id	sid	tid	token	lemma	pos
ajst-19320	1	1	academic	academic	ADJ
ajst-19320	1	2	journal	journal	NOUN
ajst-19320	1	3	of	of	ADP
ajst-19320	1	4	science	science	NOUN
ajst-19320	1	5	and	and	CCONJ
ajst-19320	1	6	technology	technology	NOUN
ajst-19320	1	7	issn	issn	NOUN
ajst-19320	1	8	:	:	PUNCT
ajst-19320	1	9	2771	2771	NUM
ajst-19320	1	10	-	-	SYM
ajst-19320	1	11	3032	3032	NUM
ajst-19320	1	12	|	|	NOUN
ajst-19320	1	13	vol	vol	NOUN
ajst-19320	1	14	.	.	PROPN
ajst-19320	2	1	10	10	NUM
ajst-19320	2	2	,	,	PUNCT
ajst-19320	2	3	no	no	INTJ
ajst-19320	2	4	.	.	NOUN
ajst-19320	2	5	1	1	NUM
ajst-19320	2	6	,	,	PUNCT
ajst-19320	2	7	2024	2024	NUM
ajst-19320	2	8	354	354	NUM
ajst-19320	2	9	research	research	NOUN
ajst-19320	2	10	on	on	ADP
ajst-19320	2	11	target	target	NOUN
ajst-19320	2	12	detection	detection	NOUN
ajst-19320	2	13	algorithm	algorithm	NOUN
ajst-19320	2	14	based	base	VERB
ajst-19320	2	15	on	on	ADP
ajst-19320	2	16	aerial	aerial	ADJ
ajst-19320	2	17	video	video	NOUN
ajst-19320	3	1	xiangchen	xiangchen	PROPN
ajst-19320	3	2	liu	liu	PROPN
ajst-19320	3	3	,	,	PUNCT
ajst-19320	3	4	lin	lin	PROPN
ajst-19320	3	5	zhang	zhang	PROPN
ajst-19320	3	6	north	north	PROPN
ajst-19320	3	7	china	china	PROPN
ajst-19320	3	8	university	university	PROPN
ajst-19320	3	9	of	of	ADP
ajst-19320	3	10	science	science	NOUN
ajst-19320	3	11	and	and	CCONJ
ajst-19320	3	12	technology	technology	NOUN
ajst-19320	3	13	,	,	PUNCT
ajst-19320	3	14	hebei	hebei	PROPN
ajst-19320	3	15	,	,	PUNCT
ajst-19320	3	16	tangshan	tangshan	NOUN
ajst-19320	3	17	,	,	PUNCT
ajst-19320	3	18	063210	063210	NUM
ajst-19320	3	19	,	,	PUNCT
ajst-19320	3	20	china	china	PROPN
ajst-19320	3	21	abstract	abstract	PROPN
ajst-19320	3	22	:	:	PUNCT
ajst-19320	3	23	for	for	ADP
ajst-19320	3	24	the	the	DET
ajst-19320	3	25	yolov5	yolov5	NOUN
ajst-19320	3	26	target	target	NOUN
ajst-19320	3	27	detection	detection	NOUN
ajst-19320	3	28	algorithm	algorithm	NOUN
ajst-19320	3	29	,	,	PUNCT
ajst-19320	3	30	firstly	firstly	ADV
ajst-19320	3	31	,	,	PUNCT
ajst-19320	3	32	the	the	DET
ajst-19320	3	33	dpfem	dpfem	PROPN
ajst-19320	3	34	network	network	NOUN
ajst-19320	3	35	is	be	AUX
ajst-19320	3	36	proposed	propose	VERB
ajst-19320	3	37	to	to	PART
ajst-19320	3	38	replace	replace	VERB
ajst-19320	3	39	the	the	DET
ajst-19320	3	40	original	original	ADJ
ajst-19320	3	41	bottleneckcsp	bottleneckcsp	NOUN
ajst-19320	3	42	and	and	CCONJ
ajst-19320	3	43	c3	c3	PROPN
ajst-19320	3	44	network	network	NOUN
ajst-19320	3	45	structure	structure	NOUN
ajst-19320	3	46	for	for	ADP
ajst-19320	3	47	the	the	DET
ajst-19320	3	48	problem	problem	NOUN
ajst-19320	3	49	of	of	ADP
ajst-19320	3	50	inadequate	inadequate	ADJ
ajst-19320	3	51	feature	feature	NOUN
ajst-19320	3	52	extraction	extraction	NOUN
ajst-19320	3	53	for	for	ADP
ajst-19320	3	54	small	small	ADJ
ajst-19320	3	55	targets	target	NOUN
ajst-19320	3	56	,	,	PUNCT
ajst-19320	3	57	secondly	secondly	ADV
ajst-19320	3	58	,	,	PUNCT
ajst-19320	3	59	the	the	DET
ajst-19320	3	60	maffem	maffem	NOUN
ajst-19320	3	61	module	module	NOUN
ajst-19320	3	62	is	be	AUX
ajst-19320	3	63	proposed	propose	VERB
ajst-19320	3	64	to	to	PART
ajst-19320	3	65	alleviate	alleviate	VERB
ajst-19320	3	66	the	the	DET
ajst-19320	3	67	conflict	conflict	NOUN
ajst-19320	3	68	of	of	ADP
ajst-19320	3	69	feature	feature	NOUN
ajst-19320	3	70	fusion	fusion	NOUN
ajst-19320	3	71	due	due	ADP
ajst-19320	3	72	to	to	ADP
ajst-19320	3	73	the	the	DET
ajst-19320	3	74	fusion	fusion	NOUN
ajst-19320	3	75	conflicts	conflict	NOUN
ajst-19320	3	76	brought	bring	VERB
ajst-19320	3	77	by	by	ADP
ajst-19320	3	78	the	the	DET
ajst-19320	3	79	different	different	ADJ
ajst-19320	3	80	scales	scale	NOUN
ajst-19320	3	81	of	of	ADP
ajst-19320	3	82	the	the	DET
ajst-19320	3	83	feature	feature	NOUN
ajst-19320	3	84	maps	map	NOUN
ajst-19320	3	85	during	during	ADP
ajst-19320	3	86	the	the	DET
ajst-19320	3	87	feature	feature	NOUN
ajst-19320	3	88	fusion	fusion	NOUN
ajst-19320	3	89	,	,	PUNCT
ajst-19320	3	90	finally	finally	ADV
ajst-19320	3	91	,	,	PUNCT
ajst-19320	3	92	the	the	DET
ajst-19320	3	93	training	training	NOUN
ajst-19320	3	94	the	the	DET
ajst-19320	3	95	results	result	NOUN
ajst-19320	3	96	show	show	VERB
ajst-19320	3	97	that	that	SCONJ
ajst-19320	3	98	the	the	DET
ajst-19320	3	99	improved	improved	ADJ
ajst-19320	3	100	yolov5	yolov5	NOUN
ajst-19320	3	101	algorithm	algorithm	PROPN
ajst-19320	3	102	map0.5	map0.5	PROPN
ajst-19320	3	103	and	and	CCONJ
ajst-19320	3	104	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	3	105	are	be	AUX
ajst-19320	3	106	improved	improve	VERB
ajst-19320	3	107	by	by	ADP
ajst-19320	3	108	2.2	2.2	NUM
ajst-19320	3	109	%	%	NOUN
ajst-19320	3	110	and	and	CCONJ
ajst-19320	3	111	1.7	1.7	NUM
ajst-19320	3	112	%	%	NOUN
ajst-19320	3	113	,	,	PUNCT
ajst-19320	3	114	respectively	respectively	ADV
ajst-19320	3	115	,	,	PUNCT
ajst-19320	3	116	and	and	CCONJ
ajst-19320	3	117	have	have	VERB
ajst-19320	3	118	certain	certain	ADJ
ajst-19320	3	119	application	application	NOUN
ajst-19320	3	120	potential	potential	NOUN
ajst-19320	3	121	.	.	PUNCT
ajst-19320	4	1	keywords	keyword	NOUN
ajst-19320	4	2	:	:	PUNCT
ajst-19320	4	3	deep	deep	ADJ
ajst-19320	4	4	learning	learning	NOUN
ajst-19320	4	5	;	;	PUNCT
ajst-19320	4	6	uav	uav	PROPN
ajst-19320	4	7	aerial	aerial	ADJ
ajst-19320	4	8	photography	photography	NOUN
ajst-19320	4	9	;	;	PUNCT
ajst-19320	4	10	yolov5	yolov5	NOUN
ajst-19320	4	11	;	;	PUNCT
ajst-19320	4	12	attention	attention	NOUN
ajst-19320	4	13	module	module	NOUN
ajst-19320	4	14	;	;	PUNCT
ajst-19320	4	15	target	target	NOUN
ajst-19320	4	16	detection	detection	NOUN
ajst-19320	4	17	algorithm	algorithm	NOUN
ajst-19320	4	18	.	.	PUNCT
ajst-19320	5	1	1	1	X
ajst-19320	5	2	.	.	X
ajst-19320	5	3	introductory	introductory	ADJ
ajst-19320	5	4	deep	deep	ADJ
ajst-19320	5	5	learning	learning	NOUN
ajst-19320	5	6	based	base	VERB
ajst-19320	5	7	target	target	NOUN
ajst-19320	5	8	detection	detection	NOUN
ajst-19320	5	9	method	method	NOUN
ajst-19320	5	10	through	through	ADP
ajst-19320	5	11	deep	deep	ADJ
ajst-19320	5	12	learning	learn	VERB
ajst-19320	5	13	convolutional	convolutional	ADJ
ajst-19320	5	14	neural	neural	ADJ
ajst-19320	5	15	network	network	NOUN
ajst-19320	5	16	and	and	CCONJ
ajst-19320	5	17	other	other	ADJ
ajst-19320	5	18	technologies	technology	NOUN
ajst-19320	5	19	,	,	PUNCT
ajst-19320	5	20	so	so	SCONJ
ajst-19320	5	21	that	that	SCONJ
ajst-19320	5	22	the	the	DET
ajst-19320	5	23	computer	computer	NOUN
ajst-19320	5	24	autonomously	autonomously	ADV
ajst-19320	5	25	on	on	ADP
ajst-19320	5	26	the	the	DET
ajst-19320	5	27	detection	detection	NOUN
ajst-19320	5	28	of	of	ADP
ajst-19320	5	29	the	the	DET
ajst-19320	5	30	target	target	NOUN
ajst-19320	5	31	feature	feature	NOUN
ajst-19320	5	32	extraction	extraction	NOUN
ajst-19320	5	33	,	,	PUNCT
ajst-19320	5	34	autonomous	autonomous	ADJ
ajst-19320	5	35	learning	learning	NOUN
ajst-19320	5	36	to	to	PART
ajst-19320	5	37	update	update	VERB
ajst-19320	5	38	the	the	DET
ajst-19320	5	39	weight	weight	NOUN
ajst-19320	5	40	parameters	parameter	NOUN
ajst-19320	5	41	,	,	PUNCT
ajst-19320	5	42	so	so	SCONJ
ajst-19320	5	43	as	as	SCONJ
ajst-19320	5	44	to	to	PART
ajst-19320	5	45	realize	realize	VERB
ajst-19320	5	46	the	the	DET
ajst-19320	5	47	detection	detection	NOUN
ajst-19320	5	48	and	and	CCONJ
ajst-19320	5	49	classification	classification	NOUN
ajst-19320	5	50	.	.	PUNCT
ajst-19320	6	1	according	accord	VERB
ajst-19320	6	2	to	to	ADP
ajst-19320	6	3	the	the	DET
ajst-19320	6	4	algorithm	algorithm	NOUN
ajst-19320	6	5	steps	step	NOUN
ajst-19320	6	6	are	be	AUX
ajst-19320	6	7	divided	divide	VERB
ajst-19320	6	8	into	into	ADP
ajst-19320	6	9	two	two	NUM
ajst-19320	6	10	-	-	PUNCT
ajst-19320	6	11	stage	stage	NOUN
ajst-19320	6	12	algorithm	algorithm	NOUN
ajst-19320	6	13	and	and	CCONJ
ajst-19320	6	14	one	one	NUM
ajst-19320	6	15	-	-	PUNCT
ajst-19320	6	16	stage	stage	NOUN
ajst-19320	6	17	algorithm	algorithm	NOUN
ajst-19320	6	18	.	.	PUNCT
ajst-19320	7	1	1.two	1.two	NUM
ajst-19320	7	2	-	-	PUNCT
ajst-19320	7	3	stage	stage	NOUN
ajst-19320	7	4	algorithm	algorithm	NOUN
ajst-19320	7	5	the	the	DET
ajst-19320	7	6	two	two	NUM
ajst-19320	7	7	-	-	PUNCT
ajst-19320	7	8	stage	stage	NOUN
ajst-19320	7	9	algorithm	algorithm	NOUN
ajst-19320	7	10	,	,	PUNCT
ajst-19320	7	11	as	as	SCONJ
ajst-19320	7	12	the	the	DET
ajst-19320	7	13	name	name	NOUN
ajst-19320	7	14	suggests	suggest	VERB
ajst-19320	7	15	,	,	PUNCT
ajst-19320	7	16	divides	divide	VERB
ajst-19320	7	17	the	the	DET
ajst-19320	7	18	detection	detection	NOUN
ajst-19320	7	19	process	process	NOUN
ajst-19320	7	20	into	into	ADP
ajst-19320	7	21	two	two	NUM
ajst-19320	7	22	stages	stage	NOUN
ajst-19320	7	23	,	,	PUNCT
ajst-19320	7	24	the	the	DET
ajst-19320	7	25	first	first	ADJ
ajst-19320	7	26	step	step	NOUN
ajst-19320	7	27	is	be	AUX
ajst-19320	7	28	to	to	PART
ajst-19320	7	29	screen	screen	VERB
ajst-19320	7	30	the	the	DET
ajst-19320	7	31	image	image	NOUN
ajst-19320	7	32	information	information	NOUN
ajst-19320	7	33	first	first	ADV
ajst-19320	7	34	,	,	PUNCT
ajst-19320	7	35	select	select	VERB
ajst-19320	7	36	the	the	DET
ajst-19320	7	37	region	region	NOUN
ajst-19320	7	38	containing	contain	VERB
ajst-19320	7	39	the	the	DET
ajst-19320	7	40	detection	detection	NOUN
ajst-19320	7	41	target	target	NOUN
ajst-19320	7	42	,	,	PUNCT
ajst-19320	7	43	sieve	sieve	VERB
ajst-19320	7	44	out	out	ADP
ajst-19320	7	45	the	the	DET
ajst-19320	7	46	redundant	redundant	ADJ
ajst-19320	7	47	background	background	NOUN
ajst-19320	7	48	information	information	NOUN
ajst-19320	7	49	,	,	PUNCT
ajst-19320	7	50	and	and	CCONJ
ajst-19320	7	51	then	then	ADV
ajst-19320	7	52	the	the	DET
ajst-19320	7	53	second	second	ADJ
ajst-19320	7	54	step	step	NOUN
ajst-19320	7	55	is	be	AUX
ajst-19320	7	56	to	to	PART
ajst-19320	7	57	regress	regress	VERB
ajst-19320	7	58	the	the	DET
ajst-19320	7	59	position	position	NOUN
ajst-19320	7	60	of	of	ADP
ajst-19320	7	61	the	the	DET
ajst-19320	7	62	target	target	NOUN
ajst-19320	7	63	in	in	ADP
ajst-19320	7	64	these	these	DET
ajst-19320	7	65	selected	select	VERB
ajst-19320	7	66	regions	region	NOUN
ajst-19320	7	67	and	and	CCONJ
ajst-19320	7	68	classify	classify	VERB
ajst-19320	7	69	them	they	PRON
ajst-19320	7	70	.	.	PUNCT
ajst-19320	8	1	therefore	therefore	ADV
ajst-19320	8	2	the	the	DET
ajst-19320	8	3	two	two	NUM
ajst-19320	8	4	-	-	PUNCT
ajst-19320	8	5	stage	stage	NOUN
ajst-19320	8	6	algorithm	algorithm	NOUN
ajst-19320	8	7	is	be	AUX
ajst-19320	8	8	also	also	ADV
ajst-19320	8	9	known	know	VERB
ajst-19320	8	10	as	as	ADP
ajst-19320	8	11	candidate	candidate	NOUN
ajst-19320	8	12	region	region	NOUN
ajst-19320	8	13	(	(	PUNCT
ajst-19320	8	14	region	region	NOUN
ajst-19320	8	15	proposal	proposal	NOUN
ajst-19320	8	16	)	)	PUNCT
ajst-19320	8	17	based	base	VERB
ajst-19320	8	18	target	target	NOUN
ajst-19320	8	19	detection	detection	NOUN
ajst-19320	8	20	.	.	PUNCT
ajst-19320	9	1	it	it	PRON
ajst-19320	9	2	is	be	AUX
ajst-19320	9	3	the	the	DET
ajst-19320	9	4	forerunner	forerunner	NOUN
ajst-19320	9	5	of	of	ADP
ajst-19320	9	6	deep	deep	ADJ
ajst-19320	9	7	learning	learning	NOUN
ajst-19320	9	8	based	base	VERB
ajst-19320	9	9	detection	detection	NOUN
ajst-19320	9	10	algorithms	algorithm	NOUN
ajst-19320	9	11	,	,	PUNCT
ajst-19320	9	12	represented	represent	VERB
ajst-19320	9	13	by	by	ADP
ajst-19320	9	14	algorithms	algorithm	NOUN
ajst-19320	9	15	such	such	ADJ
ajst-19320	9	16	as	as	ADP
ajst-19320	9	17	rcnn	rcnn	PROPN
ajst-19320	9	18	series	series	PROPN
ajst-19320	9	19	and	and	CCONJ
ajst-19320	9	20	sppnet	sppnet	NOUN
ajst-19320	9	21	.	.	PUNCT
ajst-19320	10	1	r	r	X
ajst-19320	10	2	-	-	PUNCT
ajst-19320	10	3	cnn	cnn	NOUN
ajst-19320	10	4	algorithm[1	algorithm[1	NOUN
ajst-19320	10	5	]	]	PUNCT
ajst-19320	10	6	in	in	ADP
ajst-19320	10	7	2014	2014	NUM
ajst-19320	10	8	,	,	PUNCT
ajst-19320	10	9	girshick	girshick	ADJ
ajst-19320	10	10	r	r	NOUN
ajst-19320	10	11	et	et	PROPN
ajst-19320	10	12	al	al	PROPN
ajst-19320	10	13	.	.	PROPN
ajst-19320	10	14	proposed	propose	VERB
ajst-19320	10	15	the	the	DET
ajst-19320	10	16	rcnn	rcnn	PROPN
ajst-19320	10	17	algorithm	algorithm	PROPN
ajst-19320	10	18	inspired	inspire	VERB
ajst-19320	10	19	by	by	ADP
ajst-19320	10	20	the	the	DET
ajst-19320	10	21	idea	idea	NOUN
ajst-19320	10	22	of	of	ADP
ajst-19320	10	23	imagenet	imagenet	ADJ
ajst-19320	10	24	algorithm.the	algorithm.the	DET
ajst-19320	10	25	workflow	workflow	NOUN
ajst-19320	10	26	of	of	ADP
ajst-19320	10	27	rcnn	rcnn	PROPN
ajst-19320	10	28	algorithm	algorithm	PROPN
ajst-19320	10	29	is	be	AUX
ajst-19320	10	30	to	to	PART
ajst-19320	10	31	first	first	ADV
ajst-19320	10	32	obtain	obtain	VERB
ajst-19320	10	33	the	the	DET
ajst-19320	10	34	input	input	NOUN
ajst-19320	10	35	image	image	NOUN
ajst-19320	10	36	,	,	PUNCT
ajst-19320	10	37	extract	extract	VERB
ajst-19320	10	38	the	the	DET
ajst-19320	10	39	candidate	candidate	NOUN
ajst-19320	10	40	regions	region	NOUN
ajst-19320	10	41	,	,	PUNCT
ajst-19320	10	42	and	and	CCONJ
ajst-19320	10	43	then	then	ADV
ajst-19320	10	44	scale	scale	VERB
ajst-19320	10	45	the	the	DET
ajst-19320	10	46	image	image	NOUN
ajst-19320	10	47	of	of	ADP
ajst-19320	10	48	each	each	DET
ajst-19320	10	49	candidate	candidate	NOUN
ajst-19320	10	50	region	region	NOUN
ajst-19320	10	51	to	to	ADP
ajst-19320	10	52	a	a	DET
ajst-19320	10	53	fixed	fix	VERB
ajst-19320	10	54	size	size	NOUN
ajst-19320	10	55	of	of	ADP
ajst-19320	10	56	224x224	224x224	NUM
ajst-19320	10	57	,	,	PUNCT
ajst-19320	10	58	input	input	NOUN
ajst-19320	10	59	into	into	ADP
ajst-19320	10	60	the	the	DET
ajst-19320	10	61	cnn	cnn	PROPN
ajst-19320	10	62	network	network	NOUN
ajst-19320	10	63	,	,	PUNCT
ajst-19320	10	64	and	and	CCONJ
ajst-19320	10	65	then	then	ADV
ajst-19320	10	66	input	input	VERB
ajst-19320	10	67	the	the	DET
ajst-19320	10	68	results	result	NOUN
ajst-19320	10	69	into	into	ADP
ajst-19320	10	70	the	the	DET
ajst-19320	10	71	classifier	classifier	NOUN
ajst-19320	10	72	for	for	ADP
ajst-19320	10	73	category	category	NOUN
ajst-19320	10	74	determination.the	determination.the	PROPN
ajst-19320	10	75	cnn	cnn	PROPN
ajst-19320	10	76	network	network	NOUN
ajst-19320	10	77	task	task	PROPN
ajst-19320	10	78	includes	include	VERB
ajst-19320	10	79	feature	feature	NOUN
ajst-19320	10	80	classification	classification	NOUN
ajst-19320	10	81	and	and	CCONJ
ajst-19320	10	82	edge	edge	NOUN
ajst-19320	10	83	regression	regression	NOUN
ajst-19320	10	84	.	.	PUNCT
ajst-19320	11	1	the	the	DET
ajst-19320	11	2	cnn	cnn	PROPN
ajst-19320	11	3	network	network	NOUN
ajst-19320	11	4	tasks	task	NOUN
ajst-19320	11	5	include	include	VERB
ajst-19320	11	6	feature	feature	NOUN
ajst-19320	11	7	classification	classification	NOUN
ajst-19320	11	8	and	and	CCONJ
ajst-19320	11	9	edge	edge	VERB
ajst-19320	11	10	regression.the	regression.the	DET
ajst-19320	11	11	rcnn	rcnn	PROPN
ajst-19320	11	12	algorithm	algorithm	PROPN
ajst-19320	11	13	mainly	mainly	ADV
ajst-19320	11	14	has	have	VERB
ajst-19320	11	15	the	the	DET
ajst-19320	11	16	following	follow	VERB
ajst-19320	11	17	problems	problem	NOUN
ajst-19320	11	18	,	,	PUNCT
ajst-19320	11	19	for	for	ADP
ajst-19320	11	20	example	example	NOUN
ajst-19320	11	21	,	,	PUNCT
ajst-19320	11	22	each	each	DET
ajst-19320	11	23	candidate	candidate	NOUN
ajst-19320	11	24	region	region	NOUN
ajst-19320	11	25	needs	need	VERB
ajst-19320	11	26	to	to	PART
ajst-19320	11	27	be	be	AUX
ajst-19320	11	28	repeated	repeat	VERB
ajst-19320	11	29	in	in	ADP
ajst-19320	11	30	the	the	DET
ajst-19320	11	31	subsequent	subsequent	ADJ
ajst-19320	11	32	judgment	judgment	NOUN
ajst-19320	11	33	,	,	PUNCT
ajst-19320	11	34	this	this	DET
ajst-19320	11	35	step	step	NOUN
ajst-19320	11	36	greatly	greatly	ADV
ajst-19320	11	37	wastes	waste	VERB
ajst-19320	11	38	the	the	DET
ajst-19320	11	39	arithmetic	arithmetic	ADJ
ajst-19320	11	40	power	power	NOUN
ajst-19320	11	41	,	,	PUNCT
ajst-19320	11	42	so	so	SCONJ
ajst-19320	11	43	the	the	DET
ajst-19320	11	44	detection	detection	NOUN
ajst-19320	11	45	speed	speed	NOUN
ajst-19320	11	46	of	of	ADP
ajst-19320	11	47	this	this	DET
ajst-19320	11	48	algorithm	algorithm	NOUN
ajst-19320	11	49	is	be	AUX
ajst-19320	11	50	very	very	ADV
ajst-19320	11	51	slow	slow	ADJ
ajst-19320	11	52	,	,	PUNCT
ajst-19320	11	53	and	and	CCONJ
ajst-19320	11	54	due	due	ADP
ajst-19320	11	55	to	to	ADP
ajst-19320	11	56	the	the	DET
ajst-19320	11	57	fixed	fix	VERB
ajst-19320	11	58	size	size	NOUN
ajst-19320	11	59	of	of	ADP
ajst-19320	11	60	the	the	DET
ajst-19320	11	61	output	output	NOUN
ajst-19320	11	62	,	,	PUNCT
ajst-19320	11	63	the	the	DET
ajst-19320	11	64	detection	detection	NOUN
ajst-19320	11	65	algorithm	algorithm	NOUN
ajst-19320	11	66	is	be	AUX
ajst-19320	11	67	often	often	ADV
ajst-19320	11	68	unsatisfactory	unsatisfactory	ADJ
ajst-19320	11	69	for	for	ADP
ajst-19320	11	70	images	image	NOUN
ajst-19320	11	71	with	with	ADP
ajst-19320	11	72	various	various	ADJ
ajst-19320	11	73	types	type	NOUN
ajst-19320	11	74	of	of	ADP
ajst-19320	11	75	targets	target	NOUN
ajst-19320	11	76	.	.	PUNCT
ajst-19320	12	1	aiming	aim	VERB
ajst-19320	12	2	at	at	ADP
ajst-19320	12	3	the	the	DET
ajst-19320	12	4	above	above	ADJ
ajst-19320	12	5	problems	problem	NOUN
ajst-19320	12	6	of	of	ADP
ajst-19320	12	7	r	r	NOUN
ajst-19320	12	8	-	-	PUNCT
ajst-19320	12	9	cnn	cnn	PROPN
ajst-19320	12	10	,	,	PUNCT
ajst-19320	12	11	fast	fast	ADJ
ajst-19320	12	12	r	r	NOUN
ajst-19320	12	13	-	-	PUNCT
ajst-19320	12	14	cnn	cnn	PROPN
ajst-19320	12	15	algorithm	algorithm	NOUN
ajst-19320	12	16	[	[	X
ajst-19320	12	17	2]proposed	2]propose	VERB
ajst-19320	12	18	by	by	ADP
ajst-19320	12	19	girshick	girshick	ADJ
ajst-19320	12	20	r	r	NOUN
ajst-19320	12	21	in	in	ADP
ajst-19320	12	22	2015	2015	NUM
ajst-19320	12	23	has	have	AUX
ajst-19320	12	24	been	be	AUX
ajst-19320	12	25	improved	improve	VERB
ajst-19320	12	26	in	in	ADP
ajst-19320	12	27	many	many	ADJ
ajst-19320	12	28	aspects	aspect	NOUN
ajst-19320	12	29	.	.	PUNCT
ajst-19320	13	1	for	for	ADP
ajst-19320	13	2	example	example	NOUN
ajst-19320	13	3	,	,	PUNCT
ajst-19320	13	4	the	the	DET
ajst-19320	13	5	backbone	backbone	NOUN
ajst-19320	13	6	network	network	NOUN
ajst-19320	13	7	adopts	adopt	VERB
ajst-19320	13	8	the	the	DET
ajst-19320	13	9	vgg16	vgg16	NOUN
ajst-19320	13	10	network	network	NOUN
ajst-19320	13	11	to	to	PART
ajst-19320	13	12	realize	realize	VERB
ajst-19320	13	13	the	the	DET
ajst-19320	13	14	lightweight	lightweight	ADJ
ajst-19320	13	15	calculation	calculation	NOUN
ajst-19320	13	16	,	,	PUNCT
ajst-19320	13	17	and	and	CCONJ
ajst-19320	13	18	compared	compare	VERB
ajst-19320	13	19	with	with	ADP
ajst-19320	13	20	r	r	NOUN
ajst-19320	13	21	-	-	PUNCT
ajst-19320	13	22	cnn	cnn	PROPN
ajst-19320	13	23	,	,	PUNCT
ajst-19320	13	24	the	the	DET
ajst-19320	13	25	training	training	NOUN
ajst-19320	13	26	speed	speed	NOUN
ajst-19320	13	27	of	of	ADP
ajst-19320	13	28	fast	fast	ADJ
ajst-19320	13	29	r	r	NOUN
ajst-19320	13	30	-	-	PUNCT
ajst-19320	13	31	cnn	cnn	PROPN
ajst-19320	13	32	is	be	AUX
ajst-19320	13	33	10	10	NUM
ajst-19320	13	34	times	time	NOUN
ajst-19320	13	35	faster	fast	ADJ
ajst-19320	13	36	than	than	ADP
ajst-19320	13	37	that	that	PRON
ajst-19320	13	38	of	of	ADP
ajst-19320	13	39	r	r	NOUN
ajst-19320	13	40	-	-	PUNCT
ajst-19320	13	41	cnn	cnn	PROPN
ajst-19320	13	42	,	,	PUNCT
ajst-19320	13	43	and	and	CCONJ
ajst-19320	13	44	the	the	DET
ajst-19320	13	45	accuracy	accuracy	NOUN
ajst-19320	13	46	of	of	ADP
ajst-19320	13	47	the	the	DET
ajst-19320	13	48	voc2012	voc2012	NOUN
ajst-19320	13	49	dataset	dataset	NOUN
ajst-19320	13	50	can	can	AUX
ajst-19320	13	51	reach	reach	VERB
ajst-19320	13	52	68.4	68.4	NUM
ajst-19320	13	53	%	%	NOUN
ajst-19320	13	54	.	.	PUNCT
ajst-19320	14	1	the	the	DET
ajst-19320	14	2	faster	fast	ADJ
ajst-19320	14	3	r	r	NOUN
ajst-19320	14	4	-	-	PUNCT
ajst-19320	14	5	cnn	cnn	PROPN
ajst-19320	14	6	algorithm[3]proposed	algorithm[3]propose	VERB
ajst-19320	14	7	by	by	ADP
ajst-19320	14	8	ren	ren	PROPN
ajst-19320	14	9	s	s	PROPN
ajst-19320	14	10	et	et	NOUN
ajst-19320	14	11	al	al	PROPN
ajst-19320	14	12	.	.	PROPN
ajst-19320	15	1	in	in	ADP
ajst-19320	15	2	2016	2016	NUM
ajst-19320	15	3	was	be	AUX
ajst-19320	15	4	further	far	ADV
ajst-19320	15	5	improved	improve	VERB
ajst-19320	15	6	by	by	ADP
ajst-19320	15	7	introducing	introduce	VERB
ajst-19320	15	8	an	an	DET
ajst-19320	15	9	additional	additional	ADJ
ajst-19320	15	10	rpn	rpn	NOUN
ajst-19320	15	11	structure	structure	NOUN
ajst-19320	15	12	and	and	CCONJ
ajst-19320	15	13	adding	add	VERB
ajst-19320	15	14	an	an	DET
ajst-19320	15	15	anchor	anchor	NOUN
ajst-19320	15	16	,	,	PUNCT
ajst-19320	15	17	i.e.	i.e.	X
ajst-19320	15	18	,	,	PUNCT
ajst-19320	15	19	there	there	PRON
ajst-19320	15	20	will	will	AUX
ajst-19320	15	21	be	be	AUX
ajst-19320	15	22	some	some	DET
ajst-19320	15	23	prior	prior	ADJ
ajst-19320	15	24	frames	frame	NOUN
ajst-19320	15	25	before	before	ADP
ajst-19320	15	26	learning	learn	VERB
ajst-19320	15	27	,	,	PUNCT
ajst-19320	15	28	and	and	CCONJ
ajst-19320	15	29	the	the	DET
ajst-19320	15	30	subsequent	subsequent	ADJ
ajst-19320	15	31	learning	learning	NOUN
ajst-19320	15	32	can	can	AUX
ajst-19320	15	33	go	go	VERB
ajst-19320	15	34	according	accord	VERB
ajst-19320	15	35	to	to	ADP
ajst-19320	15	36	these	these	DET
ajst-19320	15	37	prior	prior	ADJ
ajst-19320	15	38	frames	frame	NOUN
ajst-19320	15	39	,	,	PUNCT
ajst-19320	15	40	thus	thus	ADV
ajst-19320	15	41	reducing	reduce	VERB
ajst-19320	15	42	the	the	DET
ajst-19320	15	43	difficulty	difficulty	NOUN
ajst-19320	15	44	of	of	ADP
ajst-19320	15	45	learning	learn	VERB
ajst-19320	15	46	.	.	PUNCT
ajst-19320	16	1	fast	fast	ADJ
ajst-19320	16	2	r	r	NOUN
ajst-19320	16	3	-	-	PUNCT
ajst-19320	16	4	cnn	cnn	PROPN
ajst-19320	16	5	analyzes	analyze	VERB
ajst-19320	16	6	the	the	DET
ajst-19320	16	7	region	region	NOUN
ajst-19320	16	8	of	of	ADP
ajst-19320	16	9	interest	interest	NOUN
ajst-19320	16	10	after	after	ADP
ajst-19320	16	11	convolutional	convolutional	ADJ
ajst-19320	16	12	operation	operation	NOUN
ajst-19320	16	13	for	for	ADP
ajst-19320	16	14	classification	classification	NOUN
ajst-19320	16	15	and	and	CCONJ
ajst-19320	16	16	regression	regression	NOUN
ajst-19320	16	17	,	,	PUNCT
ajst-19320	16	18	but	but	CCONJ
ajst-19320	16	19	it	it	PRON
ajst-19320	16	20	contains	contain	VERB
ajst-19320	16	21	a	a	DET
ajst-19320	16	22	lot	lot	NOUN
ajst-19320	16	23	of	of	ADP
ajst-19320	16	24	useless	useless	ADJ
ajst-19320	16	25	operations	operation	NOUN
ajst-19320	16	26	,	,	PUNCT
ajst-19320	16	27	so	so	CCONJ
ajst-19320	16	28	the	the	DET
ajst-19320	16	29	rate	rate	NOUN
ajst-19320	16	30	is	be	AUX
ajst-19320	16	31	low	low	ADJ
ajst-19320	16	32	,	,	PUNCT
ajst-19320	16	33	while	while	SCONJ
ajst-19320	16	34	faster	fast	ADJ
ajst-19320	16	35	r	r	NOUN
ajst-19320	16	36	-	-	PUNCT
ajst-19320	16	37	cnn	cnn	PROPN
ajst-19320	16	38	improves	improve	VERB
ajst-19320	16	39	this	this	DET
ajst-19320	16	40	problem	problem	NOUN
ajst-19320	16	41	,	,	PUNCT
ajst-19320	16	42	so	so	SCONJ
ajst-19320	16	43	that	that	SCONJ
ajst-19320	16	44	the	the	DET
ajst-19320	16	45	speed	speed	NOUN
ajst-19320	16	46	of	of	ADP
ajst-19320	16	47	the	the	DET
ajst-19320	16	48	operation	operation	NOUN
ajst-19320	16	49	is	be	AUX
ajst-19320	16	50	greatly	greatly	ADV
ajst-19320	16	51	improved	improve	VERB
ajst-19320	16	52	,	,	PUNCT
ajst-19320	16	53	and	and	CCONJ
ajst-19320	16	54	the	the	DET
ajst-19320	16	55	accuracy	accuracy	NOUN
ajst-19320	16	56	in	in	ADP
ajst-19320	16	57	the	the	DET
ajst-19320	16	58	voc2012	voc2012	NOUN
ajst-19320	16	59	dataset	dataset	NOUN
ajst-19320	16	60	reaches	reach	VERB
ajst-19320	16	61	70.4	70.4	NUM
ajst-19320	16	62	%	%	NOUN
ajst-19320	16	63	.	.	PUNCT
ajst-19320	17	1	many	many	ADJ
ajst-19320	17	2	scholars	scholar	NOUN
ajst-19320	17	3	in	in	ADP
ajst-19320	17	4	china	china	PROPN
ajst-19320	17	5	have	have	AUX
ajst-19320	17	6	studied	study	VERB
ajst-19320	17	7	the	the	DET
ajst-19320	17	8	rcnn	rcnn	PROPN
ajst-19320	17	9	series	series	PROPN
ajst-19320	17	10	of	of	ADP
ajst-19320	17	11	algorithms	algorithms	PROPN
ajst-19320	17	12	,	,	PUNCT
ajst-19320	17	13	wang	wang	PROPN
ajst-19320	17	14	xinze	xinze	PROPN
ajst-19320	17	15	,	,	PUNCT
ajst-19320	17	16	he	he	PRON
ajst-19320	17	17	chao[4	chao[4	VERB
ajst-19320	17	18	]	]	PUNCT
ajst-19320	17	19	and	and	CCONJ
ajst-19320	17	20	others	other	NOUN
ajst-19320	17	21	have	have	AUX
ajst-19320	17	22	improved	improve	VERB
ajst-19320	17	23	the	the	DET
ajst-19320	17	24	faster	fast	ADJ
ajst-19320	17	25	rcnn	rcnn	PROPN
ajst-19320	17	26	model	model	PROPN
ajst-19320	17	27	,	,	PUNCT
ajst-19320	17	28	which	which	PRON
ajst-19320	17	29	integrates	integrate	VERB
ajst-19320	17	30	the	the	DET
ajst-19320	17	31	advantages	advantage	NOUN
ajst-19320	17	32	of	of	ADP
ajst-19320	17	33	the	the	DET
ajst-19320	17	34	transformer	transformer	NOUN
ajst-19320	17	35	model	model	NOUN
ajst-19320	17	36	by	by	ADP
ajst-19320	17	37	increasing	increase	VERB
ajst-19320	17	38	the	the	DET
ajst-19320	17	39	convolution	convolution	NOUN
ajst-19320	17	40	kernel	kernel	NOUN
ajst-19320	17	41	to	to	PART
ajst-19320	17	42	increase	increase	VERB
ajst-19320	17	43	the	the	DET
ajst-19320	17	44	receptive	receptive	ADJ
ajst-19320	17	45	field	field	NOUN
ajst-19320	17	46	,	,	PUNCT
ajst-19320	17	47	replacing	replace	VERB
ajst-19320	17	48	the	the	DET
ajst-19320	17	49	ordinary	ordinary	ADJ
ajst-19320	17	50	convolution	convolution	NOUN
ajst-19320	17	51	with	with	ADP
ajst-19320	17	52	the	the	DET
ajst-19320	17	53	dw	dw	PROPN
ajst-19320	17	54	convolution	convolution	NOUN
ajst-19320	17	55	to	to	PART
ajst-19320	17	56	enhance	enhance	VERB
ajst-19320	17	57	the	the	DET
ajst-19320	17	58	detection	detection	NOUN
ajst-19320	17	59	performance	performance	NOUN
ajst-19320	17	60	of	of	ADP
ajst-19320	17	61	the	the	DET
ajst-19320	17	62	model	model	NOUN
ajst-19320	17	63	and	and	CCONJ
ajst-19320	17	64	improving	improve	VERB
ajst-19320	17	65	the	the	DET
ajst-19320	17	66	loss	loss	NOUN
ajst-19320	17	67	parameter	parameter	NOUN
ajst-19320	17	68	,	,	PUNCT
ajst-19320	17	69	which	which	PRON
ajst-19320	17	70	improves	improve	VERB
ajst-19320	17	71	the	the	DET
ajst-19320	17	72	model	model	NOUN
ajst-19320	17	73	's	's	PART
ajst-19320	17	74	ability	ability	NOUN
ajst-19320	17	75	of	of	ADP
ajst-19320	17	76	detecting	detect	VERB
ajst-19320	17	77	objects	object	NOUN
ajst-19320	17	78	at	at	ADP
ajst-19320	17	79	different	different	ADJ
ajst-19320	17	80	scales	scale	NOUN
ajst-19320	17	81	.	.	PUNCT
ajst-19320	18	1	zhou	zhou	PROPN
ajst-19320	18	2	shaohong	shaohong	PROPN
ajst-19320	18	3	,	,	PUNCT
ajst-19320	18	4	fang	fang	X
ajst-19320	18	5	xinxin[5	xinxin[5	X
ajst-19320	18	6	]	]	X
ajst-19320	18	7	et	et	PROPN
ajst-19320	18	8	al	al	PROPN
ajst-19320	18	9	.	.	PROPN
ajst-19320	18	10	used	use	VERB
ajst-19320	18	11	resnet50	resnet50	NOUN
ajst-19320	18	12	network	network	NOUN
ajst-19320	18	13	instead	instead	ADV
ajst-19320	18	14	of	of	ADP
ajst-19320	18	15	the	the	DET
ajst-19320	18	16	original	original	ADJ
ajst-19320	18	17	vgg16	vgg16	NOUN
ajst-19320	18	18	network	network	NOUN
ajst-19320	18	19	for	for	ADP
ajst-19320	18	20	feature	feature	NOUN
ajst-19320	18	21	extraction	extraction	NOUN
ajst-19320	18	22	of	of	ADP
ajst-19320	18	23	the	the	DET
ajst-19320	18	24	target	target	NOUN
ajst-19320	18	25	and	and	CCONJ
ajst-19320	18	26	used	use	VERB
ajst-19320	18	27	the	the	DET
ajst-19320	18	28	pre	pre	ADJ
ajst-19320	18	29	-	-	ADJ
ajst-19320	18	30	trained	train	VERB
ajst-19320	18	31	weights	weight	NOUN
ajst-19320	18	32	as	as	ADP
ajst-19320	18	33	the	the	DET
ajst-19320	18	34	initial	initial	ADJ
ajst-19320	18	35	weights	weight	NOUN
ajst-19320	18	36	to	to	PART
ajst-19320	18	37	improve	improve	VERB
ajst-19320	18	38	the	the	DET
ajst-19320	18	39	training	training	NOUN
ajst-19320	18	40	effect	effect	NOUN
ajst-19320	18	41	,	,	PUNCT
ajst-19320	18	42	and	and	CCONJ
ajst-19320	18	43	also	also	ADV
ajst-19320	18	44	increased	increase	VERB
ajst-19320	18	45	the	the	DET
ajst-19320	18	46	number	number	NOUN
ajst-19320	18	47	of	of	ADP
ajst-19320	18	48	anchor	anchor	NOUN
ajst-19320	18	49	frames	frame	NOUN
ajst-19320	18	50	of	of	ADP
ajst-19320	18	51	the	the	DET
ajst-19320	18	52	original	original	ADJ
ajst-19320	18	53	algorithm	algorithm	NOUN
ajst-19320	18	54	to	to	PART
ajst-19320	18	55	obtain	obtain	VERB
ajst-19320	18	56	richer	rich	ADJ
ajst-19320	18	57	feature	feature	NOUN
ajst-19320	18	58	map	map	NOUN
ajst-19320	18	59	information	information	NOUN
ajst-19320	18	60	,	,	PUNCT
ajst-19320	18	61	and	and	CCONJ
ajst-19320	18	62	the	the	DET
ajst-19320	18	63	improved	improve	VERB
ajst-19320	18	64	faster	fast	ADJ
ajst-19320	18	65	rcnn	rcnn	PROPN
ajst-19320	18	66	model	model	PROPN
ajst-19320	18	67	improved	improve	VERB
ajst-19320	18	68	the	the	DET
ajst-19320	18	69	detection	detection	NOUN
ajst-19320	18	70	effect	effect	NOUN
ajst-19320	18	71	and	and	CCONJ
ajst-19320	18	72	stability	stability	NOUN
ajst-19320	18	73	.	.	PUNCT
ajst-19320	19	1	du	du	PROPN
ajst-19320	19	2	yunyan[6]et	yunyan[6]et	PROPN
ajst-19320	19	3	al	al	PROPN
ajst-19320	19	4	.	.	PROPN
ajst-19320	19	5	proposed	propose	VERB
ajst-19320	19	6	a	a	DET
ajst-19320	19	7	faster	fast	ADJ
ajst-19320	19	8	rcnn	rcnn	PROPN
ajst-19320	19	9	model	model	NOUN
ajst-19320	19	10	for	for	ADP
ajst-19320	19	11	a	a	DET
ajst-19320	19	12	small	small	ADJ
ajst-19320	19	13	number	number	NOUN
ajst-19320	19	14	of	of	ADP
ajst-19320	19	15	target	target	NOUN
ajst-19320	19	16	samples	sample	NOUN
ajst-19320	19	17	in	in	ADP
ajst-19320	19	18	response	response	NOUN
ajst-19320	19	19	to	to	ADP
ajst-19320	19	20	the	the	DET
ajst-19320	19	21	problem	problem	NOUN
ajst-19320	19	22	of	of	ADP
ajst-19320	19	23	a	a	DET
ajst-19320	19	24	small	small	ADJ
ajst-19320	19	25	amount	amount	NOUN
ajst-19320	19	26	of	of	ADP
ajst-19320	19	27	model	model	NOUN
ajst-19320	19	28	training	training	NOUN
ajst-19320	19	29	samples	sample	NOUN
ajst-19320	19	30	,	,	PUNCT
ajst-19320	19	31	by	by	ADP
ajst-19320	19	32	incorporating	incorporate	VERB
ajst-19320	19	33	the	the	DET
ajst-19320	19	34	cbam	cbam	NOUN
ajst-19320	19	35	attention	attention	NOUN
ajst-19320	19	36	mechanism	mechanism	NOUN
ajst-19320	19	37	into	into	ADP
ajst-19320	19	38	the	the	DET
ajst-19320	19	39	rpn	rpn	PROPN
ajst-19320	19	40	module	module	NOUN
ajst-19320	19	41	,	,	PUNCT
ajst-19320	19	42	screening	screen	VERB
ajst-19320	19	43	out	out	ADP
ajst-19320	19	44	a	a	DET
ajst-19320	19	45	portion	portion	NOUN
ajst-19320	19	46	of	of	ADP
ajst-19320	19	47	the	the	DET
ajst-19320	19	48	candidate	candidate	NOUN
ajst-19320	19	49	frames	frame	NOUN
ajst-19320	19	50	,	,	PUNCT
ajst-19320	19	51	and	and	CCONJ
ajst-19320	19	52	proposing	propose	VERB
ajst-19320	19	53	a	a	DET
ajst-19320	19	54	global	global	ADJ
ajst-19320	19	55	and	and	CCONJ
ajst-19320	19	56	local	local	ADJ
ajst-19320	19	57	relationship	relationship	NOUN
ajst-19320	19	58	detector	detector	NOUN
ajst-19320	19	59	to	to	PART
ajst-19320	19	60	obtain	obtain	VERB
ajst-19320	19	61	the	the	DET
ajst-19320	19	62	relationship	relationship	NOUN
ajst-19320	19	63	between	between	ADP
ajst-19320	19	64	a	a	DET
ajst-19320	19	65	small	small	ADJ
ajst-19320	19	66	number	number	NOUN
ajst-19320	19	67	of	of	ADP
ajst-19320	19	68	labeled	label	VERB
ajst-19320	19	69	samples	sample	NOUN
ajst-19320	19	70	and	and	CCONJ
ajst-19320	19	71	features	feature	NOUN
ajst-19320	19	72	of	of	ADP
ajst-19320	19	73	the	the	DET
ajst-19320	19	74	samples	sample	NOUN
ajst-19320	19	75	to	to	PART
ajst-19320	19	76	be	be	AUX
ajst-19320	19	77	detected	detect	VERB
ajst-19320	19	78	,	,	PUNCT
ajst-19320	19	79	which	which	PRON
ajst-19320	19	80	reduces	reduce	VERB
ajst-19320	19	81	the	the	DET
ajst-19320	19	82	interference	interference	NOUN
ajst-19320	19	83	of	of	ADP
ajst-19320	19	84	useless	useless	ADJ
ajst-19320	19	85	information	information	NOUN
ajst-19320	19	86	and	and	CCONJ
ajst-19320	19	87	improves	improve	VERB
ajst-19320	19	88	the	the	DET
ajst-19320	19	89	model	model	NOUN
ajst-19320	19	90	detection	detection	NOUN
ajst-19320	19	91	accuracy	accuracy	NOUN
ajst-19320	19	92	.	.	PUNCT
ajst-19320	20	1	2	2	X
ajst-19320	20	2	.	.	X
ajst-19320	20	3	one	one	NUM
ajst-19320	20	4	-	-	PUNCT
ajst-19320	20	5	stage	stage	NOUN
ajst-19320	20	6	algorithm	algorithm	NOUN
ajst-19320	20	7	single	single	ADJ
ajst-19320	20	8	-	-	PUNCT
ajst-19320	20	9	stage	stage	NOUN
ajst-19320	20	10	algorithms	algorithm	NOUN
ajst-19320	20	11	directly	directly	ADV
ajst-19320	20	12	input	input	VERB
ajst-19320	20	13	image	image	NOUN
ajst-19320	20	14	through	through	ADP
ajst-19320	20	15	the	the	DET
ajst-19320	20	16	convolutional	convolutional	ADJ
ajst-19320	20	17	neural	neural	ADJ
ajst-19320	20	18	network	network	NOUN
ajst-19320	20	19	directly	directly	ADV
ajst-19320	20	20	on	on	ADP
ajst-19320	20	21	the	the	DET
ajst-19320	20	22	target	target	NOUN
ajst-19320	20	23	detection	detection	NOUN
ajst-19320	20	24	,	,	PUNCT
ajst-19320	20	25	to	to	PART
ajst-19320	20	26	achieve	achieve	VERB
ajst-19320	20	27	the	the	DET
ajst-19320	20	28	detection	detection	NOUN
ajst-19320	20	29	of	of	ADP
ajst-19320	20	30	the	the	DET
ajst-19320	20	31	target	target	NOUN
ajst-19320	20	32	position	position	NOUN
ajst-19320	20	33	regression	regression	NOUN
ajst-19320	20	34	and	and	CCONJ
ajst-19320	20	35	classification	classification	NOUN
ajst-19320	20	36	.	.	PUNCT
ajst-19320	21	1	the	the	DET
ajst-19320	21	2	representative	representative	ADJ
ajst-19320	21	3	algorithms	algorithm	NOUN
ajst-19320	21	4	are	be	AUX
ajst-19320	21	5	yolo	yolo	ADJ
ajst-19320	21	6	series	series	NOUN
ajst-19320	21	7	algorithm	algorithm	PROPN
ajst-19320	21	8	and	and	CCONJ
ajst-19320	21	9	ssd	ssd	NOUN
ajst-19320	21	10	algorithm.in	algorithm.in	PROPN
ajst-19320	21	11	2016	2016	NUM
ajst-19320	21	12	,	,	PUNCT
ajst-19320	21	13	yolo[7]proposed	yolo[7]propose	VERB
ajst-19320	21	14	by	by	ADP
ajst-19320	21	15	redmon	redmon	PROPN
ajst-19320	21	16	et	et	PROPN
ajst-19320	21	17	al	al	PROPN
ajst-19320	21	18	.	.	PROPN
ajst-19320	22	1	combines	combine	VERB
ajst-19320	22	2	target	target	NOUN
ajst-19320	22	3	recognition	recognition	NOUN
ajst-19320	22	4	and	and	CCONJ
ajst-19320	22	5	determination	determination	NOUN
ajst-19320	22	6	355	355	NUM
ajst-19320	22	7	by	by	ADP
ajst-19320	22	8	segmenting	segment	VERB
ajst-19320	22	9	a	a	DET
ajst-19320	22	10	448	448	NUM
ajst-19320	22	11	×	×	NOUN
ajst-19320	22	12	448	448	NUM
ajst-19320	22	13	image	image	NOUN
ajst-19320	22	14	into	into	ADP
ajst-19320	22	15	grids	grid	NOUN
ajst-19320	22	16	at	at	ADP
ajst-19320	22	17	the	the	DET
ajst-19320	22	18	same	same	ADJ
ajst-19320	22	19	intervals	interval	NOUN
ajst-19320	22	20	,	,	PUNCT
ajst-19320	22	21	each	each	PRON
ajst-19320	22	22	of	of	ADP
ajst-19320	22	23	which	which	PRON
ajst-19320	22	24	predicts	predict	VERB
ajst-19320	22	25	the	the	DET
ajst-19320	22	26	position	position	NOUN
ajst-19320	22	27	of	of	ADP
ajst-19320	22	28	the	the	DET
ajst-19320	22	29	two	two	NUM
ajst-19320	22	30	candidate	candidate	NOUN
ajst-19320	22	31	frames	frame	NOUN
ajst-19320	22	32	as	as	ADV
ajst-19320	22	33	well	well	ADV
ajst-19320	22	34	as	as	ADP
ajst-19320	22	35	the	the	DET
ajst-19320	22	36	confidence	confidence	NOUN
ajst-19320	22	37	of	of	ADP
ajst-19320	22	38	whether	whether	SCONJ
ajst-19320	22	39	they	they	PRON
ajst-19320	22	40	contain	contain	VERB
ajst-19320	22	41	an	an	DET
ajst-19320	22	42	object	object	NOUN
ajst-19320	22	43	or	or	CCONJ
ajst-19320	22	44	not	not	PART
ajst-19320	22	45	,	,	PUNCT
ajst-19320	22	46	and	and	CCONJ
ajst-19320	22	47	then	then	ADV
ajst-19320	22	48	obtains	obtain	VERB
ajst-19320	22	49	detection	detection	NOUN
ajst-19320	22	50	results	result	NOUN
ajst-19320	22	51	by	by	ADP
ajst-19320	22	52	non	non	ADJ
ajst-19320	22	53	-	-	ADJ
ajst-19320	22	54	maximum	maximum	ADJ
ajst-19320	22	55	suppression	suppression	NOUN
ajst-19320	22	56	(	(	PUNCT
ajst-19320	22	57	nms)[8]obtain	nms)[8]obtain	ADP
ajst-19320	22	58	the	the	DET
ajst-19320	22	59	detection	detection	NOUN
ajst-19320	22	60	results	result	NOUN
ajst-19320	22	61	,	,	PUNCT
ajst-19320	22	62	this	this	DET
ajst-19320	22	63	approach	approach	NOUN
ajst-19320	22	64	makes	make	VERB
ajst-19320	22	65	yolo	yolo	ADJ
ajst-19320	22	66	detection	detection	NOUN
ajst-19320	22	67	of	of	ADP
ajst-19320	22	68	an	an	DET
ajst-19320	22	69	image	image	NOUN
ajst-19320	22	70	takes	take	VERB
ajst-19320	22	71	only	only	ADV
ajst-19320	22	72	20ms	20ms	NUM
ajst-19320	22	73	,	,	PUNCT
ajst-19320	22	74	and	and	CCONJ
ajst-19320	22	75	the	the	DET
ajst-19320	22	76	inference	inference	NOUN
ajst-19320	22	77	speed	speed	NOUN
ajst-19320	22	78	of	of	ADP
ajst-19320	22	79	the	the	DET
ajst-19320	22	80	related	relate	VERB
ajst-19320	22	81	lightweight	lightweight	ADJ
ajst-19320	22	82	model	model	NOUN
ajst-19320	22	83	can	can	AUX
ajst-19320	22	84	reach	reach	VERB
ajst-19320	22	85	155fps	155fps	NUM
ajst-19320	22	86	.	.	PUNCT
ajst-19320	23	1	although	although	SCONJ
ajst-19320	23	2	this	this	DET
ajst-19320	23	3	approach	approach	NOUN
ajst-19320	23	4	leads	lead	VERB
ajst-19320	23	5	to	to	ADP
ajst-19320	23	6	a	a	DET
ajst-19320	23	7	slightly	slightly	ADV
ajst-19320	23	8	lower	low	ADJ
ajst-19320	23	9	detection	detection	NOUN
ajst-19320	23	10	accuracy	accuracy	NOUN
ajst-19320	23	11	than	than	ADP
ajst-19320	23	12	the	the	DET
ajst-19320	23	13	faster	fast	ADJ
ajst-19320	23	14	rcnn	rcnn	NOUN
ajst-19320	23	15	,	,	PUNCT
ajst-19320	23	16	but	but	CCONJ
ajst-19320	23	17	it	it	PRON
ajst-19320	23	18	is	be	AUX
ajst-19320	23	19	still	still	ADV
ajst-19320	23	20	much	much	ADV
ajst-19320	23	21	higher	high	ADJ
ajst-19320	23	22	than	than	ADP
ajst-19320	23	23	the	the	DET
ajst-19320	23	24	traditional	traditional	ADJ
ajst-19320	23	25	algorithms	algorithm	NOUN
ajst-19320	23	26	,	,	PUNCT
ajst-19320	23	27	and	and	CCONJ
ajst-19320	23	28	its	its	PRON
ajst-19320	23	29	high	high	ADJ
ajst-19320	23	30	detection	detection	NOUN
ajst-19320	23	31	speed	speed	NOUN
ajst-19320	23	32	to	to	PART
ajst-19320	23	33	better	well	ADV
ajst-19320	23	34	meet	meet	VERB
ajst-19320	23	35	real	real	ADJ
ajst-19320	23	36	-	-	PUNCT
ajst-19320	23	37	time	time	NOUN
ajst-19320	23	38	demand	demand	NOUN
ajst-19320	23	39	.	.	PUNCT
ajst-19320	24	1	in	in	ADP
ajst-19320	24	2	the	the	DET
ajst-19320	24	3	same	same	ADJ
ajst-19320	24	4	year	year	NOUN
ajst-19320	24	5	,	,	PUNCT
ajst-19320	24	6	ssd[9]was	ssd[9]was	PROPN
ajst-19320	24	7	proposed	propose	VERB
ajst-19320	24	8	to	to	PART
ajst-19320	24	9	improve	improve	VERB
ajst-19320	24	10	the	the	DET
ajst-19320	24	11	generalization	generalization	NOUN
ajst-19320	24	12	ability	ability	NOUN
ajst-19320	24	13	of	of	ADP
ajst-19320	24	14	yolo	yolo	NOUN
ajst-19320	24	15	,	,	PUNCT
ajst-19320	24	16	using	use	VERB
ajst-19320	24	17	different	different	ADJ
ajst-19320	24	18	resolutions	resolution	NOUN
ajst-19320	24	19	of	of	ADP
ajst-19320	24	20	feature	feature	NOUN
ajst-19320	24	21	maps	map	NOUN
ajst-19320	24	22	to	to	PART
ajst-19320	24	23	calculate	calculate	VERB
ajst-19320	24	24	the	the	DET
ajst-19320	24	25	classification	classification	NOUN
ajst-19320	24	26	information	information	NOUN
ajst-19320	24	27	and	and	CCONJ
ajst-19320	24	28	regression	regression	NOUN
ajst-19320	24	29	results	result	NOUN
ajst-19320	24	30	,	,	PUNCT
ajst-19320	24	31	in	in	ADP
ajst-19320	24	32	order	order	NOUN
ajst-19320	24	33	to	to	PART
ajst-19320	24	34	optimize	optimize	VERB
ajst-19320	24	35	the	the	DET
ajst-19320	24	36	scale	scale	NOUN
ajst-19320	24	37	-	-	PUNCT
ajst-19320	24	38	insensitive	insensitive	ADJ
ajst-19320	24	39	problem	problem	NOUN
ajst-19320	24	40	of	of	ADP
ajst-19320	24	41	yolo.yolo	yolo.yolo	NUM
ajst-19320	24	42	algorithms	algorithm	NOUN
ajst-19320	24	43	have	have	AUX
ajst-19320	24	44	also	also	ADV
ajst-19320	24	45	been	be	AUX
ajst-19320	24	46	followed	follow	VERB
ajst-19320	24	47	up	up	ADP
ajst-19320	24	48	to	to	PART
ajst-19320	24	49	address	address	VERB
ajst-19320	24	50	this	this	DET
ajst-19320	24	51	problem	problem	NOUN
ajst-19320	24	52	,	,	PUNCT
ajst-19320	24	53	and	and	CCONJ
ajst-19320	24	54	multi	multi	ADJ
ajst-19320	24	55	-	-	ADJ
ajst-19320	24	56	scale	scale	ADJ
ajst-19320	24	57	detectors	detector	NOUN
ajst-19320	24	58	have	have	AUX
ajst-19320	24	59	been	be	AUX
ajst-19320	24	60	added	add	VERB
ajst-19320	24	61	to	to	PART
ajst-19320	24	62	improve	improve	VERB
ajst-19320	24	63	the	the	DET
ajst-19320	24	64	feature	feature	NOUN
ajst-19320	24	65	extraction	extraction	NOUN
ajst-19320	24	66	ability	ability	NOUN
ajst-19320	24	67	of	of	ADP
ajst-19320	24	68	the	the	DET
ajst-19320	24	69	network	network	NOUN
ajst-19320	24	70	,	,	PUNCT
ajst-19320	24	71	which	which	PRON
ajst-19320	24	72	led	lead	VERB
ajst-19320	24	73	to	to	ADP
ajst-19320	24	74	the	the	DET
ajst-19320	24	75	derivation	derivation	NOUN
ajst-19320	24	76	of	of	ADP
ajst-19320	24	77	yolov2[10]and	yolov2[10]and	NOUN
ajst-19320	24	78	yolov3[11]and	yolov3[11]and	PROPN
ajst-19320	24	79	algorithms	algorithm	NOUN
ajst-19320	24	80	.	.	PUNCT
ajst-19320	25	1	however	however	ADV
ajst-19320	25	2	,	,	PUNCT
ajst-19320	25	3	these	these	DET
ajst-19320	25	4	improvements	improvement	NOUN
ajst-19320	25	5	did	do	AUX
ajst-19320	25	6	not	not	PART
ajst-19320	25	7	change	change	VERB
ajst-19320	25	8	the	the	DET
ajst-19320	25	9	phenomenon	phenomenon	NOUN
ajst-19320	25	10	that	that	SCONJ
ajst-19320	25	11	the	the	DET
ajst-19320	25	12	detection	detection	NOUN
ajst-19320	25	13	accuracy	accuracy	NOUN
ajst-19320	25	14	of	of	ADP
ajst-19320	25	15	the	the	DET
ajst-19320	25	16	single	single	ADJ
ajst-19320	25	17	-	-	PUNCT
ajst-19320	25	18	stage	stage	NOUN
ajst-19320	25	19	algorithm	algorithm	NOUN
ajst-19320	25	20	is	be	AUX
ajst-19320	25	21	lower	low	ADJ
ajst-19320	25	22	than	than	ADP
ajst-19320	25	23	that	that	PRON
ajst-19320	25	24	of	of	ADP
ajst-19320	25	25	the	the	DET
ajst-19320	25	26	two	two	NUM
ajst-19320	25	27	-	-	PUNCT
ajst-19320	25	28	stage	stage	NOUN
ajst-19320	25	29	algorithm.lin	algorithm.lin	PROPN
ajst-19320	25	30	et	et	PROPN
ajst-19320	25	31	al	al	PROPN
ajst-19320	25	32	.	.	PROPN
ajst-19320	25	33	argued	argue	VERB
ajst-19320	25	34	that	that	SCONJ
ajst-19320	25	35	the	the	DET
ajst-19320	25	36	single	single	ADJ
ajst-19320	25	37	-	-	PUNCT
ajst-19320	25	38	stage	stage	NOUN
ajst-19320	25	39	algorithm	algorithm	NOUN
ajst-19320	25	40	is	be	AUX
ajst-19320	25	41	trained	train	VERB
ajst-19320	25	42	to	to	PART
ajst-19320	25	43	learn	learn	VERB
ajst-19320	25	44	the	the	DET
ajst-19320	25	45	whole	whole	ADJ
ajst-19320	25	46	map	map	NOUN
ajst-19320	25	47	,	,	PUNCT
ajst-19320	25	48	while	while	SCONJ
ajst-19320	25	49	the	the	DET
ajst-19320	25	50	two	two	NUM
ajst-19320	25	51	-	-	PUNCT
ajst-19320	25	52	stage	stage	NOUN
ajst-19320	25	53	algorithm	algorithm	NOUN
ajst-19320	25	54	only	only	ADV
ajst-19320	25	55	needs	need	VERB
ajst-19320	25	56	to	to	PART
ajst-19320	25	57	train	train	VERB
ajst-19320	25	58	the	the	DET
ajst-19320	25	59	positive	positive	ADJ
ajst-19320	25	60	samples	sample	NOUN
ajst-19320	25	61	,	,	PUNCT
ajst-19320	25	62	and	and	CCONJ
ajst-19320	25	63	therefore	therefore	ADV
ajst-19320	25	64	there	there	PRON
ajst-19320	25	65	is	be	VERB
ajst-19320	25	66	a	a	DET
ajst-19320	25	67	sample	sample	NOUN
ajst-19320	25	68	imbalance	imbalance	NOUN
ajst-19320	25	69	problem	problem	NOUN
ajst-19320	25	70	,	,	PUNCT
ajst-19320	25	71	to	to	PART
ajst-19320	25	72	solve	solve	VERB
ajst-19320	25	73	this	this	DET
ajst-19320	25	74	problem	problem	NOUN
ajst-19320	25	75	,	,	PUNCT
ajst-19320	26	1	lin	lin	PROPN
ajst-19320	26	2	et	et	PROPN
ajst-19320	26	3	al	al	PROPN
ajst-19320	26	4	.	.	PROPN
ajst-19320	26	5	proposed	propose	VERB
ajst-19320	26	6	focal	focal	ADJ
ajst-19320	26	7	loss	loss	NOUN
ajst-19320	26	8	,	,	PUNCT
ajst-19320	26	9	to	to	PART
ajst-19320	26	10	improve	improve	VERB
ajst-19320	26	11	the	the	DET
ajst-19320	26	12	detector	detector	NOUN
ajst-19320	26	13	's	's	PART
ajst-19320	26	14	learning	learning	NOUN
ajst-19320	26	15	for	for	ADP
ajst-19320	26	16	difficult	difficult	ADJ
ajst-19320	26	17	samples	sample	NOUN
ajst-19320	26	18	,	,	PUNCT
ajst-19320	26	19	and	and	CCONJ
ajst-19320	26	20	used	use	VERB
ajst-19320	26	21	this	this	PRON
ajst-19320	26	22	to	to	AUX
ajst-19320	26	23	established	establish	VERB
ajst-19320	26	24	the	the	DET
ajst-19320	26	25	retinanet	retinanet	NOUN
ajst-19320	27	1	[	[	X
ajst-19320	27	2	12]algorithm	12]algorithm	NUM
ajst-19320	27	3	to	to	PART
ajst-19320	27	4	further	far	ADV
ajst-19320	27	5	improve	improve	VERB
ajst-19320	27	6	the	the	DET
ajst-19320	27	7	detection	detection	NOUN
ajst-19320	27	8	accuracy	accuracy	NOUN
ajst-19320	27	9	,	,	PUNCT
ajst-19320	27	10	thus	thus	ADV
ajst-19320	27	11	narrowing	narrow	VERB
ajst-19320	27	12	the	the	DET
ajst-19320	27	13	accuracy	accuracy	NOUN
ajst-19320	27	14	difference	difference	NOUN
ajst-19320	27	15	between	between	ADP
ajst-19320	27	16	the	the	DET
ajst-19320	27	17	single	single	ADJ
ajst-19320	27	18	-	-	PUNCT
ajst-19320	27	19	stage	stage	NOUN
ajst-19320	27	20	algorithm	algorithm	NOUN
ajst-19320	27	21	and	and	CCONJ
ajst-19320	27	22	the	the	DET
ajst-19320	27	23	two	two	NUM
ajst-19320	27	24	-	-	PUNCT
ajst-19320	27	25	stage	stage	NOUN
ajst-19320	27	26	algorithm	algorithm	NOUN
ajst-19320	27	27	.	.	PUNCT
ajst-19320	28	1	the	the	DET
ajst-19320	28	2	target	target	NOUN
ajst-19320	28	3	detection	detection	NOUN
ajst-19320	28	4	algorithms	algorithm	NOUN
ajst-19320	28	5	proposed	propose	VERB
ajst-19320	28	6	since	since	SCONJ
ajst-19320	28	7	then	then	ADV
ajst-19320	28	8	have	have	AUX
ajst-19320	28	9	focused	focus	VERB
ajst-19320	28	10	more	more	ADJ
ajst-19320	28	11	on	on	ADP
ajst-19320	28	12	the	the	DET
ajst-19320	28	13	improvement	improvement	NOUN
ajst-19320	28	14	of	of	ADP
ajst-19320	28	15	feature	feature	NOUN
ajst-19320	28	16	fusion	fusion	NOUN
ajst-19320	28	17	,	,	PUNCT
ajst-19320	28	18	training	training	NOUN
ajst-19320	28	19	techniques	technique	NOUN
ajst-19320	28	20	,	,	PUNCT
ajst-19320	28	21	and	and	CCONJ
ajst-19320	28	22	the	the	DET
ajst-19320	28	23	improvement	improvement	NOUN
ajst-19320	28	24	of	of	ADP
ajst-19320	28	25	the	the	DET
ajst-19320	28	26	network	network	NOUN
ajst-19320	28	27	infrastructure	infrastructure	NOUN
ajst-19320	28	28	module	module	NOUN
ajst-19320	28	29	.	.	PUNCT
ajst-19320	29	1	yolov4[13	yolov4[13	PROPN
ajst-19320	29	2	]	]	PUNCT
ajst-19320	29	3	proposed	propose	VERB
ajst-19320	29	4	in	in	ADP
ajst-19320	29	5	2020	2020	NUM
ajst-19320	29	6	was	be	AUX
ajst-19320	29	7	oriented	orient	VERB
ajst-19320	29	8	in	in	ADP
ajst-19320	29	9	this	this	DET
ajst-19320	29	10	direction	direction	NOUN
ajst-19320	29	11	,	,	PUNCT
ajst-19320	29	12	and	and	CCONJ
ajst-19320	29	13	listed	list	VERB
ajst-19320	29	14	the	the	DET
ajst-19320	29	15	latest	late	ADJ
ajst-19320	29	16	technologies	technology	NOUN
ajst-19320	29	17	and	and	CCONJ
ajst-19320	29	18	achievements	achievement	NOUN
ajst-19320	29	19	at	at	ADP
ajst-19320	29	20	that	that	DET
ajst-19320	29	21	time	time	NOUN
ajst-19320	29	22	,	,	PUNCT
ajst-19320	29	23	and	and	CCONJ
ajst-19320	29	24	conducted	conduct	VERB
ajst-19320	29	25	a	a	DET
ajst-19320	29	26	series	series	NOUN
ajst-19320	29	27	of	of	ADP
ajst-19320	29	28	ablation	ablation	NOUN
ajst-19320	29	29	experiments	experiment	NOUN
ajst-19320	29	30	by	by	ADP
ajst-19320	29	31	permutation	permutation	NOUN
ajst-19320	29	32	and	and	CCONJ
ajst-19320	29	33	combination	combination	NOUN
ajst-19320	29	34	,	,	PUNCT
ajst-19320	29	35	and	and	CCONJ
ajst-19320	29	36	selected	select	VERB
ajst-19320	29	37	the	the	DET
ajst-19320	29	38	optimal	optimal	ADJ
ajst-19320	29	39	combination	combination	NOUN
ajst-19320	29	40	of	of	ADP
ajst-19320	29	41	the	the	DET
ajst-19320	29	42	yolov3	yolov3	PROPN
ajst-19320	29	43	to	to	PART
ajst-19320	29	44	form	form	VERB
ajst-19320	29	45	yolov4	yolov4	PROPN
ajst-19320	29	46	,	,	PUNCT
ajst-19320	29	47	which	which	PRON
ajst-19320	29	48	further	far	ADV
ajst-19320	29	49	improved	improve	VERB
ajst-19320	29	50	the	the	DET
ajst-19320	29	51	overall	overall	ADJ
ajst-19320	29	52	progress	progress	NOUN
ajst-19320	29	53	.	.	PUNCT
ajst-19320	30	1	in	in	ADP
ajst-19320	30	2	2020	2020	NUM
ajst-19320	30	3	,	,	PUNCT
ajst-19320	30	4	yolov5	yolov5	NOUN
ajst-19320	30	5	code	code	NOUN
ajst-19320	30	6	was	be	AUX
ajst-19320	30	7	open	open	ADV
ajst-19320	30	8	-	-	PUNCT
ajst-19320	30	9	sourced	source	VERB
ajst-19320	30	10	and	and	CCONJ
ajst-19320	30	11	upgraded	upgrade	VERB
ajst-19320	30	12	in	in	ADP
ajst-19320	30	13	engineering	engineering	NOUN
ajst-19320	30	14	applications	application	NOUN
ajst-19320	30	15	,	,	PUNCT
ajst-19320	30	16	becoming	become	VERB
ajst-19320	30	17	the	the	DET
ajst-19320	30	18	first	first	ADJ
ajst-19320	30	19	choice	choice	NOUN
ajst-19320	30	20	for	for	ADP
ajst-19320	30	21	major	major	ADJ
ajst-19320	30	22	projects	project	NOUN
ajst-19320	30	23	.	.	PUNCT
ajst-19320	31	1	in	in	ADP
ajst-19320	31	2	recent	recent	ADJ
ajst-19320	31	3	years	year	NOUN
ajst-19320	31	4	,	,	PUNCT
ajst-19320	31	5	yolov6	yolov6	PROPN
ajst-19320	31	6	,	,	PUNCT
ajst-19320	31	7	yolov7	yolov7	NOUN
ajst-19320	31	8	,	,	PUNCT
ajst-19320	31	9	and	and	CCONJ
ajst-19320	31	10	yolov8	yolov8	NOUN
ajst-19320	31	11	have	have	AUX
ajst-19320	31	12	been	be	AUX
ajst-19320	31	13	proposed	propose	VERB
ajst-19320	31	14	to	to	PART
ajst-19320	31	15	further	far	ADV
ajst-19320	31	16	develop	develop	VERB
ajst-19320	31	17	the	the	DET
ajst-19320	31	18	yolo	yolo	ADJ
ajst-19320	31	19	family	family	NOUN
ajst-19320	31	20	.	.	PUNCT
ajst-19320	32	1	2	2	X
ajst-19320	32	2	.	.	X
ajst-19320	32	3	introduction	introduction	NOUN
ajst-19320	32	4	to	to	ADP
ajst-19320	32	5	the	the	DET
ajst-19320	32	6	yolov5	yolov5	NOUN
ajst-19320	32	7	network	network	NOUN
ajst-19320	32	8	yolov5	yolov5	NOUN
ajst-19320	32	9	consists	consist	VERB
ajst-19320	32	10	of	of	ADP
ajst-19320	32	11	four	four	NUM
ajst-19320	32	12	parts	part	NOUN
ajst-19320	32	13	:	:	PUNCT
ajst-19320	32	14	input	input	NOUN
ajst-19320	32	15	,	,	PUNCT
ajst-19320	32	16	backbone	backbone	NOUN
ajst-19320	32	17	network	network	NOUN
ajst-19320	32	18	,	,	PUNCT
ajst-19320	32	19	neck	neck	NOUN
ajst-19320	32	20	network	network	NOUN
ajst-19320	32	21	and	and	CCONJ
ajst-19320	32	22	prediction	prediction	NOUN
ajst-19320	32	23	.	.	PUNCT
ajst-19320	33	1	the	the	DET
ajst-19320	33	2	network	network	NOUN
ajst-19320	33	3	structure	structure	NOUN
ajst-19320	33	4	is	be	AUX
ajst-19320	33	5	shown	show	VERB
ajst-19320	33	6	in	in	ADP
ajst-19320	33	7	figure	figure	NOUN
ajst-19320	33	8	1	1	NUM
ajst-19320	33	9	.	.	PUNCT
ajst-19320	34	1	(	(	PUNCT
ajst-19320	34	2	1	1	X
ajst-19320	34	3	)	)	PUNCT
ajst-19320	34	4	input	input	NOUN
ajst-19320	34	5	the	the	DET
ajst-19320	34	6	input	input	NOUN
ajst-19320	34	7	side	side	NOUN
ajst-19320	34	8	includes	include	VERB
ajst-19320	34	9	three	three	NUM
ajst-19320	34	10	parts	part	NOUN
ajst-19320	34	11	:	:	PUNCT
ajst-19320	34	12	mosaic	mosaic	ADJ
ajst-19320	34	13	data	datum	NOUN
ajst-19320	34	14	enhancement	enhancement	NOUN
ajst-19320	34	15	,	,	PUNCT
ajst-19320	34	16	adaptive	adaptive	ADJ
ajst-19320	34	17	anchor	anchor	NOUN
ajst-19320	34	18	frame	frame	NOUN
ajst-19320	34	19	calculation	calculation	NOUN
ajst-19320	34	20	and	and	CCONJ
ajst-19320	34	21	adaptive	adaptive	ADJ
ajst-19320	34	22	image	image	NOUN
ajst-19320	34	23	scaling	scaling	NOUN
ajst-19320	34	24	.	.	PUNCT
ajst-19320	35	1	mosaic	mosaic	ADJ
ajst-19320	35	2	data	datum	NOUN
ajst-19320	35	3	enhancement	enhancement	NOUN
ajst-19320	35	4	is	be	AUX
ajst-19320	35	5	done	do	VERB
ajst-19320	35	6	by	by	ADP
ajst-19320	35	7	enhancing	enhance	VERB
ajst-19320	35	8	the	the	DET
ajst-19320	35	9	data	datum	NOUN
ajst-19320	35	10	of	of	ADP
ajst-19320	35	11	four	four	NUM
ajst-19320	35	12	randomly	randomly	ADV
ajst-19320	35	13	selected	select	VERB
ajst-19320	35	14	images	image	NOUN
ajst-19320	35	15	and	and	CCONJ
ajst-19320	35	16	finally	finally	ADV
ajst-19320	35	17	stitching	stitch	VERB
ajst-19320	35	18	and	and	CCONJ
ajst-19320	35	19	combining	combine	VERB
ajst-19320	35	20	them	they	PRON
ajst-19320	35	21	.	.	PUNCT
ajst-19320	36	1	the	the	DET
ajst-19320	36	2	advantage	advantage	NOUN
ajst-19320	36	3	of	of	ADP
ajst-19320	36	4	this	this	DET
ajst-19320	36	5	method	method	NOUN
ajst-19320	36	6	is	be	AUX
ajst-19320	36	7	that	that	SCONJ
ajst-19320	36	8	it	it	PRON
ajst-19320	36	9	increases	increase	VERB
ajst-19320	36	10	the	the	DET
ajst-19320	36	11	amount	amount	NOUN
ajst-19320	36	12	of	of	ADP
ajst-19320	36	13	data	datum	NOUN
ajst-19320	36	14	,	,	PUNCT
ajst-19320	36	15	which	which	PRON
ajst-19320	36	16	makes	make	VERB
ajst-19320	36	17	the	the	DET
ajst-19320	36	18	network	network	NOUN
ajst-19320	36	19	more	more	ADV
ajst-19320	36	20	robust	robust	ADJ
ajst-19320	36	21	and	and	CCONJ
ajst-19320	36	22	reduces	reduce	VERB
ajst-19320	36	23	the	the	DET
ajst-19320	36	24	burden	burden	NOUN
ajst-19320	36	25	of	of	ADP
ajst-19320	36	26	gpu	gpu	PROPN
ajst-19320	36	27	computing	computing	NOUN
ajst-19320	36	28	.	.	PUNCT
ajst-19320	37	1	adaptive	adaptive	ADJ
ajst-19320	37	2	anchor	anchor	NOUN
ajst-19320	37	3	frame	frame	NOUN
ajst-19320	37	4	computation	computation	NOUN
ajst-19320	37	5	is	be	AUX
ajst-19320	37	6	based	base	VERB
ajst-19320	37	7	on	on	ADP
ajst-19320	37	8	the	the	DET
ajst-19320	37	9	characteristics	characteristic	NOUN
ajst-19320	37	10	of	of	ADP
ajst-19320	37	11	different	different	ADJ
ajst-19320	37	12	datasets	dataset	NOUN
ajst-19320	37	13	and	and	CCONJ
ajst-19320	37	14	outputs	output	NOUN
ajst-19320	37	15	predicted	predict	VERB
ajst-19320	37	16	frames	frame	NOUN
ajst-19320	37	17	based	base	VERB
ajst-19320	37	18	on	on	ADP
ajst-19320	37	19	the	the	DET
ajst-19320	37	20	initial	initial	ADJ
ajst-19320	37	21	anchor	anchor	NOUN
ajst-19320	37	22	frames	frame	NOUN
ajst-19320	37	23	,	,	PUNCT
ajst-19320	37	24	and	and	CCONJ
ajst-19320	37	25	then	then	ADV
ajst-19320	37	26	compares	compare	VERB
ajst-19320	37	27	them	they	PRON
ajst-19320	37	28	with	with	ADP
ajst-19320	37	29	the	the	DET
ajst-19320	37	30	real	real	ADJ
ajst-19320	37	31	frames	frame	NOUN
ajst-19320	37	32	to	to	PART
ajst-19320	37	33	compute	compute	VERB
ajst-19320	37	34	the	the	DET
ajst-19320	37	35	differences	difference	NOUN
ajst-19320	37	36	between	between	ADP
ajst-19320	37	37	them	they	PRON
ajst-19320	37	38	.	.	PUNCT
ajst-19320	38	1	adaptive	adaptive	ADJ
ajst-19320	38	2	image	image	NOUN
ajst-19320	38	3	scalingfor	scalingfor	NOUN
ajst-19320	38	4	images	image	NOUN
ajst-19320	38	5	with	with	ADP
ajst-19320	38	6	different	different	ADJ
ajst-19320	38	7	aspect	aspect	NOUN
ajst-19320	38	8	ratios	ratio	NOUN
ajst-19320	38	9	,	,	PUNCT
ajst-19320	38	10	adaptive	adaptive	ADJ
ajst-19320	38	11	scaling	scaling	NOUN
ajst-19320	38	12	fills	fill	VERB
ajst-19320	38	13	the	the	DET
ajst-19320	38	14	image	image	NOUN
ajst-19320	38	15	according	accord	VERB
ajst-19320	38	16	to	to	ADP
ajst-19320	38	17	the	the	DET
ajst-19320	38	18	standard	standard	ADJ
ajst-19320	38	19	size	size	NOUN
ajst-19320	38	20	to	to	PART
ajst-19320	38	21	meet	meet	VERB
ajst-19320	38	22	the	the	DET
ajst-19320	38	23	training	training	NOUN
ajst-19320	38	24	requirements	requirement	NOUN
ajst-19320	38	25	,	,	PUNCT
ajst-19320	38	26	reduce	reduce	VERB
ajst-19320	38	27	the	the	DET
ajst-19320	38	28	amount	amount	NOUN
ajst-19320	38	29	of	of	ADP
ajst-19320	38	30	computation	computation	NOUN
ajst-19320	38	31	and	and	CCONJ
ajst-19320	38	32	improve	improve	VERB
ajst-19320	38	33	the	the	DET
ajst-19320	38	34	detection	detection	NOUN
ajst-19320	38	35	speed	speed	NOUN
ajst-19320	38	36	.	.	PUNCT
ajst-19320	39	1	(	(	PUNCT
ajst-19320	39	2	2	2	X
ajst-19320	39	3	)	)	PUNCT
ajst-19320	39	4	backbone	backbone	NOUN
ajst-19320	39	5	the	the	DET
ajst-19320	39	6	backbone	backbone	NOUN
ajst-19320	39	7	network	network	NOUN
ajst-19320	39	8	consists	consist	VERB
ajst-19320	39	9	of	of	ADP
ajst-19320	39	10	three	three	NUM
ajst-19320	39	11	main	main	ADJ
ajst-19320	39	12	structures	structure	NOUN
ajst-19320	39	13	:	:	PUNCT
ajst-19320	39	14	the	the	DET
ajst-19320	39	15	cbs	cbs	PROPN
ajst-19320	39	16	convolution	convolution	NOUN
ajst-19320	39	17	module	module	NOUN
ajst-19320	39	18	,	,	PUNCT
ajst-19320	39	19	the	the	DET
ajst-19320	39	20	csp	csp	PROPN
ajst-19320	39	21	feature	feature	NOUN
ajst-19320	39	22	extraction	extraction	NOUN
ajst-19320	39	23	network	network	NOUN
ajst-19320	39	24	,	,	PUNCT
ajst-19320	39	25	and	and	CCONJ
ajst-19320	39	26	the	the	DET
ajst-19320	39	27	spatial	spatial	ADJ
ajst-19320	39	28	pyramid	pyramid	NOUN
ajst-19320	39	29	pooling	pool	VERB
ajst-19320	39	30	sppf.the	sppf.the	DET
ajst-19320	39	31	cbs	cbs	PROPN
ajst-19320	39	32	consists	consist	VERB
ajst-19320	39	33	of	of	ADP
ajst-19320	39	34	convolution	convolution	NOUN
ajst-19320	39	35	,	,	PUNCT
ajst-19320	39	36	batch	batch	NOUN
ajst-19320	39	37	normalization	normalization	NOUN
ajst-19320	39	38	,	,	PUNCT
ajst-19320	39	39	and	and	CCONJ
ajst-19320	39	40	silu	silu	ADJ
ajst-19320	39	41	activation	activation	NOUN
ajst-19320	39	42	functions.the	functions.the	PRON
ajst-19320	39	43	sppf	sppf	ADJ
ajst-19320	39	44	concatenates	concatenate	VERB
ajst-19320	39	45	multiple	multiple	ADJ
ajst-19320	39	46	maxpool	maxpool	ADJ
ajst-19320	39	47	layers	layer	NOUN
ajst-19320	39	48	to	to	PART
ajst-19320	39	49	achieve	achieve	VERB
ajst-19320	39	50	feature	feature	NOUN
ajst-19320	39	51	fusion	fusion	NOUN
ajst-19320	39	52	at	at	ADP
ajst-19320	39	53	different	different	ADJ
ajst-19320	39	54	scales	scale	NOUN
ajst-19320	39	55	.	.	PUNCT
ajst-19320	40	1	to	to	PART
ajst-19320	40	2	achieve	achieve	VERB
ajst-19320	40	3	feature	feature	NOUN
ajst-19320	40	4	fusion	fusion	NOUN
ajst-19320	40	5	at	at	ADP
ajst-19320	40	6	different	different	ADJ
ajst-19320	40	7	scales	scale	NOUN
ajst-19320	40	8	.	.	PUNCT
ajst-19320	41	1	(	(	PUNCT
ajst-19320	41	2	3	3	X
ajst-19320	41	3	)	)	PUNCT
ajst-19320	41	4	neck	neck	NOUN
ajst-19320	41	5	neck	neck	NOUN
ajst-19320	41	6	feature	feature	NOUN
ajst-19320	41	7	fusion	fusion	NOUN
ajst-19320	41	8	network	network	NOUN
ajst-19320	41	9	is	be	AUX
ajst-19320	41	10	mainly	mainly	ADV
ajst-19320	41	11	composed	compose	VERB
ajst-19320	41	12	of	of	ADP
ajst-19320	41	13	two	two	NUM
ajst-19320	41	14	parts	part	NOUN
ajst-19320	41	15	:	:	PUNCT
ajst-19320	41	16	feature	feature	NOUN
ajst-19320	41	17	pyramid	pyramid	NOUN
ajst-19320	41	18	network	network	NOUN
ajst-19320	41	19	and	and	CCONJ
ajst-19320	41	20	path	path	NOUN
ajst-19320	41	21	aggregation	aggregation	NOUN
ajst-19320	41	22	network.fpn	network.fpn	PROPN
ajst-19320	41	23	is	be	AUX
ajst-19320	41	24	up	up	ADV
ajst-19320	41	25	-	-	PUNCT
ajst-19320	41	26	sampling	sample	VERB
ajst-19320	41	27	the	the	DET
ajst-19320	41	28	high	high	ADJ
ajst-19320	41	29	-	-	PUNCT
ajst-19320	41	30	level	level	NOUN
ajst-19320	41	31	features	feature	NOUN
ajst-19320	41	32	from	from	ADP
ajst-19320	41	33	bottom	bottom	NOUN
ajst-19320	41	34	to	to	ADP
ajst-19320	41	35	top	top	NOUN
ajst-19320	41	36	,	,	PUNCT
ajst-19320	41	37	and	and	CCONJ
ajst-19320	41	38	enhances	enhance	VERB
ajst-19320	41	39	the	the	DET
ajst-19320	41	40	semantic	semantic	ADJ
ajst-19320	41	41	information	information	NOUN
ajst-19320	41	42	by	by	ADP
ajst-19320	41	43	fusing	fuse	VERB
ajst-19320	41	44	the	the	DET
ajst-19320	41	45	information	information	NOUN
ajst-19320	41	46	with	with	ADP
ajst-19320	41	47	the	the	DET
ajst-19320	41	48	low	low	ADJ
ajst-19320	41	49	-	-	PUNCT
ajst-19320	41	50	level	level	NOUN
ajst-19320	41	51	features.pan	features.pan	X
ajst-19320	41	52	adds	add	VERB
ajst-19320	41	53	top	top	ADJ
ajst-19320	41	54	-	-	PUNCT
ajst-19320	41	55	to	to	ADP
ajst-19320	41	56	-	-	PUNCT
ajst-19320	41	57	bottom	bottom	NOUN
ajst-19320	41	58	feature	feature	NOUN
ajst-19320	41	59	fusion	fusion	NOUN
ajst-19320	41	60	on	on	ADP
ajst-19320	41	61	the	the	DET
ajst-19320	41	62	basis	basis	NOUN
ajst-19320	41	63	of	of	ADP
ajst-19320	41	64	fpn	fpn	PROPN
ajst-19320	41	65	,	,	PUNCT
ajst-19320	41	66	and	and	CCONJ
ajst-19320	41	67	adopts	adopt	VERB
ajst-19320	41	68	down	down	ADV
ajst-19320	41	69	-	-	PUNCT
ajst-19320	41	70	sampling	sample	VERB
ajst-19320	41	71	method	method	NOUN
ajst-19320	41	72	to	to	PART
ajst-19320	41	73	transfer	transfer	VERB
ajst-19320	41	74	the	the	DET
ajst-19320	41	75	low	low	ADJ
ajst-19320	41	76	-	-	PUNCT
ajst-19320	41	77	level	level	NOUN
ajst-19320	41	78	features	feature	NOUN
ajst-19320	41	79	to	to	ADP
ajst-19320	41	80	the	the	DET
ajst-19320	41	81	high	high	ADJ
ajst-19320	41	82	-	-	PUNCT
ajst-19320	41	83	level	level	NOUN
ajst-19320	41	84	for	for	ADP
ajst-19320	41	85	information	information	NOUN
ajst-19320	41	86	fusion	fusion	NOUN
ajst-19320	41	87	,	,	PUNCT
ajst-19320	41	88	which	which	PRON
ajst-19320	41	89	strengthens	strengthen	VERB
ajst-19320	41	90	the	the	DET
ajst-19320	41	91	ability	ability	NOUN
ajst-19320	41	92	of	of	ADP
ajst-19320	41	93	bottom	bottom	ADJ
ajst-19320	41	94	information	information	NOUN
ajst-19320	41	95	localization	localization	NOUN
ajst-19320	41	96	.	.	PUNCT
ajst-19320	42	1	the	the	DET
ajst-19320	42	2	the	the	DET
ajst-19320	42	3	combination	combination	NOUN
ajst-19320	42	4	of	of	ADP
ajst-19320	42	5	the	the	DET
ajst-19320	42	6	two	two	NUM
ajst-19320	42	7	enhances	enhance	NOUN
ajst-19320	42	8	the	the	DET
ajst-19320	42	9	sensitivity	sensitivity	NOUN
ajst-19320	42	10	of	of	ADP
ajst-19320	42	11	the	the	DET
ajst-19320	42	12	model	model	NOUN
ajst-19320	42	13	to	to	ADP
ajst-19320	42	14	small	small	ADJ
ajst-19320	42	15	targets	target	NOUN
ajst-19320	42	16	.	.	PUNCT
ajst-19320	43	1	(	(	PUNCT
ajst-19320	43	2	4	4	X
ajst-19320	43	3	)	)	PUNCT
ajst-19320	43	4	head	head	NOUN
ajst-19320	43	5	the	the	DET
ajst-19320	43	6	head	head	NOUN
ajst-19320	43	7	detection	detection	NOUN
ajst-19320	43	8	layer	layer	NOUN
ajst-19320	43	9	consists	consist	VERB
ajst-19320	43	10	of	of	ADP
ajst-19320	43	11	three	three	NUM
ajst-19320	43	12	detection	detection	NOUN
ajst-19320	43	13	heads	head	NOUN
ajst-19320	43	14	that	that	PRON
ajst-19320	43	15	detect	detect	VERB
ajst-19320	43	16	feature	feature	NOUN
ajst-19320	43	17	maps	map	NOUN
ajst-19320	43	18	of	of	ADP
ajst-19320	43	19	different	different	ADJ
ajst-19320	43	20	sizes	size	NOUN
ajst-19320	43	21	for	for	ADP
ajst-19320	43	22	final	final	ADJ
ajst-19320	43	23	detection	detection	NOUN
ajst-19320	43	24	of	of	ADP
ajst-19320	43	25	the	the	DET
ajst-19320	43	26	target	target	NOUN
ajst-19320	43	27	.	.	PUNCT
ajst-19320	44	1	in	in	ADP
ajst-19320	44	2	order	order	NOUN
ajst-19320	44	3	to	to	PART
ajst-19320	44	4	make	make	VERB
ajst-19320	44	5	the	the	DET
ajst-19320	44	6	prediction	prediction	NOUN
ajst-19320	44	7	frame	frame	NOUN
ajst-19320	44	8	regression	regression	NOUN
ajst-19320	44	9	faster	fast	ADV
ajst-19320	44	10	and	and	CCONJ
ajst-19320	44	11	more	more	ADV
ajst-19320	44	12	accurate	accurate	ADJ
ajst-19320	44	13	,	,	PUNCT
ajst-19320	44	14	the	the	DET
ajst-19320	44	15	loss	loss	NOUN
ajst-19320	44	16	function	function	NOUN
ajst-19320	44	17	in	in	ADP
ajst-19320	44	18	yolov5	yolov5	NOUN
ajst-19320	44	19	uses	use	VERB
ajst-19320	44	20	ciou	ciou	NOUN
ajst-19320	44	21	-	-	PUNCT
ajst-19320	44	22	loss	loss	NOUN
ajst-19320	44	23	,	,	PUNCT
ajst-19320	44	24	and	and	CCONJ
ajst-19320	44	25	in	in	ADP
ajst-19320	44	26	order	order	NOUN
ajst-19320	44	27	to	to	PART
ajst-19320	44	28	prevent	prevent	VERB
ajst-19320	44	29	the	the	DET
ajst-19320	44	30	repetition	repetition	NOUN
ajst-19320	44	31	of	of	ADP
ajst-19320	44	32	similarly	similarly	ADV
ajst-19320	44	33	sized	sized	ADJ
ajst-19320	44	34	frames	frame	NOUN
ajst-19320	44	35	,	,	PUNCT
ajst-19320	44	36	the	the	DET
ajst-19320	44	37	final	final	ADJ
ajst-19320	44	38	prediction	prediction	NOUN
ajst-19320	44	39	results	result	NOUN
ajst-19320	44	40	are	be	AUX
ajst-19320	44	41	filtered	filter	VERB
ajst-19320	44	42	by	by	ADP
ajst-19320	44	43	nonmaximum	nonmaximum	ADJ
ajst-19320	44	44	suppression	suppression	NOUN
ajst-19320	44	45	method	method	NOUN
ajst-19320	44	46	.	.	PUNCT
ajst-19320	45	1	figure	figure	NOUN
ajst-19320	45	2	1	1	NUM
ajst-19320	45	3	.	.	PUNCT
ajst-19320	46	1	yolov5	yolov5	NOUN
ajst-19320	46	2	network	network	NOUN
ajst-19320	46	3	architecture	architecture	NOUN
ajst-19320	46	4	yolov5	yolov5	NOUN
ajst-19320	46	5	contains	contain	VERB
ajst-19320	46	6	a	a	DET
ajst-19320	46	7	variety	variety	NOUN
ajst-19320	46	8	of	of	ADP
ajst-19320	46	9	models	model	NOUN
ajst-19320	46	10	,	,	PUNCT
ajst-19320	46	11	such	such	ADJ
ajst-19320	46	12	as	as	ADP
ajst-19320	46	13	yolov5s	yolov5s	PROPN
ajst-19320	46	14	and	and	CCONJ
ajst-19320	46	15	yolov5	yolov5	PROPN
ajst-19320	46	16	m	m	PROPN
ajst-19320	46	17	models	model	NOUN
ajst-19320	46	18	,	,	PUNCT
ajst-19320	46	19	whose	whose	DET
ajst-19320	46	20	main	main	ADJ
ajst-19320	46	21	difference	difference	NOUN
ajst-19320	46	22	lies	lie	VERB
ajst-19320	46	23	in	in	ADP
ajst-19320	46	24	the	the	DET
ajst-19320	46	25	fact	fact	NOUN
ajst-19320	46	26	that	that	SCONJ
ajst-19320	46	27	the	the	DET
ajst-19320	46	28	depth	depth	NOUN
ajst-19320	46	29	and	and	CCONJ
ajst-19320	46	30	width	width	NOUN
ajst-19320	46	31	of	of	ADP
ajst-19320	46	32	the	the	DET
ajst-19320	46	33	network	network	NOUN
ajst-19320	46	34	can	can	AUX
ajst-19320	46	35	be	be	AUX
ajst-19320	46	36	controlled	control	VERB
ajst-19320	46	37	to	to	PART
ajst-19320	46	38	get	get	VERB
ajst-19320	46	39	different	different	ADJ
ajst-19320	46	40	sizes	size	NOUN
ajst-19320	46	41	of	of	ADP
ajst-19320	46	42	models	model	NOUN
ajst-19320	46	43	.	.	PUNCT
ajst-19320	47	1	in	in	ADP
ajst-19320	47	2	order	order	NOUN
ajst-19320	47	3	to	to	PART
ajst-19320	47	4	speed	speed	VERB
ajst-19320	47	5	up	up	ADP
ajst-19320	47	6	the	the	DET
ajst-19320	47	7	detection	detection	NOUN
ajst-19320	47	8	and	and	CCONJ
ajst-19320	47	9	achieve	achieve	VERB
ajst-19320	47	10	real	real	ADJ
ajst-19320	47	11	-	-	PUNCT
ajst-19320	47	12	time	time	NOUN
ajst-19320	47	13	detection	detection	NOUN
ajst-19320	47	14	,	,	PUNCT
ajst-19320	47	15	this	this	DET
ajst-19320	47	16	paper	paper	NOUN
ajst-19320	47	17	uses	use	VERB
ajst-19320	47	18	yolov5s	yolov5s	PROPN
ajst-19320	47	19	as	as	ADP
ajst-19320	47	20	the	the	DET
ajst-19320	47	21	benchmark	benchmark	NOUN
ajst-19320	47	22	model	model	NOUN
ajst-19320	47	23	.	.	PUNCT
ajst-19320	48	1	3	3	X
ajst-19320	48	2	.	.	X
ajst-19320	48	3	improvement	improvement	NOUN
ajst-19320	48	4	of	of	ADP
ajst-19320	48	5	yolov5	yolov5	NOUN
ajst-19320	48	6	algorithm	algorithm	PROPN
ajst-19320	48	7	3.1	3.1	NUM
ajst-19320	48	8	.	.	PUNCT
ajst-19320	48	9	convolutional	convolutional	ADJ
ajst-19320	48	10	block	block	NOUN
ajst-19320	48	11	attention	attention	NOUN
ajst-19320	48	12	module	module	NOUN
ajst-19320	48	13	cbam	cbam	NOUN
ajst-19320	48	14	,	,	PUNCT
ajst-19320	48	15	as	as	ADP
ajst-19320	48	16	an	an	DET
ajst-19320	48	17	effective	effective	ADJ
ajst-19320	48	18	convolutional	convolutional	ADJ
ajst-19320	48	19	attention	attention	NOUN
ajst-19320	48	20	module	module	NOUN
ajst-19320	48	21	,	,	PUNCT
ajst-19320	48	22	can	can	AUX
ajst-19320	48	23	be	be	AUX
ajst-19320	48	24	seamlessly	seamlessly	ADV
ajst-19320	48	25	integrated	integrate	VERB
ajst-19320	48	26	into	into	ADP
ajst-19320	48	27	cnn	cnn	PROPN
ajst-19320	48	28	architectures	architecture	NOUN
ajst-19320	48	29	and	and	CCONJ
ajst-19320	48	30	trained	train	VERB
ajst-19320	48	31	356	356	NUM
ajst-19320	48	32	end	end	NOUN
ajst-19320	48	33	-	-	PUNCT
ajst-19320	48	34	to	to	ADP
ajst-19320	48	35	-	-	PUNCT
ajst-19320	48	36	end	end	NOUN
ajst-19320	48	37	with	with	ADP
ajst-19320	48	38	basic	basic	ADJ
ajst-19320	48	39	cnns	cnn	NOUN
ajst-19320	48	40	with	with	ADP
ajst-19320	48	41	high	high	ADJ
ajst-19320	48	42	applicability.cbam	applicability.cbam	NOUN
ajst-19320	48	43	is	be	AUX
ajst-19320	48	44	divided	divide	VERB
ajst-19320	48	45	into	into	ADP
ajst-19320	48	46	two	two	NUM
ajst-19320	48	47	independent	independent	ADJ
ajst-19320	48	48	sub	sub	NOUN
ajst-19320	48	49	-	-	NOUN
ajst-19320	48	50	modules	module	NOUN
ajst-19320	48	51	,	,	PUNCT
ajst-19320	48	52	channel	channel	NOUN
ajst-19320	48	53	attention	attention	NOUN
ajst-19320	48	54	module	module	NOUN
ajst-19320	48	55	and	and	CCONJ
ajst-19320	48	56	spatial	spatial	ADJ
ajst-19320	48	57	attention	attention	NOUN
ajst-19320	48	58	module	module	NOUN
ajst-19320	48	59	,	,	PUNCT
ajst-19320	48	60	which	which	PRON
ajst-19320	48	61	perform	perform	VERB
ajst-19320	48	62	attentional	attentional	ADJ
ajst-19320	48	63	feature	feature	NOUN
ajst-19320	48	64	fusion	fusion	NOUN
ajst-19320	48	65	in	in	ADP
ajst-19320	48	66	channel	channel	NOUN
ajst-19320	48	67	and	and	CCONJ
ajst-19320	48	68	spatial	spatial	ADJ
ajst-19320	48	69	dimensions	dimension	NOUN
ajst-19320	48	70	,	,	PUNCT
ajst-19320	48	71	respectively.the	respectively.the	DET
ajst-19320	48	72	structure	structure	NOUN
ajst-19320	48	73	of	of	ADP
ajst-19320	48	74	cbam	cbam	NOUN
ajst-19320	48	75	is	be	AUX
ajst-19320	48	76	shown	show	VERB
ajst-19320	48	77	in	in	ADP
ajst-19320	48	78	figure	figure	NOUN
ajst-19320	48	79	2	2	NUM
ajst-19320	48	80	.	.	PUNCT
ajst-19320	48	81	module	module	NOUN
ajst-19320	48	82	,	,	PUNCT
ajst-19320	48	83	respectively	respectively	ADV
ajst-19320	48	84	,	,	PUNCT
ajst-19320	48	85	for	for	ADP
ajst-19320	48	86	the	the	DET
ajst-19320	48	87	fusion	fusion	NOUN
ajst-19320	48	88	of	of	ADP
ajst-19320	48	89	attention	attention	NOUN
ajst-19320	48	90	features	feature	NOUN
ajst-19320	48	91	in	in	ADP
ajst-19320	48	92	the	the	DET
ajst-19320	48	93	channel	channel	NOUN
ajst-19320	48	94	and	and	CCONJ
ajst-19320	48	95	spatial	spatial	ADJ
ajst-19320	48	96	dimensions	dimension	NOUN
ajst-19320	48	97	,	,	PUNCT
ajst-19320	48	98	the	the	DET
ajst-19320	48	99	structure	structure	NOUN
ajst-19320	48	100	of	of	ADP
ajst-19320	48	101	cbam	cbam	NOUN
ajst-19320	48	102	is	be	AUX
ajst-19320	48	103	shown	show	VERB
ajst-19320	48	104	in	in	ADP
ajst-19320	48	105	figure	figure	NOUN
ajst-19320	48	106	2	2	NUM
ajst-19320	48	107	.	.	PUNCT
ajst-19320	48	108	figure	figure	NOUN
ajst-19320	48	109	2	2	NUM
ajst-19320	48	110	.	.	PUNCT
ajst-19320	49	1	cbam	cbam	NOUN
ajst-19320	49	2	structure	structure	NOUN
ajst-19320	49	3	(	(	PUNCT
ajst-19320	49	4	1	1	NUM
ajst-19320	49	5	)	)	PUNCT
ajst-19320	49	6	channel	channel	NOUN
ajst-19320	49	7	attention	attention	NOUN
ajst-19320	49	8	module	module	NOUN
ajst-19320	49	9	the	the	DET
ajst-19320	49	10	channel	channel	NOUN
ajst-19320	49	11	attention	attention	NOUN
ajst-19320	49	12	firstly	firstly	ADV
ajst-19320	49	13	performs	perform	VERB
ajst-19320	49	14	max	max	PROPN
ajst-19320	49	15	pooling	pooling	NOUN
ajst-19320	49	16	and	and	CCONJ
ajst-19320	49	17	average	average	ADJ
ajst-19320	49	18	pooling	pool	VERB
ajst-19320	49	19	operations	operation	NOUN
ajst-19320	49	20	on	on	ADP
ajst-19320	49	21	the	the	DET
ajst-19320	49	22	input	input	NOUN
ajst-19320	49	23	feature	feature	NOUN
ajst-19320	49	24	map	map	NOUN
ajst-19320	49	25	f	f	X
ajst-19320	49	26	to	to	PART
ajst-19320	49	27	obtain	obtain	VERB
ajst-19320	49	28	the	the	DET
ajst-19320	49	29	corresponding	correspond	VERB
ajst-19320	49	30	two	two	NUM
ajst-19320	49	31	feature	feature	NOUN
ajst-19320	49	32	vectors	vector	NOUN
ajst-19320	49	33	,	,	PUNCT
ajst-19320	49	34	which	which	PRON
ajst-19320	49	35	are	be	AUX
ajst-19320	49	36	respectively	respectively	ADV
ajst-19320	49	37	summed	sum	VERB
ajst-19320	49	38	up	up	ADP
ajst-19320	49	39	to	to	PART
ajst-19320	49	40	obtain	obtain	VERB
ajst-19320	49	41	the	the	DET
ajst-19320	49	42	feature	feature	NOUN
ajst-19320	49	43	vector	vector	NOUN
ajst-19320	49	44	of	of	ADP
ajst-19320	49	45	1x1xc	1x1xc	NUM
ajst-19320	49	46	through	through	ADP
ajst-19320	49	47	the	the	DET
ajst-19320	49	48	shared	share	VERB
ajst-19320	49	49	fully	fully	ADV
ajst-19320	49	50	-	-	PUNCT
ajst-19320	49	51	connected	connect	VERB
ajst-19320	49	52	layer	layer	NOUN
ajst-19320	49	53	,	,	PUNCT
ajst-19320	49	54	and	and	CCONJ
ajst-19320	49	55	then	then	ADV
ajst-19320	49	56	performs	perform	VERB
ajst-19320	49	57	sigmoid	sigmoid	NOUN
ajst-19320	49	58	activation	activation	NOUN
ajst-19320	49	59	operations	operation	NOUN
ajst-19320	49	60	on	on	ADP
ajst-19320	49	61	it	it	PRON
ajst-19320	49	62	to	to	PART
ajst-19320	49	63	obtain	obtain	VERB
ajst-19320	49	64	the	the	DET
ajst-19320	49	65	channel	channel	NOUN
ajst-19320	49	66	attention	attention	NOUN
ajst-19320	49	67	feature	feature	NOUN
ajst-19320	49	68	.	.	PUNCT
ajst-19320	50	1	the	the	DET
ajst-19320	50	2	channel	channel	NOUN
ajst-19320	50	3	attention	attention	NOUN
ajst-19320	50	4	feature	feature	NOUN
ajst-19320	50	5	map	map	NOUN
ajst-19320	50	6	is	be	AUX
ajst-19320	50	7	obtained	obtain	VERB
ajst-19320	50	8	by	by	ADP
ajst-19320	50	9	multiplying	multiply	VERB
ajst-19320	50	10	with	with	ADP
ajst-19320	50	11	the	the	DET
ajst-19320	50	12	input	input	NOUN
ajst-19320	50	13	feature	feature	NOUN
ajst-19320	50	14	map	map	NOUN
ajst-19320	50	15	f	f	PROPN
ajst-19320	50	16	.	.	PUNCT
ajst-19320	51	1	the	the	DET
ajst-19320	51	2	structure	structure	NOUN
ajst-19320	51	3	of	of	ADP
ajst-19320	51	4	the	the	DET
ajst-19320	51	5	channel	channel	NOUN
ajst-19320	51	6	attention	attention	NOUN
ajst-19320	51	7	module	module	NOUN
ajst-19320	51	8	(	(	PUNCT
ajst-19320	51	9	cam	cam	NOUN
ajst-19320	51	10	)	)	PUNCT
ajst-19320	51	11	is	be	AUX
ajst-19320	51	12	shown	show	VERB
ajst-19320	51	13	in	in	ADP
ajst-19320	51	14	fig	fig	NOUN
ajst-19320	51	15	.	.	PUNCT
ajst-19320	52	1	3	3	X
ajst-19320	52	2	.	.	X
ajst-19320	52	3	figure	figure	NOUN
ajst-19320	52	4	3	3	NUM
ajst-19320	52	5	.	.	NOUN
ajst-19320	52	6	channel	channel	NOUN
ajst-19320	52	7	attention	attention	NOUN
ajst-19320	52	8	module	module	NOUN
ajst-19320	52	9	the	the	DET
ajst-19320	52	10	mathematical	mathematical	ADJ
ajst-19320	52	11	expression	expression	NOUN
ajst-19320	52	12	for	for	ADP
ajst-19320	52	13	the	the	DET
ajst-19320	52	14	channel	channel	NOUN
ajst-19320	52	15	attention	attention	NOUN
ajst-19320	52	16	characteristic	characteristic	NOUN
ajst-19320	52	17	is	be	AUX
ajst-19320	52	18	shown	show	VERB
ajst-19320	52	19	in	in	ADP
ajst-19320	52	20	equation	equation	NOUN
ajst-19320	52	21	(	(	PUNCT
ajst-19320	52	22	1	1	NUM
ajst-19320	52	23	):	):	SYM
ajst-19320	52	24	1	1	NUM
ajst-19320	52	25	0	0	NUM
ajst-19320	52	26	max	max	PROPN
ajst-19320	52	27	1	1	NUM
ajst-19320	52	28	0	0	NUM
ajst-19320	52	29	a	a	PRON
ajst-19320	52	30	(	(	PUNCT
ajst-19320	52	31	)	)	PUNCT
ajst-19320	52	32	(	(	PUNCT
ajst-19320	52	33	(	(	PUNCT
ajst-19320	52	34	(	(	PUNCT
ajst-19320	52	35	)	)	PUNCT
ajst-19320	52	36	)	)	PUNCT
ajst-19320	53	1	(	(	PUNCT
ajst-19320	53	2	(	(	PUNCT
ajst-19320	53	3	)	)	PUNCT
ajst-19320	53	4	)	)	PUNCT
ajst-19320	53	5	)	)	PUNCT
ajst-19320	54	1	(	(	PUNCT
ajst-19320	54	2	(	(	PUNCT
ajst-19320	54	3	(	(	PUNCT
ajst-19320	54	4	)	)	PUNCT
ajst-19320	54	5	)	)	PUNCT
ajst-19320	54	6	(	(	PUNCT
ajst-19320	54	7	(	(	PUNCT
ajst-19320	54	8	)	)	PUNCT
ajst-19320	54	9	)	)	PUNCT
ajst-19320	54	10	)	)	PUNCT
ajst-19320	55	1	c	c	NOUN
ajst-19320	55	2	c	c	NOUN
ajst-19320	55	3	vg	vg	NOUN
ajst-19320	55	4	mc	mc	PROPN
ajst-19320	55	5	f	f	PROPN
ajst-19320	55	6	mlp	mlp	PROPN
ajst-19320	55	7	maxpool	maxpool	PROPN
ajst-19320	55	8	f	f	PROPN
ajst-19320	56	1	mlp	mlp	PROPN
ajst-19320	56	2	avgpool	avgpool	PROPN
ajst-19320	56	3	f	f	PROPN
ajst-19320	56	4	w	w	PROPN
ajst-19320	56	5	w	w	PROPN
ajst-19320	56	6	f	f	PROPN
ajst-19320	56	7	w	w	PROPN
ajst-19320	56	8	w	w	PROPN
ajst-19320	56	9	f	f	PROPN
ajst-19320	56	10			PROPN
ajst-19320	56	11			PROPN
ajst-19320	56	12			NOUN
ajst-19320	56	13			ADV
ajst-19320	56	14			NUM
ajst-19320	56	15			X
ajst-19320	56	16	(	(	PUNCT
ajst-19320	56	17	1	1	X
ajst-19320	56	18	)	)	PUNCT
ajst-19320	56	19	the	the	DET
ajst-19320	56	20	mathematical	mathematical	ADJ
ajst-19320	56	21	expression	expression	NOUN
ajst-19320	56	22	for	for	ADP
ajst-19320	56	23	the	the	DET
ajst-19320	56	24	channel	channel	NOUN
ajst-19320	56	25	attention	attention	NOUN
ajst-19320	56	26	feature	feature	NOUN
ajst-19320	56	27	map	map	NOUN
ajst-19320	56	28	is	be	AUX
ajst-19320	56	29	shown	show	VERB
ajst-19320	56	30	in	in	ADP
ajst-19320	56	31	equation	equation	NOUN
ajst-19320	56	32	(	(	PUNCT
ajst-19320	56	33	2	2	NUM
ajst-19320	56	34	)	)	PUNCT
ajst-19320	56	35	:	:	PUNCT
ajst-19320	56	36	(	(	PUNCT
ajst-19320	56	37	)	)	PUNCT
ajst-19320	56	38	fc	fc	PROPN
ajst-19320	57	1	mc	mc	PROPN
ajst-19320	57	2	f	f	PROPN
ajst-19320	57	3	f	f	PROPN
ajst-19320	57	4			PROPN
ajst-19320	57	5	(	(	PUNCT
ajst-19320	57	6	2	2	NUM
ajst-19320	57	7	)	)	PUNCT
ajst-19320	57	8	(	(	PUNCT
ajst-19320	57	9	2	2	X
ajst-19320	57	10	)	)	PUNCT
ajst-19320	57	11	spartial	spartial	ADJ
ajst-19320	57	12	attention	attention	NOUN
ajst-19320	57	13	module	module	NOUN
ajst-19320	57	14	spatial	spatial	ADJ
ajst-19320	57	15	attention	attention	NOUN
ajst-19320	57	16	focuses	focus	VERB
ajst-19320	57	17	on	on	ADP
ajst-19320	57	18	the	the	DET
ajst-19320	57	19	location	location	NOUN
ajst-19320	57	20	information	information	NOUN
ajst-19320	57	21	of	of	ADP
ajst-19320	57	22	the	the	DET
ajst-19320	57	23	target	target	NOUN
ajst-19320	57	24	in	in	ADP
ajst-19320	57	25	the	the	DET
ajst-19320	57	26	input	input	NOUN
ajst-19320	57	27	image	image	NOUN
ajst-19320	57	28	.	.	PUNCT
ajst-19320	58	1	take	take	VERB
ajst-19320	58	2	the	the	DET
ajst-19320	58	3	final	final	ADJ
ajst-19320	58	4	output	output	NOUN
ajst-19320	58	5	feature	feature	NOUN
ajst-19320	58	6	map	map	NOUN
ajst-19320	58	7	fc	fc	PROPN
ajst-19320	58	8	from	from	ADP
ajst-19320	58	9	the	the	DET
ajst-19320	58	10	channel	channel	NOUN
ajst-19320	58	11	attention	attention	NOUN
ajst-19320	58	12	module	module	NOUN
ajst-19320	58	13	as	as	ADP
ajst-19320	58	14	input	input	NOUN
ajst-19320	58	15	,	,	PUNCT
ajst-19320	58	16	perform	perform	VERB
ajst-19320	58	17	max	max	PROPN
ajst-19320	58	18	pooling	pooling	NOUN
ajst-19320	58	19	and	and	CCONJ
ajst-19320	58	20	average	average	ADJ
ajst-19320	58	21	pooling	pool	VERB
ajst-19320	58	22	operations	operation	NOUN
ajst-19320	58	23	on	on	ADP
ajst-19320	58	24	it	it	PRON
ajst-19320	58	25	,	,	PUNCT
ajst-19320	58	26	splice	splice	VERB
ajst-19320	58	27	the	the	DET
ajst-19320	58	28	two	two	NUM
ajst-19320	58	29	output	output	NOUN
ajst-19320	58	30	feature	feature	NOUN
ajst-19320	58	31	vectors	vector	NOUN
ajst-19320	58	32	,	,	PUNCT
ajst-19320	58	33	and	and	CCONJ
ajst-19320	58	34	then	then	ADV
ajst-19320	58	35	go	go	VERB
ajst-19320	58	36	through	through	ADP
ajst-19320	58	37	a	a	DET
ajst-19320	58	38	7x7	7x7	NUM
ajst-19320	58	39	convolutional	convolutional	ADJ
ajst-19320	58	40	layer	layer	NOUN
ajst-19320	58	41	and	and	CCONJ
ajst-19320	58	42	sigmoid	sigmoid	NOUN
ajst-19320	58	43	activation	activation	NOUN
ajst-19320	58	44	to	to	PART
ajst-19320	58	45	get	get	VERB
ajst-19320	58	46	the	the	DET
ajst-19320	58	47	spatial	spatial	ADJ
ajst-19320	58	48	attention	attention	NOUN
ajst-19320	58	49	features	feature	VERB
ajst-19320	58	50	.	.	PUNCT
ajst-19320	59	1	the	the	DET
ajst-19320	59	2	input	input	NOUN
ajst-19320	59	3	feature	feature	NOUN
ajst-19320	59	4	map	map	NOUN
ajst-19320	59	5	is	be	AUX
ajst-19320	59	6	multiplied	multiply	VERB
ajst-19320	59	7	with	with	ADP
ajst-19320	59	8	the	the	DET
ajst-19320	59	9	input	input	NOUN
ajst-19320	59	10	feature	feature	NOUN
ajst-19320	59	11	map	map	NOUN
ajst-19320	59	12	to	to	PART
ajst-19320	59	13	obtain	obtain	VERB
ajst-19320	59	14	the	the	DET
ajst-19320	59	15	channel	channel	NOUN
ajst-19320	59	16	attention	attention	NOUN
ajst-19320	59	17	feature	feature	NOUN
ajst-19320	59	18	map	map	NOUN
ajst-19320	59	19	.	.	PUNCT
ajst-19320	60	1	the	the	DET
ajst-19320	60	2	structure	structure	NOUN
ajst-19320	60	3	of	of	ADP
ajst-19320	60	4	the	the	DET
ajst-19320	60	5	spatial	spatial	ADJ
ajst-19320	60	6	attention	attention	NOUN
ajst-19320	60	7	module	module	NOUN
ajst-19320	60	8	(	(	PUNCT
ajst-19320	60	9	sam	sam	PROPN
ajst-19320	60	10	)	)	PUNCT
ajst-19320	60	11	is	be	AUX
ajst-19320	60	12	shown	show	VERB
ajst-19320	60	13	in	in	ADP
ajst-19320	60	14	fig	fig	NOUN
ajst-19320	60	15	.	.	PUNCT
ajst-19320	61	1	4	4	X
ajst-19320	61	2	.	.	X
ajst-19320	61	3	figure	figure	VERB
ajst-19320	61	4	4	4	NUM
ajst-19320	61	5	.	.	PUNCT
ajst-19320	62	1	spatial	spatial	ADJ
ajst-19320	62	2	attention	attention	NOUN
ajst-19320	62	3	module	module	NOUN
ajst-19320	62	4	the	the	DET
ajst-19320	62	5	mathematical	mathematical	ADJ
ajst-19320	62	6	expression	expression	NOUN
ajst-19320	62	7	for	for	ADP
ajst-19320	62	8	the	the	DET
ajst-19320	62	9	spatial	spatial	ADJ
ajst-19320	62	10	attention	attention	NOUN
ajst-19320	62	11	feature	feature	NOUN
ajst-19320	62	12	is	be	AUX
ajst-19320	62	13	shown	show	VERB
ajst-19320	62	14	in	in	ADP
ajst-19320	62	15	equation	equation	NOUN
ajst-19320	62	16	(	(	PUNCT
ajst-19320	62	17	3	3	NUM
ajst-19320	62	18	):	):	PUNCT
ajst-19320	62	19	7	7	NUM
ajst-19320	62	20	7	7	NUM
ajst-19320	62	21	7	7	NUM
ajst-19320	62	22	7	7	NUM
ajst-19320	62	23	max	max	NOUN
ajst-19320	62	24	(	(	PUNCT
ajst-19320	62	25	)	)	PUNCT
ajst-19320	62	26	(	(	PUNCT
ajst-19320	62	27	(	(	PUNCT
ajst-19320	62	28	[	[	PUNCT
ajst-19320	62	29	(	(	PUNCT
ajst-19320	62	30	)	)	PUNCT
ajst-19320	62	31	;	;	PUNCT
ajst-19320	62	32	(	(	PUNCT
ajst-19320	62	33	)	)	PUNCT
ajst-19320	62	34	]	]	X
ajst-19320	62	35	)	)	PUNCT
ajst-19320	62	36	)	)	PUNCT
ajst-19320	63	1	(	(	PUNCT
ajst-19320	63	2	(	(	PUNCT
ajst-19320	63	3	[	[	PUNCT
ajst-19320	63	4	]	]	X
ajst-19320	63	5	;	;	PUNCT
ajst-19320	63	6	)	)	PUNCT
ajst-19320	63	7	)	)	PUNCT
ajst-19320	64	1	x	x	PUNCT
ajst-19320	64	2	x	x	X
ajst-19320	64	3	s	s	NOUN
ajst-19320	64	4	s	s	NOUN
ajst-19320	64	5	avg	avg	NOUN
ajst-19320	64	6	ms	ms	PROPN
ajst-19320	64	7	f	f	PROPN
ajst-19320	64	8	f	f	PROPN
ajst-19320	64	9	maxpool	maxpool	PROPN
ajst-19320	64	10	fc	fc	PROPN
ajst-19320	64	11	avgpool	avgpool	PROPN
ajst-19320	64	12	fc	fc	PROPN
ajst-19320	65	1	f	f	PROPN
ajst-19320	65	2	f	f	PROPN
ajst-19320	65	3	f	f	PROPN
ajst-19320	65	4			PROPN
ajst-19320	65	5			PROPN
ajst-19320	66	1			NUM
ajst-19320	66	2			NOUN
ajst-19320	66	3	(	(	PUNCT
ajst-19320	66	4	3	3	NUM
ajst-19320	66	5	)	)	PUNCT
ajst-19320	66	6	the	the	DET
ajst-19320	66	7	mathematical	mathematical	ADJ
ajst-19320	66	8	expression	expression	NOUN
ajst-19320	66	9	of	of	ADP
ajst-19320	66	10	the	the	DET
ajst-19320	66	11	spatial	spatial	ADJ
ajst-19320	66	12	attention	attention	NOUN
ajst-19320	66	13	feature	feature	NOUN
ajst-19320	66	14	map	map	NOUN
ajst-19320	66	15	is	be	AUX
ajst-19320	66	16	shown	show	VERB
ajst-19320	66	17	in	in	ADP
ajst-19320	66	18	equation	equation	NOUN
ajst-19320	66	19	(	(	PUNCT
ajst-19320	66	20	4	4	NUM
ajst-19320	66	21	):	):	PUNCT
ajst-19320	66	22	s	s	X
ajst-19320	66	23	(	(	PUNCT
ajst-19320	66	24	)	)	PUNCT
ajst-19320	66	25	f	f	PROPN
ajst-19320	66	26	ms	ms	PROPN
ajst-19320	66	27	fc	fc	PROPN
ajst-19320	66	28	fc	fc	PROPN
ajst-19320	66	29			INTJ
ajst-19320	66	30	(	(	PUNCT
ajst-19320	66	31	4	4	NUM
ajst-19320	66	32	)	)	PUNCT
ajst-19320	66	33	yolov5	yolov5	NOUN
ajst-19320	66	34	uses	use	VERB
ajst-19320	66	35	the	the	DET
ajst-19320	66	36	same	same	ADJ
ajst-19320	66	37	weighting	weighting	NOUN
ajst-19320	66	38	for	for	ADP
ajst-19320	66	39	all	all	DET
ajst-19320	66	40	features	feature	NOUN
ajst-19320	66	41	of	of	ADP
ajst-19320	66	42	different	different	ADJ
ajst-19320	66	43	sizes	size	NOUN
ajst-19320	66	44	and	and	CCONJ
ajst-19320	66	45	has	have	VERB
ajst-19320	66	46	no	no	DET
ajst-19320	66	47	attentional	attentional	ADJ
ajst-19320	66	48	bias	bias	NOUN
ajst-19320	66	49	in	in	ADP
ajst-19320	66	50	the	the	DET
ajst-19320	66	51	feature	feature	NOUN
ajst-19320	66	52	extraction	extraction	NOUN
ajst-19320	66	53	process	process	NOUN
ajst-19320	66	54	.	.	PUNCT
ajst-19320	67	1	while	while	SCONJ
ajst-19320	67	2	the	the	DET
ajst-19320	67	3	background	background	NOUN
ajst-19320	67	4	of	of	ADP
ajst-19320	67	5	uav	uav	PROPN
ajst-19320	67	6	aerial	aerial	ADJ
ajst-19320	67	7	images	image	NOUN
ajst-19320	67	8	is	be	AUX
ajst-19320	67	9	complex	complex	ADJ
ajst-19320	67	10	,	,	PUNCT
ajst-19320	67	11	there	there	PRON
ajst-19320	67	12	is	be	VERB
ajst-19320	67	13	a	a	DET
ajst-19320	67	14	large	large	ADJ
ajst-19320	67	15	amount	amount	NOUN
ajst-19320	67	16	of	of	ADP
ajst-19320	67	17	redundant	redundant	ADJ
ajst-19320	67	18	information	information	NOUN
ajst-19320	67	19	affecting	affect	VERB
ajst-19320	67	20	the	the	DET
ajst-19320	67	21	network	network	NOUN
ajst-19320	67	22	to	to	PART
ajst-19320	67	23	extract	extract	VERB
ajst-19320	67	24	vehicle	vehicle	NOUN
ajst-19320	67	25	features	feature	NOUN
ajst-19320	67	26	.	.	PUNCT
ajst-19320	68	1	therefore	therefore	ADV
ajst-19320	68	2	,	,	PUNCT
ajst-19320	68	3	in	in	ADP
ajst-19320	68	4	this	this	DET
ajst-19320	68	5	paper	paper	NOUN
ajst-19320	68	6	,	,	PUNCT
ajst-19320	68	7	by	by	ADP
ajst-19320	68	8	introducing	introduce	VERB
ajst-19320	68	9	the	the	DET
ajst-19320	68	10	cbam	cbam	NOUN
ajst-19320	68	11	module	module	NOUN
ajst-19320	68	12	,	,	PUNCT
ajst-19320	68	13	the	the	DET
ajst-19320	68	14	network	network	NOUN
ajst-19320	68	15	can	can	AUX
ajst-19320	68	16	suppress	suppress	VERB
ajst-19320	68	17	the	the	DET
ajst-19320	68	18	background	background	NOUN
ajst-19320	68	19	information	information	NOUN
ajst-19320	68	20	interference	interference	NOUN
ajst-19320	68	21	to	to	PART
ajst-19320	68	22	pay	pay	VERB
ajst-19320	68	23	more	more	ADJ
ajst-19320	68	24	attention	attention	NOUN
ajst-19320	68	25	to	to	ADP
ajst-19320	68	26	the	the	DET
ajst-19320	68	27	uav	uav	PROPN
ajst-19320	68	28	aerial	aerial	ADJ
ajst-19320	68	29	vehicle	vehicle	NOUN
ajst-19320	68	30	in	in	ADP
ajst-19320	68	31	the	the	DET
ajst-19320	68	32	detection	detection	NOUN
ajst-19320	68	33	process	process	NOUN
ajst-19320	68	34	.	.	PUNCT
ajst-19320	69	1	3.2	3.2	NUM
ajst-19320	69	2	.	.	PUNCT
ajst-19320	70	1	dpfam	dpfam	NOUN
ajst-19320	70	2	networks	network	VERB
ajst-19320	70	3	our	our	PRON
ajst-19320	70	4	algorithm	algorithm	NOUN
ajst-19320	70	5	model	model	NOUN
ajst-19320	70	6	uses	use	VERB
ajst-19320	70	7	csp	csp	PROPN
ajst-19320	70	8	darknet	darknet	PROPN
ajst-19320	70	9	as	as	ADP
ajst-19320	70	10	the	the	DET
ajst-19320	70	11	backbone	backbone	NOUN
ajst-19320	70	12	network	network	NOUN
ajst-19320	70	13	,	,	PUNCT
ajst-19320	70	14	and	and	CCONJ
ajst-19320	70	15	dpfem	dpfem	PROPN
ajst-19320	70	16	(	(	PUNCT
ajst-19320	70	17	double	double	ADJ
ajst-19320	70	18	path	path	NOUN
ajst-19320	70	19	feature	feature	NOUN
ajst-19320	70	20	enhancement	enhancement	NOUN
ajst-19320	70	21	module	module	NOUN
ajst-19320	70	22	)	)	PUNCT
ajst-19320	70	23	is	be	AUX
ajst-19320	70	24	used	use	VERB
ajst-19320	70	25	in	in	ADP
ajst-19320	70	26	the	the	DET
ajst-19320	70	27	neck	neck	NOUN
ajst-19320	70	28	part	part	NOUN
ajst-19320	70	29	instead	instead	ADV
ajst-19320	70	30	of	of	ADP
ajst-19320	70	31	the	the	DET
ajst-19320	70	32	traditional	traditional	ADJ
ajst-19320	70	33	bottleneck	bottleneck	NOUN
ajst-19320	70	34	csp	csp	PROPN
ajst-19320	70	35	and	and	CCONJ
ajst-19320	70	36	c3	c3	PROPN
ajst-19320	70	37	network	network	NOUN
ajst-19320	70	38	structure	structure	NOUN
ajst-19320	70	39	,	,	PUNCT
ajst-19320	70	40	which	which	PRON
ajst-19320	70	41	combines	combine	VERB
ajst-19320	70	42	the	the	DET
ajst-19320	70	43	advantages	advantage	NOUN
ajst-19320	70	44	of	of	ADP
ajst-19320	70	45	resnet	resnet	NOUN
ajst-19320	70	46	and	and	CCONJ
ajst-19320	70	47	densenet	densenet	NOUN
ajst-19320	70	48	,	,	PUNCT
ajst-19320	70	49	and	and	CCONJ
ajst-19320	70	50	can	can	AUX
ajst-19320	70	51	adapt	adapt	VERB
ajst-19320	70	52	better	well	ADV
ajst-19320	70	53	to	to	ADP
ajst-19320	70	54	the	the	DET
ajst-19320	70	55	the	the	DET
ajst-19320	70	56	structure	structure	NOUN
ajst-19320	70	57	of	of	ADP
ajst-19320	70	58	dpfem	dpfem	PROPN
ajst-19320	70	59	is	be	AUX
ajst-19320	70	60	shown	show	VERB
ajst-19320	70	61	in	in	ADP
ajst-19320	70	62	fig	fig	NOUN
ajst-19320	70	63	.	.	PUNCT
ajst-19320	71	1	5	5	X
ajst-19320	71	2	.	.	PUNCT
ajst-19320	71	3	this	this	DET
ajst-19320	71	4	module	module	NOUN
ajst-19320	71	5	is	be	AUX
ajst-19320	71	6	improved	improve	VERB
ajst-19320	71	7	by	by	ADP
ajst-19320	71	8	bottleneck	bottleneck	PROPN
ajst-19320	71	9	csp	csp	PROPN
ajst-19320	71	10	,	,	PUNCT
ajst-19320	71	11	inspired	inspire	VERB
ajst-19320	71	12	by	by	ADP
ajst-19320	71	13	dual	dual	ADJ
ajst-19320	71	14	path	path	NOUN
ajst-19320	71	15	networks	network	NOUN
ajst-19320	71	16	(	(	PUNCT
ajst-19320	71	17	dpn)[14	dpn)[14	PROPN
ajst-19320	71	18	]	]	X
ajst-19320	71	19	,	,	PUNCT
ajst-19320	71	20	we	we	PRON
ajst-19320	71	21	divide	divide	VERB
ajst-19320	71	22	the	the	DET
ajst-19320	71	23	input	input	NOUN
ajst-19320	71	24	features	feature	VERB
ajst-19320	71	25	into	into	ADP
ajst-19320	71	26	two	two	NUM
ajst-19320	71	27	parts	part	NOUN
ajst-19320	71	28	f[:i	f[:i	PART
ajst-19320	71	29	]	]	PUNCT
ajst-19320	71	30	and	and	CCONJ
ajst-19320	71	31	f[i	f[i	ADP
ajst-19320	71	32	:]	:]	PROPN
ajst-19320	71	33	for	for	ADP
ajst-19320	71	34	residual	residual	ADJ
ajst-19320	71	35	learning	learning	NOUN
ajst-19320	71	36	and	and	CCONJ
ajst-19320	71	37	dense	dense	ADJ
ajst-19320	71	38	connectivity	connectivity	NOUN
ajst-19320	71	39	respectively	respectively	ADV
ajst-19320	71	40	.	.	PUNCT
ajst-19320	72	1	where	where	SCONJ
ajst-19320	72	2	i	i	PRON
ajst-19320	72	3	is	be	AUX
ajst-19320	72	4	a	a	DET
ajst-19320	72	5	hyperparameter	hyperparameter	NOUN
ajst-19320	72	6	.	.	PUNCT
ajst-19320	73	1	in	in	ADP
ajst-19320	73	2	the	the	DET
ajst-19320	73	3	main	main	ADJ
ajst-19320	73	4	path	path	NOUN
ajst-19320	73	5	,	,	PUNCT
ajst-19320	73	6	1×1	1×1	NUM
ajst-19320	73	7	convolution	convolution	NOUN
ajst-19320	73	8	is	be	AUX
ajst-19320	73	9	first	first	ADV
ajst-19320	73	10	used	use	VERB
ajst-19320	73	11	to	to	PART
ajst-19320	73	12	reduce	reduce	VERB
ajst-19320	73	13	the	the	DET
ajst-19320	73	14	number	number	NOUN
ajst-19320	73	15	of	of	ADP
ajst-19320	73	16	channels	channel	NOUN
ajst-19320	73	17	and	and	CCONJ
ajst-19320	73	18	thus	thus	ADV
ajst-19320	73	19	the	the	DET
ajst-19320	73	20	number	number	NOUN
ajst-19320	73	21	of	of	ADP
ajst-19320	73	22	parameters	parameter	NOUN
ajst-19320	73	23	.	.	PUNCT
ajst-19320	74	1	in	in	ADP
ajst-19320	74	2	order	order	NOUN
ajst-19320	74	3	to	to	PART
ajst-19320	74	4	extend	extend	VERB
ajst-19320	74	5	the	the	DET
ajst-19320	74	6	sensory	sensory	ADJ
ajst-19320	74	7	field	field	NOUN
ajst-19320	74	8	,	,	PUNCT
ajst-19320	74	9	as	as	ADV
ajst-19320	74	10	well	well	ADV
ajst-19320	74	11	as	as	ADP
ajst-19320	74	12	to	to	PART
ajst-19320	74	13	better	well	ADV
ajst-19320	74	14	adapt	adapt	VERB
ajst-19320	74	15	to	to	ADP
ajst-19320	74	16	the	the	DET
ajst-19320	74	17	shape	shape	NOUN
ajst-19320	74	18	and	and	CCONJ
ajst-19320	74	19	orientation	orientation	NOUN
ajst-19320	74	20	changes	change	NOUN
ajst-19320	74	21	of	of	ADP
ajst-19320	74	22	different	different	ADJ
ajst-19320	74	23	targets	target	NOUN
ajst-19320	74	24	,	,	PUNCT
ajst-19320	74	25	we	we	PRON
ajst-19320	74	26	use	use	VERB
ajst-19320	74	27	the	the	DET
ajst-19320	74	28	residual	residual	ADJ
ajst-19320	74	29	unit	unit	NOUN
ajst-19320	74	30	resunit	resunit	VERB
ajst-19320	74	31	instead	instead	ADV
ajst-19320	74	32	of	of	ADP
ajst-19320	74	33	the	the	DET
ajst-19320	74	34	traditional	traditional	ADJ
ajst-19320	74	35	3×3	3×3	NUM
ajst-19320	74	36	convolutional	convolutional	ADJ
ajst-19320	74	37	layer	layer	NOUN
ajst-19320	74	38	to	to	PART
ajst-19320	74	39	improve	improve	VERB
ajst-19320	74	40	the	the	DET
ajst-19320	74	41	feature	feature	NOUN
ajst-19320	74	42	extraction	extraction	NOUN
ajst-19320	74	43	ability	ability	NOUN
ajst-19320	74	44	of	of	ADP
ajst-19320	74	45	the	the	DET
ajst-19320	74	46	network	network	NOUN
ajst-19320	74	47	for	for	ADP
ajst-19320	74	48	small	small	ADJ
ajst-19320	74	49	-	-	PUNCT
ajst-19320	74	50	sized	sized	ADJ
ajst-19320	74	51	targets	target	NOUN
ajst-19320	74	52	.	.	PUNCT
ajst-19320	75	1	second	second	ADJ
ajst-19320	75	2	order	order	NOUN
ajst-19320	75	3	response	response	NOUN
ajst-19320	75	4	transform	transform	NOUN
ajst-19320	75	5	,	,	PUNCT
ajst-19320	75	6	sort	sort	ADP
ajst-19320	75	7	[	[	X
ajst-19320	75	8	15]enhances	15]enhances	NUM
ajst-19320	75	9	the	the	DET
ajst-19320	75	10	nonlinear	nonlinear	ADJ
ajst-19320	75	11	fitting	fitting	ADJ
ajst-19320	75	12	ability	ability	NOUN
ajst-19320	75	13	of	of	ADP
ajst-19320	75	14	the	the	DET
ajst-19320	75	15	network	network	NOUN
ajst-19320	75	16	and	and	CCONJ
ajst-19320	75	17	allows	allow	VERB
ajst-19320	75	18	the	the	DET
ajst-19320	75	19	network	network	NOUN
ajst-19320	75	20	to	to	PART
ajst-19320	75	21	adapt	adapt	VERB
ajst-19320	75	22	to	to	ADP
ajst-19320	75	23	more	more	ADV
ajst-19320	75	24	complex	complex	ADJ
ajst-19320	75	25	feature	feature	NOUN
ajst-19320	75	26	distributions	distribution	NOUN
ajst-19320	75	27	.	.	PUNCT
ajst-19320	76	1	therefore	therefore	ADV
ajst-19320	76	2	,	,	PUNCT
ajst-19320	76	3	in	in	ADP
ajst-19320	76	4	the	the	DET
ajst-19320	76	5	process	process	NOUN
ajst-19320	76	6	of	of	ADP
ajst-19320	76	7	residual	residual	ADJ
ajst-19320	76	8	feature	feature	NOUN
ajst-19320	76	9	fusion	fusion	NOUN
ajst-19320	76	10	,	,	PUNCT
ajst-19320	76	11	we	we	PRON
ajst-19320	76	12	adopt	adopt	VERB
ajst-19320	76	13	sort	sort	ADV
ajst-19320	76	14	to	to	PART
ajst-19320	76	15	replace	replace	VERB
ajst-19320	76	16	the	the	DET
ajst-19320	76	17	direct	direct	ADJ
ajst-19320	76	18	summation	summation	NOUN
ajst-19320	76	19	of	of	ADP
ajst-19320	76	20	features	feature	NOUN
ajst-19320	76	21	,	,	PUNCT
ajst-19320	76	22	while	while	SCONJ
ajst-19320	76	23	further	far	ADV
ajst-19320	76	24	optimizing	optimize	VERB
ajst-19320	76	25	the	the	DET
ajst-19320	76	26	fusion	fusion	NOUN
ajst-19320	76	27	method	method	NOUN
ajst-19320	76	28	,	,	PUNCT
ajst-19320	76	29	which	which	PRON
ajst-19320	76	30	is	be	AUX
ajst-19320	76	31	represented	represent	VERB
ajst-19320	76	32	by	by	ADP
ajst-19320	76	33	the	the	DET
ajst-19320	76	34	following	follow	VERB
ajst-19320	76	35	equation	equation	NOUN
ajst-19320	76	36	:	:	PUNCT
ajst-19320	76	37	(	(	PUNCT
ajst-19320	76	38	5	5	X
ajst-19320	76	39	)	)	PUNCT
ajst-19320	76	40	where	where	SCONJ
ajst-19320	76	41	𝑁	𝑁	PROPN
ajst-19320	76	42	𝑓	𝑓	DET
ajst-19320	76	43	denotes	denote	NOUN
ajst-19320	76	44	the	the	DET
ajst-19320	76	45	output	output	NOUN
ajst-19320	76	46	features	feature	NOUN
ajst-19320	76	47	of	of	ADP
ajst-19320	76	48	the	the	DET
ajst-19320	76	49	main	main	ADJ
ajst-19320	76	50	path	path	NOUN
ajst-19320	76	51	,	,	PUNCT
ajst-19320	76	52	and	and	CCONJ
ajst-19320	76	53	𝜀	𝜀	PROPN
ajst-19320	76	54	is	be	AUX
ajst-19320	76	55	a	a	DET
ajst-19320	76	56	very	very	ADV
ajst-19320	76	57	small	small	ADJ
ajst-19320	76	58	constant	constant	ADJ
ajst-19320	76	59	to	to	PART
ajst-19320	76	60	ensure	ensure	VERB
ajst-19320	76	61	the	the	DET
ajst-19320	76	62	stability	stability	NOUN
ajst-19320	76	63	of	of	ADP
ajst-19320	76	64	the	the	DET
ajst-19320	76	65	gradient	gradient	NOUN
ajst-19320	76	66	computation	computation	NOUN
ajst-19320	76	67	,	,	PUNCT
ajst-19320	76	68	which	which	PRON
ajst-19320	76	69	is	be	AUX
ajst-19320	76	70	taken	take	VERB
ajst-19320	76	71	as	as	ADP
ajst-19320	76	72	0.00001	0.00001	NUM
ajst-19320	76	73	.	.	PUNCT
ajst-19320	77	1	next	next	ADV
ajst-19320	77	2	,	,	PUNCT
ajst-19320	77	3	we	we	PRON
ajst-19320	77	4	densely	densely	ADV
ajst-19320	77	5	concatenate	concatenate	VERB
ajst-19320	77	6	f[i	f[i	PROPN
ajst-19320	77	7	:]	:]	PROPN
ajst-19320	77	8	with	with	ADP
ajst-19320	77	9	n(f)[i	n(f)[i	VERB
ajst-19320	77	10	:]	:]	PUNCT
ajst-19320	77	11	in	in	ADP
ajst-19320	77	12	order	order	NOUN
ajst-19320	77	13	to	to	PART
ajst-19320	77	14	mine	mine	VERB
ajst-19320	77	15	new	new	ADJ
ajst-19320	77	16	features	feature	NOUN
ajst-19320	77	17	.	.	PUNCT
ajst-19320	78	1	finally	finally	ADV
ajst-19320	78	2	,	,	PUNCT
ajst-19320	78	3	we	we	PRON
ajst-19320	78	4	adjust	adjust	VERB
ajst-19320	78	5	the	the	DET
ajst-19320	78	6	number	number	NOUN
ajst-19320	78	7	of	of	ADP
ajst-19320	78	8	output	output	NOUN
ajst-19320	78	9	feature	feature	NOUN
ajst-19320	78	10	channels	channel	NOUN
ajst-19320	78	11	by	by	ADP
ajst-19320	78	12	a	a	DET
ajst-19320	78	13	1	1	NUM
ajst-19320	78	14	×	×	NOUN
ajst-19320	78	15	1	1	NUM
ajst-19320	78	16	convolutional	convolutional	ADJ
ajst-19320	78	17	layer.the	layer.the	DET
ajst-19320	78	18	operation	operation	NOUN
ajst-19320	78	19	of	of	ADP
ajst-19320	78	20	dpouts	dpout	NOUN
ajst-19320	78	21	n	n	CCONJ
ajst-19320	78	22	(	(	PUNCT
ajst-19320	78	23	f	f	PROPN
ajst-19320	78	24	)	)	PUNCT
ajst-19320	79	1	[:	[:	X
ajst-19320	79	2	i	i	X
ajst-19320	79	3	]	]	X
ajst-19320	79	4	f	f	X
ajst-19320	80	1	[:	[:	X
ajst-19320	80	2	i	i	X
ajst-19320	80	3	]	]	PUNCT
ajst-19320	80	4	n	n	CCONJ
ajst-19320	80	5	(	(	PUNCT
ajst-19320	80	6	f	f	PROPN
ajst-19320	80	7	)	)	PUNCT
ajst-19320	81	1	[:	[:	X
ajst-19320	81	2	i	i	X
ajst-19320	81	3	]	]	X
ajst-19320	81	4	f	f	X
ajst-19320	82	1	[:	[:	X
ajst-19320	82	2	i	i	X
ajst-19320	82	3	]	]	PUNCT
ajst-19320	82	4			PROPN
ajst-19320	82	5			ADV
ajst-19320	82	6			PROPN
ajst-19320	82	7			PROPN
ajst-19320	82	8			ADJ
ajst-19320	82	9	357	357	NUM
ajst-19320	82	10	fem	fem	NOUN
ajst-19320	82	11	is	be	AUX
ajst-19320	82	12	computed	compute	VERB
ajst-19320	82	13	as	as	SCONJ
ajst-19320	82	14	follows	follow	VERB
ajst-19320	82	15	:	:	PUNCT
ajst-19320	82	16	𝑁	𝑁	PROPN
ajst-19320	82	17	𝑓	𝑓	PRON
ajst-19320	82	18	𝐶	𝐶	PROPN
ajst-19320	82	19	𝑅𝑒𝑠𝑈𝑛𝑖𝑡	𝑅𝑒𝑠𝑈𝑛𝑖𝑡	PROPN
ajst-19320	82	20	𝐶	𝐶	PROPN
ajst-19320	82	21	𝑓	𝑓	PROPN
ajst-19320	82	22	(	(	PUNCT
ajst-19320	82	23	6	6	NUM
ajst-19320	82	24	)	)	PUNCT
ajst-19320	82	25	(	(	PUNCT
ajst-19320	82	26	7	7	X
ajst-19320	82	27	)	)	PUNCT
ajst-19320	82	28	figure	figure	NOUN
ajst-19320	82	29	5	5	NUM
ajst-19320	82	30	.	.	PUNCT
ajst-19320	82	31	dpfem	dpfem	PROPN
ajst-19320	82	32	network	network	NOUN
ajst-19320	82	33	structure	structure	NOUN
ajst-19320	82	34	3.3	3.3	NUM
ajst-19320	82	35	.	.	PUNCT
ajst-19320	83	1	maffem	maffem	NOUN
ajst-19320	83	2	since	since	SCONJ
ajst-19320	83	3	panet	panet	NOUN
ajst-19320	83	4	often	often	ADV
ajst-19320	83	5	directly	directly	ADV
ajst-19320	83	6	adopts	adopt	VERB
ajst-19320	83	7	the	the	DET
ajst-19320	83	8	splicing	splicing	NOUN
ajst-19320	83	9	approach	approach	NOUN
ajst-19320	83	10	when	when	SCONJ
ajst-19320	83	11	performing	perform	VERB
ajst-19320	83	12	feature	feature	NOUN
ajst-19320	83	13	fusion	fusion	NOUN
ajst-19320	83	14	of	of	ADP
ajst-19320	83	15	different	different	ADJ
ajst-19320	83	16	layers	layer	NOUN
ajst-19320	83	17	,	,	PUNCT
ajst-19320	83	18	this	this	DET
ajst-19320	83	19	practice	practice	NOUN
ajst-19320	83	20	will	will	AUX
ajst-19320	83	21	cause	cause	VERB
ajst-19320	83	22	the	the	DET
ajst-19320	83	23	obtained	obtain	VERB
ajst-19320	83	24	features	feature	NOUN
ajst-19320	83	25	of	of	ADP
ajst-19320	83	26	three	three	NUM
ajst-19320	83	27	different	different	ADJ
ajst-19320	83	28	scales	scale	NOUN
ajst-19320	83	29	to	to	PART
ajst-19320	83	30	have	have	VERB
ajst-19320	83	31	feature	feature	NOUN
ajst-19320	83	32	inconsistency	inconsistency	NOUN
ajst-19320	83	33	problems	problem	NOUN
ajst-19320	83	34	.	.	PUNCT
ajst-19320	84	1	to	to	PART
ajst-19320	84	2	address	address	VERB
ajst-19320	84	3	this	this	DET
ajst-19320	84	4	problem	problem	NOUN
ajst-19320	84	5	,	,	PUNCT
ajst-19320	84	6	we	we	PRON
ajst-19320	84	7	improve	improve	VERB
ajst-19320	84	8	a	a	DET
ajst-19320	84	9	(	(	PUNCT
ajst-19320	84	10	multi	multi	ADJ
ajst-19320	84	11	-	-	ADJ
ajst-19320	84	12	layer	layer	ADJ
ajst-19320	84	13	attention	attention	NOUN
ajst-19320	84	14	feature	feature	NOUN
ajst-19320	84	15	fusion	fusion	NOUN
ajst-19320	84	16	enhancement	enhancement	NOUN
ajst-19320	84	17	module	module	NOUN
ajst-19320	84	18	)	)	PUNCT
ajst-19320	84	19	maffem	maffem	NOUN
ajst-19320	84	20	to	to	PART
ajst-19320	84	21	be	be	AUX
ajst-19320	84	22	added	add	VERB
ajst-19320	84	23	before	before	ADP
ajst-19320	84	24	the	the	DET
ajst-19320	84	25	head	head	NOUN
ajst-19320	84	26	detector	detector	NOUN
ajst-19320	84	27	layer	layer	NOUN
ajst-19320	84	28	,	,	PUNCT
ajst-19320	84	29	which	which	PRON
ajst-19320	84	30	allows	allow	VERB
ajst-19320	84	31	each	each	DET
ajst-19320	84	32	feature	feature	NOUN
ajst-19320	84	33	layer	layer	NOUN
ajst-19320	84	34	to	to	PART
ajst-19320	84	35	autonomously	autonomously	ADV
ajst-19320	84	36	learn	learn	VERB
ajst-19320	84	37	its	its	PRON
ajst-19320	84	38	desired	desire	VERB
ajst-19320	84	39	features	feature	NOUN
ajst-19320	84	40	while	while	SCONJ
ajst-19320	84	41	retaining	retain	VERB
ajst-19320	84	42	the	the	DET
ajst-19320	84	43	key	key	ADJ
ajst-19320	84	44	features	feature	NOUN
ajst-19320	84	45	of	of	ADP
ajst-19320	84	46	the	the	DET
ajst-19320	84	47	layer	layer	NOUN
ajst-19320	84	48	,	,	PUNCT
ajst-19320	84	49	thus	thus	ADV
ajst-19320	84	50	improving	improve	VERB
ajst-19320	84	51	the	the	DET
ajst-19320	84	52	scale	scale	NOUN
ajst-19320	84	53	of	of	ADP
ajst-19320	84	54	the	the	DET
ajst-19320	84	55	features	feature	NOUN
ajst-19320	84	56	.	.	PUNCT
ajst-19320	85	1	invariance	invariance	NOUN
ajst-19320	85	2	.	.	PUNCT
ajst-19320	86	1	we	we	PRON
ajst-19320	86	2	denote	denote	VERB
ajst-19320	86	3	the	the	DET
ajst-19320	86	4	feature	feature	NOUN
ajst-19320	86	5	maps	map	NOUN
ajst-19320	86	6	of	of	ADP
ajst-19320	86	7	each	each	DET
ajst-19320	86	8	stage	stage	NOUN
ajst-19320	86	9	of	of	ADP
ajst-19320	86	10	panet	panet	NOUN
ajst-19320	86	11	as	as	ADP
ajst-19320	86	12	{	{	PUNCT
ajst-19320	86	13	f3,f4,f5	f3,f4,f5	NOUN
ajst-19320	86	14	}	}	PUNCT
ajst-19320	86	15	,	,	PUNCT
ajst-19320	86	16	and	and	CCONJ
ajst-19320	86	17	realize	realize	VERB
ajst-19320	86	18	feature	feature	NOUN
ajst-19320	86	19	fusion	fusion	NOUN
ajst-19320	86	20	in	in	ADP
ajst-19320	86	21	two	two	NUM
ajst-19320	86	22	ways	way	NOUN
ajst-19320	86	23	.	.	PUNCT
ajst-19320	87	1	first	first	ADV
ajst-19320	87	2	,	,	PUNCT
ajst-19320	87	3	we	we	PRON
ajst-19320	87	4	extract	extract	VERB
ajst-19320	87	5	the	the	DET
ajst-19320	87	6	channel	channel	NOUN
ajst-19320	87	7	attention	attention	NOUN
ajst-19320	87	8	from	from	ADP
ajst-19320	87	9	each	each	PRON
ajst-19320	87	10	of	of	ADP
ajst-19320	87	11	the	the	DET
ajst-19320	87	12	three	three	NUM
ajst-19320	87	13	feature	feature	NOUN
ajst-19320	87	14	maps	map	NOUN
ajst-19320	87	15	via	via	ADP
ajst-19320	87	16	the	the	DET
ajst-19320	87	17	cbam	cbam	NOUN
ajst-19320	87	18	(	(	PUNCT
ajst-19320	87	19	spatial	spatial	ADJ
ajst-19320	87	20	channel	channel	NOUN
ajst-19320	87	21	attention	attention	NOUN
ajst-19320	87	22	module	module	NOUN
ajst-19320	87	23	)	)	PUNCT
ajst-19320	87	24	module	module	NOUN
ajst-19320	87	25	,	,	PUNCT
ajst-19320	87	26	and	and	CCONJ
ajst-19320	87	27	realize	realize	VERB
ajst-19320	87	28	adaptive	adaptive	ADJ
ajst-19320	87	29	channel	channel	NOUN
ajst-19320	87	30	attention	attention	NOUN
ajst-19320	87	31	fusion	fusion	NOUN
ajst-19320	87	32	(	(	PUNCT
ajst-19320	87	33	acaf	acaf	NOUN
ajst-19320	87	34	)	)	PUNCT
ajst-19320	87	35	via	via	ADP
ajst-19320	87	36	network	network	PROPN
ajst-19320	87	37	autonomous	autonomous	PROPN
ajst-19320	87	38	learning	learning	NOUN
ajst-19320	87	39	.	.	PUNCT
ajst-19320	88	1	similarly	similarly	ADV
ajst-19320	88	2	,	,	PUNCT
ajst-19320	88	3	we	we	PRON
ajst-19320	88	4	directly	directly	ADV
ajst-19320	88	5	perform	perform	VERB
ajst-19320	88	6	adaptive	adaptive	ADJ
ajst-19320	88	7	feature	feature	NOUN
ajst-19320	88	8	fusion	fusion	NOUN
ajst-19320	88	9	on	on	ADP
ajst-19320	88	10	the	the	DET
ajst-19320	88	11	three	three	NUM
ajst-19320	88	12	feature	feature	NOUN
ajst-19320	88	13	maps	map	NOUN
ajst-19320	88	14	.	.	PUNCT
ajst-19320	89	1	these	these	DET
ajst-19320	89	2	two	two	NUM
ajst-19320	89	3	processes	process	NOUN
ajst-19320	89	4	can	can	AUX
ajst-19320	89	5	be	be	AUX
ajst-19320	89	6	expressed	express	VERB
ajst-19320	89	7	by	by	ADP
ajst-19320	89	8	the	the	DET
ajst-19320	89	9	following	follow	VERB
ajst-19320	89	10	equation	equation	NOUN
ajst-19320	89	11	:	:	PUNCT
ajst-19320	89	12	𝐴𝐶𝐴𝐹	𝐴𝐶𝐴𝐹	PROPN
ajst-19320	89	13	𝑥	𝑥	PROPN
ajst-19320	89	14	→	→	SYM
ajst-19320	89	15	⋅	⋅	NUM
ajst-19320	89	16	𝑎	𝑎	PRON
ajst-19320	89	17	⋅	⋅	PROPN
ajst-19320	89	18	𝐶𝐵𝐴𝑀	𝐶𝐵𝐴𝑀	NOUN
ajst-19320	89	19	𝑥	𝑥	NOUN
ajst-19320	89	20	→	→	SYM
ajst-19320	89	21	𝑥	𝑥	X
ajst-19320	89	22	→	→	SYM
ajst-19320	89	23	⋅	⋅	PROPN
ajst-19320	89	24	𝛽	𝛽	NOUN
ajst-19320	89	25	⋅	⋅	PROPN
ajst-19320	89	26	𝐶𝐵𝐴𝑀	𝐶𝐵𝐴𝑀	NOUN
ajst-19320	89	27	𝑥	𝑥	NOUN
ajst-19320	89	28	→	→	SYM
ajst-19320	89	29	𝑥	𝑥	X
ajst-19320	89	30	→	→	SYM
ajst-19320	89	31	⋅	⋅	PROPN
ajst-19320	89	32	𝛾	𝛾	ADP
ajst-19320	89	33	⋅	⋅	PROPN
ajst-19320	89	34	𝐶𝐵𝐴𝑀	𝐶𝐵𝐴𝑀	NOUN
ajst-19320	89	35	𝑥	𝑥	NOUN
ajst-19320	89	36	→	→	SYM
ajst-19320	89	37	(	(	PUNCT
ajst-19320	89	38	8)	8)	NUM
ajst-19320	89	39	where	where	SCONJ
ajst-19320	89	40	𝑥	𝑥	PROPN
ajst-19320	89	41	denotes	denote	VERB
ajst-19320	89	42	the	the	DET
ajst-19320	89	43	(	(	PUNCT
ajst-19320	89	44	i	i	PROPN
ajst-19320	89	45	,	,	PUNCT
ajst-19320	89	46	j	j	PROPN
ajst-19320	89	47	)	)	PUNCT
ajst-19320	89	48	vector	vector	NOUN
ajst-19320	89	49	of	of	ADP
ajst-19320	89	50	the	the	DET
ajst-19320	89	51	feature	feature	NOUN
ajst-19320	89	52	map	map	NOUN
ajst-19320	89	53	,	,	PUNCT
ajst-19320	89	54	𝛼	𝛼	INTJ
ajst-19320	89	55	,	,	PUNCT
ajst-19320	89	56	𝛽	𝛽	PROPN
ajst-19320	89	57	,	,	PUNCT
ajst-19320	89	58	𝛾	𝛾	PROPN
ajst-19320	89	59	are	be	AUX
ajst-19320	89	60	the	the	DET
ajst-19320	89	61	weights	weight	NOUN
ajst-19320	89	62	corresponding	correspond	VERB
ajst-19320	89	63	to	to	ADP
ajst-19320	89	64	the	the	DET
ajst-19320	89	65	three	three	NUM
ajst-19320	89	66	feature	feature	NOUN
ajst-19320	89	67	maps	map	NOUN
ajst-19320	89	68	,	,	PUNCT
ajst-19320	89	69	whose	whose	DET
ajst-19320	89	70	values	value	NOUN
ajst-19320	89	71	are	be	AUX
ajst-19320	89	72	learned	learn	VERB
ajst-19320	89	73	by	by	ADP
ajst-19320	89	74	the	the	DET
ajst-19320	89	75	network	network	NOUN
ajst-19320	89	76	autonomously	autonomously	ADV
ajst-19320	89	77	,	,	PUNCT
ajst-19320	89	78	and	and	CCONJ
ajst-19320	89	79	𝛼	𝛼	AUX
ajst-19320	89	80	𝛽	𝛽	NOUN
ajst-19320	89	81	𝛾	𝛾	PROPN
ajst-19320	89	82	1	1	NUM
ajst-19320	89	83	.	.	PUNCT
ajst-19320	90	1	finally	finally	ADV
ajst-19320	90	2	,	,	PUNCT
ajst-19320	90	3	we	we	PRON
ajst-19320	90	4	further	far	ADV
ajst-19320	90	5	adaptive	adaptive	ADJ
ajst-19320	90	6	fusion	fusion	NOUN
ajst-19320	90	7	the	the	DET
ajst-19320	90	8	feature	feature	NOUN
ajst-19320	90	9	fusion	fusion	NOUN
ajst-19320	90	10	maps	map	NOUN
ajst-19320	90	11	obtained	obtain	VERB
ajst-19320	90	12	from	from	ADP
ajst-19320	90	13	these	these	DET
ajst-19320	90	14	two	two	NUM
ajst-19320	90	15	processes	process	NOUN
ajst-19320	90	16	.	.	PUNCT
ajst-19320	91	1	thanks	thank	NOUN
ajst-19320	91	2	to	to	ADP
ajst-19320	91	3	the	the	DET
ajst-19320	91	4	above	above	ADJ
ajst-19320	91	5	improvements	improvement	NOUN
ajst-19320	91	6	,	,	PUNCT
ajst-19320	91	7	maffem	maffem	NOUN
ajst-19320	91	8	fuses	fuse	NOUN
ajst-19320	91	9	features	feature	NOUN
ajst-19320	91	10	at	at	ADP
ajst-19320	91	11	different	different	ADJ
ajst-19320	91	12	levels	level	NOUN
ajst-19320	91	13	while	while	SCONJ
ajst-19320	91	14	allowing	allow	VERB
ajst-19320	91	15	the	the	DET
ajst-19320	91	16	network	network	NOUN
ajst-19320	91	17	to	to	PART
ajst-19320	91	18	autonomously	autonomously	ADV
ajst-19320	91	19	select	select	VERB
ajst-19320	91	20	and	and	CCONJ
ajst-19320	91	21	retain	retain	VERB
ajst-19320	91	22	the	the	DET
ajst-19320	91	23	most	most	ADV
ajst-19320	91	24	appropriate	appropriate	ADJ
ajst-19320	91	25	features.the	features.the	DET
ajst-19320	91	26	structure	structure	NOUN
ajst-19320	91	27	of	of	ADP
ajst-19320	91	28	the	the	DET
ajst-19320	91	29	maffem	maffem	NOUN
ajst-19320	91	30	module	module	NOUN
ajst-19320	91	31	is	be	AUX
ajst-19320	91	32	shown	show	VERB
ajst-19320	91	33	in	in	ADP
ajst-19320	91	34	figure	figure	NOUN
ajst-19320	91	35	6	6	NUM
ajst-19320	91	36	below	below	ADV
ajst-19320	91	37	:	:	PUNCT
ajst-19320	91	38	figure	figure	VERB
ajst-19320	91	39	6	6	NUM
ajst-19320	91	40	.	.	PUNCT
ajst-19320	92	1	structure	structure	NOUN
ajst-19320	92	2	of	of	ADP
ajst-19320	92	3	maffem	maffem	NOUN
ajst-19320	92	4	network	network	NOUN
ajst-19320	92	5	figure	figure	NOUN
ajst-19320	92	6	7	7	NUM
ajst-19320	92	7	.	.	PUNCT
ajst-19320	92	8	improved	improved	ADJ
ajst-19320	92	9	yolov5	yolov5	NOUN
ajst-19320	92	10	network	network	NOUN
ajst-19320	92	11	structure	structure	NOUN
ajst-19320	92	12	out	out	ADP
ajst-19320	93	1	1	1	NUM
ajst-19320	93	2	x1	x1	NUM
ajst-19320	93	3	outf	outf	NOUN
ajst-19320	93	4	c	c	PROPN
ajst-19320	93	5	(	(	PUNCT
ajst-19320	93	6	concat	concat	X
ajst-19320	93	7	[	[	PUNCT
ajst-19320	93	8	f	f	X
ajst-19320	94	1	[	[	PUNCT
ajst-19320	94	2	i	i	PRON
ajst-19320	94	3	:]	:]	PROPN
ajst-19320	94	4	,	,	PUNCT
ajst-19320	94	5	n	n	PROPN
ajst-19320	94	6	(	(	PUNCT
ajst-19320	94	7	f	f	NOUN
ajst-19320	94	8	)	)	PUNCT
ajst-19320	95	1	[	[	PUNCT
ajst-19320	95	2	i	i	PRON
ajst-19320	95	3	:]	:]	PROPN
ajst-19320	95	4	,	,	PUNCT
ajst-19320	95	5	s	s	PART
ajst-19320	95	6	]	]	X
ajst-19320	95	7	)	)	PUNCT
ajst-19320	95	8			NOUN
ajst-19320	95	9	358	358	NUM
ajst-19320	95	10	4	4	NUM
ajst-19320	95	11	.	.	PUNCT
ajst-19320	96	1	experimental	experimental	ADJ
ajst-19320	96	2	analyses	analysis	NOUN
ajst-19320	96	3	4.1	4.1	NUM
ajst-19320	96	4	.	.	PUNCT
ajst-19320	97	1	experimental	experimental	ADJ
ajst-19320	97	2	environment	environment	NOUN
ajst-19320	97	3	and	and	CCONJ
ajst-19320	97	4	training	training	NOUN
ajst-19320	97	5	setup	setup	NOUN
ajst-19320	97	6	(	(	PUNCT
ajst-19320	97	7	1	1	X
ajst-19320	97	8	)	)	PUNCT
ajst-19320	97	9	the	the	DET
ajst-19320	97	10	experimental	experimental	ADJ
ajst-19320	97	11	configuration	configuration	NOUN
ajst-19320	97	12	environment	environment	NOUN
ajst-19320	97	13	is	be	AUX
ajst-19320	97	14	shown	show	VERB
ajst-19320	97	15	in	in	ADP
ajst-19320	97	16	table	table	NOUN
ajst-19320	97	17	1	1	NUM
ajst-19320	97	18	,	,	PUNCT
ajst-19320	97	19	and	and	CCONJ
ajst-19320	97	20	the	the	DET
ajst-19320	97	21	training	training	NOUN
ajst-19320	97	22	parameter	parameter	NOUN
ajst-19320	97	23	settings	setting	NOUN
ajst-19320	97	24	are	be	AUX
ajst-19320	97	25	shown	show	VERB
ajst-19320	97	26	in	in	ADP
ajst-19320	97	27	table	table	NOUN
ajst-19320	97	28	2	2	NUM
ajst-19320	97	29	:	:	PUNCT
ajst-19320	97	30	table	table	NOUN
ajst-19320	97	31	1	1	NUM
ajst-19320	97	32	.	.	PUNCT
ajst-19320	97	33	experimental	experimental	ADJ
ajst-19320	97	34	configuration	configuration	NOUN
ajst-19320	97	35	environment	environment	NOUN
ajst-19320	97	36	experimental	experimental	ADJ
ajst-19320	97	37	environment	environment	NOUN
ajst-19320	97	38	parameters	parameter	NOUN
ajst-19320	97	39	deep	deep	ADJ
ajst-19320	97	40	learning	learning	NOUN
ajst-19320	97	41	framework	framework	NOUN
ajst-19320	97	42	pytorch	pytorch	NOUN
ajst-19320	97	43	operating	operating	NOUN
ajst-19320	97	44	system	system	NOUN
ajst-19320	97	45	windows	window	VERB
ajst-19320	97	46	10	10	NUM
ajst-19320	97	47	gpu	gpu	PROPN
ajst-19320	97	48	nvidia	nvidia	PROPN
ajst-19320	97	49	geforce	geforce	NOUN
ajst-19320	97	50	gtx	gtx	PROPN
ajst-19320	97	51	2060	2060	NUM
ajst-19320	97	52	programming	programming	NOUN
ajst-19320	97	53	tools	tool	NOUN
ajst-19320	97	54	pycharm	pycharm	VERB
ajst-19320	97	55	programming	programming	NOUN
ajst-19320	97	56	language	language	NOUN
ajst-19320	97	57	python3.8	python3.8	ADJ
ajst-19320	97	58	table	table	NOUN
ajst-19320	97	59	2	2	NUM
ajst-19320	97	60	.	.	PUNCT
ajst-19320	97	61	training	training	NOUN
ajst-19320	97	62	settings	setting	NOUN
ajst-19320	97	63	training	train	VERB
ajst-19320	97	64	parameter	parameter	NOUN
ajst-19320	97	65	parameter	parameter	NOUN
ajst-19320	97	66	size	size	NOUN
ajst-19320	97	67	input	input	NOUN
ajst-19320	97	68	image	image	NOUN
ajst-19320	97	69	size	size	NOUN
ajst-19320	97	70	640	640	NUM
ajst-19320	97	71	640	640	NUM
ajst-19320	97	72	pre	pre	ADJ
ajst-19320	97	73	-	-	ADJ
ajst-19320	97	74	training	training	ADJ
ajst-19320	97	75	weight	weight	NOUN
ajst-19320	97	76	yolov5s	yolov5s	PROPN
ajst-19320	97	77	initial	initial	ADJ
ajst-19320	97	78	learning	learning	NOUN
ajst-19320	97	79	rate	rate	NOUN
ajst-19320	97	80	0.01	0.01	NUM
ajst-19320	97	81	weight	weight	NOUN
ajst-19320	97	82	decay	decay	NOUN
ajst-19320	97	83	factor	factor	NOUN
ajst-19320	97	84	0.0005	0.0005	NUM
ajst-19320	97	85	category	category	NOUN
ajst-19320	97	86	confidence	confidence	NOUN
ajst-19320	97	87	threshold	threshold	NOUN
ajst-19320	97	88	0.5	0.5	NUM
ajst-19320	97	89	epochs	epoch	NOUN
ajst-19320	97	90	100	100	NUM
ajst-19320	97	91	batchsize	batchsize	VERB
ajst-19320	97	92	16	16	NUM
ajst-19320	97	93	4.2	4.2	NUM
ajst-19320	97	94	.	.	PUNCT
ajst-19320	98	1	evaluation	evaluation	NOUN
ajst-19320	98	2	indicators	indicator	NOUN
ajst-19320	98	3	in	in	ADP
ajst-19320	98	4	this	this	DET
ajst-19320	98	5	paper	paper	NOUN
ajst-19320	98	6	,	,	PUNCT
ajst-19320	98	7	we	we	PRON
ajst-19320	98	8	validate	validate	VERB
ajst-19320	98	9	the	the	DET
ajst-19320	98	10	model	model	NOUN
ajst-19320	98	11	detection	detection	NOUN
ajst-19320	98	12	effect	effect	NOUN
ajst-19320	98	13	by	by	ADP
ajst-19320	98	14	three	three	NUM
ajst-19320	98	15	commonly	commonly	ADV
ajst-19320	98	16	used	use	VERB
ajst-19320	98	17	performance	performance	NOUN
ajst-19320	98	18	metrics	metric	NOUN
ajst-19320	98	19	,	,	PUNCT
ajst-19320	98	20	namely	namely	ADV
ajst-19320	98	21	precision	precision	NOUN
ajst-19320	98	22	rate	rate	NOUN
ajst-19320	98	23	(	(	PUNCT
ajst-19320	98	24	precision	precision	NOUN
ajst-19320	98	25	,	,	PUNCT
ajst-19320	98	26	p	p	NOUN
ajst-19320	98	27	)	)	PUNCT
ajst-19320	98	28	,	,	PUNCT
ajst-19320	98	29	recall	recall	NOUN
ajst-19320	98	30	rate	rate	NOUN
ajst-19320	98	31	(	(	PUNCT
ajst-19320	98	32	recall	recall	NOUN
ajst-19320	98	33	,	,	PUNCT
ajst-19320	98	34	r	r	NOUN
ajst-19320	98	35	)	)	PUNCT
ajst-19320	98	36	and	and	CCONJ
ajst-19320	98	37	average	average	ADJ
ajst-19320	98	38	precision	precision	NOUN
ajst-19320	98	39	mean[16	mean[16	PROPN
ajst-19320	98	40	]	]	PUNCT
ajst-19320	98	41	.	.	PUNCT
ajst-19320	99	1	precision	precision	NOUN
ajst-19320	99	2	rate	rate	NOUN
ajst-19320	99	3	indicates	indicate	VERB
ajst-19320	99	4	the	the	DET
ajst-19320	99	5	ratio	ratio	NOUN
ajst-19320	99	6	of	of	ADP
ajst-19320	99	7	the	the	DET
ajst-19320	99	8	number	number	NOUN
ajst-19320	99	9	of	of	ADP
ajst-19320	99	10	samples	sample	NOUN
ajst-19320	99	11	predicted	predict	VERB
ajst-19320	99	12	as	as	ADP
ajst-19320	99	13	positive	positive	ADJ
ajst-19320	99	14	to	to	ADP
ajst-19320	99	15	the	the	DET
ajst-19320	99	16	number	number	NOUN
ajst-19320	99	17	of	of	ADP
ajst-19320	99	18	true	true	ADJ
ajst-19320	99	19	positive	positive	ADJ
ajst-19320	99	20	samples	sample	NOUN
ajst-19320	99	21	,	,	PUNCT
ajst-19320	99	22	as	as	SCONJ
ajst-19320	99	23	shown	show	VERB
ajst-19320	99	24	in	in	ADP
ajst-19320	99	25	equation	equation	NOUN
ajst-19320	99	26	(	(	PUNCT
ajst-19320	99	27	9	9	NUM
ajst-19320	99	28	)	)	PUNCT
ajst-19320	99	29	.	.	PUNCT
ajst-19320	100	1	recall	recall	NOUN
ajst-19320	100	2	rate	rate	NOUN
ajst-19320	100	3	indicates	indicate	VERB
ajst-19320	100	4	the	the	DET
ajst-19320	100	5	proportion	proportion	NOUN
ajst-19320	100	6	of	of	ADP
ajst-19320	100	7	the	the	DET
ajst-19320	100	8	number	number	NOUN
ajst-19320	100	9	of	of	ADP
ajst-19320	100	10	correctly	correctly	ADV
ajst-19320	100	11	predicted	predict	VERB
ajst-19320	100	12	samples	sample	NOUN
ajst-19320	100	13	to	to	ADP
ajst-19320	100	14	the	the	DET
ajst-19320	100	15	number	number	NOUN
ajst-19320	100	16	of	of	ADP
ajst-19320	100	17	positive	positive	ADJ
ajst-19320	100	18	samples	sample	NOUN
ajst-19320	100	19	of	of	ADP
ajst-19320	100	20	the	the	DET
ajst-19320	100	21	original	original	ADJ
ajst-19320	100	22	sample[17	sample[17	VERB
ajst-19320	100	23	]	]	PUNCT
ajst-19320	100	24	,	,	PUNCT
ajst-19320	100	25	as	as	SCONJ
ajst-19320	100	26	shown	show	VERB
ajst-19320	100	27	in	in	ADP
ajst-19320	100	28	equation	equation	NOUN
ajst-19320	100	29	(	(	PUNCT
ajst-19320	100	30	10	10	NUM
ajst-19320	100	31	)	)	PUNCT
ajst-19320	100	32	.	.	PUNCT
ajst-19320	101	1	mean	mean	ADJ
ajst-19320	101	2	precision	precision	NOUN
ajst-19320	101	3	mean	mean	VERB
ajst-19320	101	4	indicates	indicate	VERB
ajst-19320	101	5	the	the	DET
ajst-19320	101	6	average	average	ADJ
ajst-19320	101	7	precision	precision	NOUN
ajst-19320	101	8	averaged	average	VERB
ajst-19320	101	9	over	over	ADP
ajst-19320	101	10	all	all	DET
ajst-19320	101	11	categories[18	categories[18	NOUN
ajst-19320	101	12	]	]	PUNCT
ajst-19320	101	13	,	,	PUNCT
ajst-19320	101	14	as	as	SCONJ
ajst-19320	101	15	shown	show	VERB
ajst-19320	101	16	in	in	ADP
ajst-19320	101	17	equation	equation	NOUN
ajst-19320	101	18	(	(	PUNCT
ajst-19320	101	19	11	11	NUM
ajst-19320	101	20	)	)	PUNCT
ajst-19320	101	21	.	.	PUNCT
ajst-19320	102	1	the	the	DET
ajst-19320	102	2	precision	precision	NOUN
ajst-19320	102	3	rate	rate	NOUN
ajst-19320	102	4	verifies	verifie	NOUN
ajst-19320	102	5	the	the	DET
ajst-19320	102	6	effectiveness	effectiveness	NOUN
ajst-19320	102	7	of	of	ADP
ajst-19320	102	8	a	a	DET
ajst-19320	102	9	classifier	classifier	NOUN
ajst-19320	102	10	,	,	PUNCT
ajst-19320	102	11	and	and	CCONJ
ajst-19320	102	12	usually	usually	ADV
ajst-19320	102	13	,	,	PUNCT
ajst-19320	102	14	the	the	DET
ajst-19320	102	15	precision	precision	NOUN
ajst-19320	102	16	rate	rate	NOUN
ajst-19320	102	17	is	be	AUX
ajst-19320	102	18	negatively	negatively	ADV
ajst-19320	102	19	correlated	correlate	VERB
ajst-19320	102	20	with	with	ADP
ajst-19320	102	21	the	the	DET
ajst-19320	102	22	recall	recall	NOUN
ajst-19320	102	23	rate	rate	NOUN
ajst-19320	102	24	,	,	PUNCT
ajst-19320	102	25	the	the	PRON
ajst-19320	102	26	higher	high	ADJ
ajst-19320	102	27	the	the	DET
ajst-19320	102	28	precision	precision	NOUN
ajst-19320	102	29	rate	rate	NOUN
ajst-19320	102	30	,	,	PUNCT
ajst-19320	102	31	the	the	PRON
ajst-19320	102	32	lower	low	ADJ
ajst-19320	102	33	the	the	DET
ajst-19320	102	34	recall	recall	NOUN
ajst-19320	102	35	rate	rate	NOUN
ajst-19320	102	36	.	.	PUNCT
ajst-19320	103	1	the	the	DET
ajst-19320	103	2	average	average	ADJ
ajst-19320	103	3	precision	precision	NOUN
ajst-19320	103	4	mean	mean	NOUN
ajst-19320	103	5	represents	represent	VERB
ajst-19320	103	6	a	a	DET
ajst-19320	103	7	comprehensive	comprehensive	ADJ
ajst-19320	103	8	evaluation	evaluation	NOUN
ajst-19320	103	9	of	of	ADP
ajst-19320	103	10	the	the	DET
ajst-19320	103	11	average	average	ADJ
ajst-19320	103	12	precision	precision	NOUN
ajst-19320	103	13	of	of	ADP
ajst-19320	103	14	the	the	DET
ajst-19320	103	15	detected	detect	VERB
ajst-19320	103	16	targets	target	NOUN
ajst-19320	103	17	,	,	PUNCT
ajst-19320	103	18	which	which	PRON
ajst-19320	103	19	can	can	AUX
ajst-19320	103	20	more	more	ADV
ajst-19320	103	21	intuitively	intuitively	ADV
ajst-19320	103	22	show	show	VERB
ajst-19320	103	23	the	the	DET
ajst-19320	103	24	performance	performance	NOUN
ajst-19320	103	25	of	of	ADP
ajst-19320	103	26	the	the	DET
ajst-19320	103	27	classifier[19	classifier[19	NOUN
ajst-19320	103	28	]	]	X
ajst-19320	103	29	.	.	PUNCT
ajst-19320	104	1	tp	tp	ADP
ajst-19320	104	2	p	p	NOUN
ajst-19320	104	3	tp	tp	ADP
ajst-19320	104	4	fp	fp	PROPN
ajst-19320	104	5			PROPN
ajst-19320	104	6			X
ajst-19320	104	7	(	(	PUNCT
ajst-19320	104	8	9	9	NUM
ajst-19320	104	9	)	)	PUNCT
ajst-19320	104	10	tp	tp	ADP
ajst-19320	104	11	r	r	NOUN
ajst-19320	104	12	tp	tp	NOUN
ajst-19320	104	13	fn	fn	PROPN
ajst-19320	105	1			PROPN
ajst-19320	105	2			PUNCT
ajst-19320	105	3	(	(	PUNCT
ajst-19320	105	4	10	10	NUM
ajst-19320	105	5	)	)	SYM
ajst-19320	105	6	1	1	NUM
ajst-19320	106	1	n	n	NOUN
ajst-19320	107	1	i	i	PRON
ajst-19320	107	2	i	i	PRON
ajst-19320	107	3	ap	ap	PROPN
ajst-19320	107	4	map	map	VERB
ajst-19320	107	5	n	n	PRON
ajst-19320	107	6			NUM
ajst-19320	107	7			X
ajst-19320	107	8	(	(	PUNCT
ajst-19320	107	9	11	11	NUM
ajst-19320	107	10	)	)	PUNCT
ajst-19320	107	11	where	where	SCONJ
ajst-19320	107	12	tp	tp	PART
ajst-19320	107	13	denotes	denote	VERB
ajst-19320	107	14	the	the	DET
ajst-19320	107	15	number	number	NOUN
ajst-19320	107	16	of	of	ADP
ajst-19320	107	17	correctly	correctly	ADV
ajst-19320	107	18	detected	detect	VERB
ajst-19320	107	19	frames	frame	NOUN
ajst-19320	107	20	;	;	PUNCT
ajst-19320	107	21	fp	fp	X
ajst-19320	107	22	denotes	denote	VERB
ajst-19320	107	23	the	the	DET
ajst-19320	107	24	number	number	NOUN
ajst-19320	107	25	of	of	ADP
ajst-19320	107	26	incorrectly	incorrectly	ADV
ajst-19320	107	27	detected	detect	VERB
ajst-19320	107	28	frames	frame	NOUN
ajst-19320	107	29	;	;	PUNCT
ajst-19320	107	30	fn	fn	NOUN
ajst-19320	107	31	denotes	denote	VERB
ajst-19320	107	32	the	the	DET
ajst-19320	107	33	number	number	NOUN
ajst-19320	107	34	of	of	ADP
ajst-19320	107	35	falsely	falsely	ADV
ajst-19320	107	36	detected	detect	VERB
ajst-19320	107	37	frames	frame	NOUN
ajst-19320	107	38	;	;	PUNCT
ajst-19320	107	39	ap	ap	PROPN
ajst-19320	107	40	denotes	denote	VERB
ajst-19320	107	41	the	the	DET
ajst-19320	107	42	area	area	NOUN
ajst-19320	107	43	enclosed	enclose	VERB
ajst-19320	107	44	by	by	ADP
ajst-19320	107	45	the	the	DET
ajst-19320	107	46	curve	curve	NOUN
ajst-19320	107	47	of	of	ADP
ajst-19320	107	48	precision	precision	NOUN
ajst-19320	107	49	p	p	NOUN
ajst-19320	107	50	and	and	CCONJ
ajst-19320	107	51	recall	recall	NOUN
ajst-19320	107	52	r	r	NOUN
ajst-19320	107	53	,	,	PUNCT
ajst-19320	107	54	i.e.	i.e.	X
ajst-19320	107	55	,	,	PUNCT
ajst-19320	107	56	the	the	DET
ajst-19320	107	57	average	average	ADJ
ajst-19320	107	58	precision	precision	NOUN
ajst-19320	107	59	;	;	PUNCT
ajst-19320	107	60	and	and	CCONJ
ajst-19320	107	61	denotes	denote	VERB
ajst-19320	107	62	the	the	DET
ajst-19320	107	63	total	total	ADJ
ajst-19320	107	64	number	number	NOUN
ajst-19320	107	65	of	of	ADP
ajst-19320	107	66	categories[20	categories[20	PROPN
ajst-19320	107	67	]	]	PUNCT
ajst-19320	107	68	.	.	PUNCT
ajst-19320	108	1	4.3	4.3	NUM
ajst-19320	108	2	.	.	PUNCT
ajst-19320	109	1	experimental	experimental	ADJ
ajst-19320	109	2	analyses	analysis	NOUN
ajst-19320	109	3	(	(	PUNCT
ajst-19320	109	4	1	1	X
ajst-19320	109	5	)	)	PUNCT
ajst-19320	109	6	dataset	dataset	NOUN
ajst-19320	109	7	creation	creation	NOUN
ajst-19320	109	8	the	the	DET
ajst-19320	109	9	detection	detection	NOUN
ajst-19320	109	10	effect	effect	NOUN
ajst-19320	109	11	of	of	ADP
ajst-19320	109	12	deep	deep	ADJ
ajst-19320	109	13	learning	learning	NOUN
ajst-19320	109	14	target	target	NOUN
ajst-19320	109	15	detection	detection	NOUN
ajst-19320	109	16	model	model	NOUN
ajst-19320	109	17	relies	rely	VERB
ajst-19320	109	18	on	on	ADP
ajst-19320	109	19	rich	rich	ADJ
ajst-19320	109	20	dataset	dataset	NOUN
ajst-19320	109	21	,	,	PUNCT
ajst-19320	109	22	in	in	ADP
ajst-19320	109	23	this	this	DET
ajst-19320	109	24	paper	paper	NOUN
ajst-19320	109	25	,	,	PUNCT
ajst-19320	109	26	on	on	ADP
ajst-19320	109	27	the	the	DET
ajst-19320	109	28	basis	basis	NOUN
ajst-19320	109	29	of	of	ADP
ajst-19320	109	30	visdrone2019	visdrone2019	PROPN
ajst-19320	109	31	aerial	aerial	ADJ
ajst-19320	109	32	photography	photography	NOUN
ajst-19320	109	33	open	open	ADJ
ajst-19320	109	34	source	source	NOUN
ajst-19320	109	35	dataset	dataset	NOUN
ajst-19320	109	36	produced	produce	VERB
ajst-19320	109	37	by	by	ADP
ajst-19320	109	38	machine	machine	NOUN
ajst-19320	109	39	learning	learning	NOUN
ajst-19320	109	40	and	and	CCONJ
ajst-19320	109	41	data	datum	NOUN
ajst-19320	109	42	mining	mining	NOUN
ajst-19320	109	43	laboratory	laboratory	NOUN
ajst-19320	109	44	of	of	ADP
ajst-19320	109	45	tianjin	tianjin	PROPN
ajst-19320	109	46	university	university	PROPN
ajst-19320	109	47	,	,	PUNCT
ajst-19320	109	48	we	we	PRON
ajst-19320	109	49	shoot	shoot	VERB
ajst-19320	109	50	aerial	aerial	ADJ
ajst-19320	109	51	video	video	NOUN
ajst-19320	109	52	to	to	PART
ajst-19320	109	53	collect	collect	VERB
ajst-19320	109	54	the	the	DET
ajst-19320	109	55	data	datum	NOUN
ajst-19320	109	56	and	and	CCONJ
ajst-19320	109	57	annotate	annotate	VERB
ajst-19320	109	58	it	it	PRON
ajst-19320	109	59	by	by	ADP
ajst-19320	109	60	ourselves	ourselves	PRON
ajst-19320	109	61	,	,	PUNCT
ajst-19320	109	62	and	and	CCONJ
ajst-19320	109	63	then	then	ADV
ajst-19320	109	64	finally	finally	ADV
ajst-19320	109	65	train	train	VERB
ajst-19320	109	66	the	the	DET
ajst-19320	109	67	model	model	NOUN
ajst-19320	109	68	by	by	ADP
ajst-19320	109	69	combining	combine	VERB
ajst-19320	109	70	with	with	ADP
ajst-19320	109	71	visdrone2019	visdrone2019	PROPN
ajst-19320	109	72	aerial	aerial	ADJ
ajst-19320	109	73	photography	photography	NOUN
ajst-19320	109	74	dataset	dataset	VERB
ajst-19320	109	75	.	.	PUNCT
ajst-19320	110	1	in	in	ADP
ajst-19320	110	2	order	order	NOUN
ajst-19320	110	3	to	to	PART
ajst-19320	110	4	enhance	enhance	VERB
ajst-19320	110	5	the	the	DET
ajst-19320	110	6	generalization	generalization	NOUN
ajst-19320	110	7	ability	ability	NOUN
ajst-19320	110	8	of	of	ADP
ajst-19320	110	9	the	the	DET
ajst-19320	110	10	model	model	NOUN
ajst-19320	110	11	,	,	PUNCT
ajst-19320	110	12	a	a	DET
ajst-19320	110	13	high	high	ADJ
ajst-19320	110	14	-	-	PUNCT
ajst-19320	110	15	precision	precision	NOUN
ajst-19320	110	16	uav	uav	PROPN
ajst-19320	110	17	aerial	aerial	ADJ
ajst-19320	110	18	photography	photography	NOUN
ajst-19320	110	19	dataset	dataset	NOUN
ajst-19320	110	20	is	be	AUX
ajst-19320	110	21	established	establish	VERB
ajst-19320	110	22	,	,	PUNCT
ajst-19320	110	23	and	and	CCONJ
ajst-19320	110	24	data	datum	NOUN
ajst-19320	110	25	collection	collection	NOUN
ajst-19320	110	26	is	be	AUX
ajst-19320	110	27	carried	carry	VERB
ajst-19320	110	28	out	out	ADP
ajst-19320	110	29	by	by	ADP
ajst-19320	110	30	using	use	VERB
ajst-19320	110	31	uavs	uavs	NOUN
ajst-19320	110	32	to	to	PART
ajst-19320	110	33	shoot	shoot	VERB
ajst-19320	110	34	on	on	ADP
ajst-19320	110	35	various	various	ADJ
ajst-19320	110	36	roads	road	NOUN
ajst-19320	110	37	in	in	ADP
ajst-19320	110	38	tangshan	tangshan	PROPN
ajst-19320	110	39	city	city	NOUN
ajst-19320	110	40	area	area	NOUN
ajst-19320	110	41	,	,	PUNCT
ajst-19320	110	42	and	and	CCONJ
ajst-19320	110	43	the	the	DET
ajst-19320	110	44	shooting	shooting	NOUN
ajst-19320	110	45	scenes	scene	NOUN
ajst-19320	110	46	include	include	VERB
ajst-19320	110	47	evening	evening	NOUN
ajst-19320	110	48	,	,	PUNCT
ajst-19320	110	49	cloudy	cloudy	ADJ
ajst-19320	110	50	and	and	CCONJ
ajst-19320	110	51	rainy	rainy	ADJ
ajst-19320	110	52	days	day	NOUN
ajst-19320	110	53	and	and	CCONJ
ajst-19320	110	54	sunny	sunny	ADJ
ajst-19320	110	55	days	day	NOUN
ajst-19320	110	56	with	with	ADP
ajst-19320	110	57	strong	strong	ADJ
ajst-19320	110	58	light	light	NOUN
ajst-19320	110	59	,	,	PUNCT
ajst-19320	110	60	which	which	PRON
ajst-19320	110	61	are	be	AUX
ajst-19320	110	62	manually	manually	ADV
ajst-19320	110	63	annotated	annotate	VERB
ajst-19320	110	64	by	by	ADP
ajst-19320	110	65	using	use	VERB
ajst-19320	110	66	labelimg	labelimg	NOUN
ajst-19320	110	67	annotation	annotation	NOUN
ajst-19320	110	68	tool	tool	NOUN
ajst-19320	110	69	,	,	PUNCT
ajst-19320	110	70	and	and	CCONJ
ajst-19320	110	71	fused	fuse	VERB
ajst-19320	110	72	with	with	SCONJ
ajst-19320	110	73	the	the	DET
ajst-19320	110	74	visdrone2019	visdrone2019	PROPN
ajst-19320	110	75	dataset	dataset	VERB
ajst-19320	110	76	into	into	ADP
ajst-19320	110	77	a	a	DET
ajst-19320	110	78	high	high	ADJ
ajst-19320	110	79	-	-	PUNCT
ajst-19320	110	80	precision	precision	NOUN
ajst-19320	110	81	uav	uav	PROPN
ajst-19320	110	82	aerial	aerial	ADJ
ajst-19320	110	83	photography	photography	NOUN
ajst-19320	110	84	dataset	dataset	VERB
ajst-19320	110	85	.	.	PUNCT
ajst-19320	111	1	the	the	DET
ajst-19320	111	2	detection	detection	NOUN
ajst-19320	111	3	effect	effect	NOUN
ajst-19320	111	4	of	of	ADP
ajst-19320	111	5	the	the	DET
ajst-19320	111	6	improved	improved	ADJ
ajst-19320	111	7	yolov5	yolov5	NOUN
ajst-19320	111	8	model	model	NOUN
ajst-19320	111	9	is	be	AUX
ajst-19320	111	10	evaluated	evaluate	VERB
ajst-19320	111	11	on	on	ADP
ajst-19320	111	12	this	this	DET
ajst-19320	111	13	dataset	dataset	NOUN
ajst-19320	111	14	.	.	PUNCT
ajst-19320	112	1	finally	finally	ADV
ajst-19320	112	2	,	,	PUNCT
ajst-19320	112	3	5000	5000	NUM
ajst-19320	112	4	images	image	NOUN
ajst-19320	112	5	with	with	ADP
ajst-19320	112	6	labels	label	NOUN
ajst-19320	112	7	are	be	AUX
ajst-19320	112	8	selected	select	VERB
ajst-19320	112	9	from	from	ADP
ajst-19320	112	10	visdrone2019	visdrone2019	PROPN
ajst-19320	112	11	dataset	dataset	VERB
ajst-19320	112	12	and	and	CCONJ
ajst-19320	112	13	fused	fuse	VERB
ajst-19320	112	14	with	with	ADP
ajst-19320	112	15	1000	1000	NUM
ajst-19320	112	16	images	image	NOUN
ajst-19320	112	17	manually	manually	ADV
ajst-19320	112	18	labeled	label	VERB
ajst-19320	112	19	to	to	PART
ajst-19320	112	20	form	form	VERB
ajst-19320	112	21	a	a	DET
ajst-19320	112	22	detection	detection	NOUN
ajst-19320	112	23	dataset	dataset	VERB
ajst-19320	112	24	.	.	PUNCT
ajst-19320	113	1	the	the	DET
ajst-19320	113	2	dataset	dataset	NOUN
ajst-19320	113	3	division	division	NOUN
ajst-19320	113	4	is	be	AUX
ajst-19320	113	5	performed	perform	VERB
ajst-19320	113	6	with	with	ADP
ajst-19320	113	7	80	80	NUM
ajst-19320	113	8	%	%	NOUN
ajst-19320	113	9	as	as	ADP
ajst-19320	113	10	training	training	NOUN
ajst-19320	113	11	set	set	VERB
ajst-19320	113	12	and	and	CCONJ
ajst-19320	113	13	20	20	NUM
ajst-19320	113	14	%	%	NOUN
ajst-19320	113	15	as	as	ADP
ajst-19320	113	16	testing	testing	NOUN
ajst-19320	113	17	set	set	VERB
ajst-19320	113	18	.	.	PUNCT
ajst-19320	114	1	(	(	PUNCT
ajst-19320	114	2	2	2	X
ajst-19320	114	3	)	)	PUNCT
ajst-19320	114	4	comparative	comparative	ADJ
ajst-19320	114	5	analysis	analysis	NOUN
ajst-19320	114	6	of	of	ADP
ajst-19320	114	7	performance	performance	NOUN
ajst-19320	114	8	test	test	NOUN
ajst-19320	114	9	results	result	VERB
ajst-19320	114	10	the	the	DET
ajst-19320	114	11	performance	performance	NOUN
ajst-19320	114	12	test	test	NOUN
ajst-19320	114	13	results	result	NOUN
ajst-19320	114	14	of	of	ADP
ajst-19320	114	15	the	the	DET
ajst-19320	114	16	improved	improved	ADJ
ajst-19320	114	17	yolov5	yolov5	NOUN
ajst-19320	114	18	algorithm	algorithm	NOUN
ajst-19320	114	19	in	in	ADP
ajst-19320	114	20	this	this	DET
ajst-19320	114	21	paper	paper	NOUN
ajst-19320	114	22	and	and	CCONJ
ajst-19320	114	23	the	the	DET
ajst-19320	114	24	classical	classical	ADJ
ajst-19320	114	25	yolov5	yolov5	NOUN
ajst-19320	114	26	algorithm	algorithm	NOUN
ajst-19320	114	27	on	on	ADP
ajst-19320	114	28	divided	divide	VERB
ajst-19320	114	29	datasets	dataset	NOUN
ajst-19320	114	30	of	of	ADP
ajst-19320	114	31	different	different	ADJ
ajst-19320	114	32	sizes	size	NOUN
ajst-19320	114	33	are	be	AUX
ajst-19320	114	34	shown	show	VERB
ajst-19320	114	35	in	in	ADP
ajst-19320	114	36	table	table	NOUN
ajst-19320	114	37	3	3	NUM
ajst-19320	114	38	.	.	PUNCT
ajst-19320	114	39	table	table	NOUN
ajst-19320	114	40	3	3	NUM
ajst-19320	114	41	.	.	PUNCT
ajst-19320	114	42	comparison	comparison	NOUN
ajst-19320	114	43	of	of	ADP
ajst-19320	114	44	performance	performance	NOUN
ajst-19320	114	45	test	test	NOUN
ajst-19320	114	46	results	result	VERB
ajst-19320	114	47	p	p	NOUN
ajst-19320	114	48	r	r	NOUN
ajst-19320	114	49	map0.5	map0.5	PROPN
ajst-19320	114	50	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	114	51	original	original	ADJ
ajst-19320	114	52	0.487	0.487	NUM
ajst-19320	114	53	0.344	0.344	NUM
ajst-19320	114	54	0.346	0.346	NUM
ajst-19320	114	55	0.185	0.185	NUM
ajst-19320	114	56	dpfem	dpfem	NOUN
ajst-19320	114	57	0.483	0.483	NUM
ajst-19320	114	58	0.391	0.391	NUM
ajst-19320	114	59	0.353	0.353	NUM
ajst-19320	114	60	0.199	0.199	NUM
ajst-19320	114	61	maffem	maffem	NOUN
ajst-19320	114	62	0.505	0.505	NUM
ajst-19320	114	63	0.38	0.38	NUM
ajst-19320	114	64	0.361	0.361	NUM
ajst-19320	114	65	0.196	0.196	NUM
ajst-19320	114	66	all	all	PRON
ajst-19320	114	67	0.523	0.523	NUM
ajst-19320	114	68	0.396	0.396	NUM
ajst-19320	114	69	0.368	0.368	NUM
ajst-19320	114	70	0.202	0.202	NUM
ajst-19320	114	71	the	the	DET
ajst-19320	114	72	dpfem	dpfem	PROPN
ajst-19320	114	73	module	module	NOUN
ajst-19320	114	74	adopts	adopt	VERB
ajst-19320	114	75	a	a	DET
ajst-19320	114	76	dual	dual	ADJ
ajst-19320	114	77	-	-	PUNCT
ajst-19320	114	78	path	path	NOUN
ajst-19320	114	79	structure	structure	NOUN
ajst-19320	114	80	,	,	PUNCT
ajst-19320	114	81	which	which	PRON
ajst-19320	114	82	is	be	AUX
ajst-19320	114	83	able	able	ADJ
ajst-19320	114	84	to	to	PART
ajst-19320	114	85	mine	mine	VERB
ajst-19320	114	86	new	new	ADJ
ajst-19320	114	87	features	feature	NOUN
ajst-19320	114	88	while	while	SCONJ
ajst-19320	114	89	realizing	realize	VERB
ajst-19320	114	90	feature	feature	NOUN
ajst-19320	114	91	extraction	extraction	NOUN
ajst-19320	114	92	,	,	PUNCT
ajst-19320	114	93	and	and	CCONJ
ajst-19320	114	94	improves	improve	VERB
ajst-19320	114	95	the	the	DET
ajst-19320	114	96	feature	feature	NOUN
ajst-19320	114	97	extraction	extraction	NOUN
ajst-19320	114	98	capability	capability	NOUN
ajst-19320	114	99	of	of	ADP
ajst-19320	114	100	the	the	DET
ajst-19320	114	101	network	network	NOUN
ajst-19320	114	102	,	,	PUNCT
ajst-19320	114	103	thus	thus	ADV
ajst-19320	114	104	improving	improve	VERB
ajst-19320	114	105	map0.5	map0.5	PROPN
ajst-19320	114	106	and	and	CCONJ
ajst-19320	114	107	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	114	108	by	by	ADP
ajst-19320	114	109	0.7	0.7	NUM
ajst-19320	114	110	%	%	NOUN
ajst-19320	114	111	and	and	CCONJ
ajst-19320	114	112	1.4	1.4	NUM
ajst-19320	114	113	%	%	NOUN
ajst-19320	114	114	,	,	PUNCT
ajst-19320	114	115	respectively.maffem	respectively.maffem	NOUN
ajst-19320	114	116	fuses	fuse	VERB
ajst-19320	114	117	different	different	ADJ
ajst-19320	114	118	levels	level	NOUN
ajst-19320	114	119	of	of	ADP
ajst-19320	114	120	features	feature	NOUN
ajst-19320	114	121	by	by	ADP
ajst-19320	114	122	autonomously	autonomously	ADV
ajst-19320	114	123	learning	learn	VERB
ajst-19320	114	124	to	to	PART
ajst-19320	114	125	select	select	VERB
ajst-19320	114	126	the	the	DET
ajst-19320	114	127	key	key	ADJ
ajst-19320	114	128	features	feature	NOUN
ajst-19320	114	129	of	of	ADP
ajst-19320	114	130	different	different	ADJ
ajst-19320	114	131	feature	feature	NOUN
ajst-19320	114	132	layers	layer	NOUN
ajst-19320	114	133	required	require	VERB
ajst-19320	114	134	by	by	ADP
ajst-19320	114	135	the	the	DET
ajst-19320	114	136	network	network	NOUN
ajst-19320	114	137	,	,	PUNCT
ajst-19320	114	138	and	and	CCONJ
ajst-19320	114	139	at	at	ADP
ajst-19320	114	140	the	the	DET
ajst-19320	114	141	same	same	ADJ
ajst-19320	114	142	time	time	NOUN
ajst-19320	114	143	allows	allow	VERB
ajst-19320	114	144	the	the	DET
ajst-19320	114	145	network	network	NOUN
ajst-19320	114	146	to	to	PART
ajst-19320	114	147	autonomously	autonomously	ADV
ajst-19320	114	148	select	select	VERB
ajst-19320	114	149	and	and	CCONJ
ajst-19320	114	150	retain	retain	VERB
ajst-19320	114	151	the	the	DET
ajst-19320	114	152	most	most	ADV
ajst-19320	114	153	appropriate	appropriate	ADJ
ajst-19320	114	154	features.the	features.the	DET
ajst-19320	114	155	use	use	NOUN
ajst-19320	114	156	of	of	ADP
ajst-19320	114	157	the	the	DET
ajst-19320	114	158	maffem	maffem	NOUN
ajst-19320	114	159	module	module	NOUN
ajst-19320	114	160	resulted	result	VERB
ajst-19320	114	161	in	in	ADP
ajst-19320	114	162	an	an	DET
ajst-19320	114	163	improvement	improvement	NOUN
ajst-19320	114	164	of	of	ADP
ajst-19320	114	165	1.5	1.5	NUM
ajst-19320	114	166	%	%	NOUN
ajst-19320	114	167	and	and	CCONJ
ajst-19320	114	168	1.1	1.1	NUM
ajst-19320	114	169	%	%	NOUN
ajst-19320	114	170	for	for	ADP
ajst-19320	114	171	map0.5	map0.5	PROPN
ajst-19320	114	172	and	and	CCONJ
ajst-19320	114	173	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	114	174	,	,	PUNCT
ajst-19320	114	175	respectively	respectively	ADV
ajst-19320	114	176	.	.	PUNCT
ajst-19320	115	1	when	when	SCONJ
ajst-19320	115	2	both	both	DET
ajst-19320	115	3	dpfem	dpfem	NOUN
ajst-19320	115	4	and	and	CCONJ
ajst-19320	115	5	maffem	maffem	NOUN
ajst-19320	115	6	modules	module	NOUN
ajst-19320	115	7	are	be	AUX
ajst-19320	115	8	added	add	VERB
ajst-19320	115	9	to	to	ADP
ajst-19320	115	10	the	the	DET
ajst-19320	115	11	network	network	NOUN
ajst-19320	115	12	,	,	PUNCT
ajst-19320	115	13	map0.5	map0.5	PROPN
ajst-19320	115	14	and	and	CCONJ
ajst-19320	115	15	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	115	16	are	be	AUX
ajst-19320	115	17	improved	improve	VERB
ajst-19320	115	18	by	by	ADP
ajst-19320	115	19	2.2	2.2	NUM
ajst-19320	115	20	%	%	NOUN
ajst-19320	115	21	and	and	CCONJ
ajst-19320	115	22	1.7	1.7	NUM
ajst-19320	115	23	%	%	NOUN
ajst-19320	115	24	,	,	PUNCT
ajst-19320	115	25	respectively	respectively	ADV
ajst-19320	115	26	.	.	PUNCT
ajst-19320	116	1	it	it	PRON
ajst-19320	116	2	can	can	AUX
ajst-19320	116	3	be	be	AUX
ajst-19320	116	4	seen	see	VERB
ajst-19320	116	5	that	that	SCONJ
ajst-19320	116	6	compared	compare	VERB
ajst-19320	116	7	with	with	ADP
ajst-19320	116	8	the	the	DET
ajst-19320	116	9	classical	classical	ADJ
ajst-19320	116	10	yolov5	yolov5	NOUN
ajst-19320	116	11	algorithm	algorithm	NOUN
ajst-19320	116	12	,	,	PUNCT
ajst-19320	116	13	the	the	DET
ajst-19320	116	14	precision	precision	NOUN
ajst-19320	116	15	,	,	PUNCT
ajst-19320	116	16	recall	recall	VERB
ajst-19320	116	17	and	and	CCONJ
ajst-19320	116	18	mean	mean	ADJ
ajst-19320	116	19	average	average	ADJ
ajst-19320	116	20	precision	precision	NOUN
ajst-19320	116	21	of	of	ADP
ajst-19320	116	22	the	the	DET
ajst-19320	116	23	improved	improve	VERB
ajst-19320	116	24	algorithm	algorithm	NOUN
ajst-19320	116	25	have	have	AUX
ajst-19320	116	26	been	be	AUX
ajst-19320	116	27	improved	improve	VERB
ajst-19320	116	28	significantly	significantly	ADV
ajst-19320	116	29	,	,	PUNCT
ajst-19320	116	30	and	and	CCONJ
ajst-19320	116	31	the	the	DET
ajst-19320	116	32	generalization	generalization	NOUN
ajst-19320	116	33	ability	ability	NOUN
ajst-19320	116	34	of	of	ADP
ajst-19320	116	35	the	the	DET
ajst-19320	116	36	model	model	NOUN
ajst-19320	116	37	has	have	AUX
ajst-19320	116	38	been	be	AUX
ajst-19320	116	39	improved	improve	VERB
ajst-19320	116	40	to	to	ADP
ajst-19320	116	41	some	some	DET
ajst-19320	116	42	extent	extent	NOUN
ajst-19320	116	43	.	.	PUNCT
ajst-19320	117	1	359	359	NUM
ajst-19320	117	2	table	table	NOUN
ajst-19320	117	3	4	4	NUM
ajst-19320	117	4	.	.	PUNCT
ajst-19320	117	5	comparison	comparison	NOUN
ajst-19320	117	6	table	table	NOUN
ajst-19320	117	7	of	of	ADP
ajst-19320	117	8	detection	detection	NOUN
ajst-19320	117	9	results	result	NOUN
ajst-19320	117	10	of	of	ADP
ajst-19320	117	11	main	main	ADJ
ajst-19320	117	12	detection	detection	NOUN
ajst-19320	117	13	targets	target	NOUN
ajst-19320	117	14	detection	detection	NOUN
ajst-19320	117	15	target	target	NOUN
ajst-19320	117	16	p	p	NOUN
ajst-19320	117	17	r	r	NOUN
ajst-19320	117	18	map0.5	map0.5	NUM
ajst-19320	117	19	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	117	20	original	original	ADJ
ajst-19320	117	21	pedestrians	pedestrian	NOUN
ajst-19320	117	22	0.488	0.488	NUM
ajst-19320	117	23	0.393	0.393	NUM
ajst-19320	117	24	0.376	0.376	NUM
ajst-19320	117	25	0.152	0.152	NUM
ajst-19320	117	26	car	car	NOUN
ajst-19320	117	27	0.634	0.634	NUM
ajst-19320	117	28	0.736	0.736	NUM
ajst-19320	117	29	0.716	0.716	NUM
ajst-19320	117	30	0.462	0.462	NUM
ajst-19320	117	31	bus	bus	NOUN
ajst-19320	117	32	0.577	0.577	NUM
ajst-19320	117	33	0.418	0.418	NUM
ajst-19320	117	34	0.417	0.417	NUM
ajst-19320	117	35	0.251	0.251	NUM
ajst-19320	117	36	revised	revise	VERB
ajst-19320	117	37	pedestrians	pedestrian	NOUN
ajst-19320	117	38	0.511	0.511	NUM
ajst-19320	117	39	0.474	0.474	NUM
ajst-19320	117	40	0.453	0.453	NUM
ajst-19320	117	41	0.192	0.192	NUM
ajst-19320	117	42	car	car	NOUN
ajst-19320	117	43	0.62	0.62	NUM
ajst-19320	117	44	0.788	0.788	NUM
ajst-19320	117	45	0.763	0.763	NUM
ajst-19320	117	46	0.505	0.505	NUM
ajst-19320	117	47	bus	bus	NOUN
ajst-19320	117	48	0.613	0.613	NUM
ajst-19320	117	49	0.446	0.446	NUM
ajst-19320	117	50	0.45	0.45	NUM
ajst-19320	117	51	0.284	0.284	NUM
ajst-19320	117	52	due	due	ADP
ajst-19320	117	53	to	to	ADP
ajst-19320	117	54	the	the	DET
ajst-19320	117	55	dataset	dataset	VERB
ajst-19320	117	56	aerial	aerial	ADJ
ajst-19320	117	57	photography	photography	NOUN
ajst-19320	117	58	target	target	NOUN
ajst-19320	117	59	scale	scale	NOUN
ajst-19320	117	60	is	be	AUX
ajst-19320	117	61	small	small	ADJ
ajst-19320	117	62	,	,	PUNCT
ajst-19320	117	63	labeling	label	VERB
ajst-19320	117	64	different	different	ADJ
ajst-19320	117	65	categories	category	NOUN
ajst-19320	117	66	of	of	ADP
ajst-19320	117	67	training	training	NOUN
ajst-19320	117	68	samples	sample	NOUN
ajst-19320	117	69	is	be	AUX
ajst-19320	117	70	not	not	PART
ajst-19320	117	71	balanced	balanced	ADJ
ajst-19320	117	72	and	and	CCONJ
ajst-19320	117	73	other	other	ADJ
ajst-19320	117	74	issues	issue	NOUN
ajst-19320	117	75	that	that	PRON
ajst-19320	117	76	lead	lead	VERB
ajst-19320	117	77	to	to	ADP
ajst-19320	117	78	greater	great	ADJ
ajst-19320	117	79	difficulty	difficulty	NOUN
ajst-19320	117	80	in	in	ADP
ajst-19320	117	81	training	train	VERB
ajst-19320	117	82	the	the	DET
ajst-19320	117	83	model	model	NOUN
ajst-19320	117	84	,	,	PUNCT
ajst-19320	117	85	training	training	NOUN
ajst-19320	117	86	to	to	PART
ajst-19320	117	87	get	get	VERB
ajst-19320	117	88	all	all	DET
ajst-19320	117	89	the	the	DET
ajst-19320	117	90	categories	category	NOUN
ajst-19320	117	91	of	of	ADP
ajst-19320	117	92	map	map	NOUN
ajst-19320	117	93	value	value	NOUN
ajst-19320	117	94	is	be	AUX
ajst-19320	117	95	low	low	ADJ
ajst-19320	117	96	,	,	PUNCT
ajst-19320	117	97	but	but	CCONJ
ajst-19320	117	98	the	the	DET
ajst-19320	117	99	training	training	NOUN
ajst-19320	117	100	samples	sample	NOUN
ajst-19320	117	101	are	be	AUX
ajst-19320	117	102	sufficient	sufficient	ADJ
ajst-19320	117	103	categories	category	NOUN
ajst-19320	117	104	of	of	ADP
ajst-19320	117	105	pedestrians	pedestrian	NOUN
ajst-19320	117	106	,	,	PUNCT
ajst-19320	117	107	vehicles	vehicle	NOUN
ajst-19320	117	108	,	,	PUNCT
ajst-19320	117	109	public	public	ADJ
ajst-19320	117	110	transport	transport	NOUN
ajst-19320	117	111	,	,	PUNCT
ajst-19320	117	112	and	and	CCONJ
ajst-19320	117	113	other	other	ADJ
ajst-19320	117	114	detection	detection	NOUN
ajst-19320	117	115	of	of	ADP
ajst-19320	117	116	detection	detection	NOUN
ajst-19320	117	117	of	of	ADP
ajst-19320	117	118	the	the	DET
ajst-19320	117	119	target	target	NOUN
ajst-19320	117	120	detection	detection	NOUN
ajst-19320	117	121	effect	effect	NOUN
ajst-19320	117	122	to	to	PART
ajst-19320	117	123	achieve	achieve	VERB
ajst-19320	117	124	a	a	DET
ajst-19320	117	125	certain	certain	ADJ
ajst-19320	117	126	degree	degree	NOUN
ajst-19320	117	127	of	of	ADP
ajst-19320	117	128	accuracy	accuracy	NOUN
ajst-19320	117	129	,	,	PUNCT
ajst-19320	117	130	in	in	ADP
ajst-19320	117	131	particular	particular	ADJ
ajst-19320	117	132	,	,	PUNCT
ajst-19320	117	133	the	the	DET
ajst-19320	117	134	detection	detection	NOUN
ajst-19320	117	135	of	of	ADP
ajst-19320	117	136	the	the	DET
ajst-19320	117	137	vehicle	vehicle	NOUN
ajst-19320	117	138	recognition	recognition	NOUN
ajst-19320	117	139	improved	improve	VERB
ajst-19320	117	140	map	map	NOUN
ajst-19320	117	141	value	value	NOUN
ajst-19320	117	142	can	can	AUX
ajst-19320	117	143	reach	reach	VERB
ajst-19320	117	144	76.3	76.3	NUM
ajst-19320	117	145	%	%	NOUN
ajst-19320	117	146	.	.	PUNCT
ajst-19320	118	1	(	(	PUNCT
ajst-19320	118	2	3	3	X
ajst-19320	118	3	)	)	PUNCT
ajst-19320	118	4	comparative	comparative	ADJ
ajst-19320	118	5	analysis	analysis	NOUN
ajst-19320	118	6	of	of	ADP
ajst-19320	118	7	aerial	aerial	ADJ
ajst-19320	118	8	photography	photography	NOUN
ajst-19320	118	9	example	example	NOUN
ajst-19320	118	10	detection	detection	NOUN
ajst-19320	118	11	the	the	DET
ajst-19320	118	12	weight	weight	NOUN
ajst-19320	118	13	parameter	parameter	NOUN
ajst-19320	118	14	matrix	matrix	NOUN
ajst-19320	118	15	generated	generate	VERB
ajst-19320	118	16	after	after	SCONJ
ajst-19320	118	17	training	training	NOUN
ajst-19320	118	18	is	be	AUX
ajst-19320	118	19	saved	save	VERB
ajst-19320	118	20	in	in	ADP
ajst-19320	118	21	the	the	DET
ajst-19320	118	22	runs	run	NOUN
ajst-19320	118	23	/	/	SYM
ajst-19320	118	24	train	train	NOUN
ajst-19320	118	25	/	/	SYM
ajst-19320	118	26	exp	exp	NOUN
ajst-19320	118	27	/	/	SYM
ajst-19320	118	28	weights	weight	NOUN
ajst-19320	118	29	folder	folder	NOUN
ajst-19320	118	30	,	,	PUNCT
ajst-19320	118	31	best.pt	best.pt	PROPN
ajst-19320	118	32	represents	represent	VERB
ajst-19320	118	33	the	the	DET
ajst-19320	118	34	weight	weight	NOUN
ajst-19320	118	35	matrix	matrix	NOUN
ajst-19320	118	36	with	with	ADP
ajst-19320	118	37	the	the	DET
ajst-19320	118	38	best	good	ADJ
ajst-19320	118	39	training	training	NOUN
ajst-19320	118	40	effect	effect	NOUN
ajst-19320	118	41	,	,	PUNCT
ajst-19320	118	42	and	and	CCONJ
ajst-19320	118	43	last.pt	last.pt	PROPN
ajst-19320	118	44	represents	represent	VERB
ajst-19320	118	45	the	the	DET
ajst-19320	118	46	weight	weight	NOUN
ajst-19320	118	47	matrix	matrix	NOUN
ajst-19320	118	48	of	of	ADP
ajst-19320	118	49	the	the	DET
ajst-19320	118	50	last	last	ADJ
ajst-19320	118	51	training	training	NOUN
ajst-19320	118	52	.	.	PUNCT
ajst-19320	119	1	the	the	DET
ajst-19320	119	2	best.pt	best.pt	PROPN
ajst-19320	119	3	weight	weight	NOUN
ajst-19320	119	4	parameter	parameter	NOUN
ajst-19320	119	5	matrix	matrix	NOUN
ajst-19320	119	6	trained	train	VERB
ajst-19320	119	7	with	with	ADP
ajst-19320	119	8	the	the	DET
ajst-19320	119	9	model	model	NOUN
ajst-19320	119	10	before	before	ADP
ajst-19320	119	11	and	and	CCONJ
ajst-19320	119	12	after	after	SCONJ
ajst-19320	119	13	the	the	DET
ajst-19320	119	14	improvement	improvement	NOUN
ajst-19320	119	15	is	be	AUX
ajst-19320	119	16	used	use	VERB
ajst-19320	119	17	to	to	PART
ajst-19320	119	18	validate	validate	VERB
ajst-19320	119	19	the	the	DET
ajst-19320	119	20	detection	detection	NOUN
ajst-19320	119	21	results	result	NOUN
ajst-19320	119	22	,	,	PUNCT
ajst-19320	119	23	as	as	SCONJ
ajst-19320	119	24	shown	show	VERB
ajst-19320	119	25	below	below	ADP
ajst-19320	119	26	.	.	PUNCT
ajst-19320	120	1	from	from	ADP
ajst-19320	120	2	the	the	DET
ajst-19320	120	3	comparison	comparison	NOUN
ajst-19320	120	4	chart	chart	NOUN
ajst-19320	120	5	of	of	ADP
ajst-19320	120	6	different	different	ADJ
ajst-19320	120	7	height	height	NOUN
ajst-19320	120	8	detection	detection	NOUN
ajst-19320	120	9	,	,	PUNCT
ajst-19320	120	10	it	it	PRON
ajst-19320	120	11	can	can	AUX
ajst-19320	120	12	be	be	AUX
ajst-19320	120	13	seen	see	VERB
ajst-19320	120	14	that	that	SCONJ
ajst-19320	120	15	with	with	ADP
ajst-19320	120	16	the	the	DET
ajst-19320	120	17	increase	increase	NOUN
ajst-19320	120	18	of	of	ADP
ajst-19320	120	19	aerial	aerial	ADJ
ajst-19320	120	20	photography	photography	NOUN
ajst-19320	120	21	height	height	NOUN
ajst-19320	120	22	,	,	PUNCT
ajst-19320	120	23	the	the	DET
ajst-19320	120	24	detection	detection	NOUN
ajst-19320	120	25	target	target	NOUN
ajst-19320	120	26	scale	scale	NOUN
ajst-19320	120	27	becomes	become	VERB
ajst-19320	120	28	smaller	small	ADJ
ajst-19320	120	29	resulting	result	VERB
ajst-19320	120	30	in	in	ADP
ajst-19320	120	31	increased	increase	VERB
ajst-19320	120	32	detection	detection	NOUN
ajst-19320	120	33	difficulty	difficulty	NOUN
ajst-19320	120	34	,	,	PUNCT
ajst-19320	120	35	and	and	CCONJ
ajst-19320	120	36	problems	problem	NOUN
ajst-19320	120	37	such	such	ADJ
ajst-19320	120	38	as	as	ADP
ajst-19320	120	39	missed	miss	VERB
ajst-19320	120	40	detection	detection	NOUN
ajst-19320	120	41	and	and	CCONJ
ajst-19320	120	42	misdetection	misdetection	NOUN
ajst-19320	120	43	obviously	obviously	ADV
ajst-19320	120	44	increase	increase	VERB
ajst-19320	120	45	,	,	PUNCT
ajst-19320	120	46	but	but	CCONJ
ajst-19320	120	47	the	the	DET
ajst-19320	120	48	improved	improved	ADJ
ajst-19320	120	49	model	model	NOUN
ajst-19320	120	50	algorithm	algorithm	NOUN
ajst-19320	120	51	reduces	reduce	VERB
ajst-19320	120	52	the	the	DET
ajst-19320	120	53	misdetection	misdetection	NOUN
ajst-19320	120	54	and	and	CCONJ
ajst-19320	120	55	misdetection	misdetection	NOUN
ajst-19320	120	56	and	and	CCONJ
ajst-19320	120	57	other	other	ADJ
ajst-19320	120	58	problems	problem	NOUN
ajst-19320	120	59	to	to	ADP
ajst-19320	120	60	a	a	DET
ajst-19320	120	61	certain	certain	ADJ
ajst-19320	120	62	extent	extent	NOUN
ajst-19320	120	63	,	,	PUNCT
ajst-19320	120	64	and	and	CCONJ
ajst-19320	120	65	the	the	DET
ajst-19320	120	66	confidence	confidence	NOUN
ajst-19320	120	67	level	level	NOUN
ajst-19320	120	68	of	of	ADP
ajst-19320	120	69	the	the	DET
ajst-19320	120	70	detection	detection	NOUN
ajst-19320	120	71	target	target	NOUN
ajst-19320	120	72	is	be	AUX
ajst-19320	120	73	increased	increase	VERB
ajst-19320	120	74	,	,	PUNCT
ajst-19320	120	75	which	which	PRON
ajst-19320	120	76	can	can	AUX
ajst-19320	120	77	be	be	AUX
ajst-19320	120	78	seen	see	VERB
ajst-19320	120	79	that	that	SCONJ
ajst-19320	120	80	the	the	DET
ajst-19320	120	81	improved	improved	ADJ
ajst-19320	120	82	model	model	NOUN
ajst-19320	120	83	detection	detection	NOUN
ajst-19320	120	84	effect	effect	NOUN
ajst-19320	120	85	is	be	AUX
ajst-19320	120	86	improved	improve	VERB
ajst-19320	120	87	.	.	PUNCT
ajst-19320	121	1	(	(	PUNCT
ajst-19320	121	2	a	a	X
ajst-19320	121	3	)	)	PUNCT
ajst-19320	121	4	drone	drone	NOUN
ajst-19320	121	5	flying	fly	VERB
ajst-19320	121	6	at	at	ADP
ajst-19320	121	7	100	100	NUM
ajst-19320	121	8	meters	meter	NOUN
ajst-19320	121	9	(	(	PUNCT
ajst-19320	121	10	b	b	NOUN
ajst-19320	121	11	)	)	PUNCT
ajst-19320	121	12	drone	drone	NOUN
ajst-19320	121	13	flying	fly	VERB
ajst-19320	121	14	at	at	ADP
ajst-19320	121	15	110	110	NUM
ajst-19320	121	16	meters	meter	NOUN
ajst-19320	121	17	(	(	PUNCT
ajst-19320	121	18	c	c	NOUN
ajst-19320	121	19	)	)	PUNCT
ajst-19320	121	20	drone	drone	NOUN
ajst-19320	121	21	flying	fly	VERB
ajst-19320	121	22	at	at	ADP
ajst-19320	121	23	120	120	NUM
ajst-19320	121	24	meters	meter	NOUN
ajst-19320	121	25	figure	figure	NOUN
ajst-19320	121	26	8	8	NUM
ajst-19320	121	27	.	.	PUNCT
ajst-19320	121	28	comparison	comparison	NOUN
ajst-19320	121	29	chart	chart	NOUN
ajst-19320	121	30	of	of	ADP
ajst-19320	121	31	detection	detection	NOUN
ajst-19320	121	32	effect	effect	NOUN
ajst-19320	121	33	5	5	NUM
ajst-19320	121	34	.	.	X
ajst-19320	121	35	conclusion	conclusion	NOUN
ajst-19320	121	36	some	some	DET
ajst-19320	121	37	problems	problem	NOUN
ajst-19320	121	38	of	of	ADP
ajst-19320	121	39	yolov5	yolov5	NOUN
ajst-19320	121	40	detection	detection	NOUN
ajst-19320	121	41	algorithm	algorithm	NOUN
ajst-19320	121	42	are	be	AUX
ajst-19320	121	43	optimized	optimize	VERB
ajst-19320	121	44	and	and	CCONJ
ajst-19320	121	45	improved	improve	VERB
ajst-19320	121	46	to	to	PART
ajst-19320	121	47	enhance	enhance	VERB
ajst-19320	121	48	the	the	DET
ajst-19320	121	49	detection	detection	NOUN
ajst-19320	121	50	accuracy	accuracy	NOUN
ajst-19320	121	51	of	of	ADP
ajst-19320	121	52	the	the	DET
ajst-19320	121	53	algorithm	algorithm	NOUN
ajst-19320	121	54	.	.	PUNCT
ajst-19320	122	1	aiming	aim	VERB
ajst-19320	122	2	at	at	ADP
ajst-19320	122	3	the	the	DET
ajst-19320	122	4	problem	problem	NOUN
ajst-19320	122	5	of	of	ADP
ajst-19320	122	6	insufficient	insufficient	ADJ
ajst-19320	122	7	feature	feature	NOUN
ajst-19320	122	8	extraction	extraction	NOUN
ajst-19320	122	9	,	,	PUNCT
ajst-19320	122	10	an	an	DET
ajst-19320	122	11	improved	improved	ADJ
ajst-19320	122	12	dpfem	dpfem	NOUN
ajst-19320	122	13	network	network	NOUN
ajst-19320	122	14	is	be	AUX
ajst-19320	122	15	proposed	propose	VERB
ajst-19320	122	16	to	to	ADP
ajst-19320	122	17	360	360	NUM
ajst-19320	122	18	replace	replace	VERB
ajst-19320	122	19	the	the	DET
ajst-19320	122	20	original	original	ADJ
ajst-19320	122	21	c3	c3	NOUN
ajst-19320	122	22	network	network	NOUN
ajst-19320	122	23	structure	structure	NOUN
ajst-19320	122	24	to	to	PART
ajst-19320	122	25	strengthen	strengthen	VERB
ajst-19320	122	26	the	the	DET
ajst-19320	122	27	feature	feature	NOUN
ajst-19320	122	28	extraction	extraction	NOUN
ajst-19320	122	29	ability	ability	NOUN
ajst-19320	122	30	of	of	ADP
ajst-19320	122	31	the	the	DET
ajst-19320	122	32	model	model	NOUN
ajst-19320	122	33	.	.	PUNCT
ajst-19320	123	1	aiming	aim	VERB
ajst-19320	123	2	at	at	ADP
ajst-19320	123	3	the	the	DET
ajst-19320	123	4	problem	problem	NOUN
ajst-19320	123	5	that	that	SCONJ
ajst-19320	123	6	the	the	DET
ajst-19320	123	7	extracted	extract	VERB
ajst-19320	123	8	features	feature	NOUN
ajst-19320	123	9	have	have	AUX
ajst-19320	123	10	different	different	ADJ
ajst-19320	123	11	scales	scale	NOUN
ajst-19320	123	12	and	and	CCONJ
ajst-19320	123	13	thus	thus	ADV
ajst-19320	123	14	cause	cause	VERB
ajst-19320	123	15	feature	feature	NOUN
ajst-19320	123	16	fusion	fusion	NOUN
ajst-19320	123	17	conflicts	conflict	NOUN
ajst-19320	123	18	,	,	PUNCT
ajst-19320	123	19	the	the	DET
ajst-19320	123	20	maffem	maffem	NOUN
ajst-19320	123	21	module	module	NOUN
ajst-19320	123	22	is	be	AUX
ajst-19320	123	23	proposed	propose	VERB
ajst-19320	123	24	to	to	PART
ajst-19320	123	25	enable	enable	VERB
ajst-19320	123	26	the	the	DET
ajst-19320	123	27	computer	computer	NOUN
ajst-19320	123	28	to	to	PART
ajst-19320	123	29	autonomously	autonomously	ADV
ajst-19320	123	30	learn	learn	VERB
ajst-19320	123	31	and	and	CCONJ
ajst-19320	123	32	regulate	regulate	VERB
ajst-19320	123	33	the	the	DET
ajst-19320	123	34	key	key	ADJ
ajst-19320	123	35	features	feature	NOUN
ajst-19320	123	36	of	of	ADP
ajst-19320	123	37	the	the	DET
ajst-19320	123	38	features	feature	NOUN
ajst-19320	123	39	,	,	PUNCT
ajst-19320	123	40	so	so	SCONJ
ajst-19320	123	41	as	as	SCONJ
ajst-19320	123	42	to	to	PART
ajst-19320	123	43	improve	improve	VERB
ajst-19320	123	44	the	the	DET
ajst-19320	123	45	problem	problem	NOUN
ajst-19320	123	46	of	of	ADP
ajst-19320	123	47	feature	feature	NOUN
ajst-19320	123	48	conflicts	conflict	NOUN
ajst-19320	123	49	.	.	PUNCT
ajst-19320	124	1	finally	finally	ADV
ajst-19320	124	2	,	,	PUNCT
ajst-19320	124	3	the	the	DET
ajst-19320	124	4	model	model	NOUN
ajst-19320	124	5	is	be	AUX
ajst-19320	124	6	trained	train	VERB
ajst-19320	124	7	on	on	ADP
ajst-19320	124	8	a	a	DET
ajst-19320	124	9	homemade	homemade	ADJ
ajst-19320	124	10	training	training	NOUN
ajst-19320	124	11	set	set	NOUN
ajst-19320	124	12	,	,	PUNCT
ajst-19320	124	13	and	and	CCONJ
ajst-19320	124	14	the	the	DET
ajst-19320	124	15	map0.5	map0.5	NOUN
ajst-19320	124	16	and	and	CCONJ
ajst-19320	124	17	map0.5:0.95	map0.5:0.95	NOUN
ajst-19320	124	18	of	of	ADP
ajst-19320	124	19	the	the	DET
ajst-19320	124	20	model	model	NOUN
ajst-19320	124	21	are	be	AUX
ajst-19320	124	22	improved	improve	VERB
ajst-19320	124	23	by	by	ADP
ajst-19320	124	24	2.2	2.2	NUM
ajst-19320	124	25	%	%	NOUN
ajst-19320	124	26	and	and	CCONJ
ajst-19320	124	27	1.7	1.7	NUM
ajst-19320	124	28	%	%	NOUN
ajst-19320	124	29	,	,	PUNCT
ajst-19320	124	30	respectively	respectively	ADV
ajst-19320	124	31	,	,	PUNCT
ajst-19320	124	32	and	and	CCONJ
ajst-19320	124	33	the	the	DET
ajst-19320	124	34	results	result	NOUN
ajst-19320	124	35	show	show	VERB
ajst-19320	124	36	that	that	SCONJ
ajst-19320	124	37	the	the	DET
ajst-19320	124	38	detection	detection	NOUN
ajst-19320	124	39	accuracy	accuracy	NOUN
ajst-19320	124	40	is	be	AUX
ajst-19320	124	41	improved	improve	VERB
ajst-19320	124	42	.	.	PUNCT
ajst-19320	125	1	as	as	ADP
ajst-19320	125	2	a	a	DET
ajst-19320	125	3	phase	phase	NOUN
ajst-19320	125	4	study	study	NOUN
ajst-19320	125	5	,	,	PUNCT
ajst-19320	125	6	the	the	DET
ajst-19320	125	7	improved	improved	ADJ
ajst-19320	125	8	algorithm	algorithm	NOUN
ajst-19320	125	9	proposed	propose	VERB
ajst-19320	125	10	in	in	ADP
ajst-19320	125	11	this	this	DET
ajst-19320	125	12	paper	paper	NOUN
ajst-19320	125	13	has	have	VERB
ajst-19320	125	14	great	great	ADJ
ajst-19320	125	15	potential	potential	NOUN
ajst-19320	125	16	for	for	ADP
ajst-19320	125	17	practical	practical	ADJ
ajst-19320	125	18	application	application	NOUN
ajst-19320	125	19	,	,	PUNCT
ajst-19320	125	20	especially	especially	ADV
ajst-19320	125	21	providing	provide	VERB
ajst-19320	125	22	algorithmic	algorithmic	ADJ
ajst-19320	125	23	support	support	NOUN
ajst-19320	125	24	for	for	ADP
ajst-19320	125	25	subsequent	subsequent	ADJ
ajst-19320	125	26	vehicle	vehicle	NOUN
ajst-19320	125	27	counting	counting	NOUN
ajst-19320	125	28	,	,	PUNCT
ajst-19320	125	29	vehicle	vehicle	NOUN
ajst-19320	125	30	tracking	tracking	NOUN
ajst-19320	125	31	and	and	CCONJ
ajst-19320	125	32	vehicle	vehicle	NOUN
ajst-19320	125	33	trajectory	trajectory	NOUN
ajst-19320	125	34	prediction	prediction	NOUN
ajst-19320	125	35	,	,	PUNCT
ajst-19320	125	36	while	while	SCONJ
ajst-19320	125	37	it	it	PRON
ajst-19320	125	38	can	can	AUX
ajst-19320	125	39	be	be	AUX
ajst-19320	125	40	further	far	ADV
ajst-19320	125	41	applied	apply	VERB
ajst-19320	125	42	to	to	ADP
ajst-19320	125	43	the	the	DET
ajst-19320	125	44	detection	detection	NOUN
ajst-19320	125	45	of	of	ADP
ajst-19320	125	46	non	non	ADJ
ajst-19320	125	47	-	-	ADJ
ajst-19320	125	48	motorized	motorized	ADJ
ajst-19320	125	49	vehicles	vehicle	NOUN
ajst-19320	125	50	and	and	CCONJ
ajst-19320	125	51	pedestrians	pedestrian	NOUN
ajst-19320	125	52	in	in	ADP
ajst-19320	125	53	future	future	ADJ
ajst-19320	125	54	transportation	transportation	NOUN
ajst-19320	125	55	fields	field	NOUN
ajst-19320	125	56	.	.	PUNCT
ajst-19320	126	1	bibliography	bibliography	NOUN
ajst-19320	127	1	[	[	X
ajst-19320	127	2	1	1	X
ajst-19320	127	3	]	]	X
ajst-19320	127	4	girshick	girshick	ADJ
ajst-19320	127	5	r	r	PROPN
ajst-19320	127	6	,	,	PUNCT
ajst-19320	127	7	donahue	donahue	PROPN
ajst-19320	127	8	j	j	PROPN
ajst-19320	127	9	,	,	PUNCT
ajst-19320	127	10	darrell	darrell	PROPN
ajst-19320	127	11	t	t	PROPN
ajst-19320	127	12	,	,	PUNCT
ajst-19320	127	13	et	et	PROPN
ajst-19320	128	1	al	al	PROPN
ajst-19320	128	2	.	.	PROPN
ajst-19320	129	1	rich	rich	ADJ
ajst-19320	129	2	feature	feature	NOUN
ajst-19320	129	3	hierarchies	hierarchy	NOUN
ajst-19320	129	4	for	for	ADP
ajst-19320	129	5	accurate	accurate	ADJ
ajst-19320	129	6	objectdetection	objectdetection	NOUN
ajst-19320	129	7	and	and	CCONJ
ajst-19320	129	8	semantic	semantic	ADJ
ajst-19320	129	9	segmentation	segmentation	NOUN
ajst-19320	129	10	[	[	X
ajst-19320	129	11	c]//proceedings	c]//proceeding	NOUN
ajst-19320	129	12	of	of	ADP
ajst-19320	129	13	the	the	DET
ajst-19320	129	14	ieee	ieee	NOUN
ajst-19320	129	15	conference	conference	NOUN
ajst-19320	129	16	on	on	ADP
ajst-19320	129	17	computervision	computervision	NOUN
ajst-19320	129	18	and	and	CCONJ
ajst-19320	129	19	pattern	pattern	NOUN
ajst-19320	129	20	recognition	recognition	NOUN
ajst-19320	129	21	.	.	PUNCT
ajst-19320	130	1	2014	2014	NUM
ajst-19320	130	2	:	:	PUNCT
ajst-19320	131	1	580	580	NUM
ajst-19320	131	2	-	-	SYM
ajst-19320	131	3	587	587	NUM
ajst-19320	131	4	.	.	PUNCT
ajst-19320	132	1	[	[	X
ajst-19320	132	2	2	2	NUM
ajst-19320	132	3	]	]	X
ajst-19320	132	4	girshick	girshick	PROPN
ajst-19320	132	5	r.	r.	PROPN
ajst-19320	132	6	fast	fast	ADV
ajst-19320	132	7	r	r	NOUN
ajst-19320	132	8	-	-	PUNCT
ajst-19320	132	9	cnn[c]//proceedings	cnn[c]//proceeding	NOUN
ajst-19320	132	10	of	of	ADP
ajst-19320	132	11	the	the	DET
ajst-19320	132	12	ieee	ieee	NOUN
ajst-19320	132	13	international	international	PROPN
ajst-19320	132	14	conference	conference	NOUN
ajst-19320	132	15	on	on	ADP
ajst-19320	132	16	computervision	computervision	NOUN
ajst-19320	132	17	.	.	PUNCT
ajst-19320	133	1	2015	2015	NUM
ajst-19320	133	2	:	:	PUNCT
ajst-19320	133	3	1440	1440	NUM
ajst-19320	133	4	-	-	SYM
ajst-19320	133	5	1448	1448	NUM
ajst-19320	133	6	.	.	PUNCT
ajst-19320	134	1	[	[	X
ajst-19320	134	2	3	3	X
ajst-19320	134	3	]	]	X
ajst-19320	134	4	ren	ren	PROPN
ajst-19320	134	5	s	s	PROPN
ajst-19320	134	6	,	,	PUNCT
ajst-19320	134	7	he	he	PRON
ajst-19320	134	8	k	k	NOUN
ajst-19320	134	9	,	,	PUNCT
ajst-19320	134	10	girshick	girshick	ADJ
ajst-19320	134	11	r	r	NOUN
ajst-19320	134	12	,	,	PUNCT
ajst-19320	134	13	et	et	PROPN
ajst-19320	134	14	al	al	PROPN
ajst-19320	134	15	.	.	PUNCT
ajst-19320	135	1	faster	fast	ADJ
ajst-19320	135	2	r	r	NOUN
ajst-19320	135	3	-	-	PUNCT
ajst-19320	135	4	cnn	cnn	NOUN
ajst-19320	135	5	:	:	PUNCT
ajst-19320	135	6	towards	towards	ADP
ajst-19320	135	7	real	real	ADJ
ajst-19320	135	8	-	-	PUNCT
ajst-19320	135	9	time	time	NOUN
ajst-19320	135	10	object	object	NOUN
ajst-19320	135	11	detection	detection	NOUN
ajst-19320	135	12	with	with	ADP
ajst-19320	135	13	regionproposal	regionproposal	ADJ
ajst-19320	135	14	networks[j].advances	networks[j].advance	NOUN
ajst-19320	135	15	in	in	ADP
ajst-19320	135	16	neural	neural	ADJ
ajst-19320	135	17	information	information	NOUN
ajst-19320	135	18	processing	processing	NOUN
ajst-19320	135	19	systems	system	NOUN
ajst-19320	135	20	,	,	PUNCT
ajst-19320	135	21	2015	2015	NUM
ajst-19320	135	22	,	,	PUNCT
ajst-19320	135	23	28	28	NUM
ajst-19320	135	24	:	:	SYM
ajst-19320	135	25	91	91	NUM
ajst-19320	135	26	-	-	SYM
ajst-19320	135	27	99	99	NUM
ajst-19320	135	28	.	.	PUNCT
ajst-19320	136	1	[	[	X
ajst-19320	136	2	4	4	X
ajst-19320	136	3	]	]	PUNCT
ajst-19320	136	4	x.	x.	NOUN
ajst-19320	136	5	z.	z.	PROPN
ajst-19320	136	6	wang	wang	PROPN
ajst-19320	136	7	,	,	PUNCT
ajst-19320	136	8	c.	c.	PROPN
ajst-19320	136	9	he	he	PRON
ajst-19320	136	10	.	.	PUNCT
ajst-19320	137	1	vehicle	vehicle	NOUN
ajst-19320	137	2	detection	detection	NOUN
ajst-19320	137	3	in	in	ADP
ajst-19320	137	4	complex	complex	ADJ
ajst-19320	137	5	environments	environment	NOUN
ajst-19320	137	6	based	base	VERB
ajst-19320	137	7	on	on	ADP
ajst-19320	137	8	transformer	transformer	NOUN
ajst-19320	137	9	improved	improve	VERB
ajst-19320	137	10	faster	fast	ADV
ajst-19320	137	11	rcnn[j	rcnn[j	PROPN
ajst-19320	137	12	/	/	SYM
ajst-19320	137	13	ol	ol	PROPN
ajst-19320	137	14	]	]	PUNCT
ajst-19320	137	15	.	.	PUNCT
ajst-19320	138	1	electromechanical	electromechanical	ADJ
ajst-19320	138	2	engineering	engineering	NOUN
ajst-19320	138	3	technology	technology	NOUN
ajst-19320	138	4	.	.	PUNCT
ajst-19320	139	1	https://link.cnki.net/urlid/44.1522.th.20240312.1557.002	https://link.cnki.net/urlid/44.1522.th.20240312.1557.002	NOUN
ajst-19320	139	2	.	.	PUNCT
ajst-19320	140	1	[	[	X
ajst-19320	140	2	5	5	NUM
ajst-19320	140	3	]	]	X
ajst-19320	140	4	shao	shao	PROPN
ajst-19320	140	5	-	-	PUNCT
ajst-19320	140	6	hong	hong	PROPN
ajst-19320	140	7	zhou	zhou	PROPN
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ajst-19320	140	9	xin	xin	PROPN
ajst-19320	140	10	-	-	PUNCT
ajst-19320	140	11	jin	jin	PROPN
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ajst-19320	140	13	,	,	PUNCT
ajst-19320	140	14	xin	xin	PROPN
ajst-19320	140	15	-	-	PUNCT
ajst-19320	140	16	yi	yi	PROPN
ajst-19320	140	17	liu	liu	PROPN
ajst-19320	140	18	,	,	PUNCT
ajst-19320	140	19	eddie	eddie	PROPN
ajst-19320	140	20	zhang	zhang	PROPN
ajst-19320	140	21	,	,	PUNCT
ajst-19320	140	22	sheng	sheng	PROPN
ajst-19320	140	23	yan	yan	PROPN
ajst-19320	140	24	.	.	PUNCT
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ajst-19320	141	2	target	target	NOUN
ajst-19320	141	3	detection	detection	NOUN
ajst-19320	141	4	based	base	VERB
ajst-19320	141	5	on	on	ADP
ajst-19320	141	6	migration	migration	NOUN
ajst-19320	141	7	learning	learning	NOUN
ajst-19320	141	8	and	and	CCONJ
ajst-19320	141	9	improved	improve	VERB
ajst-19320	141	10	faster	fast	ADJ
ajst-19320	141	11	-	-	PUNCT
ajst-19320	141	12	rcnn	rcnn	NOUN
ajst-19320	141	13	remote	remote	ADJ
ajst-19320	141	14	sensing	sense	VERB
ajst-19320	141	15	imagery[j	imagery[j	PROPN
ajst-19320	141	16	/	/	SYM
ajst-19320	141	17	ol	ol	PROPN
ajst-19320	141	18	]	]	PUNCT
ajst-19320	141	19	.	.	PUNCT
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ajst-19320	143	3	]	]	PUNCT
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ajst-19320	143	5	yunyan	yunyan	PROPN
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ajst-19320	143	7	yang	yang	PROPN
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ajst-19320	143	9	,	,	PUNCT
ajst-19320	143	10	li	li	PROPN
ajst-19320	143	11	hong	hong	PROPN
ajst-19320	143	12	,	,	PUNCT
ajst-19320	143	13	et	et	PROPN
ajst-19320	143	14	al	al	PROPN
ajst-19320	143	15	.	.	PUNCT
ajst-19320	143	16	sample	sample	PROPN
ajst-19320	143	17	less	less	ADJ
ajst-19320	143	18	target	target	NOUN
ajst-19320	143	19	detection	detection	NOUN
ajst-19320	143	20	algorithm	algorithm	NOUN
ajst-19320	143	21	based	base	VERB
ajst-19320	143	22	on	on	ADP
ajst-19320	143	23	improved	improved	ADJ
ajst-19320	143	24	faster	fast	ADV
ajst-19320	143	25	rcnn[j	rcnn[j	PROPN
ajst-19320	143	26	]	]	PUNCT
ajst-19320	143	27	.	.	PUNCT
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ajst-19320	144	2	-	-	PUNCT
ajst-19320	144	3	optics	optic	NOUN
ajst-19320	144	4	and	and	CCONJ
ajst-19320	144	5	control,2023,30(5):44	control,2023,30(5):44	NOUN
ajst-19320	144	6	-	-	PUNCT
ajst-19320	144	7	51	51	NUM
ajst-19320	144	8	.	.	PUNCT
ajst-19320	145	1	[	[	X
ajst-19320	145	2	7	7	X
ajst-19320	145	3	]	]	X
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ajst-19320	145	5	j	j	PROPN
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ajst-19320	145	7	divvala	divvala	PROPN
ajst-19320	145	8	s	s	PROPN
ajst-19320	145	9	,	,	PUNCT
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ajst-19320	145	13	et	et	PROPN
ajst-19320	145	14	al	al	PROPN
ajst-19320	145	15	.	.	PUNCT
ajst-19320	146	1	you	you	PRON
ajst-19320	146	2	only	only	ADV
ajst-19320	146	3	look	look	VERB
ajst-19320	146	4	once	once	ADV
ajst-19320	146	5	:	:	PUNCT
ajst-19320	146	6	unified	unified	ADJ
ajst-19320	146	7	,	,	PUNCT
ajst-19320	146	8	real	real	ADJ
ajst-19320	146	9	-	-	PUNCT
ajst-19320	146	10	time	time	NOUN
ajst-19320	146	11	object	object	NOUN
ajst-19320	146	12	detection[c]//proceedings	detection[c]//proceeding	NOUN
ajst-19320	146	13	of	of	ADP
ajst-19320	146	14	the	the	DET
ajst-19320	146	15	ieee	ieee	NOUN
ajst-19320	146	16	conference	conference	NOUN
ajst-19320	146	17	on	on	ADP
ajst-19320	146	18	computer	computer	NOUN
ajst-19320	146	19	vision	vision	NOUN
ajst-19320	146	20	and	and	CCONJ
ajst-19320	146	21	pattern	pattern	NOUN
ajst-19320	146	22	recognition	recognition	NOUN
ajst-19320	146	23	.	.	PUNCT
ajst-19320	147	1	2016	2016	NUM
ajst-19320	147	2	:	:	PUNCT
ajst-19320	147	3	779	779	NUM
ajst-19320	147	4	-	-	SYM
ajst-19320	147	5	788	788	NUM
ajst-19320	147	6	.	.	PUNCT
ajst-19320	148	1	[	[	X
ajst-19320	148	2	8	8	NUM
ajst-19320	148	3	]	]	X
ajst-19320	148	4	rothe	rothe	PROPN
ajst-19320	148	5	r	r	PROPN
ajst-19320	148	6	,	,	PUNCT
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ajst-19320	148	8	m	m	PROPN
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ajst-19320	148	10	van	van	PROPN
ajst-19320	148	11	gool	gool	PROPN
ajst-19320	148	12	l.	l.	PROPN
ajst-19320	148	13	non	non	PROPN
ajst-19320	148	14	-	-	ADJ
ajst-19320	148	15	maximum	maximum	ADJ
ajst-19320	148	16	suppression	suppression	NOUN
ajst-19320	148	17	for	for	ADP
ajst-19320	148	18	object	object	NOUN
ajst-19320	148	19	detection	detection	NOUN
ajst-19320	148	20	by	by	ADP
ajst-19320	148	21	passing	pass	VERB
ajst-19320	148	22	messages	message	NOUN
ajst-19320	148	23	between	between	ADP
ajst-19320	148	24	windows[c]//asian	windows[c]//asian	ADJ
ajst-19320	148	25	conference	conference	NOUN
ajst-19320	148	26	on	on	ADP
ajst-19320	148	27	computer	computer	NOUN
ajst-19320	148	28	vision	vision	NOUN
ajst-19320	148	29	.	.	PUNCT
ajst-19320	149	1	springer	springer	NOUN
ajst-19320	149	2	,	,	PUNCT
ajst-19320	149	3	cham	cham	PROPN
ajst-19320	149	4	,	,	PUNCT
ajst-19320	149	5	2014	2014	NUM
ajst-19320	149	6	:	:	PUNCT
ajst-19320	149	7	290	290	NUM
ajst-19320	149	8	-	-	SYM
ajst-19320	149	9	306	306	NUM
ajst-19320	149	10	.	.	PUNCT
ajst-19320	150	1	[	[	X
ajst-19320	150	2	9	9	NUM
ajst-19320	150	3	]	]	X
ajst-19320	150	4	liu	liu	PROPN
ajst-19320	150	5	w	w	PROPN
ajst-19320	150	6	,	,	PUNCT
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ajst-19320	150	8	d	d	NOUN
ajst-19320	150	9	,	,	PUNCT
ajst-19320	150	10	erhan	erhan	ADP
ajst-19320	150	11	d	d	PROPN
ajst-19320	150	12	,	,	PUNCT
ajst-19320	150	13	et	et	PROPN
ajst-19320	150	14	al	al	PROPN
ajst-19320	150	15	.	.	PROPN
ajst-19320	150	16	ssd	ssd	PROPN
ajst-19320	150	17	:	:	PUNCT
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ajst-19320	150	19	shot	shot	NOUN
ajst-19320	150	20	multibox	multibox	PROPN
ajst-19320	150	21	detector[c	detector[c	PROPN
ajst-19320	150	22	]	]	PUNCT
ajst-19320	150	23	.	.	PUNCT
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ajst-19320	151	2	conference	conference	PROPN
ajst-19320	151	3	on	on	ADP
ajst-19320	151	4	computer	computer	NOUN
ajst-19320	151	5	vision	vision	NOUN
ajst-19320	151	6	.	.	PUNCT
ajst-19320	152	1	springer	springer	NOUN
ajst-19320	152	2	,	,	PUNCT
ajst-19320	152	3	cham	cham	PROPN
ajst-19320	152	4	,	,	PUNCT
ajst-19320	152	5	2016	2016	NUM
ajst-19320	152	6	:	:	PUNCT
ajst-19320	152	7	21	21	NUM
ajst-19320	152	8	-	-	SYM
ajst-19320	152	9	37	37	NUM
ajst-19320	152	10	.	.	PUNCT
ajst-19320	153	1	[	[	X
ajst-19320	153	2	10	10	NUM
ajst-19320	153	3	]	]	X
ajst-19320	153	4	redmon	redmon	PROPN
ajst-19320	153	5	j	j	PROPN
ajst-19320	153	6	,	,	PUNCT
ajst-19320	153	7	farhadi	farhadi	PROPN
ajst-19320	153	8	a.	a.	PROPN
ajst-19320	153	9	yolo9000	yolo9000	PROPN
ajst-19320	153	10	:	:	PUNCT
ajst-19320	153	11	better	well	ADJ
ajst-19320	153	12	,	,	PUNCT
ajst-19320	153	13	faster	fast	ADV
ajst-19320	153	14	,	,	PUNCT
ajst-19320	153	15	stronger[c]//proceedings	stronger[c]//proceeding	NOUN
ajst-19320	153	16	of	of	ADP
ajst-19320	153	17	the	the	DET
ajst-19320	153	18	ieee	ieee	NOUN
ajst-19320	153	19	conference	conference	NOUN
ajst-19320	153	20	on	on	ADP
ajst-19320	153	21	computer	computer	NOUN
ajst-19320	153	22	vision	vision	NOUN
ajst-19320	153	23	and	and	CCONJ
ajst-19320	153	24	pattern	pattern	NOUN
ajst-19320	153	25	recognition	recognition	NOUN
ajst-19320	153	26	.	.	PUNCT
ajst-19320	154	1	2017	2017	NUM
ajst-19320	154	2	:	:	PUNCT
ajst-19320	154	3	7263	7263	NUM
ajst-19320	154	4	-	-	SYM
ajst-19320	154	5	7271	7271	NUM
ajst-19320	154	6	.	.	PUNCT
ajst-19320	155	1	[	[	X
ajst-19320	155	2	11	11	NUM
ajst-19320	155	3	]	]	X
ajst-19320	155	4	redmon	redmon	PROPN
ajst-19320	155	5	j	j	PROPN
ajst-19320	155	6	,	,	PUNCT
ajst-19320	155	7	farhadi	farhadi	PROPN
ajst-19320	155	8	a.	a.	PROPN
ajst-19320	155	9	yolov3	yolov3	PROPN
ajst-19320	155	10	:	:	PUNCT
ajst-19320	156	1	an	an	DET
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ajst-19320	156	3	improvement[j	improvement[j	NOUN
ajst-19320	156	4	]	]	PUNCT
ajst-19320	156	5	.	.	PUNCT
ajst-19320	157	1	arxiv	arxiv	PROPN
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ajst-19320	157	4	,	,	PUNCT
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ajst-19320	157	6	.	.	PUNCT
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ajst-19320	158	2	12	12	NUM
ajst-19320	158	3	]	]	X
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ajst-19320	158	5	t	t	PROPN
ajst-19320	158	6	y	y	PROPN
ajst-19320	158	7	,	,	PUNCT
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ajst-19320	158	10	,	,	PUNCT
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ajst-19320	158	12	r	r	NOUN
ajst-19320	158	13	,	,	PUNCT
ajst-19320	158	14	et	et	PROPN
ajst-19320	158	15	al	al	PROPN
ajst-19320	158	16	.	.	PROPN
ajst-19320	158	17	focal	focal	ADJ
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ajst-19320	158	19	for	for	ADP
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ajst-19320	158	21	object	object	NOUN
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ajst-19320	158	23	]	]	PUNCT
ajst-19320	158	24	//proceedings	//proceeding	NOUN
ajst-19320	158	25	of	of	ADP
ajst-19320	158	26	the	the	DET
ajst-19320	158	27	ieee	ieee	NOUN
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ajst-19320	158	29	conference	conference	NOUN
ajst-19320	158	30	on	on	ADP
ajst-19320	158	31	computer	computer	NOUN
ajst-19320	158	32	vision	vision	NOUN
ajst-19320	158	33	.	.	PUNCT
ajst-19320	159	1	2017	2017	NUM
ajst-19320	159	2	:	:	PUNCT
ajst-19320	159	3	2980	2980	NUM
ajst-19320	159	4	-	-	SYM
ajst-19320	159	5	2988	2988	NUM
ajst-19320	159	6	.	.	PUNCT
ajst-19320	160	1	[	[	X
ajst-19320	160	2	13	13	NUM
ajst-19320	160	3	]	]	X
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ajst-19320	160	5	t	t	PROPN
ajst-19320	160	6	y	y	PROPN
ajst-19320	160	7	,	,	PUNCT
ajst-19320	160	8	dollár	dollár	NOUN
ajst-19320	160	9	p	p	NOUN
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ajst-19320	160	13	,	,	PUNCT
ajst-19320	160	14	et	et	PROPN
ajst-19320	160	15	al	al	PROPN
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ajst-19320	161	3	networks	network	NOUN
ajst-19320	161	4	for	for	ADP
ajst-19320	161	5	object	object	NOUN
ajst-19320	161	6	detection[c	detection[c	VERB
ajst-19320	161	7	]	]	PUNCT
ajst-19320	161	8	.	.	PUNCT
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ajst-19320	162	2	of	of	ADP
ajst-19320	162	3	the	the	DET
ajst-19320	162	4	ieee	ieee	NOUN
ajst-19320	162	5	conference	conference	NOUN
ajst-19320	162	6	on	on	ADP
ajst-19320	162	7	computer	computer	NOUN
ajst-19320	162	8	vision	vision	NOUN
ajst-19320	162	9	and	and	CCONJ
ajst-19320	162	10	pattern	pattern	NOUN
ajst-19320	162	11	recognition	recognition	NOUN
ajst-19320	162	12	.	.	PUNCT
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ajst-19320	163	2	-	-	SYM
ajst-19320	163	3	2125	2125	NUM
ajst-19320	163	4	.	.	PUNCT
ajst-19320	164	1	[	[	X
ajst-19320	164	2	14	14	NUM
ajst-19320	164	3	]	]	X
ajst-19320	164	4	y	y	PROPN
ajst-19320	164	5	.	.	PUNCT
ajst-19320	165	1	chen	chen	PROPN
ajst-19320	165	2	et	et	PROPN
ajst-19320	165	3	al	al	PROPN
ajst-19320	165	4	.	.	PROPN
ajst-19320	165	5	,	,	PUNCT
ajst-19320	165	6	“	"	PUNCT
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ajst-19320	165	8	path	path	NOUN
ajst-19320	165	9	networks	network	NOUN
ajst-19320	165	10	,	,	PUNCT
ajst-19320	165	11	”	"	PUNCT
ajst-19320	165	12	in	in	ADP
ajst-19320	165	13	proc	proc	NOUN
ajst-19320	165	14	.	.	PUNCT
ajst-19320	166	1	adv	adv	PROPN
ajst-19320	166	2	.	.	PUNCT
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ajst-19320	166	4	inf	inf	PROPN
ajst-19320	166	5	.	.	PUNCT
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ajst-19320	167	2	.	.	PROPN
ajst-19320	167	3	,	,	PUNCT
ajst-19320	167	4	2017	2017	NUM
ajst-19320	167	5	,	,	PUNCT
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ajst-19320	167	7	.	.	PUNCT
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ajst-19320	168	2	.	.	PUNCT
ajst-19320	169	1	[	[	X
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ajst-19320	169	3	]	]	X
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ajst-19320	169	10	liu	liu	PROPN
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ajst-19320	169	13	et	et	PROPN
ajst-19320	169	14	al	al	PROPN
ajst-19320	169	15	.	.	PUNCT
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ajst-19320	170	2	:	:	PUNCT
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ajst-19320	170	4	-	-	PUNCT
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ajst-19320	170	11	of	of	ADP
ajst-19320	170	12	the	the	DET
ajst-19320	170	13	ieee	ieee	NOUN
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ajst-19320	170	15	conference	conference	NOUN
ajst-19320	170	16	on	on	ADP
ajst-19320	170	17	computer	computer	NOUN
ajst-19320	170	18	vision	vision	NOUN
ajst-19320	170	19	.	.	PUNCT
ajst-19320	171	1	2017	2017	NUM
ajst-19320	171	2	:	:	PUNCT
ajst-19320	171	3	13591368	13591368	NUM
ajst-19320	171	4	.	.	PUNCT
ajst-19320	172	1	[	[	X
ajst-19320	172	2	16	16	NUM
ajst-19320	172	3	]	]	X
ajst-19320	172	4	zhao	zhao	PROPN
ajst-19320	172	5	lulu	lulu	PROPN
ajst-19320	172	6	,	,	PUNCT
ajst-19320	172	7	wang	wang	PROPN
ajst-19320	172	8	xueying	xueying	PROPN
ajst-19320	172	9	,	,	PUNCT
ajst-19320	172	10	zhang	zhang	PROPN
ajst-19320	172	11	yi	yi	PROPN
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ajst-19320	172	13	et	et	PROPN
ajst-19320	172	14	al	al	PROPN
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ajst-19320	172	16	research	research	NOUN
ajst-19320	172	17	on	on	ADP
ajst-19320	172	18	vehicle	vehicle	NOUN
ajst-19320	172	19	target	target	NOUN
ajst-19320	172	20	detection	detection	NOUN
ajst-19320	172	21	technology	technology	NOUN
ajst-19320	172	22	based	base	VERB
ajst-19320	172	23	on	on	ADP
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