fcis-10736	65	9	labeled	label	VERB
fcis-10736	65	10	with	with	ADP
fcis-10736	65	11	specific	specific	ADJ
fcis-10736	65	12	categories	category	NOUN
fcis-10736	65	13	and	and	CCONJ
fcis-10736	65	14	positions	position	NOUN
fcis-10736	65	15	of	of	ADP
fcis-10736	65	16	objects	object	NOUN
fcis-10736	65	17	in	in	ADP
fcis-10736	65	18	the	the	DET
fcis-10736	65	19	image	image	NOUN
fcis-10736	65	20	,	,	PUNCT
fcis-10736	65	21	making	make	VERB
fcis-10736	65	22	them	they	PRON
fcis-10736	65	23	a	a	DET
fcis-10736	65	24	suitable	suitable	ADJ
fcis-10736	65	25	target	target	NOUN
fcis-10736	65	26	detection	detection	NOUN
fcis-10736	65	27	dataset	dataset	NOUN
fcis-10736	65	28	.	.	PUNCT
fcis-10736	66	1	ms	ms	PROPN
fcis-10736	66	2	-	-	PUNCT
fcis-10736	66	3	coco	coco	PROPN
fcis-10736	66	4	:	:	PUNCT
fcis-10736	66	5	the	the	DET
fcis-10736	66	6	ms	ms	PROPN
fcis-10736	66	7	-	-	PUNCT
fcis-10736	66	8	coco	coco	PROPN
fcis-10736	66	9	dataset	dataset	PROPN
fcis-10736	66	10	is	be	AUX
fcis-10736	66	11	a	a	DET
fcis-10736	66	12	data	data	NOUN
fcis-10736	66	13	set	set	VERB
fcis-10736	66	14	released	release	VERB
fcis-10736	66	15	by	by	ADP
fcis-10736	66	16	the	the	DET
fcis-10736	66	17	microsoft	microsoft	PROPN
fcis-10736	66	18	team	team	NOUN
fcis-10736	66	19	that	that	PRON
fcis-10736	66	20	can	can	AUX
fcis-10736	66	21	be	be	AUX
fcis-10736	66	22	used	use	VERB
fcis-10736	66	23	for	for	ADP
fcis-10736	66	24	image	image	NOUN
fcis-10736	66	25	recognition	recognition	NOUN
fcis-10736	66	26	,	,	PUNCT
fcis-10736	66	27	segmentation	segmentation	NOUN
fcis-10736	66	28	and	and	CCONJ
fcis-10736	66	29	annotation	annotation	NOUN
fcis-10736	66	30	.	.	PUNCT
fcis-10736	67	1	it	it	PRON
fcis-10736	67	2	consists	consist	VERB
fcis-10736	67	3	of	of	ADP
fcis-10736	67	4	91	91	NUM
fcis-10736	67	5	classes	class	NOUN
fcis-10736	67	6	of	of	ADP
fcis-10736	67	7	targets	target	NOUN
fcis-10736	67	8	and	and	CCONJ
fcis-10736	67	9	328,000	328,000	NUM
fcis-10736	67	10	images	image	NOUN
fcis-10736	67	11	.	.	PUNCT
fcis-10736	68	1	it	it	PRON
fcis-10736	68	2	features	feature	VERB
fcis-10736	68	3	images	image	NOUN
fcis-10736	68	4	that	that	PRON
fcis-10736	68	5	are	be	AUX
fcis-10736	68	6	mostly	mostly	ADV
fcis-10736	68	7	from	from	ADP
fcis-10736	68	8	life	life	NOUN
fcis-10736	68	9	,	,	PUNCT
fcis-10736	68	10	more	more	ADV
fcis-10736	68	11	complex	complex	ADJ
fcis-10736	68	12	backgrounds	background	NOUN
fcis-10736	68	13	,	,	PUNCT
fcis-10736	68	14	and	and	CCONJ
fcis-10736	68	15	more	more	ADV
fcis-10736	68	16	small	small	ADJ
fcis-10736	68	17	objects	object	NOUN
fcis-10736	68	18	.	.	PUNCT
fcis-10736	69	1	on	on	ADP
fcis-10736	69	2	the	the	DET
fcis-10736	69	3	other	other	ADJ
fcis-10736	69	4	hand	hand	NOUN
fcis-10736	69	5	,	,	PUNCT
fcis-10736	69	6	it	it	PRON
fcis-10736	69	7	also	also	ADV
fcis-10736	69	8	notes	note	VERB
fcis-10736	69	9	the	the	DET
fcis-10736	69	10	segmentation	segmentation	NOUN
fcis-10736	69	11	information	information	NOUN
fcis-10736	69	12	of	of	ADP
fcis-10736	69	13	each	each	DET
fcis-10736	69	14	instance	instance	NOUN
fcis-10736	69	15	in	in	ADP
fcis-10736	69	16	addition	addition	NOUN
fcis-10736	69	17	to	to	ADP
fcis-10736	69	18	the	the	DET
fcis-10736	69	19	border	border	NOUN
fcis-10736	69	20	of	of	ADP
fcis-10736	69	21	each	each	DET
fcis-10736	69	22	object	object	NOUN
fcis-10736	69	23	.	.	PUNCT
fcis-10736	70	1	therefore	therefore	ADV
fcis-10736	70	2	,	,	PUNCT
fcis-10736	70	3	coco	coco	PROPN
fcis-10736	70	4	has	have	AUX
fcis-10736	70	5	gradually	gradually	ADV
fcis-10736	70	6	become	become	VERB
fcis-10736	70	7	a	a	DET
fcis-10736	70	8	mainstream	mainstream	NOUN
fcis-10736	70	9	target	target	NOUN
fcis-10736	70	10	detection	detection	NOUN
fcis-10736	70	11	evaluation	evaluation	NOUN
fcis-10736	70	12	set	set	NOUN
fcis-10736	70	13	.	.	PUNCT
fcis-10736	71	1	open	open	ADJ
fcis-10736	71	2	images	image	NOUN
fcis-10736	71	3	:	:	PUNCT
fcis-10736	71	4	open	open	ADJ
fcis-10736	71	5	images	image	NOUN
fcis-10736	71	6	comes	come	VERB
fcis-10736	71	7	from	from	ADP
fcis-10736	71	8	the	the	DET
fcis-10736	71	9	google	google	PROPN
fcis-10736	71	10	team	team	NOUN
fcis-10736	71	11	19	19	NUM
fcis-10736	71	12	and	and	CCONJ
fcis-10736	71	13	includes	include	VERB
fcis-10736	71	14	9	9	NUM
fcis-10736	71	15	million	million	NUM
fcis-10736	71	16	images	image	NOUN
fcis-10736	71	17	and	and	CCONJ
fcis-10736	71	18	more	more	ADJ
fcis-10736	71	19	than	than	ADP
fcis-10736	71	20	6,000	6,000	NUM
fcis-10736	71	21	categories	category	NOUN
fcis-10736	71	22	.	.	PUNCT
fcis-10736	72	1	data	datum	NOUN
fcis-10736	72	2	sets	set	NOUN
fcis-10736	72	3	have	have	VERB
fcis-10736	72	4	more	more	ADV
fcis-10736	72	5	diverse	diverse	ADJ
fcis-10736	72	6	objects	object	NOUN
fcis-10736	72	7	,	,	PUNCT
fcis-10736	72	8	often	often	ADV
fcis-10736	72	9	containing	contain	VERB
fcis-10736	72	10	complex	complex	ADJ
fcis-10736	72	11	scenarios	scenario	NOUN
fcis-10736	72	12	of	of	ADP
fcis-10736	72	13	multiple	multiple	ADJ
fcis-10736	72	14	objects	object	NOUN
fcis-10736	72	15	.	.	PUNCT
fcis-10736	73	1	visual	visual	ADJ
fcis-10736	73	2	relationship	relationship	NOUN
fcis-10736	73	3	annotations	annotation	NOUN
fcis-10736	73	4	are	be	AUX
fcis-10736	73	5	also	also	ADV
fcis-10736	73	6	provided	provide	VERB
fcis-10736	73	7	.	.	PUNCT
fcis-10736	74	1	there	there	PRON
fcis-10736	74	2	is	be	VERB
fcis-10736	74	3	more	more	ADJ
fcis-10736	74	4	information	information	NOUN
fcis-10736	74	5	than	than	ADP
fcis-10736	74	6	in	in	ADP
fcis-10736	74	7	the	the	DET
fcis-10736	74	8	previous	previous	ADJ
fcis-10736	74	9	data	data	NOUN
fcis-10736	74	10	set	set	VERB
fcis-10736	74	11	.	.	PUNCT
fcis-10736	75	1	4	4	NUM
fcis-10736	75	2	conclusion	conclusion	NOUN
fcis-10736	75	3	in	in	ADP
fcis-10736	75	4	this	this	DET
fcis-10736	75	5	paper	paper	NOUN
fcis-10736	75	6	,	,	PUNCT
fcis-10736	75	7	the	the	DET
fcis-10736	75	8	traditional	traditional	ADJ
fcis-10736	75	9	target	target	NOUN
fcis-10736	75	10	detection	detection	NOUN
fcis-10736	75	11	methods	method	NOUN
fcis-10736	75	12	and	and	CCONJ
fcis-10736	75	13	target	target	NOUN
fcis-10736	75	14	detection	detection	NOUN
fcis-10736	75	15	methods	method	NOUN
fcis-10736	75	16	based	base	VERB
fcis-10736	75	17	on	on	ADP
fcis-10736	75	18	depth	depth	NOUN
fcis-10736	75	19	learning	learning	NOUN
fcis-10736	75	20	are	be	AUX
fcis-10736	75	21	reviewed	review	VERB
fcis-10736	75	22	.	.	PUNCT
fcis-10736	76	1	combining	combine	VERB
fcis-10736	76	2	two	two	NUM
fcis-10736	76	3	-	-	PUNCT
fcis-10736	76	4	stage	stage	NOUN
fcis-10736	76	5	target	target	NOUN
fcis-10736	76	6	detection	detection	NOUN
fcis-10736	76	7	method	method	NOUN
fcis-10736	76	8	and	and	CCONJ
fcis-10736	76	9	single	single	ADJ
fcis-10736	76	10	-	-	PUNCT
fcis-10736	76	11	stage	stage	NOUN
fcis-10736	76	12	target	target	NOUN
fcis-10736	76	13	detection	detection	NOUN
fcis-10736	76	14	method	method	NOUN
fcis-10736	76	15	,	,	PUNCT
fcis-10736	76	16	the	the	DET
fcis-10736	76	17	paper	paper	NOUN
fcis-10736	76	18	summarizes	summarize	VERB
fcis-10736	76	19	their	their	PRON
fcis-10736	76	20	advantages	advantage	NOUN
fcis-10736	76	21	and	and	CCONJ
fcis-10736	76	22	disadvantages	disadvantage	NOUN
fcis-10736	76	23	,	,	PUNCT
fcis-10736	76	24	introduces	introduce	VERB
fcis-10736	76	25	the	the	DET
fcis-10736	76	26	data	datum	NOUN
fcis-10736	76	27	set	set	VERB
fcis-10736	76	28	,	,	PUNCT
fcis-10736	76	29	and	and	CCONJ
fcis-10736	76	30	summarizes	summarize	VERB
fcis-10736	76	31	the	the	DET
fcis-10736	76	32	important	important	ADJ
fcis-10736	76	33	applications	application	NOUN
fcis-10736	76	34	of	of	ADP
fcis-10736	76	35	target	target	NOUN
fcis-10736	76	36	detection	detection	NOUN
fcis-10736	76	37	.	.	PUNCT
fcis-10736	77	1	with	with	ADP
fcis-10736	77	2	the	the	DET
fcis-10736	77	3	gradual	gradual	ADJ
fcis-10736	77	4	development	development	NOUN
fcis-10736	77	5	of	of	ADP
fcis-10736	77	6	target	target	NOUN
fcis-10736	77	7	detection	detection	NOUN
fcis-10736	77	8	technology	technology	NOUN
fcis-10736	77	9	,	,	PUNCT
fcis-10736	77	10	the	the	DET
fcis-10736	77	11	detection	detection	NOUN
fcis-10736	77	12	accuracy	accuracy	NOUN
fcis-10736	77	13	has	have	AUX
fcis-10736	77	14	been	be	AUX
fcis-10736	77	15	gradually	gradually	ADV
fcis-10736	77	16	improved	improve	VERB
fcis-10736	77	17	at	at	ADP
fcis-10736	77	18	present	present	ADJ
fcis-10736	77	19	.	.	PUNCT
fcis-10736	78	1	however	however	ADV
fcis-10736	78	2	,	,	PUNCT
fcis-10736	78	3	with	with	ADP
fcis-10736	78	4	the	the	DET
fcis-10736	78	5	development	development	NOUN
fcis-10736	78	6	of	of	ADP
fcis-10736	78	7	diversified	diversified	ADJ
fcis-10736	78	8	application	application	NOUN
fcis-10736	78	9	scenarios	scenario	NOUN
fcis-10736	78	10	,	,	PUNCT
fcis-10736	78	11	there	there	PRON
fcis-10736	78	12	are	be	VERB
fcis-10736	78	13	still	still	ADV
fcis-10736	78	14	many	many	ADJ
fcis-10736	78	15	challenges	challenge	NOUN
fcis-10736	78	16	and	and	CCONJ
fcis-10736	78	17	problems	problem	NOUN
fcis-10736	78	18	to	to	PART
fcis-10736	78	19	be	be	AUX
fcis-10736	78	20	solved	solve	VERB
fcis-10736	78	21	in	in	ADP
fcis-10736	78	22	improving	improve	VERB
fcis-10736	78	23	model	model	NOUN
fcis-10736	78	24	algorithms	algorithm	NOUN
fcis-10736	78	25	,	,	PUNCT
fcis-10736	78	26	data	datum	NOUN
fcis-10736	78	27	pre	pre	ADJ
fcis-10736	78	28	-	-	ADJ
fcis-10736	78	29	processing	processing	ADJ
fcis-10736	78	30	,	,	PUNCT
fcis-10736	78	31	deep	deep	ADJ
fcis-10736	78	32	learning	learning	NOUN
fcis-10736	78	33	network	network	NOUN
fcis-10736	78	34	design	design	NOUN
fcis-10736	78	35	and	and	CCONJ
fcis-10736	78	36	model	model	NOUN
fcis-10736	78	37	optimization	optimization	NOUN
fcis-10736	78	38	.	.	PUNCT
fcis-10736	79	1	based	base	VERB
fcis-10736	79	2	on	on	ADP
fcis-10736	79	3	the	the	DET
fcis-10736	79	4	current	current	ADJ
fcis-10736	79	5	research	research	NOUN
fcis-10736	79	6	status	status	NOUN
fcis-10736	79	7	of	of	ADP
fcis-10736	79	8	target	target	NOUN
fcis-10736	79	9	detection	detection	NOUN
fcis-10736	79	10	,	,	PUNCT
fcis-10736	79	11	the	the	DET
fcis-10736	79	12	future	future	ADJ
fcis-10736	79	13	research	research	NOUN
fcis-10736	79	14	prospects	prospect	NOUN
fcis-10736	79	15	are	be	AUX
fcis-10736	79	16	as	as	SCONJ
fcis-10736	79	17	follows	follow	VERB
fcis-10736	79	18	:	:	PUNCT
fcis-10736	79	19	(	(	PUNCT
fcis-10736	79	20	1	1	X
fcis-10736	79	21	)	)	PUNCT
fcis-10736	79	22	for	for	ADP
fcis-10736	79	23	multi	multi	ADJ
fcis-10736	79	24	-	-	ADJ
fcis-10736	79	25	scale	scale	ADJ
fcis-10736	79	26	target	target	NOUN
fcis-10736	79	27	detection	detection	NOUN
fcis-10736	79	28	under	under	ADP
fcis-10736	79	29	complex	complex	ADJ
fcis-10736	79	30	background	background	NOUN
fcis-10736	79	31	,	,	PUNCT
fcis-10736	79	32	such	such	ADJ
fcis-10736	79	33	as	as	ADP
fcis-10736	79	34	dense	dense	ADJ
fcis-10736	79	35	small	small	ADJ
fcis-10736	79	36	target	target	NOUN
fcis-10736	79	37	detection	detection	NOUN
fcis-10736	79	38	is	be	AUX
fcis-10736	79	39	still	still	ADV
fcis-10736	79	40	insufficient	insufficient	ADJ
fcis-10736	79	41	.	.	PUNCT
fcis-10736	80	1	(	(	PUNCT
fcis-10736	80	2	2	2	NUM
fcis-10736	80	3	)	)	PUNCT
fcis-10736	80	4	with	with	ADP
fcis-10736	80	5	the	the	DET
fcis-10736	80	6	increasing	increase	VERB
fcis-10736	80	7	complexity	complexity	NOUN
fcis-10736	80	8	of	of	ADP
fcis-10736	80	9	the	the	DET
fcis-10736	80	10	application	application	NOUN
fcis-10736	80	11	scene	scene	NOUN
fcis-10736	80	12	,	,	PUNCT
fcis-10736	80	13	how	how	SCONJ
fcis-10736	80	14	to	to	PART
fcis-10736	80	15	identify	identify	VERB
fcis-10736	80	16	the	the	DET
fcis-10736	80	17	target	target	NOUN
fcis-10736	80	18	quickly	quickly	ADV
fcis-10736	80	19	and	and	CCONJ
fcis-10736	80	20	accurately	accurately	ADV
fcis-10736	80	21	in	in	ADP
fcis-10736	80	22	the	the	DET
fcis-10736	80	23	complex	complex	ADJ
fcis-10736	80	24	environment	environment	NOUN
fcis-10736	80	25	,	,	PUNCT
fcis-10736	80	26	and	and	CCONJ
fcis-10736	80	27	how	how	SCONJ
fcis-10736	80	28	to	to	PART
fcis-10736	80	29	use	use	VERB
fcis-10736	80	30	the	the	DET
fcis-10736	80	31	context	context	NOUN
fcis-10736	80	32	information	information	NOUN
fcis-10736	80	33	of	of	ADP
fcis-10736	80	34	the	the	DET
fcis-10736	80	35	scene	scene	NOUN
fcis-10736	80	36	and	and	CCONJ
fcis-10736	80	37	the	the	DET
fcis-10736	80	38	relationship	relationship	NOUN
fcis-10736	80	39	between	between	ADP
fcis-10736	80	40	objects	object	NOUN
fcis-10736	80	41	are	be	AUX
fcis-10736	80	42	the	the	DET
fcis-10736	80	43	future	future	ADJ
fcis-10736	80	44	research	research	NOUN
fcis-10736	80	45	directions	direction	NOUN
fcis-10736	80	46	in	in	ADP
fcis-10736	80	47	the	the	DET
fcis-10736	80	48	field	field	NOUN
fcis-10736	80	49	of	of	ADP
fcis-10736	80	50	target	target	NOUN
fcis-10736	80	51	detection	detection	NOUN
fcis-10736	80	52	.	.	PUNCT
fcis-10736	81	1	acknowledgments	acknowledgment	NOUN
fcis-10736	81	2	this	this	DET
fcis-10736	81	3	work	work	NOUN
fcis-10736	81	4	was	be	AUX
fcis-10736	81	5	supported	support	VERB
fcis-10736	81	6	in	in	ADP
fcis-10736	81	7	part	part	NOUN
fcis-10736	81	8	by	by	ADP
fcis-10736	81	9	the	the	DET
fcis-10736	81	10	2022	2022	NUM
fcis-10736	81	11	graduate	graduate	NOUN
fcis-10736	81	12	innovation	innovation	NOUN
fcis-10736	81	13	fund	fund	NOUN
fcis-10736	81	14	project	project	NOUN
fcis-10736	81	15	of	of	ADP
fcis-10736	81	16	sichuan	sichuan	PROPN
fcis-10736	81	17	university	university	PROPN
fcis-10736	81	18	of	of	ADP
fcis-10736	81	19	science	science	NOUN
fcis-10736	81	20	and	and	CCONJ
fcis-10736	81	21	engineering	engineering	NOUN
fcis-10736	81	22	(	(	PUNCT
fcis-10736	81	23	y2022162	y2022162	NOUN
fcis-10736	81	24	)	)	PUNCT
fcis-10736	81	25	.	.	PUNCT
fcis-10736	82	1	the	the	DET
fcis-10736	82	2	authors	author	NOUN
fcis-10736	82	3	express	express	VERB
fcis-10736	82	4	their	their	PRON
fcis-10736	82	5	acknowledgement	acknowledgement	NOUN
fcis-10736	82	6	for	for	ADP
fcis-10736	82	7	the	the	DET
fcis-10736	82	8	anonymous	anonymous	PROPN
fcis-10736	82	9	review	review	NOUN
fcis-10736	82	10	.	.	PUNCT
fcis-10736	83	1	references	reference	NOUN
fcis-10736	83	2	[	[	X
fcis-10736	83	3	1	1	NUM
fcis-10736	83	4	]	]	X
fcis-10736	83	5	liu	liu	PROPN
fcis-10736	83	6	x	x	PROPN
fcis-10736	83	7	,	,	PUNCT
fcis-10736	83	8	deng	deng	PROPN
fcis-10736	83	9	z	z	PROPN
fcis-10736	83	10	,	,	PUNCT
fcis-10736	83	11	yang	yang	PROPN
fcis-10736	83	12	y.	y.	PROPN
fcis-10736	83	13	recent	recent	ADJ
fcis-10736	83	14	progress	progress	NOUN
fcis-10736	83	15	in	in	ADP
fcis-10736	83	16	semantic	semantic	ADJ
fcis-10736	83	17	image	image	NOUN
fcis-10736	83	18	segmentation[j	segmentation[j	PROPN
fcis-10736	83	19	]	]	PUNCT
fcis-10736	83	20	.	.	PUNCT
fcis-10736	84	1	artificial	artificial	ADJ
fcis-10736	84	2	intelligence	intelligence	NOUN
fcis-10736	84	3	review	review	NOUN
fcis-10736	84	4	,	,	PUNCT
fcis-10736	84	5	2019	2019	NUM
fcis-10736	84	6	,	,	PUNCT
fcis-10736	84	7	52(2	52(2	NUM
fcis-10736	84	8	):	):	PUNCT
fcis-10736	84	9	1089	1089	NUM
fcis-10736	84	10	-	-	SYM
fcis-10736	84	11	1106	1106	NUM
fcis-10736	84	12	.	.	PUNCT
fcis-10736	85	1	[	[	X
fcis-10736	85	2	2	2	X
fcis-10736	85	3	]	]	X
fcis-10736	85	4	hafiz	hafiz	PROPN
fcis-10736	85	5	a	a	PROPN
fcis-10736	85	6	m	m	PROPN
fcis-10736	85	7	,	,	PUNCT
fcis-10736	85	8	bhat	bhat	PROPN
fcis-10736	85	9	g	g	PROPN
fcis-10736	85	10	m.	m.	NOUN
fcis-10736	85	11	a	a	DET
fcis-10736	85	12	survey	survey	NOUN
fcis-10736	85	13	on	on	ADP
fcis-10736	85	14	instance	instance	NOUN
fcis-10736	85	15	segmentation	segmentation	NOUN
fcis-10736	85	16	:	:	PUNCT
fcis-10736	85	17	state	state	NOUN
fcis-10736	85	18	of	of	ADP
fcis-10736	85	19	the	the	DET
fcis-10736	85	20	art	art	NOUN
fcis-10736	86	1	[	[	X
fcis-10736	86	2	j	j	X
fcis-10736	86	3	]	]	X
fcis-10736	86	4	.	.	PUNCT
fcis-10736	87	1	international	international	ADJ
fcis-10736	87	2	journal	journal	PROPN
fcis-10736	87	3	of	of	ADP
fcis-10736	87	4	multimedia	multimedia	PROPN
fcis-10736	87	5	information	information	NOUN
fcis-10736	87	6	retrieval	retrieval	NOUN
fcis-10736	87	7	,	,	PUNCT
fcis-10736	87	8	2020	2020	NUM
fcis-10736	87	9	,	,	PUNCT
fcis-10736	87	10	9(3	9(3	NUM
fcis-10736	87	11	):	):	PUNCT
fcis-10736	87	12	171	171	NUM
fcis-10736	87	13	-	-	SYM
fcis-10736	87	14	189	189	NUM
fcis-10736	87	15	.	.	PUNCT
fcis-10736	88	1	[	[	X
fcis-10736	88	2	3	3	X
fcis-10736	88	3	]	]	X
fcis-10736	88	4	viola	viola	NOUN
fcis-10736	88	5	p	p	X
fcis-10736	88	6	,	,	PUNCT
fcis-10736	88	7	jones	jones	PROPN
fcis-10736	88	8	m	m	PROPN
fcis-10736	88	9	j.	j.	PROPN
fcis-10736	88	10	robust	robust	ADJ
fcis-10736	88	11	real	real	ADJ
fcis-10736	88	12	-	-	PUNCT
fcis-10736	88	13	time	time	NOUN
fcis-10736	88	14	face	face	NOUN
fcis-10736	88	15	detection[j	detection[j	PROPN
fcis-10736	88	16	]	]	PUNCT
fcis-10736	88	17	.	.	PUNCT
fcis-10736	89	1	international	international	ADJ
fcis-10736	89	2	journal	journal	PROPN
fcis-10736	89	3	of	of	ADP
fcis-10736	89	4	computer	computer	NOUN
fcis-10736	89	5	vision,2004,57(2):137	vision,2004,57(2):137	NOUN
fcis-10736	89	6	-	-	SYM
fcis-10736	89	7	154	154	NUM
fcis-10736	89	8	.	.	PUNCT
fcis-10736	90	1	[	[	X
fcis-10736	90	2	4	4	X
fcis-10736	90	3	]	]	X
fcis-10736	90	4	dalal	dalal	PROPN
fcis-10736	90	5	n	n	SYM
fcis-10736	90	6	,	,	PUNCT
fcis-10736	90	7	triggs	triggs	PROPN
fcis-10736	90	8	b.	b.	PROPN
fcis-10736	90	9	histograms	histograms	PROPN
fcis-10736	90	10	of	of	ADP
fcis-10736	90	11	oriented	orient	VERB
fcis-10736	90	12	gradients	gradient	NOUN
fcis-10736	90	13	for	for	ADP
fcis-10736	90	14	human	human	NOUN
fcis-10736	90	15	detection[c	detection[c	PROPN
fcis-10736	90	16	]	]	PUNCT
fcis-10736	90	17	.	.	PUNCT
fcis-10736	91	1	ieee	ieee	PROPN
fcis-10736	91	2	computer	computer	PROPN
fcis-10736	91	3	society	society	PROPN
fcis-10736	91	4	conference	conference	NOUN
fcis-10736	91	5	on	on	ADP
fcis-10736	91	6	computer	computer	NOUN
fcis-10736	91	7	vision	vision	NOUN
fcis-10736	91	8	&	&	CCONJ
fcis-10736	91	9	pattern	pattern	NOUN
fcis-10736	91	10	recognition	recognition	NOUN
fcis-10736	91	11	.	.	PUNCT
fcis-10736	92	1	ieee,2005	ieee,2005	NOUN
fcis-10736	92	2	.	.	PUNCT
fcis-10736	93	1	[	[	X
fcis-10736	93	2	5	5	NUM
fcis-10736	93	3	]	]	PUNCT
fcis-10736	93	4	felzenszwalb	felzenszwalb	NOUN
fcis-10736	93	5	p	p	PROPN
fcis-10736	93	6	f	f	PROPN
fcis-10736	93	7	,	,	PUNCT
fcis-10736	93	8	girshick	girshick	ADJ
fcis-10736	93	9	r	r	PROPN
fcis-10736	93	10	b	b	PROPN
fcis-10736	93	11	,	,	PUNCT
fcis-10736	93	12	mcallester	mcallester	NOUN
fcis-10736	93	13	d	d	NOUN
fcis-10736	93	14	,	,	PUNCT
fcis-10736	93	15	et	et	PROPN
fcis-10736	93	16	al	al	PROPN
fcis-10736	93	17	.	.	PROPN
fcis-10736	93	18	object	object	PROPN
fcis-10736	93	19	detection	detection	NOUN
fcis-10736	93	20	with	with	ADP
fcis-10736	93	21	discriminatively	discriminatively	ADV
fcis-10736	93	22	trained	train	VERB
fcis-10736	93	23	part	part	NOUN
fcis-10736	93	24	-	-	PUNCT
fcis-10736	93	25	based	base	VERB
fcis-10736	93	26	models[j	models[j	NOUN
fcis-10736	93	27	]	]	PUNCT
fcis-10736	93	28	.	.	PUNCT
fcis-10736	94	1	ieee	ieee	NOUN
fcis-10736	94	2	transactions	transaction	NOUN
fcis-10736	94	3	on	on	ADP
fcis-10736	94	4	pattern	pattern	NOUN
fcis-10736	94	5	analysis	analysis	NOUN
fcis-10736	94	6	and	and	CCONJ
fcis-10736	94	7	machine	machine	NOUN
fcis-10736	94	8	intelligence	intelligence	NOUN
fcis-10736	94	9	,	,	PUNCT
fcis-10736	94	10	2009	2009	NUM
fcis-10736	94	11	,	,	PUNCT
fcis-10736	94	12	32(9	32(9	NUM
fcis-10736	94	13	):	):	PUNCT
fcis-10736	94	14	1627	1627	NUM
fcis-10736	94	15	-	-	SYM
fcis-10736	94	16	1645	1645	NUM
fcis-10736	94	17	.	.	PUNCT
fcis-10736	95	1	[	[	X
fcis-10736	95	2	6	6	NUM
fcis-10736	95	3	]	]	PUNCT
fcis-10736	95	4	gu	gu	PROPN
fcis-10736	95	5	j	j	PROPN
fcis-10736	95	6	,	,	PUNCT
fcis-10736	95	7	wang	wang	PROPN
fcis-10736	95	8	z	z	PROPN
fcis-10736	95	9	,	,	PUNCT
fcis-10736	95	10	kuen	kuen	PROPN
fcis-10736	95	11	j	j	PROPN
fcis-10736	95	12	,	,	PUNCT
fcis-10736	95	13	et	et	PROPN
fcis-10736	95	14	al	al	PROPN
fcis-10736	95	15	.	.	PUNCT
fcis-10736	96	1	recent	recent	ADJ
fcis-10736	96	2	advances	advance	NOUN
fcis-10736	96	3	in	in	ADP
fcis-10736	96	4	convolutional	convolutional	ADJ
fcis-10736	96	5	neural	neural	ADJ
fcis-10736	96	6	networks[j	networks[j	NOUN
fcis-10736	96	7	]	]	X
fcis-10736	96	8	.	.	PUNCT
fcis-10736	97	1	pattern	pattern	NOUN
fcis-10736	97	2	recognition	recognition	NOUN
fcis-10736	97	3	,	,	PUNCT
fcis-10736	97	4	2018	2018	NUM
fcis-10736	97	5	,	,	PUNCT
fcis-10736	97	6	77	77	NUM
fcis-10736	97	7	:	:	PUNCT
fcis-10736	97	8	354	354	NUM
fcis-10736	97	9	-	-	SYM
fcis-10736	97	10	377	377	NUM
fcis-10736	97	11	.	.	PUNCT
fcis-10736	98	1	[	[	X
fcis-10736	98	2	7	7	X
fcis-10736	98	3	]	]	X
fcis-10736	98	4	redmon	redmon	PROPN
fcis-10736	98	5	j	j	PROPN
fcis-10736	98	6	,	,	PUNCT
fcis-10736	98	7	divvala	divvala	PROPN
fcis-10736	98	8	s	s	PROPN
fcis-10736	98	9	,	,	PUNCT
fcis-10736	98	10	girshick	girshick	ADJ
fcis-10736	98	11	r	r	NOUN
fcis-10736	98	12	,	,	PUNCT
fcis-10736	98	13	et	et	NOUN
fcis-10736	98	14	al.you	al.you	PRON
fcis-10736	98	15	only	only	ADV
fcis-10736	98	16	look	look	VERB
fcis-10736	98	17	once	once	ADV
fcis-10736	98	18	:	:	PUNCT
fcis-10736	98	19	unified	unified	ADJ
fcis-10736	98	20	,	,	PUNCT
fcis-10736	98	21	real	real	ADJ
fcis-10736	98	22	-	-	PUNCT
fcis-10736	98	23	time	time	NOUN
fcis-10736	98	24	object	object	NOUN
fcis-10736	98	25	detection[c]//proceedings	detection[c]//proceeding	NOUN
fcis-10736	98	26	of	of	ADP
fcis-10736	98	27	the	the	DET
fcis-10736	98	28	ieee	ieee	NOUN
fcis-10736	98	29	conference	conference	NOUN
fcis-10736	98	30	on	on	ADP
fcis-10736	98	31	computer	computer	NOUN
fcis-10736	98	32	vision	vision	NOUN
fcis-10736	98	33	and	and	CCONJ
fcis-10736	98	34	pattern	pattern	NOUN
fcis-10736	98	35	recognition	recognition	NOUN
fcis-10736	98	36	,	,	PUNCT
fcis-10736	98	37	2016:779–788	2016:779–788	X
fcis-10736	98	38	.	.	PUNCT
fcis-10736	99	1	[	[	X
fcis-10736	99	2	8	8	NUM
fcis-10736	99	3	]	]	X
fcis-10736	99	4	redmon	redmon	PROPN
fcis-10736	99	5	j	j	PROPN
fcis-10736	99	6	,	,	PUNCT
fcis-10736	99	7	farhadi	farhadi	PROPN
fcis-10736	99	8	a.yolo9000	a.yolo9000	PROPN
fcis-10736	99	9	:	:	PUNCT
fcis-10736	99	10	better	well	ADJ
fcis-10736	99	11	,	,	PUNCT
fcis-10736	99	12	faster	fast	ADV
fcis-10736	99	13	,	,	PUNCT
fcis-10736	99	14	stronger	strong	ADJ
fcis-10736	99	15	[	[	PUNCT
fcis-10736	99	16	c]//	c]//	ADJ
fcis-10736	99	17	proceedings	proceeding	NOUN
fcis-10736	99	18	of	of	ADP
fcis-10736	99	19	the	the	DET
fcis-10736	99	20	ieee	ieee	NOUN
fcis-10736	99	21	conference	conference	NOUN
fcis-10736	99	22	on	on	ADP
fcis-10736	99	23	computer	computer	NOUN
fcis-10736	99	24	vision	vision	NOUN
fcis-10736	99	25	and	and	CCONJ
fcis-10736	99	26	pattern	pattern	NOUN
fcis-10736	99	27	recognition,2017:6517–6525	recognition,2017:6517–6525	NOUN
fcis-10736	99	28	.	.	PUNCT
fcis-10736	100	1	[	[	X
fcis-10736	100	2	9	9	NUM
fcis-10736	100	3	]	]	X
fcis-10736	100	4	redmon	redmon	PROPN
fcis-10736	100	5	j	j	PROPN
fcis-10736	100	6	,	,	PUNCT
fcis-10736	100	7	farhadi	farhadi	PROPN
fcis-10736	100	8	a	a	PRON
fcis-10736	100	9	.yolov3	.yolov3	PROPN
fcis-10736	100	10	:	:	PUNCT
fcis-10736	100	11	an	an	DET
fcis-10736	100	12	incremental	incremental	ADJ
fcis-10736	100	13	improvement[j].arxiv	improvement[j].arxiv	NOUN
fcis-10736	100	14	e	e	NOUN
fcis-10736	100	15	-	-	NOUN
fcis-10736	100	16	prints	print	NOUN
fcis-10736	100	17	,	,	PUNCT
fcis-10736	100	18	2018	2018	NUM
fcis-10736	100	19	.	.	PUNCT
fcis-10736	101	1	doi	doi	NOUN
fcis-10736	101	2	:	:	PUNCT
fcis-10736	101	3	10.48550/	10.48550/	NUM
fcis-10736	101	4	arxiv	arxiv	NOUN
fcis-10736	101	5	.	.	PUNCT
fcis-10736	102	1	1804.02767	1804.02767	X
fcis-10736	102	2	.	.	PUNCT
fcis-10736	103	1	[	[	X
fcis-10736	103	2	10	10	NUM
fcis-10736	103	3	]	]	X
fcis-10736	103	4	jocher	jocher	PROPN
fcis-10736	103	5	g	g	PROPN
fcis-10736	103	6	,	,	PUNCT
fcis-10736	103	7	stoken	stoken	VERB
fcis-10736	103	8	a	a	PRON
fcis-10736	103	9	,	,	PUNCT
fcis-10736	103	10	borovec	borovec	PROPN
fcis-10736	103	11	j	j	PROPN
fcis-10736	103	12	,	,	PUNCT
fcis-10736	103	13	et	et	PROPN
fcis-10736	103	14	al.yolov5	al.yolov5	PROPN
fcis-10736	103	15	:	:	PUNCT
fcis-10736	103	16	v3.1	v3.1	ADP
fcis-10736	103	17	bug	bug	NOUN
fcis-10736	103	18	fixes	fix	NOUN
fcis-10736	103	19	and	and	CCONJ
fcis-10736	103	20	performance	performance	NOUN
fcis-10736	103	21	improvements	improvement	NOUN
fcis-10736	103	22	[	[	X
fcis-10736	103	23	eb	eb	PROPN
fcis-10736	103	24	/	/	SYM
fcis-10736	103	25	ol].2020	ol].2020	PROPN
fcis-10736	103	26	.	.	PUNCT
fcis-10736	104	1	doi	doi	NOUN
fcis-10736	104	2	:	:	PUNCT
fcis-10736	104	3	10.5281	10.5281	NUM
fcis-10736	104	4	/	/	SYM
fcis-10736	104	5	zenodo.4154370	zenodo.4154370	NUM
fcis-10736	104	6	,	,	PUNCT
fcis-10736	104	7	2020	2020	NUM
fcis-10736	104	8	.	.	PUNCT
fcis-10736	105	1	[	[	X
fcis-10736	105	2	11	11	NUM
fcis-10736	105	3	]	]	PUNCT
fcis-10736	105	4	wang	wang	PROPN
fcis-10736	105	5	c	c	PROPN
fcis-10736	105	6	y	y	PROPN
fcis-10736	105	7	,	,	PUNCT
fcis-10736	105	8	bochkovskiy	bochkovskiy	VERB
fcis-10736	105	9	a	a	PRON
fcis-10736	105	10	,	,	PUNCT
fcis-10736	105	11	liao	liao	PROPN
fcis-10736	105	12	h.	h.	PROPN
fcis-10736	105	13	yolov7	yolov7	PROPN
fcis-10736	105	14	:	:	PUNCT
fcis-10736	105	15	trainable	trainable	ADJ
fcis-10736	105	16	bag	bag	NOUN
fcis-10736	105	17	-	-	PUNCT
fcis-10736	105	18	of	of	ADP
fcis-10736	105	19	-	-	PUNCT
fcis-10736	105	20	freebies	freebie	NOUN
fcis-10736	105	21	sets	set	VERB
fcis-10736	105	22	new	new	ADJ
fcis-10736	105	23	state	state	NOUN
fcis-10736	105	24	-	-	PUNCT
fcis-10736	105	25	of	of	ADP
fcis-10736	105	26	-	-	PUNCT
fcis-10736	105	27	the	the	DET
fcis-10736	105	28	-	-	PUNCT
fcis-10736	105	29	art	art	NOUN
fcis-10736	105	30	for	for	ADP
fcis-10736	105	31	realtime	realtime	ADJ
fcis-10736	105	32	object	object	NOUN
fcis-10736	105	33	detectors[j	detectors[j	PROPN
fcis-10736	105	34	]	]	PUNCT
fcis-10736	105	35	.	.	PUNCT
fcis-10736	106	1	arxiv	arxiv	PROPN
fcis-10736	106	2	preprints	preprint	NOUN
fcis-10736	106	3	arxiv	arxiv	PROPN
fcis-10736	106	4	:	:	PUNCT
fcis-10736	106	5	2207	2207	NUM
fcis-10736	106	6	,	,	PUNCT
fcis-10736	106	7	02696	02696	NUM
fcis-10736	106	8	,	,	PUNCT
fcis-10736	106	9	2022	2022	NUM
fcis-10736	106	10	.	.	PUNCT
fcis-10736	107	1	[	[	X
fcis-10736	107	2	12	12	NUM
fcis-10736	107	3	]	]	X
fcis-10736	107	4	girshick	girshick	ADJ
fcis-10736	107	5	r	r	PROPN
fcis-10736	107	6	,	,	PUNCT
fcis-10736	107	7	donahue	donahue	PROPN
fcis-10736	107	8	j	j	PROPN
fcis-10736	107	9	,	,	PUNCT
fcis-10736	107	10	darrell	darrell	PROPN
fcis-10736	107	11	t	t	PROPN
fcis-10736	107	12	,	,	PUNCT
fcis-10736	107	13	et	et	NOUN
fcis-10736	107	14	al.rich	al.rich	ADP
fcis-10736	107	15	feature	feature	NOUN
fcis-10736	107	16	hierarchies	hierarchy	NOUN
fcis-10736	107	17	for	for	ADP
fcis-10736	107	18	accurate	accurate	ADJ
fcis-10736	107	19	object	object	NOUN
fcis-10736	107	20	detection	detection	NOUN
fcis-10736	107	21	and	and	CCONJ
fcis-10736	107	22	semantic	semantic	ADJ
fcis-10736	107	23	segmentation	segmentation	NOUN
fcis-10736	107	24	[	[	X
fcis-10736	107	25	c]//	c]//	ADJ
fcis-10736	107	26	proceedings	proceeding	NOUN
fcis-10736	107	27	of	of	ADP
fcis-10736	107	28	the	the	DET
fcis-10736	107	29	ieee	ieee	NOUN
fcis-10736	107	30	conference	conference	NOUN
fcis-10736	107	31	on	on	ADP
fcis-10736	107	32	computer	computer	NOUN
fcis-10736	107	33	vision	vision	NOUN
fcis-10736	107	34	and	and	CCONJ
fcis-10736	107	35	pattern	pattern	NOUN
fcis-10736	107	36	recognition	recognition	NOUN
fcis-10736	107	37	,	,	PUNCT
fcis-10736	107	38	2014:580–587	2014:580–587	NOUN
fcis-10736	107	39	.	.	PUNCT