id	sid	tid	token	lemma	pos
fcis-3182	1	1	frontiers	frontier	NOUN
fcis-3182	1	2	in	in	ADP
fcis-3182	1	3	computing	computing	NOUN
fcis-3182	1	4	and	and	CCONJ
fcis-3182	1	5	intelligent	intelligent	ADJ
fcis-3182	1	6	systems	system	NOUN
fcis-3182	1	7	issn	issn	VERB
fcis-3182	1	8	:	:	PUNCT
fcis-3182	1	9	2832	2832	NUM
fcis-3182	1	10	-	-	SYM
fcis-3182	1	11	6024	6024	NUM
fcis-3182	1	12	|	|	NOUN
fcis-3182	1	13	vol	vol	NOUN
fcis-3182	1	14	.	.	PROPN
fcis-3182	2	1	2	2	NUM
fcis-3182	2	2	,	,	PUNCT
fcis-3182	2	3	no	no	INTJ
fcis-3182	2	4	.	.	NOUN
fcis-3182	2	5	1	1	NUM
fcis-3182	2	6	,	,	PUNCT
fcis-3182	2	7	2022	2022	NUM
fcis-3182	2	8	126	126	NUM
fcis-3182	2	9	detection	detection	NOUN
fcis-3182	2	10	of	of	ADP
fcis-3182	2	11	domestic	domestic	ADJ
fcis-3182	2	12	waste	waste	NOUN
fcis-3182	2	13	based	base	VERB
fcis-3182	2	14	on	on	ADP
fcis-3182	2	15	yolo	yolo	PROPN
fcis-3182	2	16	yaohui	yaohui	PROPN
fcis-3182	2	17	hou	hou	PROPN
fcis-3182	2	18	school	school	PROPN
fcis-3182	2	19	of	of	ADP
fcis-3182	2	20	information	information	NOUN
fcis-3182	2	21	and	and	CCONJ
fcis-3182	2	22	communication	communication	NOUN
fcis-3182	2	23	engineering	engineering	NOUN
fcis-3182	2	24	,	,	PUNCT
fcis-3182	2	25	beijing	beijing	PROPN
fcis-3182	2	26	university	university	PROPN
fcis-3182	2	27	of	of	ADP
fcis-3182	2	28	posts	post	NOUN
fcis-3182	2	29	and	and	CCONJ
fcis-3182	2	30	telecommunications	telecommunication	NOUN
fcis-3182	2	31	,	,	PUNCT
fcis-3182	2	32	beijing	beijing	PROPN
fcis-3182	2	33	100876	100876	NUM
fcis-3182	2	34	,	,	PUNCT
fcis-3182	2	35	china	china	PROPN
fcis-3182	2	36	houyh_0817@bupt.edu.cn	houyh_0817@bupt.edu.cn	NOUN
fcis-3182	2	37	abstract	abstract	NOUN
fcis-3182	2	38	:	:	PUNCT
fcis-3182	2	39	deep	deep	ADJ
fcis-3182	2	40	learning	learning	NOUN
fcis-3182	2	41	has	have	AUX
fcis-3182	2	42	increasingly	increasingly	ADV
fcis-3182	2	43	permeated	permeate	VERB
fcis-3182	2	44	every	every	DET
fcis-3182	2	45	aspect	aspect	NOUN
fcis-3182	2	46	of	of	ADP
fcis-3182	2	47	our	our	PRON
fcis-3182	2	48	lives	life	NOUN
fcis-3182	2	49	.	.	PUNCT
fcis-3182	3	1	currently	currently	ADV
fcis-3182	3	2	,	,	PUNCT
fcis-3182	3	3	the	the	DET
fcis-3182	3	4	economy	economy	NOUN
fcis-3182	3	5	develops	develop	VERB
fcis-3182	3	6	rapidly	rapidly	ADV
fcis-3182	3	7	and	and	CCONJ
fcis-3182	3	8	the	the	DET
fcis-3182	3	9	population	population	NOUN
fcis-3182	3	10	living	live	VERB
fcis-3182	3	11	in	in	ADP
fcis-3182	3	12	cities	city	NOUN
fcis-3182	3	13	grows	grow	VERB
fcis-3182	3	14	quickly	quickly	ADV
fcis-3182	3	15	.	.	PUNCT
fcis-3182	4	1	in	in	ADP
fcis-3182	4	2	the	the	DET
fcis-3182	4	3	meanwhile	meanwhile	NOUN
fcis-3182	4	4	,	,	PUNCT
fcis-3182	4	5	the	the	DET
fcis-3182	4	6	amount	amount	NOUN
fcis-3182	4	7	of	of	ADP
fcis-3182	4	8	domestic	domestic	ADJ
fcis-3182	4	9	waste	waste	NOUN
fcis-3182	4	10	is	be	AUX
fcis-3182	4	11	constantly	constantly	ADV
fcis-3182	4	12	rising	rise	VERB
fcis-3182	4	13	,	,	PUNCT
fcis-3182	4	14	and	and	CCONJ
fcis-3182	4	15	the	the	DET
fcis-3182	4	16	serious	serious	ADJ
fcis-3182	4	17	shortage	shortage	NOUN
fcis-3182	4	18	of	of	ADP
fcis-3182	4	19	waste	waste	NOUN
fcis-3182	4	20	treatment	treatment	NOUN
fcis-3182	4	21	competence	competence	NOUN
fcis-3182	4	22	has	have	AUX
fcis-3182	4	23	become	become	VERB
fcis-3182	4	24	increasingly	increasingly	ADV
fcis-3182	4	25	prominent	prominent	ADJ
fcis-3182	4	26	.	.	PUNCT
fcis-3182	5	1	many	many	ADJ
fcis-3182	5	2	cities	city	NOUN
fcis-3182	5	3	can	can	AUX
fcis-3182	5	4	only	only	ADV
fcis-3182	5	5	use	use	VERB
fcis-3182	5	6	landfill	landfill	NOUN
fcis-3182	5	7	or	or	CCONJ
fcis-3182	5	8	incineration	incineration	NOUN
fcis-3182	5	9	to	to	PART
fcis-3182	5	10	generate	generate	VERB
fcis-3182	5	11	electricity	electricity	NOUN
fcis-3182	5	12	for	for	ADP
fcis-3182	5	13	most	most	ADJ
fcis-3182	5	14	of	of	ADP
fcis-3182	5	15	the	the	DET
fcis-3182	5	16	waste	waste	NOUN
fcis-3182	5	17	,	,	PUNCT
fcis-3182	5	18	but	but	CCONJ
fcis-3182	5	19	this	this	PRON
fcis-3182	5	20	is	be	AUX
fcis-3182	5	21	not	not	PART
fcis-3182	5	22	a	a	DET
fcis-3182	5	23	long	long	ADJ
fcis-3182	5	24	-	-	PUNCT
fcis-3182	5	25	term	term	NOUN
fcis-3182	5	26	solution	solution	NOUN
fcis-3182	5	27	.	.	PUNCT
fcis-3182	6	1	in	in	ADP
fcis-3182	6	2	this	this	DET
fcis-3182	6	3	paper	paper	NOUN
fcis-3182	6	4	,	,	PUNCT
fcis-3182	6	5	the	the	DET
fcis-3182	6	6	yolo	yolo	NOUN
fcis-3182	6	7	-	-	PUNCT
fcis-3182	6	8	based	base	VERB
fcis-3182	6	9	domestic	domestic	ADJ
fcis-3182	6	10	waste	waste	NOUN
fcis-3182	6	11	classification	classification	NOUN
fcis-3182	6	12	and	and	CCONJ
fcis-3182	6	13	treatment	treatment	NOUN
fcis-3182	6	14	method	method	NOUN
fcis-3182	6	15	are	be	AUX
fcis-3182	6	16	proposed	propose	VERB
fcis-3182	6	17	to	to	PART
fcis-3182	6	18	improve	improve	VERB
fcis-3182	6	19	the	the	DET
fcis-3182	6	20	efficiency	efficiency	NOUN
fcis-3182	6	21	of	of	ADP
fcis-3182	6	22	waste	waste	NOUN
fcis-3182	6	23	treatment	treatment	NOUN
fcis-3182	6	24	.	.	PUNCT
fcis-3182	7	1	keywords	keyword	NOUN
fcis-3182	7	2	:	:	PUNCT
fcis-3182	8	1	yolo	yolo	ADJ
fcis-3182	8	2	;	;	PUNCT
fcis-3182	8	3	domestic	domestic	ADJ
fcis-3182	8	4	waste	waste	NOUN
fcis-3182	8	5	;	;	PUNCT
fcis-3182	8	6	objection	objection	NOUN
fcis-3182	8	7	detection	detection	NOUN
fcis-3182	8	8	;	;	PUNCT
fcis-3182	8	9	deep	deep	ADJ
fcis-3182	8	10	learning	learning	NOUN
fcis-3182	8	11	.	.	PUNCT
fcis-3182	9	1	1	1	X
fcis-3182	9	2	.	.	X
fcis-3182	9	3	introduction	introduction	NOUN
fcis-3182	9	4	in	in	ADP
fcis-3182	9	5	the	the	DET
fcis-3182	9	6	domain	domain	NOUN
fcis-3182	9	7	of	of	ADP
fcis-3182	9	8	artificial	artificial	ADJ
fcis-3182	9	9	intelligence	intelligence	NOUN
fcis-3182	9	10	and	and	CCONJ
fcis-3182	9	11	pattern	pattern	NOUN
fcis-3182	9	12	recognition	recognition	NOUN
fcis-3182	9	13	,	,	PUNCT
fcis-3182	9	14	machine	machine	NOUN
fcis-3182	9	15	learning	learning	NOUN
fcis-3182	9	16	especially	especially	ADV
fcis-3182	9	17	deep	deep	ADJ
fcis-3182	9	18	learning	learning	NOUN
fcis-3182	9	19	is	be	AUX
fcis-3182	9	20	an	an	DET
fcis-3182	9	21	enduring	endure	VERB
fcis-3182	9	22	research	research	NOUN
fcis-3182	9	23	hotspot	hotspot	NOUN
fcis-3182	9	24	,	,	PUNCT
fcis-3182	9	25	and	and	CCONJ
fcis-3182	9	26	its	its	PRON
fcis-3182	9	27	theories	theory	NOUN
fcis-3182	9	28	and	and	CCONJ
fcis-3182	9	29	techniques	technique	NOUN
fcis-3182	9	30	have	have	AUX
fcis-3182	9	31	been	be	AUX
fcis-3182	9	32	widely	widely	ADV
fcis-3182	9	33	used	use	VERB
fcis-3182	9	34	to	to	PART
fcis-3182	9	35	settle	settle	VERB
fcis-3182	9	36	complicated	complicated	ADJ
fcis-3182	9	37	engineering	engineering	NOUN
fcis-3182	9	38	applications	application	NOUN
fcis-3182	9	39	and	and	CCONJ
fcis-3182	9	40	science	science	NOUN
fcis-3182	9	41	problems	problem	NOUN
fcis-3182	9	42	.	.	PUNCT
fcis-3182	10	1	among	among	ADP
fcis-3182	10	2	them	they	PRON
fcis-3182	10	3	,	,	PUNCT
fcis-3182	10	4	the	the	DET
fcis-3182	10	5	yolo	yolo	ADJ
fcis-3182	10	6	algorithm	algorithm	NOUN
fcis-3182	10	7	is	be	AUX
fcis-3182	10	8	one	one	NUM
fcis-3182	10	9	of	of	ADP
fcis-3182	10	10	the	the	DET
fcis-3182	10	11	representative	representative	ADJ
fcis-3182	10	12	algorithms	algorithm	NOUN
fcis-3182	10	13	.	.	PUNCT
fcis-3182	11	1	yolo	yolo	ADJ
fcis-3182	11	2	,	,	PUNCT
fcis-3182	11	3	short	short	ADJ
fcis-3182	11	4	for	for	ADP
fcis-3182	11	5	you	you	PRON
fcis-3182	11	6	only	only	ADV
fcis-3182	11	7	look	look	VERB
fcis-3182	11	8	once	once	ADV
fcis-3182	11	9	,	,	PUNCT
fcis-3182	11	10	as	as	SCONJ
fcis-3182	11	11	the	the	DET
fcis-3182	11	12	name	name	NOUN
fcis-3182	11	13	suggests	suggest	VERB
fcis-3182	11	14	,	,	PUNCT
fcis-3182	11	15	you	you	PRON
fcis-3182	11	16	just	just	ADV
fcis-3182	11	17	need	need	VERB
fcis-3182	11	18	to	to	PART
fcis-3182	11	19	look	look	VERB
fcis-3182	11	20	once	once	ADV
fcis-3182	11	21	.	.	PUNCT
fcis-3182	12	1	we	we	PRON
fcis-3182	12	2	humans	human	NOUN
fcis-3182	12	3	can	can	AUX
fcis-3182	12	4	know	know	VERB
fcis-3182	12	5	what	what	PRON
fcis-3182	12	6	objects	object	NOUN
fcis-3182	12	7	in	in	ADP
fcis-3182	12	8	an	an	DET
fcis-3182	12	9	image	image	NOUN
fcis-3182	12	10	are	be	AUX
fcis-3182	12	11	,	,	PUNCT
fcis-3182	12	12	where	where	SCONJ
fcis-3182	12	13	they	they	PRON
fcis-3182	12	14	are	be	AUX
fcis-3182	12	15	,	,	PUNCT
fcis-3182	12	16	and	and	CCONJ
fcis-3182	12	17	how	how	SCONJ
fcis-3182	12	18	they	they	PRON
fcis-3182	12	19	relate	relate	VERB
fcis-3182	12	20	to	to	ADP
fcis-3182	12	21	each	each	DET
fcis-3182	12	22	other	other	ADJ
fcis-3182	12	23	with	with	ADP
fcis-3182	12	24	a	a	DET
fcis-3182	12	25	glance	glance	NOUN
fcis-3182	12	26	.	.	PUNCT
fcis-3182	13	1	in	in	ADP
fcis-3182	13	2	the	the	DET
fcis-3182	13	3	first	first	ADJ
fcis-3182	13	4	generation	generation	NOUN
fcis-3182	13	5	of	of	ADP
fcis-3182	13	6	yolo	yolo	PROPN
fcis-3182	13	7	,	,	PUNCT
fcis-3182	13	8	the	the	DET
fcis-3182	13	9	input	input	NOUN
fcis-3182	13	10	was	be	AUX
fcis-3182	13	11	resized	resize	VERB
fcis-3182	13	12	to	to	ADP
fcis-3182	13	13	a	a	DET
fcis-3182	13	14	specific	specific	ADJ
fcis-3182	13	15	size	size	NOUN
fcis-3182	13	16	,	,	PUNCT
fcis-3182	13	17	then	then	ADV
fcis-3182	13	18	passed	pass	VERB
fcis-3182	13	19	through	through	ADP
fcis-3182	13	20	a	a	DET
fcis-3182	13	21	single	single	ADJ
fcis-3182	13	22	convolutional	convolutional	ADJ
fcis-3182	13	23	neural	neural	ADJ
fcis-3182	13	24	network	network	NOUN
fcis-3182	13	25	,	,	PUNCT
fcis-3182	13	26	and	and	CCONJ
fcis-3182	13	27	the	the	DET
fcis-3182	13	28	detection	detection	NOUN
fcis-3182	13	29	results	result	NOUN
fcis-3182	13	30	were	be	AUX
fcis-3182	13	31	thresholder	thresholder	VERB
fcis-3182	13	32	by	by	ADP
fcis-3182	13	33	the	the	DET
fcis-3182	13	34	confidence	confidence	NOUN
fcis-3182	13	35	of	of	ADP
fcis-3182	13	36	the	the	DET
fcis-3182	13	37	model	model	NOUN
fcis-3182	13	38	.	.	PUNCT
fcis-3182	14	1	figure	figure	NOUN
fcis-3182	14	2	1	1	NUM
fcis-3182	14	3	shows	show	VERB
fcis-3182	14	4	the	the	DET
fcis-3182	14	5	working	work	VERB
fcis-3182	14	6	principle	principle	NOUN
fcis-3182	14	7	of	of	ADP
fcis-3182	14	8	a	a	DET
fcis-3182	14	9	simple	simple	ADJ
fcis-3182	14	10	yolo	yolo	ADJ
fcis-3182	14	11	detection	detection	NOUN
fcis-3182	14	12	system	system	NOUN
fcis-3182	14	13	.	.	PUNCT
fcis-3182	15	1	figure1	figure1	PROPN
fcis-3182	15	2	.	.	PUNCT
fcis-3182	16	1	the	the	DET
fcis-3182	16	2	yolo	yolo	ADJ
fcis-3182	16	3	detection	detection	NOUN
fcis-3182	16	4	system	system	NOUN
fcis-3182	16	5	the	the	DET
fcis-3182	16	6	second	second	ADJ
fcis-3182	16	7	-	-	PUNCT
fcis-3182	16	8	generation	generation	NOUN
fcis-3182	16	9	yolo	yolo	ADJ
fcis-3182	16	10	algorithm	algorithm	NOUN
fcis-3182	16	11	improves	improve	VERB
fcis-3182	16	12	some	some	PRON
fcis-3182	16	13	of	of	ADP
fcis-3182	16	14	the	the	DET
fcis-3182	16	15	shortcomings	shortcoming	NOUN
fcis-3182	16	16	of	of	ADP
fcis-3182	16	17	the	the	DET
fcis-3182	16	18	first	first	ADJ
fcis-3182	16	19	generation	generation	NOUN
fcis-3182	16	20	.	.	PUNCT
fcis-3182	17	1	the	the	DET
fcis-3182	17	2	author	author	NOUN
fcis-3182	17	3	used	use	VERB
fcis-3182	17	4	batch	batch	NOUN
fcis-3182	17	5	normalization	normalization	NOUN
fcis-3182	17	6	,	,	PUNCT
fcis-3182	17	7	direct	direct	ADJ
fcis-3182	17	8	location	location	NOUN
fcis-3182	17	9	prediction	prediction	NOUN
fcis-3182	17	10	,	,	PUNCT
fcis-3182	17	11	multi	multi	ADJ
fcis-3182	17	12	-	-	ADJ
fcis-3182	17	13	scale	scale	ADJ
fcis-3182	17	14	training	training	NOUN
fcis-3182	17	15	and	and	CCONJ
fcis-3182	17	16	other	other	ADJ
fcis-3182	17	17	methods	method	NOUN
fcis-3182	17	18	to	to	PART
fcis-3182	17	19	solve	solve	VERB
fcis-3182	17	20	the	the	DET
fcis-3182	17	21	problem	problem	NOUN
fcis-3182	17	22	of	of	ADP
fcis-3182	17	23	inaccurate	inaccurate	ADJ
fcis-3182	17	24	localization	localization	NOUN
fcis-3182	17	25	and	and	CCONJ
fcis-3182	17	26	low	low	ADJ
fcis-3182	17	27	recall	recall	NOUN
fcis-3182	17	28	.	.	PUNCT
fcis-3182	18	1	in	in	ADP
fcis-3182	18	2	addition	addition	NOUN
fcis-3182	18	3	to	to	ADP
fcis-3182	18	4	this	this	PRON
fcis-3182	18	5	,	,	PUNCT
fcis-3182	18	6	the	the	DET
fcis-3182	18	7	training	training	NOUN
fcis-3182	18	8	network	network	NOUN
fcis-3182	18	9	is	be	AUX
fcis-3182	18	10	also	also	ADV
fcis-3182	18	11	modified	modify	VERB
fcis-3182	18	12	.	.	PUNCT
fcis-3182	19	1	the	the	DET
fcis-3182	19	2	author	author	NOUN
fcis-3182	19	3	uses	use	VERB
fcis-3182	19	4	darknet-19	darknet-19	PROPN
fcis-3182	19	5	as	as	ADP
fcis-3182	19	6	the	the	DET
fcis-3182	19	7	base	base	NOUN
fcis-3182	19	8	network	network	NOUN
fcis-3182	19	9	,	,	PUNCT
fcis-3182	19	10	which	which	PRON
fcis-3182	19	11	reduces	reduce	VERB
fcis-3182	19	12	the	the	DET
fcis-3182	19	13	amount	amount	NOUN
fcis-3182	19	14	of	of	ADP
fcis-3182	19	15	computation	computation	NOUN
fcis-3182	19	16	to	to	ADP
fcis-3182	19	17	some	some	DET
fcis-3182	19	18	extent	extent	NOUN
fcis-3182	19	19	.	.	PUNCT
fcis-3182	20	1	by	by	ADP
fcis-3182	20	2	comparison	comparison	NOUN
fcis-3182	20	3	with	with	ADP
fcis-3182	20	4	the	the	DET
fcis-3182	20	5	previous	previous	ADJ
fcis-3182	20	6	generation	generation	NOUN
fcis-3182	20	7	,	,	PUNCT
fcis-3182	20	8	the	the	DET
fcis-3182	20	9	thirdgeneration	thirdgeneration	NOUN
fcis-3182	20	10	algorithm	algorithm	NOUN
fcis-3182	20	11	has	have	AUX
fcis-3182	20	12	mainly	mainly	ADV
fcis-3182	20	13	made	make	VERB
fcis-3182	20	14	two	two	NUM
fcis-3182	20	15	improvements	improvement	NOUN
fcis-3182	20	16	.	.	PUNCT
fcis-3182	21	1	firstly	firstly	ADV
fcis-3182	21	2	,	,	PUNCT
fcis-3182	21	3	drawing	draw	VERB
fcis-3182	21	4	on	on	ADP
fcis-3182	21	5	the	the	DET
fcis-3182	21	6	method	method	NOUN
fcis-3182	21	7	of	of	ADP
fcis-3182	21	8	resnet	resnet	NOUN
fcis-3182	21	9	and	and	CCONJ
fcis-3182	21	10	adding	add	VERB
fcis-3182	21	11	some	some	DET
fcis-3182	21	12	short	short	ADJ
fcis-3182	21	13	connections	connection	NOUN
fcis-3182	21	14	,	,	PUNCT
fcis-3182	21	15	darknet-19	darknet-19	PROPN
fcis-3182	21	16	is	be	AUX
fcis-3182	21	17	improved	improve	VERB
fcis-3182	21	18	to	to	ADP
fcis-3182	21	19	darknet-53	darknet-53	PROPN
fcis-3182	21	20	.	.	PUNCT
fcis-3182	21	21	secondly	secondly	ADV
fcis-3182	21	22	,	,	PUNCT
fcis-3182	21	23	using	use	VERB
fcis-3182	21	24	feature	feature	NOUN
fcis-3182	21	25	pyramid	pyramid	NOUN
fcis-3182	21	26	networks	network	NOUN
fcis-3182	21	27	for	for	ADP
fcis-3182	21	28	object	object	NOUN
fcis-3182	21	29	detection	detection	NOUN
fcis-3182	21	30	enabled	enable	VERB
fcis-3182	21	31	a	a	DET
fcis-3182	21	32	better	well	ADJ
fcis-3182	21	33	detection	detection	NOUN
fcis-3182	21	34	for	for	ADP
fcis-3182	21	35	small	small	ADJ
fcis-3182	21	36	object	object	NOUN
fcis-3182	21	37	.	.	PUNCT
fcis-3182	22	1	however	however	ADV
fcis-3182	22	2	,	,	PUNCT
fcis-3182	22	3	unfortunately	unfortunately	ADV
fcis-3182	22	4	,	,	PUNCT
fcis-3182	22	5	the	the	DET
fcis-3182	22	6	detection	detection	NOUN
fcis-3182	22	7	performance	performance	NOUN
fcis-3182	22	8	for	for	ADP
fcis-3182	22	9	large	large	ADJ
fcis-3182	22	10	objects	object	NOUN
fcis-3182	22	11	degraded	degrade	VERB
fcis-3182	22	12	slightly	slightly	ADV
fcis-3182	22	13	.	.	PUNCT
fcis-3182	23	1	the	the	DET
fcis-3182	23	2	authors	author	NOUN
fcis-3182	23	3	have	have	AUX
fcis-3182	23	4	done	do	VERB
fcis-3182	23	5	quite	quite	DET
fcis-3182	23	6	a	a	DET
fcis-3182	23	7	bit	bit	NOUN
fcis-3182	23	8	of	of	ADP
fcis-3182	23	9	work	work	NOUN
fcis-3182	23	10	to	to	PART
fcis-3182	23	11	get	get	VERB
fcis-3182	23	12	yolov4	yolov4	NOUN
fcis-3182	23	13	to	to	ADP
fcis-3182	23	14	high	high	ADJ
fcis-3182	23	15	performance	performance	NOUN
fcis-3182	23	16	.	.	PUNCT
fcis-3182	24	1	the	the	DET
fcis-3182	24	2	main	main	ADJ
fcis-3182	24	3	work	work	NOUN
fcis-3182	24	4	is	be	AUX
fcis-3182	24	5	reflected	reflect	VERB
fcis-3182	24	6	in	in	ADP
fcis-3182	24	7	the	the	DET
fcis-3182	24	8	integration	integration	NOUN
fcis-3182	24	9	and	and	CCONJ
fcis-3182	24	10	parameter	parameter	NOUN
fcis-3182	24	11	adjustment	adjustment	NOUN
fcis-3182	24	12	of	of	ADP
fcis-3182	24	13	the	the	DET
fcis-3182	24	14	network	network	NOUN
fcis-3182	24	15	model	model	NOUN
fcis-3182	24	16	.	.	PUNCT
fcis-3182	25	1	yolov4	yolov4	PROPN
fcis-3182	25	2	makes	make	VERB
fcis-3182	25	3	everybody	everybody	PRON
fcis-3182	25	4	can	can	AUX
fcis-3182	25	5	train	train	VERB
fcis-3182	25	6	a	a	DET
fcis-3182	25	7	fast	fast	ADJ
fcis-3182	25	8	and	and	CCONJ
fcis-3182	25	9	accurate	accurate	ADJ
fcis-3182	25	10	object	object	NOUN
fcis-3182	25	11	detector	detector	NOUN
fcis-3182	25	12	by	by	ADP
fcis-3182	25	13	applying	apply	VERB
fcis-3182	25	14	a	a	DET
fcis-3182	25	15	common	common	ADJ
fcis-3182	25	16	gpu	gpu	NOUN
fcis-3182	25	17	,	,	PUNCT
fcis-3182	25	18	like	like	ADP
fcis-3182	25	19	1080	1080	NUM
fcis-3182	25	20	ti	ti	NOUN
fcis-3182	25	21	.	.	PROPN
fcis-3182	25	22	2	2	NUM
fcis-3182	25	23	.	.	PUNCT
fcis-3182	26	1	the	the	DET
fcis-3182	26	2	reason	reason	NOUN
fcis-3182	26	3	for	for	ADP
fcis-3182	26	4	choosing	choose	VERB
fcis-3182	26	5	yolo	yolo	PROPN
fcis-3182	26	6	prior	prior	ADV
fcis-3182	26	7	to	to	ADP
fcis-3182	26	8	yolo	yolo	PROPN
fcis-3182	26	9	,	,	PUNCT
fcis-3182	26	10	algorithms	algorithm	NOUN
fcis-3182	26	11	such	such	ADJ
fcis-3182	26	12	as	as	ADP
fcis-3182	26	13	r	r	NOUN
fcis-3182	26	14	-	-	PUNCT
fcis-3182	26	15	cnn(region	cnn(region	NOUN
fcis-3182	26	16	-	-	PUNCT
fcis-3182	26	17	cnn	cnn	NOUN
fcis-3182	26	18	)	)	PUNCT
fcis-3182	26	19	used	use	VERB
fcis-3182	26	20	the	the	DET
fcis-3182	26	21	region	region	NOUN
fcis-3182	26	22	proposal	proposal	NOUN
fcis-3182	26	23	method	method	NOUN
fcis-3182	26	24	which	which	PRON
fcis-3182	26	25	can	can	AUX
fcis-3182	26	26	generate	generate	VERB
fcis-3182	26	27	potential	potential	ADJ
fcis-3182	26	28	bounding	bounding	NOUN
fcis-3182	26	29	boxes	box	NOUN
fcis-3182	26	30	on	on	ADP
fcis-3182	26	31	images	image	NOUN
fcis-3182	26	32	.	.	PUNCT
fcis-3182	27	1	each	each	DET
fcis-3182	27	2	image	image	NOUN
fcis-3182	27	3	would	would	AUX
fcis-3182	27	4	generate	generate	VERB
fcis-3182	27	5	about	about	ADV
fcis-3182	27	6	2000	2000	NUM
fcis-3182	27	7	category	category	NOUN
fcis-3182	27	8	-	-	PUNCT
fcis-3182	27	9	independent	independent	ADJ
fcis-3182	27	10	potential	potential	ADJ
fcis-3182	27	11	bounding	bounding	NOUN
fcis-3182	27	12	boxes	box	NOUN
fcis-3182	27	13	,	,	PUNCT
fcis-3182	27	14	extract	extract	VERB
fcis-3182	27	15	a	a	DET
fcis-3182	27	16	fixed	fix	VERB
fcis-3182	27	17	-	-	PUNCT
fcis-3182	27	18	length	length	NOUN
fcis-3182	27	19	feature	feature	NOUN
fcis-3182	27	20	vector	vector	NOUN
fcis-3182	27	21	from	from	ADP
fcis-3182	27	22	every	every	DET
fcis-3182	27	23	proposal	proposal	NOUN
fcis-3182	27	24	by	by	ADP
fcis-3182	27	25	using	use	VERB
fcis-3182	27	26	a	a	DET
fcis-3182	27	27	convolutional	convolutional	ADJ
fcis-3182	27	28	network	network	NOUN
fcis-3182	27	29	and	and	CCONJ
fcis-3182	27	30	then	then	ADV
fcis-3182	27	31	perform	perform	VERB
fcis-3182	27	32	classification	classification	NOUN
fcis-3182	27	33	and	and	CCONJ
fcis-3182	27	34	detection	detection	NOUN
fcis-3182	27	35	on	on	ADP
fcis-3182	27	36	the	the	DET
fcis-3182	27	37	bounding	bounding	NOUN
fcis-3182	27	38	boxes	box	NOUN
fcis-3182	27	39	.	.	PUNCT
fcis-3182	28	1	the	the	DET
fcis-3182	28	2	box	box	PROPN
fcis-3182	28	3	is	be	AUX
fcis-3182	28	4	then	then	ADV
fcis-3182	28	5	refined	refine	VERB
fcis-3182	28	6	to	to	PART
fcis-3182	28	7	remove	remove	VERB
fcis-3182	28	8	reduplicate	reduplicate	NOUN
fcis-3182	28	9	detections	detection	NOUN
fcis-3182	28	10	.	.	PUNCT
fcis-3182	29	1	the	the	DET
fcis-3182	29	2	problem	problem	NOUN
fcis-3182	29	3	with	with	ADP
fcis-3182	29	4	this	this	DET
fcis-3182	29	5	approach	approach	NOUN
fcis-3182	29	6	,	,	PUNCT
fcis-3182	29	7	however	however	ADV
fcis-3182	29	8	,	,	PUNCT
fcis-3182	29	9	is	be	AUX
fcis-3182	29	10	that	that	SCONJ
fcis-3182	29	11	the	the	DET
fcis-3182	29	12	process	process	NOUN
fcis-3182	29	13	is	be	AUX
fcis-3182	29	14	overly	overly	ADV
fcis-3182	29	15	complex	complex	ADJ
fcis-3182	29	16	,	,	PUNCT
fcis-3182	29	17	slow	slow	ADJ
fcis-3182	29	18	and	and	CCONJ
fcis-3182	29	19	difficult	difficult	ADJ
fcis-3182	29	20	to	to	PART
fcis-3182	29	21	optimize	optimize	VERB
fcis-3182	29	22	.	.	PUNCT
fcis-3182	30	1	in	in	ADP
fcis-3182	30	2	contrast	contrast	NOUN
fcis-3182	30	3	,	,	PUNCT
fcis-3182	30	4	the	the	DET
fcis-3182	30	5	yolo	yolo	ADJ
fcis-3182	30	6	algorithm	algorithm	NOUN
fcis-3182	30	7	is	be	AUX
fcis-3182	30	8	much	much	ADV
fcis-3182	30	9	simpler	simple	ADJ
fcis-3182	30	10	,	,	PUNCT
fcis-3182	30	11	a	a	DET
fcis-3182	30	12	simplex	simplex	NOUN
fcis-3182	30	13	neural	neural	ADJ
fcis-3182	30	14	network	network	NOUN
fcis-3182	30	15	can	can	AUX
fcis-3182	30	16	predict	predict	VERB
fcis-3182	30	17	multiple	multiple	ADJ
fcis-3182	30	18	bounding	bounding	NOUN
fcis-3182	30	19	boxes	box	NOUN
fcis-3182	30	20	at	at	ADP
fcis-3182	30	21	the	the	DET
fcis-3182	30	22	same	same	ADJ
fcis-3182	30	23	time	time	NOUN
fcis-3182	30	24	,	,	PUNCT
fcis-3182	30	25	and	and	CCONJ
fcis-3182	30	26	it	it	PRON
fcis-3182	30	27	trains	train	VERB
fcis-3182	30	28	on	on	ADP
fcis-3182	30	29	the	the	DET
fcis-3182	30	30	entire	entire	ADJ
fcis-3182	30	31	image	image	NOUN
fcis-3182	30	32	and	and	CCONJ
fcis-3182	30	33	optimizes	optimize	VERB
fcis-3182	30	34	detection	detection	NOUN
fcis-3182	30	35	performance	performance	NOUN
fcis-3182	30	36	directly	directly	ADV
fcis-3182	30	37	.	.	PUNCT
fcis-3182	31	1	compared	compare	VERB
fcis-3182	31	2	with	with	ADP
fcis-3182	31	3	traditional	traditional	ADJ
fcis-3182	31	4	detection	detection	NOUN
fcis-3182	31	5	models	model	NOUN
fcis-3182	31	6	,	,	PUNCT
fcis-3182	31	7	yolo	yolo	PROPN
fcis-3182	31	8	has	have	VERB
fcis-3182	31	9	a	a	DET
fcis-3182	31	10	couple	couple	NOUN
fcis-3182	31	11	of	of	ADP
fcis-3182	31	12	advantages	advantage	NOUN
fcis-3182	31	13	.	.	PUNCT
fcis-3182	32	1	first	first	ADV
fcis-3182	32	2	of	of	ADP
fcis-3182	32	3	all	all	PRON
fcis-3182	32	4	,	,	PUNCT
fcis-3182	32	5	it	it	PRON
fcis-3182	32	6	is	be	AUX
fcis-3182	32	7	really	really	ADV
fcis-3182	32	8	fast	fast	ADJ
fcis-3182	32	9	.	.	PUNCT
fcis-3182	33	1	the	the	DET
fcis-3182	33	2	object	object	NOUN
fcis-3182	33	3	detection	detection	NOUN
fcis-3182	33	4	is	be	AUX
fcis-3182	33	5	served	serve	VERB
fcis-3182	33	6	as	as	ADP
fcis-3182	33	7	a	a	DET
fcis-3182	33	8	simple	simple	ADJ
fcis-3182	33	9	regression	regression	NOUN
fcis-3182	33	10	problem	problem	NOUN
fcis-3182	33	11	,	,	PUNCT
fcis-3182	33	12	which	which	PRON
fcis-3182	33	13	means	mean	VERB
fcis-3182	33	14	we	we	PRON
fcis-3182	33	15	do	do	AUX
fcis-3182	33	16	not	not	PART
fcis-3182	33	17	need	need	VERB
fcis-3182	33	18	a	a	DET
fcis-3182	33	19	complicated	complicated	ADJ
fcis-3182	33	20	procedure	procedure	NOUN
fcis-3182	33	21	.	.	PUNCT
fcis-3182	34	1	secondly	secondly	ADV
fcis-3182	34	2	,	,	PUNCT
fcis-3182	34	3	yolo	yolo	PROPN
fcis-3182	34	4	can	can	AUX
fcis-3182	34	5	detect	detect	VERB
fcis-3182	34	6	the	the	DET
fcis-3182	34	7	image	image	NOUN
fcis-3182	34	8	as	as	ADP
fcis-3182	34	9	a	a	DET
fcis-3182	34	10	whole	whole	NOUN
fcis-3182	34	11	during	during	ADP
fcis-3182	34	12	prediction	prediction	NOUN
fcis-3182	34	13	.	.	PUNCT
fcis-3182	35	1	it	it	PRON
fcis-3182	35	2	is	be	AUX
fcis-3182	35	3	different	different	ADJ
fcis-3182	35	4	from	from	ADP
fcis-3182	35	5	the	the	DET
fcis-3182	35	6	r	r	PROPN
fcis-3182	35	7	-	-	PUNCT
fcis-3182	35	8	cnn	cnn	PROPN
fcis-3182	35	9	series	series	NOUN
fcis-3182	35	10	of	of	ADP
fcis-3182	35	11	algorithms	algorithm	NOUN
fcis-3182	35	12	,	,	PUNCT
fcis-3182	35	13	since	since	SCONJ
fcis-3182	35	14	it	it	PRON
fcis-3182	35	15	sees	see	VERB
fcis-3182	35	16	larger	large	ADJ
fcis-3182	35	17	context	context	NOUN
fcis-3182	35	18	and	and	CCONJ
fcis-3182	35	19	makes	make	VERB
fcis-3182	35	20	less	less	ADJ
fcis-3182	35	21	than	than	ADP
fcis-3182	35	22	half	half	NOUN
fcis-3182	35	23	of	of	ADP
fcis-3182	35	24	background	background	NOUN
fcis-3182	35	25	errors	error	NOUN
fcis-3182	35	26	.	.	PUNCT
fcis-3182	36	1	finally	finally	ADV
fcis-3182	36	2	,	,	PUNCT
fcis-3182	36	3	yolo	yolo	PROPN
fcis-3182	36	4	learns	learn	VERB
fcis-3182	36	5	abstract	abstract	ADJ
fcis-3182	36	6	features	feature	NOUN
fcis-3182	36	7	of	of	ADP
fcis-3182	36	8	images	image	NOUN
fcis-3182	36	9	.	.	PUNCT
fcis-3182	37	1	when	when	SCONJ
fcis-3182	37	2	ported	port	VERB
fcis-3182	37	3	to	to	ADP
fcis-3182	37	4	a	a	DET
fcis-3182	37	5	new	new	ADJ
fcis-3182	37	6	field	field	NOUN
fcis-3182	37	7	or	or	CCONJ
fcis-3182	37	8	unexpected	unexpected	ADJ
fcis-3182	37	9	input	input	NOUN
fcis-3182	37	10	emerges	emerge	VERB
fcis-3182	37	11	,	,	PUNCT
fcis-3182	37	12	it	it	PRON
fcis-3182	37	13	is	be	AUX
fcis-3182	37	14	less	less	ADV
fcis-3182	37	15	likely	likely	ADJ
fcis-3182	37	16	to	to	PART
fcis-3182	37	17	break	break	VERB
fcis-3182	37	18	down	down	ADP
fcis-3182	37	19	.	.	PUNCT
fcis-3182	38	1	3	3	X
fcis-3182	38	2	.	.	X
fcis-3182	38	3	experimental	experimental	ADJ
fcis-3182	38	4	work	work	NOUN
fcis-3182	38	5	in	in	ADP
fcis-3182	38	6	this	this	DET
fcis-3182	38	7	experiment	experiment	NOUN
fcis-3182	38	8	,	,	PUNCT
fcis-3182	38	9	the	the	DET
fcis-3182	38	10	writer	writer	NOUN
fcis-3182	38	11	uses	use	VERB
fcis-3182	38	12	the	the	DET
fcis-3182	38	13	yolov5	yolov5	NOUN
fcis-3182	38	14	algorithm	algorithm	PROPN
fcis-3182	38	15	,	,	PUNCT
fcis-3182	38	16	the	the	DET
fcis-3182	38	17	windows	window	NOUN
fcis-3182	38	18	operating	operating	NOUN
fcis-3182	38	19	system	system	NOUN
fcis-3182	38	20	,	,	PUNCT
fcis-3182	38	21	the	the	DET
fcis-3182	38	22	gpu	gpu	NOUN
fcis-3182	38	23	model	model	NOUN
fcis-3182	38	24	mx250	mx250	PROPN
fcis-3182	38	25	,	,	PUNCT
fcis-3182	38	26	the	the	DET
fcis-3182	38	27	pytorch	pytorch	NOUN
fcis-3182	38	28	framework	framework	NOUN
fcis-3182	38	29	,	,	PUNCT
fcis-3182	38	30	and	and	CCONJ
fcis-3182	38	31	the	the	DET
fcis-3182	38	32	python	python	NOUN
fcis-3182	38	33	programming	programming	NOUN
fcis-3182	38	34	language	language	NOUN
fcis-3182	38	35	.	.	PUNCT
fcis-3182	39	1	the	the	DET
fcis-3182	39	2	voc	voc	NOUN
fcis-3182	39	3	dataset	dataset	VERB
fcis-3182	39	4	used	use	VERB
fcis-3182	39	5	in	in	ADP
fcis-3182	39	6	the	the	DET
fcis-3182	39	7	experiment	experiment	NOUN
fcis-3182	39	8	contains	contain	VERB
fcis-3182	39	9	44	44	NUM
fcis-3182	39	10	classes	class	NOUN
fcis-3182	39	11	common	common	ADJ
fcis-3182	39	12	domestic	domestic	ADJ
fcis-3182	39	13	wastes	waste	NOUN
fcis-3182	39	14	and	and	CCONJ
fcis-3182	39	15	more	more	ADJ
fcis-3182	39	16	than	than	ADP
fcis-3182	39	17	15,000	15,000	NUM
fcis-3182	39	18	image	image	NOUN
fcis-3182	39	19	,	,	PUNCT
fcis-3182	39	20	such	such	ADJ
fcis-3182	39	21	as	as	ADP
fcis-3182	39	22	disposable	disposable	ADJ
fcis-3182	39	23	snack	snack	NOUN
fcis-3182	39	24	box	box	NOUN
fcis-3182	39	25	,	,	PUNCT
fcis-3182	39	26	washing	washing	NOUN
fcis-3182	39	27	product	product	NOUN
fcis-3182	39	28	,	,	PUNCT
fcis-3182	39	29	stained	stain	VERB
fcis-3182	39	30	plastic	plastic	NOUN
fcis-3182	39	31	and	and	CCONJ
fcis-3182	39	32	127	127	NUM
fcis-3182	39	33	so	so	ADV
fcis-3182	39	34	on	on	ADV
fcis-3182	39	35	.	.	PUNCT
fcis-3182	40	1	the	the	DET
fcis-3182	40	2	yolov5	yolov5	PROPN
fcis-3182	40	3	source	source	NOUN
fcis-3182	40	4	code	code	NOUN
fcis-3182	40	5	is	be	AUX
fcis-3182	40	6	available	available	ADJ
fcis-3182	40	7	at	at	ADP
fcis-3182	40	8	https://github.com/ultralytics/yolov5	https://github.com/ultralytics/yolov5	NOUN
fcis-3182	40	9	.	.	PUNCT
fcis-3182	41	1	in	in	ADP
fcis-3182	41	2	addition	addition	NOUN
fcis-3182	41	3	,	,	PUNCT
fcis-3182	41	4	the	the	DET
fcis-3182	41	5	source	source	NOUN
fcis-3182	41	6	code	code	NOUN
fcis-3182	41	7	was	be	AUX
fcis-3182	41	8	also	also	ADV
fcis-3182	41	9	modified	modify	VERB
fcis-3182	41	10	as	as	SCONJ
fcis-3182	41	11	follows	follow	VERB
fcis-3182	41	12	:	:	PUNCT
fcis-3182	41	13	(	(	PUNCT
fcis-3182	41	14	1	1	X
fcis-3182	41	15	)	)	PUNCT
fcis-3182	41	16	create	create	VERB
fcis-3182	41	17	a	a	DET
fcis-3182	41	18	split_train_val.py	split_train_val.py	PROPN
fcis-3182	41	19	file	file	NOUN
fcis-3182	41	20	in	in	ADP
fcis-3182	41	21	the	the	DET
fcis-3182	41	22	dataset	dataset	NOUN
fcis-3182	41	23	directory	directory	NOUN
fcis-3182	41	24	vocdata	vocdata	NOUN
fcis-3182	41	25	to	to	PART
fcis-3182	41	26	divide	divide	VERB
fcis-3182	41	27	the	the	DET
fcis-3182	41	28	training	training	NOUN
fcis-3182	41	29	dataset	dataset	NOUN
fcis-3182	41	30	and	and	CCONJ
fcis-3182	41	31	validation	validation	NOUN
fcis-3182	41	32	dataset	dataset	NOUN
fcis-3182	41	33	.	.	PUNCT
fcis-3182	42	1	the	the	DET
fcis-3182	42	2	training	training	NOUN
fcis-3182	42	3	dataset	dataset	NOUN
fcis-3182	42	4	accounts	account	NOUN
fcis-3182	42	5	for	for	ADP
fcis-3182	42	6	90	90	NUM
fcis-3182	42	7	%	%	NOUN
fcis-3182	42	8	.	.	PUNCT
fcis-3182	43	1	(	(	PUNCT
fcis-3182	43	2	2	2	X
fcis-3182	43	3	)	)	PUNCT
fcis-3182	43	4	create	create	VERB
fcis-3182	43	5	a	a	DET
fcis-3182	43	6	xml_to_yolo.py	xml_to_yolo.py	PROPN
fcis-3182	43	7	file	file	NOUN
fcis-3182	43	8	in	in	ADP
fcis-3182	43	9	the	the	DET
fcis-3182	43	10	vocdata	vocdata	PROPN
fcis-3182	43	11	directory	directory	NOUN
fcis-3182	43	12	to	to	PART
fcis-3182	43	13	convert	convert	VERB
fcis-3182	43	14	name	name	NOUN
fcis-3182	43	15	,	,	PUNCT
fcis-3182	43	16	width	width	ADJ
fcis-3182	43	17	,	,	PUNCT
fcis-3182	43	18	height	height	NOUN
fcis-3182	43	19	,	,	PUNCT
fcis-3182	43	20	and	and	CCONJ
fcis-3182	43	21	other	other	ADJ
fcis-3182	43	22	information	information	NOUN
fcis-3182	43	23	into	into	ADP
fcis-3182	43	24	yolo_txt	yolo_txt	NUM
fcis-3182	43	25	format	format	NOUN
fcis-3182	43	26	.	.	PUNCT
fcis-3182	44	1	(	(	PUNCT
fcis-3182	44	2	3	3	X
fcis-3182	44	3	)	)	PUNCT
fcis-3182	44	4	create	create	VERB
fcis-3182	44	5	a	a	DET
fcis-3182	44	6	my.yaml	my.yaml	NOUN
fcis-3182	44	7	file	file	NOUN
fcis-3182	44	8	in	in	ADP
fcis-3182	44	9	the	the	DET
fcis-3182	44	10	data	data	NOUN
fcis-3182	44	11	folder	folder	NOUN
fcis-3182	44	12	of	of	ADP
fcis-3182	44	13	the	the	DET
fcis-3182	44	14	source	source	NOUN
fcis-3182	44	15	code	code	NOUN
fcis-3182	44	16	and	and	CCONJ
fcis-3182	44	17	write	write	VERB
fcis-3182	44	18	the	the	DET
fcis-3182	44	19	training	training	NOUN
fcis-3182	44	20	dataset	dataset	NOUN
fcis-3182	44	21	and	and	CCONJ
fcis-3182	44	22	validation	validation	NOUN
fcis-3182	44	23	dataset	dataset	NOUN
fcis-3182	44	24	paths	path	NOUN
fcis-3182	44	25	and	and	CCONJ
fcis-3182	44	26	the	the	DET
fcis-3182	44	27	types	type	NOUN
fcis-3182	44	28	and	and	CCONJ
fcis-3182	44	29	names	name	NOUN
fcis-3182	44	30	of	of	ADP
fcis-3182	44	31	the	the	DET
fcis-3182	44	32	waste	waste	NOUN
fcis-3182	44	33	.	.	PUNCT
fcis-3182	45	1	(	(	PUNCT
fcis-3182	45	2	4	4	X
fcis-3182	45	3	)	)	PUNCT
fcis-3182	45	4	the	the	DET
fcis-3182	45	5	number	number	NOUN
fcis-3182	45	6	of	of	ADP
fcis-3182	45	7	classes(nc	classes(nc	NOUN
fcis-3182	45	8	)	)	PUNCT
fcis-3182	45	9	is	be	AUX
fcis-3182	45	10	changed	change	VERB
fcis-3182	45	11	to	to	ADP
fcis-3182	45	12	44	44	NUM
fcis-3182	45	13	in	in	ADP
fcis-3182	45	14	the	the	DET
fcis-3182	45	15	configuration	configuration	NOUN
fcis-3182	45	16	file	file	NOUN
fcis-3182	45	17	in	in	ADP
fcis-3182	45	18	the	the	DET
fcis-3182	45	19	model	model	NOUN
fcis-3182	45	20	’s	’s	PART
fcis-3182	45	21	directory	directory	NOUN
fcis-3182	45	22	.	.	PUNCT
fcis-3182	46	1	(	(	PUNCT
fcis-3182	46	2	5	5	NUM
fcis-3182	46	3	)	)	PUNCT
fcis-3182	46	4	in	in	ADP
fcis-3182	46	5	this	this	DET
fcis-3182	46	6	experiment	experiment	NOUN
fcis-3182	46	7	,	,	PUNCT
fcis-3182	46	8	the	the	DET
fcis-3182	46	9	yolov5s	yolov5s	PROPN
fcis-3182	46	10	model	model	NOUN
fcis-3182	46	11	is	be	AUX
fcis-3182	46	12	used	use	VERB
fcis-3182	46	13	,	,	PUNCT
fcis-3182	46	14	so	so	CCONJ
fcis-3182	46	15	the	the	DET
fcis-3182	46	16	model	model	NOUN
fcis-3182	46	17	is	be	AUX
fcis-3182	46	18	supposed	suppose	VERB
fcis-3182	46	19	to	to	PART
fcis-3182	46	20	be	be	AUX
fcis-3182	46	21	downloaded	download	VERB
fcis-3182	46	22	in	in	ADP
fcis-3182	46	23	advance	advance	NOUN
fcis-3182	46	24	and	and	CCONJ
fcis-3182	46	25	added	add	VERB
fcis-3182	46	26	to	to	ADP
fcis-3182	46	27	the	the	DET
fcis-3182	46	28	directory	directory	NOUN
fcis-3182	46	29	.	.	PUNCT
fcis-3182	47	1	4	4	X
fcis-3182	47	2	.	.	NOUN
fcis-3182	47	3	result	result	VERB
fcis-3182	47	4	analysis	analysis	NOUN
fcis-3182	47	5	the	the	DET
fcis-3182	47	6	representation	representation	NOUN
fcis-3182	47	7	of	of	ADP
fcis-3182	47	8	an	an	DET
fcis-3182	47	9	object	object	NOUN
fcis-3182	47	10	detection	detection	NOUN
fcis-3182	47	11	model	model	NOUN
fcis-3182	47	12	is	be	AUX
fcis-3182	47	13	mainly	mainly	ADV
fcis-3182	47	14	reflected	reflect	VERB
fcis-3182	47	15	by	by	ADP
fcis-3182	47	16	the	the	DET
fcis-3182	47	17	precision	precision	NOUN
fcis-3182	47	18	ratio(p	ratio(p	PROPN
fcis-3182	47	19	)	)	PUNCT
fcis-3182	47	20	,	,	PUNCT
fcis-3182	47	21	recall	recall	VERB
fcis-3182	47	22	ratio(r	ratio(r	PROPN
fcis-3182	47	23	)	)	PUNCT
fcis-3182	47	24	,	,	PUNCT
fcis-3182	47	25	and	and	CCONJ
fcis-3182	47	26	mean	mean	VERB
fcis-3182	47	27	average	average	ADJ
fcis-3182	47	28	precision	precision	NOUN
fcis-3182	47	29	(	(	PUNCT
fcis-3182	47	30	map	map	NOUN
fcis-3182	47	31	)	)	PUNCT
fcis-3182	47	32	.	.	PUNCT
fcis-3182	48	1	the	the	DET
fcis-3182	48	2	calculation	calculation	NOUN
fcis-3182	48	3	method	method	NOUN
fcis-3182	48	4	is	be	AUX
fcis-3182	48	5	as	as	SCONJ
fcis-3182	48	6	follows	follow	VERB
fcis-3182	48	7	:	:	PUNCT
fcis-3182	48	8	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	NOUN
fcis-3182	48	9	=	=	SYM
fcis-3182	48	10	𝑇𝑃	𝑇𝑃	PROPN
fcis-3182	48	11	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NUM
fcis-3182	48	12	(	(	PUNCT
fcis-3182	48	13	1	1	NUM
fcis-3182	48	14	)	)	PUNCT
fcis-3182	48	15	𝑟𝑒𝑐𝑎𝑙𝑙	𝑟𝑒𝑐𝑎𝑙𝑙	NOUN
fcis-3182	48	16	=	=	SYM
fcis-3182	48	17	𝑇𝑃	𝑇𝑃	PROPN
fcis-3182	48	18	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
fcis-3182	48	19	(	(	PUNCT
fcis-3182	48	20	2	2	NUM
fcis-3182	48	21	)	)	PUNCT
fcis-3182	48	22	𝑃𝑠𝑚𝑜𝑜𝑡ℎ(𝑟	𝑃𝑠𝑚𝑜𝑜𝑡ℎ(𝑟	NOUN
fcis-3182	48	23	)	)	PUNCT
fcis-3182	48	24	=	=	SYM
fcis-3182	48	25	max	max	PROPN
fcis-3182	48	26	𝑟′≥𝑟	𝑟′≥𝑟	PROPN
fcis-3182	48	27	𝑃(𝑟′	𝑃(𝑟′	PROPN
fcis-3182	48	28	)	)	PUNCT
fcis-3182	48	29	(	(	PUNCT
fcis-3182	48	30	3	3	X
fcis-3182	48	31	)	)	PUNCT
fcis-3182	48	32	𝐴𝑃	𝐴𝑃	PROPN
fcis-3182	48	33	=	=	SYM
fcis-3182	48	34	∫	∫	PROPN
fcis-3182	48	35	𝑝(𝑟)𝑑𝑟	𝑝(𝑟)𝑑𝑟	PROPN
fcis-3182	48	36	1	1	NUM
fcis-3182	48	37	0	0	NUM
fcis-3182	48	38	(	(	PUNCT
fcis-3182	48	39	4	4	NUM
fcis-3182	48	40	)	)	PUNCT
fcis-3182	48	41	among	among	ADP
fcis-3182	48	42	them	they	PRON
fcis-3182	48	43	,	,	PUNCT
fcis-3182	48	44	tp	tp	X
fcis-3182	48	45	(	(	PUNCT
fcis-3182	48	46	true	true	ADJ
fcis-3182	48	47	positive	positive	ADJ
fcis-3182	48	48	)	)	PUNCT
fcis-3182	48	49	represents	represent	VERB
fcis-3182	48	50	a	a	DET
fcis-3182	48	51	sample	sample	NOUN
fcis-3182	48	52	that	that	PRON
fcis-3182	48	53	is	be	AUX
fcis-3182	48	54	accurately	accurately	ADV
fcis-3182	48	55	predicted	predict	VERB
fcis-3182	48	56	as	as	ADP
fcis-3182	48	57	a	a	DET
fcis-3182	48	58	positive	positive	ADJ
fcis-3182	48	59	sample	sample	NOUN
fcis-3182	48	60	,	,	PUNCT
fcis-3182	48	61	fp	fp	INTJ
fcis-3182	48	62	(	(	PUNCT
fcis-3182	48	63	false	false	ADJ
fcis-3182	48	64	positive	positive	ADJ
fcis-3182	48	65	)	)	PUNCT
fcis-3182	48	66	represents	represent	VERB
fcis-3182	48	67	a	a	DET
fcis-3182	48	68	sample	sample	NOUN
fcis-3182	48	69	that	that	PRON
fcis-3182	48	70	is	be	AUX
fcis-3182	48	71	not	not	PART
fcis-3182	48	72	accurately	accurately	ADV
fcis-3182	48	73	predicted	predict	VERB
fcis-3182	48	74	to	to	PART
fcis-3182	48	75	be	be	AUX
fcis-3182	48	76	positive	positive	ADJ
fcis-3182	48	77	,	,	PUNCT
fcis-3182	48	78	and	and	CCONJ
fcis-3182	48	79	fn	fn	INTJ
fcis-3182	48	80	(	(	PUNCT
fcis-3182	48	81	false	false	ADJ
fcis-3182	48	82	negative	negative	NOUN
fcis-3182	48	83	)	)	PUNCT
fcis-3182	48	84	represents	represent	VERB
fcis-3182	48	85	a	a	DET
fcis-3182	48	86	sample	sample	NOUN
fcis-3182	48	87	that	that	PRON
fcis-3182	48	88	is	be	AUX
fcis-3182	48	89	not	not	PART
fcis-3182	48	90	accurately	accurately	ADV
fcis-3182	48	91	predicted	predict	VERB
fcis-3182	48	92	to	to	PART
fcis-3182	48	93	be	be	AUX
fcis-3182	48	94	negative	negative	ADJ
fcis-3182	48	95	.	.	PUNCT
fcis-3182	49	1	precision	precision	NOUN
fcis-3182	49	2	shows	show	VERB
fcis-3182	49	3	the	the	DET
fcis-3182	49	4	ratio	ratio	NOUN
fcis-3182	49	5	of	of	ADP
fcis-3182	49	6	accurately	accurately	ADV
fcis-3182	49	7	predicted	predict	VERB
fcis-3182	49	8	positive	positive	ADJ
fcis-3182	49	9	samples	sample	NOUN
fcis-3182	49	10	to	to	PART
fcis-3182	49	11	all	all	PRON
fcis-3182	49	12	detected	detect	VERB
fcis-3182	49	13	positive	positive	ADJ
fcis-3182	49	14	samples	sample	NOUN
fcis-3182	49	15	.	.	PUNCT
fcis-3182	50	1	recall	recall	PROPN
fcis-3182	50	2	shows	show	VERB
fcis-3182	50	3	the	the	DET
fcis-3182	50	4	ratio	ratio	NOUN
fcis-3182	50	5	of	of	ADP
fcis-3182	50	6	accurately	accurately	ADV
fcis-3182	50	7	predicted	predict	VERB
fcis-3182	50	8	positive	positive	ADJ
fcis-3182	50	9	samples	sample	NOUN
fcis-3182	50	10	to	to	ADP
fcis-3182	50	11	all	all	DET
fcis-3182	50	12	positive	positive	ADJ
fcis-3182	50	13	samples	sample	NOUN
fcis-3182	50	14	.	.	PUNCT
fcis-3182	51	1	taking	take	VERB
fcis-3182	51	2	the	the	DET
fcis-3182	51	3	recall	recall	NOUN
fcis-3182	51	4	value	value	NOUN
fcis-3182	51	5	as	as	ADP
fcis-3182	51	6	the	the	DET
fcis-3182	51	7	horizontal	horizontal	ADJ
fcis-3182	51	8	axis	axis	NOUN
fcis-3182	51	9	and	and	CCONJ
fcis-3182	51	10	the	the	DET
fcis-3182	51	11	precision	precision	NOUN
fcis-3182	51	12	value	value	NOUN
fcis-3182	51	13	as	as	ADP
fcis-3182	51	14	the	the	DET
fcis-3182	51	15	vertical	vertical	ADJ
fcis-3182	51	16	axis	axis	NOUN
fcis-3182	51	17	,	,	PUNCT
fcis-3182	51	18	we	we	PRON
fcis-3182	51	19	can	can	AUX
fcis-3182	51	20	get	get	VERB
fcis-3182	51	21	the	the	DET
fcis-3182	51	22	precision	precision	NOUN
fcis-3182	51	23	-	-	PUNCT
fcis-3182	51	24	recall	recall	NOUN
fcis-3182	51	25	curve	curve	NOUN
fcis-3182	51	26	(	(	PUNCT
fcis-3182	51	27	p	p	NOUN
fcis-3182	51	28	-	-	PUNCT
fcis-3182	51	29	r	r	NOUN
fcis-3182	51	30	curve	curve	NOUN
fcis-3182	51	31	)	)	PUNCT
fcis-3182	51	32	.	.	PUNCT
fcis-3182	52	1	we	we	PRON
fcis-3182	52	2	will	will	AUX
fcis-3182	52	3	find	find	VERB
fcis-3182	52	4	that	that	SCONJ
fcis-3182	52	5	the	the	DET
fcis-3182	52	6	values	value	NOUN
fcis-3182	52	7	of	of	ADP
fcis-3182	52	8	precision	precision	NOUN
fcis-3182	52	9	and	and	CCONJ
fcis-3182	52	10	recall	recall	NOUN
fcis-3182	52	11	are	be	AUX
fcis-3182	52	12	negatively	negatively	ADV
fcis-3182	52	13	correlated	correlate	VERB
fcis-3182	52	14	,	,	PUNCT
fcis-3182	52	15	and	and	CCONJ
fcis-3182	52	16	will	will	AUX
fcis-3182	52	17	fluctuate	fluctuate	VERB
fcis-3182	52	18	up	up	ADP
fcis-3182	52	19	and	and	CCONJ
fcis-3182	52	20	down	down	ADV
fcis-3182	52	21	in	in	ADP
fcis-3182	52	22	the	the	DET
fcis-3182	52	23	local	local	ADJ
fcis-3182	52	24	area	area	NOUN
fcis-3182	52	25	.	.	PUNCT
fcis-3182	53	1	in	in	ADP
fcis-3182	53	2	practical	practical	ADJ
fcis-3182	53	3	applications	application	NOUN
fcis-3182	53	4	,	,	PUNCT
fcis-3182	53	5	since	since	SCONJ
fcis-3182	53	6	this	this	DET
fcis-3182	53	7	pr	pr	NOUN
fcis-3182	53	8	curve	curve	NOUN
fcis-3182	53	9	is	be	AUX
fcis-3182	53	10	not	not	PART
fcis-3182	53	11	convenient	convenient	ADJ
fcis-3182	53	12	to	to	PART
fcis-3182	53	13	calculate	calculate	VERB
fcis-3182	53	14	directly	directly	ADV
fcis-3182	53	15	,	,	PUNCT
fcis-3182	53	16	we	we	PRON
fcis-3182	53	17	need	need	VERB
fcis-3182	53	18	to	to	PART
fcis-3182	53	19	smooth	smooth	VERB
fcis-3182	53	20	it	it	PRON
fcis-3182	53	21	,	,	PUNCT
fcis-3182	53	22	as	as	SCONJ
fcis-3182	53	23	shown	show	VERB
fcis-3182	53	24	in	in	ADP
fcis-3182	53	25	the	the	DET
fcis-3182	53	26	above	above	ADJ
fcis-3182	53	27	formula	formula	NOUN
fcis-3182	53	28	.	.	PUNCT
fcis-3182	54	1	then	then	ADV
fcis-3182	54	2	the	the	DET
fcis-3182	54	3	performance	performance	NOUN
fcis-3182	54	4	of	of	ADP
fcis-3182	54	5	the	the	DET
fcis-3182	54	6	model	model	NOUN
fcis-3182	54	7	can	can	AUX
fcis-3182	54	8	be	be	AUX
fcis-3182	54	9	evaluated	evaluate	VERB
fcis-3182	54	10	by	by	ADP
fcis-3182	54	11	the	the	DET
fcis-3182	54	12	p	p	PROPN
fcis-3182	54	13	-	-	PUNCT
fcis-3182	54	14	r	r	NOUN
fcis-3182	54	15	curve	curve	NOUN
fcis-3182	54	16	.	.	PUNCT
fcis-3182	55	1	the	the	DET
fcis-3182	55	2	area	area	NOUN
fcis-3182	55	3	between	between	ADP
fcis-3182	55	4	it	it	PRON
fcis-3182	55	5	and	and	CCONJ
fcis-3182	55	6	the	the	DET
fcis-3182	55	7	coordinate	coordinate	NOUN
fcis-3182	55	8	axis	axis	NOUN
fcis-3182	55	9	represents	represent	VERB
fcis-3182	55	10	the	the	DET
fcis-3182	55	11	average	average	ADJ
fcis-3182	55	12	precision	precision	NOUN
fcis-3182	55	13	(	(	PUNCT
fcis-3182	55	14	ap	ap	PROPN
fcis-3182	55	15	)	)	PUNCT
fcis-3182	55	16	,	,	PUNCT
fcis-3182	55	17	and	and	CCONJ
fcis-3182	55	18	map	map	NOUN
fcis-3182	55	19	is	be	AUX
fcis-3182	55	20	the	the	DET
fcis-3182	55	21	mean	mean	ADJ
fcis-3182	55	22	value	value	NOUN
fcis-3182	55	23	of	of	ADP
fcis-3182	55	24	ap	ap	PROPN
fcis-3182	55	25	.	.	PUNCT
fcis-3182	56	1	as	as	SCONJ
fcis-3182	56	2	the	the	DET
fcis-3182	56	3	figure2(a	figure2(a	NOUN
fcis-3182	56	4	)	)	PUNCT
fcis-3182	56	5	shows	show	NOUN
fcis-3182	56	6	,	,	PUNCT
fcis-3182	56	7	this	this	DET
fcis-3182	56	8	experiment	experiment	NOUN
fcis-3182	56	9	has	have	VERB
fcis-3182	56	10	a	a	DET
fcis-3182	56	11	total	total	NOUN
fcis-3182	56	12	of	of	ADP
fcis-3182	56	13	10	10	NUM
fcis-3182	56	14	rounds	round	NOUN
fcis-3182	56	15	of	of	ADP
fcis-3182	56	16	training	training	NOUN
fcis-3182	56	17	,	,	PUNCT
fcis-3182	56	18	the	the	DET
fcis-3182	56	19	precision	precision	NOUN
fcis-3182	56	20	reaches	reach	VERB
fcis-3182	56	21	65	65	NUM
fcis-3182	56	22	%	%	NOUN
fcis-3182	56	23	,	,	PUNCT
fcis-3182	56	24	and	and	CCONJ
fcis-3182	56	25	the	the	DET
fcis-3182	56	26	recall	recall	NOUN
fcis-3182	56	27	is	be	AUX
fcis-3182	56	28	about	about	ADV
fcis-3182	56	29	50	50	NUM
fcis-3182	56	30	%	%	NOUN
fcis-3182	56	31	.	.	PUNCT
fcis-3182	57	1	the	the	DET
fcis-3182	57	2	map	map	NOUN
fcis-3182	57	3	also	also	ADV
fcis-3182	57	4	increases	increase	VERB
fcis-3182	57	5	with	with	ADP
fcis-3182	57	6	the	the	DET
fcis-3182	57	7	add	add	NOUN
fcis-3182	57	8	in	in	ADP
fcis-3182	57	9	experimental	experimental	ADJ
fcis-3182	57	10	rounds	round	NOUN
fcis-3182	57	11	.	.	PUNCT
fcis-3182	58	1	from	from	ADP
fcis-3182	58	2	figure2(b	figure2(b	ADJ
fcis-3182	58	3	)	)	PUNCT
fcis-3182	58	4	above	above	ADV
fcis-3182	58	5	,	,	PUNCT
fcis-3182	58	6	we	we	PRON
fcis-3182	58	7	can	can	AUX
fcis-3182	58	8	see	see	VERB
fcis-3182	58	9	that	that	SCONJ
fcis-3182	58	10	the	the	DET
fcis-3182	58	11	model	model	NOUN
fcis-3182	58	12	performs	perform	VERB
fcis-3182	58	13	well	well	ADV
fcis-3182	58	14	on	on	ADP
fcis-3182	58	15	some	some	PRON
fcis-3182	58	16	such	such	ADJ
fcis-3182	58	17	as	as	ADP
fcis-3182	58	18	stuffed	stuffed	ADJ
fcis-3182	58	19	toy	toy	NOUN
fcis-3182	58	20	,	,	PUNCT
fcis-3182	58	21	cooking	cook	VERB
fcis-3182	58	22	oil	oil	NOUN
fcis-3182	58	23	bucket	bucket	NOUN
fcis-3182	58	24	and	and	CCONJ
fcis-3182	58	25	washing	washing	NOUN
fcis-3182	58	26	product	product	NOUN
fcis-3182	58	27	.	.	PUNCT
fcis-3182	59	1	however	however	ADV
fcis-3182	59	2	,	,	PUNCT
fcis-3182	59	3	it	it	PRON
fcis-3182	59	4	does	do	AUX
fcis-3182	59	5	not	not	PART
fcis-3182	59	6	perform	perform	VERB
fcis-3182	59	7	well	well	ADV
fcis-3182	59	8	on	on	ADP
fcis-3182	59	9	others	other	NOUN
fcis-3182	59	10	,	,	PUNCT
fcis-3182	59	11	for	for	ADP
fcis-3182	59	12	instance	instance	NOUN
fcis-3182	59	13	,	,	PUNCT
fcis-3182	59	14	the	the	DET
fcis-3182	59	15	detections	detection	NOUN
fcis-3182	59	16	of	of	ADP
fcis-3182	59	17	book	book	NOUN
fcis-3182	59	18	paper	paper	NOUN
fcis-3182	59	19	,	,	PUNCT
fcis-3182	59	20	metal	metal	NOUN
fcis-3182	59	21	kitchen	kitchen	NOUN
fcis-3182	59	22	ware	ware	NOUN
fcis-3182	59	23	and	and	CCONJ
fcis-3182	59	24	stained	stain	VERB
fcis-3182	59	25	paper	paper	NOUN
fcis-3182	59	26	are	be	AUX
fcis-3182	59	27	not	not	PART
fcis-3182	59	28	very	very	ADV
fcis-3182	59	29	good	good	ADJ
fcis-3182	59	30	.	.	PUNCT
fcis-3182	60	1	limited	limit	VERB
fcis-3182	60	2	by	by	ADP
fcis-3182	60	3	hardware	hardware	NOUN
fcis-3182	60	4	conditions	condition	NOUN
fcis-3182	60	5	,	,	PUNCT
fcis-3182	60	6	the	the	DET
fcis-3182	60	7	results	result	NOUN
fcis-3182	60	8	of	of	ADP
fcis-3182	60	9	this	this	DET
fcis-3182	60	10	experiment	experiment	NOUN
fcis-3182	60	11	still	still	ADV
fcis-3182	60	12	have	have	VERB
fcis-3182	60	13	room	room	NOUN
fcis-3182	60	14	for	for	ADP
fcis-3182	60	15	improvement	improvement	NOUN
fcis-3182	60	16	.	.	PUNCT
fcis-3182	61	1	from	from	ADP
fcis-3182	61	2	the	the	DET
fcis-3182	61	3	result	result	NOUN
fcis-3182	61	4	on	on	ADP
fcis-3182	61	5	test	test	NOUN
fcis-3182	61	6	dataset	dataset	VERB
fcis-3182	61	7	,	,	PUNCT
fcis-3182	61	8	it	it	PRON
fcis-3182	61	9	can	can	AUX
fcis-3182	61	10	be	be	AUX
fcis-3182	61	11	seen	see	VERB
fcis-3182	61	12	that	that	SCONJ
fcis-3182	61	13	there	there	PRON
fcis-3182	61	14	are	be	VERB
fcis-3182	61	15	still	still	ADV
fcis-3182	61	16	some	some	DET
fcis-3182	61	17	items	item	NOUN
fcis-3182	61	18	that	that	PRON
fcis-3182	61	19	have	have	AUX
fcis-3182	61	20	not	not	PART
fcis-3182	61	21	been	be	AUX
fcis-3182	61	22	detected	detect	VERB
fcis-3182	61	23	,	,	PUNCT
fcis-3182	61	24	which	which	PRON
fcis-3182	61	25	may	may	AUX
fcis-3182	61	26	be	be	AUX
fcis-3182	61	27	due	due	ADJ
fcis-3182	61	28	to	to	ADP
fcis-3182	61	29	the	the	DET
fcis-3182	61	30	shortcomings	shortcoming	NOUN
fcis-3182	61	31	of	of	ADP
fcis-3182	61	32	yolo	yolo	PROPN
fcis-3182	61	33	.	.	PUNCT
fcis-3182	62	1	for	for	ADP
fcis-3182	62	2	example	example	NOUN
fcis-3182	62	3	,	,	PUNCT
fcis-3182	62	4	it	it	PRON
fcis-3182	62	5	is	be	AUX
fcis-3182	62	6	really	really	ADV
fcis-3182	62	7	difficult	difficult	ADJ
fcis-3182	62	8	to	to	PART
fcis-3182	62	9	detect	detect	VERB
fcis-3182	62	10	objects	object	NOUN
fcis-3182	62	11	which	which	PRON
fcis-3182	62	12	are	be	AUX
fcis-3182	62	13	next	next	ADJ
fcis-3182	62	14	to	to	ADP
fcis-3182	62	15	each	each	DET
fcis-3182	62	16	other	other	ADJ
fcis-3182	62	17	because	because	SCONJ
fcis-3182	62	18	each	each	DET
fcis-3182	62	19	grid	grid	NOUN
fcis-3182	62	20	can	can	AUX
fcis-3182	62	21	propose	propose	VERB
fcis-3182	62	22	just	just	ADV
fcis-3182	62	23	2	2	NUM
fcis-3182	62	24	bounding	bounding	NOUN
fcis-3182	62	25	boxes	box	NOUN
fcis-3182	62	26	and	and	CCONJ
fcis-3182	62	27	it	it	PRON
fcis-3182	62	28	is	be	AUX
fcis-3182	62	29	also	also	ADV
fcis-3182	62	30	prone	prone	ADJ
fcis-3182	62	31	to	to	ADP
fcis-3182	62	32	positioning	position	VERB
fcis-3182	62	33	errors	error	NOUN
fcis-3182	62	34	of	of	ADP
fcis-3182	62	35	objects	object	NOUN
fcis-3182	62	36	.	.	PUNCT
fcis-3182	63	1	(	(	PUNCT
fcis-3182	63	2	a	a	X
fcis-3182	63	3	)	)	PUNCT
fcis-3182	63	4	(	(	PUNCT
fcis-3182	63	5	b	b	NOUN
fcis-3182	63	6	)	)	PUNCT
fcis-3182	63	7	figure2	figure2	ADJ
fcis-3182	63	8	.	.	PUNCT
fcis-3182	63	9	visualization	visualization	NOUN
fcis-3182	63	10	of	of	ADP
fcis-3182	63	11	experimental	experimental	ADJ
fcis-3182	63	12	results	result	NOUN
fcis-3182	63	13	,	,	PUNCT
fcis-3182	63	14	(	(	PUNCT
fcis-3182	63	15	a	a	X
fcis-3182	63	16	)	)	PUNCT
fcis-3182	63	17	precision	precision	NOUN
fcis-3182	63	18	,	,	PUNCT
fcis-3182	63	19	recall	recall	NOUN
fcis-3182	63	20	and	and	CCONJ
fcis-3182	63	21	map	map	NOUN
fcis-3182	63	22	,	,	PUNCT
fcis-3182	63	23	(	(	PUNCT
fcis-3182	63	24	b	b	NOUN
fcis-3182	63	25	)	)	PUNCT
fcis-3182	63	26	confusion	confusion	NOUN
fcis-3182	63	27	matrix	matrix	NOUN
fcis-3182	63	28	https://github.com/ultralytics/yolov5	https://github.com/ultralytics/yolov5	NOUN
fcis-3182	63	29	128	128	NUM
fcis-3182	63	30	5	5	NUM
fcis-3182	63	31	.	.	PUNCT
fcis-3182	64	1	conclusion	conclusion	NOUN
fcis-3182	64	2	this	this	DET
fcis-3182	64	3	paper	paper	NOUN
fcis-3182	64	4	proposes	propose	VERB
fcis-3182	64	5	a	a	DET
fcis-3182	64	6	domestic	domestic	ADJ
fcis-3182	64	7	waste	waste	NOUN
fcis-3182	64	8	detection	detection	NOUN
fcis-3182	64	9	model	model	NOUN
fcis-3182	64	10	base	base	NOUN
fcis-3182	64	11	on	on	ADP
fcis-3182	64	12	yolo	yolo	PROPN
fcis-3182	64	13	.	.	PUNCT
fcis-3182	65	1	it	it	PRON
fcis-3182	65	2	has	have	VERB
fcis-3182	65	3	a	a	DET
fcis-3182	65	4	simple	simple	ADJ
fcis-3182	65	5	structure	structure	NOUN
fcis-3182	65	6	and	and	CCONJ
fcis-3182	65	7	the	the	DET
fcis-3182	65	8	accuracy	accuracy	NOUN
fcis-3182	65	9	of	of	ADP
fcis-3182	65	10	the	the	DET
fcis-3182	65	11	detected	detect	VERB
fcis-3182	65	12	items	item	NOUN
fcis-3182	65	13	is	be	AUX
fcis-3182	65	14	really	really	ADV
fcis-3182	65	15	high	high	ADJ
fcis-3182	65	16	,	,	PUNCT
fcis-3182	65	17	which	which	PRON
fcis-3182	65	18	can	can	AUX
fcis-3182	65	19	meet	meet	VERB
fcis-3182	65	20	daily	daily	ADJ
fcis-3182	65	21	use	use	NOUN
fcis-3182	65	22	.	.	PUNCT
fcis-3182	66	1	in	in	ADP
fcis-3182	66	2	the	the	DET
fcis-3182	66	3	follow	follow	VERB
fcis-3182	66	4	-	-	PUNCT
fcis-3182	66	5	up	up	ADP
fcis-3182	66	6	research	research	NOUN
fcis-3182	66	7	,	,	PUNCT
fcis-3182	66	8	the	the	DET
fcis-3182	66	9	focus	focus	NOUN
fcis-3182	66	10	will	will	AUX
fcis-3182	66	11	be	be	AUX
fcis-3182	66	12	on	on	ADP
fcis-3182	66	13	improving	improve	VERB
fcis-3182	66	14	the	the	DET
fcis-3182	66	15	recall	recall	NOUN
fcis-3182	66	16	rate	rate	NOUN
fcis-3182	66	17	to	to	PART
fcis-3182	66	18	enhance	enhance	VERB
fcis-3182	66	19	the	the	DET
fcis-3182	66	20	practical	practical	ADJ
fcis-3182	66	21	value	value	NOUN
fcis-3182	66	22	.	.	PUNCT
fcis-3182	67	1	references	reference	NOUN
fcis-3182	67	2	[	[	X
fcis-3182	67	3	1	1	NUM
fcis-3182	67	4	]	]	X
fcis-3182	67	5	redmon	redmon	PROPN
fcis-3182	67	6	j	j	PROPN
fcis-3182	67	7	,	,	PUNCT
fcis-3182	67	8	divvala	divvala	PROPN
fcis-3182	67	9	s	s	PROPN
fcis-3182	67	10	,	,	PUNCT
fcis-3182	67	11	girshick	girshick	ADJ
fcis-3182	67	12	r	r	NOUN
fcis-3182	67	13	,	,	PUNCT
fcis-3182	67	14	et	et	PROPN
fcis-3182	67	15	al	al	PROPN
fcis-3182	67	16	.	.	PUNCT
fcis-3182	68	1	(	(	PUNCT
fcis-3182	68	2	2016	2016	NUM
fcis-3182	68	3	)	)	PUNCT
fcis-3182	68	4	you	you	PRON
fcis-3182	68	5	only	only	ADV
fcis-3182	68	6	look	look	VERB
fcis-3182	68	7	once	once	ADV
fcis-3182	68	8	:	:	PUNCT
fcis-3182	68	9	unified	unified	ADJ
fcis-3182	68	10	,	,	PUNCT
fcis-3182	68	11	real	real	ADJ
fcis-3182	68	12	-	-	PUNCT
fcis-3182	68	13	time	time	NOUN
fcis-3182	68	14	object	object	NOUN
fcis-3182	68	15	detection	detection	NOUN
fcis-3182	68	16	.	.	PUNCT
fcis-3182	69	1	in	in	ADP
fcis-3182	69	2	:	:	PUNCT
fcis-3182	69	3	ieee	ieee	NOUN
fcis-3182	69	4	conference	conference	NOUN
fcis-3182	69	5	on	on	ADP
fcis-3182	69	6	computer	computer	NOUN
fcis-3182	69	7	vision	vision	NOUN
fcis-3182	69	8	and	and	CCONJ
fcis-3182	69	9	pattern	pattern	NOUN
fcis-3182	69	10	recognition	recognition	NOUN
fcis-3182	69	11	.	.	PUNCT
fcis-3182	70	1	las	las	PROPN
fcis-3182	70	2	vegas	vegas	PROPN
fcis-3182	70	3	.	.	PUNCT
fcis-3182	71	1	pp	pp	ADJ
fcis-3182	71	2	.	.	PUNCT
fcis-3182	72	1	779	779	NUM
fcis-3182	72	2	-	-	SYM
fcis-3182	72	3	788	788	NUM
fcis-3182	72	4	.	.	PUNCT
fcis-3182	73	1	[	[	X
fcis-3182	73	2	2	2	X
fcis-3182	73	3	]	]	X
fcis-3182	73	4	redmon	redmon	PROPN
fcis-3182	73	5	j	j	PROPN
fcis-3182	73	6	,	,	PUNCT
fcis-3182	73	7	farhadi	farhadi	PROPN
fcis-3182	73	8	a.	a.	PROPN
fcis-3182	73	9	(	(	PUNCT
fcis-3182	73	10	2017	2017	NUM
fcis-3182	73	11	)	)	PUNCT
fcis-3182	73	12	yolo9000	yolo9000	PROPN
fcis-3182	73	13	:	:	PUNCT
fcis-3182	73	14	better	well	ADJ
fcis-3182	73	15	,	,	PUNCT
fcis-3182	73	16	faster	fast	ADJ
fcis-3182	73	17	,	,	PUNCT
fcis-3182	73	18	stronger	strong	ADJ
fcis-3182	73	19	.	.	PUNCT
fcis-3182	74	1	in	in	ADP
fcis-3182	74	2	:	:	PUNCT
fcis-3182	74	3	ieee	ieee	NOUN
fcis-3182	74	4	conference	conference	NOUN
fcis-3182	74	5	on	on	ADP
fcis-3182	74	6	computer	computer	NOUN
fcis-3182	74	7	vision	vision	NOUN
fcis-3182	74	8	and	and	CCONJ
fcis-3182	74	9	pattern	pattern	NOUN
fcis-3182	74	10	recognition	recognition	NOUN
fcis-3182	74	11	.	.	PUNCT
fcis-3182	75	1	honolulu	honolulu	PROPN
fcis-3182	75	2	.	.	PUNCT
fcis-3182	76	1	pp	pp	X
fcis-3182	76	2	.	.	PUNCT
fcis-3182	77	1	7263	7263	NUM
fcis-3182	77	2	-	-	SYM
fcis-3182	77	3	7271	7271	NUM
fcis-3182	77	4	.	.	PUNCT
fcis-3182	78	1	[	[	X
fcis-3182	78	2	3	3	X
fcis-3182	78	3	]	]	X
fcis-3182	78	4	redmon	redmon	PROPN
fcis-3182	78	5	j	j	PROPN
fcis-3182	78	6	,	,	PUNCT
fcis-3182	78	7	farhadi	farhadi	PROPN
fcis-3182	78	8	a.	a.	PROPN
fcis-3182	78	9	(	(	PUNCT
fcis-3182	78	10	2018	2018	NUM
fcis-3182	78	11	)	)	PUNCT
fcis-3182	79	1	yolov3	yolov3	PROPN
fcis-3182	79	2	:	:	PUNCT
fcis-3182	79	3	an	an	DET
fcis-3182	79	4	incremental	incremental	ADJ
fcis-3182	79	5	improvement	improvement	NOUN
fcis-3182	79	6	.	.	PUNCT
fcis-3182	80	1	arxiv	arxiv	PROPN
fcis-3182	80	2	preprint	preprint	VERB
fcis-3182	80	3	arxiv:1804.02767	arxiv:1804.02767	PROPN
fcis-3182	80	4	.	.	PUNCT
fcis-3182	81	1	[	[	X
fcis-3182	81	2	4	4	NUM
fcis-3182	81	3	]	]	PUNCT
fcis-3182	81	4	bochkovskiy	bochkovskiy	X
fcis-3182	81	5	a	a	PRON
fcis-3182	81	6	,	,	PUNCT
fcis-3182	81	7	wang	wang	PROPN
fcis-3182	81	8	c	c	PROPN
fcis-3182	81	9	y	y	PROPN
fcis-3182	81	10	,	,	PUNCT
fcis-3182	81	11	liao	liao	PROPN
fcis-3182	81	12	h	h	PROPN
fcis-3182	81	13	y	y	PROPN
fcis-3182	81	14	m.	m.	NOUN
fcis-3182	81	15	(	(	PUNCT
fcis-3182	81	16	2020	2020	NUM
fcis-3182	81	17	)	)	PUNCT
fcis-3182	81	18	yolov4	yolov4	NOUN
fcis-3182	82	1	:	:	PUNCT
fcis-3182	82	2	optimal	optimal	ADJ
fcis-3182	82	3	speed	speed	NOUN
fcis-3182	82	4	and	and	CCONJ
fcis-3182	82	5	accuracy	accuracy	NOUN
fcis-3182	82	6	of	of	ADP
fcis-3182	82	7	object	object	NOUN
fcis-3182	82	8	detection	detection	NOUN
fcis-3182	82	9	.	.	PUNCT
fcis-3182	83	1	arxiv	arxiv	PROPN
fcis-3182	83	2	preprint	preprint	NOUN
fcis-3182	83	3	arxiv:2004.10934	arxiv:2004.10934	NOUN
fcis-3182	83	4	,	,	PUNCT
fcis-3182	83	5	2020	2020	NUM
fcis-3182	83	6	.	.	PUNCT
fcis-3182	84	1	[	[	X
fcis-3182	84	2	5	5	X
fcis-3182	84	3	]	]	X
fcis-3182	84	4	girshick	girshick	ADJ
fcis-3182	84	5	r	r	PROPN
fcis-3182	84	6	,	,	PUNCT
fcis-3182	84	7	donahue	donahue	PROPN
fcis-3182	84	8	j	j	PROPN
fcis-3182	84	9	,	,	PUNCT
fcis-3182	84	10	darrell	darrell	PROPN
fcis-3182	84	11	t	t	PROPN
fcis-3182	84	12	,	,	PUNCT
fcis-3182	84	13	et	et	PROPN
fcis-3182	84	14	al	al	PROPN
fcis-3182	84	15	.	.	PUNCT
fcis-3182	84	16	(	(	PUNCT
fcis-3182	84	17	2014	2014	NUM
fcis-3182	84	18	)	)	PUNCT
fcis-3182	84	19	rich	rich	ADJ
fcis-3182	84	20	feature	feature	NOUN
fcis-3182	84	21	hierarchies	hierarchy	NOUN
fcis-3182	84	22	for	for	ADP
fcis-3182	84	23	accurate	accurate	ADJ
fcis-3182	84	24	object	object	NOUN
fcis-3182	84	25	detection	detection	NOUN
fcis-3182	84	26	and	and	CCONJ
fcis-3182	84	27	semantic	semantic	ADJ
fcis-3182	84	28	segmentation	segmentation	NOUN
fcis-3182	84	29	.	.	PUNCT
fcis-3182	85	1	in	in	ADP
fcis-3182	85	2	:	:	PUNCT
fcis-3182	85	3	ieee	ieee	NOUN
fcis-3182	85	4	conference	conference	NOUN
fcis-3182	85	5	on	on	ADP
fcis-3182	85	6	computer	computer	NOUN
fcis-3182	85	7	vision	vision	NOUN
fcis-3182	85	8	and	and	CCONJ
fcis-3182	85	9	pattern	pattern	NOUN
fcis-3182	85	10	recognition	recognition	NOUN
fcis-3182	85	11	.	.	PUNCT
fcis-3182	86	1	columbus	columbus	PROPN
fcis-3182	86	2	.	.	PUNCT
fcis-3182	87	1	pp	pp	ADJ
fcis-3182	87	2	.	.	PUNCT
fcis-3182	88	1	580	580	NUM
fcis-3182	88	2	-	-	SYM
fcis-3182	88	3	587	587	NUM
fcis-3182	88	4	.	.	PUNCT
fcis-3182	89	1	[	[	X
fcis-3182	89	2	6	6	NUM
fcis-3182	89	3	]	]	PUNCT
fcis-3182	89	4	y.	y.	NOUN
fcis-3182	89	5	he	he	PRON
fcis-3182	89	6	,	,	PUNCT
fcis-3182	89	7	j.w	j.w	PROPN
fcis-3182	89	8	.	.	PROPN
fcis-3182	89	9	tian	tian	PROPN
fcis-3182	89	10	,	,	PUNCT
fcis-3182	89	11	z.	z.	PROPN
fcis-3182	89	12	zhang	zhang	PROPN
fcis-3182	89	13	,	,	PUNCT
fcis-3182	89	14	q.	q.	PROPN
fcis-3182	89	15	wang	wang	PROPN
fcis-3182	89	16	and	and	CCONJ
fcis-3182	89	17	p.	p.	NOUN
fcis-3182	89	18	zhao	zhao	PROPN
fcis-3182	89	19	(	(	PUNCT
fcis-3182	89	20	2022	2022	NUM
fcis-3182	89	21	)	)	PUNCT
fcis-3182	89	22	lightweight	lightweight	ADJ
fcis-3182	89	23	research	research	NOUN
fcis-3182	89	24	of	of	ADP
fcis-3182	89	25	yolov5	yolov5	NOUN
fcis-3182	89	26	target	target	NOUN
fcis-3182	89	27	detection	detection	NOUN
fcis-3182	89	28	.	.	PUNCT
fcis-3182	90	1	computer	computer	NOUN
fcis-3182	90	2	engineering	engineering	NOUN
fcis-3182	90	3	and	and	CCONJ
fcis-3182	90	4	applications	application	NOUN
fcis-3182	90	5	.	.	PUNCT
fcis-3182	91	1	,	,	PUNCT
fcis-3182	91	2	1	1	NUM
fcis-3182	91	3	-	-	SYM
fcis-3182	91	4	9	9	NUM
fcis-3182	91	5	(	(	PUNCT
fcis-3182	91	6	in	in	ADP
fcis-3182	91	7	chinese	chinese	PROPN
fcis-3182	91	8	)	)	PUNCT
fcis-3182	91	9	.	.	PUNCT
fcis-3182	92	1	[	[	X
fcis-3182	92	2	7	7	NUM
fcis-3182	92	3	]	]	X
fcis-3182	92	4	pawangfg	pawangfg	ADJ
fcis-3182	92	5	.	.	PUNCT
fcis-3182	93	1	(	(	PUNCT
fcis-3182	93	2	2022	2022	NUM
fcis-3182	93	3	)	)	PUNCT
fcis-3182	93	4	yolo	yolo	NOUN
fcis-3182	93	5	:	:	PUNCT
fcis-3182	93	6	you	you	PRON
fcis-3182	93	7	only	only	ADV
fcis-3182	93	8	look	look	VERB
fcis-3182	93	9	once	once	ADV
fcis-3182	93	10	–	–	PUNCT
fcis-3182	93	11	real	real	ADJ
fcis-3182	93	12	time	time	NOUN
fcis-3182	93	13	object	object	NOUN
fcis-3182	93	14	detection	detection	NOUN
fcis-3182	93	15	.	.	PUNCT
fcis-3182	94	1	https://www.geeksforgeeks.org/yolo-youonly-look-once-real-time-object-detection/.	https://www.geeksforgeeks.org/yolo-youonly-look-once-real-time-object-detection/.	PROPN
fcis-3182	94	2	https://www.geeksforgeeks.org/yolo-you-only-look-once-real-time-object-detection/	https://www.geeksforgeeks.org/yolo-you-only-look-once-real-time-object-detection/	PUNCT
fcis-3182	94	3	https://www.geeksforgeeks.org/yolo-you-only-look-once-real-time-object-detection/	https://www.geeksforgeeks.org/yolo-you-only-look-once-real-time-object-detection/	PUNCT