id	sid	tid	token	lemma	pos
fcis-10765	1	1	frontiers	frontier	NOUN
fcis-10765	1	2	in	in	ADP
fcis-10765	1	3	computing	computing	NOUN
fcis-10765	1	4	and	and	CCONJ
fcis-10765	1	5	intelligent	intelligent	ADJ
fcis-10765	1	6	systems	system	NOUN
fcis-10765	1	7	issn	issn	VERB
fcis-10765	1	8	:	:	PUNCT
fcis-10765	1	9	2832	2832	NUM
fcis-10765	1	10	-	-	SYM
fcis-10765	1	11	6024	6024	NUM
fcis-10765	1	12	|	|	NOUN
fcis-10765	1	13	vol	vol	NOUN
fcis-10765	1	14	.	.	PROPN
fcis-10765	2	1	4	4	NUM
fcis-10765	2	2	,	,	PUNCT
fcis-10765	2	3	no	no	INTJ
fcis-10765	2	4	.	.	NOUN
fcis-10765	2	5	3	3	NUM
fcis-10765	2	6	,	,	PUNCT
fcis-10765	2	7	2023	2023	NUM
fcis-10765	2	8	28	28	NUM
fcis-10765	2	9	fire	fire	NOUN
fcis-10765	2	10	detection	detection	NOUN
fcis-10765	2	11	of	of	ADP
fcis-10765	2	12	yolov8	yolov8	PROPN
fcis-10765	2	13	model	model	NOUN
fcis-10765	2	14	based	base	VERB
fcis-10765	2	15	on	on	ADP
fcis-10765	2	16	integrated	integrate	VERB
fcis-10765	2	17	se	se	PROPN
fcis-10765	2	18	attention	attention	NOUN
fcis-10765	2	19	mechanism	mechanism	NOUN
fcis-10765	2	20	zihe	zihe	PROPN
fcis-10765	2	21	wei	wei	PROPN
fcis-10765	2	22	*	*	PUNCT
fcis-10765	2	23	university	university	PROPN
fcis-10765	2	24	of	of	ADP
fcis-10765	2	25	science	science	NOUN
fcis-10765	2	26	and	and	CCONJ
fcis-10765	2	27	technology	technology	NOUN
fcis-10765	2	28	liaoning	liaoning	NOUN
fcis-10765	2	29	,	,	PUNCT
fcis-10765	2	30	anshan	anshan	PROPN
fcis-10765	2	31	114051	114051	NUM
fcis-10765	2	32	,	,	PUNCT
fcis-10765	2	33	china	china	PROPN
fcis-10765	2	34	abstract	abstract	NOUN
fcis-10765	2	35	:	:	PUNCT
fcis-10765	2	36	our	our	PRON
fcis-10765	2	37	proposed	propose	VERB
fcis-10765	2	38	method	method	NOUN
fcis-10765	2	39	utilizes	utilize	VERB
fcis-10765	2	40	yolov8	yolov8	NOUN
fcis-10765	2	41	and	and	CCONJ
fcis-10765	2	42	se	se	ADJ
fcis-10765	2	43	attention	attention	NOUN
fcis-10765	2	44	mechanism	mechanism	NOUN
fcis-10765	2	45	for	for	ADP
fcis-10765	2	46	detecting	detect	VERB
fcis-10765	2	47	fires	fire	NOUN
fcis-10765	2	48	,	,	PUNCT
fcis-10765	2	49	which	which	PRON
fcis-10765	2	50	is	be	AUX
fcis-10765	2	51	crucial	crucial	ADJ
fcis-10765	2	52	for	for	ADP
fcis-10765	2	53	early	early	ADJ
fcis-10765	2	54	detection	detection	NOUN
fcis-10765	2	55	and	and	CCONJ
fcis-10765	2	56	prevention	prevention	NOUN
fcis-10765	2	57	.	.	PUNCT
fcis-10765	3	1	our	our	PRON
fcis-10765	3	2	method	method	NOUN
fcis-10765	3	3	balances	balance	NOUN
fcis-10765	3	4	accuracy	accuracy	NOUN
fcis-10765	3	5	and	and	CCONJ
fcis-10765	3	6	real	real	ADJ
fcis-10765	3	7	-	-	PUNCT
fcis-10765	3	8	time	time	NOUN
fcis-10765	3	9	performance	performance	NOUN
fcis-10765	3	10	while	while	SCONJ
fcis-10765	3	11	detecting	detect	VERB
fcis-10765	3	12	fires	fire	NOUN
fcis-10765	3	13	in	in	ADP
fcis-10765	3	14	various	various	ADJ
fcis-10765	3	15	scenarios	scenario	NOUN
fcis-10765	3	16	.	.	PUNCT
fcis-10765	4	1	the	the	DET
fcis-10765	4	2	proposed	propose	VERB
fcis-10765	4	3	method	method	NOUN
fcis-10765	4	4	achieves	achieve	VERB
fcis-10765	4	5	an	an	DET
fcis-10765	4	6	average	average	ADJ
fcis-10765	4	7	map0.5	map0.5	PROPN
fcis-10765	4	8	value	value	NOUN
fcis-10765	4	9	of	of	ADP
fcis-10765	4	10	0.730	0.730	NUM
fcis-10765	4	11	,	,	PUNCT
fcis-10765	4	12	with	with	ADP
fcis-10765	4	13	an	an	DET
fcis-10765	4	14	improvement	improvement	NOUN
fcis-10765	4	15	from	from	ADP
fcis-10765	4	16	the	the	DET
fcis-10765	4	17	original	original	ADJ
fcis-10765	4	18	model	model	NOUN
fcis-10765	4	19	's	's	PART
fcis-10765	4	20	map0.5	map0.5	PROPN
fcis-10765	4	21	value	value	NOUN
fcis-10765	4	22	of	of	ADP
fcis-10765	4	23	0.707	0.707	NUM
fcis-10765	4	24	after	after	ADP
fcis-10765	4	25	incorporating	incorporate	VERB
fcis-10765	4	26	the	the	DET
fcis-10765	4	27	se	se	ADJ
fcis-10765	4	28	attention	attention	NOUN
fcis-10765	4	29	mechanism	mechanism	NOUN
fcis-10765	4	30	.	.	PUNCT
fcis-10765	5	1	we	we	PRON
fcis-10765	5	2	evaluated	evaluate	VERB
fcis-10765	5	3	our	our	PRON
fcis-10765	5	4	model	model	NOUN
fcis-10765	5	5	on	on	ADP
fcis-10765	5	6	a	a	DET
fcis-10765	5	7	benchmark	benchmark	NOUN
fcis-10765	5	8	dataset	dataset	NOUN
fcis-10765	5	9	and	and	CCONJ
fcis-10765	5	10	demonstrated	demonstrate	VERB
fcis-10765	5	11	its	its	PRON
fcis-10765	5	12	effectiveness	effectiveness	NOUN
fcis-10765	5	13	in	in	ADP
fcis-10765	5	14	accurately	accurately	ADV
fcis-10765	5	15	detecting	detect	VERB
fcis-10765	5	16	and	and	CCONJ
fcis-10765	5	17	localizing	localize	VERB
fcis-10765	5	18	fires	fire	NOUN
fcis-10765	5	19	with	with	ADP
fcis-10765	5	20	high	high	ADJ
fcis-10765	5	21	precision	precision	NOUN
fcis-10765	5	22	and	and	CCONJ
fcis-10765	5	23	recall	recall	NOUN
fcis-10765	5	24	rates	rate	NOUN
fcis-10765	5	25	.	.	PUNCT
fcis-10765	6	1	experimental	experimental	ADJ
fcis-10765	6	2	results	result	NOUN
fcis-10765	6	3	confirm	confirm	VERB
fcis-10765	6	4	the	the	DET
fcis-10765	6	5	effectiveness	effectiveness	NOUN
fcis-10765	6	6	of	of	ADP
fcis-10765	6	7	our	our	PRON
fcis-10765	6	8	proposed	propose	VERB
fcis-10765	6	9	method	method	NOUN
fcis-10765	6	10	in	in	ADP
fcis-10765	6	11	accurately	accurately	ADV
fcis-10765	6	12	detecting	detect	VERB
fcis-10765	6	13	and	and	CCONJ
fcis-10765	6	14	localizing	localize	VERB
fcis-10765	6	15	fires	fire	NOUN
fcis-10765	6	16	,	,	PUNCT
fcis-10765	6	17	demonstrating	demonstrate	VERB
fcis-10765	6	18	its	its	PRON
fcis-10765	6	19	potential	potential	NOUN
fcis-10765	6	20	for	for	ADP
fcis-10765	6	21	wide	wide	ADJ
fcis-10765	6	22	application	application	NOUN
fcis-10765	6	23	and	and	CCONJ
fcis-10765	6	24	promotion	promotion	NOUN
fcis-10765	6	25	in	in	ADP
fcis-10765	6	26	the	the	DET
fcis-10765	6	27	fire	fire	NOUN
fcis-10765	6	28	safety	safety	NOUN
fcis-10765	6	29	industry	industry	NOUN
fcis-10765	6	30	.	.	PUNCT
fcis-10765	7	1	keywords	keyword	NOUN
fcis-10765	7	2	:	:	PUNCT
fcis-10765	7	3	fire	fire	NOUN
fcis-10765	7	4	detection	detection	NOUN
fcis-10765	7	5	;	;	PUNCT
fcis-10765	7	6	yolov8	yolov8	NOUN
fcis-10765	7	7	;	;	PUNCT
fcis-10765	7	8	se	se	X
fcis-10765	7	9	attention	attention	NOUN
fcis-10765	7	10	mechanism	mechanism	NOUN
fcis-10765	7	11	.	.	PUNCT
fcis-10765	8	1	1	1	X
fcis-10765	8	2	.	.	X
fcis-10765	8	3	introduction	introduction	NOUN
fcis-10765	8	4	fire	fire	NOUN
fcis-10765	8	5	detection	detection	NOUN
fcis-10765	8	6	is	be	AUX
fcis-10765	8	7	crucial	crucial	ADJ
fcis-10765	8	8	for	for	ADP
fcis-10765	8	9	ensuring	ensure	VERB
fcis-10765	8	10	the	the	DET
fcis-10765	8	11	safety	safety	NOUN
fcis-10765	8	12	of	of	ADP
fcis-10765	8	13	people	people	NOUN
fcis-10765	8	14	and	and	CCONJ
fcis-10765	8	15	property	property	NOUN
fcis-10765	8	16	,	,	PUNCT
fcis-10765	8	17	as	as	SCONJ
fcis-10765	8	18	fires	fire	NOUN
fcis-10765	8	19	can	can	AUX
fcis-10765	8	20	cause	cause	VERB
fcis-10765	8	21	significant	significant	ADJ
fcis-10765	8	22	damage	damage	NOUN
fcis-10765	8	23	and	and	CCONJ
fcis-10765	8	24	threaten	threaten	VERB
fcis-10765	8	25	lives	life	NOUN
fcis-10765	8	26	.	.	PUNCT
fcis-10765	9	1	various	various	ADJ
fcis-10765	9	2	methods	method	NOUN
fcis-10765	9	3	have	have	AUX
fcis-10765	9	4	been	be	AUX
fcis-10765	9	5	proposed	propose	VERB
fcis-10765	9	6	for	for	ADP
fcis-10765	9	7	fire	fire	NOUN
fcis-10765	9	8	detection	detection	NOUN
fcis-10765	9	9	,	,	PUNCT
fcis-10765	9	10	including	include	VERB
fcis-10765	9	11	those	those	PRON
fcis-10765	9	12	based	base	VERB
fcis-10765	9	13	on	on	ADP
fcis-10765	9	14	traditional	traditional	ADJ
fcis-10765	9	15	sensors	sensor	NOUN
fcis-10765	9	16	such	such	ADJ
fcis-10765	9	17	as	as	ADP
fcis-10765	9	18	smoke	smoke	NOUN
fcis-10765	9	19	and	and	CCONJ
fcis-10765	9	20	heat	heat	NOUN
fcis-10765	9	21	detectors	detector	NOUN
fcis-10765	9	22	,	,	PUNCT
fcis-10765	9	23	as	as	ADV
fcis-10765	9	24	well	well	ADV
fcis-10765	9	25	as	as	ADP
fcis-10765	9	26	those	those	PRON
fcis-10765	9	27	based	base	VERB
fcis-10765	9	28	on	on	ADP
fcis-10765	9	29	computer	computer	NOUN
fcis-10765	9	30	vision	vision	NOUN
fcis-10765	9	31	and	and	CCONJ
fcis-10765	9	32	deep	deep	ADJ
fcis-10765	9	33	learning	learning	NOUN
fcis-10765	9	34	technology	technology	NOUN
fcis-10765	9	35	.	.	PUNCT
fcis-10765	10	1	among	among	ADP
fcis-10765	10	2	these	these	DET
fcis-10765	10	3	methods	method	NOUN
fcis-10765	10	4	,	,	PUNCT
fcis-10765	10	5	the	the	DET
fcis-10765	10	6	fire	fire	NOUN
fcis-10765	10	7	detection	detection	NOUN
fcis-10765	10	8	method	method	NOUN
fcis-10765	10	9	using	use	VERB
fcis-10765	10	10	yolov8	yolov8	NOUN
fcis-10765	10	11	and	and	CCONJ
fcis-10765	10	12	se	se	ADJ
fcis-10765	10	13	attention	attention	NOUN
fcis-10765	10	14	mechanism	mechanism	NOUN
fcis-10765	10	15	has	have	AUX
fcis-10765	10	16	shown	show	VERB
fcis-10765	10	17	promising	promising	ADJ
fcis-10765	10	18	results	result	NOUN
fcis-10765	10	19	in	in	ADP
fcis-10765	10	20	achieving	achieve	VERB
fcis-10765	10	21	high	high	ADJ
fcis-10765	10	22	accuracy	accuracy	NOUN
fcis-10765	10	23	and	and	CCONJ
fcis-10765	10	24	real	real	ADJ
fcis-10765	10	25	-	-	PUNCT
fcis-10765	10	26	time	time	NOUN
fcis-10765	10	27	performance	performance	NOUN
fcis-10765	10	28	.	.	PUNCT
fcis-10765	11	1	this	this	DET
fcis-10765	11	2	method	method	NOUN
fcis-10765	11	3	combines	combine	VERB
fcis-10765	11	4	the	the	DET
fcis-10765	11	5	yolov8	yolov8	NOUN
fcis-10765	11	6	object	object	NOUN
fcis-10765	11	7	detection	detection	NOUN
fcis-10765	11	8	algorithm	algorithm	NOUN
fcis-10765	11	9	with	with	ADP
fcis-10765	11	10	se	se	NOUN
fcis-10765	11	11	attention	attention	NOUN
fcis-10765	11	12	mechanism	mechanism	NOUN
fcis-10765	11	13	to	to	PART
fcis-10765	11	14	enhance	enhance	VERB
fcis-10765	11	15	the	the	DET
fcis-10765	11	16	model	model	NOUN
fcis-10765	11	17	's	's	PART
fcis-10765	11	18	ability	ability	NOUN
fcis-10765	11	19	to	to	PART
fcis-10765	11	20	identify	identify	VERB
fcis-10765	11	21	fires	fire	NOUN
fcis-10765	11	22	in	in	ADP
fcis-10765	11	23	various	various	ADJ
fcis-10765	11	24	scenarios	scenario	NOUN
fcis-10765	11	25	.	.	PUNCT
fcis-10765	12	1	compared	compare	VERB
fcis-10765	12	2	to	to	ADP
fcis-10765	12	3	traditional	traditional	ADJ
fcis-10765	12	4	fire	fire	NOUN
fcis-10765	12	5	detection	detection	NOUN
fcis-10765	12	6	methods	method	NOUN
fcis-10765	12	7	,	,	PUNCT
fcis-10765	12	8	which	which	PRON
fcis-10765	12	9	often	often	ADV
fcis-10765	12	10	rely	rely	VERB
fcis-10765	12	11	on	on	ADP
fcis-10765	12	12	costly	costly	ADJ
fcis-10765	12	13	and	and	CCONJ
fcis-10765	12	14	complex	complex	ADJ
fcis-10765	12	15	sensor	sensor	NOUN
fcis-10765	12	16	systems	system	NOUN
fcis-10765	12	17	[	[	X
fcis-10765	12	18	1][2	1][2	NUM
fcis-10765	12	19	]	]	PUNCT
fcis-10765	12	20	,	,	PUNCT
fcis-10765	12	21	and	and	CCONJ
fcis-10765	12	22	it	it	PRON
fcis-10765	12	23	is	be	AUX
fcis-10765	12	24	not	not	PART
fcis-10765	12	25	easy	easy	ADJ
fcis-10765	12	26	to	to	PART
fcis-10765	12	27	achieve	achieve	VERB
fcis-10765	12	28	a	a	DET
fcis-10765	12	29	good	good	ADJ
fcis-10765	12	30	detection	detection	NOUN
fcis-10765	12	31	effect	effect	NOUN
fcis-10765	12	32	outdoors	outdoors	ADV
fcis-10765	12	33	.	.	PUNCT
fcis-10765	13	1	today	today	NOUN
fcis-10765	13	2	,	,	PUNCT
fcis-10765	13	3	many	many	ADJ
fcis-10765	13	4	application	application	NOUN
fcis-10765	13	5	cases	case	NOUN
fcis-10765	13	6	of	of	ADP
fcis-10765	13	7	yolov8	yolov8	NOUN
fcis-10765	13	8	have	have	AUX
fcis-10765	13	9	also	also	ADV
fcis-10765	13	10	emerged	emerge	VERB
fcis-10765	13	11	.	.	PUNCT
fcis-10765	14	1	for	for	ADP
fcis-10765	14	2	example	example	NOUN
fcis-10765	14	3	,	,	PUNCT
fcis-10765	14	4	yuan	yuan	PROPN
fcis-10765	14	5	's	's	PART
fcis-10765	14	6	team	team	NOUN
fcis-10765	14	7	[	[	X
fcis-10765	14	8	3	3	X
fcis-10765	14	9	]	]	PUNCT
fcis-10765	14	10	used	use	VERB
fcis-10765	14	11	the	the	DET
fcis-10765	14	12	improved	improved	ADJ
fcis-10765	14	13	yolov8	yolov8	NOUN
fcis-10765	14	14	model	model	NOUN
fcis-10765	14	15	to	to	PART
fcis-10765	14	16	detect	detect	VERB
fcis-10765	14	17	and	and	CCONJ
fcis-10765	14	18	identify	identify	VERB
fcis-10765	14	19	fish	fish	NOUN
fcis-10765	14	20	in	in	ADP
fcis-10765	14	21	the	the	DET
fcis-10765	14	22	electronic	electronic	ADJ
fcis-10765	14	23	monitoring	monitoring	NOUN
fcis-10765	14	24	data	datum	NOUN
fcis-10765	14	25	of	of	ADP
fcis-10765	14	26	commercial	commercial	ADJ
fcis-10765	14	27	fishing	fishing	NOUN
fcis-10765	14	28	boats	boat	NOUN
fcis-10765	14	29	,	,	PUNCT
fcis-10765	14	30	the	the	DET
fcis-10765	14	31	yolov8	yolov8	NOUN
fcis-10765	14	32	and	and	CCONJ
fcis-10765	14	33	se	se	PROPN
fcis-10765	14	34	attentionbased	attentionbase	VERB
fcis-10765	14	35	method	method	NOUN
fcis-10765	14	36	is	be	AUX
fcis-10765	14	37	more	more	ADJ
fcis-10765	14	38	cost	cost	NOUN
fcis-10765	14	39	-	-	PUNCT
fcis-10765	14	40	effective	effective	ADJ
fcis-10765	14	41	and	and	CCONJ
fcis-10765	14	42	easier	easy	ADJ
fcis-10765	14	43	to	to	PART
fcis-10765	14	44	implement	implement	VERB
fcis-10765	14	45	and	and	CCONJ
fcis-10765	14	46	scale	scale	NOUN
fcis-10765	14	47	.	.	PUNCT
fcis-10765	15	1	additionally	additionally	ADV
fcis-10765	15	2	,	,	PUNCT
fcis-10765	15	3	it	it	PRON
fcis-10765	15	4	can	can	AUX
fcis-10765	15	5	detect	detect	VERB
fcis-10765	15	6	and	and	CCONJ
fcis-10765	15	7	locate	locate	ADJ
fcis-10765	15	8	fires	fire	NOUN
fcis-10765	15	9	in	in	ADP
fcis-10765	15	10	realtime	realtime	NOUN
fcis-10765	15	11	,	,	PUNCT
fcis-10765	15	12	which	which	PRON
fcis-10765	15	13	is	be	AUX
fcis-10765	15	14	essential	essential	ADJ
fcis-10765	15	15	for	for	ADP
fcis-10765	15	16	early	early	ADJ
fcis-10765	15	17	detection	detection	NOUN
fcis-10765	15	18	and	and	CCONJ
fcis-10765	15	19	prevention	prevention	NOUN
fcis-10765	15	20	.	.	PUNCT
fcis-10765	16	1	overall	overall	ADV
fcis-10765	16	2	,	,	PUNCT
fcis-10765	16	3	the	the	DET
fcis-10765	16	4	fire	fire	NOUN
fcis-10765	16	5	detection	detection	NOUN
fcis-10765	16	6	method	method	NOUN
fcis-10765	16	7	using	use	VERB
fcis-10765	16	8	yolov8	yolov8	NOUN
fcis-10765	16	9	and	and	CCONJ
fcis-10765	16	10	se	se	ADJ
fcis-10765	16	11	attention	attention	NOUN
fcis-10765	16	12	mechanism	mechanism	NOUN
fcis-10765	16	13	has	have	VERB
fcis-10765	16	14	great	great	ADJ
fcis-10765	16	15	potential	potential	NOUN
fcis-10765	16	16	to	to	PART
fcis-10765	16	17	be	be	AUX
fcis-10765	16	18	applied	apply	VERB
fcis-10765	16	19	in	in	ADP
fcis-10765	16	20	various	various	ADJ
fcis-10765	16	21	settings	setting	NOUN
fcis-10765	16	22	,	,	PUNCT
fcis-10765	16	23	such	such	ADJ
fcis-10765	16	24	as	as	ADP
fcis-10765	16	25	public	public	ADJ
fcis-10765	16	26	spaces	space	NOUN
fcis-10765	16	27	,	,	PUNCT
fcis-10765	16	28	homes	home	NOUN
fcis-10765	16	29	,	,	PUNCT
fcis-10765	16	30	and	and	CCONJ
fcis-10765	16	31	industrial	industrial	ADJ
fcis-10765	16	32	environments	environment	NOUN
fcis-10765	16	33	,	,	PUNCT
fcis-10765	16	34	to	to	PART
fcis-10765	16	35	enhance	enhance	VERB
fcis-10765	16	36	fire	fire	NOUN
fcis-10765	16	37	safety	safety	NOUN
fcis-10765	16	38	and	and	CCONJ
fcis-10765	16	39	prevent	prevent	VERB
fcis-10765	16	40	potential	potential	ADJ
fcis-10765	16	41	damages	damage	NOUN
fcis-10765	16	42	and	and	CCONJ
fcis-10765	16	43	losses	loss	NOUN
fcis-10765	16	44	.	.	PUNCT
fcis-10765	17	1	2	2	X
fcis-10765	17	2	.	.	X
fcis-10765	17	3	introduction	introduction	NOUN
fcis-10765	17	4	of	of	ADP
fcis-10765	17	5	yolov8	yolov8	NOUN
fcis-10765	17	6	figure	figure	NOUN
fcis-10765	17	7	1	1	NUM
fcis-10765	17	8	.	.	PUNCT
fcis-10765	18	1	the	the	DET
fcis-10765	18	2	network	network	NOUN
fcis-10765	18	3	structure	structure	NOUN
fcis-10765	18	4	of	of	ADP
fcis-10765	18	5	yolov8	yolov8	NOUN
fcis-10765	18	6	as	as	SCONJ
fcis-10765	18	7	shown	show	VERB
fcis-10765	18	8	in	in	ADP
fcis-10765	18	9	figure	figure	NOUN
fcis-10765	18	10	1	1	NUM
fcis-10765	18	11	,	,	PUNCT
fcis-10765	18	12	yolov8	yolov8	NOUN
fcis-10765	18	13	is	be	AUX
fcis-10765	18	14	an	an	DET
fcis-10765	18	15	advanced	advanced	ADJ
fcis-10765	18	16	model	model	NOUN
fcis-10765	18	17	that	that	PRON
fcis-10765	18	18	builds	build	VERB
fcis-10765	18	19	on	on	ADP
fcis-10765	18	20	the	the	DET
fcis-10765	18	21	design	design	NOUN
fcis-10765	18	22	principles	principle	NOUN
fcis-10765	18	23	of	of	ADP
fcis-10765	18	24	yolov5	yolov5	NOUN
fcis-10765	18	25	and	and	CCONJ
fcis-10765	18	26	yolov7	yolov7	PROPN
fcis-10765	18	27	elan	elan	PROPN
fcis-10765	18	28	to	to	PART
fcis-10765	18	29	enhance	enhance	VERB
fcis-10765	18	30	performance	performance	NOUN
fcis-10765	18	31	and	and	CCONJ
fcis-10765	18	32	flexibility	flexibility	NOUN
fcis-10765	18	33	.	.	PUNCT
fcis-10765	19	1	it	it	PRON
fcis-10765	19	2	retains	retain	VERB
fcis-10765	19	3	the	the	DET
fcis-10765	19	4	basic	basic	ADJ
fcis-10765	19	5	framework	framework	NOUN
fcis-10765	19	6	of	of	ADP
fcis-10765	19	7	yolov5	yolov5	NOUN
fcis-10765	19	8	while	while	SCONJ
fcis-10765	19	9	introducing	introduce	VERB
fcis-10765	19	10	new	new	ADJ
fcis-10765	19	11	features	feature	NOUN
fcis-10765	19	12	,	,	PUNCT
fcis-10765	19	13	such	such	ADJ
fcis-10765	19	14	as	as	ADP
fcis-10765	19	15	a	a	DET
fcis-10765	19	16	new	new	ADJ
fcis-10765	19	17	backbone	backbone	NOUN
fcis-10765	19	18	network	network	NOUN
fcis-10765	19	19	architecture	architecture	NOUN
fcis-10765	19	20	,	,	PUNCT
fcis-10765	19	21	an	an	DET
fcis-10765	19	22	anchor	anchor	NOUN
fcis-10765	19	23	-	-	PUNCT
fcis-10765	19	24	free	free	ADJ
fcis-10765	19	25	detection	detection	NOUN
fcis-10765	19	26	head	head	NOUN
fcis-10765	19	27	,	,	PUNCT
fcis-10765	19	28	and	and	CCONJ
fcis-10765	19	29	a	a	DET
fcis-10765	19	30	new	new	ADJ
fcis-10765	19	31	loss	loss	NOUN
fcis-10765	19	32	function	function	NOUN
fcis-10765	19	33	.	.	PUNCT
fcis-10765	20	1	the	the	DET
fcis-10765	20	2	model	model	NOUN
fcis-10765	20	3	offers	offer	VERB
fcis-10765	20	4	different	different	ADJ
fcis-10765	20	5	size	size	NOUN
fcis-10765	20	6	models	model	NOUN
fcis-10765	20	7	,	,	PUNCT
fcis-10765	20	8	ranging	range	VERB
fcis-10765	20	9	from	from	ADP
fcis-10765	20	10	n	n	CCONJ
fcis-10765	20	11	/	/	SYM
fcis-10765	20	12	s	s	PROPN
fcis-10765	20	13	/	/	SYM
fcis-10765	20	14	m	m	NOUN
fcis-10765	20	15	/	/	SYM
fcis-10765	20	16	l	l	NOUN
fcis-10765	20	17	/	/	SYM
fcis-10765	20	18	x	x	SYM
fcis-10765	20	19	scales	scale	NOUN
fcis-10765	20	20	,	,	PUNCT
fcis-10765	20	21	which	which	PRON
fcis-10765	20	22	are	be	AUX
fcis-10765	20	23	adjusted	adjust	VERB
fcis-10765	20	24	based	base	VERB
fcis-10765	20	25	on	on	ADP
fcis-10765	20	26	scaling	scale	VERB
fcis-10765	20	27	coefficients	coefficient	NOUN
fcis-10765	20	28	.	.	PUNCT
fcis-10765	21	1	the	the	DET
fcis-10765	21	2	backbone	backbone	NOUN
fcis-10765	21	3	network	network	NOUN
fcis-10765	21	4	and	and	CCONJ
fcis-10765	21	5	neck	neck	NOUN
fcis-10765	21	6	sections	section	NOUN
fcis-10765	21	7	of	of	ADP
fcis-10765	21	8	yolov8	yolov8	NOUN
fcis-10765	21	9	are	be	AUX
fcis-10765	21	10	based	base	VERB
fcis-10765	21	11	on	on	ADP
fcis-10765	21	12	the	the	DET
fcis-10765	21	13	design	design	NOUN
fcis-10765	21	14	philosophy	philosophy	NOUN
fcis-10765	21	15	of	of	ADP
fcis-10765	21	16	yolov7	yolov7	PROPN
fcis-10765	21	17	elan	elan	PROPN
fcis-10765	21	18	,	,	PUNCT
fcis-10765	21	19	with	with	ADP
fcis-10765	21	20	adjustments	adjustment	NOUN
fcis-10765	21	21	made	make	VERB
fcis-10765	21	22	to	to	PART
fcis-10765	21	23	improve	improve	VERB
fcis-10765	21	24	model	model	NOUN
fcis-10765	21	25	performance	performance	NOUN
fcis-10765	21	26	.	.	PUNCT
fcis-10765	22	1	in	in	ADP
fcis-10765	22	2	the	the	DET
fcis-10765	22	3	head	head	NOUN
fcis-10765	22	4	section	section	NOUN
fcis-10765	22	5	,	,	PUNCT
fcis-10765	22	6	yolov8	yolov8	PROPN
fcis-10765	22	7	has	have	AUX
fcis-10765	22	8	undergone	undergo	VERB
fcis-10765	22	9	significant	significant	ADJ
fcis-10765	22	10	changes	change	NOUN
fcis-10765	22	11	,	,	PUNCT
fcis-10765	22	12	using	use	VERB
fcis-10765	22	13	the	the	DET
fcis-10765	22	14	decoupled	decouple	VERB
fcis-10765	22	15	head	head	NOUN
fcis-10765	22	16	structure	structure	NOUN
fcis-10765	22	17	to	to	PART
fcis-10765	22	18	separate	separate	VERB
fcis-10765	22	19	the	the	DET
fcis-10765	22	20	classification	classification	NOUN
fcis-10765	22	21	and	and	CCONJ
fcis-10765	22	22	detection	detection	NOUN
fcis-10765	22	23	heads	head	NOUN
fcis-10765	22	24	and	and	CCONJ
fcis-10765	22	25	changing	change	VERB
fcis-10765	22	26	the	the	DET
fcis-10765	22	27	detection	detection	NOUN
fcis-10765	22	28	head	head	NOUN
fcis-10765	22	29	from	from	ADP
fcis-10765	22	30	anchor	anchor	NOUN
fcis-10765	22	31	-	-	PUNCT
fcis-10765	22	32	based	base	VERB
fcis-10765	22	33	to	to	AUX
fcis-10765	22	34	anchor	anchor	NOUN
fcis-10765	22	35	-	-	PUNCT
fcis-10765	22	36	free	free	ADJ
fcis-10765	22	37	.	.	PUNCT
fcis-10765	23	1	the	the	DET
fcis-10765	23	2	loss	loss	NOUN
fcis-10765	23	3	calculation	calculation	NOUN
fcis-10765	23	4	uses	use	VERB
fcis-10765	23	5	the	the	DET
fcis-10765	23	6	taskalignedassigner	taskalignedassigner	NOUN
fcis-10765	23	7	positive	positive	ADJ
fcis-10765	23	8	sample	sample	NOUN
fcis-10765	23	9	allocation	allocation	NOUN
fcis-10765	23	10	strategy	strategy	NOUN
fcis-10765	23	11	and	and	CCONJ
fcis-10765	23	12	the	the	DET
fcis-10765	23	13	distribution	distribution	NOUN
fcis-10765	23	14	focal	focal	ADJ
fcis-10765	23	15	loss	loss	NOUN
fcis-10765	23	16	.	.	PUNCT
fcis-10765	24	1	overall	overall	ADV
fcis-10765	24	2	,	,	PUNCT
fcis-10765	24	3	yolov8	yolov8	PROPN
fcis-10765	24	4	is	be	AUX
fcis-10765	24	5	an	an	DET
fcis-10765	24	6	advanced	advanced	ADJ
fcis-10765	24	7	model	model	NOUN
fcis-10765	24	8	that	that	PRON
fcis-10765	24	9	builds	build	VERB
fcis-10765	24	10	upon	upon	SCONJ
fcis-10765	24	11	the	the	DET
fcis-10765	24	12	success	success	NOUN
fcis-10765	24	13	of	of	ADP
fcis-10765	24	14	previous	previous	ADJ
fcis-10765	24	15	yolo	yolo	ADJ
fcis-10765	24	16	models	model	NOUN
fcis-10765	24	17	while	while	SCONJ
fcis-10765	24	18	introducing	introduce	VERB
fcis-10765	24	19	new	new	ADJ
fcis-10765	24	20	features	feature	NOUN
fcis-10765	24	21	and	and	CCONJ
fcis-10765	24	22	improvements	improvement	NOUN
fcis-10765	24	23	.	.	PUNCT
fcis-10765	25	1	it	it	PRON
fcis-10765	25	2	offers	offer	VERB
fcis-10765	25	3	different	different	ADJ
fcis-10765	25	4	size	size	NOUN
fcis-10765	25	5	models	model	NOUN
fcis-10765	25	6	and	and	CCONJ
fcis-10765	25	7	incorporates	incorporate	VERB
fcis-10765	25	8	various	various	ADJ
fcis-10765	25	9	design	design	NOUN
fcis-10765	25	10	changes	change	NOUN
fcis-10765	25	11	to	to	PART
fcis-10765	25	12	improve	improve	VERB
fcis-10765	25	13	performance	performance	NOUN
fcis-10765	25	14	and	and	CCONJ
fcis-10765	25	15	flexibility	flexibility	NOUN
fcis-10765	25	16	.	.	PUNCT
fcis-10765	26	1	3	3	X
fcis-10765	26	2	.	.	X
fcis-10765	26	3	introduction	introduction	NOUN
fcis-10765	26	4	of	of	ADP
fcis-10765	26	5	se	se	PROPN
fcis-10765	26	6	attention	attention	NOUN
fcis-10765	26	7	mechanism	mechanism	NOUN
fcis-10765	26	8	figure	figure	NOUN
fcis-10765	26	9	2	2	NUM
fcis-10765	26	10	.	.	PUNCT
fcis-10765	26	11	structure	structure	NOUN
fcis-10765	26	12	of	of	ADP
fcis-10765	26	13	se	se	PROPN
fcis-10765	26	14	attention	attention	NOUN
fcis-10765	26	15	as	as	SCONJ
fcis-10765	26	16	shown	show	VERB
fcis-10765	26	17	in	in	ADP
fcis-10765	26	18	figure	figure	NOUN
fcis-10765	26	19	2	2	NUM
fcis-10765	26	20	,	,	PUNCT
fcis-10765	26	21	the	the	DET
fcis-10765	26	22	se	se	X
fcis-10765	26	23	(	(	PUNCT
fcis-10765	26	24	squeeze	squeeze	NOUN
fcis-10765	26	25	-	-	PUNCT
fcis-10765	26	26	and	and	CCONJ
fcis-10765	26	27	-	-	PUNCT
fcis-10765	26	28	excitation	excitation	NOUN
fcis-10765	26	29	)	)	PUNCT
fcis-10765	27	1	[	[	X
fcis-10765	27	2	4	4	NUM
fcis-10765	27	3	]	]	PUNCT
fcis-10765	27	4	attention	attention	NOUN
fcis-10765	27	5	mechanism	mechanism	NOUN
fcis-10765	27	6	is	be	AUX
fcis-10765	27	7	a	a	DET
fcis-10765	27	8	channel	channel	NOUN
fcis-10765	27	9	-	-	PUNCT
fcis-10765	27	10	wise	wise	ADJ
fcis-10765	27	11	attention	attention	NOUN
fcis-10765	27	12	mechanism	mechanism	NOUN
fcis-10765	27	13	29	29	NUM
fcis-10765	27	14	that	that	PRON
fcis-10765	27	15	improves	improve	VERB
fcis-10765	27	16	the	the	DET
fcis-10765	27	17	interdependencies	interdependency	NOUN
fcis-10765	27	18	between	between	ADP
fcis-10765	27	19	channels	channel	NOUN
fcis-10765	27	20	in	in	ADP
fcis-10765	27	21	a	a	DET
fcis-10765	27	22	feature	feature	NOUN
fcis-10765	27	23	map	map	NOUN
fcis-10765	27	24	.	.	PUNCT
fcis-10765	28	1	se	se	PROPN
fcis-10765	28	2	attention	attention	NOUN
fcis-10765	28	3	mechanisms	mechanism	NOUN
fcis-10765	28	4	(	(	PUNCT
fcis-10765	28	5	squeeze	squeeze	NOUN
fcis-10765	28	6	and	and	CCONJ
fcis-10765	28	7	excitation	excitation	NOUN
fcis-10765	28	8	networks	network	NOUN
fcis-10765	28	9	)	)	PUNCT
fcis-10765	28	10	incorporate	incorporate	VERB
fcis-10765	28	11	attention	attention	NOUN
fcis-10765	28	12	mechanisms	mechanism	NOUN
fcis-10765	28	13	in	in	ADP
fcis-10765	28	14	the	the	DET
fcis-10765	28	15	channel	channel	NOUN
fcis-10765	28	16	dimension	dimension	NOUN
fcis-10765	28	17	,	,	PUNCT
fcis-10765	28	18	with	with	ADP
fcis-10765	28	19	the	the	DET
fcis-10765	28	20	main	main	ADJ
fcis-10765	28	21	operations	operation	NOUN
fcis-10765	28	22	being	be	AUX
fcis-10765	28	23	squeeze	squeeze	NOUN
fcis-10765	28	24	and	and	CCONJ
fcis-10765	28	25	excitation	excitation	NOUN
fcis-10765	28	26	.	.	PUNCT
fcis-10765	29	1	by	by	ADP
fcis-10765	29	2	means	mean	NOUN
fcis-10765	29	3	of	of	ADP
fcis-10765	29	4	automatic	automatic	ADJ
fcis-10765	29	5	learning	learning	NOUN
fcis-10765	29	6	,	,	PUNCT
fcis-10765	29	7	a	a	DET
fcis-10765	29	8	new	new	ADJ
fcis-10765	29	9	neural	neural	ADJ
fcis-10765	29	10	network	network	NOUN
fcis-10765	29	11	is	be	AUX
fcis-10765	29	12	utilized	utilize	VERB
fcis-10765	29	13	to	to	PART
fcis-10765	29	14	determine	determine	VERB
fcis-10765	29	15	the	the	DET
fcis-10765	29	16	significance	significance	NOUN
fcis-10765	29	17	of	of	ADP
fcis-10765	29	18	each	each	DET
fcis-10765	29	19	channel	channel	NOUN
fcis-10765	29	20	within	within	ADP
fcis-10765	29	21	the	the	DET
fcis-10765	29	22	feature	feature	NOUN
fcis-10765	29	23	map	map	NOUN
fcis-10765	29	24	.	.	PUNCT
fcis-10765	30	1	this	this	DET
fcis-10765	30	2	importance	importance	NOUN
fcis-10765	30	3	score	score	NOUN
fcis-10765	30	4	is	be	AUX
fcis-10765	30	5	then	then	ADV
fcis-10765	30	6	used	use	VERB
fcis-10765	30	7	to	to	PART
fcis-10765	30	8	assign	assign	VERB
fcis-10765	30	9	a	a	DET
fcis-10765	30	10	weight	weight	NOUN
fcis-10765	30	11	value	value	NOUN
fcis-10765	30	12	to	to	ADP
fcis-10765	30	13	each	each	DET
fcis-10765	30	14	feature	feature	NOUN
fcis-10765	30	15	,	,	PUNCT
fcis-10765	30	16	allowing	allow	VERB
fcis-10765	30	17	the	the	DET
fcis-10765	30	18	neural	neural	ADJ
fcis-10765	30	19	network	network	NOUN
fcis-10765	30	20	to	to	PART
fcis-10765	30	21	concentrate	concentrate	VERB
fcis-10765	30	22	on	on	ADP
fcis-10765	30	23	particular	particular	ADJ
fcis-10765	30	24	feature	feature	NOUN
fcis-10765	30	25	channels	channel	NOUN
fcis-10765	30	26	that	that	PRON
fcis-10765	30	27	are	be	AUX
fcis-10765	30	28	pertinent	pertinent	ADJ
fcis-10765	30	29	to	to	ADP
fcis-10765	30	30	the	the	DET
fcis-10765	30	31	current	current	ADJ
fcis-10765	30	32	task	task	NOUN
fcis-10765	30	33	,	,	PUNCT
fcis-10765	30	34	while	while	SCONJ
fcis-10765	30	35	suppressing	suppress	VERB
fcis-10765	30	36	those	those	PRON
fcis-10765	30	37	that	that	PRON
fcis-10765	30	38	are	be	AUX
fcis-10765	30	39	not	not	PART
fcis-10765	30	40	.	.	PUNCT
fcis-10765	31	1	the	the	DET
fcis-10765	31	2	figure	figure	NOUN
fcis-10765	31	3	2	2	NUM
fcis-10765	31	4	illustrates	illustrate	VERB
fcis-10765	31	5	the	the	DET
fcis-10765	31	6	impact	impact	NOUN
fcis-10765	31	7	of	of	ADP
fcis-10765	31	8	se	se	X
fcis-10765	31	9	attention	attention	NOUN
fcis-10765	31	10	mechanism	mechanism	NOUN
fcis-10765	31	11	(	(	PUNCT
fcis-10765	31	12	figure	figure	NOUN
fcis-10765	31	13	c	c	NOUN
fcis-10765	31	14	on	on	ADP
fcis-10765	31	15	the	the	DET
fcis-10765	31	16	right	right	NOUN
fcis-10765	31	17	)	)	PUNCT
fcis-10765	31	18	in	in	ADP
fcis-10765	31	19	contrast	contrast	NOUN
fcis-10765	31	20	to	to	ADP
fcis-10765	31	21	its	its	PRON
fcis-10765	31	22	absence	absence	NOUN
fcis-10765	31	23	(	(	PUNCT
fcis-10765	31	24	figure	figure	NOUN
fcis-10765	31	25	c	c	NOUN
fcis-10765	31	26	on	on	ADP
fcis-10765	31	27	the	the	DET
fcis-10765	31	28	left	left	NOUN
fcis-10765	31	29	)	)	PUNCT
fcis-10765	31	30	,	,	PUNCT
fcis-10765	31	31	where	where	SCONJ
fcis-10765	31	32	the	the	DET
fcis-10765	31	33	feature	feature	NOUN
fcis-10765	31	34	map	map	NOUN
fcis-10765	31	35	channels	channel	NOUN
fcis-10765	31	36	all	all	PRON
fcis-10765	31	37	have	have	VERB
fcis-10765	31	38	the	the	DET
fcis-10765	31	39	same	same	ADJ
fcis-10765	31	40	level	level	NOUN
fcis-10765	31	41	of	of	ADP
fcis-10765	31	42	importance	importance	NOUN
fcis-10765	31	43	prior	prior	ADV
fcis-10765	31	44	to	to	ADP
fcis-10765	31	45	processing	processing	NOUN
fcis-10765	31	46	,	,	PUNCT
fcis-10765	31	47	but	but	CCONJ
fcis-10765	31	48	after	after	ADP
fcis-10765	31	49	se	se	PROPN
fcis-10765	31	50	attention	attention	NOUN
fcis-10765	31	51	,	,	PUNCT
fcis-10765	31	52	different	different	ADJ
fcis-10765	31	53	weights	weight	NOUN
fcis-10765	31	54	are	be	AUX
fcis-10765	31	55	assigned	assign	VERB
fcis-10765	31	56	to	to	ADP
fcis-10765	31	57	different	different	ADJ
fcis-10765	31	58	channels	channel	NOUN
fcis-10765	31	59	,	,	PUNCT
fcis-10765	31	60	represented	represent	VERB
fcis-10765	31	61	by	by	ADP
fcis-10765	31	62	different	different	ADJ
fcis-10765	31	63	colors	color	NOUN
fcis-10765	31	64	.	.	PUNCT
fcis-10765	32	1	this	this	PRON
fcis-10765	32	2	enables	enable	VERB
fcis-10765	32	3	the	the	DET
fcis-10765	32	4	neural	neural	ADJ
fcis-10765	32	5	network	network	NOUN
fcis-10765	32	6	to	to	PART
fcis-10765	32	7	prioritize	prioritize	VERB
fcis-10765	32	8	channels	channel	NOUN
fcis-10765	32	9	with	with	ADP
fcis-10765	32	10	high	high	ADJ
fcis-10765	32	11	weight	weight	NOUN
fcis-10765	32	12	values	value	NOUN
fcis-10765	32	13	,	,	PUNCT
fcis-10765	32	14	which	which	PRON
fcis-10765	32	15	correspond	correspond	VERB
fcis-10765	32	16	to	to	ADP
fcis-10765	32	17	those	those	PRON
fcis-10765	32	18	that	that	PRON
fcis-10765	32	19	are	be	AUX
fcis-10765	32	20	most	most	ADV
fcis-10765	32	21	relevant	relevant	ADJ
fcis-10765	32	22	to	to	ADP
fcis-10765	32	23	the	the	DET
fcis-10765	32	24	given	give	VERB
fcis-10765	32	25	task	task	NOUN
fcis-10765	32	26	.	.	PUNCT
fcis-10765	33	1	figure	figure	NOUN
fcis-10765	33	2	3	3	NUM
fcis-10765	33	3	.	.	PUNCT
fcis-10765	34	1	the	the	DET
fcis-10765	34	2	location	location	NOUN
fcis-10765	34	3	of	of	ADP
fcis-10765	34	4	the	the	DET
fcis-10765	34	5	se	se	PROPN
fcis-10765	34	6	attention	attention	NOUN
fcis-10765	34	7	mechanism	mechanism	NOUN
fcis-10765	34	8	addition	addition	NOUN
fcis-10765	34	9	as	as	SCONJ
fcis-10765	34	10	shown	show	VERB
fcis-10765	34	11	in	in	ADP
fcis-10765	34	12	figure	figure	NOUN
fcis-10765	34	13	3	3	NUM
fcis-10765	34	14	,	,	PUNCT
fcis-10765	34	15	in	in	ADP
fcis-10765	34	16	this	this	DET
fcis-10765	34	17	modified	modify	VERB
fcis-10765	34	18	yolov8	yolov8	NOUN
fcis-10765	34	19	network	network	NOUN
fcis-10765	34	20	architecture	architecture	NOUN
fcis-10765	34	21	,	,	PUNCT
fcis-10765	34	22	an	an	DET
fcis-10765	34	23	se	se	ADJ
fcis-10765	34	24	attention	attention	NOUN
fcis-10765	34	25	mechanism	mechanism	NOUN
fcis-10765	34	26	has	have	AUX
fcis-10765	34	27	been	be	AUX
fcis-10765	34	28	added	add	VERB
fcis-10765	34	29	to	to	ADP
fcis-10765	34	30	the	the	DET
fcis-10765	34	31	final	final	ADJ
fcis-10765	34	32	layer	layer	NOUN
fcis-10765	34	33	of	of	ADP
fcis-10765	34	34	the	the	DET
fcis-10765	34	35	backbone	backbone	NOUN
fcis-10765	34	36	.	.	PUNCT
fcis-10765	35	1	this	this	DET
fcis-10765	35	2	mechanism	mechanism	NOUN
fcis-10765	35	3	uses	use	VERB
fcis-10765	35	4	a	a	DET
fcis-10765	35	5	new	new	ADJ
fcis-10765	35	6	neural	neural	ADJ
fcis-10765	35	7	network	network	NOUN
fcis-10765	35	8	to	to	PART
fcis-10765	35	9	determine	determine	VERB
fcis-10765	35	10	the	the	DET
fcis-10765	35	11	importance	importance	NOUN
fcis-10765	35	12	of	of	ADP
fcis-10765	35	13	each	each	DET
fcis-10765	35	14	channel	channel	NOUN
fcis-10765	35	15	within	within	ADP
fcis-10765	35	16	the	the	DET
fcis-10765	35	17	feature	feature	NOUN
fcis-10765	35	18	map	map	NOUN
fcis-10765	35	19	,	,	PUNCT
fcis-10765	35	20	and	and	CCONJ
fcis-10765	35	21	assigns	assign	VERB
fcis-10765	35	22	weight	weight	NOUN
fcis-10765	35	23	values	value	NOUN
fcis-10765	35	24	to	to	ADP
fcis-10765	35	25	each	each	DET
fcis-10765	35	26	feature	feature	NOUN
fcis-10765	35	27	based	base	VERB
fcis-10765	35	28	on	on	ADP
fcis-10765	35	29	this	this	DET
fcis-10765	35	30	importance	importance	NOUN
fcis-10765	35	31	.	.	PUNCT
fcis-10765	36	1	this	this	PRON
fcis-10765	36	2	allows	allow	VERB
fcis-10765	36	3	the	the	DET
fcis-10765	36	4	neural	neural	ADJ
fcis-10765	36	5	network	network	NOUN
fcis-10765	36	6	to	to	PART
fcis-10765	36	7	prioritize	prioritize	VERB
fcis-10765	36	8	feature	feature	NOUN
fcis-10765	36	9	channels	channel	NOUN
fcis-10765	36	10	that	that	PRON
fcis-10765	36	11	are	be	AUX
fcis-10765	36	12	most	most	ADV
fcis-10765	36	13	relevant	relevant	ADJ
fcis-10765	36	14	to	to	ADP
fcis-10765	36	15	the	the	DET
fcis-10765	36	16	current	current	ADJ
fcis-10765	36	17	task	task	NOUN
fcis-10765	36	18	,	,	PUNCT
fcis-10765	36	19	while	while	SCONJ
fcis-10765	36	20	suppressing	suppress	VERB
fcis-10765	36	21	those	those	PRON
fcis-10765	36	22	that	that	PRON
fcis-10765	36	23	are	be	AUX
fcis-10765	36	24	not	not	PART
fcis-10765	36	25	.	.	PUNCT
fcis-10765	37	1	the	the	DET
fcis-10765	37	2	addition	addition	NOUN
fcis-10765	37	3	of	of	ADP
fcis-10765	37	4	the	the	DET
fcis-10765	37	5	se	se	PROPN
fcis-10765	37	6	attention	attention	NOUN
fcis-10765	37	7	mechanism	mechanism	NOUN
fcis-10765	37	8	can	can	AUX
fcis-10765	37	9	improve	improve	VERB
fcis-10765	37	10	the	the	DET
fcis-10765	37	11	performance	performance	NOUN
fcis-10765	37	12	of	of	ADP
fcis-10765	37	13	the	the	DET
fcis-10765	37	14	yolov8	yolov8	NOUN
fcis-10765	37	15	network	network	NOUN
fcis-10765	37	16	by	by	ADP
fcis-10765	37	17	allowing	allow	VERB
fcis-10765	37	18	it	it	PRON
fcis-10765	37	19	to	to	PART
fcis-10765	37	20	better	well	ADV
fcis-10765	37	21	focus	focus	VERB
fcis-10765	37	22	on	on	ADP
fcis-10765	37	23	the	the	DET
fcis-10765	37	24	most	most	ADV
fcis-10765	37	25	important	important	ADJ
fcis-10765	37	26	features	feature	NOUN
fcis-10765	37	27	in	in	ADP
fcis-10765	37	28	the	the	DET
fcis-10765	37	29	input	input	NOUN
fcis-10765	37	30	data	datum	NOUN
fcis-10765	37	31	.	.	PUNCT
fcis-10765	38	1	this	this	PRON
fcis-10765	38	2	can	can	AUX
fcis-10765	38	3	lead	lead	VERB
fcis-10765	38	4	to	to	ADP
fcis-10765	38	5	more	more	ADV
fcis-10765	38	6	accurate	accurate	ADJ
fcis-10765	38	7	object	object	NOUN
fcis-10765	38	8	detection	detection	NOUN
fcis-10765	38	9	,	,	PUNCT
fcis-10765	38	10	especially	especially	ADV
fcis-10765	38	11	in	in	ADP
fcis-10765	38	12	complex	complex	ADJ
fcis-10765	38	13	scenes	scene	NOUN
fcis-10765	38	14	with	with	ADP
fcis-10765	38	15	many	many	ADJ
fcis-10765	38	16	objects	object	NOUN
fcis-10765	38	17	or	or	CCONJ
fcis-10765	38	18	cluttered	cluttered	ADJ
fcis-10765	38	19	backgrounds	background	NOUN
fcis-10765	38	20	.	.	PUNCT
fcis-10765	39	1	the	the	DET
fcis-10765	39	2	se	se	PROPN
fcis-10765	39	3	attention	attention	NOUN
fcis-10765	39	4	mechanism	mechanism	NOUN
fcis-10765	39	5	also	also	ADV
fcis-10765	39	6	has	have	VERB
fcis-10765	39	7	the	the	DET
fcis-10765	39	8	advantage	advantage	NOUN
fcis-10765	39	9	of	of	ADP
fcis-10765	39	10	being	be	AUX
fcis-10765	39	11	relatively	relatively	ADV
fcis-10765	39	12	lightweight	lightweight	ADJ
fcis-10765	39	13	and	and	CCONJ
fcis-10765	39	14	computationally	computationally	ADV
fcis-10765	39	15	efficient	efficient	ADJ
fcis-10765	39	16	,	,	PUNCT
fcis-10765	39	17	making	make	VERB
fcis-10765	39	18	it	it	PRON
fcis-10765	39	19	well	well	ADV
fcis-10765	39	20	-	-	PUNCT
fcis-10765	39	21	suited	suit	VERB
fcis-10765	39	22	for	for	ADP
fcis-10765	39	23	use	use	NOUN
fcis-10765	39	24	in	in	ADP
fcis-10765	39	25	real	real	ADJ
fcis-10765	39	26	-	-	PUNCT
fcis-10765	39	27	time	time	NOUN
fcis-10765	39	28	applications	application	NOUN
fcis-10765	39	29	.	.	PUNCT
fcis-10765	40	1	overall	overall	ADV
fcis-10765	40	2	,	,	PUNCT
fcis-10765	40	3	the	the	DET
fcis-10765	40	4	addition	addition	NOUN
fcis-10765	40	5	of	of	ADP
fcis-10765	40	6	the	the	DET
fcis-10765	40	7	se	se	PROPN
fcis-10765	40	8	attention	attention	NOUN
fcis-10765	40	9	mechanism	mechanism	NOUN
fcis-10765	40	10	to	to	ADP
fcis-10765	40	11	the	the	DET
fcis-10765	40	12	yolov8	yolov8	NOUN
fcis-10765	40	13	network	network	NOUN
fcis-10765	40	14	architecture	architecture	NOUN
fcis-10765	40	15	represents	represent	VERB
fcis-10765	40	16	a	a	DET
fcis-10765	40	17	significant	significant	ADJ
fcis-10765	40	18	improvement	improvement	NOUN
fcis-10765	40	19	over	over	ADP
fcis-10765	40	20	the	the	DET
fcis-10765	40	21	original	original	ADJ
fcis-10765	40	22	architecture	architecture	NOUN
fcis-10765	40	23	,	,	PUNCT
fcis-10765	40	24	and	and	CCONJ
fcis-10765	40	25	has	have	VERB
fcis-10765	40	26	the	the	DET
fcis-10765	40	27	potential	potential	NOUN
fcis-10765	40	28	to	to	PART
fcis-10765	40	29	greatly	greatly	ADV
fcis-10765	40	30	enhance	enhance	VERB
fcis-10765	40	31	the	the	DET
fcis-10765	40	32	accuracy	accuracy	NOUN
fcis-10765	40	33	and	and	CCONJ
fcis-10765	40	34	efficiency	efficiency	NOUN
fcis-10765	40	35	of	of	ADP
fcis-10765	40	36	object	object	NOUN
fcis-10765	40	37	detection	detection	NOUN
fcis-10765	40	38	tasks	task	NOUN
fcis-10765	40	39	.	.	PUNCT
fcis-10765	41	1	4	4	X
fcis-10765	41	2	.	.	X
fcis-10765	41	3	experiment	experiment	NOUN
fcis-10765	41	4	and	and	CCONJ
fcis-10765	41	5	analysis	analysis	NOUN
fcis-10765	41	6	the	the	DET
fcis-10765	41	7	dataset	dataset	NOUN
fcis-10765	41	8	we	we	PRON
fcis-10765	41	9	used	use	VERB
fcis-10765	41	10	in	in	ADP
fcis-10765	41	11	our	our	PRON
fcis-10765	41	12	research	research	NOUN
fcis-10765	41	13	is	be	AUX
fcis-10765	41	14	called	call	VERB
fcis-10765	41	15	"	"	PUNCT
fcis-10765	41	16	fire	fire	NOUN
fcis-10765	41	17	-	-	PUNCT
fcis-10765	41	18	dataset2000	dataset2000	PROPN
fcis-10765	41	19	"	"	PUNCT
fcis-10765	41	20	,	,	PUNCT
fcis-10765	41	21	which	which	PRON
fcis-10765	41	22	contains	contain	VERB
fcis-10765	41	23	a	a	DET
fcis-10765	41	24	total	total	NOUN
fcis-10765	41	25	of	of	ADP
fcis-10765	41	26	2059	2059	NUM
fcis-10765	41	27	images	image	NOUN
fcis-10765	41	28	.	.	PUNCT
fcis-10765	42	1	each	each	DET
fcis-10765	42	2	image	image	NOUN
fcis-10765	42	3	includes	include	VERB
fcis-10765	42	4	one	one	NUM
fcis-10765	42	5	or	or	CCONJ
fcis-10765	42	6	more	more	ADV
fcis-10765	42	7	annotated	annotated	ADJ
fcis-10765	42	8	bounding	bounding	NOUN
fcis-10765	42	9	boxes	box	NOUN
fcis-10765	42	10	of	of	ADP
fcis-10765	42	11	flames	flame	NOUN
fcis-10765	42	12	.	.	PUNCT
fcis-10765	43	1	an	an	DET
fcis-10765	43	2	example	example	NOUN
fcis-10765	43	3	image	image	NOUN
fcis-10765	43	4	is	be	AUX
fcis-10765	43	5	shown	show	VERB
fcis-10765	43	6	in	in	ADP
fcis-10765	43	7	figure	figure	NOUN
fcis-10765	43	8	4	4	NUM
fcis-10765	43	9	.	.	PUNCT
fcis-10765	43	10	figure	figure	VERB
fcis-10765	43	11	4	4	NUM
fcis-10765	43	12	.	.	NOUN
fcis-10765	43	13	example	example	NOUN
fcis-10765	43	14	graphs	graph	NOUN
fcis-10765	43	15	for	for	ADP
fcis-10765	43	16	each	each	DET
fcis-10765	43	17	category	category	NOUN
fcis-10765	43	18	in	in	ADP
fcis-10765	43	19	our	our	PRON
fcis-10765	43	20	study	study	NOUN
fcis-10765	43	21	,	,	PUNCT
fcis-10765	43	22	we	we	PRON
fcis-10765	43	23	trained	train	VERB
fcis-10765	43	24	the	the	DET
fcis-10765	43	25	yolov8	yolov8	NOUN
fcis-10765	43	26	model	model	NOUN
fcis-10765	43	27	using	use	VERB
fcis-10765	43	28	the	the	DET
fcis-10765	43	29	firedataset-2000	firedataset-2000	ADJ
fcis-10765	43	30	.	.	PUNCT
fcis-10765	44	1	we	we	PRON
fcis-10765	44	2	used	use	VERB
fcis-10765	44	3	a	a	DET
fcis-10765	44	4	stochastic	stochastic	ADJ
fcis-10765	44	5	gradient	gradient	ADJ
fcis-10765	44	6	descent	descent	NOUN
fcis-10765	44	7	algorithm	algorithm	NOUN
fcis-10765	44	8	with	with	ADP
fcis-10765	44	9	a	a	DET
fcis-10765	44	10	batch	batch	NOUN
fcis-10765	44	11	size	size	NOUN
fcis-10765	44	12	of	of	ADP
fcis-10765	44	13	8	8	NUM
fcis-10765	44	14	and	and	CCONJ
fcis-10765	44	15	sgd	sgd	NOUN
fcis-10765	44	16	as	as	ADP
fcis-10765	44	17	the	the	DET
fcis-10765	44	18	optimizer	optimizer	NOUN
fcis-10765	44	19	.	.	PUNCT
fcis-10765	45	1	the	the	DET
fcis-10765	45	2	learning	learning	NOUN
fcis-10765	45	3	rate	rate	NOUN
fcis-10765	45	4	was	be	AUX
fcis-10765	45	5	decayed	decay	VERB
fcis-10765	45	6	to	to	ADP
fcis-10765	45	7	0.01	0.01	NUM
fcis-10765	45	8	after	after	ADP
fcis-10765	45	9	100	100	NUM
fcis-10765	45	10	iterations	iteration	NOUN
fcis-10765	45	11	,	,	PUNCT
fcis-10765	45	12	and	and	CCONJ
fcis-10765	45	13	the	the	DET
fcis-10765	45	14	training	training	NOUN
fcis-10765	45	15	process	process	NOUN
fcis-10765	45	16	continued	continue	VERB
fcis-10765	45	17	for	for	ADP
fcis-10765	45	18	a	a	DET
fcis-10765	45	19	total	total	NOUN
fcis-10765	45	20	of	of	ADP
fcis-10765	45	21	2.2	2.2	NUM
fcis-10765	45	22	hours	hour	NOUN
fcis-10765	45	23	on	on	ADP
fcis-10765	45	24	an	an	DET
fcis-10765	45	25	rtx3060	rtx3060	NOUN
fcis-10765	45	26	device	device	NOUN
fcis-10765	45	27	.	.	PUNCT
fcis-10765	46	1	figure	figure	NOUN
fcis-10765	46	2	5	5	NUM
fcis-10765	46	3	.	.	PUNCT
fcis-10765	47	1	the	the	DET
fcis-10765	47	2	pr	pr	NOUN
fcis-10765	47	3	(	(	PUNCT
fcis-10765	47	4	precision	precision	NOUN
fcis-10765	47	5	-	-	PUNCT
fcis-10765	47	6	recall	recall	NOUN
fcis-10765	47	7	)	)	PUNCT
fcis-10765	47	8	curve	curve	NOUN
fcis-10765	47	9	comparison	comparison	NOUN
fcis-10765	47	10	between	between	ADP
fcis-10765	47	11	the	the	DET
fcis-10765	47	12	original	original	ADJ
fcis-10765	47	13	model	model	NOUN
fcis-10765	47	14	(	(	PUNCT
fcis-10765	47	15	a	a	NOUN
fcis-10765	47	16	)	)	PUNCT
fcis-10765	47	17	and	and	CCONJ
fcis-10765	47	18	the	the	DET
fcis-10765	47	19	model	model	NOUN
fcis-10765	47	20	(	(	PUNCT
fcis-10765	47	21	b	b	NOUN
fcis-10765	47	22	)	)	PUNCT
fcis-10765	47	23	with	with	ADP
fcis-10765	47	24	the	the	DET
fcis-10765	47	25	added	add	VERB
fcis-10765	47	26	se	se	NOUN
fcis-10765	47	27	attention	attention	NOUN
fcis-10765	47	28	mechanism	mechanism	NOUN
fcis-10765	47	29	.	.	PUNCT
fcis-10765	48	1	the	the	DET
fcis-10765	48	2	pr	pr	NOUN
fcis-10765	48	3	(	(	PUNCT
fcis-10765	48	4	precision	precision	NOUN
fcis-10765	48	5	-	-	PUNCT
fcis-10765	48	6	recall	recall	NOUN
fcis-10765	48	7	)	)	PUNCT
fcis-10765	48	8	curve	curve	NOUN
fcis-10765	48	9	is	be	AUX
fcis-10765	48	10	a	a	DET
fcis-10765	48	11	graphical	graphical	ADJ
fcis-10765	48	12	representation	representation	NOUN
fcis-10765	48	13	that	that	PRON
fcis-10765	48	14	shows	show	VERB
fcis-10765	48	15	the	the	DET
fcis-10765	48	16	trade	trade	NOUN
fcis-10765	48	17	-	-	PUNCT
fcis-10765	48	18	off	off	NOUN
fcis-10765	48	19	between	between	ADP
fcis-10765	48	20	precision	precision	NOUN
fcis-10765	48	21	and	and	CCONJ
fcis-10765	48	22	recall	recall	NOUN
fcis-10765	48	23	for	for	ADP
fcis-10765	48	24	different	different	ADJ
fcis-10765	48	25	classification	classification	NOUN
fcis-10765	48	26	thresholds	threshold	NOUN
fcis-10765	48	27	in	in	ADP
fcis-10765	48	28	a	a	DET
fcis-10765	48	29	machine	machine	NOUN
fcis-10765	48	30	learning	learn	VERB
fcis-10765	48	31	model	model	NOUN
fcis-10765	48	32	.	.	PUNCT
fcis-10765	49	1	the	the	DET
fcis-10765	49	2	pr	pr	NOUN
fcis-10765	49	3	curve	curve	NOUN
fcis-10765	49	4	plots	plot	NOUN
fcis-10765	49	5	precision	precision	NOUN
fcis-10765	49	6	on	on	ADP
fcis-10765	49	7	the	the	DET
fcis-10765	49	8	y	y	NOUN
fcis-10765	49	9	-	-	PUNCT
fcis-10765	49	10	axis	axis	NOUN
fcis-10765	49	11	and	and	CCONJ
fcis-10765	49	12	recall	recall	NOUN
fcis-10765	49	13	on	on	ADP
fcis-10765	49	14	the	the	DET
fcis-10765	49	15	x	x	NOUN
fcis-10765	49	16	-	-	NOUN
fcis-10765	49	17	axis	axis	ADJ
fcis-10765	49	18	,	,	PUNCT
fcis-10765	49	19	where	where	SCONJ
fcis-10765	49	20	each	each	DET
fcis-10765	49	21	point	point	NOUN
fcis-10765	49	22	on	on	ADP
fcis-10765	49	23	the	the	DET
fcis-10765	49	24	curve	curve	NOUN
fcis-10765	49	25	corresponds	correspond	VERB
fcis-10765	49	26	to	to	ADP
fcis-10765	49	27	a	a	DET
fcis-10765	49	28	different	different	ADJ
fcis-10765	49	29	threshold	threshold	NOUN
fcis-10765	49	30	value	value	NOUN
fcis-10765	49	31	.	.	PUNCT
fcis-10765	50	1	a	a	DET
fcis-10765	50	2	higher	high	ADJ
fcis-10765	50	3	area	area	NOUN
fcis-10765	50	4	under	under	ADP
fcis-10765	50	5	the	the	DET
fcis-10765	50	6	pr	pr	NOUN
fcis-10765	50	7	curve	curve	NOUN
fcis-10765	50	8	indicates	indicate	VERB
fcis-10765	50	9	better	well	ADJ
fcis-10765	50	10	model	model	NOUN
fcis-10765	50	11	performance	performance	NOUN
fcis-10765	50	12	in	in	ADP
fcis-10765	50	13	terms	term	NOUN
fcis-10765	50	14	of	of	ADP
fcis-10765	50	15	both	both	DET
fcis-10765	50	16	precision	precision	NOUN
fcis-10765	50	17	and	and	CCONJ
fcis-10765	50	18	recall	recall	NOUN
fcis-10765	50	19	.	.	PUNCT
fcis-10765	51	1	the	the	DET
fcis-10765	51	2	map@0.5	map@0.5	PROPN
fcis-10765	51	3	is	be	AUX
fcis-10765	51	4	a	a	DET
fcis-10765	51	5	widely	widely	ADV
fcis-10765	51	6	used	use	VERB
fcis-10765	51	7	metric	metric	NOUN
fcis-10765	51	8	for	for	ADP
fcis-10765	51	9	evaluating	evaluate	VERB
fcis-10765	51	10	the	the	DET
fcis-10765	51	11	performance	performance	NOUN
fcis-10765	51	12	of	of	ADP
fcis-10765	51	13	object	object	NOUN
fcis-10765	51	14	detection	detection	NOUN
fcis-10765	51	15	models	model	NOUN
fcis-10765	51	16	.	.	PUNCT
fcis-10765	52	1	it	it	PRON
fcis-10765	52	2	is	be	AUX
fcis-10765	52	3	the	the	DET
fcis-10765	52	4	mean	mean	ADJ
fcis-10765	52	5	average	average	ADJ
fcis-10765	52	6	precision	precision	NOUN
fcis-10765	52	7	computed	compute	VERB
fcis-10765	52	8	at	at	ADP
fcis-10765	52	9	an	an	DET
fcis-10765	52	10	iou	iou	NOUN
fcis-10765	52	11	threshold	threshold	NOUN
fcis-10765	52	12	of	of	ADP
fcis-10765	52	13	0.5	0.5	NUM
fcis-10765	52	14	,	,	PUNCT
fcis-10765	52	15	which	which	PRON
fcis-10765	52	16	represents	represent	VERB
fcis-10765	52	17	the	the	DET
fcis-10765	52	18	overlap	overlap	NOUN
fcis-10765	52	19	between	between	ADP
fcis-10765	52	20	the	the	DET
fcis-10765	52	21	predicted	predict	VERB
fcis-10765	52	22	and	and	CCONJ
fcis-10765	52	23	ground	ground	NOUN
fcis-10765	52	24	truth	truth	NOUN
fcis-10765	52	25	bounding	bounding	NOUN
fcis-10765	52	26	boxes	box	NOUN
fcis-10765	52	27	.	.	PUNCT
fcis-10765	53	1	as	as	SCONJ
fcis-10765	53	2	shown	show	VERB
fcis-10765	53	3	in	in	ADP
fcis-10765	53	4	figure	figure	NOUN
fcis-10765	53	5	5	5	NUM
fcis-10765	53	6	,	,	PUNCT
fcis-10765	53	7	the	the	DET
fcis-10765	53	8	map@0.5	map@0.5	NOUN
fcis-10765	53	9	value	value	NOUN
fcis-10765	53	10	for	for	ADP
fcis-10765	53	11	the	the	DET
fcis-10765	53	12	original	original	ADJ
fcis-10765	53	13	model	model	NOUN
fcis-10765	53	14	(	(	PUNCT
fcis-10765	53	15	a	a	X
fcis-10765	53	16	)	)	PUNCT
fcis-10765	53	17	on	on	ADP
fcis-10765	53	18	the	the	DET
fcis-10765	53	19	left	left	NOUN
fcis-10765	53	20	is	be	AUX
fcis-10765	53	21	0.707	0.707	NUM
fcis-10765	53	22	,	,	PUNCT
fcis-10765	53	23	while	while	SCONJ
fcis-10765	53	24	the	the	DET
fcis-10765	53	25	map@0.5	map@0.5	NOUN
fcis-10765	53	26	value	value	NOUN
fcis-10765	53	27	for	for	ADP
fcis-10765	53	28	the	the	DET
fcis-10765	53	29	model	model	NOUN
fcis-10765	53	30	(	(	PUNCT
fcis-10765	53	31	b	b	NOUN
fcis-10765	53	32	)	)	PUNCT
fcis-10765	53	33	with	with	ADP
fcis-10765	53	34	the	the	DET
fcis-10765	53	35	added	add	VERB
fcis-10765	53	36	se	se	NOUN
fcis-10765	53	37	attention	attention	NOUN
fcis-10765	53	38	mechanism	mechanism	NOUN
fcis-10765	53	39	on	on	ADP
fcis-10765	53	40	the	the	DET
fcis-10765	53	41	right	right	NOUN
fcis-10765	53	42	is	be	AUX
fcis-10765	53	43	0.730	0.730	NUM
fcis-10765	53	44	.	.	PUNCT
fcis-10765	54	1	this	this	PRON
fcis-10765	54	2	suggests	suggest	VERB
fcis-10765	54	3	that	that	SCONJ
fcis-10765	54	4	the	the	DET
fcis-10765	54	5	model	model	NOUN
fcis-10765	54	6	with	with	ADP
fcis-10765	54	7	the	the	DET
fcis-10765	54	8	se	se	PROPN
fcis-10765	54	9	attention	attention	NOUN
fcis-10765	54	10	mechanism	mechanism	NOUN
fcis-10765	54	11	has	have	AUX
fcis-10765	54	12	improved	improve	VERB
fcis-10765	54	13	the	the	DET
fcis-10765	54	14	overall	overall	ADJ
fcis-10765	54	15	performance	performance	NOUN
fcis-10765	54	16	of	of	ADP
fcis-10765	54	17	the	the	DET
fcis-10765	54	18	object	object	NOUN
fcis-10765	54	19	detection	detection	NOUN
fcis-10765	54	20	system	system	NOUN
fcis-10765	54	21	,	,	PUNCT
fcis-10765	54	22	as	as	SCONJ
fcis-10765	54	23	it	it	PRON
fcis-10765	54	24	has	have	AUX
fcis-10765	54	25	achieved	achieve	VERB
fcis-10765	54	26	a	a	DET
fcis-10765	54	27	higher	high	ADJ
fcis-10765	54	28	map@0.5	map@0.5	NOUN
fcis-10765	54	29	value	value	NOUN
fcis-10765	54	30	.	.	PUNCT
fcis-10765	55	1	the	the	DET
fcis-10765	55	2	improvement	improvement	NOUN
fcis-10765	55	3	in	in	ADP
fcis-10765	55	4	map@0.5	map@0.5	PROPN
fcis-10765	55	5	indicates	indicate	VERB
fcis-10765	55	6	that	that	SCONJ
fcis-10765	55	7	the	the	DET
fcis-10765	55	8	model	model	NOUN
fcis-10765	55	9	with	with	ADP
fcis-10765	55	10	the	the	DET
fcis-10765	55	11	se	se	PROPN
fcis-10765	55	12	attention	attention	NOUN
fcis-10765	55	13	mechanism	mechanism	NOUN
fcis-10765	55	14	is	be	AUX
fcis-10765	55	15	better	well	ADJ
fcis-10765	55	16	at	at	ADP
fcis-10765	55	17	accurately	accurately	ADV
fcis-10765	55	18	detecting	detect	VERB
fcis-10765	55	19	and	and	CCONJ
fcis-10765	55	20	localizing	localize	VERB
fcis-10765	55	21	objects	object	NOUN
fcis-10765	55	22	with	with	ADP
fcis-10765	55	23	a	a	DET
fcis-10765	55	24	moderate	moderate	ADJ
fcis-10765	55	25	level	level	NOUN
fcis-10765	55	26	of	of	ADP
fcis-10765	55	27	overlap	overlap	NOUN
fcis-10765	55	28	between	between	ADP
fcis-10765	55	29	the	the	DET
fcis-10765	55	30	predicted	predict	VERB
fcis-10765	55	31	and	and	CCONJ
fcis-10765	55	32	ground	ground	NOUN
fcis-10765	55	33	truth	truth	NOUN
fcis-10765	55	34	bounding	bounding	NOUN
fcis-10765	55	35	boxes	box	NOUN
fcis-10765	55	36	.	.	PUNCT
fcis-10765	56	1	30	30	NUM
fcis-10765	56	2	5	5	NUM
fcis-10765	56	3	.	.	PUNCT
fcis-10765	56	4	experimental	experimental	ADJ
fcis-10765	56	5	application	application	NOUN
fcis-10765	56	6	and	and	CCONJ
fcis-10765	56	7	results	result	NOUN
fcis-10765	56	8	figure	figure	VERB
fcis-10765	56	9	6	6	NUM
fcis-10765	56	10	demonstrates	demonstrate	VERB
fcis-10765	56	11	that	that	SCONJ
fcis-10765	56	12	our	our	PRON
fcis-10765	56	13	trained	train	VERB
fcis-10765	56	14	model	model	NOUN
fcis-10765	56	15	accurately	accurately	ADV
fcis-10765	56	16	detects	detect	NOUN
fcis-10765	56	17	and	and	CCONJ
fcis-10765	56	18	labels	label	NOUN
fcis-10765	56	19	all	all	DET
fcis-10765	56	20	potential	potential	ADJ
fcis-10765	56	21	targets	target	NOUN
fcis-10765	56	22	in	in	ADP
fcis-10765	56	23	the	the	DET
fcis-10765	56	24	images	image	NOUN
fcis-10765	56	25	,	,	PUNCT
fcis-10765	56	26	even	even	ADV
fcis-10765	56	27	when	when	SCONJ
fcis-10765	56	28	they	they	PRON
fcis-10765	56	29	are	be	AUX
fcis-10765	56	30	partially	partially	ADV
fcis-10765	56	31	or	or	CCONJ
fcis-10765	56	32	fully	fully	ADV
fcis-10765	56	33	obscured	obscure	VERB
fcis-10765	56	34	.	.	PUNCT
fcis-10765	57	1	notably	notably	ADV
fcis-10765	57	2	,	,	PUNCT
fcis-10765	57	3	the	the	DET
fcis-10765	57	4	model	model	NOUN
fcis-10765	57	5	is	be	AUX
fcis-10765	57	6	not	not	PART
fcis-10765	57	7	limited	limit	VERB
fcis-10765	57	8	to	to	ADP
fcis-10765	57	9	distinguishing	distinguish	VERB
fcis-10765	57	10	a	a	DET
fcis-10765	57	11	single	single	ADJ
fcis-10765	57	12	category	category	NOUN
fcis-10765	57	13	per	per	ADP
fcis-10765	57	14	image	image	NOUN
fcis-10765	57	15	but	but	CCONJ
fcis-10765	57	16	can	can	AUX
fcis-10765	57	17	identify	identify	VERB
fcis-10765	57	18	multiple	multiple	ADJ
fcis-10765	57	19	targets	target	NOUN
fcis-10765	57	20	.	.	PUNCT
fcis-10765	58	1	it	it	PRON
fcis-10765	58	2	is	be	AUX
fcis-10765	58	3	important	important	ADJ
fcis-10765	58	4	to	to	PART
fcis-10765	58	5	highlight	highlight	VERB
fcis-10765	58	6	the	the	DET
fcis-10765	58	7	remarkable	remarkable	ADJ
fcis-10765	58	8	performance	performance	NOUN
fcis-10765	58	9	of	of	ADP
fcis-10765	58	10	the	the	DET
fcis-10765	58	11	model	model	NOUN
fcis-10765	58	12	,	,	PUNCT
fcis-10765	58	13	considering	consider	VERB
fcis-10765	58	14	its	its	PRON
fcis-10765	58	15	ability	ability	NOUN
fcis-10765	58	16	to	to	PART
fcis-10765	58	17	process	process	VERB
fcis-10765	58	18	each	each	DET
fcis-10765	58	19	image	image	NOUN
fcis-10765	58	20	in	in	ADP
fcis-10765	58	21	just	just	ADV
fcis-10765	58	22	20	20	NUM
fcis-10765	58	23	milliseconds	millisecond	NOUN
fcis-10765	58	24	.	.	PUNCT
fcis-10765	59	1	this	this	DET
fcis-10765	59	2	exceptional	exceptional	ADJ
fcis-10765	59	3	efficiency	efficiency	NOUN
fcis-10765	59	4	and	and	CCONJ
fcis-10765	59	5	speed	speed	NOUN
fcis-10765	59	6	make	make	VERB
fcis-10765	59	7	it	it	PRON
fcis-10765	59	8	an	an	DET
fcis-10765	59	9	ideal	ideal	ADJ
fcis-10765	59	10	choice	choice	NOUN
fcis-10765	59	11	for	for	ADP
fcis-10765	59	12	real	real	ADJ
fcis-10765	59	13	-	-	PUNCT
fcis-10765	59	14	time	time	NOUN
fcis-10765	59	15	applications	application	NOUN
fcis-10765	59	16	.	.	PUNCT
fcis-10765	60	1	figure	figure	VERB
fcis-10765	60	2	6	6	NUM
fcis-10765	60	3	.	.	PUNCT
fcis-10765	60	4	classification	classification	NOUN
fcis-10765	60	5	prediction	prediction	NOUN
fcis-10765	60	6	result	result	VERB
fcis-10765	60	7	6	6	NUM
fcis-10765	60	8	.	.	X
fcis-10765	61	1	conclusion	conclusion	NOUN
fcis-10765	61	2	our	our	PRON
fcis-10765	61	3	study	study	NOUN
fcis-10765	61	4	demonstrates	demonstrate	VERB
fcis-10765	61	5	the	the	DET
fcis-10765	61	6	impressive	impressive	ADJ
fcis-10765	61	7	performance	performance	NOUN
fcis-10765	61	8	of	of	ADP
fcis-10765	61	9	our	our	PRON
fcis-10765	61	10	trained	train	VERB
fcis-10765	61	11	model	model	NOUN
fcis-10765	61	12	for	for	ADP
fcis-10765	61	13	fire	fire	NOUN
fcis-10765	61	14	detection	detection	NOUN
fcis-10765	61	15	and	and	CCONJ
fcis-10765	61	16	recognition	recognition	NOUN
fcis-10765	61	17	tasks	task	NOUN
fcis-10765	61	18	.	.	PUNCT
fcis-10765	62	1	the	the	DET
fcis-10765	62	2	model	model	NOUN
fcis-10765	62	3	accurately	accurately	ADV
fcis-10765	62	4	predicts	predict	VERB
fcis-10765	62	5	and	and	CCONJ
fcis-10765	62	6	marks	mark	VERB
fcis-10765	62	7	all	all	DET
fcis-10765	62	8	possible	possible	ADJ
fcis-10765	62	9	targets	target	NOUN
fcis-10765	62	10	in	in	ADP
fcis-10765	62	11	images	image	NOUN
fcis-10765	62	12	,	,	PUNCT
fcis-10765	62	13	even	even	ADV
fcis-10765	62	14	when	when	SCONJ
fcis-10765	62	15	targets	target	NOUN
fcis-10765	62	16	are	be	AUX
fcis-10765	62	17	partially	partially	ADV
fcis-10765	62	18	or	or	CCONJ
fcis-10765	62	19	fully	fully	ADV
fcis-10765	62	20	blocked	block	VERB
fcis-10765	62	21	,	,	PUNCT
fcis-10765	62	22	which	which	PRON
fcis-10765	62	23	is	be	AUX
fcis-10765	62	24	crucial	crucial	ADJ
fcis-10765	62	25	for	for	ADP
fcis-10765	62	26	detecting	detect	VERB
fcis-10765	62	27	and	and	CCONJ
fcis-10765	62	28	locating	locate	VERB
fcis-10765	62	29	fires	fire	NOUN
fcis-10765	62	30	in	in	ADP
fcis-10765	62	31	complex	complex	ADJ
fcis-10765	62	32	scenarios	scenario	NOUN
fcis-10765	62	33	.	.	PUNCT
fcis-10765	63	1	additionally	additionally	ADV
fcis-10765	63	2	,	,	PUNCT
fcis-10765	63	3	the	the	DET
fcis-10765	63	4	model	model	NOUN
fcis-10765	63	5	's	's	PART
fcis-10765	63	6	efficiency	efficiency	NOUN
fcis-10765	63	7	and	and	CCONJ
fcis-10765	63	8	speed	speed	NOUN
fcis-10765	63	9	,	,	PUNCT
fcis-10765	63	10	with	with	SCONJ
fcis-10765	63	11	each	each	DET
fcis-10765	63	12	image	image	NOUN
fcis-10765	63	13	processed	process	VERB
fcis-10765	63	14	in	in	ADP
fcis-10765	63	15	just	just	ADV
fcis-10765	63	16	20	20	NUM
fcis-10765	63	17	milliseconds	millisecond	NOUN
fcis-10765	63	18	,	,	PUNCT
fcis-10765	63	19	make	make	VERB
fcis-10765	63	20	it	it	PRON
fcis-10765	63	21	well	well	ADV
fcis-10765	63	22	-	-	PUNCT
fcis-10765	63	23	suited	suit	VERB
fcis-10765	63	24	for	for	ADP
fcis-10765	63	25	real	real	ADJ
fcis-10765	63	26	-	-	PUNCT
fcis-10765	63	27	time	time	NOUN
fcis-10765	63	28	fire	fire	NOUN
fcis-10765	63	29	detection	detection	NOUN
fcis-10765	63	30	applications	application	NOUN
fcis-10765	63	31	.	.	PUNCT
fcis-10765	64	1	overall	overall	ADV
fcis-10765	64	2	,	,	PUNCT
fcis-10765	64	3	our	our	PRON
fcis-10765	64	4	model	model	NOUN
fcis-10765	64	5	has	have	VERB
fcis-10765	64	6	great	great	ADJ
fcis-10765	64	7	potential	potential	NOUN
fcis-10765	64	8	for	for	ADP
fcis-10765	64	9	detecting	detect	VERB
fcis-10765	64	10	fires	fire	NOUN
fcis-10765	64	11	and	and	CCONJ
fcis-10765	64	12	other	other	ADJ
fcis-10765	64	13	image	image	NOUN
fcis-10765	64	14	recognition	recognition	NOUN
fcis-10765	64	15	tasks	task	NOUN
fcis-10765	64	16	,	,	PUNCT
fcis-10765	64	17	improving	improve	VERB
fcis-10765	64	18	the	the	DET
fcis-10765	64	19	accuracy	accuracy	NOUN
fcis-10765	64	20	and	and	CCONJ
fcis-10765	64	21	speed	speed	NOUN
fcis-10765	64	22	of	of	ADP
fcis-10765	64	23	fire	fire	NOUN
fcis-10765	64	24	detection	detection	NOUN
fcis-10765	64	25	systems	system	NOUN
fcis-10765	64	26	,	,	PUNCT
fcis-10765	64	27	and	and	CCONJ
fcis-10765	64	28	ultimately	ultimately	ADV
fcis-10765	64	29	reducing	reduce	VERB
fcis-10765	64	30	the	the	DET
fcis-10765	64	31	risk	risk	NOUN
fcis-10765	64	32	and	and	CCONJ
fcis-10765	64	33	impact	impact	NOUN
fcis-10765	64	34	of	of	ADP
fcis-10765	64	35	fires	fire	NOUN
fcis-10765	64	36	.	.	PUNCT
fcis-10765	65	1	references	reference	NOUN
fcis-10765	65	2	[	[	X
fcis-10765	65	3	1	1	NUM
fcis-10765	65	4	]	]	X
fcis-10765	65	5	guo	guo	PROPN
fcis-10765	65	6	zhuohao	zhuohao	PROPN
fcis-10765	65	7	,	,	PUNCT
fcis-10765	65	8	peng	peng	PROPN
fcis-10765	65	9	jiechen	jiechen	PROPN
fcis-10765	65	10	,	,	PUNCT
fcis-10765	66	1	zhuang	zhuang	PROPN
fcis-10765	66	2	yihui	yihui	PROPN
fcis-10765	66	3	et	et	PROPN
fcis-10765	66	4	al	al	PROPN
fcis-10765	66	5	.	.	PROPN
fcis-10765	66	6	design	design	NOUN
fcis-10765	66	7	of	of	ADP
fcis-10765	66	8	multi	multi	ADJ
fcis-10765	66	9	-	-	ADJ
fcis-10765	66	10	mode	mode	ADJ
fcis-10765	66	11	fire	fire	NOUN
fcis-10765	66	12	detection	detection	NOUN
fcis-10765	66	13	and	and	CCONJ
fcis-10765	66	14	alarm	alarm	NOUN
fcis-10765	66	15	system	system	NOUN
fcis-10765	67	1	[	[	X
fcis-10765	67	2	j	j	X
fcis-10765	67	3	]	]	X
fcis-10765	67	4	.	.	PUNCT
fcis-10765	68	1	journal	journal	PROPN
fcis-10765	68	2	of	of	ADP
fcis-10765	68	3	shaoguan	shaoguan	PROPN
fcis-10765	68	4	university,2022,43(09):54	university,2022,43(09):54	PROPN
fcis-10765	68	5	-	-	PUNCT
fcis-10765	68	6	59	59	NUM
fcis-10765	68	7	.	.	PUNCT
fcis-10765	69	1	[	[	X
fcis-10765	69	2	2	2	NUM
fcis-10765	69	3	]	]	PUNCT
fcis-10765	69	4	xun	xun	PROPN
fcis-10765	69	5	lei	lei	PROPN
fcis-10765	69	6	.	.	PROPN
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fcis-10765	69	9	laboratory	laboratory	NOUN
fcis-10765	69	10	fire	fire	NOUN
fcis-10765	69	11	monitoring	monitoring	NOUN
fcis-10765	69	12	and	and	CCONJ
fcis-10765	69	13	control	control	NOUN
fcis-10765	69	14	system	system	NOUN
fcis-10765	69	15	based	base	VERB
fcis-10765	69	16	on	on	ADP
fcis-10765	69	17	multi	multi	ADJ
fcis-10765	69	18	-	-	ADJ
fcis-10765	69	19	sensor	sensor	ADJ
fcis-10765	69	20	fusion	fusion	NOUN
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fcis-10765	69	22	j	j	X
fcis-10765	69	23	]	]	X
fcis-10765	69	24	.	.	PUNCT
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fcis-10765	70	2	of	of	ADP
fcis-10765	70	3	anhui	anhui	PROPN
fcis-10765	70	4	electronic	electronic	ADJ
fcis-10765	70	5	information	information	PROPN
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fcis-10765	70	7	college	college	NOUN
fcis-10765	70	8	,	,	PUNCT
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fcis-10765	70	10	-	-	SYM
fcis-10765	70	11	13	13	NUM
fcis-10765	70	12	.	.	PUNCT
fcis-10765	71	1	[	[	X
fcis-10765	71	2	3	3	X
fcis-10765	71	3	]	]	X
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fcis-10765	71	5	hongchun	hongchun	PROPN
fcis-10765	71	6	,	,	PUNCT
fcis-10765	71	7	tao	tao	PROPN
fcis-10765	71	8	lei	lei	PROPN
fcis-10765	71	9	.	.	PROPN
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fcis-10765	71	11	detection	detection	NOUN
fcis-10765	71	12	and	and	CCONJ
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fcis-10765	71	14	based	base	VERB
fcis-10765	71	15	on	on	ADP
fcis-10765	71	16	improved	improved	ADJ
fcis-10765	71	17	yolov8	yolov8	NOUN
fcis-10765	71	18	electronic	electronic	ADJ
fcis-10765	71	19	monitoring	monitoring	NOUN
fcis-10765	71	20	data	datum	NOUN
fcis-10765	71	21	of	of	ADP
fcis-10765	71	22	commercial	commercial	ADJ
fcis-10765	71	23	fishing	fishing	NOUN
fcis-10765	71	24	vessels	vessel	NOUN
fcis-10765	71	25	[	[	X
fcis-10765	71	26	j	j	X
fcis-10765	71	27	]	]	X
fcis-10765	71	28	.	.	PUNCT
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fcis-10765	72	2	of	of	ADP
fcis-10765	72	3	dalian	dalian	PROPN
fcis-10765	72	4	ocean	ocean	PROPN
fcis-10765	72	5	university	university	PROPN
fcis-10765	72	6	,	,	PUNCT
fcis-10765	72	7	2023	2023	NUM
fcis-10765	72	8	,	,	PUNCT
fcis-10765	72	9	38	38	NUM
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fcis-10765	72	16	.	.	PUNCT
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fcis-10765	73	2	doi	doi	NOUN
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fcis-10765	73	11	.	.	PUNCT
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fcis-10765	74	2	-	-	SYM
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fcis-10765	74	4	.	.	PUNCT
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fcis-10765	75	3	]	]	X
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fcis-10765	75	5	pengcheng	pengcheng	PROPN
fcis-10765	75	6	,	,	PUNCT
fcis-10765	75	7	xu	xu	PROPN
fcis-10765	75	8	wenguang	wenguang	PROPN
fcis-10765	75	9	,	,	PUNCT
fcis-10765	75	10	zhang	zhang	PROPN
fcis-10765	75	11	yan	yan	PROPN
fcis-10765	75	12	,	,	PUNCT
fcis-10765	75	13	zhu	zhu	PROPN
fcis-10765	75	14	liguang	liguang	PROPN
fcis-10765	75	15	,	,	PUNCT
fcis-10765	75	16	zhu	zhu	PROPN
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fcis-10765	75	21	.	.	PUNCT
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fcis-10765	76	3	rating	rating	NOUN
fcis-10765	76	4	method	method	NOUN
fcis-10765	76	5	based	base	VERB
fcis-10765	76	6	on	on	ADP
fcis-10765	76	7	se	se	X
fcis-10765	76	8	attention	attention	NOUN
fcis-10765	76	9	mechanism	mechanism	NOUN
fcis-10765	77	1	[	[	X
fcis-10765	77	2	j	j	X
fcis-10765	77	3	]	]	X
fcis-10765	77	4	.	.	PUNCT
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fcis-10765	78	4	science	science	NOUN
fcis-10765	78	5	,	,	PUNCT
fcis-10765	78	6	2023	2023	NUM
fcis-10765	78	7	,	,	PUNCT
fcis-10765	78	8	(	(	PUNCT
fcis-10765	78	9	8)	8)	NUM
fcis-10765	78	10	:	:	PUNCT
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fcis-10765	78	12	-	-	SYM
fcis-10765	78	13	1352	1352	NUM
fcis-10765	78	14	.	.	PUNCT
fcis-10765	79	1	the	the	DET
fcis-10765	79	2	doi	doi	NOUN
fcis-10765	79	3	:	:	PUNCT
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fcis-10765	79	5	/	/	SYM
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fcis-10765	79	9	-	-	SYM
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fcis-10765	79	11	.	.	PUNCT