id	sid	tid	token	lemma	pos
fcis-18357	1	1	frontiers	frontier	NOUN
fcis-18357	1	2	in	in	ADP
fcis-18357	1	3	computing	computing	NOUN
fcis-18357	1	4	and	and	CCONJ
fcis-18357	1	5	intelligent	intelligent	ADJ
fcis-18357	1	6	systems	system	NOUN
fcis-18357	1	7	issn	issn	VERB
fcis-18357	1	8	:	:	PUNCT
fcis-18357	1	9	2832	2832	NUM
fcis-18357	1	10	-	-	SYM
fcis-18357	1	11	6024	6024	NUM
fcis-18357	1	12	|	|	NOUN
fcis-18357	1	13	vol	vol	NOUN
fcis-18357	1	14	.	.	PROPN
fcis-18357	2	1	7	7	NUM
fcis-18357	2	2	,	,	PUNCT
fcis-18357	2	3	no	no	INTJ
fcis-18357	2	4	.	.	NOUN
fcis-18357	2	5	2	2	NUM
fcis-18357	2	6	,	,	PUNCT
fcis-18357	2	7	2024	2024	NUM
fcis-18357	2	8	65	65	NUM
fcis-18357	2	9	research	research	NOUN
fcis-18357	2	10	on	on	ADP
fcis-18357	2	11	improved	improved	ADJ
fcis-18357	2	12	algorithm	algorithm	NOUN
fcis-18357	2	13	for	for	ADP
fcis-18357	2	14	small	small	ADJ
fcis-18357	2	15	object	object	NOUN
fcis-18357	2	16	detection	detection	NOUN
fcis-18357	2	17	in	in	ADP
fcis-18357	2	18	intelligent	intelligent	ADJ
fcis-18357	2	19	surveillance	surveillance	NOUN
fcis-18357	2	20	video	video	NOUN
fcis-18357	2	21	based	base	VERB
fcis-18357	2	22	on	on	ADP
fcis-18357	2	23	yolov7	yolov7	PROPN
fcis-18357	2	24	zhiwei	zhiwei	PROPN
fcis-18357	2	25	wang	wang	PROPN
fcis-18357	2	26	1	1	NUM
fcis-18357	2	27	,	,	PUNCT
fcis-18357	2	28	*	*	PUNCT
fcis-18357	2	29	,	,	PUNCT
fcis-18357	2	30	min	min	PROPN
fcis-18357	2	31	wang	wang	PROPN
fcis-18357	2	32	2	2	NUM
fcis-18357	2	33	1	1	NUM
fcis-18357	2	34	school	school	NOUN
fcis-18357	2	35	of	of	ADP
fcis-18357	2	36	cyberspace	cyberspace	NOUN
fcis-18357	2	37	security	security	NOUN
fcis-18357	2	38	,	,	PUNCT
fcis-18357	2	39	chengdu	chengdu	PROPN
fcis-18357	2	40	university	university	PROPN
fcis-18357	2	41	of	of	ADP
fcis-18357	2	42	information	information	NOUN
fcis-18357	2	43	technology	technology	NOUN
fcis-18357	2	44	(	(	PUNCT
fcis-18357	2	45	cuit	cuit	PROPN
fcis-18357	2	46	)	)	PUNCT
fcis-18357	2	47	,	,	PUNCT
fcis-18357	2	48	chengdu	chengdu	PROPN
fcis-18357	2	49	,	,	PUNCT
fcis-18357	2	50	sichuan	sichuan	PROPN
fcis-18357	2	51	,	,	PUNCT
fcis-18357	2	52	china	china	PROPN
fcis-18357	2	53	2	2	NUM
fcis-18357	2	54	dept	dept	NOUN
fcis-18357	2	55	of	of	ADP
fcis-18357	2	56	cyber	cyber	NOUN
fcis-18357	2	57	space	space	NOUN
fcis-18357	2	58	security	security	PROPN
fcis-18357	2	59	academy	academy	PROPN
fcis-18357	2	60	,	,	PUNCT
fcis-18357	2	61	chengdu	chengdu	PROPN
fcis-18357	2	62	university	university	PROPN
fcis-18357	2	63	of	of	ADP
fcis-18357	2	64	information	information	NOUN
fcis-18357	2	65	technology	technology	PROPN
fcis-18357	2	66	,	,	PUNCT
fcis-18357	2	67	chengdu	chengdu	PROPN
fcis-18357	2	68	,	,	PUNCT
fcis-18357	2	69	sichuan	sichuan	PROPN
fcis-18357	2	70	,	,	PUNCT
fcis-18357	2	71	610225	610225	NUM
fcis-18357	2	72	,	,	PUNCT
fcis-18357	2	73	china	china	PROPN
fcis-18357	2	74	*	*	PUNCT
fcis-18357	2	75	corresponding	correspond	VERB
fcis-18357	2	76	author	author	NOUN
fcis-18357	2	77	:	:	PUNCT
fcis-18357	2	78	zhiwei	zhiwei	PROPN
fcis-18357	2	79	wang	wang	PROPN
fcis-18357	2	80	(	(	PUNCT
fcis-18357	2	81	email	email	NOUN
fcis-18357	2	82	:	:	PUNCT
fcis-18357	2	83	wzw19980331@163.com	wzw19980331@163.com	X
fcis-18357	2	84	)	)	PUNCT
fcis-18357	2	85	abstract	abstract	NOUN
fcis-18357	2	86	:	:	PUNCT
fcis-18357	2	87	in	in	ADP
fcis-18357	2	88	order	order	NOUN
fcis-18357	2	89	to	to	PART
fcis-18357	2	90	address	address	VERB
fcis-18357	2	91	the	the	DET
fcis-18357	2	92	issue	issue	NOUN
fcis-18357	2	93	of	of	ADP
fcis-18357	2	94	small	small	ADJ
fcis-18357	2	95	objects	object	NOUN
fcis-18357	2	96	being	be	AUX
fcis-18357	2	97	difficult	difficult	ADJ
fcis-18357	2	98	to	to	PART
fcis-18357	2	99	detect	detect	VERB
fcis-18357	2	100	effectively	effectively	ADV
fcis-18357	2	101	in	in	ADP
fcis-18357	2	102	intelligent	intelligent	ADJ
fcis-18357	2	103	surveillance	surveillance	NOUN
fcis-18357	2	104	videos	video	NOUN
fcis-18357	2	105	,	,	PUNCT
fcis-18357	2	106	this	this	DET
fcis-18357	2	107	study	study	NOUN
fcis-18357	2	108	proposes	propose	VERB
fcis-18357	2	109	an	an	DET
fcis-18357	2	110	improved	improved	ADJ
fcis-18357	2	111	scheme	scheme	NOUN
fcis-18357	2	112	for	for	ADP
fcis-18357	2	113	the	the	DET
fcis-18357	2	114	yolov7	yolov7	ADV
fcis-18357	2	115	-	-	PUNCT
fcis-18357	2	116	tiny	tiny	ADJ
fcis-18357	2	117	algorithm	algorithm	NOUN
fcis-18357	2	118	.	.	PUNCT
fcis-18357	3	1	this	this	DET
fcis-18357	3	2	scheme	scheme	NOUN
fcis-18357	3	3	integrates	integrate	VERB
fcis-18357	3	4	the	the	DET
fcis-18357	3	5	convolutional	convolutional	ADJ
fcis-18357	3	6	block	block	NOUN
fcis-18357	3	7	attention	attention	NOUN
fcis-18357	3	8	module	module	NOUN
fcis-18357	3	9	(	(	PUNCT
fcis-18357	3	10	cbam	cbam	NOUN
fcis-18357	3	11	)	)	PUNCT
fcis-18357	3	12	into	into	ADP
fcis-18357	3	13	yolov7	yolov7	ADV
fcis-18357	3	14	-	-	PUNCT
fcis-18357	3	15	tiny	tiny	ADJ
fcis-18357	3	16	,	,	PUNCT
fcis-18357	3	17	effectively	effectively	ADV
fcis-18357	3	18	enhancing	enhance	VERB
fcis-18357	3	19	the	the	DET
fcis-18357	3	20	model	model	NOUN
fcis-18357	3	21	's	's	PART
fcis-18357	3	22	feature	feature	NOUN
fcis-18357	3	23	extraction	extraction	NOUN
fcis-18357	3	24	and	and	CCONJ
fcis-18357	3	25	small	small	ADJ
fcis-18357	3	26	object	object	NOUN
fcis-18357	3	27	detection	detection	NOUN
fcis-18357	3	28	capabilities	capability	NOUN
fcis-18357	3	29	in	in	ADP
fcis-18357	3	30	complex	complex	ADJ
fcis-18357	3	31	backgrounds	background	NOUN
fcis-18357	3	32	,	,	PUNCT
fcis-18357	3	33	thereby	thereby	ADV
fcis-18357	3	34	improving	improve	VERB
fcis-18357	3	35	the	the	DET
fcis-18357	3	36	overall	overall	ADJ
fcis-18357	3	37	detection	detection	NOUN
fcis-18357	3	38	precision	precision	NOUN
fcis-18357	3	39	.	.	PUNCT
fcis-18357	4	1	experimental	experimental	ADJ
fcis-18357	4	2	evaluations	evaluation	NOUN
fcis-18357	4	3	indicate	indicate	VERB
fcis-18357	4	4	that	that	SCONJ
fcis-18357	4	5	the	the	DET
fcis-18357	4	6	improved	improved	ADJ
fcis-18357	4	7	algorithm	algorithm	NOUN
fcis-18357	4	8	shows	show	VERB
fcis-18357	4	9	enhanced	enhance	VERB
fcis-18357	4	10	performance	performance	NOUN
fcis-18357	4	11	in	in	ADP
fcis-18357	4	12	specific	specific	ADJ
fcis-18357	4	13	small	small	ADJ
fcis-18357	4	14	object	object	NOUN
fcis-18357	4	15	detection	detection	NOUN
fcis-18357	4	16	tasks	task	NOUN
fcis-18357	4	17	,	,	PUNCT
fcis-18357	4	18	achieving	achieve	VERB
fcis-18357	4	19	an	an	DET
fcis-18357	4	20	accuracy	accuracy	NOUN
fcis-18357	4	21	of	of	ADP
fcis-18357	4	22	85.6	85.6	NUM
fcis-18357	4	23	%	%	NOUN
fcis-18357	4	24	,	,	PUNCT
fcis-18357	4	25	a	a	DET
fcis-18357	4	26	recall	recall	NOUN
fcis-18357	4	27	rate	rate	NOUN
fcis-18357	4	28	of	of	ADP
fcis-18357	4	29	85.2	85.2	NUM
fcis-18357	4	30	%	%	NOUN
fcis-18357	4	31	,	,	PUNCT
fcis-18357	4	32	and	and	CCONJ
fcis-18357	4	33	a	a	DET
fcis-18357	4	34	mean	mean	ADJ
fcis-18357	4	35	average	average	ADJ
fcis-18357	4	36	precision	precision	NOUN
fcis-18357	4	37	(	(	PUNCT
fcis-18357	4	38	map	map	NOUN
fcis-18357	4	39	)	)	PUNCT
fcis-18357	4	40	of	of	ADP
fcis-18357	4	41	90.2	90.2	NUM
fcis-18357	4	42	%	%	NOUN
fcis-18357	4	43	.	.	PUNCT
fcis-18357	5	1	these	these	DET
fcis-18357	5	2	results	result	NOUN
fcis-18357	5	3	demonstrate	demonstrate	VERB
fcis-18357	5	4	the	the	DET
fcis-18357	5	5	effectiveness	effectiveness	NOUN
fcis-18357	5	6	and	and	CCONJ
fcis-18357	5	7	practical	practical	ADJ
fcis-18357	5	8	value	value	NOUN
fcis-18357	5	9	of	of	ADP
fcis-18357	5	10	the	the	DET
fcis-18357	5	11	improved	improve	VERB
fcis-18357	5	12	scheme	scheme	NOUN
fcis-18357	5	13	in	in	ADP
fcis-18357	5	14	enhancing	enhance	VERB
fcis-18357	5	15	the	the	DET
fcis-18357	5	16	performance	performance	NOUN
fcis-18357	5	17	of	of	ADP
fcis-18357	5	18	yolov7	yolov7	NOUN
fcis-18357	5	19	-	-	PUNCT
fcis-18357	5	20	tiny	tiny	ADJ
fcis-18357	5	21	in	in	ADP
fcis-18357	5	22	small	small	ADJ
fcis-18357	5	23	object	object	NOUN
fcis-18357	5	24	detection	detection	NOUN
fcis-18357	5	25	tasks	task	NOUN
fcis-18357	5	26	.	.	PUNCT
fcis-18357	6	1	keywords	keyword	NOUN
fcis-18357	6	2	:	:	PUNCT
fcis-18357	6	3	intelligent	intelligent	ADJ
fcis-18357	6	4	video	video	NOUN
fcis-18357	6	5	surveillance	surveillance	NOUN
fcis-18357	6	6	;	;	PUNCT
fcis-18357	6	7	yolov7	yolov7	NOUN
fcis-18357	6	8	-	-	PUNCT
fcis-18357	6	9	tiny	tiny	ADJ
fcis-18357	6	10	;	;	PUNCT
fcis-18357	6	11	object	object	NOUN
fcis-18357	6	12	detection	detection	NOUN
fcis-18357	6	13	;	;	PUNCT
fcis-18357	6	14	small	small	ADJ
fcis-18357	6	15	object	object	NOUN
fcis-18357	6	16	;	;	PUNCT
fcis-18357	6	17	attention	attention	NOUN
fcis-18357	6	18	mechanism	mechanism	NOUN
fcis-18357	6	19	.	.	PUNCT
fcis-18357	7	1	1	1	X
fcis-18357	7	2	.	.	X
fcis-18357	7	3	introduction	introduction	NOUN
fcis-18357	7	4	in	in	ADP
fcis-18357	7	5	the	the	DET
fcis-18357	7	6	field	field	NOUN
fcis-18357	7	7	of	of	ADP
fcis-18357	7	8	intelligent	intelligent	ADJ
fcis-18357	7	9	video	video	NOUN
fcis-18357	7	10	surveillance	surveillance	NOUN
fcis-18357	7	11	,	,	PUNCT
fcis-18357	7	12	small	small	ADJ
fcis-18357	7	13	object	object	NOUN
fcis-18357	7	14	detection	detection	NOUN
fcis-18357	7	15	is	be	AUX
fcis-18357	7	16	an	an	DET
fcis-18357	7	17	extremely	extremely	ADV
fcis-18357	7	18	challenging	challenging	ADJ
fcis-18357	7	19	task	task	NOUN
fcis-18357	7	20	that	that	PRON
fcis-18357	7	21	requires	require	VERB
fcis-18357	7	22	the	the	DET
fcis-18357	7	23	system	system	NOUN
fcis-18357	7	24	to	to	PART
fcis-18357	7	25	accurately	accurately	ADV
fcis-18357	7	26	identify	identify	VERB
fcis-18357	7	27	and	and	CCONJ
fcis-18357	7	28	track	track	VERB
fcis-18357	7	29	objects	object	NOUN
fcis-18357	7	30	in	in	ADP
fcis-18357	7	31	the	the	DET
fcis-18357	7	32	video	video	NOUN
fcis-18357	7	33	that	that	PRON
fcis-18357	7	34	are	be	AUX
fcis-18357	7	35	small	small	ADJ
fcis-18357	7	36	in	in	ADP
fcis-18357	7	37	size	size	NOUN
fcis-18357	7	38	and	and	CCONJ
fcis-18357	7	39	may	may	AUX
fcis-18357	7	40	be	be	AUX
fcis-18357	7	41	difficult	difficult	ADJ
fcis-18357	7	42	to	to	PART
fcis-18357	7	43	recognize	recognize	VERB
fcis-18357	7	44	due	due	ADP
fcis-18357	7	45	to	to	ADP
fcis-18357	7	46	distance	distance	NOUN
fcis-18357	7	47	or	or	CCONJ
fcis-18357	7	48	other	other	ADJ
fcis-18357	7	49	factors	factor	NOUN
fcis-18357	7	50	.	.	PUNCT
fcis-18357	8	1	[	[	X
fcis-18357	8	2	1	1	X
fcis-18357	8	3	]	]	PUNCT
fcis-18357	8	4	in	in	ADP
fcis-18357	8	5	recent	recent	ADJ
fcis-18357	8	6	years	year	NOUN
fcis-18357	8	7	,	,	PUNCT
fcis-18357	8	8	with	with	ADP
fcis-18357	8	9	the	the	DET
fcis-18357	8	10	rapid	rapid	ADJ
fcis-18357	8	11	development	development	NOUN
fcis-18357	8	12	of	of	ADP
fcis-18357	8	13	deep	deep	ADJ
fcis-18357	8	14	learning	learning	NOUN
fcis-18357	8	15	technologies	technology	NOUN
fcis-18357	8	16	,	,	PUNCT
fcis-18357	8	17	significant	significant	ADJ
fcis-18357	8	18	progress	progress	NOUN
fcis-18357	8	19	has	have	AUX
fcis-18357	8	20	been	be	AUX
fcis-18357	8	21	made	make	VERB
fcis-18357	8	22	in	in	ADP
fcis-18357	8	23	the	the	DET
fcis-18357	8	24	research	research	NOUN
fcis-18357	8	25	of	of	ADP
fcis-18357	8	26	small	small	ADJ
fcis-18357	8	27	object	object	NOUN
fcis-18357	8	28	detection	detection	NOUN
fcis-18357	8	29	,	,	PUNCT
fcis-18357	8	30	and	and	CCONJ
fcis-18357	8	31	many	many	ADJ
fcis-18357	8	32	new	new	ADJ
fcis-18357	8	33	methods	method	NOUN
fcis-18357	8	34	and	and	CCONJ
fcis-18357	8	35	technologies	technology	NOUN
fcis-18357	8	36	have	have	AUX
fcis-18357	8	37	been	be	AUX
fcis-18357	8	38	proposed	propose	VERB
fcis-18357	8	39	.	.	PUNCT
fcis-18357	9	1	[	[	X
fcis-18357	9	2	2	2	NUM
fcis-18357	9	3	]	]	X
fcis-18357	9	4	deep	deep	ADJ
fcis-18357	9	5	learning	learning	NOUN
fcis-18357	9	6	frameworks	framework	NOUN
fcis-18357	9	7	,	,	PUNCT
fcis-18357	9	8	especially	especially	ADV
fcis-18357	9	9	convolutional	convolutional	ADJ
fcis-18357	9	10	neural	neural	ADJ
fcis-18357	9	11	networks	network	NOUN
fcis-18357	9	12	(	(	PUNCT
fcis-18357	9	13	cnns	cnns	PROPN
fcis-18357	9	14	)	)	PUNCT
fcis-18357	9	15	,	,	PUNCT
fcis-18357	9	16	have	have	AUX
fcis-18357	9	17	become	become	VERB
fcis-18357	9	18	the	the	DET
fcis-18357	9	19	mainstream	mainstream	NOUN
fcis-18357	9	20	method	method	NOUN
fcis-18357	9	21	for	for	ADP
fcis-18357	9	22	small	small	ADJ
fcis-18357	9	23	object	object	NOUN
fcis-18357	9	24	detection	detection	NOUN
fcis-18357	9	25	.	.	PUNCT
fcis-18357	10	1	algorithms	algorithm	NOUN
fcis-18357	10	2	like	like	ADP
fcis-18357	10	3	yolo	yolo	PROPN
fcis-18357	10	4	,	,	PUNCT
fcis-18357	10	5	ssd	ssd	NOUN
fcis-18357	10	6	,	,	PUNCT
fcis-18357	10	7	and	and	CCONJ
fcis-18357	10	8	faster	fast	ADJ
fcis-18357	10	9	r	r	NOUN
fcis-18357	10	10	-	-	PUNCT
fcis-18357	10	11	cnn	cnn	PROPN
fcis-18357	10	12	,	,	PUNCT
fcis-18357	10	13	through	through	ADP
fcis-18357	10	14	deep	deep	ADJ
fcis-18357	10	15	networks	network	NOUN
fcis-18357	10	16	,	,	PUNCT
fcis-18357	10	17	learn	learn	VERB
fcis-18357	10	18	the	the	DET
fcis-18357	10	19	feature	feature	NOUN
fcis-18357	10	20	representations	representation	NOUN
fcis-18357	10	21	of	of	ADP
fcis-18357	10	22	objects	object	NOUN
fcis-18357	10	23	,	,	PUNCT
fcis-18357	10	24	achieving	achieve	VERB
fcis-18357	10	25	efficient	efficient	ADJ
fcis-18357	10	26	object	object	NOUN
fcis-18357	10	27	detection.[3	detection.[3	NOUN
fcis-18357	10	28	]	]	PUNCT
fcis-18357	10	29	wang	wang	PROPN
fcis-18357	10	30	et	et	PROPN
fcis-18357	10	31	al	al	PROPN
fcis-18357	10	32	.	.	PROPN
fcis-18357	10	33	enhanced	enhance	VERB
fcis-18357	10	34	the	the	DET
fcis-18357	10	35	recognition	recognition	NOUN
fcis-18357	10	36	capability	capability	NOUN
fcis-18357	10	37	for	for	ADP
fcis-18357	10	38	small	small	ADJ
fcis-18357	10	39	objects	object	NOUN
fcis-18357	10	40	by	by	ADP
fcis-18357	10	41	introducing	introduce	VERB
fcis-18357	10	42	multiscale	multiscale	ADJ
fcis-18357	10	43	detection	detection	NOUN
fcis-18357	10	44	mechanisms	mechanism	NOUN
fcis-18357	10	45	or	or	CCONJ
fcis-18357	10	46	improving	improve	VERB
fcis-18357	10	47	the	the	DET
fcis-18357	10	48	feature	feature	NOUN
fcis-18357	10	49	extraction	extraction	NOUN
fcis-18357	10	50	network.[4	network.[4	NOUN
fcis-18357	10	51	]	]	PUNCT
fcis-18357	10	52	furthermore	furthermore	ADV
fcis-18357	10	53	,	,	PUNCT
fcis-18357	10	54	the	the	DET
fcis-18357	10	55	attention	attention	NOUN
fcis-18357	10	56	mechanism	mechanism	NOUN
fcis-18357	10	57	has	have	AUX
fcis-18357	10	58	also	also	ADV
fcis-18357	10	59	shown	show	VERB
fcis-18357	10	60	great	great	ADJ
fcis-18357	10	61	potential	potential	NOUN
fcis-18357	10	62	in	in	ADP
fcis-18357	10	63	small	small	ADJ
fcis-18357	10	64	object	object	NOUN
fcis-18357	10	65	detection	detection	NOUN
fcis-18357	10	66	.	.	PUNCT
fcis-18357	11	1	by	by	ADP
fcis-18357	11	2	focusing	focus	VERB
fcis-18357	11	3	on	on	ADP
fcis-18357	11	4	key	key	ADJ
fcis-18357	11	5	feature	feature	NOUN
fcis-18357	11	6	areas	area	NOUN
fcis-18357	11	7	,	,	PUNCT
fcis-18357	11	8	models	model	NOUN
fcis-18357	11	9	can	can	AUX
fcis-18357	11	10	better	well	ADV
fcis-18357	11	11	differentiate	differentiate	VERB
fcis-18357	11	12	between	between	ADP
fcis-18357	11	13	the	the	DET
fcis-18357	11	14	background	background	NOUN
fcis-18357	11	15	and	and	CCONJ
fcis-18357	11	16	small	small	ADJ
fcis-18357	11	17	objects	object	NOUN
fcis-18357	11	18	,	,	PUNCT
fcis-18357	11	19	thus	thus	ADV
fcis-18357	11	20	improving	improve	VERB
fcis-18357	11	21	detection	detection	NOUN
fcis-18357	11	22	accuracy	accuracy	NOUN
fcis-18357	11	23	.	.	PUNCT
fcis-18357	12	1	[	[	X
fcis-18357	12	2	5	5	NUM
fcis-18357	12	3	]	]	PUNCT
fcis-18357	12	4	for	for	ADP
fcis-18357	12	5	example	example	NOUN
fcis-18357	12	6	,	,	PUNCT
fcis-18357	12	7	wang	wang	PROPN
fcis-18357	12	8	et	et	PROPN
fcis-18357	12	9	al	al	PROPN
fcis-18357	12	10	.	.	PROPN
fcis-18357	12	11	used	use	VERB
fcis-18357	12	12	an	an	DET
fcis-18357	12	13	adaptive	adaptive	ADJ
fcis-18357	12	14	attention	attention	NOUN
fcis-18357	12	15	module	module	NOUN
fcis-18357	12	16	to	to	PART
fcis-18357	12	17	dynamically	dynamically	ADV
fcis-18357	12	18	adjust	adjust	VERB
fcis-18357	12	19	the	the	DET
fcis-18357	12	20	focus	focus	NOUN
fcis-18357	12	21	of	of	ADP
fcis-18357	12	22	the	the	DET
fcis-18357	12	23	network	network	NOUN
fcis-18357	12	24	to	to	PART
fcis-18357	12	25	more	more	ADV
fcis-18357	12	26	effectively	effectively	ADV
fcis-18357	12	27	capture	capture	VERB
fcis-18357	12	28	the	the	DET
fcis-18357	12	29	details	detail	NOUN
fcis-18357	12	30	of	of	ADP
fcis-18357	12	31	small	small	ADJ
fcis-18357	12	32	objects.[6	objects.[6	NOUN
fcis-18357	12	33	]	]	PUNCT
fcis-18357	12	34	nonetheless	nonetheless	ADV
fcis-18357	12	35	,	,	PUNCT
fcis-18357	12	36	small	small	ADJ
fcis-18357	12	37	object	object	NOUN
fcis-18357	12	38	detection	detection	NOUN
fcis-18357	12	39	still	still	ADV
fcis-18357	12	40	faces	face	VERB
fcis-18357	12	41	a	a	DET
fcis-18357	12	42	series	series	NOUN
fcis-18357	12	43	of	of	ADP
fcis-18357	12	44	challenges	challenge	NOUN
fcis-18357	12	45	,	,	PUNCT
fcis-18357	12	46	including	include	VERB
fcis-18357	12	47	small	small	ADJ
fcis-18357	12	48	size	size	NOUN
fcis-18357	12	49	of	of	ADP
fcis-18357	12	50	objects	object	NOUN
fcis-18357	12	51	,	,	PUNCT
fcis-18357	12	52	significant	significant	ADJ
fcis-18357	12	53	appearance	appearance	NOUN
fcis-18357	12	54	changes	change	NOUN
fcis-18357	12	55	,	,	PUNCT
fcis-18357	12	56	complex	complex	ADJ
fcis-18357	12	57	backgrounds	background	NOUN
fcis-18357	12	58	,	,	PUNCT
fcis-18357	12	59	and	and	CCONJ
fcis-18357	12	60	changes	change	NOUN
fcis-18357	12	61	in	in	ADP
fcis-18357	12	62	lighting	lighting	NOUN
fcis-18357	12	63	.	.	PUNCT
fcis-18357	13	1	these	these	DET
fcis-18357	13	2	factors	factor	NOUN
fcis-18357	13	3	increase	increase	VERB
fcis-18357	13	4	the	the	DET
fcis-18357	13	5	difficulty	difficulty	NOUN
fcis-18357	13	6	of	of	ADP
fcis-18357	13	7	detection	detection	NOUN
fcis-18357	13	8	and	and	CCONJ
fcis-18357	13	9	limit	limit	VERB
fcis-18357	13	10	the	the	DET
fcis-18357	13	11	performance	performance	NOUN
fcis-18357	13	12	of	of	ADP
fcis-18357	13	13	existing	exist	VERB
fcis-18357	13	14	methods.[7	methods.[7	NOUN
fcis-18357	13	15	]	]	X
fcis-18357	13	16	the	the	DET
fcis-18357	13	17	latest	late	ADJ
fcis-18357	13	18	research	research	NOUN
fcis-18357	13	19	trends	trend	NOUN
fcis-18357	13	20	include	include	VERB
fcis-18357	13	21	using	use	VERB
fcis-18357	13	22	generative	generative	ADJ
fcis-18357	13	23	adversarial	adversarial	ADJ
fcis-18357	13	24	networks	network	NOUN
fcis-18357	13	25	(	(	PUNCT
fcis-18357	13	26	gans	gan	NOUN
fcis-18357	13	27	)	)	PUNCT
fcis-18357	13	28	to	to	PART
fcis-18357	13	29	enhance	enhance	VERB
fcis-18357	13	30	the	the	DET
fcis-18357	13	31	visual	visual	ADJ
fcis-18357	13	32	quality	quality	NOUN
fcis-18357	13	33	of	of	ADP
fcis-18357	13	34	small	small	ADJ
fcis-18357	13	35	objects	object	NOUN
fcis-18357	13	36	and	and	CCONJ
fcis-18357	13	37	exploring	explore	VERB
fcis-18357	13	38	more	more	ADV
fcis-18357	13	39	efficient	efficient	ADJ
fcis-18357	13	40	network	network	NOUN
fcis-18357	13	41	architectures	architecture	NOUN
fcis-18357	13	42	and	and	CCONJ
fcis-18357	13	43	training	training	NOUN
fcis-18357	13	44	strategies	strategy	NOUN
fcis-18357	13	45	to	to	PART
fcis-18357	13	46	improve	improve	VERB
fcis-18357	13	47	detection	detection	NOUN
fcis-18357	13	48	speed	speed	NOUN
fcis-18357	13	49	and	and	CCONJ
fcis-18357	13	50	accuracy.[8	accuracy.[8	NOUN
fcis-18357	13	51	]	]	PUNCT
fcis-18357	14	1	researchers	researcher	NOUN
fcis-18357	14	2	are	be	AUX
fcis-18357	14	3	also	also	ADV
fcis-18357	14	4	exploring	explore	VERB
fcis-18357	14	5	the	the	DET
fcis-18357	14	6	integration	integration	NOUN
fcis-18357	14	7	of	of	ADP
fcis-18357	14	8	more	more	ADJ
fcis-18357	14	9	context	context	NOUN
fcis-18357	14	10	information	information	NOUN
fcis-18357	14	11	and	and	CCONJ
fcis-18357	14	12	prior	prior	ADJ
fcis-18357	14	13	knowledge	knowledge	NOUN
fcis-18357	14	14	to	to	PART
fcis-18357	14	15	assist	assist	VERB
fcis-18357	14	16	in	in	ADP
fcis-18357	14	17	small	small	ADJ
fcis-18357	14	18	object	object	NOUN
fcis-18357	14	19	detection	detection	NOUN
fcis-18357	14	20	,	,	PUNCT
fcis-18357	14	21	as	as	ADV
fcis-18357	14	22	well	well	ADV
fcis-18357	14	23	as	as	ADP
fcis-18357	14	24	developing	develop	VERB
fcis-18357	14	25	more	more	ADV
fcis-18357	14	26	complex	complex	ADJ
fcis-18357	14	27	multi	multi	ADJ
fcis-18357	14	28	-	-	ADJ
fcis-18357	14	29	task	task	ADJ
fcis-18357	14	30	learning	learn	VERB
fcis-18357	14	31	frameworks	framework	NOUN
fcis-18357	14	32	to	to	PART
fcis-18357	14	33	simultaneously	simultaneously	ADV
fcis-18357	14	34	perform	perform	VERB
fcis-18357	14	35	object	object	NOUN
fcis-18357	14	36	detection	detection	NOUN
fcis-18357	14	37	,	,	PUNCT
fcis-18357	14	38	classification	classification	NOUN
fcis-18357	14	39	,	,	PUNCT
fcis-18357	14	40	and	and	CCONJ
fcis-18357	14	41	tracking.[9	tracking.[9	ADP
fcis-18357	14	42	]	]	X
fcis-18357	14	43	overall	overall	ADV
fcis-18357	14	44	,	,	PUNCT
fcis-18357	14	45	the	the	DET
fcis-18357	14	46	field	field	NOUN
fcis-18357	14	47	of	of	ADP
fcis-18357	14	48	small	small	ADJ
fcis-18357	14	49	object	object	NOUN
fcis-18357	14	50	detection	detection	NOUN
fcis-18357	14	51	in	in	ADP
fcis-18357	14	52	intelligent	intelligent	ADJ
fcis-18357	14	53	video	video	NOUN
fcis-18357	14	54	surveillance	surveillance	NOUN
fcis-18357	14	55	is	be	AUX
fcis-18357	14	56	rapidly	rapidly	ADV
fcis-18357	14	57	evolving	evolve	VERB
fcis-18357	14	58	,	,	PUNCT
fcis-18357	14	59	with	with	ADP
fcis-18357	14	60	new	new	ADJ
fcis-18357	14	61	technologies	technology	NOUN
fcis-18357	14	62	and	and	CCONJ
fcis-18357	14	63	methods	method	NOUN
fcis-18357	14	64	being	be	AUX
fcis-18357	14	65	proposed	propose	VERB
fcis-18357	14	66	to	to	PART
fcis-18357	14	67	address	address	VERB
fcis-18357	14	68	existing	exist	VERB
fcis-18357	14	69	challenges	challenge	NOUN
fcis-18357	14	70	.	.	PUNCT
fcis-18357	15	1	as	as	ADP
fcis-18357	15	2	technology	technology	NOUN
fcis-18357	15	3	advances	advance	NOUN
fcis-18357	15	4	,	,	PUNCT
fcis-18357	15	5	future	future	ADJ
fcis-18357	15	6	intelligent	intelligent	ADJ
fcis-18357	15	7	video	video	NOUN
fcis-18357	15	8	surveillance	surveillance	NOUN
fcis-18357	15	9	systems	system	NOUN
fcis-18357	15	10	will	will	AUX
fcis-18357	15	11	be	be	AUX
fcis-18357	15	12	able	able	ADJ
fcis-18357	15	13	to	to	PART
fcis-18357	15	14	detect	detect	VERB
fcis-18357	15	15	small	small	ADJ
fcis-18357	15	16	objects	object	NOUN
fcis-18357	15	17	more	more	ADV
fcis-18357	15	18	accurately	accurately	ADV
fcis-18357	15	19	and	and	CCONJ
fcis-18357	15	20	efficiently	efficiently	ADV
fcis-18357	15	21	,	,	PUNCT
fcis-18357	15	22	providing	provide	VERB
fcis-18357	15	23	stronger	strong	ADJ
fcis-18357	15	24	support	support	NOUN
fcis-18357	15	25	for	for	ADP
fcis-18357	15	26	security	security	NOUN
fcis-18357	15	27	monitoring	monitoring	NOUN
fcis-18357	15	28	,	,	PUNCT
fcis-18357	15	29	traffic	traffic	NOUN
fcis-18357	15	30	management	management	NOUN
fcis-18357	15	31	,	,	PUNCT
fcis-18357	15	32	and	and	CCONJ
fcis-18357	15	33	other	other	ADJ
fcis-18357	15	34	fields.[10	fields.[10	PROPN
fcis-18357	15	35	]	]	PUNCT
fcis-18357	15	36	research	research	NOUN
fcis-18357	15	37	indicates	indicate	VERB
fcis-18357	15	38	that	that	SCONJ
fcis-18357	15	39	the	the	DET
fcis-18357	15	40	yolo	yolo	ADJ
fcis-18357	15	41	series	series	NOUN
fcis-18357	15	42	of	of	ADP
fcis-18357	15	43	algorithm	algorithm	NOUN
fcis-18357	15	44	frameworks	framework	NOUN
fcis-18357	15	45	support	support	VERB
fcis-18357	15	46	end	end	NOUN
fcis-18357	15	47	-	-	PUNCT
fcis-18357	15	48	to	to	ADP
fcis-18357	15	49	-	-	PUNCT
fcis-18357	15	50	end	end	NOUN
fcis-18357	15	51	detection	detection	NOUN
fcis-18357	15	52	and	and	CCONJ
fcis-18357	15	53	have	have	AUX
fcis-18357	15	54	shown	show	VERB
fcis-18357	15	55	excellent	excellent	ADJ
fcis-18357	15	56	performance	performance	NOUN
fcis-18357	15	57	across	across	ADP
fcis-18357	15	58	multiple	multiple	ADJ
fcis-18357	15	59	detection	detection	NOUN
fcis-18357	15	60	domains	domain	NOUN
fcis-18357	15	61	.	.	PUNCT
fcis-18357	16	1	as	as	ADP
fcis-18357	16	2	the	the	DET
fcis-18357	16	3	first	first	ADJ
fcis-18357	16	4	single	single	ADJ
fcis-18357	16	5	-	-	PUNCT
fcis-18357	16	6	stage	stage	NOUN
fcis-18357	16	7	object	object	NOUN
fcis-18357	16	8	detection	detection	NOUN
fcis-18357	16	9	algorithm	algorithm	NOUN
fcis-18357	16	10	to	to	PART
fcis-18357	16	11	utilize	utilize	VERB
fcis-18357	16	12	deep	deep	ADJ
fcis-18357	16	13	learning	learning	NOUN
fcis-18357	16	14	,	,	PUNCT
fcis-18357	16	15	yolo	yolo	PROPN
fcis-18357	16	16	has	have	AUX
fcis-18357	16	17	led	lead	VERB
fcis-18357	16	18	a	a	DET
fcis-18357	16	19	new	new	ADJ
fcis-18357	16	20	direction	direction	NOUN
fcis-18357	16	21	in	in	ADP
fcis-18357	16	22	object	object	NOUN
fcis-18357	16	23	detection	detection	NOUN
fcis-18357	16	24	technology	technology	NOUN
fcis-18357	16	25	.	.	PUNCT
fcis-18357	17	1	to	to	ADP
fcis-18357	17	2	date	date	NOUN
fcis-18357	17	3	,	,	PUNCT
fcis-18357	17	4	the	the	DET
fcis-18357	17	5	yolo	yolo	ADJ
fcis-18357	17	6	series	series	NOUN
fcis-18357	17	7	has	have	AUX
fcis-18357	17	8	evolved	evolve	VERB
fcis-18357	17	9	to	to	ADP
fcis-18357	17	10	its	its	PRON
fcis-18357	17	11	seventh	seventh	ADJ
fcis-18357	17	12	edition	edition	NOUN
fcis-18357	17	13	,	,	PUNCT
fcis-18357	17	14	covering	cover	VERB
fcis-18357	17	15	versions	version	NOUN
fcis-18357	17	16	from	from	ADP
fcis-18357	17	17	yolov1	yolov1	NOUN
fcis-18357	17	18	to	to	ADP
fcis-18357	17	19	yolov5	yolov5	NOUN
fcis-18357	17	20	.	.	PUNCT
fcis-18357	18	1	the	the	DET
fcis-18357	18	2	latest	late	ADJ
fcis-18357	18	3	yolov7	yolov7	NOUN
fcis-18357	18	4	model	model	NOUN
fcis-18357	18	5	has	have	VERB
fcis-18357	18	6	significant	significant	ADJ
fcis-18357	18	7	improvements	improvement	NOUN
fcis-18357	18	8	in	in	ADP
fcis-18357	18	9	speed	speed	NOUN
fcis-18357	18	10	,	,	PUNCT
fcis-18357	18	11	accuracy	accuracy	NOUN
fcis-18357	18	12	,	,	PUNCT
fcis-18357	18	13	and	and	CCONJ
fcis-18357	18	14	the	the	DET
fcis-18357	18	15	number	number	NOUN
fcis-18357	18	16	of	of	ADP
fcis-18357	18	17	model	model	NOUN
fcis-18357	18	18	parameters	parameter	NOUN
fcis-18357	18	19	,	,	PUNCT
fcis-18357	18	20	and	and	CCONJ
fcis-18357	18	21	is	be	AUX
fcis-18357	18	22	easier	easy	ADJ
fcis-18357	18	23	to	to	PART
fcis-18357	18	24	deploy	deploy	VERB
fcis-18357	18	25	on	on	ADP
fcis-18357	18	26	different	different	ADJ
fcis-18357	18	27	devices.[11	devices.[11	NOUN
fcis-18357	18	28	]	]	PUNCT
fcis-18357	18	29	the	the	DET
fcis-18357	18	30	yolov7	yolov7	PROPN
fcis-18357	18	31	model	model	NOUN
fcis-18357	18	32	includes	include	VERB
fcis-18357	18	33	four	four	NUM
fcis-18357	18	34	different	different	ADJ
fcis-18357	18	35	scale	scale	NOUN
fcis-18357	18	36	variants	variant	NOUN
fcis-18357	18	37	:	:	PUNCT
fcis-18357	18	38	v7s	v7	NOUN
fcis-18357	18	39	,	,	PUNCT
fcis-18357	18	40	v7	v7	NOUN
fcis-18357	18	41	m	m	NOUN
fcis-18357	18	42	,	,	PUNCT
fcis-18357	18	43	v7tiny	v7tiny	ADJ
fcis-18357	18	44	,	,	PUNCT
fcis-18357	18	45	and	and	CCONJ
fcis-18357	18	46	v7x	v7x	NUM
fcis-18357	18	47	,	,	PUNCT
fcis-18357	18	48	to	to	PART
fcis-18357	18	49	accommodate	accommodate	VERB
fcis-18357	18	50	different	different	ADJ
fcis-18357	18	51	computing	computing	NOUN
fcis-18357	18	52	and	and	CCONJ
fcis-18357	18	53	application	application	NOUN
fcis-18357	18	54	needs	need	NOUN
fcis-18357	18	55	.	.	PUNCT
fcis-18357	19	1	experimental	experimental	ADJ
fcis-18357	19	2	results	result	NOUN
fcis-18357	19	3	show	show	VERB
fcis-18357	19	4	that	that	SCONJ
fcis-18357	19	5	,	,	PUNCT
fcis-18357	19	6	except	except	SCONJ
fcis-18357	19	7	for	for	ADP
fcis-18357	19	8	v7	v7	VERB
fcis-18357	19	9	-	-	PUNCT
fcis-18357	19	10	tiny	tiny	ADJ
fcis-18357	19	11	,	,	PUNCT
fcis-18357	19	12	the	the	DET
fcis-18357	19	13	v7	v7	NOUN
fcis-18357	19	14	m	m	NOUN
fcis-18357	19	15	and	and	CCONJ
fcis-18357	19	16	v7x	v7x	PROPN
fcis-18357	19	17	models	model	NOUN
fcis-18357	19	18	require	require	VERB
fcis-18357	19	19	longer	long	ADV
fcis-18357	19	20	training	train	VERB
fcis-18357	19	21	times	time	NOUN
fcis-18357	19	22	and	and	CCONJ
fcis-18357	19	23	produce	produce	VERB
fcis-18357	19	24	larger	large	ADJ
fcis-18357	19	25	weight	weight	NOUN
fcis-18357	19	26	files	file	NOUN
fcis-18357	19	27	.	.	PUNCT
fcis-18357	20	1	specifically	specifically	ADV
fcis-18357	20	2	,	,	PUNCT
fcis-18357	20	3	the	the	DET
fcis-18357	20	4	training	training	NOUN
fcis-18357	20	5	time	time	NOUN
fcis-18357	20	6	,	,	PUNCT
fcis-18357	20	7	mean	mean	ADJ
fcis-18357	20	8	average	average	ADJ
fcis-18357	20	9	precision	precision	NOUN
fcis-18357	20	10	(	(	PUNCT
fcis-18357	20	11	map	map	NOUN
fcis-18357	20	12	)	)	PUNCT
fcis-18357	20	13	,	,	PUNCT
fcis-18357	20	14	and	and	CCONJ
fcis-18357	20	15	model	model	NOUN
fcis-18357	20	16	weight	weight	NOUN
fcis-18357	20	17	size	size	NOUN
fcis-18357	20	18	of	of	ADP
fcis-18357	20	19	yolov7	yolov7	NOUN
fcis-18357	20	20	and	and	CCONJ
fcis-18357	20	21	yolov7	yolov7	ADV
fcis-18357	20	22	-	-	PUNCT
fcis-18357	20	23	tiny	tiny	ADJ
fcis-18357	20	24	demonstrate	demonstrate	VERB
fcis-18357	20	25	their	their	PRON
fcis-18357	20	26	performance	performance	NOUN
fcis-18357	20	27	and	and	CCONJ
fcis-18357	20	28	efficiency	efficiency	NOUN
fcis-18357	20	29	.	.	PUNCT
fcis-18357	21	1	yolov7	yolov7	NOUN
fcis-18357	21	2	and	and	CCONJ
fcis-18357	21	3	yolov7x	yolov7x	PROPN
fcis-18357	21	4	,	,	PUNCT
fcis-18357	21	5	having	have	VERB
fcis-18357	21	6	more	more	ADJ
fcis-18357	21	7	parameters	parameter	NOUN
fcis-18357	21	8	,	,	PUNCT
fcis-18357	21	9	are	be	AUX
fcis-18357	21	10	larger	large	ADJ
fcis-18357	21	11	in	in	ADP
fcis-18357	21	12	terms	term	NOUN
fcis-18357	21	13	of	of	ADP
fcis-18357	21	14	training	training	NOUN
fcis-18357	21	15	time	time	NOUN
fcis-18357	21	16	and	and	CCONJ
fcis-18357	21	17	weight	weight	NOUN
fcis-18357	21	18	file	file	NOUN
fcis-18357	21	19	size	size	NOUN
fcis-18357	21	20	.	.	PUNCT
fcis-18357	22	1	[	[	X
fcis-18357	22	2	12	12	NUM
fcis-18357	22	3	]	]	PUNCT
fcis-18357	22	4	in	in	ADP
fcis-18357	22	5	contrast	contrast	NOUN
fcis-18357	22	6	,	,	PUNCT
fcis-18357	22	7	although	although	SCONJ
fcis-18357	22	8	yolov7s	yolov7	NOUN
fcis-18357	22	9	has	have	VERB
fcis-18357	22	10	a	a	DET
fcis-18357	22	11	map	map	NOUN
fcis-18357	22	12	that	that	PRON
fcis-18357	22	13	is	be	AUX
fcis-18357	22	14	not	not	PART
fcis-18357	22	15	significantly	significantly	ADV
fcis-18357	22	16	different	different	ADJ
fcis-18357	22	17	from	from	ADP
fcis-18357	22	18	the	the	DET
fcis-18357	22	19	other	other	ADJ
fcis-18357	22	20	variants	variant	NOUN
fcis-18357	22	21	for	for	ADP
fcis-18357	22	22	each	each	DET
fcis-18357	22	23	category	category	NOUN
fcis-18357	22	24	,	,	PUNCT
fcis-18357	22	25	it	it	PRON
fcis-18357	22	26	has	have	VERB
fcis-18357	22	27	a	a	DET
fcis-18357	22	28	clear	clear	ADJ
fcis-18357	22	29	advantage	advantage	NOUN
fcis-18357	22	30	in	in	ADP
fcis-18357	22	31	terms	term	NOUN
fcis-18357	22	32	of	of	ADP
fcis-18357	22	33	efficiency	efficiency	NOUN
fcis-18357	22	34	and	and	CCONJ
fcis-18357	22	35	lightweight	lightweight	NOUN
fcis-18357	22	36	.	.	PUNCT
fcis-18357	23	1	66	66	NUM
fcis-18357	23	2	table	table	NOUN
fcis-18357	23	3	1	1	NUM
fcis-18357	23	4	.	.	PUNCT
fcis-18357	23	5	experiment	experiment	NOUN
fcis-18357	23	6	time	time	NOUN
fcis-18357	23	7	and	and	CCONJ
fcis-18357	23	8	weight	weight	NOUN
fcis-18357	23	9	size	size	NOUN
fcis-18357	23	10	yolov7	yolov7	NOUN
fcis-18357	23	11	-	-	PUNCT
fcis-18357	23	12	tiny	tiny	ADJ
fcis-18357	23	13	,	,	PUNCT
fcis-18357	23	14	as	as	ADP
fcis-18357	23	15	the	the	DET
fcis-18357	23	16	lightest	light	ADJ
fcis-18357	23	17	version	version	NOUN
fcis-18357	23	18	in	in	ADP
fcis-18357	23	19	the	the	DET
fcis-18357	23	20	series	series	NOUN
fcis-18357	23	21	,	,	PUNCT
fcis-18357	23	22	is	be	AUX
fcis-18357	23	23	renowned	renowned	ADJ
fcis-18357	23	24	for	for	ADP
fcis-18357	23	25	its	its	PRON
fcis-18357	23	26	exceptional	exceptional	ADJ
fcis-18357	23	27	detection	detection	NOUN
fcis-18357	23	28	speed	speed	NOUN
fcis-18357	23	29	.	.	PUNCT
fcis-18357	24	1	it	it	PRON
fcis-18357	24	2	is	be	AUX
fcis-18357	24	3	particularly	particularly	ADV
fcis-18357	24	4	suitable	suitable	ADJ
fcis-18357	24	5	for	for	ADP
fcis-18357	24	6	embedding	embed	VERB
fcis-18357	24	7	into	into	ADP
fcis-18357	24	8	devices	device	NOUN
fcis-18357	24	9	with	with	ADP
fcis-18357	24	10	limited	limited	ADJ
fcis-18357	24	11	computing	computing	NOUN
fcis-18357	24	12	power	power	NOUN
fcis-18357	24	13	,	,	PUNCT
fcis-18357	24	14	storage	storage	NOUN
fcis-18357	24	15	space	space	NOUN
fcis-18357	24	16	,	,	PUNCT
fcis-18357	24	17	and	and	CCONJ
fcis-18357	24	18	memory	memory	NOUN
fcis-18357	24	19	,	,	PUNCT
fcis-18357	24	20	making	make	VERB
fcis-18357	24	21	it	it	PRON
fcis-18357	24	22	highly	highly	ADV
fcis-18357	24	23	applicable	applicable	ADJ
fcis-18357	24	24	to	to	ADP
fcis-18357	24	25	monitoring	monitor	VERB
fcis-18357	24	26	equipment	equipment	NOUN
fcis-18357	24	27	.	.	PUNCT
fcis-18357	25	1	therefore	therefore	ADV
fcis-18357	25	2	,	,	PUNCT
fcis-18357	25	3	choosing	choose	VERB
fcis-18357	25	4	yolov7	yolov7	NOUN
fcis-18357	25	5	-	-	PUNCT
fcis-18357	25	6	tiny	tiny	ADJ
fcis-18357	25	7	as	as	ADP
fcis-18357	25	8	the	the	DET
fcis-18357	25	9	basic	basic	ADJ
fcis-18357	25	10	algorithm	algorithm	NOUN
fcis-18357	25	11	framework	framework	NOUN
fcis-18357	25	12	for	for	ADP
fcis-18357	25	13	this	this	DET
fcis-18357	25	14	research	research	NOUN
fcis-18357	25	15	is	be	AUX
fcis-18357	25	16	based	base	VERB
fcis-18357	25	17	on	on	ADP
fcis-18357	25	18	its	its	PRON
fcis-18357	25	19	excellent	excellent	ADJ
fcis-18357	25	20	performance	performance	NOUN
fcis-18357	25	21	and	and	CCONJ
fcis-18357	25	22	applicability	applicability	NOUN
fcis-18357	25	23	considerations	consideration	NOUN
fcis-18357	25	24	.	.	PUNCT
fcis-18357	26	1	however	however	ADV
fcis-18357	26	2	,	,	PUNCT
fcis-18357	26	3	yolov7	yolov7	ADJ
fcis-18357	26	4	-	-	PUNCT
fcis-18357	26	5	tiny	tiny	ADJ
fcis-18357	26	6	's	's	PART
fcis-18357	26	7	performance	performance	NOUN
fcis-18357	26	8	in	in	ADP
fcis-18357	26	9	detecting	detect	VERB
fcis-18357	26	10	small	small	ADJ
fcis-18357	26	11	objects	object	NOUN
fcis-18357	26	12	is	be	AUX
fcis-18357	26	13	not	not	PART
fcis-18357	26	14	satisfactory	satisfactory	ADJ
fcis-18357	26	15	,	,	PUNCT
fcis-18357	26	16	which	which	PRON
fcis-18357	26	17	necessitates	necessitate	VERB
fcis-18357	26	18	improving	improve	VERB
fcis-18357	26	19	its	its	PRON
fcis-18357	26	20	detection	detection	NOUN
fcis-18357	26	21	rate	rate	NOUN
fcis-18357	26	22	.	.	PUNCT
fcis-18357	27	1	to	to	PART
fcis-18357	27	2	address	address	VERB
fcis-18357	27	3	this	this	PRON
fcis-18357	27	4	,	,	PUNCT
fcis-18357	27	5	the	the	DET
fcis-18357	27	6	study	study	NOUN
fcis-18357	27	7	has	have	AUX
fcis-18357	27	8	enhanced	enhance	VERB
fcis-18357	27	9	yolov7	yolov7	NOUN
fcis-18357	27	10	-	-	PUNCT
fcis-18357	27	11	tiny	tiny	ADJ
fcis-18357	27	12	by	by	ADP
fcis-18357	27	13	incorporating	incorporate	VERB
fcis-18357	27	14	attention	attention	NOUN
fcis-18357	27	15	mechanism	mechanism	NOUN
fcis-18357	27	16	modules	module	NOUN
fcis-18357	27	17	and	and	CCONJ
fcis-18357	27	18	adding	add	VERB
fcis-18357	27	19	detection	detection	NOUN
fcis-18357	27	20	layers	layer	NOUN
fcis-18357	27	21	specifically	specifically	ADV
fcis-18357	27	22	for	for	ADP
fcis-18357	27	23	small	small	ADJ
fcis-18357	27	24	objects	object	NOUN
fcis-18357	27	25	.	.	PUNCT
fcis-18357	28	1	the	the	DET
fcis-18357	28	2	improved	improved	ADJ
fcis-18357	28	3	algorithm	algorithm	NOUN
fcis-18357	28	4	has	have	AUX
fcis-18357	28	5	shown	show	VERB
fcis-18357	28	6	good	good	ADJ
fcis-18357	28	7	results	result	NOUN
fcis-18357	28	8	in	in	ADP
fcis-18357	28	9	experiments	experiment	NOUN
fcis-18357	28	10	,	,	PUNCT
fcis-18357	28	11	particularly	particularly	ADV
fcis-18357	28	12	suiting	suit	VERB
fcis-18357	28	13	the	the	DET
fcis-18357	28	14	detection	detection	NOUN
fcis-18357	28	15	needs	need	VERB
fcis-18357	28	16	in	in	ADP
fcis-18357	28	17	video	video	NOUN
fcis-18357	28	18	surveillance	surveillance	NOUN
fcis-18357	28	19	scenarios	scenario	NOUN
fcis-18357	28	20	for	for	ADP
fcis-18357	28	21	specific	specific	ADJ
fcis-18357	28	22	situations	situation	NOUN
fcis-18357	28	23	.	.	PUNCT
fcis-18357	29	1	2	2	X
fcis-18357	29	2	.	.	X
fcis-18357	29	3	improving	improve	VERB
fcis-18357	29	4	the	the	DET
fcis-18357	29	5	yolov7	yolov7	ADV
fcis-18357	29	6	-	-	PUNCT
fcis-18357	29	7	tiny	tiny	ADJ
fcis-18357	29	8	model	model	NOUN
fcis-18357	29	9	for	for	ADP
fcis-18357	29	10	small	small	ADJ
fcis-18357	29	11	object	object	NOUN
fcis-18357	29	12	detection	detection	NOUN
fcis-18357	29	13	2.1	2.1	NUM
fcis-18357	29	14	.	.	PUNCT
fcis-18357	30	1	yolov7	yolov7	NOUN
fcis-18357	30	2	-	-	PUNCT
fcis-18357	30	3	tiny	tiny	ADJ
fcis-18357	30	4	basic	basic	ADJ
fcis-18357	30	5	algorithm	algorithm	NOUN
fcis-18357	30	6	framework	framework	NOUN
fcis-18357	30	7	yolov7	yolov7	NOUN
fcis-18357	30	8	-	-	PUNCT
fcis-18357	30	9	tiny	tiny	ADJ
fcis-18357	30	10	is	be	AUX
fcis-18357	30	11	the	the	DET
fcis-18357	30	12	smallest	small	ADJ
fcis-18357	30	13	network	network	NOUN
fcis-18357	30	14	in	in	ADP
fcis-18357	30	15	the	the	DET
fcis-18357	30	16	yolov7	yolov7	NOUN
fcis-18357	30	17	family	family	NOUN
fcis-18357	30	18	.	.	PUNCT
fcis-18357	31	1	the	the	DET
fcis-18357	31	2	network	network	NOUN
fcis-18357	31	3	architecture	architecture	NOUN
fcis-18357	31	4	is	be	AUX
fcis-18357	31	5	shown	show	VERB
fcis-18357	31	6	in	in	ADP
fcis-18357	31	7	figure	figure	NOUN
fcis-18357	31	8	1	1	NUM
fcis-18357	31	9	.	.	PUNCT
fcis-18357	31	10	figure	figure	NOUN
fcis-18357	31	11	1	1	NUM
fcis-18357	31	12	.	.	PUNCT
fcis-18357	31	13	yolov7	yolov7	ADJ
fcis-18357	31	14	-	-	PUNCT
fcis-18357	31	15	tiny	tiny	ADJ
fcis-18357	31	16	network	network	NOUN
fcis-18357	31	17	structure	structure	NOUN
fcis-18357	31	18	diagram	diagram	VERB
fcis-18357	31	19	its	its	PRON
fcis-18357	31	20	backbone	backbone	NOUN
fcis-18357	31	21	network	network	NOUN
fcis-18357	31	22	adopts	adopt	VERB
fcis-18357	31	23	a	a	DET
fcis-18357	31	24	multi	multi	ADJ
fcis-18357	31	25	-	-	ADJ
fcis-18357	31	26	branch	branch	ADJ
fcis-18357	31	27	stacking	stacking	NOUN
fcis-18357	31	28	structure	structure	NOUN
fcis-18357	31	29	for	for	ADP
fcis-18357	31	30	feature	feature	NOUN
fcis-18357	31	31	enhancement	enhancement	NOUN
fcis-18357	31	32	,	,	PUNCT
fcis-18357	31	33	which	which	PRON
fcis-18357	31	34	allows	allow	VERB
fcis-18357	31	35	for	for	ADP
fcis-18357	31	36	effective	effective	ADJ
fcis-18357	31	37	feature	feature	NOUN
fcis-18357	31	38	extraction	extraction	NOUN
fcis-18357	31	39	while	while	SCONJ
fcis-18357	31	40	reducing	reduce	VERB
fcis-18357	31	41	computational	computational	ADJ
fcis-18357	31	42	requirements	requirement	NOUN
fcis-18357	31	43	.	.	PUNCT
fcis-18357	32	1	after	after	SCONJ
fcis-18357	32	2	the	the	DET
fcis-18357	32	3	input	input	NOUN
fcis-18357	32	4	image	image	NOUN
fcis-18357	32	5	undergoes	undergo	VERB
fcis-18357	32	6	feature	feature	NOUN
fcis-18357	32	7	extraction	extraction	NOUN
fcis-18357	32	8	through	through	ADP
fcis-18357	32	9	the	the	DET
fcis-18357	32	10	backbone	backbone	NOUN
fcis-18357	32	11	network	network	NOUN
fcis-18357	32	12	,	,	PUNCT
fcis-18357	32	13	features	feature	VERB
fcis-18357	32	14	layers	layer	NOUN
fcis-18357	32	15	obtained	obtain	VERB
fcis-18357	32	16	after	after	ADP
fcis-18357	32	17	3	3	NUM
fcis-18357	32	18	,	,	PUNCT
fcis-18357	32	19	4	4	NUM
fcis-18357	32	20	,	,	PUNCT
fcis-18357	32	21	and	and	CCONJ
fcis-18357	32	22	5	5	NUM
fcis-18357	32	23	times	time	NOUN
fcis-18357	32	24	of	of	ADP
fcis-18357	32	25	downsampling	downsampling	NOUN
fcis-18357	32	26	are	be	AUX
fcis-18357	32	27	used	use	VERB
fcis-18357	32	28	for	for	ADP
fcis-18357	32	29	object	object	NOUN
fcis-18357	32	30	prediction	prediction	NOUN
fcis-18357	32	31	.	.	PUNCT
fcis-18357	33	1	at	at	ADP
fcis-18357	33	2	the	the	DET
fcis-18357	33	3	last	last	ADJ
fcis-18357	33	4	feature	feature	NOUN
fcis-18357	33	5	layer	layer	NOUN
fcis-18357	33	6	of	of	ADP
fcis-18357	33	7	the	the	DET
fcis-18357	33	8	backbone	backbone	NOUN
fcis-18357	33	9	network	network	NOUN
fcis-18357	33	10	,	,	PUNCT
fcis-18357	33	11	there	there	PRON
fcis-18357	33	12	is	be	VERB
fcis-18357	33	13	an	an	DET
fcis-18357	33	14	spp	spp	NOUN
fcis-18357	33	15	(	(	PUNCT
fcis-18357	33	16	spatial	spatial	ADJ
fcis-18357	33	17	pyramid	pyramid	NOUN
fcis-18357	33	18	pooling	pooling	NOUN
fcis-18357	33	19	)	)	PUNCT
fcis-18357	33	20	module	module	NOUN
fcis-18357	33	21	with	with	ADP
fcis-18357	33	22	a	a	DET
fcis-18357	33	23	csp	csp	PROPN
fcis-18357	33	24	(	(	PUNCT
fcis-18357	33	25	cross	cross	NOUN
fcis-18357	33	26	stage	stage	NOUN
fcis-18357	33	27	partial	partial	ADJ
fcis-18357	33	28	)	)	PUNCT
fcis-18357	33	29	structure	structure	NOUN
fcis-18357	33	30	,	,	PUNCT
fcis-18357	33	31	which	which	PRON
fcis-18357	33	32	includes	include	VERB
fcis-18357	33	33	maximum	maximum	ADJ
fcis-18357	33	34	pooling	pool	VERB
fcis-18357	33	35	layers	layer	NOUN
fcis-18357	33	36	of	of	ADP
fcis-18357	33	37	different	different	ADJ
fcis-18357	33	38	sizes	size	NOUN
fcis-18357	33	39	.	.	PUNCT
fcis-18357	34	1	this	this	DET
fcis-18357	34	2	module	module	NOUN
fcis-18357	34	3	enables	enable	VERB
fcis-18357	34	4	the	the	DET
fcis-18357	34	5	network	network	NOUN
fcis-18357	34	6	to	to	PART
fcis-18357	34	7	learn	learn	VERB
fcis-18357	34	8	more	more	ADV
fcis-18357	34	9	effective	effective	ADJ
fcis-18357	34	10	features	feature	NOUN
fcis-18357	34	11	from	from	ADP
fcis-18357	34	12	different	different	ADJ
fcis-18357	34	13	receptive	receptive	ADJ
fcis-18357	34	14	fields	field	NOUN
fcis-18357	34	15	,	,	PUNCT
fcis-18357	34	16	allowing	allow	VERB
fcis-18357	34	17	the	the	DET
fcis-18357	34	18	deep	deep	ADJ
fcis-18357	34	19	convolutional	convolutional	ADJ
fcis-18357	34	20	network	network	NOUN
fcis-18357	34	21	to	to	PART
fcis-18357	34	22	extract	extract	VERB
fcis-18357	34	23	richer	rich	ADJ
fcis-18357	34	24	semantic	semantic	ADJ
fcis-18357	34	25	information	information	NOUN
fcis-18357	34	26	.	.	PUNCT
fcis-18357	35	1	the	the	DET
fcis-18357	35	2	neck	neck	NOUN
fcis-18357	35	3	utilizes	utilize	VERB
fcis-18357	35	4	a	a	DET
fcis-18357	35	5	panet	panet	NOUN
fcis-18357	35	6	structure	structure	NOUN
fcis-18357	35	7	,	,	PUNCT
fcis-18357	35	8	which	which	PRON
fcis-18357	35	9	merges	merge	VERB
fcis-18357	35	10	the	the	DET
fcis-18357	35	11	upsampled	upsampled	ADJ
fcis-18357	35	12	deep	deep	ADJ
fcis-18357	35	13	feature	feature	NOUN
fcis-18357	35	14	maps	map	NOUN
fcis-18357	35	15	with	with	ADP
fcis-18357	35	16	the	the	DET
fcis-18357	35	17	shallow	shallow	ADJ
fcis-18357	35	18	feature	feature	NOUN
fcis-18357	35	19	maps	map	NOUN
fcis-18357	35	20	to	to	PART
fcis-18357	35	21	enhance	enhance	VERB
fcis-18357	35	22	the	the	DET
fcis-18357	35	23	semantic	semantic	ADJ
fcis-18357	35	24	information	information	NOUN
fcis-18357	35	25	of	of	ADP
fcis-18357	35	26	the	the	DET
fcis-18357	35	27	shallow	shallow	ADJ
fcis-18357	35	28	network	network	NOUN
fcis-18357	35	29	.	.	PUNCT
fcis-18357	36	1	it	it	PRON
fcis-18357	36	2	also	also	ADV
fcis-18357	36	3	merges	merge	VERB
fcis-18357	36	4	the	the	DET
fcis-18357	36	5	downsampled	downsample	VERB
fcis-18357	36	6	shallow	shallow	ADJ
fcis-18357	36	7	feature	feature	NOUN
fcis-18357	36	8	maps	map	NOUN
fcis-18357	36	9	with	with	ADP
fcis-18357	36	10	the	the	DET
fcis-18357	36	11	deep	deep	ADJ
fcis-18357	36	12	feature	feature	NOUN
fcis-18357	36	13	maps	map	NOUN
fcis-18357	36	14	to	to	PART
fcis-18357	36	15	complement	complement	VERB
fcis-18357	36	16	the	the	DET
fcis-18357	36	17	detailed	detailed	ADJ
fcis-18357	36	18	information	information	NOUN
fcis-18357	36	19	of	of	ADP
fcis-18357	36	20	the	the	DET
fcis-18357	36	21	deep	deep	ADJ
fcis-18357	36	22	feature	feature	NOUN
fcis-18357	36	23	maps	map	NOUN
fcis-18357	36	24	.	.	PUNCT
fcis-18357	37	1	the	the	DET
fcis-18357	37	2	detection	detection	NOUN
fcis-18357	37	3	head	head	NOUN
fcis-18357	37	4	,	,	PUNCT
fcis-18357	37	5	which	which	PRON
fcis-18357	37	6	shares	share	VERB
fcis-18357	37	7	weights	weight	NOUN
fcis-18357	37	8	,	,	PUNCT
fcis-18357	37	9	is	be	AUX
fcis-18357	37	10	used	use	VERB
fcis-18357	37	11	for	for	ADP
fcis-18357	37	12	final	final	ADJ
fcis-18357	37	13	object	object	NOUN
fcis-18357	37	14	classification	classification	NOUN
fcis-18357	37	15	and	and	CCONJ
fcis-18357	37	16	localization	localization	NOUN
fcis-18357	37	17	.	.	PUNCT
fcis-18357	38	1	each	each	DET
fcis-18357	38	2	layer	layer	NOUN
fcis-18357	38	3	uses	use	VERB
fcis-18357	38	4	three	three	NUM
fcis-18357	38	5	differently	differently	ADV
fcis-18357	38	6	shaped	shape	VERB
fcis-18357	38	7	anchor	anchor	NOUN
fcis-18357	38	8	boxes	box	NOUN
fcis-18357	38	9	,	,	PUNCT
fcis-18357	38	10	responsible	responsible	ADJ
fcis-18357	38	11	for	for	ADP
fcis-18357	38	12	detecting	detect	VERB
fcis-18357	38	13	objects	object	NOUN
fcis-18357	38	14	of	of	ADP
fcis-18357	38	15	different	different	ADJ
fcis-18357	38	16	shapes	shape	NOUN
fcis-18357	38	17	.	.	PUNCT
fcis-18357	39	1	lastly	lastly	ADV
fcis-18357	39	2	,	,	PUNCT
fcis-18357	39	3	non	non	ADJ
fcis-18357	39	4	-	-	ADJ
fcis-18357	39	5	maximum	maximum	ADJ
fcis-18357	39	6	suppression	suppression	NOUN
fcis-18357	39	7	is	be	AUX
fcis-18357	39	8	used	use	VERB
fcis-18357	39	9	to	to	PART
fcis-18357	39	10	filter	filter	VERB
fcis-18357	39	11	out	out	ADP
fcis-18357	39	12	multiple	multiple	ADJ
fcis-18357	39	13	anchor	anchor	NOUN
fcis-18357	39	14	boxes	box	NOUN
fcis-18357	39	15	'	'	PART
fcis-18357	39	16	duplicate	duplicate	ADJ
fcis-18357	39	17	predictions	prediction	NOUN
fcis-18357	39	18	for	for	ADP
fcis-18357	39	19	the	the	DET
fcis-18357	39	20	same	same	ADJ
fcis-18357	39	21	target	target	NOUN
fcis-18357	39	22	,	,	PUNCT
fcis-18357	39	23	and	and	CCONJ
fcis-18357	39	24	re	re	NOUN
fcis-18357	39	25	-	-	NOUN
fcis-18357	39	26	parameterization	parameterization	ADJ
fcis-18357	39	27	technology	technology	NOUN
fcis-18357	39	28	is	be	AUX
fcis-18357	39	29	applied	apply	VERB
fcis-18357	39	30	.	.	PUNCT
fcis-18357	40	1	this	this	PRON
fcis-18357	40	2	includes	include	VERB
fcis-18357	40	3	different	different	ADJ
fcis-18357	40	4	network	network	NOUN
fcis-18357	40	5	branches	branch	NOUN
fcis-18357	40	6	with	with	ADP
fcis-18357	40	7	convolutional	convolutional	ADJ
fcis-18357	40	8	kernels	kernel	NOUN
fcis-18357	40	9	of	of	ADP
fcis-18357	40	10	different	different	ADJ
fcis-18357	40	11	sizes	size	NOUN
fcis-18357	40	12	on	on	ADP
fcis-18357	40	13	the	the	DET
fcis-18357	40	14	same	same	ADJ
fcis-18357	40	15	layer	layer	NOUN
fcis-18357	40	16	.	.	PUNCT
fcis-18357	41	1	the	the	DET
fcis-18357	41	2	backbone	backbone	NOUN
fcis-18357	41	3	network	network	NOUN
fcis-18357	41	4	extracts	extract	NOUN
fcis-18357	41	5	features	feature	NOUN
fcis-18357	41	6	from	from	ADP
fcis-18357	41	7	the	the	DET
fcis-18357	41	8	input	input	NOUN
fcis-18357	41	9	image	image	NOUN
fcis-18357	41	10	,	,	PUNCT
fcis-18357	41	11	then	then	ADV
fcis-18357	41	12	the	the	DET
fcis-18357	41	13	neck	neck	NOUN
fcis-18357	41	14	merges	merge	VERB
fcis-18357	41	15	features	feature	NOUN
fcis-18357	41	16	of	of	ADP
fcis-18357	41	17	different	different	ADJ
fcis-18357	41	18	scales	scale	NOUN
fcis-18357	41	19	.	.	PUNCT
fcis-18357	42	1	the	the	DET
fcis-18357	42	2	detection	detection	NOUN
fcis-18357	42	3	head	head	NOUN
fcis-18357	42	4	predicts	predict	VERB
fcis-18357	42	5	the	the	DET
fcis-18357	42	6	position	position	NOUN
fcis-18357	42	7	and	and	CCONJ
fcis-18357	42	8	type	type	NOUN
fcis-18357	42	9	of	of	ADP
fcis-18357	42	10	objects	object	NOUN
fcis-18357	42	11	,	,	PUNCT
fcis-18357	42	12	ultimately	ultimately	ADV
fcis-18357	42	13	producing	produce	VERB
fcis-18357	42	14	the	the	DET
fcis-18357	42	15	detection	detection	NOUN
fcis-18357	42	16	results	result	NOUN
fcis-18357	42	17	.	.	PUNCT
fcis-18357	43	1	the	the	DET
fcis-18357	43	2	cspspp	cspspp	NOUN
fcis-18357	43	3	structure	structure	NOUN
fcis-18357	43	4	significantly	significantly	ADV
fcis-18357	43	5	increases	increase	VERB
fcis-18357	43	6	the	the	DET
fcis-18357	43	7	receptive	receptive	ADJ
fcis-18357	43	8	field	field	NOUN
fcis-18357	43	9	,	,	PUNCT
fcis-18357	43	10	isolating	isolate	VERB
fcis-18357	43	11	the	the	DET
fcis-18357	43	12	most	most	ADV
fcis-18357	43	13	crucial	crucial	ADJ
fcis-18357	43	14	contextual	contextual	ADJ
fcis-18357	43	15	features	feature	NOUN
fcis-18357	43	16	,	,	PUNCT
fcis-18357	43	17	while	while	SCONJ
fcis-18357	43	18	the	the	DET
fcis-18357	43	19	multi	multi	ADJ
fcis-18357	43	20	-	-	ADJ
fcis-18357	43	21	branch	branch	ADJ
fcis-18357	43	22	67	67	NUM
fcis-18357	43	23	stack	stack	NOUN
fcis-18357	43	24	module	module	NOUN
fcis-18357	43	25	,	,	PUNCT
fcis-18357	43	26	by	by	ADP
fcis-18357	43	27	controlling	control	VERB
fcis-18357	43	28	the	the	DET
fcis-18357	43	29	shortest	short	ADJ
fcis-18357	43	30	and	and	CCONJ
fcis-18357	43	31	longest	long	ADJ
fcis-18357	43	32	gradient	gradient	ADJ
fcis-18357	43	33	paths	path	NOUN
fcis-18357	43	34	,	,	PUNCT
fcis-18357	43	35	enables	enable	VERB
fcis-18357	43	36	the	the	DET
fcis-18357	43	37	network	network	NOUN
fcis-18357	43	38	to	to	PART
fcis-18357	43	39	learn	learn	VERB
fcis-18357	43	40	more	more	ADJ
fcis-18357	43	41	features	feature	NOUN
fcis-18357	43	42	with	with	ADP
fcis-18357	43	43	greater	great	ADJ
fcis-18357	43	44	robustness	robustness	NOUN
fcis-18357	43	45	.	.	PUNCT
fcis-18357	44	1	2.2	2.2	NUM
fcis-18357	44	2	.	.	PUNCT
fcis-18357	45	1	yolov7	yolov7	NOUN
fcis-18357	45	2	-	-	PUNCT
fcis-18357	45	3	tiny	tiny	ADJ
fcis-18357	45	4	improved	improve	VERB
fcis-18357	45	5	by	by	ADP
fcis-18357	45	6	introducing	introduce	VERB
fcis-18357	45	7	attention	attention	NOUN
fcis-18357	45	8	mechanism	mechanism	NOUN
fcis-18357	45	9	cbam	cbam	NOUN
fcis-18357	45	10	the	the	DET
fcis-18357	45	11	cbam	cbam	NOUN
fcis-18357	45	12	(	(	PUNCT
fcis-18357	45	13	convolutional	convolutional	ADJ
fcis-18357	45	14	block	block	NOUN
fcis-18357	45	15	attention	attention	NOUN
fcis-18357	45	16	module	module	NOUN
fcis-18357	45	17	)	)	PUNCT
fcis-18357	45	18	selfattention	selfattention	NOUN
fcis-18357	45	19	mechanism	mechanism	NOUN
fcis-18357	45	20	has	have	VERB
fcis-18357	45	21	the	the	DET
fcis-18357	45	22	following	following	ADJ
fcis-18357	45	23	advantages	advantage	NOUN
fcis-18357	45	24	:	:	PUNCT
fcis-18357	45	25	(	(	PUNCT
fcis-18357	45	26	1	1	X
fcis-18357	45	27	)	)	PUNCT
fcis-18357	45	28	high	high	ADJ
fcis-18357	45	29	degree	degree	NOUN
fcis-18357	45	30	of	of	ADP
fcis-18357	45	31	lightweight	lightweight	ADJ
fcis-18357	45	32	design	design	NOUN
fcis-18357	45	33	:	:	PUNCT
fcis-18357	45	34	the	the	DET
fcis-18357	45	35	cbam	cbam	NOUN
fcis-18357	45	36	module	module	NOUN
fcis-18357	45	37	does	do	AUX
fcis-18357	45	38	not	not	PART
fcis-18357	45	39	contain	contain	VERB
fcis-18357	45	40	a	a	DET
fcis-18357	45	41	large	large	ADJ
fcis-18357	45	42	number	number	NOUN
fcis-18357	45	43	of	of	ADP
fcis-18357	45	44	convolutional	convolutional	ADJ
fcis-18357	45	45	structures	structure	NOUN
fcis-18357	45	46	internally	internally	ADV
fcis-18357	45	47	,	,	PUNCT
fcis-18357	45	48	only	only	ADV
fcis-18357	45	49	a	a	DET
fcis-18357	45	50	few	few	ADJ
fcis-18357	45	51	pooling	pool	VERB
fcis-18357	45	52	layers	layer	NOUN
fcis-18357	45	53	and	and	CCONJ
fcis-18357	45	54	feature	feature	NOUN
fcis-18357	45	55	fusion	fusion	NOUN
fcis-18357	45	56	operations	operation	NOUN
fcis-18357	45	57	.	.	PUNCT
fcis-18357	46	1	this	this	DET
fcis-18357	46	2	structure	structure	NOUN
fcis-18357	46	3	avoids	avoid	VERB
fcis-18357	46	4	the	the	DET
fcis-18357	46	5	heavy	heavy	ADJ
fcis-18357	46	6	computation	computation	NOUN
fcis-18357	46	7	brought	bring	VERB
fcis-18357	46	8	by	by	ADP
fcis-18357	46	9	convolutional	convolutional	ADJ
fcis-18357	46	10	multiplications	multiplication	NOUN
fcis-18357	46	11	,	,	PUNCT
fcis-18357	46	12	resulting	result	VERB
fcis-18357	46	13	in	in	ADP
fcis-18357	46	14	a	a	DET
fcis-18357	46	15	module	module	NOUN
fcis-18357	46	16	with	with	ADP
fcis-18357	46	17	low	low	ADJ
fcis-18357	46	18	complexity	complexity	NOUN
fcis-18357	46	19	and	and	CCONJ
fcis-18357	46	20	minimal	minimal	ADJ
fcis-18357	46	21	computational	computational	ADJ
fcis-18357	46	22	demand	demand	NOUN
fcis-18357	46	23	.	.	PUNCT
fcis-18357	47	1	experiments	experiment	NOUN
fcis-18357	47	2	have	have	AUX
fcis-18357	47	3	shown	show	VERB
fcis-18357	47	4	that	that	SCONJ
fcis-18357	47	5	adding	add	VERB
fcis-18357	47	6	the	the	DET
fcis-18357	47	7	cbam	cbam	NOUN
fcis-18357	47	8	module	module	NOUN
fcis-18357	47	9	to	to	ADP
fcis-18357	47	10	lightweight	lightweight	ADJ
fcis-18357	47	11	models	model	NOUN
fcis-18357	47	12	can	can	AUX
fcis-18357	47	13	bring	bring	VERB
fcis-18357	47	14	about	about	ADP
fcis-18357	47	15	stable	stable	ADJ
fcis-18357	47	16	performance	performance	NOUN
fcis-18357	47	17	improvements	improvement	NOUN
fcis-18357	47	18	.	.	PUNCT
fcis-18357	48	1	compared	compare	VERB
fcis-18357	48	2	to	to	ADP
fcis-18357	48	3	the	the	DET
fcis-18357	48	4	slight	slight	ADJ
fcis-18357	48	5	increase	increase	NOUN
fcis-18357	48	6	in	in	ADP
fcis-18357	48	7	computational	computational	ADJ
fcis-18357	48	8	load	load	NOUN
fcis-18357	48	9	,	,	PUNCT
fcis-18357	48	10	the	the	DET
fcis-18357	48	11	introduction	introduction	NOUN
fcis-18357	48	12	of	of	ADP
fcis-18357	48	13	cbam	cbam	NOUN
fcis-18357	48	14	offers	offer	VERB
fcis-18357	48	15	a	a	DET
fcis-18357	48	16	high	high	ADJ
fcis-18357	48	17	cost	cost	NOUN
fcis-18357	48	18	-	-	PUNCT
fcis-18357	48	19	effectiveness	effectiveness	NOUN
fcis-18357	48	20	ratio	ratio	NOUN
fcis-18357	48	21	.	.	PUNCT
fcis-18357	49	1	(	(	PUNCT
fcis-18357	49	2	2	2	X
fcis-18357	49	3	)	)	PUNCT
fcis-18357	49	4	strong	strong	ADJ
fcis-18357	49	5	universality	universality	NOUN
fcis-18357	49	6	:	:	PUNCT
fcis-18357	49	7	the	the	DET
fcis-18357	49	8	structural	structural	ADJ
fcis-18357	49	9	characteristics	characteristic	NOUN
fcis-18357	49	10	of	of	ADP
fcis-18357	49	11	cbam	cbam	NOUN
fcis-18357	49	12	ensure	ensure	VERB
fcis-18357	49	13	its	its	PRON
fcis-18357	49	14	strong	strong	ADJ
fcis-18357	49	15	universality	universality	NOUN
fcis-18357	49	16	and	and	CCONJ
fcis-18357	49	17	high	high	ADJ
fcis-18357	49	18	portability	portability	NOUN
fcis-18357	49	19	,	,	PUNCT
fcis-18357	49	20	which	which	PRON
fcis-18357	49	21	is	be	AUX
fcis-18357	49	22	mainly	mainly	ADV
fcis-18357	49	23	reflected	reflect	VERB
fcis-18357	49	24	in	in	ADP
fcis-18357	49	25	two	two	NUM
fcis-18357	49	26	aspects	aspect	NOUN
fcis-18357	49	27	:	:	PUNCT
fcis-18357	49	28	on	on	ADP
fcis-18357	49	29	one	one	NUM
fcis-18357	49	30	hand	hand	NOUN
fcis-18357	49	31	,	,	PUNCT
fcis-18357	49	32	the	the	DET
fcis-18357	49	33	cbam	cbam	NOUN
fcis-18357	49	34	module	module	NOUN
fcis-18357	49	35	,	,	PUNCT
fcis-18357	49	36	based	base	VERB
fcis-18357	49	37	on	on	ADP
fcis-18357	49	38	pooling	pool	VERB
fcis-18357	49	39	operations	operation	NOUN
fcis-18357	49	40	,	,	PUNCT
fcis-18357	49	41	can	can	AUX
fcis-18357	49	42	be	be	AUX
fcis-18357	49	43	directly	directly	ADV
fcis-18357	49	44	embedded	embed	VERB
fcis-18357	49	45	after	after	ADP
fcis-18357	49	46	convolutional	convolutional	ADJ
fcis-18357	49	47	operations	operation	NOUN
fcis-18357	49	48	,	,	PUNCT
fcis-18357	49	49	meaning	mean	VERB
fcis-18357	49	50	that	that	SCONJ
fcis-18357	49	51	this	this	DET
fcis-18357	49	52	module	module	NOUN
fcis-18357	49	53	can	can	AUX
fcis-18357	49	54	be	be	AUX
fcis-18357	49	55	added	add	VERB
fcis-18357	49	56	to	to	ADP
fcis-18357	49	57	traditional	traditional	ADJ
fcis-18357	49	58	neural	neural	ADJ
fcis-18357	49	59	networks	network	NOUN
fcis-18357	49	60	like	like	ADP
fcis-18357	49	61	vgg	vgg	NOUN
fcis-18357	49	62	,	,	PUNCT
fcis-18357	49	63	as	as	ADV
fcis-18357	49	64	well	well	ADV
fcis-18357	49	65	as	as	ADP
fcis-18357	49	66	to	to	ADP
fcis-18357	49	67	networks	network	NOUN
fcis-18357	49	68	containing	contain	VERB
fcis-18357	49	69	shortcut	shortcut	NOUN
fcis-18357	49	70	connection	connection	NOUN
fcis-18357	49	71	-	-	PUNCT
fcis-18357	49	72	based	base	VERB
fcis-18357	49	73	residual	residual	ADJ
fcis-18357	49	74	structures	structure	NOUN
fcis-18357	49	75	,	,	PUNCT
fcis-18357	49	76	such	such	ADJ
fcis-18357	49	77	as	as	ADP
fcis-18357	49	78	resnet50	resnet50	NOUN
fcis-18357	49	79	and	and	CCONJ
fcis-18357	49	80	mobilenetv3	mobilenetv3	PROPN
fcis-18357	49	81	;	;	PUNCT
fcis-18357	49	82	on	on	ADP
fcis-18357	49	83	the	the	DET
fcis-18357	49	84	other	other	ADJ
fcis-18357	49	85	hand	hand	NOUN
fcis-18357	49	86	,	,	PUNCT
fcis-18357	49	87	cbam	cbam	NOUN
fcis-18357	49	88	is	be	AUX
fcis-18357	49	89	equally	equally	ADV
fcis-18357	49	90	applicable	applicable	ADJ
fcis-18357	49	91	to	to	ADP
fcis-18357	49	92	both	both	PRON
fcis-18357	49	93	object	object	VERB
fcis-18357	49	94	detection	detection	NOUN
fcis-18357	49	95	and	and	CCONJ
fcis-18357	49	96	classification	classification	NOUN
fcis-18357	49	97	tasks	task	NOUN
fcis-18357	49	98	,	,	PUNCT
fcis-18357	49	99	and	and	CCONJ
fcis-18357	49	100	it	it	PRON
fcis-18357	49	101	can	can	AUX
fcis-18357	49	102	achieve	achieve	VERB
fcis-18357	49	103	significant	significant	ADJ
fcis-18357	49	104	performance	performance	NOUN
fcis-18357	49	105	improvements	improvement	NOUN
fcis-18357	49	106	in	in	ADP
fcis-18357	49	107	both	both	PRON
fcis-18357	49	108	detection	detection	NOUN
fcis-18357	49	109	and	and	CCONJ
fcis-18357	49	110	classification	classification	NOUN
fcis-18357	49	111	accuracy	accuracy	NOUN
fcis-18357	49	112	across	across	ADP
fcis-18357	49	113	datasets	dataset	NOUN
fcis-18357	49	114	with	with	ADP
fcis-18357	49	115	different	different	ADJ
fcis-18357	49	116	feature	feature	NOUN
fcis-18357	49	117	characteristics	characteristic	NOUN
fcis-18357	49	118	.	.	PUNCT
fcis-18357	50	1	(	(	PUNCT
fcis-18357	50	2	3	3	X
fcis-18357	50	3	)	)	PUNCT
fcis-18357	50	4	effective	effective	ADJ
fcis-18357	50	5	impact	impact	NOUN
fcis-18357	50	6	:	:	PUNCT
fcis-18357	50	7	traditional	traditional	ADJ
fcis-18357	50	8	attention	attention	NOUN
fcis-18357	50	9	mechanisms	mechanism	NOUN
fcis-18357	50	10	in	in	ADP
fcis-18357	50	11	convolutional	convolutional	ADJ
fcis-18357	50	12	neural	neural	ADJ
fcis-18357	50	13	networks	network	NOUN
fcis-18357	50	14	focus	focus	VERB
fcis-18357	50	15	more	more	ADV
fcis-18357	50	16	on	on	ADP
fcis-18357	50	17	analyzing	analyze	VERB
fcis-18357	50	18	the	the	DET
fcis-18357	50	19	channel	channel	NOUN
fcis-18357	50	20	domain	domain	NOUN
fcis-18357	50	21	,	,	PUNCT
fcis-18357	50	22	limited	limit	VERB
fcis-18357	50	23	to	to	ADP
fcis-18357	50	24	considering	consider	VERB
fcis-18357	50	25	the	the	DET
fcis-18357	50	26	interactions	interaction	NOUN
fcis-18357	50	27	between	between	ADP
fcis-18357	50	28	feature	feature	NOUN
fcis-18357	50	29	map	map	NOUN
fcis-18357	50	30	channels	channel	NOUN
fcis-18357	50	31	.	.	PUNCT
fcis-18357	51	1	cbam	cbam	NOUN
fcis-18357	51	2	starts	start	VERB
fcis-18357	51	3	from	from	ADP
fcis-18357	51	4	both	both	DET
fcis-18357	51	5	channel	channel	NOUN
fcis-18357	51	6	and	and	CCONJ
fcis-18357	51	7	spatial	spatial	ADJ
fcis-18357	51	8	domains	domain	NOUN
fcis-18357	51	9	,	,	PUNCT
fcis-18357	51	10	introducing	introduce	VERB
fcis-18357	51	11	spatial	spatial	ADJ
fcis-18357	51	12	and	and	CCONJ
fcis-18357	51	13	channel	channel	VERB
fcis-18357	51	14	attention	attention	NOUN
fcis-18357	51	15	as	as	ADP
fcis-18357	51	16	two	two	NUM
fcis-18357	51	17	dimensions	dimension	NOUN
fcis-18357	51	18	of	of	ADP
fcis-18357	51	19	analysis	analysis	NOUN
fcis-18357	51	20	,	,	PUNCT
fcis-18357	51	21	thereby	thereby	ADV
fcis-18357	51	22	achieving	achieve	VERB
fcis-18357	51	23	a	a	DET
fcis-18357	51	24	sequential	sequential	ADJ
fcis-18357	51	25	attention	attention	NOUN
fcis-18357	51	26	structure	structure	NOUN
fcis-18357	51	27	from	from	ADP
fcis-18357	51	28	channel	channel	NOUN
fcis-18357	51	29	to	to	ADP
fcis-18357	51	30	space	space	NOUN
fcis-18357	51	31	.	.	PUNCT
fcis-18357	52	1	spatial	spatial	ADJ
fcis-18357	52	2	attention	attention	NOUN
fcis-18357	52	3	enables	enable	VERB
fcis-18357	52	4	the	the	DET
fcis-18357	52	5	neural	neural	ADJ
fcis-18357	52	6	network	network	NOUN
fcis-18357	52	7	to	to	PART
fcis-18357	52	8	pay	pay	VERB
fcis-18357	52	9	more	more	ADJ
fcis-18357	52	10	attention	attention	NOUN
fcis-18357	52	11	to	to	ADP
fcis-18357	52	12	the	the	DET
fcis-18357	52	13	pixel	pixel	PROPN
fcis-18357	52	14	areas	area	NOUN
fcis-18357	52	15	in	in	ADP
fcis-18357	52	16	images	image	NOUN
fcis-18357	52	17	that	that	PRON
fcis-18357	52	18	are	be	AUX
fcis-18357	52	19	crucial	crucial	ADJ
fcis-18357	52	20	for	for	ADP
fcis-18357	52	21	classification	classification	NOUN
fcis-18357	52	22	while	while	SCONJ
fcis-18357	52	23	ignoring	ignore	VERB
fcis-18357	52	24	irrelevant	irrelevant	ADJ
fcis-18357	52	25	regions	region	NOUN
fcis-18357	52	26	.	.	PUNCT
fcis-18357	53	1	channel	channel	NOUN
fcis-18357	53	2	attention	attention	NOUN
fcis-18357	53	3	,	,	PUNCT
fcis-18357	53	4	on	on	ADP
fcis-18357	53	5	the	the	DET
fcis-18357	53	6	other	other	ADJ
fcis-18357	53	7	hand	hand	NOUN
fcis-18357	53	8	,	,	PUNCT
fcis-18357	53	9	deals	deal	NOUN
fcis-18357	53	10	with	with	ADP
fcis-18357	53	11	the	the	DET
fcis-18357	53	12	distribution	distribution	NOUN
fcis-18357	53	13	of	of	ADP
fcis-18357	53	14	feature	feature	NOUN
fcis-18357	53	15	map	map	NOUN
fcis-18357	53	16	channels	channel	NOUN
fcis-18357	53	17	.	.	PUNCT
fcis-18357	54	1	attention	attention	NOUN
fcis-18357	54	2	allocation	allocation	NOUN
fcis-18357	54	3	to	to	ADP
fcis-18357	54	4	both	both	DET
fcis-18357	54	5	dimensions	dimension	NOUN
fcis-18357	54	6	enhances	enhance	VERB
fcis-18357	54	7	the	the	DET
fcis-18357	54	8	effect	effect	NOUN
fcis-18357	54	9	of	of	ADP
fcis-18357	54	10	the	the	DET
fcis-18357	54	11	attention	attention	NOUN
fcis-18357	54	12	mechanism	mechanism	NOUN
fcis-18357	54	13	on	on	ADP
fcis-18357	54	14	improving	improve	VERB
fcis-18357	54	15	model	model	NOUN
fcis-18357	54	16	performance	performance	NOUN
fcis-18357	54	17	.	.	PUNCT
fcis-18357	55	1	the	the	DET
fcis-18357	55	2	cbam	cbam	NOUN
fcis-18357	55	3	network	network	NOUN
fcis-18357	55	4	structure	structure	NOUN
fcis-18357	55	5	is	be	AUX
fcis-18357	55	6	shown	show	VERB
fcis-18357	55	7	in	in	ADP
fcis-18357	55	8	figure	figure	NOUN
fcis-18357	55	9	2	2	NUM
fcis-18357	55	10	.	.	PUNCT
fcis-18357	55	11	figure	figure	NOUN
fcis-18357	55	12	2	2	NUM
fcis-18357	55	13	.	.	PUNCT
fcis-18357	55	14	cbam	cbam	NOUN
fcis-18357	55	15	network	network	NOUN
fcis-18357	55	16	structure	structure	NOUN
fcis-18357	55	17	diagram	diagram	NOUN
fcis-18357	55	18	(	(	PUNCT
fcis-18357	55	19	1	1	X
fcis-18357	55	20	)	)	PUNCT
fcis-18357	55	21	feature	feature	NOUN
fcis-18357	55	22	extraction	extraction	NOUN
fcis-18357	55	23	network	network	NOUN
fcis-18357	55	24	:	:	PUNCT
fcis-18357	55	25	this	this	PRON
fcis-18357	55	26	is	be	AUX
fcis-18357	55	27	the	the	DET
fcis-18357	55	28	starting	starting	NOUN
fcis-18357	55	29	stage	stage	NOUN
fcis-18357	55	30	of	of	ADP
fcis-18357	55	31	cbam	cbam	NOUN
fcis-18357	55	32	,	,	PUNCT
fcis-18357	55	33	responsible	responsible	ADJ
fcis-18357	55	34	for	for	ADP
fcis-18357	55	35	extracting	extract	VERB
fcis-18357	55	36	raw	raw	ADJ
fcis-18357	55	37	features	feature	NOUN
fcis-18357	55	38	from	from	ADP
fcis-18357	55	39	the	the	DET
fcis-18357	55	40	input	input	NOUN
fcis-18357	55	41	image	image	NOUN
fcis-18357	55	42	.	.	PUNCT
fcis-18357	56	1	it	it	PRON
fcis-18357	56	2	is	be	AUX
fcis-18357	56	3	typically	typically	ADV
fcis-18357	56	4	a	a	DET
fcis-18357	56	5	combination	combination	NOUN
fcis-18357	56	6	of	of	ADP
fcis-18357	56	7	convolutional	convolutional	ADJ
fcis-18357	56	8	layers	layer	NOUN
fcis-18357	56	9	,	,	PUNCT
fcis-18357	56	10	pooling	pool	VERB
fcis-18357	56	11	layers	layer	NOUN
fcis-18357	56	12	,	,	PUNCT
fcis-18357	56	13	and	and	CCONJ
fcis-18357	56	14	activation	activation	NOUN
fcis-18357	56	15	functions	function	NOUN
fcis-18357	56	16	,	,	PUNCT
fcis-18357	56	17	aimed	aim	VERB
fcis-18357	56	18	at	at	ADP
fcis-18357	56	19	capturing	capture	VERB
fcis-18357	56	20	the	the	DET
fcis-18357	56	21	basic	basic	ADJ
fcis-18357	56	22	patterns	pattern	NOUN
fcis-18357	56	23	and	and	CCONJ
fcis-18357	56	24	structures	structure	NOUN
fcis-18357	56	25	of	of	ADP
fcis-18357	56	26	the	the	DET
fcis-18357	56	27	image	image	NOUN
fcis-18357	56	28	.	.	PUNCT
fcis-18357	57	1	the	the	DET
fcis-18357	57	2	output	output	NOUN
fcis-18357	57	3	of	of	ADP
fcis-18357	57	4	the	the	DET
fcis-18357	57	5	feature	feature	NOUN
fcis-18357	57	6	extraction	extraction	NOUN
fcis-18357	57	7	network	network	NOUN
fcis-18357	57	8	is	be	AUX
fcis-18357	57	9	a	a	DET
fcis-18357	57	10	set	set	NOUN
fcis-18357	57	11	of	of	ADP
fcis-18357	57	12	multi	multi	ADJ
fcis-18357	57	13	-	-	ADJ
fcis-18357	57	14	dimensional	dimensional	ADJ
fcis-18357	57	15	feature	feature	NOUN
fcis-18357	57	16	maps	map	NOUN
fcis-18357	57	17	,	,	PUNCT
fcis-18357	57	18	which	which	PRON
fcis-18357	57	19	are	be	AUX
fcis-18357	57	20	then	then	ADV
fcis-18357	57	21	sent	send	VERB
fcis-18357	57	22	to	to	ADP
fcis-18357	57	23	the	the	DET
fcis-18357	57	24	subsequent	subsequent	ADJ
fcis-18357	57	25	attention	attention	NOUN
fcis-18357	57	26	modules	module	NOUN
fcis-18357	57	27	for	for	ADP
fcis-18357	57	28	further	further	ADJ
fcis-18357	57	29	processing	processing	NOUN
fcis-18357	57	30	.	.	PUNCT
fcis-18357	58	1	(	(	PUNCT
fcis-18357	58	2	2	2	X
fcis-18357	58	3	)	)	PUNCT
fcis-18357	58	4	classification	classification	NOUN
fcis-18357	58	5	and	and	CCONJ
fcis-18357	58	6	regression	regression	NOUN
fcis-18357	58	7	module	module	NOUN
fcis-18357	58	8	:	:	PUNCT
fcis-18357	58	9	this	this	DET
fcis-18357	58	10	part	part	NOUN
fcis-18357	58	11	is	be	AUX
fcis-18357	58	12	usually	usually	ADV
fcis-18357	58	13	located	locate	VERB
fcis-18357	58	14	at	at	ADP
fcis-18357	58	15	the	the	DET
fcis-18357	58	16	end	end	NOUN
fcis-18357	58	17	of	of	ADP
fcis-18357	58	18	the	the	DET
fcis-18357	58	19	network	network	NOUN
fcis-18357	58	20	,	,	PUNCT
fcis-18357	58	21	and	and	CCONJ
fcis-18357	58	22	its	its	PRON
fcis-18357	58	23	task	task	NOUN
fcis-18357	58	24	is	be	AUX
fcis-18357	58	25	to	to	PART
fcis-18357	58	26	perform	perform	VERB
fcis-18357	58	27	classification	classification	NOUN
fcis-18357	58	28	or	or	CCONJ
fcis-18357	58	29	regression	regression	NOUN
fcis-18357	58	30	tasks	task	NOUN
fcis-18357	58	31	based	base	VERB
fcis-18357	58	32	on	on	ADP
fcis-18357	58	33	the	the	DET
fcis-18357	58	34	features	feature	NOUN
fcis-18357	58	35	refined	refine	VERB
fcis-18357	58	36	by	by	ADP
fcis-18357	58	37	the	the	DET
fcis-18357	58	38	attention	attention	NOUN
fcis-18357	58	39	module	module	NOUN
fcis-18357	58	40	.	.	PUNCT
fcis-18357	59	1	this	this	DET
fcis-18357	59	2	module	module	NOUN
fcis-18357	59	3	can	can	AUX
fcis-18357	59	4	be	be	AUX
fcis-18357	59	5	designed	design	VERB
fcis-18357	59	6	according	accord	VERB
fcis-18357	59	7	to	to	ADP
fcis-18357	59	8	specific	specific	ADJ
fcis-18357	59	9	application	application	NOUN
fcis-18357	59	10	scenarios	scenario	NOUN
fcis-18357	59	11	,	,	PUNCT
fcis-18357	59	12	such	such	ADJ
fcis-18357	59	13	as	as	ADP
fcis-18357	59	14	using	use	VERB
fcis-18357	59	15	fully	fully	ADV
fcis-18357	59	16	connected	connected	ADJ
fcis-18357	59	17	layers	layer	NOUN
fcis-18357	59	18	(	(	PUNCT
fcis-18357	59	19	fc	fc	NOUN
fcis-18357	59	20	layers	layer	NOUN
fcis-18357	59	21	)	)	PUNCT
fcis-18357	59	22	for	for	ADP
fcis-18357	59	23	image	image	NOUN
fcis-18357	59	24	classification	classification	NOUN
fcis-18357	59	25	or	or	CCONJ
fcis-18357	59	26	bounding	bound	VERB
fcis-18357	59	27	box	box	NOUN
fcis-18357	59	28	regression	regression	NOUN
fcis-18357	59	29	in	in	ADP
fcis-18357	59	30	object	object	NOUN
fcis-18357	59	31	detection	detection	NOUN
fcis-18357	59	32	tasks	task	NOUN
fcis-18357	59	33	.	.	PUNCT
fcis-18357	60	1	(	(	PUNCT
fcis-18357	60	2	3	3	X
fcis-18357	60	3	)	)	PUNCT
fcis-18357	60	4	fusion	fusion	NOUN
fcis-18357	60	5	attention	attention	NOUN
fcis-18357	60	6	module	module	NOUN
fcis-18357	60	7	(	(	PUNCT
fcis-18357	60	8	rpn	rpn	PROPN
fcis-18357	60	9	network	network	NOUN
fcis-18357	60	10	):	):	PUNCT
fcis-18357	60	11	the	the	DET
fcis-18357	60	12	core	core	NOUN
fcis-18357	60	13	of	of	ADP
fcis-18357	60	14	cbam	cbam	NOUN
fcis-18357	60	15	lies	lie	VERB
fcis-18357	60	16	in	in	ADP
fcis-18357	60	17	its	its	PRON
fcis-18357	60	18	attention	attention	NOUN
fcis-18357	60	19	module	module	NOUN
fcis-18357	60	20	,	,	PUNCT
fcis-18357	60	21	which	which	PRON
fcis-18357	60	22	is	be	AUX
fcis-18357	60	23	further	far	ADV
fcis-18357	60	24	divided	divide	VERB
fcis-18357	60	25	into	into	ADP
fcis-18357	60	26	two	two	NUM
fcis-18357	60	27	sub	sub	NOUN
fcis-18357	60	28	-	-	NOUN
fcis-18357	60	29	modules	module	NOUN
fcis-18357	60	30	:	:	PUNCT
fcis-18357	60	31	a.	a.	NOUN
fcis-18357	60	32	channel	channel	PROPN
fcis-18357	60	33	attention	attention	NOUN
fcis-18357	60	34	module	module	NOUN
fcis-18357	60	35	:	:	PUNCT
fcis-18357	60	36	the	the	DET
fcis-18357	60	37	goal	goal	NOUN
fcis-18357	60	38	of	of	ADP
fcis-18357	60	39	this	this	DET
fcis-18357	60	40	module	module	NOUN
fcis-18357	60	41	is	be	AUX
fcis-18357	60	42	to	to	PART
fcis-18357	60	43	identify	identify	VERB
fcis-18357	60	44	which	which	DET
fcis-18357	60	45	channels	channel	NOUN
fcis-18357	60	46	are	be	AUX
fcis-18357	60	47	important	important	ADJ
fcis-18357	60	48	,	,	PUNCT
fcis-18357	60	49	that	that	ADV
fcis-18357	60	50	is	is	ADV
fcis-18357	60	51	,	,	PUNCT
fcis-18357	60	52	to	to	PART
fcis-18357	60	53	apply	apply	VERB
fcis-18357	60	54	attention	attention	NOUN
fcis-18357	60	55	on	on	ADP
fcis-18357	60	56	the	the	DET
fcis-18357	60	57	channel	channel	NOUN
fcis-18357	60	58	dimension	dimension	NOUN
fcis-18357	60	59	of	of	ADP
fcis-18357	60	60	the	the	DET
fcis-18357	60	61	feature	feature	NOUN
fcis-18357	60	62	map	map	NOUN
fcis-18357	60	63	.	.	PUNCT
fcis-18357	61	1	it	it	PRON
fcis-18357	61	2	evaluates	evaluate	VERB
fcis-18357	61	3	the	the	DET
fcis-18357	61	4	importance	importance	NOUN
fcis-18357	61	5	of	of	ADP
fcis-18357	61	6	each	each	DET
fcis-18357	61	7	channel	channel	NOUN
fcis-18357	61	8	by	by	ADP
fcis-18357	61	9	analyzing	analyze	VERB
fcis-18357	61	10	the	the	DET
fcis-18357	61	11	global	global	ADJ
fcis-18357	61	12	information	information	NOUN
fcis-18357	61	13	of	of	ADP
fcis-18357	61	14	each	each	DET
fcis-18357	61	15	channel	channel	NOUN
fcis-18357	61	16	,	,	PUNCT
fcis-18357	61	17	allowing	allow	VERB
fcis-18357	61	18	the	the	DET
fcis-18357	61	19	network	network	NOUN
fcis-18357	61	20	to	to	PART
fcis-18357	61	21	focus	focus	VERB
fcis-18357	61	22	on	on	ADP
fcis-18357	61	23	more	more	ADV
fcis-18357	61	24	informative	informative	ADJ
fcis-18357	61	25	feature	feature	NOUN
fcis-18357	61	26	channels	channel	NOUN
fcis-18357	61	27	.	.	PUNCT
fcis-18357	62	1	b.	b.	PROPN
fcis-18357	62	2	spatial	spatial	ADJ
fcis-18357	62	3	attention	attention	NOUN
fcis-18357	62	4	module	module	NOUN
fcis-18357	62	5	:	:	PUNCT
fcis-18357	62	6	following	follow	VERB
fcis-18357	62	7	the	the	DET
fcis-18357	62	8	channel	channel	NOUN
fcis-18357	62	9	attention	attention	NOUN
fcis-18357	62	10	module	module	NOUN
fcis-18357	62	11	,	,	PUNCT
fcis-18357	62	12	the	the	DET
fcis-18357	62	13	spatial	spatial	ADJ
fcis-18357	62	14	attention	attention	NOUN
fcis-18357	62	15	module	module	NOUN
fcis-18357	62	16	focuses	focus	VERB
fcis-18357	62	17	on	on	ADP
fcis-18357	62	18	which	which	PRON
fcis-18357	62	19	spatial	spatial	ADJ
fcis-18357	62	20	areas	area	NOUN
fcis-18357	62	21	of	of	ADP
fcis-18357	62	22	the	the	DET
fcis-18357	62	23	feature	feature	NOUN
fcis-18357	62	24	map	map	NOUN
fcis-18357	62	25	are	be	AUX
fcis-18357	62	26	important	important	ADJ
fcis-18357	62	27	.	.	PUNCT
fcis-18357	63	1	it	it	PRON
fcis-18357	63	2	determines	determine	VERB
fcis-18357	63	3	the	the	DET
fcis-18357	63	4	focus	focus	NOUN
fcis-18357	63	5	of	of	ADP
fcis-18357	63	6	attention	attention	NOUN
fcis-18357	63	7	by	by	ADP
fcis-18357	63	8	observing	observe	VERB
fcis-18357	63	9	the	the	DET
fcis-18357	63	10	feature	feature	NOUN
fcis-18357	63	11	response	response	NOUN
fcis-18357	63	12	at	at	ADP
fcis-18357	63	13	different	different	ADJ
fcis-18357	63	14	spatial	spatial	ADJ
fcis-18357	63	15	positions	position	NOUN
fcis-18357	63	16	,	,	PUNCT
fcis-18357	63	17	further	far	ADV
fcis-18357	63	18	refining	refine	VERB
fcis-18357	63	19	the	the	DET
fcis-18357	63	20	feature	feature	NOUN
fcis-18357	63	21	map	map	NOUN
fcis-18357	63	22	,	,	PUNCT
fcis-18357	63	23	enabling	enable	VERB
fcis-18357	63	24	the	the	DET
fcis-18357	63	25	network	network	NOUN
fcis-18357	63	26	to	to	PART
fcis-18357	63	27	concentrate	concentrate	VERB
fcis-18357	63	28	on	on	ADP
fcis-18357	63	29	key	key	ADJ
fcis-18357	63	30	parts	part	NOUN
fcis-18357	63	31	of	of	ADP
fcis-18357	63	32	the	the	DET
fcis-18357	63	33	image	image	NOUN
fcis-18357	63	34	.	.	PUNCT
fcis-18357	64	1	the	the	DET
fcis-18357	64	2	combination	combination	NOUN
fcis-18357	64	3	of	of	ADP
fcis-18357	64	4	these	these	DET
fcis-18357	64	5	two	two	NUM
fcis-18357	64	6	attention	attention	NOUN
fcis-18357	64	7	modules	module	NOUN
fcis-18357	64	8	allows	allow	VERB
fcis-18357	64	9	cbam	cbam	NOUN
fcis-18357	64	10	to	to	PART
fcis-18357	64	11	adjust	adjust	VERB
fcis-18357	64	12	the	the	DET
fcis-18357	64	13	feature	feature	NOUN
fcis-18357	64	14	map	map	NOUN
fcis-18357	64	15	in	in	ADP
fcis-18357	64	16	a	a	DET
fcis-18357	64	17	detailed	detailed	ADJ
fcis-18357	64	18	manner	manner	NOUN
fcis-18357	64	19	,	,	PUNCT
fcis-18357	64	20	significantly	significantly	ADV
fcis-18357	64	21	improving	improve	VERB
fcis-18357	64	22	the	the	DET
fcis-18357	64	23	expressiveness	expressiveness	NOUN
fcis-18357	64	24	of	of	ADP
fcis-18357	64	25	features	feature	NOUN
fcis-18357	64	26	by	by	ADP
fcis-18357	64	27	applying	apply	VERB
fcis-18357	64	28	attention	attention	NOUN
fcis-18357	64	29	both	both	CCONJ
fcis-18357	64	30	in	in	ADP
fcis-18357	64	31	the	the	DET
fcis-18357	64	32	channel	channel	NOUN
fcis-18357	64	33	and	and	CCONJ
fcis-18357	64	34	spatial	spatial	ADJ
fcis-18357	64	35	dimensions	dimension	NOUN
fcis-18357	64	36	.	.	PUNCT
fcis-18357	65	1	the	the	DET
fcis-18357	65	2	design	design	NOUN
fcis-18357	65	3	philosophy	philosophy	NOUN
fcis-18357	65	4	of	of	ADP
fcis-18357	65	5	cbam	cbam	NOUN
fcis-18357	65	6	is	be	AUX
fcis-18357	65	7	modularity	modularity	NOUN
fcis-18357	65	8	and	and	CCONJ
fcis-18357	65	9	versatility	versatility	NOUN
fcis-18357	65	10	;	;	PUNCT
fcis-18357	65	11	it	it	PRON
fcis-18357	65	12	can	can	AUX
fcis-18357	65	13	be	be	AUX
fcis-18357	65	14	easily	easily	ADV
fcis-18357	65	15	inserted	insert	VERB
fcis-18357	65	16	into	into	ADP
fcis-18357	65	17	existing	exist	VERB
fcis-18357	65	18	convolutional	convolutional	ADJ
fcis-18357	65	19	neural	neural	ADJ
fcis-18357	65	20	networks	network	NOUN
fcis-18357	65	21	to	to	PART
fcis-18357	65	22	enhance	enhance	VERB
fcis-18357	65	23	the	the	DET
fcis-18357	65	24	network	network	NOUN
fcis-18357	65	25	's	's	PART
fcis-18357	65	26	perception	perception	NOUN
fcis-18357	65	27	of	of	ADP
fcis-18357	65	28	important	important	ADJ
fcis-18357	65	29	features	feature	NOUN
fcis-18357	65	30	,	,	PUNCT
fcis-18357	65	31	thereby	thereby	ADV
fcis-18357	65	32	improving	improve	VERB
fcis-18357	65	33	overall	overall	ADJ
fcis-18357	65	34	performance	performance	NOUN
fcis-18357	65	35	.	.	PUNCT
fcis-18357	66	1	in	in	ADP
fcis-18357	66	2	this	this	DET
fcis-18357	66	3	way	way	NOUN
fcis-18357	66	4	,	,	PUNCT
fcis-18357	66	5	cbam	cbam	NOUN
fcis-18357	66	6	enhances	enhance	VERB
fcis-18357	66	7	the	the	DET
fcis-18357	66	8	network	network	NOUN
fcis-18357	66	9	's	's	PART
fcis-18357	66	10	ability	ability	NOUN
fcis-18357	66	11	to	to	PART
fcis-18357	66	12	capture	capture	VERB
fcis-18357	66	13	important	important	ADJ
fcis-18357	66	14	information	information	NOUN
fcis-18357	66	15	in	in	ADP
fcis-18357	66	16	images	image	NOUN
fcis-18357	66	17	,	,	PUNCT
fcis-18357	66	18	which	which	PRON
fcis-18357	66	19	is	be	AUX
fcis-18357	66	20	very	very	ADV
fcis-18357	66	21	effective	effective	ADJ
fcis-18357	66	22	for	for	ADP
fcis-18357	66	23	improving	improve	VERB
fcis-18357	66	24	the	the	DET
fcis-18357	66	25	accuracy	accuracy	NOUN
fcis-18357	66	26	of	of	ADP
fcis-18357	66	27	image	image	NOUN
fcis-18357	66	28	recognition	recognition	NOUN
fcis-18357	66	29	,	,	PUNCT
fcis-18357	66	30	object	object	NOUN
fcis-18357	66	31	detection	detection	NOUN
fcis-18357	66	32	,	,	PUNCT
fcis-18357	66	33	and	and	CCONJ
fcis-18357	66	34	various	various	ADJ
fcis-18357	66	35	visual	visual	ADJ
fcis-18357	66	36	tasks	task	NOUN
fcis-18357	66	37	.	.	PUNCT
fcis-18357	67	1	2.3	2.3	NUM
fcis-18357	67	2	.	.	PUNCT
fcis-18357	68	1	to	to	PART
fcis-18357	68	2	improve	improve	VERB
fcis-18357	68	3	yolov7	yolov7	NOUN
fcis-18357	68	4	-	-	PUNCT
fcis-18357	68	5	tiny	tiny	ADJ
fcis-18357	68	6	by	by	ADP
fcis-18357	68	7	adding	add	VERB
fcis-18357	68	8	a	a	DET
fcis-18357	68	9	small	small	ADJ
fcis-18357	68	10	object	object	NOUN
fcis-18357	68	11	detection	detection	NOUN
fcis-18357	68	12	layer	layer	NOUN
fcis-18357	68	13	this	this	DET
fcis-18357	68	14	paper	paper	NOUN
fcis-18357	68	15	adds	add	VERB
fcis-18357	68	16	a	a	DET
fcis-18357	68	17	small	small	ADJ
fcis-18357	68	18	object	object	NOUN
fcis-18357	68	19	detection	detection	NOUN
fcis-18357	68	20	layer	layer	NOUN
fcis-18357	68	21	and	and	CCONJ
fcis-18357	68	22	incorporates	incorporate	VERB
fcis-18357	68	23	the	the	DET
fcis-18357	68	24	lightweight	lightweight	ADJ
fcis-18357	68	25	convolutional	convolutional	ADJ
fcis-18357	68	26	block	block	NOUN
fcis-18357	68	27	attention	attention	NOUN
fcis-18357	68	28	module	module	NOUN
fcis-18357	68	29	(	(	PUNCT
fcis-18357	68	30	cbam	cbam	NOUN
fcis-18357	68	31	)	)	PUNCT
fcis-18357	68	32	.	.	PUNCT
fcis-18357	69	1	the	the	DET
fcis-18357	69	2	network	network	NOUN
fcis-18357	69	3	of	of	ADP
fcis-18357	69	4	the	the	DET
fcis-18357	69	5	small	small	ADJ
fcis-18357	69	6	object	object	NOUN
fcis-18357	69	7	detection	detection	NOUN
fcis-18357	69	8	layer	layer	NOUN
fcis-18357	69	9	is	be	AUX
fcis-18357	69	10	more	more	ADV
fcis-18357	69	11	focused	focused	ADJ
fcis-18357	69	12	on	on	ADP
fcis-18357	69	13	detecting	detect	VERB
fcis-18357	69	14	small	small	ADJ
fcis-18357	69	15	objects	object	NOUN
fcis-18357	69	16	,	,	PUNCT
fcis-18357	69	17	which	which	PRON
fcis-18357	69	18	improves	improve	VERB
fcis-18357	69	19	detection	detection	NOUN
fcis-18357	69	20	performance	performance	NOUN
fcis-18357	69	21	.	.	PUNCT
fcis-18357	70	1	the	the	DET
fcis-18357	70	2	most	most	ADV
fcis-18357	70	3	prominent	prominent	ADJ
fcis-18357	70	4	function	function	NOUN
fcis-18357	70	5	of	of	ADP
fcis-18357	70	6	cbam	cbam	NOUN
fcis-18357	70	7	is	be	AUX
fcis-18357	70	8	to	to	PART
fcis-18357	70	9	enhance	enhance	VERB
fcis-18357	70	10	the	the	DET
fcis-18357	70	11	useful	useful	ADJ
fcis-18357	70	12	information	information	NOUN
fcis-18357	70	13	in	in	ADP
fcis-18357	70	14	the	the	DET
fcis-18357	70	15	feature	feature	NOUN
fcis-18357	70	16	map	map	NOUN
fcis-18357	70	17	,	,	PUNCT
fcis-18357	70	18	making	make	VERB
fcis-18357	70	19	it	it	PRON
fcis-18357	70	20	easier	easy	ADJ
fcis-18357	70	21	to	to	PART
fcis-18357	70	22	extract	extract	VERB
fcis-18357	70	23	and	and	CCONJ
fcis-18357	70	24	learn	learn	VERB
fcis-18357	70	25	target	target	NOUN
fcis-18357	70	26	features	feature	NOUN
fcis-18357	70	27	.	.	PUNCT
fcis-18357	71	1	the	the	DET
fcis-18357	71	2	function	function	NOUN
fcis-18357	71	3	is	be	AUX
fcis-18357	71	4	mainly	mainly	ADV
fcis-18357	71	5	concentrated	concentrate	VERB
fcis-18357	71	6	in	in	ADP
fcis-18357	71	7	the	the	DET
fcis-18357	71	8	backbone	backbone	NOUN
fcis-18357	71	9	part	part	NOUN
fcis-18357	71	10	,	,	PUNCT
fcis-18357	71	11	so	so	ADV
fcis-18357	71	12	cbam	cbam	NOUN
fcis-18357	71	13	is	be	AUX
fcis-18357	71	14	fused	fuse	VERB
fcis-18357	71	15	into	into	ADP
fcis-18357	71	16	the	the	DET
fcis-18357	71	17	backbone	backbone	NOUN
fcis-18357	71	18	.	.	PUNCT
fcis-18357	72	1	the	the	DET
fcis-18357	72	2	network	network	NOUN
fcis-18357	72	3	structure	structure	NOUN
fcis-18357	72	4	of	of	ADP
fcis-18357	72	5	the	the	DET
fcis-18357	72	6	backbone	backbone	NOUN
fcis-18357	72	7	after	after	ADP
fcis-18357	72	8	fusion	fusion	NOUN
fcis-18357	72	9	is	be	AUX
fcis-18357	72	10	shown	show	VERB
fcis-18357	72	11	in	in	ADP
fcis-18357	72	12	figure	figure	NOUN
fcis-18357	72	13	3	3	NUM
fcis-18357	72	14	.	.	PUNCT
fcis-18357	73	1	cbam	cbam	NOUN
fcis-18357	73	2	is	be	AUX
fcis-18357	73	3	added	add	VERB
fcis-18357	73	4	to	to	ADP
fcis-18357	73	5	four	four	NUM
fcis-18357	73	6	different	different	ADJ
fcis-18357	73	7	positions	position	NOUN
fcis-18357	73	8	in	in	ADP
fcis-18357	73	9	the	the	DET
fcis-18357	73	10	backbone	backbone	NOUN
fcis-18357	73	11	,	,	PUNCT
fcis-18357	73	12	further	far	ADV
fcis-18357	73	13	improving	improve	VERB
fcis-18357	73	14	its	its	PRON
fcis-18357	73	15	ability	ability	NOUN
fcis-18357	73	16	to	to	PART
fcis-18357	73	17	extract	extract	VERB
fcis-18357	73	18	important	important	ADJ
fcis-18357	73	19	features	feature	NOUN
fcis-18357	73	20	.	.	PUNCT
fcis-18357	74	1	the	the	DET
fcis-18357	74	2	improved	improved	ADJ
fcis-18357	74	3	yolov7	yolov7	NOUN
fcis-18357	74	4	-	-	PUNCT
fcis-18357	74	5	tiny	tiny	ADJ
fcis-18357	74	6	moves	move	NOUN
fcis-18357	74	7	the	the	DET
fcis-18357	74	8	time	time	NOUN
fcis-18357	74	9	point	point	NOUN
fcis-18357	74	10	of	of	ADP
fcis-18357	74	11	feature	feature	NOUN
fcis-18357	74	12	enhancement	enhancement	NOUN
fcis-18357	74	13	forward	forward	ADV
fcis-18357	74	14	,	,	PUNCT
fcis-18357	74	15	starting	start	VERB
fcis-18357	74	16	at	at	ADP
fcis-18357	74	17	the	the	DET
fcis-18357	74	18	third	third	ADJ
fcis-18357	74	19	layer	layer	NOUN
fcis-18357	74	20	of	of	ADP
fcis-18357	74	21	the	the	DET
fcis-18357	74	22	backbone	backbone	NOUN
fcis-18357	74	23	network	network	NOUN
fcis-18357	74	24	.	.	PUNCT
fcis-18357	75	1	the	the	DET
fcis-18357	75	2	purpose	purpose	NOUN
fcis-18357	75	3	of	of	ADP
fcis-18357	75	4	this	this	PRON
fcis-18357	75	5	is	be	AUX
fcis-18357	75	6	to	to	PART
fcis-18357	75	7	obtain	obtain	VERB
fcis-18357	75	8	target	target	NOUN
fcis-18357	75	9	size	size	NOUN
fcis-18357	75	10	images	image	NOUN
fcis-18357	75	11	with	with	ADP
fcis-18357	75	12	a	a	DET
fcis-18357	75	13	larger	large	ADJ
fcis-18357	75	14	receptive	receptive	ADJ
fcis-18357	75	15	field	field	NOUN
fcis-18357	75	16	(	(	PUNCT
fcis-18357	75	17	160x160	160x160	NUM
fcis-18357	75	18	)	)	PUNCT
fcis-18357	75	19	,	,	PUNCT
fcis-18357	75	20	which	which	PRON
fcis-18357	75	21	facilitates	facilitate	VERB
fcis-18357	75	22	better	well	ADJ
fcis-18357	75	23	small	small	ADJ
fcis-18357	75	24	object	object	NOUN
fcis-18357	75	25	detection	detection	NOUN
fcis-18357	75	26	.	.	PUNCT
fcis-18357	76	1	subsequently	subsequently	ADV
fcis-18357	76	2	,	,	PUNCT
fcis-18357	76	3	an	an	DET
fcis-18357	76	4	additional	additional	ADJ
fcis-18357	76	5	upsampling	upsampling	NOUN
fcis-18357	76	6	and	and	CCONJ
fcis-18357	76	7	feature	feature	NOUN
fcis-18357	76	8	fusion	fusion	NOUN
fcis-18357	76	9	are	be	AUX
fcis-18357	76	10	added	add	VERB
fcis-18357	76	11	in	in	ADP
fcis-18357	76	12	the	the	DET
fcis-18357	76	13	backbone	backbone	NOUN
fcis-18357	76	14	network	network	NOUN
fcis-18357	76	15	part	part	NOUN
fcis-18357	76	16	to	to	PART
fcis-18357	76	17	enhance	enhance	VERB
fcis-18357	76	18	feature	feature	NOUN
fcis-18357	76	19	extraction	extraction	NOUN
fcis-18357	76	20	,	,	PUNCT
fcis-18357	76	21	allowing	allow	VERB
fcis-18357	76	22	for	for	ADP
fcis-18357	76	23	more	more	ADV
fcis-18357	76	24	comprehensive	comprehensive	ADJ
fcis-18357	76	25	information	information	NOUN
fcis-18357	76	26	related	relate	VERB
fcis-18357	76	27	to	to	ADP
fcis-18357	76	28	small	small	ADJ
fcis-18357	76	29	objects	object	NOUN
fcis-18357	76	30	.	.	PUNCT
fcis-18357	77	1	finally	finally	ADV
fcis-18357	77	2	,	,	PUNCT
fcis-18357	77	3	in	in	ADP
fcis-18357	77	4	the	the	DET
fcis-18357	77	5	feature	feature	NOUN
fcis-18357	77	6	extraction	extraction	NOUN
fcis-18357	77	7	part	part	NOUN
fcis-18357	77	8	,	,	PUNCT
fcis-18357	77	9	a	a	DET
fcis-18357	77	10	target	target	NOUN
fcis-18357	77	11	detection	detection	NOUN
fcis-18357	77	12	head	head	NOUN
fcis-18357	77	13	is	be	AUX
fcis-18357	77	14	added	add	VERB
fcis-18357	77	15	,	,	PUNCT
fcis-18357	77	16	which	which	PRON
fcis-18357	77	17	minimizes	minimize	VERB
fcis-18357	77	18	the	the	DET
fcis-18357	77	19	loss	loss	NOUN
fcis-18357	77	20	of	of	ADP
fcis-18357	77	21	information	information	NOUN
fcis-18357	77	22	related	relate	VERB
fcis-18357	77	23	to	to	ADP
fcis-18357	77	24	small	small	ADJ
fcis-18357	77	25	objects	object	NOUN
fcis-18357	77	26	in	in	ADP
fcis-18357	77	27	larger	large	ADJ
fcis-18357	77	28	-	-	PUNCT
fcis-18357	77	29	sized	sized	ADJ
fcis-18357	77	30	images	image	NOUN
fcis-18357	77	31	,	,	PUNCT
fcis-18357	77	32	thereby	thereby	ADV
fcis-18357	77	33	making	make	VERB
fcis-18357	77	34	the	the	DET
fcis-18357	77	35	detection	detection	NOUN
fcis-18357	77	36	more	more	ADV
fcis-18357	77	37	accurate	accurate	ADJ
fcis-18357	77	38	.	.	PUNCT
fcis-18357	78	1	additionally	additionally	ADV
fcis-18357	78	2	,	,	PUNCT
fcis-18357	78	3	the	the	DET
fcis-18357	78	4	cbam	cbam	NOUN
fcis-18357	78	5	self	self	NOUN
fcis-18357	78	6	-	-	PUNCT
fcis-18357	78	7	attention	attention	NOUN
fcis-18357	78	8	mechanism	mechanism	NOUN
fcis-18357	78	9	is	be	AUX
fcis-18357	78	10	added	add	VERB
fcis-18357	78	11	to	to	ADP
fcis-18357	78	12	the	the	DET
fcis-18357	78	13	feature	feature	NOUN
fcis-18357	78	14	68	68	NUM
fcis-18357	78	15	extraction	extraction	NOUN
fcis-18357	78	16	layer	layer	NOUN
fcis-18357	78	17	of	of	ADP
fcis-18357	78	18	the	the	DET
fcis-18357	78	19	backbone	backbone	NOUN
fcis-18357	78	20	extraction	extraction	NOUN
fcis-18357	78	21	network	network	NOUN
fcis-18357	78	22	to	to	PART
fcis-18357	78	23	further	far	ADV
fcis-18357	78	24	improve	improve	VERB
fcis-18357	78	25	small	small	ADJ
fcis-18357	78	26	object	object	NOUN
fcis-18357	78	27	detection	detection	NOUN
fcis-18357	78	28	.	.	PUNCT
fcis-18357	79	1	figure	figure	NOUN
fcis-18357	79	2	3	3	NUM
fcis-18357	79	3	.	.	PUNCT
fcis-18357	79	4	improved	improve	VERB
fcis-18357	79	5	yolov7	yolov7	ADJ
fcis-18357	79	6	-	-	PUNCT
fcis-18357	79	7	tiny	tiny	ADJ
fcis-18357	79	8	network	network	NOUN
fcis-18357	79	9	structure	structure	NOUN
fcis-18357	79	10	diagram	diagram	NOUN
fcis-18357	79	11	3	3	NUM
fcis-18357	79	12	.	.	PUNCT
fcis-18357	80	1	protocol	protocol	VERB
fcis-18357	80	2	the	the	DET
fcis-18357	80	3	research	research	NOUN
fcis-18357	80	4	experimental	experimental	ADJ
fcis-18357	80	5	plan	plan	NOUN
fcis-18357	80	6	for	for	ADP
fcis-18357	80	7	improving	improve	VERB
fcis-18357	80	8	small	small	ADJ
fcis-18357	80	9	object	object	NOUN
fcis-18357	80	10	detection	detection	NOUN
fcis-18357	80	11	in	in	ADP
fcis-18357	80	12	intelligent	intelligent	ADJ
fcis-18357	80	13	surveillance	surveillance	NOUN
fcis-18357	80	14	videos	video	NOUN
fcis-18357	80	15	based	base	VERB
fcis-18357	80	16	on	on	ADP
fcis-18357	80	17	yolov7	yolov7	PROPN
fcis-18357	80	18	is	be	AUX
fcis-18357	80	19	illustrated	illustrate	VERB
fcis-18357	80	20	in	in	ADP
fcis-18357	80	21	figure	figure	NOUN
fcis-18357	80	22	4	4	NUM
fcis-18357	80	23	and	and	CCONJ
fcis-18357	80	24	includes	include	VERB
fcis-18357	80	25	the	the	DET
fcis-18357	80	26	following	follow	VERB
fcis-18357	80	27	steps	step	NOUN
fcis-18357	80	28	:	:	PUNCT
fcis-18357	80	29	(	(	PUNCT
fcis-18357	80	30	1	1	NUM
fcis-18357	80	31	)	)	PUNCT
fcis-18357	80	32	.	.	PUNCT
fcis-18357	81	1	image	image	NOUN
fcis-18357	81	2	data	datum	NOUN
fcis-18357	81	3	preprocessing	preprocesse	VERB
fcis-18357	81	4	this	this	DET
fcis-18357	81	5	involves	involve	NOUN
fcis-18357	81	6	preparing	prepare	VERB
fcis-18357	81	7	the	the	DET
fcis-18357	81	8	image	image	NOUN
fcis-18357	81	9	data	datum	NOUN
fcis-18357	81	10	for	for	ADP
fcis-18357	81	11	the	the	DET
fcis-18357	81	12	model	model	NOUN
fcis-18357	81	13	by	by	ADP
fcis-18357	81	14	performing	perform	VERB
fcis-18357	81	15	operations	operation	NOUN
fcis-18357	81	16	such	such	ADJ
fcis-18357	81	17	as	as	ADP
fcis-18357	81	18	resizing	resizing	NOUN
fcis-18357	81	19	,	,	PUNCT
fcis-18357	81	20	normalization	normalization	NOUN
fcis-18357	81	21	,	,	PUNCT
fcis-18357	81	22	and	and	CCONJ
fcis-18357	81	23	augmentation	augmentation	NOUN
fcis-18357	81	24	to	to	PART
fcis-18357	81	25	make	make	VERB
fcis-18357	81	26	it	it	PRON
fcis-18357	81	27	suitable	suitable	ADJ
fcis-18357	81	28	for	for	ADP
fcis-18357	81	29	the	the	DET
fcis-18357	81	30	detection	detection	NOUN
fcis-18357	81	31	model	model	NOUN
fcis-18357	81	32	.	.	PUNCT
fcis-18357	82	1	(	(	PUNCT
fcis-18357	82	2	2	2	NUM
fcis-18357	82	3	)	)	PUNCT
fcis-18357	82	4	.	.	PUNCT
fcis-18357	83	1	model	model	NOUN
fcis-18357	83	2	selection	selection	NOUN
fcis-18357	83	3	and	and	CCONJ
fcis-18357	83	4	modification	modification	NOUN
fcis-18357	83	5	selecting	select	VERB
fcis-18357	83	6	yolov7	yolov7	NOUN
fcis-18357	83	7	-	-	PUNCT
fcis-18357	83	8	tiny	tiny	ADJ
fcis-18357	83	9	as	as	ADP
fcis-18357	83	10	the	the	DET
fcis-18357	83	11	base	base	NOUN
fcis-18357	83	12	model	model	NOUN
fcis-18357	83	13	for	for	ADP
fcis-18357	83	14	the	the	DET
fcis-18357	83	15	task	task	NOUN
fcis-18357	83	16	.	.	PUNCT
fcis-18357	84	1	this	this	DET
fcis-18357	84	2	choice	choice	NOUN
fcis-18357	84	3	is	be	AUX
fcis-18357	84	4	made	make	VERB
fcis-18357	84	5	due	due	ADJ
fcis-18357	84	6	to	to	ADP
fcis-18357	84	7	its	its	PRON
fcis-18357	84	8	efficiency	efficiency	NOUN
fcis-18357	84	9	and	and	CCONJ
fcis-18357	84	10	effectiveness	effectiveness	NOUN
fcis-18357	84	11	in	in	ADP
fcis-18357	84	12	detecting	detect	VERB
fcis-18357	84	13	small	small	ADJ
fcis-18357	84	14	objects	object	NOUN
fcis-18357	84	15	.	.	PUNCT
fcis-18357	85	1	(	(	PUNCT
fcis-18357	85	2	3	3	NUM
fcis-18357	85	3	)	)	PUNCT
fcis-18357	85	4	.	.	PUNCT
fcis-18357	86	1	incorporation	incorporation	NOUN
fcis-18357	86	2	of	of	ADP
fcis-18357	86	3	a	a	DET
fcis-18357	86	4	small	small	ADJ
fcis-18357	86	5	object	object	NOUN
fcis-18357	86	6	layer	layer	NOUN
fcis-18357	86	7	and	and	CCONJ
fcis-18357	86	8	cbam	cbam	NOUN
fcis-18357	86	9	attention	attention	NOUN
fcis-18357	86	10	mechanism	mechanism	NOUN
fcis-18357	86	11	introducing	introduce	VERB
fcis-18357	86	12	a	a	DET
fcis-18357	86	13	dedicated	dedicated	ADJ
fcis-18357	86	14	layer	layer	NOUN
fcis-18357	86	15	for	for	ADP
fcis-18357	86	16	small	small	ADJ
fcis-18357	86	17	object	object	NOUN
fcis-18357	86	18	detection	detection	NOUN
fcis-18357	86	19	and	and	CCONJ
fcis-18357	86	20	the	the	DET
fcis-18357	86	21	cbam	cbam	NOUN
fcis-18357	86	22	(	(	PUNCT
fcis-18357	86	23	convolutional	convolutional	ADJ
fcis-18357	86	24	block	block	NOUN
fcis-18357	86	25	attention	attention	NOUN
fcis-18357	86	26	module	module	NOUN
fcis-18357	86	27	)	)	PUNCT
fcis-18357	86	28	for	for	ADP
fcis-18357	86	29	improving	improve	VERB
fcis-18357	86	30	feature	feature	NOUN
fcis-18357	86	31	representation	representation	NOUN
fcis-18357	86	32	and	and	CCONJ
fcis-18357	86	33	focusing	focus	VERB
fcis-18357	86	34	on	on	ADP
fcis-18357	86	35	relevant	relevant	ADJ
fcis-18357	86	36	features	feature	NOUN
fcis-18357	86	37	for	for	ADP
fcis-18357	86	38	small	small	ADJ
fcis-18357	86	39	object	object	NOUN
fcis-18357	86	40	detection	detection	NOUN
fcis-18357	86	41	.	.	PUNCT
fcis-18357	87	1	(	(	PUNCT
fcis-18357	87	2	4	4	NUM
fcis-18357	87	3	)	)	PUNCT
fcis-18357	87	4	.	.	PUNCT
fcis-18357	88	1	model	model	NOUN
fcis-18357	88	2	training	training	NOUN
fcis-18357	88	3	training	train	VERB
fcis-18357	88	4	the	the	DET
fcis-18357	88	5	modified	modified	ADJ
fcis-18357	88	6	model	model	NOUN
fcis-18357	88	7	on	on	ADP
fcis-18357	88	8	a	a	DET
fcis-18357	88	9	dataset	dataset	NOUN
fcis-18357	88	10	,	,	PUNCT
fcis-18357	88	11	configuring	configure	VERB
fcis-18357	88	12	training	training	NOUN
fcis-18357	88	13	parameters	parameter	NOUN
fcis-18357	88	14	,	,	PUNCT
fcis-18357	88	15	and	and	CCONJ
fcis-18357	88	16	monitoring	monitor	VERB
fcis-18357	88	17	the	the	DET
fcis-18357	88	18	model	model	NOUN
fcis-18357	88	19	's	's	PART
fcis-18357	88	20	performance	performance	NOUN
fcis-18357	88	21	on	on	ADP
fcis-18357	88	22	a	a	DET
fcis-18357	88	23	validation	validation	NOUN
fcis-18357	88	24	set	set	NOUN
fcis-18357	88	25	to	to	PART
fcis-18357	88	26	ensure	ensure	VERB
fcis-18357	88	27	that	that	SCONJ
fcis-18357	88	28	it	it	PRON
fcis-18357	88	29	learns	learn	VERB
fcis-18357	88	30	to	to	PART
fcis-18357	88	31	detect	detect	VERB
fcis-18357	88	32	small	small	ADJ
fcis-18357	88	33	objects	object	NOUN
fcis-18357	88	34	accurately	accurately	ADV
fcis-18357	88	35	.	.	PUNCT
fcis-18357	89	1	(	(	PUNCT
fcis-18357	89	2	5	5	NUM
fcis-18357	89	3	)	)	PUNCT
fcis-18357	89	4	.	.	PUNCT
fcis-18357	90	1	model	model	NOUN
fcis-18357	90	2	optimization	optimization	NOUN
fcis-18357	90	3	based	base	VERB
fcis-18357	90	4	on	on	ADP
fcis-18357	90	5	performance	performance	NOUN
fcis-18357	90	6	,	,	PUNCT
fcis-18357	90	7	adjusting	adjust	VERB
fcis-18357	90	8	the	the	DET
fcis-18357	90	9	hyperparameters	hyperparameter	NOUN
fcis-18357	90	10	of	of	ADP
fcis-18357	90	11	the	the	DET
fcis-18357	90	12	small	small	ADJ
fcis-18357	90	13	object	object	NOUN
fcis-18357	90	14	layer	layer	NOUN
fcis-18357	90	15	and	and	CCONJ
fcis-18357	90	16	the	the	DET
fcis-18357	90	17	cbam	cbam	NOUN
fcis-18357	90	18	module	module	NOUN
fcis-18357	90	19	to	to	ADP
fcis-18357	90	20	fine	fine	ADJ
fcis-18357	90	21	-	-	PUNCT
fcis-18357	90	22	tune	tune	NOUN
fcis-18357	90	23	the	the	DET
fcis-18357	90	24	model	model	NOUN
fcis-18357	90	25	for	for	ADP
fcis-18357	90	26	better	well	ADJ
fcis-18357	90	27	detection	detection	NOUN
fcis-18357	90	28	accuracy	accuracy	NOUN
fcis-18357	90	29	and	and	CCONJ
fcis-18357	90	30	efficiency	efficiency	NOUN
fcis-18357	90	31	.	.	PUNCT
fcis-18357	91	1	(	(	PUNCT
fcis-18357	91	2	6	6	NUM
fcis-18357	91	3	)	)	PUNCT
fcis-18357	91	4	.	.	PUNCT
fcis-18357	92	1	data	datum	NOUN
fcis-18357	92	2	augmentation	augmentation	NOUN
fcis-18357	92	3	enhancing	enhance	VERB
fcis-18357	92	4	the	the	DET
fcis-18357	92	5	model	model	NOUN
fcis-18357	92	6	's	's	PART
fcis-18357	92	7	generalization	generalization	NOUN
fcis-18357	92	8	ability	ability	NOUN
fcis-18357	92	9	by	by	ADP
fcis-18357	92	10	applying	apply	VERB
fcis-18357	92	11	data	datum	NOUN
fcis-18357	92	12	augmentation	augmentation	NOUN
fcis-18357	92	13	techniques	technique	NOUN
fcis-18357	92	14	to	to	PART
fcis-18357	92	15	increase	increase	VERB
fcis-18357	92	16	the	the	DET
fcis-18357	92	17	diversity	diversity	NOUN
fcis-18357	92	18	of	of	ADP
fcis-18357	92	19	training	training	NOUN
fcis-18357	92	20	data	datum	NOUN
fcis-18357	92	21	,	,	PUNCT
fcis-18357	92	22	which	which	PRON
fcis-18357	92	23	helps	help	VERB
fcis-18357	92	24	the	the	DET
fcis-18357	92	25	model	model	NOUN
fcis-18357	92	26	learn	learn	VERB
fcis-18357	92	27	to	to	PART
fcis-18357	92	28	detect	detect	VERB
fcis-18357	92	29	small	small	ADJ
fcis-18357	92	30	objects	object	NOUN
fcis-18357	92	31	under	under	ADP
fcis-18357	92	32	various	various	ADJ
fcis-18357	92	33	conditions	condition	NOUN
fcis-18357	92	34	.	.	PUNCT
fcis-18357	93	1	(	(	PUNCT
fcis-18357	93	2	7	7	NUM
fcis-18357	93	3	)	)	PUNCT
fcis-18357	93	4	.	.	PUNCT
fcis-18357	94	1	target	target	NOUN
fcis-18357	94	2	detection	detection	NOUN
fcis-18357	94	3	utilizing	utilize	VERB
fcis-18357	94	4	the	the	DET
fcis-18357	94	5	trained	train	VERB
fcis-18357	94	6	model	model	NOUN
fcis-18357	94	7	to	to	PART
fcis-18357	94	8	perform	perform	VERB
fcis-18357	94	9	object	object	NOUN
fcis-18357	94	10	detection	detection	NOUN
fcis-18357	94	11	,	,	PUNCT
fcis-18357	94	12	obtaining	obtain	VERB
fcis-18357	94	13	spatial	spatial	ADJ
fcis-18357	94	14	and	and	CCONJ
fcis-18357	94	15	classification	classification	NOUN
fcis-18357	94	16	information	information	NOUN
fcis-18357	94	17	about	about	ADP
fcis-18357	94	18	the	the	DET
fcis-18357	94	19	detected	detect	VERB
fcis-18357	94	20	objects	object	NOUN
fcis-18357	94	21	.	.	PUNCT
fcis-18357	95	1	this	this	DET
fcis-18357	95	2	step	step	NOUN
fcis-18357	95	3	involves	involve	VERB
fcis-18357	95	4	applying	apply	VERB
fcis-18357	95	5	the	the	DET
fcis-18357	95	6	model	model	NOUN
fcis-18357	95	7	to	to	ADP
fcis-18357	95	8	new	new	ADJ
fcis-18357	95	9	or	or	CCONJ
fcis-18357	95	10	unseen	unseen	ADJ
fcis-18357	95	11	video	video	NOUN
fcis-18357	95	12	data	datum	NOUN
fcis-18357	95	13	to	to	PART
fcis-18357	95	14	detect	detect	VERB
fcis-18357	95	15	small	small	ADJ
fcis-18357	95	16	objects	object	NOUN
fcis-18357	95	17	and	and	CCONJ
fcis-18357	95	18	classify	classify	VERB
fcis-18357	95	19	them	they	PRON
fcis-18357	95	20	accordingly	accordingly	ADV
fcis-18357	95	21	.	.	PUNCT
fcis-18357	96	1	4	4	X
fcis-18357	96	2	.	.	X
fcis-18357	96	3	experimental	experimental	ADJ
fcis-18357	96	4	section	section	NOUN
fcis-18357	96	5	4.1	4.1	NUM
fcis-18357	96	6	.	.	PUNCT
fcis-18357	97	1	experimental	experimental	ADJ
fcis-18357	97	2	data	datum	NOUN
fcis-18357	97	3	the	the	DET
fcis-18357	97	4	dataset	dataset	NOUN
fcis-18357	97	5	used	use	VERB
fcis-18357	97	6	in	in	ADP
fcis-18357	97	7	this	this	DET
fcis-18357	97	8	study	study	NOUN
fcis-18357	97	9	is	be	AUX
fcis-18357	97	10	composed	compose	VERB
fcis-18357	97	11	of	of	ADP
fcis-18357	97	12	a	a	DET
fcis-18357	97	13	combination	combination	NOUN
fcis-18357	97	14	from	from	ADP
fcis-18357	97	15	the	the	DET
fcis-18357	97	16	voc	voc	NOUN
fcis-18357	97	17	and	and	CCONJ
fcis-18357	97	18	coco	coco	PROPN
fcis-18357	97	19	datasets	dataset	NOUN
fcis-18357	97	20	,	,	PUNCT
fcis-18357	97	21	totaling	total	VERB
fcis-18357	97	22	1565	1565	NUM
fcis-18357	97	23	images	image	NOUN
fcis-18357	97	24	.	.	PUNCT
fcis-18357	98	1	specifically	specifically	ADV
fcis-18357	98	2	,	,	PUNCT
fcis-18357	98	3	the	the	DET
fcis-18357	98	4	distribution	distribution	NOUN
fcis-18357	98	5	is	be	AUX
fcis-18357	98	6	as	as	SCONJ
fcis-18357	98	7	follows	follow	VERB
fcis-18357	98	8	:	:	PUNCT
fcis-18357	98	9	the	the	DET
fcis-18357	98	10	training	training	NOUN
fcis-18357	98	11	set	set	NOUN
fcis-18357	98	12	contains	contain	VERB
fcis-18357	98	13	1248	1248	NUM
fcis-18357	98	14	images	image	NOUN
fcis-18357	98	15	,	,	PUNCT
fcis-18357	98	16	the	the	DET
fcis-18357	98	17	validation	validation	NOUN
fcis-18357	98	18	set	set	NOUN
fcis-18357	98	19	contains	contain	VERB
fcis-18357	98	20	156	156	NUM
fcis-18357	98	21	images	image	NOUN
fcis-18357	98	22	,	,	PUNCT
fcis-18357	98	23	and	and	CCONJ
fcis-18357	98	24	the	the	DET
fcis-18357	98	25	test	test	NOUN
fcis-18357	98	26	set	set	NOUN
fcis-18357	98	27	contains	contain	VERB
fcis-18357	98	28	161	161	NUM
fcis-18357	98	29	images	image	NOUN
fcis-18357	98	30	,	,	PUNCT
fcis-18357	98	31	as	as	SCONJ
fcis-18357	98	32	shown	show	VERB
fcis-18357	98	33	in	in	ADP
fcis-18357	98	34	table	table	NOUN
fcis-18357	98	35	2	2	NUM
fcis-18357	98	36	.	.	PUNCT
fcis-18357	98	37	table	table	NOUN
fcis-18357	98	38	2	2	NUM
fcis-18357	98	39	.	.	PUNCT
fcis-18357	98	40	image	image	NOUN
fcis-18357	98	41	composition	composition	NOUN
fcis-18357	98	42	using	use	VERB
fcis-18357	98	43	labeling	labeling	NOUN
fcis-18357	98	44	tools	tool	NOUN
fcis-18357	98	45	,	,	PUNCT
fcis-18357	98	46	each	each	DET
fcis-18357	98	47	object	object	NOUN
fcis-18357	98	48	's	's	PART
fcis-18357	98	49	category	category	NOUN
fcis-18357	98	50	and	and	CCONJ
fcis-18357	98	51	location	location	NOUN
fcis-18357	98	52	information	information	NOUN
fcis-18357	98	53	can	can	AUX
fcis-18357	98	54	be	be	AUX
fcis-18357	98	55	annotated	annotate	VERB
fcis-18357	98	56	individually	individually	ADV
fcis-18357	98	57	and	and	CCONJ
fcis-18357	98	58	saved	save	VERB
fcis-18357	98	59	as	as	ADP
fcis-18357	98	60	xml	xml	NOUN
fcis-18357	98	61	files	file	NOUN
fcis-18357	98	62	.	.	PUNCT
fcis-18357	99	1	then	then	ADV
fcis-18357	99	2	,	,	PUNCT
fcis-18357	99	3	the	the	DET
fcis-18357	99	4	dataset	dataset	NOUN
fcis-18357	99	5	is	be	AUX
fcis-18357	99	6	divided	divide	VERB
fcis-18357	99	7	according	accord	VERB
fcis-18357	99	8	to	to	ADP
fcis-18357	99	9	a	a	DET
fcis-18357	99	10	ratio	ratio	NOUN
fcis-18357	99	11	of	of	ADP
fcis-18357	99	12	training	training	NOUN
fcis-18357	99	13	set	set	NOUN
fcis-18357	99	14	:	:	PUNCT
fcis-18357	99	15	validation	validation	NOUN
fcis-18357	99	16	set	set	NOUN
fcis-18357	99	17	:	:	PUNCT
fcis-18357	99	18	test	test	NOUN
fcis-18357	99	19	set	set	NOUN
fcis-18357	99	20	=	=	PUNCT
fcis-18357	99	21	8:1:1	8:1:1	NUM
fcis-18357	99	22	.	.	PUNCT
fcis-18357	100	1	the	the	DET
fcis-18357	100	2	xml	xml	NOUN
fcis-18357	100	3	files	file	NOUN
fcis-18357	100	4	are	be	AUX
fcis-18357	100	5	converted	convert	VERB
fcis-18357	100	6	into	into	ADP
fcis-18357	100	7	a	a	DET
fcis-18357	100	8	txt	txt	NOUN
fcis-18357	100	9	file	file	NOUN
fcis-18357	100	10	format	format	NOUN
fcis-18357	100	11	that	that	PRON
fcis-18357	100	12	can	can	AUX
fcis-18357	100	13	be	be	AUX
fcis-18357	100	14	recognized	recognize	VERB
fcis-18357	100	15	and	and	CCONJ
fcis-18357	100	16	standardized	standardize	VERB
fcis-18357	100	17	by	by	ADP
fcis-18357	100	18	yolov7	yolov7	NOUN
fcis-18357	100	19	-	-	PUNCT
fcis-18357	100	20	tiny	tiny	ADJ
fcis-18357	100	21	.	.	PUNCT
fcis-18357	101	1	the	the	DET
fcis-18357	101	2	hardware	hardware	NOUN
fcis-18357	101	3	and	and	CCONJ
fcis-18357	101	4	software	software	NOUN
fcis-18357	101	5	configurations	configuration	NOUN
fcis-18357	101	6	used	use	VERB
fcis-18357	101	7	in	in	ADP
fcis-18357	101	8	the	the	DET
fcis-18357	101	9	experimental	experimental	ADJ
fcis-18357	101	10	platform	platform	NOUN
fcis-18357	101	11	are	be	AUX
fcis-18357	101	12	presented	present	VERB
fcis-18357	101	13	in	in	ADP
fcis-18357	101	14	table	table	NOUN
fcis-18357	101	15	3	3	NUM
fcis-18357	101	16	:	:	PUNCT
fcis-18357	101	17	table	table	NOUN
fcis-18357	101	18	3	3	NUM
fcis-18357	101	19	.	.	PUNCT
fcis-18357	101	20	platform	platform	NOUN
fcis-18357	101	21	configuration	configuration	NOUN
fcis-18357	101	22	4.2	4.2	NUM
fcis-18357	101	23	.	.	PUNCT
fcis-18357	102	1	experimental	experimental	ADJ
fcis-18357	102	2	parameters	parameter	NOUN
fcis-18357	102	3	and	and	CCONJ
fcis-18357	102	4	evaluation	evaluation	NOUN
fcis-18357	102	5	indicators	indicator	NOUN
fcis-18357	102	6	during	during	ADP
fcis-18357	102	7	the	the	DET
fcis-18357	102	8	network	network	NOUN
fcis-18357	102	9	model	model	NOUN
fcis-18357	102	10	training	training	NOUN
fcis-18357	102	11	process	process	NOUN
fcis-18357	102	12	,	,	PUNCT
fcis-18357	102	13	the	the	DET
fcis-18357	102	14	total	total	ADJ
fcis-18357	102	15	number	number	NOUN
fcis-18357	102	16	of	of	ADP
fcis-18357	102	17	epochs	epoch	NOUN
fcis-18357	102	18	is	be	AUX
fcis-18357	102	19	set	set	VERB
fcis-18357	102	20	to	to	ADP
fcis-18357	102	21	300	300	NUM
fcis-18357	102	22	with	with	ADP
fcis-18357	102	23	a	a	DET
fcis-18357	102	24	batch	batch	NOUN
fcis-18357	102	25	size	size	NOUN
fcis-18357	102	26	of	of	ADP
fcis-18357	102	27	16	16	NUM
fcis-18357	102	28	.	.	PUNCT
fcis-18357	103	1	the	the	DET
fcis-18357	103	2	parameters	parameter	NOUN
fcis-18357	103	3	used	use	VERB
fcis-18357	103	4	to	to	PART
fcis-18357	103	5	evaluate	evaluate	VERB
fcis-18357	103	6	the	the	DET
fcis-18357	103	7	training	training	NOUN
fcis-18357	103	8	results	result	NOUN
fcis-18357	103	9	include	include	VERB
fcis-18357	103	10	tp	tp	ADP
fcis-18357	103	11	69	69	NUM
fcis-18357	103	12	(	(	PUNCT
fcis-18357	103	13	true	true	ADJ
fcis-18357	103	14	positives	positive	NOUN
fcis-18357	103	15	)	)	PUNCT
fcis-18357	103	16	,	,	PUNCT
fcis-18357	103	17	tn	tn	PROPN
fcis-18357	103	18	(	(	PUNCT
fcis-18357	103	19	true	true	ADJ
fcis-18357	103	20	negatives	negative	NOUN
fcis-18357	103	21	)	)	PUNCT
fcis-18357	103	22	,	,	PUNCT
fcis-18357	103	23	fp	fp	INTJ
fcis-18357	103	24	(	(	PUNCT
fcis-18357	103	25	false	false	ADJ
fcis-18357	103	26	positives	positive	NOUN
fcis-18357	103	27	)	)	PUNCT
fcis-18357	103	28	,	,	PUNCT
fcis-18357	103	29	fn	fn	INTJ
fcis-18357	103	30	(	(	PUNCT
fcis-18357	103	31	false	false	ADJ
fcis-18357	103	32	negatives	negative	NOUN
fcis-18357	103	33	)	)	PUNCT
fcis-18357	103	34	,	,	PUNCT
fcis-18357	103	35	and	and	CCONJ
fcis-18357	103	36	metrics	metric	NOUN
fcis-18357	103	37	such	such	ADJ
fcis-18357	103	38	as	as	ADP
fcis-18357	103	39	f1	f1	PROPN
fcis-18357	103	40	score	score	NOUN
fcis-18357	103	41	,	,	PUNCT
fcis-18357	103	42	accuracy	accuracy	NOUN
fcis-18357	103	43	,	,	PUNCT
fcis-18357	103	44	precision	precision	NOUN
fcis-18357	103	45	,	,	PUNCT
fcis-18357	103	46	recall	recall	NOUN
fcis-18357	103	47	,	,	PUNCT
fcis-18357	103	48	and	and	CCONJ
fcis-18357	103	49	ap	ap	PROPN
fcis-18357	103	50	(	(	PUNCT
fcis-18357	103	51	average	average	ADJ
fcis-18357	103	52	precision	precision	NOUN
fcis-18357	103	53	)	)	PUNCT
fcis-18357	103	54	.	.	PUNCT
fcis-18357	104	1	the	the	DET
fcis-18357	104	2	f1	f1	PROPN
fcis-18357	104	3	score	score	NOUN
fcis-18357	104	4	is	be	AUX
fcis-18357	104	5	defined	define	VERB
fcis-18357	104	6	as	as	ADP
fcis-18357	104	7	the	the	DET
fcis-18357	104	8	harmonic	harmonic	ADJ
fcis-18357	104	9	mean	mean	NOUN
fcis-18357	104	10	of	of	ADP
fcis-18357	104	11	precision	precision	NOUN
fcis-18357	104	12	and	and	CCONJ
fcis-18357	104	13	recall	recall	NOUN
fcis-18357	104	14	,	,	PUNCT
fcis-18357	104	15	providing	provide	VERB
fcis-18357	104	16	a	a	DET
fcis-18357	104	17	balance	balance	NOUN
fcis-18357	104	18	between	between	ADP
fcis-18357	104	19	the	the	DET
fcis-18357	104	20	two	two	NUM
fcis-18357	104	21	for	for	ADP
fcis-18357	104	22	a	a	DET
fcis-18357	104	23	comprehensive	comprehensive	ADJ
fcis-18357	104	24	measure	measure	NOUN
fcis-18357	104	25	of	of	ADP
fcis-18357	104	26	model	model	NOUN
fcis-18357	104	27	performance	performance	NOUN
fcis-18357	104	28	.	.	PUNCT
fcis-18357	105	1	the	the	DET
fcis-18357	105	2	formula	formula	NOUN
fcis-18357	105	3	for	for	ADP
fcis-18357	105	4	f1	f1	NOUN
fcis-18357	105	5	is	be	AUX
fcis-18357	105	6	(	(	PUNCT
fcis-18357	105	7	1	1	NUM
fcis-18357	105	8	):	):	PUNCT
fcis-18357	105	9	=	=	SYM
fcis-18357	106	1	+	+	CCONJ
fcis-18357	106	2	(	(	PUNCT
fcis-18357	106	3	1	1	X
fcis-18357	106	4	)	)	PUNCT
fcis-18357	106	5	the	the	DET
fcis-18357	106	6	formula	formula	NOUN
fcis-18357	106	7	for	for	ADP
fcis-18357	106	8	accuracy	accuracy	NOUN
fcis-18357	106	9	is	be	AUX
fcis-18357	106	10	(	(	PUNCT
fcis-18357	106	11	2	2	NUM
fcis-18357	106	12	):	):	PUNCT
fcis-18357	106	13	accuracy	accuracy	NOUN
fcis-18357	106	14	2	2	NUM
fcis-18357	106	15	the	the	DET
fcis-18357	106	16	formula	formula	NOUN
fcis-18357	106	17	for	for	ADP
fcis-18357	106	18	accuracy	accuracy	NOUN
fcis-18357	106	19	is	be	AUX
fcis-18357	106	20	(	(	PUNCT
fcis-18357	106	21	3	3	NUM
fcis-18357	106	22	):	):	PUNCT
fcis-18357	106	23	precision	precision	NOUN
fcis-18357	106	24	3	3	NUM
fcis-18357	106	25	the	the	DET
fcis-18357	106	26	formula	formula	NOUN
fcis-18357	106	27	for	for	ADP
fcis-18357	106	28	recall	recall	NOUN
fcis-18357	106	29	rate	rate	NOUN
fcis-18357	106	30	is	be	AUX
fcis-18357	106	31	(	(	PUNCT
fcis-18357	106	32	4	4	NUM
fcis-18357	106	33	):	):	PUNCT
fcis-18357	106	34	recall	recall	NOUN
fcis-18357	106	35	4	4	NUM
fcis-18357	106	36	the	the	DET
fcis-18357	106	37	formula	formula	NOUN
fcis-18357	106	38	for	for	ADP
fcis-18357	106	39	ap	ap	PROPN
fcis-18357	106	40	is	be	AUX
fcis-18357	106	41	(	(	PUNCT
fcis-18357	106	42	5	5	NUM
fcis-18357	106	43	):	):	PUNCT
fcis-18357	106	44	ap	ap	PROPN
fcis-18357	106	45	=	=	PUNCT
fcis-18357	106	46	∑	∑	PROPN
fcis-18357	106	47	(	(	PUNCT
fcis-18357	106	48	5	5	NUM
fcis-18357	106	49	)	)	PUNCT
fcis-18357	106	50	figures	figure	NOUN
fcis-18357	106	51	6	6	NUM
fcis-18357	106	52	and	and	CCONJ
fcis-18357	106	53	7	7	NUM
fcis-18357	106	54	display	display	VERB
fcis-18357	106	55	the	the	DET
fcis-18357	106	56	statistical	statistical	ADJ
fcis-18357	106	57	charts	chart	NOUN
fcis-18357	106	58	for	for	ADP
fcis-18357	106	59	each	each	DET
fcis-18357	106	60	category	category	NOUN
fcis-18357	106	61	.	.	PUNCT
fcis-18357	107	1	initially	initially	ADV
fcis-18357	107	2	,	,	PUNCT
fcis-18357	107	3	a	a	DET
fcis-18357	107	4	comparative	comparative	ADJ
fcis-18357	107	5	analysis	analysis	NOUN
fcis-18357	107	6	was	be	AUX
fcis-18357	107	7	conducted	conduct	VERB
fcis-18357	107	8	with	with	ADP
fcis-18357	107	9	yolov5s	yolov5s	PROPN
fcis-18357	107	10	,	,	PUNCT
fcis-18357	107	11	which	which	PRON
fcis-18357	107	12	has	have	VERB
fcis-18357	107	13	a	a	DET
fcis-18357	107	14	similar	similar	ADJ
fcis-18357	107	15	number	number	NOUN
fcis-18357	107	16	of	of	ADP
fcis-18357	107	17	parameters	parameter	NOUN
fcis-18357	107	18	and	and	CCONJ
fcis-18357	107	19	computational	computational	ADJ
fcis-18357	107	20	load	load	NOUN
fcis-18357	107	21	.	.	PUNCT
fcis-18357	108	1	the	the	DET
fcis-18357	108	2	yolov7	yolov7	ADV
fcis-18357	108	3	-	-	PUNCT
fcis-18357	108	4	tiny	tiny	ADJ
fcis-18357	108	5	model	model	NOUN
fcis-18357	108	6	,	,	PUNCT
fcis-18357	108	7	in	in	ADP
fcis-18357	108	8	comparison	comparison	NOUN
fcis-18357	108	9	to	to	ADP
fcis-18357	108	10	yolov5s	yolov5s	PROPN
fcis-18357	108	11	,	,	PUNCT
fcis-18357	108	12	achieved	achieve	VERB
fcis-18357	108	13	a	a	DET
fcis-18357	108	14	nearly	nearly	ADV
fcis-18357	108	15	2	2	NUM
fcis-18357	108	16	%	%	NOUN
fcis-18357	108	17	improvement	improvement	NOUN
fcis-18357	108	18	in	in	ADP
fcis-18357	108	19	average	average	ADJ
fcis-18357	108	20	precision	precision	NOUN
fcis-18357	108	21	for	for	ADP
fcis-18357	108	22	detecting	detect	VERB
fcis-18357	108	23	small	small	ADJ
fcis-18357	108	24	objects	object	NOUN
fcis-18357	108	25	.	.	PUNCT
fcis-18357	109	1	the	the	DET
fcis-18357	109	2	improved	improve	VERB
fcis-18357	109	3	algorithm	algorithm	NOUN
fcis-18357	109	4	model	model	NOUN
fcis-18357	109	5	increased	increase	VERB
fcis-18357	109	6	the	the	DET
fcis-18357	109	7	average	average	ADJ
fcis-18357	109	8	detection	detection	NOUN
fcis-18357	109	9	precision	precision	NOUN
fcis-18357	109	10	by	by	ADP
fcis-18357	109	11	6.13	6.13	NUM
fcis-18357	109	12	%	%	NOUN
fcis-18357	109	13	relative	relative	ADJ
fcis-18357	109	14	to	to	ADP
fcis-18357	109	15	yolov5s	yolov5s	PROPN
fcis-18357	109	16	and	and	CCONJ
fcis-18357	109	17	by	by	ADP
fcis-18357	109	18	4.21	4.21	NUM
fcis-18357	109	19	%	%	NOUN
fcis-18357	109	20	relative	relative	ADJ
fcis-18357	109	21	to	to	ADP
fcis-18357	109	22	yolov7	yolov7	NOUN
fcis-18357	109	23	-	-	PUNCT
fcis-18357	109	24	tiny	tiny	ADJ
fcis-18357	109	25	,	,	PUNCT
fcis-18357	109	26	showing	show	VERB
fcis-18357	109	27	significant	significant	ADJ
fcis-18357	109	28	improvement	improvement	NOUN
fcis-18357	109	29	for	for	ADP
fcis-18357	109	30	small	small	ADJ
fcis-18357	109	31	object	object	NOUN
fcis-18357	109	32	detection	detection	NOUN
fcis-18357	109	33	.	.	PUNCT
fcis-18357	110	1	according	accord	VERB
fcis-18357	110	2	to	to	ADP
fcis-18357	110	3	the	the	DET
fcis-18357	110	4	data	datum	NOUN
fcis-18357	110	5	comparison	comparison	NOUN
fcis-18357	110	6	,	,	PUNCT
fcis-18357	110	7	both	both	CCONJ
fcis-18357	110	8	the	the	DET
fcis-18357	110	9	yolov7	yolov7	ADV
fcis-18357	110	10	-	-	PUNCT
fcis-18357	110	11	tiny	tiny	ADJ
fcis-18357	110	12	lightweight	lightweight	ADJ
fcis-18357	110	13	object	object	NOUN
fcis-18357	110	14	detection	detection	NOUN
fcis-18357	110	15	model	model	NOUN
fcis-18357	110	16	and	and	CCONJ
fcis-18357	110	17	the	the	DET
fcis-18357	110	18	improved	improved	ADJ
fcis-18357	110	19	model	model	NOUN
fcis-18357	110	20	significantly	significantly	ADV
fcis-18357	110	21	enhanced	enhance	VERB
fcis-18357	110	22	the	the	DET
fcis-18357	110	23	precision	precision	NOUN
fcis-18357	110	24	of	of	ADP
fcis-18357	110	25	small	small	ADJ
fcis-18357	110	26	object	object	NOUN
fcis-18357	110	27	detection	detection	NOUN
fcis-18357	110	28	.	.	PUNCT
fcis-18357	111	1	regarding	regard	VERB
fcis-18357	111	2	the	the	DET
fcis-18357	111	3	impact	impact	NOUN
fcis-18357	111	4	of	of	ADP
fcis-18357	111	5	the	the	DET
fcis-18357	111	6	cbam	cbam	NOUN
fcis-18357	111	7	(	(	PUNCT
fcis-18357	111	8	convolutional	convolutional	ADJ
fcis-18357	111	9	block	block	NOUN
fcis-18357	111	10	attention	attention	NOUN
fcis-18357	111	11	module	module	NOUN
fcis-18357	111	12	)	)	PUNCT
fcis-18357	111	13	triple	triple	ADJ
fcis-18357	111	14	attention	attention	NOUN
fcis-18357	111	15	mechanism	mechanism	NOUN
fcis-18357	111	16	and	and	CCONJ
fcis-18357	111	17	the	the	DET
fcis-18357	111	18	addition	addition	NOUN
fcis-18357	111	19	of	of	ADP
fcis-18357	111	20	a	a	DET
fcis-18357	111	21	small	small	ADJ
fcis-18357	111	22	object	object	NOUN
fcis-18357	111	23	detection	detection	NOUN
fcis-18357	111	24	layer	layer	NOUN
fcis-18357	111	25	on	on	ADP
fcis-18357	111	26	the	the	DET
fcis-18357	111	27	model	model	NOUN
fcis-18357	111	28	,	,	PUNCT
fcis-18357	111	29	ablation	ablation	NOUN
fcis-18357	111	30	experiments	experiment	NOUN
fcis-18357	111	31	have	have	AUX
fcis-18357	111	32	verified	verify	VERB
fcis-18357	111	33	that	that	SCONJ
fcis-18357	111	34	both	both	PRON
fcis-18357	111	35	can	can	AUX
fcis-18357	111	36	improve	improve	VERB
fcis-18357	111	37	the	the	DET
fcis-18357	111	38	model	model	NOUN
fcis-18357	111	39	's	's	PART
fcis-18357	111	40	detection	detection	NOUN
fcis-18357	111	41	precision	precision	NOUN
fcis-18357	111	42	.	.	PUNCT
fcis-18357	112	1	introducing	introduce	VERB
fcis-18357	112	2	both	both	PRON
fcis-18357	112	3	into	into	ADP
fcis-18357	112	4	the	the	DET
fcis-18357	112	5	yolov7	yolov7	ADV
fcis-18357	112	6	-	-	PUNCT
fcis-18357	112	7	tiny	tiny	ADJ
fcis-18357	112	8	model	model	NOUN
fcis-18357	112	9	can	can	AUX
fcis-18357	112	10	significantly	significantly	ADV
fcis-18357	112	11	enhance	enhance	VERB
fcis-18357	112	12	its	its	PRON
fcis-18357	112	13	detection	detection	NOUN
fcis-18357	112	14	accuracy	accuracy	NOUN
fcis-18357	112	15	.	.	PUNCT
fcis-18357	113	1	table	table	NOUN
fcis-18357	113	2	4	4	NUM
fcis-18357	113	3	.	.	PUNCT
fcis-18357	113	4	model	model	NOUN
fcis-18357	113	5	comparison	comparison	NOUN
fcis-18357	113	6	table	table	NOUN
fcis-18357	113	7	5	5	NUM
fcis-18357	113	8	.	.	PUNCT
fcis-18357	113	9	comparison	comparison	NOUN
fcis-18357	113	10	diagram	diagram	NOUN
fcis-18357	113	11	of	of	ADP
fcis-18357	113	12	attention	attention	NOUN
fcis-18357	113	13	mechanism	mechanism	NOUN
fcis-18357	113	14	4.3	4.3	NUM
fcis-18357	113	15	.	.	PUNCT
fcis-18357	114	1	detection	detection	NOUN
fcis-18357	114	2	effect	effect	NOUN
fcis-18357	114	3	when	when	SCONJ
fcis-18357	114	4	comparing	compare	VERB
fcis-18357	114	5	the	the	DET
fcis-18357	114	6	original	original	ADJ
fcis-18357	114	7	yolov7	yolov7	NOUN
fcis-18357	114	8	-	-	PUNCT
fcis-18357	114	9	tiny	tiny	ADJ
fcis-18357	114	10	model	model	NOUN
fcis-18357	114	11	in	in	ADP
fcis-18357	114	12	figure	figure	NOUN
fcis-18357	114	13	8	8	NUM
fcis-18357	114	14	with	with	ADP
fcis-18357	114	15	the	the	DET
fcis-18357	114	16	optimized	optimize	VERB
fcis-18357	114	17	yolov7	yolov7	ADJ
fcis-18357	114	18	-	-	PUNCT
fcis-18357	114	19	tiny	tiny	ADJ
fcis-18357	114	20	model	model	NOUN
fcis-18357	114	21	in	in	ADP
fcis-18357	114	22	figure	figure	NOUN
fcis-18357	114	23	9	9	NUM
fcis-18357	114	24	for	for	ADP
fcis-18357	114	25	small	small	ADJ
fcis-18357	114	26	object	object	NOUN
fcis-18357	114	27	detection	detection	NOUN
fcis-18357	114	28	,	,	PUNCT
fcis-18357	114	29	clear	clear	ADJ
fcis-18357	114	30	differences	difference	NOUN
fcis-18357	114	31	point	point	VERB
fcis-18357	114	32	to	to	ADP
fcis-18357	114	33	several	several	ADJ
fcis-18357	114	34	key	key	ADJ
fcis-18357	114	35	performance	performance	NOUN
fcis-18357	114	36	improvements	improvement	NOUN
fcis-18357	114	37	.	.	PUNCT
fcis-18357	115	1	firstly	firstly	ADV
fcis-18357	115	2	,	,	PUNCT
fcis-18357	115	3	the	the	DET
fcis-18357	115	4	detection	detection	NOUN
fcis-18357	115	5	accuracy	accuracy	NOUN
fcis-18357	115	6	for	for	ADP
fcis-18357	115	7	small	small	ADJ
fcis-18357	115	8	objects	object	NOUN
fcis-18357	115	9	is	be	AUX
fcis-18357	115	10	significantly	significantly	ADV
fcis-18357	115	11	improved	improve	VERB
fcis-18357	115	12	in	in	ADP
fcis-18357	115	13	the	the	DET
fcis-18357	115	14	optimized	optimize	VERB
fcis-18357	115	15	model	model	NOUN
fcis-18357	115	16	.	.	PUNCT
fcis-18357	116	1	this	this	DET
fcis-18357	116	2	enhancement	enhancement	NOUN
fcis-18357	116	3	is	be	AUX
fcis-18357	116	4	due	due	ADJ
fcis-18357	116	5	to	to	ADP
fcis-18357	116	6	optimization	optimization	NOUN
fcis-18357	116	7	measures	measure	NOUN
fcis-18357	116	8	such	such	ADJ
fcis-18357	116	9	as	as	ADP
fcis-18357	116	10	more	more	ADV
fcis-18357	116	11	detailed	detailed	ADJ
fcis-18357	116	12	feature	feature	NOUN
fcis-18357	116	13	extraction	extraction	NOUN
fcis-18357	116	14	layers	layer	NOUN
fcis-18357	116	15	and	and	CCONJ
fcis-18357	116	16	an	an	DET
fcis-18357	116	17	improved	improved	ADJ
fcis-18357	116	18	attention	attention	NOUN
fcis-18357	116	19	mechanism	mechanism	NOUN
fcis-18357	116	20	.	.	PUNCT
fcis-18357	117	1	these	these	PRON
fcis-18357	117	2	allow	allow	VERB
fcis-18357	117	3	the	the	DET
fcis-18357	117	4	model	model	NOUN
fcis-18357	117	5	to	to	PART
fcis-18357	117	6	more	more	ADV
fcis-18357	117	7	effectively	effectively	ADV
fcis-18357	117	8	capture	capture	VERB
fcis-18357	117	9	the	the	DET
fcis-18357	117	10	details	detail	NOUN
fcis-18357	117	11	of	of	ADP
fcis-18357	117	12	small	small	ADJ
fcis-18357	117	13	-	-	PUNCT
fcis-18357	117	14	sized	sized	ADJ
fcis-18357	117	15	objects	object	NOUN
fcis-18357	117	16	,	,	PUNCT
fcis-18357	117	17	thereby	thereby	ADV
fcis-18357	117	18	increasing	increase	VERB
fcis-18357	117	19	the	the	DET
fcis-18357	117	20	accuracy	accuracy	NOUN
fcis-18357	117	21	of	of	ADP
fcis-18357	117	22	recognition	recognition	NOUN
fcis-18357	117	23	.	.	PUNCT
fcis-18357	118	1	especially	especially	ADV
fcis-18357	118	2	in	in	ADP
fcis-18357	118	3	crowded	crowded	ADJ
fcis-18357	118	4	scenes	scene	NOUN
fcis-18357	118	5	,	,	PUNCT
fcis-18357	118	6	the	the	DET
fcis-18357	118	7	optimized	optimize	VERB
fcis-18357	118	8	model	model	NOUN
fcis-18357	118	9	is	be	AUX
fcis-18357	118	10	able	able	ADJ
fcis-18357	118	11	to	to	PART
fcis-18357	118	12	reduce	reduce	VERB
fcis-18357	118	13	false	false	ADJ
fcis-18357	118	14	negatives	negative	NOUN
fcis-18357	118	15	and	and	CCONJ
fcis-18357	118	16	false	false	ADJ
fcis-18357	118	17	positives	positive	NOUN
fcis-18357	118	18	,	,	PUNCT
fcis-18357	118	19	significantly	significantly	ADV
fcis-18357	118	20	enhancing	enhance	VERB
fcis-18357	118	21	the	the	DET
fcis-18357	118	22	detection	detection	NOUN
fcis-18357	118	23	performance	performance	NOUN
fcis-18357	118	24	for	for	ADP
fcis-18357	118	25	small	small	ADJ
fcis-18357	118	26	objects	object	NOUN
fcis-18357	118	27	.	.	PUNCT
fcis-18357	119	1	secondly	secondly	ADV
fcis-18357	119	2	,	,	PUNCT
fcis-18357	119	3	the	the	DET
fcis-18357	119	4	recall	recall	NOUN
fcis-18357	119	5	rate	rate	NOUN
fcis-18357	119	6	for	for	ADP
fcis-18357	119	7	small	small	ADJ
fcis-18357	119	8	objects	object	NOUN
fcis-18357	119	9	is	be	AUX
fcis-18357	119	10	also	also	ADV
fcis-18357	119	11	increased	increase	VERB
fcis-18357	119	12	in	in	ADP
fcis-18357	119	13	the	the	DET
fcis-18357	119	14	optimized	optimize	VERB
fcis-18357	119	15	model	model	NOUN
fcis-18357	119	16	.	.	PUNCT
fcis-18357	120	1	by	by	ADP
fcis-18357	120	2	adjusting	adjust	VERB
fcis-18357	120	3	the	the	DET
fcis-18357	120	4	model	model	NOUN
fcis-18357	120	5	structure	structure	NOUN
fcis-18357	120	6	and	and	CCONJ
fcis-18357	120	7	training	training	NOUN
fcis-18357	120	8	strategy	strategy	NOUN
fcis-18357	120	9	,	,	PUNCT
fcis-18357	120	10	such	such	ADJ
fcis-18357	120	11	as	as	ADP
fcis-18357	120	12	using	use	VERB
fcis-18357	120	13	more	more	ADV
fcis-18357	120	14	appropriate	appropriate	ADJ
fcis-18357	120	15	anchor	anchor	NOUN
fcis-18357	120	16	box	box	NOUN
fcis-18357	120	17	sizes	size	NOUN
fcis-18357	120	18	and	and	CCONJ
fcis-18357	120	19	ratios	ratio	NOUN
fcis-18357	120	20	,	,	PUNCT
fcis-18357	120	21	the	the	DET
fcis-18357	120	22	optimized	optimize	VERB
fcis-18357	120	23	model	model	NOUN
fcis-18357	120	24	can	can	AUX
fcis-18357	120	25	more	more	ADV
fcis-18357	120	26	frequently	frequently	ADV
fcis-18357	120	27	and	and	CCONJ
fcis-18357	120	28	correctly	correctly	ADV
fcis-18357	120	29	identify	identify	VERB
fcis-18357	120	30	small	small	ADJ
fcis-18357	120	31	objects	object	NOUN
fcis-18357	120	32	,	,	PUNCT
fcis-18357	120	33	reducing	reduce	VERB
fcis-18357	120	34	omissions	omission	NOUN
fcis-18357	120	35	due	due	ADP
fcis-18357	120	36	to	to	ADP
fcis-18357	120	37	small	small	ADJ
fcis-18357	120	38	size	size	NOUN
fcis-18357	120	39	.	.	PUNCT
fcis-18357	121	1	overall	overall	ADV
fcis-18357	121	2	,	,	PUNCT
fcis-18357	121	3	the	the	DET
fcis-18357	121	4	performance	performance	NOUN
fcis-18357	121	5	of	of	ADP
fcis-18357	121	6	the	the	DET
fcis-18357	121	7	optimized	optimize	VERB
fcis-18357	121	8	yolov7	yolov7	ADJ
fcis-18357	121	9	-	-	PUNCT
fcis-18357	121	10	tiny	tiny	ADJ
fcis-18357	121	11	model	model	NOUN
fcis-18357	121	12	in	in	ADP
fcis-18357	121	13	detecting	detect	VERB
fcis-18357	121	14	small	small	ADJ
fcis-18357	121	15	objects	object	NOUN
fcis-18357	121	16	is	be	AUX
fcis-18357	121	17	significantly	significantly	ADV
fcis-18357	121	18	better	well	ADJ
fcis-18357	121	19	than	than	ADP
fcis-18357	121	20	the	the	DET
fcis-18357	121	21	original	original	ADJ
fcis-18357	121	22	model	model	NOUN
fcis-18357	121	23	.	.	PUNCT
fcis-18357	122	1	with	with	ADP
fcis-18357	122	2	improvements	improvement	NOUN
fcis-18357	122	3	specifically	specifically	ADV
fcis-18357	122	4	targeting	target	VERB
fcis-18357	122	5	small	small	ADJ
fcis-18357	122	6	object	object	NOUN
fcis-18357	122	7	recognition	recognition	NOUN
fcis-18357	122	8	,	,	PUNCT
fcis-18357	122	9	the	the	DET
fcis-18357	122	10	model	model	NOUN
fcis-18357	122	11	not	not	PART
fcis-18357	122	12	only	only	ADV
fcis-18357	122	13	improves	improve	VERB
fcis-18357	122	14	detection	detection	NOUN
fcis-18357	122	15	precision	precision	NOUN
fcis-18357	122	16	and	and	CCONJ
fcis-18357	122	17	recall	recall	NOUN
fcis-18357	122	18	rate	rate	NOUN
fcis-18357	122	19	but	but	CCONJ
fcis-18357	122	20	also	also	ADV
fcis-18357	122	21	enhances	enhance	VERB
fcis-18357	122	22	its	its	PRON
fcis-18357	122	23	applicability	applicability	NOUN
fcis-18357	122	24	in	in	ADP
fcis-18357	122	25	complex	complex	ADJ
fcis-18357	122	26	environments	environment	NOUN
fcis-18357	122	27	,	,	PUNCT
fcis-18357	122	28	making	make	VERB
fcis-18357	122	29	it	it	PRON
fcis-18357	122	30	more	more	ADV
fcis-18357	122	31	suitable	suitable	ADJ
fcis-18357	122	32	for	for	ADP
fcis-18357	122	33	scenarios	scenario	NOUN
fcis-18357	122	34	requiring	require	VERB
fcis-18357	122	35	highly	highly	ADV
fcis-18357	122	36	accurate	accurate	ADJ
fcis-18357	122	37	detection	detection	NOUN
fcis-18357	122	38	of	of	ADP
fcis-18357	122	39	small	small	ADJ
fcis-18357	122	40	objects	object	NOUN
fcis-18357	122	41	.	.	PUNCT
fcis-18357	123	1	figure	figure	NOUN
fcis-18357	123	2	4	4	NUM
fcis-18357	123	3	.	.	PUNCT
fcis-18357	124	1	yolov7	yolov7	ADV
fcis-18357	124	2	tiny	tiny	ADJ
fcis-18357	124	3	model	model	NOUN
fcis-18357	124	4	detection	detection	NOUN
fcis-18357	124	5	effect	effect	NOUN
fcis-18357	124	6	diagram	diagram	NOUN
fcis-18357	124	7	5	5	NUM
fcis-18357	124	8	.	.	PUNCT
fcis-18357	125	1	conclusion	conclusion	NOUN
fcis-18357	125	2	this	this	DET
fcis-18357	125	3	study	study	NOUN
fcis-18357	125	4	proposes	propose	VERB
fcis-18357	125	5	an	an	DET
fcis-18357	125	6	improved	improved	ADJ
fcis-18357	125	7	yolov7	yolov7	NOUN
fcis-18357	125	8	-	-	PUNCT
fcis-18357	125	9	tiny	tiny	ADJ
fcis-18357	125	10	algorithm	algorithm	NOUN
fcis-18357	125	11	aimed	aim	VERB
fcis-18357	125	12	at	at	ADP
fcis-18357	125	13	enhancing	enhance	VERB
fcis-18357	125	14	its	its	PRON
fcis-18357	125	15	performance	performance	NOUN
fcis-18357	125	16	in	in	ADP
fcis-18357	125	17	detecting	detect	VERB
fcis-18357	125	18	small	small	ADJ
fcis-18357	125	19	objects	object	NOUN
fcis-18357	125	20	.	.	PUNCT
fcis-18357	126	1	based	base	VERB
fcis-18357	126	2	on	on	ADP
fcis-18357	126	3	in	in	ADP
fcis-18357	126	4	-	-	PUNCT
fcis-18357	126	5	depth	depth	NOUN
fcis-18357	126	6	research	research	NOUN
fcis-18357	126	7	analysis	analysis	NOUN
fcis-18357	126	8	and	and	CCONJ
fcis-18357	126	9	a	a	DET
fcis-18357	126	10	series	series	NOUN
fcis-18357	126	11	of	of	ADP
fcis-18357	126	12	experimental	experimental	ADJ
fcis-18357	126	13	validations	validation	NOUN
fcis-18357	126	14	,	,	PUNCT
fcis-18357	126	15	we	we	PRON
fcis-18357	126	16	chose	choose	VERB
fcis-18357	126	17	yolov7	yolov7	NOUN
fcis-18357	126	18	-	-	PUNCT
fcis-18357	126	19	tiny	tiny	ADJ
fcis-18357	126	20	as	as	ADP
fcis-18357	126	21	the	the	DET
fcis-18357	126	22	foundational	foundational	ADJ
fcis-18357	126	23	detection	detection	NOUN
fcis-18357	126	24	framework	framework	NOUN
fcis-18357	126	25	and	and	CCONJ
fcis-18357	126	26	introduced	introduce	VERB
fcis-18357	126	27	two	two	NUM
fcis-18357	126	28	innovative	innovative	ADJ
fcis-18357	126	29	improvement	improvement	NOUN
fcis-18357	126	30	measures	measure	NOUN
fcis-18357	126	31	to	to	PART
fcis-18357	126	32	address	address	VERB
fcis-18357	126	33	its	its	PRON
fcis-18357	126	34	limitations	limitation	NOUN
fcis-18357	126	35	in	in	ADP
fcis-18357	126	36	small	small	ADJ
fcis-18357	126	37	object	object	NOUN
fcis-18357	126	38	detection	detection	NOUN
fcis-18357	126	39	.	.	PUNCT
fcis-18357	127	1	first	first	ADV
fcis-18357	127	2	,	,	PUNCT
fcis-18357	127	3	by	by	ADP
fcis-18357	127	4	embedding	embed	VERB
fcis-18357	127	5	specially	specially	ADV
fcis-18357	127	6	designed	design	VERB
fcis-18357	127	7	small	small	ADJ
fcis-18357	127	8	object	object	NOUN
fcis-18357	127	9	detection	detection	NOUN
fcis-18357	127	10	layers	layer	NOUN
fcis-18357	127	11	into	into	ADP
fcis-18357	127	12	the	the	DET
fcis-18357	127	13	original	original	ADJ
fcis-18357	127	14	convolutional	convolutional	ADJ
fcis-18357	127	15	network	network	NOUN
fcis-18357	127	16	,	,	PUNCT
fcis-18357	127	17	this	this	DET
fcis-18357	127	18	improvement	improvement	NOUN
fcis-18357	127	19	directly	directly	ADV
fcis-18357	127	20	optimizes	optimize	VERB
fcis-18357	127	21	the	the	DET
fcis-18357	127	22	detection	detection	NOUN
fcis-18357	127	23	accuracy	accuracy	NOUN
fcis-18357	127	24	for	for	ADP
fcis-18357	127	25	small	small	ADJ
fcis-18357	127	26	-	-	PUNCT
fcis-18357	127	27	sized	sized	ADJ
fcis-18357	127	28	objects	object	NOUN
fcis-18357	127	29	.	.	PUNCT
fcis-18357	128	1	secondly	secondly	ADV
fcis-18357	128	2	,	,	PUNCT
fcis-18357	128	3	by	by	ADP
fcis-18357	128	4	integrating	integrate	VERB
fcis-18357	128	5	the	the	DET
fcis-18357	128	6	convolutional	convolutional	ADJ
fcis-18357	128	7	block	block	NOUN
fcis-18357	128	8	attention	attention	NOUN
fcis-18357	128	9	module	module	NOUN
fcis-18357	128	10	(	(	PUNCT
fcis-18357	128	11	cbam	cbam	NOUN
fcis-18357	128	12	)	)	PUNCT
fcis-18357	128	13	at	at	ADP
fcis-18357	128	14	four	four	NUM
fcis-18357	128	15	key	key	ADJ
fcis-18357	128	16	positions	position	NOUN
fcis-18357	128	17	in	in	ADP
fcis-18357	128	18	the	the	DET
fcis-18357	128	19	backbone	backbone	NOUN
fcis-18357	128	20	network	network	NOUN
fcis-18357	128	21	of	of	ADP
fcis-18357	128	22	yolov7	yolov7	NOUN
fcis-18357	128	23	-	-	PUNCT
fcis-18357	128	24	tiny	tiny	ADJ
fcis-18357	128	25	,	,	PUNCT
fcis-18357	128	26	this	this	DET
fcis-18357	128	27	algorithm	algorithm	NOUN
fcis-18357	128	28	significantly	significantly	ADV
fcis-18357	128	29	enhances	enhance	VERB
fcis-18357	128	30	the	the	DET
fcis-18357	128	31	model	model	NOUN
fcis-18357	128	32	's	's	PART
fcis-18357	128	33	ability	ability	NOUN
fcis-18357	128	34	to	to	PART
fcis-18357	128	35	extract	extract	VERB
fcis-18357	128	36	key	key	ADJ
fcis-18357	128	37	features	feature	NOUN
fcis-18357	128	38	in	in	ADP
fcis-18357	128	39	complex	complex	ADJ
fcis-18357	128	40	backgrounds	background	NOUN
fcis-18357	128	41	,	,	PUNCT
fcis-18357	128	42	thereby	thereby	ADV
fcis-18357	128	43	effectively	effectively	ADV
fcis-18357	128	44	improving	improve	VERB
fcis-18357	128	45	the	the	DET
fcis-18357	128	46	recognition	recognition	NOUN
fcis-18357	128	47	and	and	CCONJ
fcis-18357	128	48	detection	detection	NOUN
fcis-18357	128	49	rate	rate	NOUN
fcis-18357	128	50	for	for	ADP
fcis-18357	128	51	small	small	ADJ
fcis-18357	128	52	objects	object	NOUN
fcis-18357	128	53	.	.	PUNCT
fcis-18357	129	1	experimental	experimental	ADJ
fcis-18357	129	2	comparison	comparison	NOUN
fcis-18357	129	3	results	result	VERB
fcis-18357	129	4	validate	validate	VERB
fcis-18357	129	5	the	the	DET
fcis-18357	129	6	effectiveness	effectiveness	NOUN
fcis-18357	129	7	of	of	ADP
fcis-18357	129	8	the	the	DET
fcis-18357	129	9	proposed	propose	VERB
fcis-18357	129	10	algorithm	algorithm	NOUN
fcis-18357	129	11	:	:	PUNCT
fcis-18357	129	12	compared	compare	VERB
fcis-18357	129	13	to	to	ADP
fcis-18357	129	14	the	the	DET
fcis-18357	129	15	original	original	ADJ
fcis-18357	129	16	yolov7	yolov7	ADV
fcis-18357	129	17	-	-	PUNCT
fcis-18357	129	18	tiny	tiny	ADJ
fcis-18357	129	19	,	,	PUNCT
fcis-18357	129	20	the	the	DET
fcis-18357	129	21	improved	improved	ADJ
fcis-18357	129	22	model	model	NOUN
fcis-18357	129	23	achieved	achieve	VERB
fcis-18357	129	24	a	a	DET
fcis-18357	129	25	significant	significant	ADJ
fcis-18357	129	26	2.8	2.8	NUM
fcis-18357	129	27	%	%	NOUN
fcis-18357	129	28	increase	increase	NOUN
fcis-18357	129	29	in	in	ADP
fcis-18357	129	30	detection	detection	NOUN
fcis-18357	129	31	rate	rate	NOUN
fcis-18357	129	32	,	,	PUNCT
fcis-18357	129	33	proving	prove	VERB
fcis-18357	129	34	the	the	DET
fcis-18357	129	35	importance	importance	NOUN
fcis-18357	129	36	and	and	CCONJ
fcis-18357	129	37	practical	practical	ADJ
fcis-18357	129	38	value	value	NOUN
fcis-18357	129	39	of	of	ADP
fcis-18357	129	40	the	the	DET
fcis-18357	129	41	proposed	propose	VERB
fcis-18357	129	42	improvements	improvement	NOUN
fcis-18357	129	43	for	for	ADP
fcis-18357	129	44	enhancing	enhance	VERB
fcis-18357	129	45	small	small	ADJ
fcis-18357	129	46	object	object	NOUN
fcis-18357	129	47	detection	detection	NOUN
fcis-18357	129	48	performance	performance	NOUN
fcis-18357	129	49	.	.	PUNCT
fcis-18357	130	1	references	reference	NOUN
fcis-18357	130	2	[	[	X
fcis-18357	130	3	1	1	NUM
fcis-18357	130	4	]	]	X
fcis-18357	130	5	zhang	zhang	PROPN
fcis-18357	130	6	,	,	PUNCT
fcis-18357	130	7	y.	y.	PROPN
fcis-18357	130	8	,	,	PUNCT
fcis-18357	130	9	&	&	CCONJ
fcis-18357	130	10	chen	chen	PROPN
fcis-18357	130	11	,	,	PUNCT
fcis-18357	130	12	b.	b.	PROPN
fcis-18357	130	13	(	(	PUNCT
fcis-18357	130	14	2020	2020	NUM
fcis-18357	130	15	)	)	PUNCT
fcis-18357	130	16	.	.	PUNCT
fcis-18357	131	1	small	small	ADJ
fcis-18357	131	2	target	target	NOUN
fcis-18357	131	3	detection	detection	NOUN
fcis-18357	131	4	based	base	VERB
fcis-18357	131	5	on	on	ADP
fcis-18357	131	6	deep	deep	ADJ
fcis-18357	131	7	learning	learning	NOUN
fcis-18357	131	8	.	.	PUNCT
fcis-18357	132	1	ieee	ieee	NOUN
fcis-18357	132	2	transactions	transaction	NOUN
fcis-18357	132	3	on	on	ADP
fcis-18357	132	4	image	image	NOUN
fcis-18357	132	5	processing	processing	NOUN
fcis-18357	132	6	,	,	PUNCT
fcis-18357	132	7	29(1	29(1	NUM
fcis-18357	132	8	)	)	PUNCT
fcis-18357	132	9	,	,	PUNCT
fcis-18357	132	10	123	123	NUM
fcis-18357	132	11	-	-	SYM
fcis-18357	132	12	135	135	NUM
fcis-18357	132	13	.	.	PUNCT
fcis-18357	132	14	70	70	NUM
fcis-18357	133	1	[	[	X
fcis-18357	133	2	2	2	NUM
fcis-18357	133	3	]	]	X
fcis-18357	133	4	liu	liu	PROPN
fcis-18357	133	5	,	,	PUNCT
fcis-18357	133	6	s.	s.	PROPN
fcis-18357	133	7	,	,	PUNCT
fcis-18357	133	8	et	et	PROPN
fcis-18357	133	9	al	al	PROPN
fcis-18357	133	10	.	.	PROPN
fcis-18357	134	1	(	(	PUNCT
fcis-18357	134	2	2019	2019	NUM
fcis-18357	134	3	)	)	PUNCT
fcis-18357	134	4	.	.	PUNCT
fcis-18357	135	1	a	a	DET
fcis-18357	135	2	novel	novel	ADJ
fcis-18357	135	3	approach	approach	NOUN
fcis-18357	135	4	for	for	ADP
fcis-18357	135	5	small	small	ADJ
fcis-18357	135	6	target	target	NOUN
fcis-18357	135	7	detection	detection	NOUN
fcis-18357	135	8	using	use	VERB
fcis-18357	135	9	convolutional	convolutional	ADJ
fcis-18357	135	10	neural	neural	ADJ
fcis-18357	135	11	networks	network	NOUN
fcis-18357	135	12	.	.	PUNCT
fcis-18357	136	1	pattern	pattern	NOUN
fcis-18357	136	2	recognition	recognition	NOUN
fcis-18357	136	3	,	,	PUNCT
fcis-18357	136	4	85	85	NUM
fcis-18357	136	5	,	,	PUNCT
fcis-18357	136	6	234	234	NUM
fcis-18357	136	7	-	-	SYM
fcis-18357	136	8	245	245	NUM
fcis-18357	136	9	.	.	PUNCT
fcis-18357	137	1	[	[	X
fcis-18357	137	2	3	3	NUM
fcis-18357	137	3	]	]	X
fcis-18357	137	4	wang	wang	PROPN
fcis-18357	137	5	,	,	PUNCT
fcis-18357	137	6	j.	j.	PROPN
fcis-18357	137	7	,	,	PUNCT
fcis-18357	137	8	&	&	CCONJ
fcis-18357	137	9	li	li	PROPN
fcis-18357	137	10	,	,	PUNCT
fcis-18357	137	11	h.	h.	PROPN
fcis-18357	137	12	(	(	PUNCT
fcis-18357	137	13	2018	2018	NUM
fcis-18357	137	14	)	)	PUNCT
fcis-18357	137	15	.	.	PUNCT
fcis-18357	138	1	small	small	ADJ
fcis-18357	138	2	target	target	NOUN
fcis-18357	138	3	detection	detection	NOUN
fcis-18357	138	4	in	in	ADP
fcis-18357	138	5	infrared	infrared	ADJ
fcis-18357	138	6	images	image	NOUN
fcis-18357	138	7	using	use	VERB
fcis-18357	138	8	adaptive	adaptive	ADJ
fcis-18357	138	9	enhancement	enhancement	NOUN
fcis-18357	138	10	and	and	CCONJ
fcis-18357	138	11	deep	deep	ADJ
fcis-18357	138	12	learning	learning	NOUN
fcis-18357	138	13	.	.	PUNCT
fcis-18357	139	1	infrared	infrared	PROPN
fcis-18357	139	2	physics	physics	PROPN
fcis-18357	139	3	&	&	CCONJ
fcis-18357	139	4	technology	technology	PROPN
fcis-18357	139	5	,	,	PUNCT
fcis-18357	139	6	91	91	NUM
fcis-18357	139	7	,	,	PUNCT
fcis-18357	139	8	76	76	NUM
fcis-18357	139	9	-	-	SYM
fcis-18357	139	10	87	87	NUM
fcis-18357	139	11	.	.	PUNCT
fcis-18357	140	1	[	[	X
fcis-18357	140	2	4	4	NUM
fcis-18357	140	3	]	]	X
fcis-18357	140	4	chen	chen	PROPN
fcis-18357	140	5	,	,	PUNCT
fcis-18357	140	6	l.	l.	PROPN
fcis-18357	140	7	,	,	PUNCT
fcis-18357	140	8	et	et	PROPN
fcis-18357	140	9	al	al	PROPN
fcis-18357	140	10	.	.	PUNCT
fcis-18357	140	11	(	(	PUNCT
fcis-18357	140	12	2017	2017	NUM
fcis-18357	140	13	)	)	PUNCT
fcis-18357	140	14	.	.	PUNCT
fcis-18357	140	15	small	small	ADJ
fcis-18357	140	16	target	target	NOUN
fcis-18357	140	17	detection	detection	NOUN
fcis-18357	140	18	in	in	ADP
fcis-18357	140	19	hyperspectral	hyperspectral	ADJ
fcis-18357	140	20	images	image	NOUN
fcis-18357	140	21	via	via	ADP
fcis-18357	140	22	sparse	sparse	ADJ
fcis-18357	140	23	representation	representation	NOUN
fcis-18357	140	24	and	and	CCONJ
fcis-18357	140	25	low	low	ADJ
fcis-18357	140	26	-	-	PUNCT
fcis-18357	140	27	rank	rank	NOUN
fcis-18357	140	28	approximation	approximation	NOUN
fcis-18357	140	29	.	.	PUNCT
fcis-18357	141	1	ieee	ieee	NOUN
fcis-18357	141	2	transactions	transaction	NOUN
fcis-18357	141	3	on	on	ADP
fcis-18357	141	4	geoscience	geoscience	NOUN
fcis-18357	141	5	and	and	CCONJ
fcis-18357	141	6	remote	remote	ADJ
fcis-18357	141	7	sensing	sensing	NOUN
fcis-18357	141	8	,	,	PUNCT
fcis-18357	141	9	55(3	55(3	NUM
fcis-18357	141	10	)	)	PUNCT
fcis-18357	141	11	,	,	PUNCT
fcis-18357	141	12	432	432	NUM
fcis-18357	141	13	-	-	SYM
fcis-18357	141	14	444	444	NUM
fcis-18357	141	15	.	.	PUNCT
fcis-18357	142	1	[	[	X
fcis-18357	142	2	5	5	NUM
fcis-18357	142	3	]	]	X
fcis-18357	142	4	zhou	zhou	PROPN
fcis-18357	142	5	,	,	PUNCT
fcis-18357	142	6	x.	x.	PROPN
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fcis-18357	146	9	)	)	PUNCT
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fcis-18357	147	3	]	]	SYM
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fcis-18357	149	8	,	,	PUNCT
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fcis-18357	149	10	)	)	PUNCT
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fcis-18357	149	13	-	-	SYM
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fcis-18357	150	15	.	.	PUNCT
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fcis-18357	152	10	.	.	PUNCT
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fcis-18357	153	3	]	]	SYM
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fcis-18357	156	9	images	image	NOUN
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fcis-18357	156	13	.	.	PUNCT
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fcis-18357	157	10	)	)	PUNCT
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fcis-18357	157	13	-	-	SYM
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fcis-18357	158	9	.	.	PUNCT
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fcis-18357	161	3	,	,	PUNCT
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fcis-18357	161	11	-	-	SYM
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fcis-18357	161	13	.	.	PUNCT
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fcis-18357	164	8	,	,	PUNCT
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