id	sid	tid	token	lemma	pos
ajst-25603	1	1	academic	academic	ADJ
ajst-25603	1	2	journal	journal	NOUN
ajst-25603	1	3	of	of	ADP
ajst-25603	1	4	science	science	NOUN
ajst-25603	1	5	and	and	CCONJ
ajst-25603	1	6	technology	technology	NOUN
ajst-25603	1	7	issn	issn	NOUN
ajst-25603	1	8	:	:	PUNCT
ajst-25603	1	9	2771	2771	NUM
ajst-25603	1	10	-	-	SYM
ajst-25603	1	11	3032	3032	NUM
ajst-25603	1	12	|	|	NOUN
ajst-25603	1	13	vol	vol	NOUN
ajst-25603	1	14	.	.	PROPN
ajst-25603	2	1	12	12	NUM
ajst-25603	2	2	,	,	PUNCT
ajst-25603	2	3	no	no	INTJ
ajst-25603	2	4	.	.	NOUN
ajst-25603	2	5	2	2	NUM
ajst-25603	2	6	,	,	PUNCT
ajst-25603	2	7	2024	2024	NUM
ajst-25603	2	8	144	144	NUM
ajst-25603	2	9	improved	improved	ADJ
ajst-25603	2	10	target	target	NOUN
ajst-25603	2	11	detection	detection	NOUN
ajst-25603	2	12	algorithm	algorithm	NOUN
ajst-25603	2	13	for	for	ADP
ajst-25603	2	14	foggy	foggy	ADJ
ajst-25603	2	15	conditions	condition	NOUN
ajst-25603	2	16	in	in	ADP
ajst-25603	2	17	yolov8	yolov8	PROPN
ajst-25603	2	18	hanwei	hanwei	PROPN
ajst-25603	2	19	mao	mao	PROPN
ajst-25603	3	1	*	*	PROPN
ajst-25603	3	2	,	,	PUNCT
ajst-25603	3	3	peng	peng	PROPN
ajst-25603	3	4	wang	wang	PROPN
ajst-25603	3	5	,	,	PUNCT
ajst-25603	3	6	liming	lime	VERB
ajst-25603	3	7	zhou	zhou	PROPN
ajst-25603	3	8	,	,	PUNCT
ajst-25603	3	9	zhiren	zhiren	PROPN
ajst-25603	3	10	zhu	zhu	PROPN
ajst-25603	3	11	,	,	PUNCT
ajst-25603	3	12	xiangmeng	xiangmeng	PROPN
ajst-25603	3	13	ren	ren	PROPN
ajst-25603	3	14	college	college	PROPN
ajst-25603	3	15	of	of	ADP
ajst-25603	3	16	electrical	electrical	ADJ
ajst-25603	3	17	engineering	engineering	NOUN
ajst-25603	3	18	,	,	PUNCT
ajst-25603	3	19	tianjin	tianjin	PROPN
ajst-25603	3	20	university	university	PROPN
ajst-25603	3	21	of	of	ADP
ajst-25603	3	22	technology	technology	NOUN
ajst-25603	3	23	and	and	CCONJ
ajst-25603	3	24	education	education	NOUN
ajst-25603	3	25	,	,	PUNCT
ajst-25603	3	26	tianjin	tianjin	PROPN
ajst-25603	3	27	,	,	PUNCT
ajst-25603	3	28	china	china	PROPN
ajst-25603	3	29	*	*	PUNCT
ajst-25603	3	30	corresponding	correspond	VERB
ajst-25603	3	31	author	author	NOUN
ajst-25603	3	32	:	:	PUNCT
ajst-25603	3	33	hanwei	hanwei	PROPN
ajst-25603	3	34	mao	mao	PROPN
ajst-25603	3	35	(	(	PUNCT
ajst-25603	3	36	email	email	NOUN
ajst-25603	3	37	:	:	PUNCT
ajst-25603	3	38	15022471661@163.com	15022471661@163.com	NUM
ajst-25603	3	39	)	)	PUNCT
ajst-25603	3	40	abstract	abstract	NOUN
ajst-25603	3	41	:	:	PUNCT
ajst-25603	3	42	in	in	ADP
ajst-25603	3	43	a	a	DET
ajst-25603	3	44	foggy	foggy	ADJ
ajst-25603	3	45	environment	environment	NOUN
ajst-25603	3	46	,	,	PUNCT
ajst-25603	3	47	due	due	ADP
ajst-25603	3	48	to	to	ADP
ajst-25603	3	49	the	the	DET
ajst-25603	3	50	significant	significant	ADJ
ajst-25603	3	51	decrease	decrease	NOUN
ajst-25603	3	52	in	in	ADP
ajst-25603	3	53	visibility	visibility	NOUN
ajst-25603	3	54	,	,	PUNCT
ajst-25603	3	55	the	the	DET
ajst-25603	3	56	target	target	NOUN
ajst-25603	3	57	image	image	NOUN
ajst-25603	3	58	captured	capture	VERB
ajst-25603	3	59	by	by	ADP
ajst-25603	3	60	the	the	DET
ajst-25603	3	61	vehicle	vehicle	NOUN
ajst-25603	3	62	camera	camera	NOUN
ajst-25603	3	63	becomes	become	VERB
ajst-25603	3	64	blurred	blur	VERB
ajst-25603	3	65	and	and	CCONJ
ajst-25603	3	66	the	the	DET
ajst-25603	3	67	information	information	NOUN
ajst-25603	3	68	is	be	AUX
ajst-25603	3	69	incomplete	incomplete	ADJ
ajst-25603	3	70	,	,	PUNCT
ajst-25603	3	71	which	which	PRON
ajst-25603	3	72	exacerbates	exacerbate	VERB
ajst-25603	3	73	the	the	DET
ajst-25603	3	74	problem	problem	NOUN
ajst-25603	3	75	of	of	ADP
ajst-25603	3	76	misdetection	misdetection	NOUN
ajst-25603	3	77	and	and	CCONJ
ajst-25603	3	78	omission	omission	NOUN
ajst-25603	3	79	in	in	ADP
ajst-25603	3	80	the	the	DET
ajst-25603	3	81	process	process	NOUN
ajst-25603	3	82	of	of	ADP
ajst-25603	3	83	target	target	NOUN
ajst-25603	3	84	detection	detection	NOUN
ajst-25603	3	85	,	,	PUNCT
ajst-25603	3	86	and	and	CCONJ
ajst-25603	3	87	poses	pose	VERB
ajst-25603	3	88	a	a	DET
ajst-25603	3	89	serious	serious	ADJ
ajst-25603	3	90	challenge	challenge	NOUN
ajst-25603	3	91	to	to	ADP
ajst-25603	3	92	driving	drive	VERB
ajst-25603	3	93	safety	safety	NOUN
ajst-25603	3	94	and	and	CCONJ
ajst-25603	3	95	navigation	navigation	NOUN
ajst-25603	3	96	accuracy	accuracy	NOUN
ajst-25603	3	97	.	.	PUNCT
ajst-25603	4	1	in	in	ADP
ajst-25603	4	2	this	this	DET
ajst-25603	4	3	regard	regard	NOUN
ajst-25603	4	4	,	,	PUNCT
ajst-25603	4	5	a	a	DET
ajst-25603	4	6	foggy	foggy	ADJ
ajst-25603	4	7	target	target	NOUN
ajst-25603	4	8	detection	detection	NOUN
ajst-25603	4	9	algorithm	algorithm	NOUN
ajst-25603	4	10	based	base	VERB
ajst-25603	4	11	on	on	ADP
ajst-25603	4	12	improved	improved	ADJ
ajst-25603	4	13	yolov8	yolov8	NOUN
ajst-25603	4	14	is	be	AUX
ajst-25603	4	15	proposed	propose	VERB
ajst-25603	4	16	.	.	PUNCT
ajst-25603	5	1	first	first	ADV
ajst-25603	5	2	,	,	PUNCT
ajst-25603	5	3	by	by	ADP
ajst-25603	5	4	introducing	introduce	VERB
ajst-25603	5	5	a	a	DET
ajst-25603	5	6	bidirectional	bidirectional	ADJ
ajst-25603	5	7	feature	feature	NOUN
ajst-25603	5	8	pyramid	pyramid	NOUN
ajst-25603	5	9	bifpn	bifpn	PROPN
ajst-25603	5	10	structure	structure	NOUN
ajst-25603	5	11	in	in	ADP
ajst-25603	5	12	the	the	DET
ajst-25603	5	13	backbone	backbone	NOUN
ajst-25603	5	14	network	network	NOUN
ajst-25603	5	15	,	,	PUNCT
ajst-25603	5	16	the	the	DET
ajst-25603	5	17	algorithm	algorithm	NOUN
ajst-25603	5	18	is	be	AUX
ajst-25603	5	19	able	able	ADJ
ajst-25603	5	20	to	to	PART
ajst-25603	5	21	better	well	ADV
ajst-25603	5	22	capture	capture	VERB
ajst-25603	5	23	target	target	NOUN
ajst-25603	5	24	detection	detection	NOUN
ajst-25603	5	25	features	feature	VERB
ajst-25603	5	26	through	through	ADP
ajst-25603	5	27	bidirectional	bidirectional	ADJ
ajst-25603	5	28	connections	connection	NOUN
ajst-25603	5	29	.	.	PUNCT
ajst-25603	6	1	secondly	secondly	ADV
ajst-25603	6	2	,	,	PUNCT
ajst-25603	6	3	the	the	DET
ajst-25603	6	4	shuffle	shuffle	NOUN
ajst-25603	6	5	attention	attention	NOUN
ajst-25603	6	6	mechanism	mechanism	NOUN
ajst-25603	6	7	is	be	AUX
ajst-25603	6	8	added	add	VERB
ajst-25603	6	9	to	to	ADP
ajst-25603	6	10	the	the	DET
ajst-25603	6	11	neck	neck	NOUN
ajst-25603	6	12	network	network	NOUN
ajst-25603	6	13	to	to	PART
ajst-25603	6	14	enhance	enhance	VERB
ajst-25603	6	15	the	the	DET
ajst-25603	6	16	diversity	diversity	NOUN
ajst-25603	6	17	of	of	ADP
ajst-25603	6	18	the	the	DET
ajst-25603	6	19	input	input	NOUN
ajst-25603	6	20	sequences	sequence	NOUN
ajst-25603	6	21	by	by	ADP
ajst-25603	6	22	randomly	randomly	ADV
ajst-25603	6	23	disrupting	disrupt	VERB
ajst-25603	6	24	and	and	CCONJ
ajst-25603	6	25	grouping	group	VERB
ajst-25603	6	26	them	they	PRON
ajst-25603	6	27	,	,	PUNCT
ajst-25603	6	28	thus	thus	ADV
ajst-25603	6	29	improving	improve	VERB
ajst-25603	6	30	the	the	DET
ajst-25603	6	31	performance	performance	NOUN
ajst-25603	6	32	of	of	ADP
ajst-25603	6	33	the	the	DET
ajst-25603	6	34	self	self	NOUN
ajst-25603	6	35	-	-	PUNCT
ajst-25603	6	36	attention	attention	NOUN
ajst-25603	6	37	mechanism	mechanism	NOUN
ajst-25603	6	38	,	,	PUNCT
ajst-25603	6	39	and	and	CCONJ
ajst-25603	6	40	thus	thus	ADV
ajst-25603	6	41	improving	improve	VERB
ajst-25603	6	42	the	the	DET
ajst-25603	6	43	detection	detection	NOUN
ajst-25603	6	44	accuracy	accuracy	NOUN
ajst-25603	6	45	of	of	ADP
ajst-25603	6	46	the	the	DET
ajst-25603	6	47	network	network	NOUN
ajst-25603	6	48	model	model	NOUN
ajst-25603	6	49	.	.	PUNCT
ajst-25603	7	1	the	the	DET
ajst-25603	7	2	experimental	experimental	ADJ
ajst-25603	7	3	results	result	NOUN
ajst-25603	7	4	show	show	VERB
ajst-25603	7	5	that	that	SCONJ
ajst-25603	7	6	the	the	DET
ajst-25603	7	7	average	average	ADJ
ajst-25603	7	8	accuracy	accuracy	NOUN
ajst-25603	7	9	of	of	ADP
ajst-25603	7	10	the	the	DET
ajst-25603	7	11	improved	improved	ADJ
ajst-25603	7	12	model	model	NOUN
ajst-25603	7	13	on	on	ADP
ajst-25603	7	14	the	the	DET
ajst-25603	7	15	rtts	rtt	NOUN
ajst-25603	7	16	dataset	dataset	NOUN
ajst-25603	7	17	is	be	AUX
ajst-25603	7	18	increased	increase	VERB
ajst-25603	7	19	by	by	ADP
ajst-25603	7	20	1.3	1.3	NUM
ajst-25603	7	21	%	%	NOUN
ajst-25603	7	22	map@0.5	map@0.5	NOUN
ajst-25603	7	23	and	and	CCONJ
ajst-25603	7	24	1.2	1.2	NUM
ajst-25603	7	25	%	%	NOUN
ajst-25603	7	26	by	by	ADP
ajst-25603	7	27	map@0.5:0.95	map@0.5:0.95	NOUN
ajst-25603	7	28	.	.	PUNCT
ajst-25603	8	1	in	in	ADP
ajst-25603	8	2	summary	summary	NOUN
ajst-25603	8	3	,	,	PUNCT
ajst-25603	8	4	the	the	DET
ajst-25603	8	5	improved	improved	ADJ
ajst-25603	8	6	model	model	NOUN
ajst-25603	8	7	yolo_bis	yolo_bis	NUM
ajst-25603	8	8	can	can	AUX
ajst-25603	8	9	show	show	VERB
ajst-25603	8	10	good	good	ADJ
ajst-25603	8	11	performance	performance	NOUN
ajst-25603	8	12	when	when	SCONJ
ajst-25603	8	13	dealing	deal	VERB
ajst-25603	8	14	with	with	ADP
ajst-25603	8	15	target	target	NOUN
ajst-25603	8	16	detection	detection	NOUN
ajst-25603	8	17	tasks	task	NOUN
ajst-25603	8	18	in	in	ADP
ajst-25603	8	19	foggy	foggy	ADJ
ajst-25603	8	20	scenarios	scenario	NOUN
ajst-25603	8	21	.	.	PUNCT
ajst-25603	9	1	keywords	keyword	NOUN
ajst-25603	9	2	:	:	PUNCT
ajst-25603	9	3	target	target	VERB
ajst-25603	9	4	detection	detection	NOUN
ajst-25603	9	5	;	;	PUNCT
ajst-25603	9	6	yolov8	yolov8	PROPN
ajst-25603	9	7	;	;	PUNCT
ajst-25603	9	8	bifpn	bifpn	PROPN
ajst-25603	9	9	;	;	PUNCT
ajst-25603	9	10	shuffle	shuffle	VERB
ajst-25603	9	11	attention	attention	NOUN
ajst-25603	9	12	.	.	PUNCT
ajst-25603	10	1	1	1	X
ajst-25603	10	2	.	.	X
ajst-25603	10	3	introduction	introduction	NOUN
ajst-25603	10	4	with	with	ADP
ajst-25603	10	5	the	the	DET
ajst-25603	10	6	rapid	rapid	ADJ
ajst-25603	10	7	development	development	NOUN
ajst-25603	10	8	of	of	ADP
ajst-25603	10	9	science	science	NOUN
ajst-25603	10	10	and	and	CCONJ
ajst-25603	10	11	technology	technology	NOUN
ajst-25603	10	12	,	,	PUNCT
ajst-25603	10	13	automatic	automatic	ADJ
ajst-25603	10	14	driving	driving	NOUN
ajst-25603	11	1	[	[	X
ajst-25603	11	2	1	1	NUM
ajst-25603	11	3	]	]	PUNCT
ajst-25603	11	4	technology	technology	NOUN
ajst-25603	11	5	,	,	PUNCT
ajst-25603	11	6	as	as	ADP
ajst-25603	11	7	a	a	DET
ajst-25603	11	8	core	core	NOUN
ajst-25603	11	9	component	component	NOUN
ajst-25603	11	10	of	of	ADP
ajst-25603	11	11	intelligent	intelligent	ADJ
ajst-25603	11	12	transportation	transportation	NOUN
ajst-25603	11	13	system	system	NOUN
ajst-25603	11	14	,	,	PUNCT
ajst-25603	11	15	is	be	AUX
ajst-25603	11	16	gradually	gradually	ADV
ajst-25603	11	17	moving	move	VERB
ajst-25603	11	18	from	from	ADP
ajst-25603	11	19	concept	concept	NOUN
ajst-25603	11	20	to	to	ADP
ajst-25603	11	21	reality	reality	NOUN
ajst-25603	11	22	,	,	PUNCT
ajst-25603	11	23	leading	lead	VERB
ajst-25603	11	24	a	a	DET
ajst-25603	11	25	profound	profound	ADJ
ajst-25603	11	26	change	change	NOUN
ajst-25603	11	27	in	in	ADP
ajst-25603	11	28	the	the	DET
ajst-25603	11	29	future	future	ADJ
ajst-25603	11	30	travel	travel	NOUN
ajst-25603	11	31	mode	mode	NOUN
ajst-25603	11	32	.	.	PUNCT
ajst-25603	12	1	the	the	DET
ajst-25603	12	2	realization	realization	NOUN
ajst-25603	12	3	of	of	ADP
ajst-25603	12	4	automatic	automatic	ADJ
ajst-25603	12	5	driving	driving	NOUN
ajst-25603	12	6	technology	technology	NOUN
ajst-25603	12	7	relies	rely	VERB
ajst-25603	12	8	on	on	ADP
ajst-25603	12	9	the	the	DET
ajst-25603	12	10	in	in	ADP
ajst-25603	12	11	-	-	PUNCT
ajst-25603	12	12	depth	depth	NOUN
ajst-25603	12	13	integration	integration	NOUN
ajst-25603	12	14	of	of	ADP
ajst-25603	12	15	multiple	multiple	ADJ
ajst-25603	12	16	advanced	advanced	ADJ
ajst-25603	12	17	technologies	technology	NOUN
ajst-25603	12	18	,	,	PUNCT
ajst-25603	12	19	among	among	ADP
ajst-25603	12	20	which	which	PRON
ajst-25603	12	21	,	,	PUNCT
ajst-25603	12	22	target	target	NOUN
ajst-25603	12	23	detection	detection	NOUN
ajst-25603	12	24	technology	technology	NOUN
ajst-25603	12	25	,	,	PUNCT
ajst-25603	12	26	as	as	ADP
ajst-25603	12	27	a	a	DET
ajst-25603	12	28	key	key	ADJ
ajst-25603	12	29	part	part	NOUN
ajst-25603	12	30	of	of	ADP
ajst-25603	12	31	environment	environment	NOUN
ajst-25603	12	32	perception	perception	NOUN
ajst-25603	12	33	,	,	PUNCT
ajst-25603	12	34	is	be	AUX
ajst-25603	12	35	crucial	crucial	ADJ
ajst-25603	12	36	for	for	ADP
ajst-25603	12	37	ensuring	ensure	VERB
ajst-25603	12	38	vehicle	vehicle	NOUN
ajst-25603	12	39	driving	drive	VERB
ajst-25603	12	40	safety	safety	NOUN
ajst-25603	12	41	and	and	CCONJ
ajst-25603	12	42	enhancing	enhance	VERB
ajst-25603	12	43	the	the	DET
ajst-25603	12	44	decision	decision	NOUN
ajst-25603	12	45	-	-	PUNCT
ajst-25603	12	46	making	make	VERB
ajst-25603	12	47	ability	ability	NOUN
ajst-25603	12	48	of	of	ADP
ajst-25603	12	49	automatic	automatic	ADJ
ajst-25603	12	50	driving	driving	NOUN
ajst-25603	12	51	system	system	NOUN
ajst-25603	12	52	.	.	PUNCT
ajst-25603	13	1	at	at	ADP
ajst-25603	13	2	present	present	ADJ
ajst-25603	13	3	,	,	PUNCT
ajst-25603	13	4	the	the	DET
ajst-25603	13	5	target	target	NOUN
ajst-25603	13	6	detection	detection	NOUN
ajst-25603	13	7	technology	technology	NOUN
ajst-25603	13	8	based	base	VERB
ajst-25603	13	9	on	on	ADP
ajst-25603	13	10	yolo	yolo	ADJ
ajst-25603	13	11	algorithm	algorithm	NOUN
ajst-25603	13	12	is	be	AUX
ajst-25603	13	13	widely	widely	ADV
ajst-25603	13	14	used	use	VERB
ajst-25603	13	15	in	in	ADP
ajst-25603	13	16	foggy	foggy	ADJ
ajst-25603	13	17	scenes	scene	NOUN
ajst-25603	13	18	.	.	PUNCT
ajst-25603	14	1	su	su	PROPN
ajst-25603	14	2	tong	tong	PROPN
ajst-25603	14	3	et	et	PROPN
ajst-25603	14	4	al	al	PROPN
ajst-25603	15	1	[	[	X
ajst-25603	15	2	2	2	NUM
ajst-25603	15	3	]	]	PUNCT
ajst-25603	15	4	based	base	VERB
ajst-25603	15	5	on	on	ADP
ajst-25603	15	6	dehazenet	dehazenet	ADJ
ajst-25603	15	7	defogging	defogge	VERB
ajst-25603	15	8	algorithm	algorithm	NOUN
ajst-25603	15	9	combined	combine	VERB
ajst-25603	15	10	with	with	ADP
ajst-25603	15	11	improved	improved	ADJ
ajst-25603	15	12	yolov5	yolov5	NOUN
ajst-25603	15	13	algorithm	algorithm	NOUN
ajst-25603	15	14	to	to	PART
ajst-25603	15	15	detect	detect	VERB
ajst-25603	15	16	vehicles	vehicle	NOUN
ajst-25603	15	17	and	and	CCONJ
ajst-25603	15	18	people	people	NOUN
ajst-25603	15	19	in	in	ADP
ajst-25603	15	20	foggy	foggy	ADJ
ajst-25603	15	21	days	day	NOUN
ajst-25603	15	22	,	,	PUNCT
ajst-25603	15	23	using	use	VERB
ajst-25603	15	24	lightweight	lightweight	ADJ
ajst-25603	15	25	gsconv	gsconv	NOUN
ajst-25603	15	26	instead	instead	ADV
ajst-25603	15	27	of	of	ADP
ajst-25603	15	28	the	the	DET
ajst-25603	15	29	standard	standard	ADJ
ajst-25603	15	30	convolution	convolution	NOUN
ajst-25603	15	31	,	,	PUNCT
ajst-25603	15	32	which	which	PRON
ajst-25603	15	33	improves	improve	VERB
ajst-25603	15	34	the	the	DET
ajst-25603	15	35	network	network	NOUN
ajst-25603	15	36	feature	feature	NOUN
ajst-25603	15	37	fusion	fusion	NOUN
ajst-25603	15	38	ability	ability	NOUN
ajst-25603	15	39	.	.	PUNCT
ajst-25603	16	1	lirens	liren	NOUN
ajst-25603	16	2	et	et	PROPN
ajst-25603	16	3	al[3]proposed	al[3]propose	VERB
ajst-25603	16	4	an	an	DET
ajst-25603	16	5	improved	improved	ADJ
ajst-25603	16	6	method	method	NOUN
ajst-25603	16	7	based	base	VERB
ajst-25603	16	8	on	on	ADP
ajst-25603	16	9	the	the	DET
ajst-25603	16	10	doublehead	doublehead	NOUN
ajst-25603	16	11	framework	framework	NOUN
ajst-25603	16	12	,	,	PUNCT
ajst-25603	16	13	which	which	PRON
ajst-25603	16	14	optimizes	optimize	VERB
ajst-25603	16	15	the	the	DET
ajst-25603	16	16	feature	feature	NOUN
ajst-25603	16	17	extraction	extraction	NOUN
ajst-25603	16	18	part	part	NOUN
ajst-25603	16	19	of	of	ADP
ajst-25603	16	20	the	the	DET
ajst-25603	16	21	image	image	NOUN
ajst-25603	16	22	and	and	CCONJ
ajst-25603	16	23	the	the	DET
ajst-25603	16	24	prediction	prediction	NOUN
ajst-25603	16	25	head	head	NOUN
ajst-25603	16	26	,	,	PUNCT
ajst-25603	16	27	thus	thus	ADV
ajst-25603	16	28	improving	improve	VERB
ajst-25603	16	29	the	the	DET
ajst-25603	16	30	ability	ability	NOUN
ajst-25603	16	31	of	of	ADP
ajst-25603	16	32	the	the	DET
ajst-25603	16	33	network	network	NOUN
ajst-25603	16	34	to	to	PART
ajst-25603	16	35	focus	focus	VERB
ajst-25603	16	36	on	on	ADP
ajst-25603	16	37	the	the	DET
ajst-25603	16	38	target	target	NOUN
ajst-25603	16	39	.	.	PUNCT
ajst-25603	17	1	zhang	zhang	PROPN
ajst-25603	17	2	et	et	PROPN
ajst-25603	17	3	al	al	PROPN
ajst-25603	18	1	[	[	X
ajst-25603	18	2	4	4	X
ajst-25603	18	3	]	]	PUNCT
ajst-25603	18	4	used	use	VERB
ajst-25603	18	5	yolov7	yolov7	NOUN
ajst-25603	18	6	as	as	ADP
ajst-25603	18	7	an	an	DET
ajst-25603	18	8	augmented	augmented	ADJ
ajst-25603	18	9	network	network	NOUN
ajst-25603	18	10	and	and	CCONJ
ajst-25603	18	11	introduced	introduce	VERB
ajst-25603	18	12	the	the	DET
ajst-25603	18	13	msffa	msffa	NOUN
ajst-25603	18	14	structure	structure	NOUN
ajst-25603	18	15	to	to	PART
ajst-25603	18	16	improve	improve	VERB
ajst-25603	18	17	visibility	visibility	NOUN
ajst-25603	18	18	,	,	PUNCT
ajst-25603	18	19	which	which	PRON
ajst-25603	18	20	improves	improve	VERB
ajst-25603	18	21	the	the	DET
ajst-25603	18	22	effectiveness	effectiveness	NOUN
ajst-25603	18	23	of	of	ADP
ajst-25603	18	24	the	the	DET
ajst-25603	18	25	network	network	NOUN
ajst-25603	18	26	in	in	ADP
ajst-25603	18	27	multi	multi	ADJ
ajst-25603	18	28	-	-	ADJ
ajst-25603	18	29	class	class	ADJ
ajst-25603	18	30	object	object	NOUN
ajst-25603	18	31	target	target	NOUN
ajst-25603	18	32	detection	detection	NOUN
ajst-25603	18	33	.	.	PUNCT
ajst-25603	19	1	although	although	SCONJ
ajst-25603	19	2	the	the	DET
ajst-25603	19	3	target	target	NOUN
ajst-25603	19	4	detection	detection	NOUN
ajst-25603	19	5	technology	technology	NOUN
ajst-25603	19	6	for	for	ADP
ajst-25603	19	7	vehicles	vehicle	NOUN
ajst-25603	19	8	and	and	CCONJ
ajst-25603	19	9	pedestrians	pedestrian	NOUN
ajst-25603	19	10	in	in	ADP
ajst-25603	19	11	foggy	foggy	ADJ
ajst-25603	19	12	scenarios	scenario	NOUN
ajst-25603	19	13	has	have	AUX
ajst-25603	19	14	made	make	VERB
ajst-25603	19	15	some	some	DET
ajst-25603	19	16	progress	progress	NOUN
ajst-25603	19	17	in	in	ADP
ajst-25603	19	18	recent	recent	ADJ
ajst-25603	19	19	years	year	NOUN
ajst-25603	19	20	,	,	PUNCT
ajst-25603	19	21	which	which	PRON
ajst-25603	19	22	significantly	significantly	ADV
ajst-25603	19	23	enhances	enhance	VERB
ajst-25603	19	24	the	the	DET
ajst-25603	19	25	detection	detection	NOUN
ajst-25603	19	26	performance	performance	NOUN
ajst-25603	19	27	,	,	PUNCT
ajst-25603	19	28	however	however	ADV
ajst-25603	19	29	,	,	PUNCT
ajst-25603	19	30	the	the	DET
ajst-25603	19	31	current	current	ADJ
ajst-25603	19	32	mainstream	mainstream	ADJ
ajst-25603	19	33	foggy	foggy	ADJ
ajst-25603	19	34	target	target	NOUN
ajst-25603	19	35	detection	detection	NOUN
ajst-25603	19	36	methods	method	NOUN
ajst-25603	19	37	are	be	AUX
ajst-25603	19	38	still	still	ADV
ajst-25603	19	39	facing	face	VERB
ajst-25603	19	40	many	many	ADJ
ajst-25603	19	41	challenges	challenge	NOUN
ajst-25603	19	42	,	,	PUNCT
ajst-25603	19	43	and	and	CCONJ
ajst-25603	19	44	their	their	PRON
ajst-25603	19	45	limitations	limitation	NOUN
ajst-25603	19	46	can	can	AUX
ajst-25603	19	47	not	not	PART
ajst-25603	19	48	be	be	AUX
ajst-25603	19	49	ignored	ignore	VERB
ajst-25603	19	50	.	.	PUNCT
ajst-25603	20	1	for	for	ADP
ajst-25603	20	2	this	this	DET
ajst-25603	20	3	reason	reason	NOUN
ajst-25603	20	4	,	,	PUNCT
ajst-25603	20	5	this	this	DET
ajst-25603	20	6	paper	paper	NOUN
ajst-25603	20	7	proposes	propose	VERB
ajst-25603	20	8	an	an	DET
ajst-25603	20	9	improved	improved	ADJ
ajst-25603	20	10	target	target	NOUN
ajst-25603	20	11	detection	detection	NOUN
ajst-25603	20	12	algorithm	algorithm	NOUN
ajst-25603	20	13	for	for	ADP
ajst-25603	20	14	yolov8	yolov8	NOUN
ajst-25603	20	15	under	under	ADP
ajst-25603	20	16	foggy	foggy	ADJ
ajst-25603	20	17	conditions	condition	NOUN
ajst-25603	20	18	.	.	PUNCT
ajst-25603	21	1	2	2	X
ajst-25603	21	2	.	.	X
ajst-25603	21	3	yolov8	yolov8	NOUN
ajst-25603	21	4	algorithm	algorithm	PROPN
ajst-25603	21	5	yolov8	yolov8	NOUN
ajst-25603	22	1	[	[	X
ajst-25603	22	2	5	5	NUM
ajst-25603	22	3	]	]	PUNCT
ajst-25603	22	4	is	be	AUX
ajst-25603	22	5	an	an	DET
ajst-25603	22	6	advanced	advanced	ADJ
ajst-25603	22	7	target	target	NOUN
ajst-25603	22	8	detection	detection	NOUN
ajst-25603	22	9	algorithm	algorithm	NOUN
ajst-25603	22	10	,	,	PUNCT
ajst-25603	22	11	which	which	PRON
ajst-25603	22	12	inherits	inherit	VERB
ajst-25603	22	13	the	the	DET
ajst-25603	22	14	advantages	advantage	NOUN
ajst-25603	22	15	of	of	ADP
ajst-25603	22	16	its	its	PRON
ajst-25603	22	17	predecessor	predecessor	NOUN
ajst-25603	22	18	yolo	yolo	ADJ
ajst-25603	22	19	series	series	PROPN
ajst-25603	22	20	algorithms	algorithm	NOUN
ajst-25603	22	21	and	and	CCONJ
ajst-25603	22	22	makes	make	VERB
ajst-25603	22	23	several	several	ADJ
ajst-25603	22	24	important	important	ADJ
ajst-25603	22	25	improvements	improvement	NOUN
ajst-25603	22	26	and	and	CCONJ
ajst-25603	22	27	optimizations.yolov8	optimizations.yolov8	ADJ
ajst-25603	22	28	uses	use	VERB
ajst-25603	22	29	bce	bce	PROPN
ajst-25603	22	30	loss	loss	PROPN
ajst-25603	22	31	(	(	PUNCT
ajst-25603	22	32	binary	binary	PROPN
ajst-25603	22	33	cross	cross	PROPN
ajst-25603	22	34	entropy	entropy	PROPN
ajst-25603	22	35	loss	loss	PROPN
ajst-25603	22	36	)	)	PUNCT
ajst-25603	22	37	as	as	ADP
ajst-25603	22	38	classification	classification	NOUN
ajst-25603	22	39	loss	loss	NOUN
ajst-25603	22	40	,	,	PUNCT
ajst-25603	22	41	dfl	dfl	PROPN
ajst-25603	22	42	loss	loss	NOUN
ajst-25603	22	43	(	(	PUNCT
ajst-25603	22	44	distribution	distribution	NOUN
ajst-25603	22	45	focal	focal	ADJ
ajst-25603	22	46	loss	loss	NOUN
ajst-25603	22	47	)	)	PUNCT
ajst-25603	22	48	and	and	CCONJ
ajst-25603	22	49	ciou	ciou	NOUN
ajst-25603	22	50	loss	loss	NOUN
ajst-25603	22	51	(	(	PUNCT
ajst-25603	22	52	complete	complete	ADJ
ajst-25603	22	53	intersection	intersection	NOUN
ajst-25603	22	54	over	over	ADP
ajst-25603	22	55	union	union	NOUN
ajst-25603	22	56	)	)	PUNCT
ajst-25603	22	57	as	as	ADP
ajst-25603	22	58	the	the	DET
ajst-25603	22	59	regression	regression	NOUN
ajst-25603	22	60	loss	loss	NOUN
ajst-25603	22	61	,	,	PUNCT
ajst-25603	22	62	which	which	PRON
ajst-25603	22	63	helps	help	VERB
ajst-25603	22	64	to	to	PART
ajst-25603	22	65	measure	measure	VERB
ajst-25603	22	66	the	the	DET
ajst-25603	22	67	similarity	similarity	NOUN
ajst-25603	22	68	of	of	ADP
ajst-25603	22	69	the	the	DET
ajst-25603	22	70	target	target	NOUN
ajst-25603	22	71	frames	frame	VERB
ajst-25603	22	72	more	more	ADV
ajst-25603	22	73	accurately	accurately	ADV
ajst-25603	22	74	and	and	CCONJ
ajst-25603	22	75	further	far	ADV
ajst-25603	22	76	improves	improve	VERB
ajst-25603	22	77	the	the	DET
ajst-25603	22	78	convergence	convergence	NOUN
ajst-25603	22	79	speed	speed	NOUN
ajst-25603	22	80	and	and	CCONJ
ajst-25603	22	81	performance	performance	NOUN
ajst-25603	22	82	performance	performance	NOUN
ajst-25603	22	83	of	of	ADP
ajst-25603	22	84	the	the	DET
ajst-25603	22	85	model	model	NOUN
ajst-25603	22	86	.	.	PUNCT
ajst-25603	23	1	compared	compare	VERB
ajst-25603	23	2	with	with	ADP
ajst-25603	23	3	the	the	DET
ajst-25603	23	4	original	original	ADJ
ajst-25603	23	5	model	model	NOUN
ajst-25603	23	6	,	,	PUNCT
ajst-25603	23	7	yolov8	yolov8	PROPN
ajst-25603	23	8	is	be	AUX
ajst-25603	23	9	able	able	ADJ
ajst-25603	23	10	to	to	PART
ajst-25603	23	11	better	well	ADV
ajst-25603	23	12	balance	balance	VERB
ajst-25603	23	13	the	the	DET
ajst-25603	23	14	relationship	relationship	NOUN
ajst-25603	23	15	between	between	ADP
ajst-25603	23	16	accuracy	accuracy	NOUN
ajst-25603	23	17	and	and	CCONJ
ajst-25603	23	18	speed	speed	NOUN
ajst-25603	23	19	,	,	PUNCT
ajst-25603	23	20	and	and	CCONJ
ajst-25603	23	21	is	be	AUX
ajst-25603	23	22	more	more	ADV
ajst-25603	23	23	suitable	suitable	ADJ
ajst-25603	23	24	for	for	ADP
ajst-25603	23	25	target	target	NOUN
ajst-25603	23	26	detection	detection	NOUN
ajst-25603	23	27	tasks	task	NOUN
ajst-25603	23	28	in	in	ADP
ajst-25603	23	29	different	different	ADJ
ajst-25603	23	30	scenarios	scenario	NOUN
ajst-25603	23	31	.	.	PUNCT
ajst-25603	24	1	3	3	X
ajst-25603	24	2	.	.	X
ajst-25603	24	3	model	model	NOUN
ajst-25603	24	4	optimisation	optimisation	PROPN
ajst-25603	24	5	3.1	3.1	NUM
ajst-25603	24	6	.	.	PUNCT
ajst-25603	24	7	bifpn	bifpn	PROPN
ajst-25603	24	8	module	module	NOUN
ajst-25603	24	9	unlike	unlike	ADP
ajst-25603	24	10	traditional	traditional	ADJ
ajst-25603	24	11	fpns	fpns	NOUN
ajst-25603	24	12	,	,	PUNCT
ajst-25603	24	13	bifpn[6]introduces	bifpn[6]introduce	VERB
ajst-25603	24	14	bidirectional	bidirectional	ADJ
ajst-25603	24	15	connections	connection	NOUN
ajst-25603	24	16	between	between	ADP
ajst-25603	24	17	neighboring	neighboring	NOUN
ajst-25603	24	18	levels	level	NOUN
ajst-25603	24	19	of	of	ADP
ajst-25603	24	20	the	the	DET
ajst-25603	24	21	feature	feature	NOUN
ajst-25603	24	22	pyramid	pyramid	NOUN
ajst-25603	24	23	.	.	PUNCT
ajst-25603	25	1	this	this	PRON
ajst-25603	25	2	means	mean	VERB
ajst-25603	25	3	that	that	SCONJ
ajst-25603	25	4	information	information	NOUN
ajst-25603	25	5	can	can	AUX
ajst-25603	25	6	flow	flow	VERB
ajst-25603	25	7	from	from	ADP
ajst-25603	25	8	higher	high	ADJ
ajst-25603	25	9	level	level	NOUN
ajst-25603	25	10	features	feature	NOUN
ajst-25603	25	11	to	to	ADP
ajst-25603	25	12	lower	low	ADJ
ajst-25603	25	13	level	level	NOUN
ajst-25603	25	14	features	feature	NOUN
ajst-25603	25	15	(	(	PUNCT
ajst-25603	25	16	top	top	ADJ
ajst-25603	25	17	-	-	PUNCT
ajst-25603	25	18	down	down	ADP
ajst-25603	25	19	path	path	NOUN
ajst-25603	25	20	)	)	PUNCT
ajst-25603	25	21	or	or	CCONJ
ajst-25603	25	22	from	from	ADP
ajst-25603	25	23	lower	low	ADJ
ajst-25603	25	24	level	level	NOUN
ajst-25603	25	25	features	feature	NOUN
ajst-25603	25	26	to	to	ADP
ajst-25603	25	27	higher	high	ADJ
ajst-25603	25	28	level	level	NOUN
ajst-25603	25	29	features	feature	NOUN
ajst-25603	25	30	(	(	PUNCT
ajst-25603	25	31	bottomup	bottomup	NOUN
ajst-25603	25	32	path	path	NOUN
ajst-25603	25	33	)	)	PUNCT
ajst-25603	25	34	.	.	PUNCT
ajst-25603	26	1	bidirectional	bidirectional	ADJ
ajst-25603	26	2	connectivity	connectivity	NOUN
ajst-25603	27	1	[	[	X
ajst-25603	27	2	7	7	X
ajst-25603	27	3	]	]	PUNCT
ajst-25603	27	4	allows	allow	VERB
ajst-25603	27	5	integration	integration	NOUN
ajst-25603	27	6	of	of	ADP
ajst-25603	27	7	information	information	NOUN
ajst-25603	27	8	from	from	ADP
ajst-25603	27	9	different	different	ADJ
ajst-25603	27	10	levels	level	NOUN
ajst-25603	27	11	of	of	ADP
ajst-25603	27	12	the	the	DET
ajst-25603	27	13	feature	feature	NOUN
ajst-25603	27	14	pyramid	pyramid	NOUN
ajst-25603	27	15	in	in	ADP
ajst-25603	27	16	both	both	DET
ajst-25603	27	17	directions	direction	NOUN
ajst-25603	27	18	.	.	PUNCT
ajst-25603	28	1	this	this	DET
ajst-25603	28	2	integration	integration	NOUN
ajst-25603	28	3	helps	help	VERB
ajst-25603	28	4	to	to	PART
ajst-25603	28	5	capture	capture	VERB
ajst-25603	28	6	multi	multi	ADJ
ajst-25603	28	7	-	-	ADJ
ajst-25603	28	8	scale	scale	ADJ
ajst-25603	28	9	features	feature	VERB
ajst-25603	28	10	efficiently	efficiently	ADV
ajst-25603	28	11	.	.	PUNCT
ajst-25603	29	1	bifpn	bifpn	PROPN
ajst-25603	29	2	uses	use	VERB
ajst-25603	29	3	a	a	DET
ajst-25603	29	4	weighted	weight	VERB
ajst-25603	29	5	feature	feature	NOUN
ajst-25603	29	6	fusion	fusion	NOUN
ajst-25603	29	7	mechanism	mechanism	NOUN
ajst-25603	29	8	to	to	PART
ajst-25603	29	9	combine	combine	VERB
ajst-25603	29	10	features	feature	NOUN
ajst-25603	29	11	from	from	ADP
ajst-25603	29	12	different	different	ADJ
ajst-25603	29	13	levels	level	NOUN
ajst-25603	29	14	.	.	PUNCT
ajst-25603	30	1	the	the	DET
ajst-25603	30	2	fused	fuse	VERB
ajst-25603	30	3	weights	weight	NOUN
ajst-25603	30	4	are	be	AUX
ajst-25603	30	5	learned	learn	VERB
ajst-25603	30	6	during	during	ADP
ajst-25603	30	7	the	the	DET
ajst-25603	30	8	training	training	NOUN
ajst-25603	30	9	process	process	NOUN
ajst-25603	30	10	,	,	PUNCT
ajst-25603	30	11	ensuring	ensure	VERB
ajst-25603	30	12	optimal	optimal	ADJ
ajst-25603	30	13	feature	feature	NOUN
ajst-25603	30	14	integration	integration	NOUN
ajst-25603	30	15	.	.	PUNCT
ajst-25603	31	1	bidirectional	bidirectional	ADJ
ajst-25603	31	2	connectivity	connectivity	NOUN
ajst-25603	31	3	in	in	ADP
ajst-25603	31	4	bifpn	bifpn	PROPN
ajst-25603	31	5	helps	help	VERB
ajst-25603	31	6	to	to	PART
ajst-25603	31	7	better	well	ADV
ajst-25603	31	8	capture	capture	VERB
ajst-25603	31	9	feature	feature	NOUN
ajst-25603	31	10	representations	representation	NOUN
ajst-25603	31	11	at	at	ADP
ajst-25603	31	12	different	different	ADJ
ajst-25603	31	13	scales	scale	NOUN
ajst-25603	31	14	,	,	PUNCT
ajst-25603	31	15	improving	improve	VERB
ajst-25603	31	16	the	the	DET
ajst-25603	31	17	network	network	NOUN
ajst-25603	31	18	's	's	PART
ajst-25603	31	19	ability	ability	NOUN
ajst-25603	31	20	to	to	PART
ajst-25603	31	21	handle	handle	VERB
ajst-25603	31	22	objects	object	NOUN
ajst-25603	31	23	of	of	ADP
ajst-25603	31	24	different	different	ADJ
ajst-25603	31	25	sizes	size	NOUN
ajst-25603	31	26	and	and	CCONJ
ajst-25603	31	27	complexity	complexity	NOUN
ajst-25603	31	28	.	.	PUNCT
ajst-25603	32	1	as	as	SCONJ
ajst-25603	32	2	shown	show	VERB
ajst-25603	32	3	in	in	ADP
ajst-25603	32	4	figure	figure	NOUN
ajst-25603	32	5	1	1	NUM
ajst-25603	32	6	,	,	PUNCT
ajst-25603	32	7	this	this	PRON
ajst-25603	32	8	is	be	AUX
ajst-25603	32	9	particularly	particularly	ADV
ajst-25603	32	10	important	important	ADJ
ajst-25603	32	11	in	in	ADP
ajst-25603	32	12	target	target	NOUN
ajst-25603	32	13	detection	detection	NOUN
ajst-25603	32	14	tasks	task	NOUN
ajst-25603	32	15	,	,	PUNCT
ajst-25603	32	16	where	where	SCONJ
ajst-25603	32	17	the	the	DET
ajst-25603	32	18	size	size	NOUN
ajst-25603	32	19	of	of	ADP
ajst-25603	32	20	objects	object	NOUN
ajst-25603	32	21	in	in	ADP
ajst-25603	32	22	an	an	DET
ajst-25603	32	23	image	image	NOUN
ajst-25603	32	24	may	may	AUX
ajst-25603	32	25	vary	vary	VERB
ajst-25603	32	26	significantly	significantly	ADV
ajst-25603	32	27	.	.	PUNCT
ajst-25603	33	1	145	145	NUM
ajst-25603	33	2	figure	figure	NOUN
ajst-25603	33	3	1	1	NUM
ajst-25603	33	4	.	.	PUNCT
ajst-25603	33	5	bifpn	bifpn	PROPN
ajst-25603	33	6	module	module	NOUN
ajst-25603	33	7	structure	structure	NOUN
ajst-25603	33	8	diagram	diagram	NOUN
ajst-25603	33	9	3.2	3.2	NUM
ajst-25603	33	10	.	.	PUNCT
ajst-25603	34	1	shuffleattention	shuffleattention	NOUN
ajst-25603	34	2	module	module	NOUN
ajst-25603	34	3	shuffle	shuffle	NOUN
ajst-25603	34	4	attention	attention	NOUN
ajst-25603	34	5	attention	attention	NOUN
ajst-25603	34	6	mechanism	mechanism	NOUN
ajst-25603	34	7	[	[	X
ajst-25603	34	8	8	8	NUM
ajst-25603	34	9	]	]	PUNCT
ajst-25603	34	10	uses	use	VERB
ajst-25603	34	11	a	a	DET
ajst-25603	34	12	selfattention	selfattention	NOUN
ajst-25603	34	13	mechanism	mechanism	NOUN
ajst-25603	34	14	to	to	PART
ajst-25603	34	15	compute	compute	VERB
ajst-25603	34	16	the	the	DET
ajst-25603	34	17	attention	attention	NOUN
ajst-25603	34	18	weights	weight	NOUN
ajst-25603	34	19	between	between	ADP
ajst-25603	34	20	each	each	DET
ajst-25603	34	21	vector	vector	NOUN
ajst-25603	34	22	and	and	CCONJ
ajst-25603	34	23	the	the	DET
ajst-25603	34	24	other	other	ADJ
ajst-25603	34	25	vectors	vector	NOUN
ajst-25603	34	26	.	.	PUNCT
ajst-25603	35	1	the	the	DET
ajst-25603	35	2	vectors	vector	NOUN
ajst-25603	35	3	within	within	ADP
ajst-25603	35	4	each	each	DET
ajst-25603	35	5	group	group	NOUN
ajst-25603	35	6	are	be	AUX
ajst-25603	35	7	weighted	weight	VERB
ajst-25603	35	8	and	and	CCONJ
ajst-25603	35	9	summarized	summarize	VERB
ajst-25603	35	10	according	accord	VERB
ajst-25603	35	11	to	to	ADP
ajst-25603	35	12	the	the	DET
ajst-25603	35	13	attention	attention	NOUN
ajst-25603	35	14	weights	weight	NOUN
ajst-25603	35	15	to	to	PART
ajst-25603	35	16	obtain	obtain	VERB
ajst-25603	35	17	the	the	DET
ajst-25603	35	18	representation	representation	NOUN
ajst-25603	35	19	within	within	ADP
ajst-25603	35	20	the	the	DET
ajst-25603	35	21	group	group	NOUN
ajst-25603	35	22	.	.	PUNCT
ajst-25603	36	1	reorganization	reorganization	NOUN
ajst-25603	36	2	can	can	AUX
ajst-25603	36	3	recombine	recombine	VERB
ajst-25603	36	4	the	the	DET
ajst-25603	36	5	representations	representation	NOUN
ajst-25603	36	6	within	within	ADP
ajst-25603	36	7	each	each	DET
ajst-25603	36	8	group	group	NOUN
ajst-25603	36	9	into	into	ADP
ajst-25603	36	10	a	a	DET
ajst-25603	36	11	new	new	ADJ
ajst-25603	36	12	sequence	sequence	NOUN
ajst-25603	36	13	.	.	PUNCT
ajst-25603	37	1	reflective	reflective	ADJ
ajst-25603	37	2	shooting	shooting	NOUN
ajst-25603	37	3	maps	map	VERB
ajst-25603	37	4	the	the	DET
ajst-25603	37	5	new	new	ADJ
ajst-25603	37	6	sequence	sequence	NOUN
ajst-25603	37	7	back	back	ADV
ajst-25603	37	8	to	to	ADP
ajst-25603	37	9	the	the	DET
ajst-25603	37	10	original	original	ADJ
ajst-25603	37	11	high	high	ADJ
ajst-25603	37	12	-	-	PUNCT
ajst-25603	37	13	dimensional	dimensional	ADJ
ajst-25603	37	14	vector	vector	NOUN
ajst-25603	37	15	space	space	NOUN
ajst-25603	37	16	via	via	ADP
ajst-25603	37	17	an	an	DET
ajst-25603	37	18	inverse	inverse	NOUN
ajst-25603	37	19	mapping	mapping	NOUN
ajst-25603	37	20	function	function	NOUN
ajst-25603	37	21	.	.	PUNCT
ajst-25603	38	1	the	the	DET
ajst-25603	38	2	output	output	NOUN
ajst-25603	38	3	yields	yield	VERB
ajst-25603	38	4	a	a	DET
ajst-25603	38	5	new	new	ADJ
ajst-25603	38	6	representation	representation	NOUN
ajst-25603	38	7	that	that	PRON
ajst-25603	38	8	can	can	AUX
ajst-25603	38	9	be	be	AUX
ajst-25603	38	10	used	use	VERB
ajst-25603	38	11	for	for	ADP
ajst-25603	38	12	subsequent	subsequent	ADJ
ajst-25603	38	13	tasks	task	NOUN
ajst-25603	38	14	such	such	ADJ
ajst-25603	38	15	as	as	ADP
ajst-25603	38	16	classification	classification	NOUN
ajst-25603	38	17	or	or	CCONJ
ajst-25603	38	18	generation	generation	NOUN
ajst-25603	38	19	.	.	PUNCT
ajst-25603	39	1	as	as	SCONJ
ajst-25603	39	2	shown	show	VERB
ajst-25603	39	3	in	in	ADP
ajst-25603	39	4	figure	figure	NOUN
ajst-25603	39	5	2	2	NUM
ajst-25603	39	6	,	,	PUNCT
ajst-25603	39	7	the	the	DET
ajst-25603	39	8	core	core	NOUN
ajst-25603	39	9	idea	idea	NOUN
ajst-25603	39	10	is	be	AUX
ajst-25603	39	11	to	to	PART
ajst-25603	39	12	enhance	enhance	VERB
ajst-25603	39	13	the	the	DET
ajst-25603	39	14	diversity	diversity	NOUN
ajst-25603	39	15	of	of	ADP
ajst-25603	39	16	input	input	NOUN
ajst-25603	39	17	sequences	sequence	NOUN
ajst-25603	39	18	by	by	ADP
ajst-25603	39	19	randomly	randomly	ADV
ajst-25603	39	20	disrupting	disrupt	VERB
ajst-25603	39	21	and	and	CCONJ
ajst-25603	39	22	grouping	group	VERB
ajst-25603	39	23	them	they	PRON
ajst-25603	39	24	,	,	PUNCT
ajst-25603	39	25	thus	thus	ADV
ajst-25603	39	26	improving	improve	VERB
ajst-25603	39	27	the	the	DET
ajst-25603	39	28	performance	performance	NOUN
ajst-25603	39	29	of	of	ADP
ajst-25603	39	30	the	the	DET
ajst-25603	39	31	self	self	NOUN
ajst-25603	39	32	-	-	PUNCT
ajst-25603	39	33	attention	attention	NOUN
ajst-25603	39	34	mechanism	mechanism	NOUN
ajst-25603	39	35	.	.	PUNCT
ajst-25603	40	1	figure	figure	NOUN
ajst-25603	40	2	2	2	NUM
ajst-25603	40	3	.	.	PUNCT
ajst-25603	40	4	shuffleattention	shuffleattention	NOUN
ajst-25603	40	5	module	module	NOUN
ajst-25603	40	6	structure	structure	NOUN
ajst-25603	40	7	in	in	ADP
ajst-25603	40	8	this	this	DET
ajst-25603	40	9	paper	paper	NOUN
ajst-25603	40	10	,	,	PUNCT
ajst-25603	40	11	based	base	VERB
ajst-25603	40	12	on	on	ADP
ajst-25603	40	13	the	the	DET
ajst-25603	40	14	yolov8n	yolov8n	PROPN
ajst-25603	40	15	model	model	NOUN
ajst-25603	40	16	,	,	PUNCT
ajst-25603	40	17	a	a	DET
ajst-25603	40	18	new	new	ADJ
ajst-25603	40	19	foggy	foggy	ADJ
ajst-25603	40	20	target	target	NOUN
ajst-25603	40	21	detection	detection	NOUN
ajst-25603	40	22	algorithm	algorithm	NOUN
ajst-25603	40	23	,	,	PUNCT
ajst-25603	40	24	yolo_bis	yolo_bis	NUM
ajst-25603	40	25	,	,	PUNCT
ajst-25603	40	26	is	be	AUX
ajst-25603	40	27	proposed	propose	VERB
ajst-25603	40	28	to	to	PART
ajst-25603	40	29	address	address	VERB
ajst-25603	40	30	the	the	DET
ajst-25603	40	31	misdetection	misdetection	NOUN
ajst-25603	40	32	and	and	CCONJ
ajst-25603	40	33	omission	omission	NOUN
ajst-25603	40	34	problems	problem	NOUN
ajst-25603	40	35	that	that	PRON
ajst-25603	40	36	occur	occur	VERB
ajst-25603	40	37	when	when	SCONJ
ajst-25603	40	38	it	it	PRON
ajst-25603	40	39	performs	perform	VERB
ajst-25603	40	40	target	target	NOUN
ajst-25603	40	41	detection	detection	NOUN
ajst-25603	40	42	in	in	ADP
ajst-25603	40	43	foggy	foggy	ADJ
ajst-25603	40	44	scenarios.through	scenarios.through	PROPN
ajst-25603	40	45	the	the	DET
ajst-25603	40	46	application	application	NOUN
ajst-25603	40	47	of	of	ADP
ajst-25603	40	48	this	this	DET
ajst-25603	40	49	algorithm	algorithm	NOUN
ajst-25603	40	50	,	,	PUNCT
ajst-25603	40	51	the	the	DET
ajst-25603	40	52	probability	probability	NOUN
ajst-25603	40	53	of	of	ADP
ajst-25603	40	54	misdetection	misdetection	NOUN
ajst-25603	40	55	and	and	CCONJ
ajst-25603	40	56	omission	omission	NOUN
ajst-25603	40	57	is	be	AUX
ajst-25603	40	58	reduced	reduce	VERB
ajst-25603	40	59	,	,	PUNCT
ajst-25603	40	60	and	and	CCONJ
ajst-25603	40	61	the	the	DET
ajst-25603	40	62	robustness	robustness	NOUN
ajst-25603	40	63	and	and	CCONJ
ajst-25603	40	64	reliability	reliability	NOUN
ajst-25603	40	65	of	of	ADP
ajst-25603	40	66	the	the	DET
ajst-25603	40	67	model	model	NOUN
ajst-25603	40	68	are	be	AUX
ajst-25603	40	69	improved	improve	VERB
ajst-25603	40	70	.	.	PUNCT
ajst-25603	41	1	a	a	DET
ajst-25603	41	2	weighted	weight	VERB
ajst-25603	41	3	bidirectional	bidirectional	ADJ
ajst-25603	41	4	feature	feature	NOUN
ajst-25603	41	5	pyramid	pyramid	NOUN
ajst-25603	41	6	network	network	PROPN
ajst-25603	41	7	,	,	PUNCT
ajst-25603	41	8	bifpn	bifpn	PROPN
ajst-25603	41	9	,	,	PUNCT
ajst-25603	41	10	is	be	AUX
ajst-25603	41	11	added	add	VERB
ajst-25603	41	12	to	to	ADP
ajst-25603	41	13	the	the	DET
ajst-25603	41	14	neck	neck	NOUN
ajst-25603	41	15	section	section	NOUN
ajst-25603	41	16	,	,	PUNCT
ajst-25603	41	17	which	which	PRON
ajst-25603	41	18	introduces	introduce	VERB
ajst-25603	41	19	learnable	learnable	ADJ
ajst-25603	41	20	weights	weight	NOUN
ajst-25603	41	21	to	to	PART
ajst-25603	41	22	learn	learn	VERB
ajst-25603	41	23	the	the	DET
ajst-25603	41	24	importance	importance	NOUN
ajst-25603	41	25	of	of	ADP
ajst-25603	41	26	different	different	ADJ
ajst-25603	41	27	input	input	NOUN
ajst-25603	41	28	features	feature	NOUN
ajst-25603	41	29	,	,	PUNCT
ajst-25603	41	30	while	while	SCONJ
ajst-25603	41	31	top	top	ADJ
ajst-25603	41	32	-	-	PUNCT
ajst-25603	41	33	down	down	NOUN
ajst-25603	41	34	and	and	CCONJ
ajst-25603	41	35	bottom	bottom	ADJ
ajst-25603	41	36	-	-	PUNCT
ajst-25603	41	37	up	up	ADP
ajst-25603	41	38	multiscale	multiscale	ADJ
ajst-25603	41	39	feature	feature	NOUN
ajst-25603	41	40	fusion	fusion	NOUN
ajst-25603	41	41	is	be	AUX
ajst-25603	41	42	applied	apply	VERB
ajst-25603	41	43	iteratively	iteratively	ADV
ajst-25603	41	44	.	.	PUNCT
ajst-25603	42	1	one	one	NUM
ajst-25603	42	2	of	of	ADP
ajst-25603	42	3	the	the	DET
ajst-25603	42	4	core	core	NOUN
ajst-25603	42	5	innovations	innovation	NOUN
ajst-25603	42	6	of	of	ADP
ajst-25603	42	7	bifpn	bifpn	PROPN
ajst-25603	42	8	is	be	AUX
ajst-25603	42	9	the	the	DET
ajst-25603	42	10	bidirectional	bidirectional	ADJ
ajst-25603	42	11	cross	cross	ADJ
ajst-25603	42	12	-	-	ADJ
ajst-25603	42	13	scale	scale	ADJ
ajst-25603	42	14	connectivity	connectivity	NOUN
ajst-25603	42	15	,	,	PUNCT
ajst-25603	42	16	which	which	PRON
ajst-25603	42	17	allows	allow	VERB
ajst-25603	42	18	for	for	SCONJ
ajst-25603	42	19	features	feature	NOUN
ajst-25603	42	20	to	to	PART
ajst-25603	42	21	be	be	AUX
ajst-25603	42	22	more	more	ADV
ajst-25603	42	23	comprehensively	comprehensively	ADV
ajst-25603	42	24	informative	informative	ADJ
ajst-25603	42	25	between	between	ADP
ajst-25603	42	26	different	different	ADJ
ajst-25603	42	27	layers	layer	NOUN
ajst-25603	42	28	through	through	ADP
ajst-25603	42	29	top	top	ADJ
ajst-25603	42	30	-	-	PUNCT
ajst-25603	42	31	down	down	NOUN
ajst-25603	42	32	and	and	CCONJ
ajst-25603	42	33	bottom	bottom	ADJ
ajst-25603	42	34	-	-	PUNCT
ajst-25603	42	35	up	up	ADP
ajst-25603	42	36	paths	path	NOUN
ajst-25603	42	37	transfer	transfer	NOUN
ajst-25603	42	38	and	and	CCONJ
ajst-25603	42	39	fusion	fusion	NOUN
ajst-25603	42	40	.	.	PUNCT
ajst-25603	43	1	this	this	PRON
ajst-25603	43	2	is	be	AUX
ajst-25603	43	3	different	different	ADJ
ajst-25603	43	4	from	from	ADP
ajst-25603	43	5	traditional	traditional	ADJ
ajst-25603	43	6	fpns	fpns	ADJ
ajst-25603	43	7	and	and	CCONJ
ajst-25603	43	8	pans	pan	NOUN
ajst-25603	43	9	,	,	PUNCT
ajst-25603	43	10	which	which	PRON
ajst-25603	43	11	mainly	mainly	ADV
ajst-25603	43	12	use	use	VERB
ajst-25603	43	13	top	top	ADJ
ajst-25603	43	14	-	-	PUNCT
ajst-25603	43	15	down	down	ADP
ajst-25603	43	16	feature	feature	NOUN
ajst-25603	43	17	propagation	propagation	NOUN
ajst-25603	43	18	.	.	PUNCT
ajst-25603	44	1	the	the	DET
ajst-25603	44	2	shuffleattention	shuffleattention	NOUN
ajst-25603	44	3	mechanism	mechanism	NOUN
ajst-25603	44	4	is	be	AUX
ajst-25603	44	5	added	add	VERB
ajst-25603	44	6	to	to	ADP
ajst-25603	44	7	layers	layer	NOUN
ajst-25603	44	8	16	16	NUM
ajst-25603	44	9	and	and	CCONJ
ajst-25603	44	10	20	20	NUM
ajst-25603	44	11	of	of	ADP
ajst-25603	44	12	the	the	DET
ajst-25603	44	13	neck	neck	NOUN
ajst-25603	44	14	part	part	NOUN
ajst-25603	44	15	,	,	PUNCT
ajst-25603	44	16	which	which	PRON
ajst-25603	44	17	can	can	AUX
ajst-25603	44	18	enhance	enhance	VERB
ajst-25603	44	19	the	the	DET
ajst-25603	44	20	diversity	diversity	NOUN
ajst-25603	44	21	of	of	ADP
ajst-25603	44	22	input	input	NOUN
ajst-25603	44	23	sequences	sequence	NOUN
ajst-25603	44	24	by	by	ADP
ajst-25603	44	25	randomly	randomly	ADV
ajst-25603	44	26	disrupting	disrupt	VERB
ajst-25603	44	27	and	and	CCONJ
ajst-25603	44	28	grouping	group	VERB
ajst-25603	44	29	to	to	PART
ajst-25603	44	30	improve	improve	VERB
ajst-25603	44	31	the	the	DET
ajst-25603	44	32	performance	performance	NOUN
ajst-25603	44	33	of	of	ADP
ajst-25603	44	34	the	the	DET
ajst-25603	44	35	self	self	NOUN
ajst-25603	44	36	-	-	PUNCT
ajst-25603	44	37	attention	attention	NOUN
ajst-25603	44	38	mechanism	mechanism	NOUN
ajst-25603	44	39	.	.	PUNCT
ajst-25603	45	1	the	the	DET
ajst-25603	45	2	structure	structure	NOUN
ajst-25603	45	3	of	of	ADP
ajst-25603	45	4	the	the	DET
ajst-25603	45	5	yolo_bis	yolo_bis	PROPN
ajst-25603	45	6	network	network	NOUN
ajst-25603	45	7	is	be	AUX
ajst-25603	45	8	shown	show	VERB
ajst-25603	45	9	in	in	ADP
ajst-25603	45	10	figure	figure	NOUN
ajst-25603	45	11	3	3	NUM
ajst-25603	45	12	.	.	PUNCT
ajst-25603	45	13	figure	figure	NOUN
ajst-25603	45	14	3	3	NUM
ajst-25603	45	15	.	.	PUNCT
ajst-25603	45	16	improved	improve	VERB
ajst-25603	45	17	yolov8	yolov8	NOUN
ajst-25603	45	18	network	network	NOUN
ajst-25603	45	19	structure	structure	NOUN
ajst-25603	45	20	4	4	NUM
ajst-25603	45	21	.	.	PUNCT
ajst-25603	45	22	experimental	experimental	ADJ
ajst-25603	45	23	result	result	NOUN
ajst-25603	45	24	and	and	CCONJ
ajst-25603	45	25	analysis	analysis	NOUN
ajst-25603	45	26	4.1	4.1	NUM
ajst-25603	45	27	.	.	PUNCT
ajst-25603	46	1	datasets	dataset	VERB
ajst-25603	46	2	the	the	DET
ajst-25603	46	3	operating	operating	NOUN
ajst-25603	46	4	system	system	NOUN
ajst-25603	46	5	for	for	ADP
ajst-25603	46	6	this	this	DET
ajst-25603	46	7	experiment	experiment	NOUN
ajst-25603	46	8	is	be	AUX
ajst-25603	46	9	windows	window	NOUN
ajst-25603	46	10	11	11	NUM
ajst-25603	46	11	professional	professional	ADJ
ajst-25603	46	12	,	,	PUNCT
ajst-25603	46	13	64	64	NUM
ajst-25603	46	14	-	-	PUNCT
ajst-25603	46	15	bit	bit	NOUN
ajst-25603	46	16	os	os	NOUN
ajst-25603	46	17	,	,	PUNCT
ajst-25603	46	18	running	run	VERB
ajst-25603	46	19	on	on	ADP
ajst-25603	46	20	128	128	NUM
ajst-25603	46	21	g	g	NOUN
ajst-25603	46	22	of	of	ADP
ajst-25603	46	23	ram	ram	NOUN
ajst-25603	46	24	,	,	PUNCT
ajst-25603	46	25	intel(r	intel(r	NOUN
ajst-25603	46	26	)	)	PUNCT
ajst-25603	46	27	xeon(r	xeon(r	NOUN
ajst-25603	46	28	)	)	PUNCT
ajst-25603	46	29	gold	gold	NOUN
ajst-25603	46	30	6226r	6226r	NOUN
ajst-25603	46	31	cpu	cpu	NOUN
ajst-25603	46	32	@	@	ADP
ajst-25603	46	33	2.90ghz	2.90ghz	NUM
ajst-25603	46	34	2.89	2.89	NUM
ajst-25603	46	35	ghz	ghz	NOUN
ajst-25603	46	36	(	(	PUNCT
ajst-25603	46	37	2	2	NUM
ajst-25603	46	38	processors	processor	NOUN
ajst-25603	46	39	)	)	PUNCT
ajst-25603	46	40	.	.	PUNCT
ajst-25603	47	1	python	python	NOUN
ajst-25603	47	2	programming	programming	NOUN
ajst-25603	47	3	language	language	NOUN
ajst-25603	47	4	was	be	AUX
ajst-25603	47	5	used	use	VERB
ajst-25603	47	6	to	to	PART
ajst-25603	47	7	complete	complete	VERB
ajst-25603	47	8	the	the	DET
ajst-25603	47	9	training	training	NOUN
ajst-25603	47	10	of	of	ADP
ajst-25603	47	11	the	the	DET
ajst-25603	47	12	model	model	NOUN
ajst-25603	47	13	in	in	ADP
ajst-25603	47	14	pycharm	pycharm	PROPN
ajst-25603	47	15	version	version	NOUN
ajst-25603	47	16	2023.1	2023.1	NUM
ajst-25603	47	17	.	.	PUNCT
ajst-25603	47	18	batch	batch	NOUN
ajst-25603	47	19	-	-	PUNCT
ajst-25603	47	20	size	size	NOUN
ajst-25603	47	21	was	be	AUX
ajst-25603	47	22	set	set	VERB
ajst-25603	47	23	to	to	ADP
ajst-25603	47	24	4	4	NUM
ajst-25603	47	25	and	and	CCONJ
ajst-25603	47	26	the	the	DET
ajst-25603	47	27	number	number	NOUN
ajst-25603	47	28	of	of	ADP
ajst-25603	47	29	iterations	iteration	NOUN
ajst-25603	47	30	epoch	epoch	NOUN
ajst-25603	47	31	was	be	AUX
ajst-25603	47	32	set	set	VERB
ajst-25603	47	33	to	to	ADP
ajst-25603	47	34	200	200	NUM
ajst-25603	47	35	.	.	PUNCT
ajst-25603	48	1	in	in	ADP
ajst-25603	48	2	order	order	NOUN
ajst-25603	48	3	to	to	PART
ajst-25603	48	4	deeply	deeply	ADV
ajst-25603	48	5	investigate	investigate	VERB
ajst-25603	48	6	the	the	DET
ajst-25603	48	7	target	target	NOUN
ajst-25603	48	8	detection	detection	NOUN
ajst-25603	48	9	effectiveness	effectiveness	NOUN
ajst-25603	48	10	of	of	ADP
ajst-25603	48	11	yolov8	yolov8	NOUN
ajst-25603	48	12	in	in	ADP
ajst-25603	48	13	foggy	foggy	ADJ
ajst-25603	48	14	environment	environment	NOUN
ajst-25603	48	15	,	,	PUNCT
ajst-25603	48	16	the	the	DET
ajst-25603	48	17	rtts	rtt	NOUN
ajst-25603	48	18	dataset	dataset	VERB
ajst-25603	48	19	[	[	X
ajst-25603	48	20	9	9	NUM
ajst-25603	48	21	]	]	PUNCT
ajst-25603	48	22	is	be	AUX
ajst-25603	48	23	chosen	choose	VERB
ajst-25603	48	24	for	for	ADP
ajst-25603	48	25	empirical	empirical	ADJ
ajst-25603	48	26	analysis	analysis	NOUN
ajst-25603	48	27	.	.	PUNCT
ajst-25603	49	1	the	the	DET
ajst-25603	49	2	rtts	rtt	NOUN
ajst-25603	49	3	dataset	dataset	NOUN
ajst-25603	49	4	is	be	AUX
ajst-25603	49	5	derived	derive	VERB
ajst-25603	49	6	from	from	ADP
ajst-25603	49	7	the	the	DET
ajst-25603	49	8	foggy	foggy	ADJ
ajst-25603	49	9	day	day	NOUN
ajst-25603	49	10	subset	subset	NOUN
ajst-25603	49	11	of	of	ADP
ajst-25603	49	12	reside	reside	NOUN
ajst-25603	49	13	dataset	dataset	NOUN
ajst-25603	49	14	(	(	PUNCT
ajst-25603	49	15	reside	reside	NOUN
ajst-25603	49	16	-	-	PUNCT
ajst-25603	49	17	foggy	foggy	ADJ
ajst-25603	49	18	day	day	NOUN
ajst-25603	49	19	dataset	dataset	NOUN
ajst-25603	49	20	)	)	PUNCT
ajst-25603	49	21	,	,	PUNCT
ajst-25603	49	22	which	which	PRON
ajst-25603	49	23	is	be	AUX
ajst-25603	49	24	a	a	DET
ajst-25603	49	25	publicly	publicly	ADV
ajst-25603	49	26	available	available	ADJ
ajst-25603	49	27	dataset	dataset	NOUN
ajst-25603	49	28	specifically	specifically	ADV
ajst-25603	49	29	designed	design	VERB
ajst-25603	49	30	for	for	ADP
ajst-25603	49	31	foggy	foggy	ADJ
ajst-25603	49	32	day	day	NOUN
ajst-25603	49	33	image	image	NOUN
ajst-25603	49	34	processing	processing	NOUN
ajst-25603	49	35	and	and	CCONJ
ajst-25603	49	36	computer	computer	NOUN
ajst-25603	49	37	vision	vision	NOUN
ajst-25603	49	38	research	research	NOUN
ajst-25603	49	39	.	.	PUNCT
ajst-25603	50	1	specifically	specifically	ADV
ajst-25603	50	2	,	,	PUNCT
ajst-25603	50	3	the	the	DET
ajst-25603	50	4	rtts	rtt	NOUN
ajst-25603	50	5	dataset	dataset	NOUN
ajst-25603	50	6	covers	cover	VERB
ajst-25603	50	7	4322	4322	NUM
ajst-25603	50	8	real	real	ADV
ajst-25603	50	9	foggy	foggy	ADJ
ajst-25603	50	10	day	day	NOUN
ajst-25603	50	11	images	image	NOUN
ajst-25603	50	12	that	that	PRON
ajst-25603	50	13	contain	contain	VERB
ajst-25603	50	14	diverse	diverse	ADJ
ajst-25603	50	15	target	target	NOUN
ajst-25603	50	16	types	type	NOUN
ajst-25603	50	17	as	as	ADV
ajst-25603	50	18	well	well	ADV
ajst-25603	50	19	as	as	ADP
ajst-25603	50	20	various	various	ADJ
ajst-25603	50	21	occlusion	occlusion	NOUN
ajst-25603	50	22	situations	situation	NOUN
ajst-25603	50	23	.	.	PUNCT
ajst-25603	51	1	in	in	ADP
ajst-25603	51	2	the	the	DET
ajst-25603	51	3	experiments	experiment	NOUN
ajst-25603	51	4	in	in	ADP
ajst-25603	51	5	this	this	DET
ajst-25603	51	6	paper	paper	NOUN
ajst-25603	51	7	,	,	PUNCT
ajst-25603	51	8	we	we	PRON
ajst-25603	51	9	divide	divide	VERB
ajst-25603	51	10	the	the	DET
ajst-25603	51	11	dataset	dataset	NOUN
ajst-25603	51	12	into	into	ADP
ajst-25603	51	13	a	a	DET
ajst-25603	51	14	test	test	NOUN
ajst-25603	51	15	set	set	NOUN
ajst-25603	51	16	(	(	PUNCT
ajst-25603	51	17	433	433	NUM
ajst-25603	51	18	images	image	NOUN
ajst-25603	51	19	)	)	PUNCT
ajst-25603	51	20	,	,	PUNCT
ajst-25603	51	21	a	a	DET
ajst-25603	51	22	training	training	NOUN
ajst-25603	51	23	set	set	NOUN
ajst-25603	51	24	(	(	PUNCT
ajst-25603	51	25	2161	2161	NUM
ajst-25603	51	26	images	image	NOUN
ajst-25603	51	27	)	)	PUNCT
ajst-25603	51	28	,	,	PUNCT
ajst-25603	51	29	and	and	CCONJ
ajst-25603	51	30	a	a	DET
ajst-25603	51	31	validation	validation	NOUN
ajst-25603	51	32	set	set	NOUN
ajst-25603	51	33	(	(	PUNCT
ajst-25603	51	34	1728	1728	NUM
ajst-25603	51	35	images	image	NOUN
ajst-25603	51	36	)	)	PUNCT
ajst-25603	51	37	.	.	PUNCT
ajst-25603	52	1	these	these	DET
ajst-25603	52	2	datasets	dataset	NOUN
ajst-25603	52	3	are	be	AUX
ajst-25603	52	4	widely	widely	ADV
ajst-25603	52	5	used	use	VERB
ajst-25603	52	6	in	in	ADP
ajst-25603	52	7	computer	computer	NOUN
ajst-25603	52	8	vision	vision	NOUN
ajst-25603	52	9	tasks	task	NOUN
ajst-25603	52	10	such	such	ADJ
ajst-25603	52	11	as	as	ADP
ajst-25603	52	12	target	target	NOUN
ajst-25603	52	13	detection	detection	NOUN
ajst-25603	52	14	,	,	PUNCT
ajst-25603	52	15	image	image	NOUN
ajst-25603	52	16	enhancement	enhancement	NOUN
ajst-25603	52	17	and	and	CCONJ
ajst-25603	52	18	defogging	defogging	NOUN
ajst-25603	52	19	.	.	PUNCT
ajst-25603	53	1	in	in	ADP
ajst-25603	53	2	order	order	NOUN
ajst-25603	53	3	to	to	PART
ajst-25603	53	4	improve	improve	VERB
ajst-25603	53	5	the	the	DET
ajst-25603	53	6	performance	performance	NOUN
ajst-25603	53	7	and	and	CCONJ
ajst-25603	53	8	robustness	robustness	NOUN
ajst-25603	53	9	of	of	ADP
ajst-25603	53	10	the	the	DET
ajst-25603	53	11	model	model	NOUN
ajst-25603	53	12	under	under	ADP
ajst-25603	53	13	foggy	foggy	ADJ
ajst-25603	53	14	conditions	condition	NOUN
ajst-25603	53	15	,	,	PUNCT
ajst-25603	53	16	we	we	PRON
ajst-25603	53	17	especially	especially	ADV
ajst-25603	53	18	use	use	VERB
ajst-25603	53	19	rectangular	rectangular	ADJ
ajst-25603	53	20	labeling	labeling	NOUN
ajst-25603	53	21	frames	frame	NOUN
ajst-25603	53	22	to	to	PART
ajst-25603	53	23	specifically	specifically	ADV
ajst-25603	53	24	filter	filter	VERB
ajst-25603	53	25	out	out	ADP
ajst-25603	53	26	a	a	DET
ajst-25603	53	27	variety	variety	NOUN
ajst-25603	53	28	of	of	ADP
ajst-25603	53	29	targets	target	NOUN
ajst-25603	53	30	such	such	ADJ
ajst-25603	53	31	as	as	ADP
ajst-25603	53	32	bicycles	bicycle	NOUN
ajst-25603	53	33	,	,	PUNCT
ajst-25603	53	34	buses	bus	NOUN
ajst-25603	53	35	,	,	PUNCT
ajst-25603	53	36	cars	car	NOUN
ajst-25603	53	37	,	,	PUNCT
ajst-25603	53	38	motorcycles	motorcycle	NOUN
ajst-25603	53	39	and	and	CCONJ
ajst-25603	53	40	pedestrians	pedestrian	NOUN
ajst-25603	53	41	for	for	ADP
ajst-25603	53	42	training	training	NOUN
ajst-25603	53	43	.	.	PUNCT
ajst-25603	54	1	figure	figure	NOUN
ajst-25603	54	2	4	4	NUM
ajst-25603	54	3	.	.	PUNCT
ajst-25603	54	4	images	image	NOUN
ajst-25603	54	5	of	of	ADP
ajst-25603	54	6	selected	select	VERB
ajst-25603	54	7	datasets	dataset	NOUN
ajst-25603	54	8	4.2	4.2	NUM
ajst-25603	54	9	.	.	PUNCT
ajst-25603	55	1	evaluation	evaluation	NOUN
ajst-25603	55	2	indicators	indicator	NOUN
ajst-25603	55	3	in	in	ADP
ajst-25603	55	4	this	this	DET
ajst-25603	55	5	paper	paper	NOUN
ajst-25603	55	6	,	,	PUNCT
ajst-25603	55	7	average	average	ADJ
ajst-25603	55	8	precision	precision	NOUN
ajst-25603	55	9	(	(	PUNCT
ajst-25603	55	10	ap	ap	PROPN
ajst-25603	55	11	)	)	PUNCT
ajst-25603	55	12	,	,	PUNCT
ajst-25603	55	13	precision	precision	NOUN
ajst-25603	55	14	(	(	PUNCT
ajst-25603	55	15	p	p	NOUN
ajst-25603	55	16	)	)	PUNCT
ajst-25603	55	17	,	,	PUNCT
ajst-25603	55	18	recall	recall	INTJ
ajst-25603	55	19	(	(	PUNCT
ajst-25603	55	20	r	r	NOUN
ajst-25603	55	21	)	)	PUNCT
ajst-25603	55	22	and	and	CCONJ
ajst-25603	55	23	mean	mean	VERB
ajst-25603	55	24	average	average	ADJ
ajst-25603	55	25	precision	precision	NOUN
ajst-25603	55	26	(	(	PUNCT
ajst-25603	55	27	map	map	NOUN
ajst-25603	55	28	)	)	PUNCT
ajst-25603	56	1	[	[	X
ajst-25603	56	2	10]are	10]are	NUM
ajst-25603	56	3	used	use	VERB
ajst-25603	56	4	as	as	ADP
ajst-25603	56	5	the	the	DET
ajst-25603	56	6	evaluation	evaluation	NOUN
ajst-25603	56	7	metrics	metric	NOUN
ajst-25603	56	8	for	for	ADP
ajst-25603	56	9	target	target	NOUN
ajst-25603	56	10	detection	detection	NOUN
ajst-25603	56	11	,	,	PUNCT
ajst-25603	56	12	and	and	CCONJ
ajst-25603	56	13	the	the	DET
ajst-25603	56	14	corresponding	corresponding	ADJ
ajst-25603	56	15	calculation	calculation	NOUN
ajst-25603	56	16	formulas	formula	NOUN
ajst-25603	56	17	are	be	AUX
ajst-25603	56	18	shown	show	VERB
ajst-25603	56	19	in	in	ADP
ajst-25603	56	20	eqs	eqs	PROPN
ajst-25603	56	21	.	.	PUNCT
ajst-25603	57	1	(	(	PUNCT
ajst-25603	57	2	1	1	X
ajst-25603	57	3	)	)	PUNCT
ajst-25603	57	4	to	to	ADP
ajst-25603	57	5	(	(	PUNCT
ajst-25603	57	6	4	4	NUM
ajst-25603	57	7	)	)	PUNCT
ajst-25603	57	8	.	.	PUNCT
ajst-25603	58	1	146	146	NUM
ajst-25603	58	2			NOUN
ajst-25603	58	3	1	1	NUM
ajst-25603	58	4	0	0	NUM
ajst-25603	58	5	)	)	PUNCT
ajst-25603	58	6	(	(	PUNCT
ajst-25603	58	7	drrpap	drrpap	NOUN
ajst-25603	58	8	(	(	PUNCT
ajst-25603	58	9	1	1	NUM
ajst-25603	58	10	)	)	PUNCT
ajst-25603	58	11	fptp	fptp	PROPN
ajst-25603	58	12	tp	tp	PART
ajst-25603	58	13	precision	precision	PROPN
ajst-25603	58	14			ADV
ajst-25603	59	1			NUM
ajst-25603	60	1	(	(	PUNCT
ajst-25603	60	2	2	2	NUM
ajst-25603	60	3	)	)	PUNCT
ajst-25603	60	4	fntp	fntp	NOUN
ajst-25603	60	5	tp	tp	PART
ajst-25603	60	6	recall	recall	VERB
ajst-25603	60	7			PROPN
ajst-25603	61	1			PROPN
ajst-25603	62	1	(	(	PUNCT
ajst-25603	62	2	3	3	NUM
ajst-25603	62	3	)	)	PUNCT
ajst-25603	62	4	n	n	NOUN
ajst-25603	62	5	ap	ap	PROPN
ajst-25603	62	6	map	map	VERB
ajst-25603	62	7			ADV
ajst-25603	62	8	(	(	PUNCT
ajst-25603	62	9	4	4	NUM
ajst-25603	62	10	)	)	PUNCT
ajst-25603	62	11	where	where	SCONJ
ajst-25603	62	12	tp	tp	PART
ajst-25603	62	13	denotes	denote	VERB
ajst-25603	62	14	the	the	DET
ajst-25603	62	15	number	number	NOUN
ajst-25603	62	16	of	of	ADP
ajst-25603	62	17	correct	correct	ADJ
ajst-25603	62	18	positive	positive	ADJ
ajst-25603	62	19	samples	sample	NOUN
ajst-25603	62	20	,	,	PUNCT
ajst-25603	62	21	fp	fp	PROPN
ajst-25603	62	22	denotes	denote	VERB
ajst-25603	62	23	the	the	DET
ajst-25603	62	24	number	number	NOUN
ajst-25603	62	25	of	of	ADP
ajst-25603	62	26	incorrect	incorrect	ADJ
ajst-25603	62	27	positive	positive	ADJ
ajst-25603	62	28	samples	sample	NOUN
ajst-25603	62	29	,	,	PUNCT
ajst-25603	62	30	i.e.	i.e.	X
ajst-25603	62	31	,	,	PUNCT
ajst-25603	62	32	the	the	DET
ajst-25603	62	33	actual	actual	ADJ
ajst-25603	62	34	category	category	NOUN
ajst-25603	62	35	is	be	AUX
ajst-25603	62	36	a	a	DET
ajst-25603	62	37	negative	negative	ADJ
ajst-25603	62	38	sample	sample	NOUN
ajst-25603	62	39	but	but	CCONJ
ajst-25603	62	40	the	the	DET
ajst-25603	62	41	model	model	NOUN
ajst-25603	62	42	predicts	predict	VERB
ajst-25603	62	43	it	it	PRON
ajst-25603	62	44	as	as	ADP
ajst-25603	62	45	a	a	DET
ajst-25603	62	46	positive	positive	ADJ
ajst-25603	62	47	sample	sample	NOUN
ajst-25603	62	48	.	.	PUNCT
ajst-25603	63	1	fn	fn	NOUN
ajst-25603	63	2	denotes	denote	VERB
ajst-25603	63	3	the	the	DET
ajst-25603	63	4	number	number	NOUN
ajst-25603	63	5	of	of	ADP
ajst-25603	63	6	incorrect	incorrect	ADJ
ajst-25603	63	7	negative	negative	ADJ
ajst-25603	63	8	samples	sample	NOUN
ajst-25603	63	9	,	,	PUNCT
ajst-25603	63	10	i.e.	i.e.	X
ajst-25603	63	11	,	,	PUNCT
ajst-25603	63	12	the	the	DET
ajst-25603	63	13	actual	actual	ADJ
ajst-25603	63	14	category	category	NOUN
ajst-25603	63	15	is	be	AUX
ajst-25603	63	16	a	a	DET
ajst-25603	63	17	positive	positive	ADJ
ajst-25603	63	18	sample	sample	NOUN
ajst-25603	63	19	and	and	CCONJ
ajst-25603	63	20	the	the	DET
ajst-25603	63	21	model	model	NOUN
ajst-25603	63	22	predicts	predict	VERB
ajst-25603	63	23	it	it	PRON
ajst-25603	63	24	as	as	ADP
ajst-25603	63	25	a	a	DET
ajst-25603	63	26	negative	negative	ADJ
ajst-25603	63	27	sample	sample	NOUN
ajst-25603	63	28	.	.	PUNCT
ajst-25603	64	1	n	n	PRON
ajst-25603	64	2	is	be	AUX
ajst-25603	64	3	the	the	DET
ajst-25603	64	4	number	number	NOUN
ajst-25603	64	5	of	of	ADP
ajst-25603	64	6	all	all	DET
ajst-25603	64	7	categories	category	NOUN
ajst-25603	64	8	.	.	PUNCT
ajst-25603	65	1	4.3	4.3	NUM
ajst-25603	65	2	.	.	PUNCT
ajst-25603	65	3	analysis	analysis	NOUN
ajst-25603	65	4	of	of	ADP
ajst-25603	65	5	the	the	DET
ajst-25603	65	6	results	result	NOUN
ajst-25603	65	7	in	in	ADP
ajst-25603	65	8	order	order	NOUN
ajst-25603	65	9	to	to	PART
ajst-25603	65	10	assess	assess	VERB
ajst-25603	65	11	the	the	DET
ajst-25603	65	12	effectiveness	effectiveness	NOUN
ajst-25603	65	13	of	of	ADP
ajst-25603	65	14	the	the	DET
ajst-25603	65	15	improved	improved	ADJ
ajst-25603	65	16	model	model	NOUN
ajst-25603	65	17	,	,	PUNCT
ajst-25603	65	18	the	the	DET
ajst-25603	65	19	original	original	ADJ
ajst-25603	65	20	algorithm	algorithm	NOUN
ajst-25603	65	21	was	be	AUX
ajst-25603	65	22	compared	compare	VERB
ajst-25603	65	23	with	with	ADP
ajst-25603	65	24	the	the	DET
ajst-25603	65	25	improved	improve	VERB
ajst-25603	65	26	algorithm	algorithm	NOUN
ajst-25603	65	27	yolo_bis	yolo_bis	PRON
ajst-25603	65	28	.	.	PUNCT
ajst-25603	66	1	map@0.5	map@0.5	VERB
ajst-25603	66	2	the	the	DET
ajst-25603	66	3	curve	curve	NOUN
ajst-25603	66	4	comparison	comparison	NOUN
ajst-25603	66	5	graph	graph	NOUN
ajst-25603	66	6	is	be	AUX
ajst-25603	66	7	shown	show	VERB
ajst-25603	66	8	in	in	ADP
ajst-25603	66	9	figure	figure	NOUN
ajst-25603	66	10	6	6	NUM
ajst-25603	66	11	.	.	PUNCT
ajst-25603	66	12	from	from	ADP
ajst-25603	66	13	fig	fig	NOUN
ajst-25603	66	14	.	.	PUNCT
ajst-25603	67	1	5	5	NUM
ajst-25603	67	2	,	,	PUNCT
ajst-25603	67	3	it	it	PRON
ajst-25603	67	4	can	can	AUX
ajst-25603	67	5	be	be	AUX
ajst-25603	67	6	seen	see	VERB
ajst-25603	67	7	that	that	SCONJ
ajst-25603	67	8	the	the	DET
ajst-25603	67	9	detection	detection	NOUN
ajst-25603	67	10	effect	effect	NOUN
ajst-25603	67	11	of	of	ADP
ajst-25603	67	12	the	the	DET
ajst-25603	67	13	improved	improved	ADJ
ajst-25603	67	14	algorithm	algorithm	NOUN
ajst-25603	67	15	is	be	AUX
ajst-25603	67	16	significantly	significantly	ADV
ajst-25603	67	17	improved	improve	VERB
ajst-25603	67	18	compared	compare	VERB
ajst-25603	67	19	with	with	ADP
ajst-25603	67	20	the	the	DET
ajst-25603	67	21	original	original	ADJ
ajst-25603	67	22	algorithm	algorithm	NOUN
ajst-25603	67	23	,	,	PUNCT
ajst-25603	67	24	which	which	PRON
ajst-25603	67	25	proves	prove	VERB
ajst-25603	67	26	that	that	SCONJ
ajst-25603	67	27	the	the	DET
ajst-25603	67	28	improved	improved	ADJ
ajst-25603	67	29	algorithm	algorithm	NOUN
ajst-25603	67	30	is	be	AUX
ajst-25603	67	31	effective	effective	ADJ
ajst-25603	67	32	for	for	ADP
ajst-25603	67	33	detection	detection	NOUN
ajst-25603	67	34	.	.	PUNCT
ajst-25603	68	1	figure	figure	NOUN
ajst-25603	68	2	5	5	NUM
ajst-25603	68	3	.	.	PUNCT
ajst-25603	68	4	comparison	comparison	NOUN
ajst-25603	68	5	chart	chart	NOUN
ajst-25603	68	6	of	of	ADP
ajst-25603	68	7	map@0.5	map@0.5	PROPN
ajst-25603	68	8	curve	curve	NOUN
ajst-25603	68	9	4.4	4.4	NUM
ajst-25603	68	10	.	.	PUNCT
ajst-25603	69	1	ablation	ablation	NOUN
ajst-25603	69	2	experiments	experiment	NOUN
ajst-25603	69	3	in	in	ADP
ajst-25603	69	4	order	order	NOUN
ajst-25603	69	5	to	to	PART
ajst-25603	69	6	verify	verify	VERB
ajst-25603	69	7	the	the	DET
ajst-25603	69	8	effectiveness	effectiveness	NOUN
ajst-25603	69	9	of	of	ADP
ajst-25603	69	10	the	the	DET
ajst-25603	69	11	model	model	NOUN
ajst-25603	69	12	improvement	improvement	NOUN
ajst-25603	69	13	methods	method	NOUN
ajst-25603	69	14	on	on	ADP
ajst-25603	69	15	the	the	DET
ajst-25603	69	16	experiments	experiment	NOUN
ajst-25603	69	17	,	,	PUNCT
ajst-25603	69	18	the	the	DET
ajst-25603	69	19	different	different	ADJ
ajst-25603	69	20	improvement	improvement	NOUN
ajst-25603	69	21	methods	method	NOUN
ajst-25603	69	22	are	be	AUX
ajst-25603	69	23	evaluated	evaluate	VERB
ajst-25603	69	24	by	by	ADP
ajst-25603	69	25	doing	do	VERB
ajst-25603	69	26	ablation	ablation	NOUN
ajst-25603	69	27	experiments	experiment	NOUN
ajst-25603	69	28	.	.	PUNCT
ajst-25603	70	1	table	table	NOUN
ajst-25603	70	2	1	1	NUM
ajst-25603	70	3	shows	show	VERB
ajst-25603	70	4	that	that	SCONJ
ajst-25603	70	5	the	the	DET
ajst-25603	70	6	yolov8	yolov8	NOUN
ajst-25603	70	7	base	base	PROPN
ajst-25603	70	8	model	model	NOUN
ajst-25603	70	9	has	have	VERB
ajst-25603	70	10	an	an	DET
ajst-25603	70	11	average	average	ADJ
ajst-25603	70	12	accuracy	accuracy	NOUN
ajst-25603	70	13	of	of	ADP
ajst-25603	70	14	67	67	NUM
ajst-25603	70	15	%	%	NOUN
ajst-25603	70	16	on	on	ADP
ajst-25603	70	17	the	the	DET
ajst-25603	70	18	dataset	dataset	NOUN
ajst-25603	70	19	.	.	PUNCT
ajst-25603	71	1	after	after	ADP
ajst-25603	71	2	adding	add	VERB
ajst-25603	71	3	bifpn	bifpn	PROPN
ajst-25603	71	4	,	,	PUNCT
ajst-25603	71	5	the	the	DET
ajst-25603	71	6	yolov8_b	yolov8_b	PROPN
ajst-25603	71	7	model	model	NOUN
ajst-25603	71	8	has	have	VERB
ajst-25603	71	9	a	a	DET
ajst-25603	71	10	0.5	0.5	NUM
ajst-25603	71	11	%	%	NOUN
ajst-25603	71	12	increase	increase	NOUN
ajst-25603	71	13	on	on	ADP
ajst-25603	71	14	map0.5	map0.5	PROPN
ajst-25603	71	15	compared	compare	VERB
ajst-25603	71	16	to	to	ADP
ajst-25603	71	17	the	the	DET
ajst-25603	71	18	original	original	ADJ
ajst-25603	71	19	base	base	NOUN
ajst-25603	71	20	model	model	NOUN
ajst-25603	71	21	.	.	PUNCT
ajst-25603	72	1	it	it	PRON
ajst-25603	72	2	shows	show	VERB
ajst-25603	72	3	that	that	SCONJ
ajst-25603	72	4	bifpn	bifpn	PROPN
ajst-25603	72	5	has	have	VERB
ajst-25603	72	6	some	some	DET
ajst-25603	72	7	effect	effect	NOUN
ajst-25603	72	8	on	on	ADP
ajst-25603	72	9	model	model	NOUN
ajst-25603	72	10	accuracy	accuracy	NOUN
ajst-25603	72	11	improvement	improvement	NOUN
ajst-25603	72	12	.	.	PUNCT
ajst-25603	73	1	after	after	ADP
ajst-25603	73	2	adding	add	VERB
ajst-25603	73	3	shuffleattention	shuffleattention	NOUN
ajst-25603	73	4	,	,	PUNCT
ajst-25603	73	5	the	the	DET
ajst-25603	73	6	yolov8_s	yolov8_s	PROPN
ajst-25603	73	7	model	model	NOUN
ajst-25603	73	8	shows	show	VERB
ajst-25603	73	9	a	a	DET
ajst-25603	73	10	growth	growth	NOUN
ajst-25603	73	11	of	of	ADP
ajst-25603	73	12	0.3	0.3	NUM
ajst-25603	73	13	%	%	NOUN
ajst-25603	73	14	on	on	ADP
ajst-25603	73	15	map0.5	map0.5	PROPN
ajst-25603	73	16	compared	compare	VERB
ajst-25603	73	17	to	to	ADP
ajst-25603	73	18	the	the	DET
ajst-25603	73	19	original	original	ADJ
ajst-25603	73	20	base	base	NOUN
ajst-25603	73	21	model	model	NOUN
ajst-25603	73	22	.	.	PUNCT
ajst-25603	74	1	in	in	ADP
ajst-25603	74	2	terms	term	NOUN
ajst-25603	74	3	of	of	ADP
ajst-25603	74	4	detection	detection	NOUN
ajst-25603	74	5	accuracy	accuracy	NOUN
ajst-25603	74	6	,	,	PUNCT
ajst-25603	74	7	the	the	DET
ajst-25603	74	8	improved	improved	ADJ
ajst-25603	74	9	model	model	NOUN
ajst-25603	74	10	improves	improve	VERB
ajst-25603	74	11	by	by	ADP
ajst-25603	74	12	1.3	1.3	NUM
ajst-25603	74	13	%	%	NOUN
ajst-25603	74	14	compared	compare	VERB
ajst-25603	74	15	to	to	ADP
ajst-25603	74	16	the	the	DET
ajst-25603	74	17	traditional	traditional	ADJ
ajst-25603	74	18	model	model	NOUN
ajst-25603	74	19	,	,	PUNCT
ajst-25603	74	20	and	and	CCONJ
ajst-25603	74	21	the	the	DET
ajst-25603	74	22	yolo_bis	yolo_bis	NUM
ajst-25603	74	23	model	model	NOUN
ajst-25603	74	24	has	have	AUX
ajst-25603	74	25	improved	improve	VERB
ajst-25603	74	26	target	target	NOUN
ajst-25603	74	27	detection	detection	NOUN
ajst-25603	74	28	accuracy	accuracy	NOUN
ajst-25603	74	29	for	for	ADP
ajst-25603	74	30	the	the	DET
ajst-25603	74	31	foggy	foggy	ADJ
ajst-25603	74	32	dataset	dataset	NOUN
ajst-25603	74	33	,	,	PUNCT
ajst-25603	74	34	which	which	PRON
ajst-25603	74	35	verifies	verify	VERB
ajst-25603	74	36	the	the	DET
ajst-25603	74	37	applicability	applicability	NOUN
ajst-25603	74	38	to	to	PART
ajst-25603	74	39	target	target	VERB
ajst-25603	74	40	detection	detection	NOUN
ajst-25603	74	41	in	in	ADP
ajst-25603	74	42	foggy	foggy	ADJ
ajst-25603	74	43	scenes	scene	NOUN
ajst-25603	74	44	.	.	PUNCT
ajst-25603	75	1	table	table	NOUN
ajst-25603	75	2	1	1	NUM
ajst-25603	75	3	.	.	PUNCT
ajst-25603	75	4	ablation	ablation	NOUN
ajst-25603	75	5	experiments	experiment	NOUN
ajst-25603	75	6	algorithm	algorithm	PROPN
ajst-25603	75	7	bifpn	bifpn	VERB
ajst-25603	75	8	shuffle	shuffle	NOUN
ajst-25603	75	9	map@0.5/%	map@0.5/%	PROPN
ajst-25603	75	10	map@0.5:0.95/%	map@0.5:0.95/%	INTJ
ajst-25603	75	11	fps	fps	PROPN
ajst-25603	75	12	yolov8n	yolov8n	PROPN
ajst-25603	75	13	67	67	NUM
ajst-25603	75	14	43.4	43.4	NUM
ajst-25603	75	15	62.11	62.11	NUM
ajst-25603	75	16	yolov8n_b	yolov8n_b	PROPN
ajst-25603	75	17	√	√	ADV
ajst-25603	76	1	67.5	67.5	NUM
ajst-25603	76	2	44	44	NUM
ajst-25603	76	3	47.39	47.39	NUM
ajst-25603	76	4	yolov8n_s	yolov8n_	NOUN
ajst-25603	76	5	√	√	PROPN
ajst-25603	76	6	67.3	67.3	NUM
ajst-25603	76	7	43.7	43.7	NUM
ajst-25603	76	8	45.87	45.87	NUM
ajst-25603	76	9	yolo_bis	yolo_bis	NUM
ajst-25603	76	10	√	√	NOUN
ajst-25603	76	11	√	√	ADP
ajst-25603	76	12	68.3	68.3	NUM
ajst-25603	76	13	44.6	44.6	NUM
ajst-25603	76	14	51.28	51.28	NUM
ajst-25603	76	15	4.5	4.5	NUM
ajst-25603	76	16	.	.	PUNCT
ajst-25603	77	1	detection	detection	NOUN
ajst-25603	77	2	effect	effect	NOUN
ajst-25603	77	3	considering	consider	VERB
ajst-25603	77	4	that	that	SCONJ
ajst-25603	77	5	the	the	DET
ajst-25603	77	6	traffic	traffic	NOUN
ajst-25603	77	7	situation	situation	NOUN
ajst-25603	77	8	in	in	ADP
ajst-25603	77	9	foggy	foggy	ADJ
ajst-25603	77	10	scenes	scene	NOUN
ajst-25603	77	11	is	be	AUX
ajst-25603	77	12	complex	complex	ADJ
ajst-25603	77	13	and	and	CCONJ
ajst-25603	77	14	changeable	changeable	ADJ
ajst-25603	77	15	,	,	PUNCT
ajst-25603	77	16	and	and	CCONJ
ajst-25603	77	17	there	there	PRON
ajst-25603	77	18	will	will	AUX
ajst-25603	77	19	be	be	AUX
ajst-25603	77	20	situations	situation	NOUN
ajst-25603	77	21	such	such	ADJ
ajst-25603	77	22	as	as	ADP
ajst-25603	77	23	target	target	NOUN
ajst-25603	77	24	occlusion	occlusion	NOUN
ajst-25603	77	25	and	and	CCONJ
ajst-25603	77	26	visible	visible	ADJ
ajst-25603	77	27	range	range	NOUN
ajst-25603	77	28	changes	change	NOUN
ajst-25603	77	29	,	,	PUNCT
ajst-25603	77	30	in	in	ADP
ajst-25603	77	31	order	order	NOUN
ajst-25603	77	32	to	to	PART
ajst-25603	77	33	verify	verify	VERB
ajst-25603	77	34	the	the	DET
ajst-25603	77	35	applicability	applicability	NOUN
ajst-25603	77	36	of	of	ADP
ajst-25603	77	37	the	the	DET
ajst-25603	77	38	improved	improved	ADJ
ajst-25603	77	39	model	model	NOUN
ajst-25603	77	40	more	more	ADV
ajst-25603	77	41	intuitively	intuitively	ADV
ajst-25603	77	42	,	,	PUNCT
ajst-25603	77	43	visual	visual	ADJ
ajst-25603	77	44	comparison	comparison	NOUN
ajst-25603	77	45	experiments	experiment	NOUN
ajst-25603	77	46	are	be	AUX
ajst-25603	77	47	carried	carry	VERB
ajst-25603	77	48	out	out	ADP
ajst-25603	77	49	after	after	ADP
ajst-25603	77	50	constructing	construct	VERB
ajst-25603	77	51	the	the	DET
ajst-25603	77	52	target	target	NOUN
ajst-25603	77	53	test	test	NOUN
ajst-25603	77	54	set	set	VERB
ajst-25603	77	55	in	in	ADP
ajst-25603	77	56	foggy	foggy	ADJ
ajst-25603	77	57	environments	environment	NOUN
ajst-25603	77	58	with	with	ADP
ajst-25603	77	59	the	the	DET
ajst-25603	77	60	aim	aim	NOUN
ajst-25603	77	61	of	of	ADP
ajst-25603	77	62	comparing	compare	VERB
ajst-25603	77	63	the	the	DET
ajst-25603	77	64	performance	performance	NOUN
ajst-25603	77	65	differences	difference	NOUN
ajst-25603	77	66	between	between	ADP
ajst-25603	77	67	the	the	DET
ajst-25603	77	68	traditional	traditional	ADJ
ajst-25603	77	69	model	model	NOUN
ajst-25603	77	70	and	and	CCONJ
ajst-25603	77	71	the	the	DET
ajst-25603	77	72	improved	improved	ADJ
ajst-25603	77	73	model	model	NOUN
ajst-25603	77	74	in	in	ADP
ajst-25603	77	75	target	target	NOUN
ajst-25603	77	76	recognition	recognition	NOUN
ajst-25603	77	77	.	.	PUNCT
ajst-25603	78	1	the	the	DET
ajst-25603	78	2	comparison	comparison	NOUN
ajst-25603	78	3	result	result	NOUN
ajst-25603	78	4	graphs	graph	NOUN
ajst-25603	78	5	are	be	AUX
ajst-25603	78	6	shown	show	VERB
ajst-25603	78	7	in	in	ADP
ajst-25603	78	8	fig	fig	NOUN
ajst-25603	78	9	.	.	PUNCT
ajst-25603	79	1	6	6	NUM
ajst-25603	79	2	,	,	PUNCT
ajst-25603	79	3	with	with	ADP
ajst-25603	79	4	the	the	DET
ajst-25603	79	5	target	target	NOUN
ajst-25603	79	6	detection	detection	NOUN
ajst-25603	79	7	graph	graph	NOUN
ajst-25603	79	8	of	of	ADP
ajst-25603	79	9	the	the	DET
ajst-25603	79	10	original	original	ADJ
ajst-25603	79	11	model	model	NOUN
ajst-25603	79	12	yolov8n	yolov8n	PROPN
ajst-25603	79	13	on	on	ADP
ajst-25603	79	14	the	the	DET
ajst-25603	79	15	left	left	NOUN
ajst-25603	79	16	and	and	CCONJ
ajst-25603	79	17	the	the	DET
ajst-25603	79	18	target	target	NOUN
ajst-25603	79	19	detection	detection	NOUN
ajst-25603	79	20	graph	graph	NOUN
ajst-25603	79	21	of	of	ADP
ajst-25603	79	22	the	the	DET
ajst-25603	79	23	improved	improved	ADJ
ajst-25603	79	24	model	model	NOUN
ajst-25603	79	25	yolo_bis	yolo_bis	NUM
ajst-25603	79	26	on	on	ADP
ajst-25603	79	27	the	the	DET
ajst-25603	79	28	right	right	NOUN
ajst-25603	79	29	.	.	PUNCT
ajst-25603	80	1	among	among	ADP
ajst-25603	80	2	them	they	PRON
ajst-25603	80	3	,	,	PUNCT
ajst-25603	80	4	fig	fig	NOUN
ajst-25603	80	5	.	.	PUNCT
ajst-25603	81	1	6(a	6(a	NUM
ajst-25603	81	2	)	)	PUNCT
ajst-25603	82	1	represents	represent	VERB
ajst-25603	82	2	the	the	DET
ajst-25603	82	3	comparison	comparison	NOUN
ajst-25603	82	4	map	map	NOUN
ajst-25603	82	5	for	for	ADP
ajst-25603	82	6	the	the	DET
ajst-25603	82	7	target	target	NOUN
ajst-25603	82	8	occlusion	occlusion	NOUN
ajst-25603	82	9	case	case	NOUN
ajst-25603	82	10	;	;	PUNCT
ajst-25603	82	11	fig6(b	fig6(b	NUM
ajst-25603	82	12	)	)	PUNCT
ajst-25603	83	1	represents	represent	VERB
ajst-25603	83	2	the	the	DET
ajst-25603	83	3	comparison	comparison	NOUN
ajst-25603	83	4	map	map	NOUN
ajst-25603	83	5	for	for	ADP
ajst-25603	83	6	the	the	DET
ajst-25603	83	7	dense	dense	PROPN
ajst-25603	83	8	fog	fog	PROPN
ajst-25603	83	9	environment	environment	NOUN
ajst-25603	83	10	;	;	PUNCT
ajst-25603	83	11	fig.6(c	fig.6(c	NUM
ajst-25603	83	12	)	)	PUNCT
ajst-25603	83	13	represents	represent	VERB
ajst-25603	83	14	the	the	DET
ajst-25603	83	15	comparison	comparison	NOUN
ajst-25603	83	16	map	map	NOUN
ajst-25603	83	17	for	for	ADP
ajst-25603	83	18	the	the	DET
ajst-25603	83	19	close	close	ADJ
ajst-25603	83	20	-	-	PUNCT
ajst-25603	83	21	range	range	NOUN
ajst-25603	83	22	case	case	NOUN
ajst-25603	83	23	.	.	PUNCT
ajst-25603	84	1	from	from	ADP
ajst-25603	84	2	fig.6	fig.6	PROPN
ajst-25603	84	3	,	,	PUNCT
ajst-25603	84	4	it	it	PRON
ajst-25603	84	5	can	can	AUX
ajst-25603	84	6	be	be	AUX
ajst-25603	84	7	seen	see	VERB
ajst-25603	84	8	that	that	SCONJ
ajst-25603	84	9	the	the	DET
ajst-25603	84	10	original	original	ADJ
ajst-25603	84	11	model	model	NOUN
ajst-25603	84	12	has	have	VERB
ajst-25603	84	13	target	target	NOUN
ajst-25603	84	14	misdetection	misdetection	NOUN
ajst-25603	84	15	and	and	CCONJ
ajst-25603	84	16	omission	omission	NOUN
ajst-25603	84	17	in	in	ADP
ajst-25603	84	18	foggy	foggy	ADJ
ajst-25603	84	19	scenarios	scenario	NOUN
ajst-25603	84	20	,	,	PUNCT
ajst-25603	84	21	and	and	CCONJ
ajst-25603	84	22	the	the	DET
ajst-25603	84	23	improved	improved	ADJ
ajst-25603	84	24	model	model	NOUN
ajst-25603	84	25	reduces	reduce	VERB
ajst-25603	84	26	the	the	DET
ajst-25603	84	27	omission	omission	NOUN
ajst-25603	84	28	of	of	ADP
ajst-25603	84	29	targets	target	NOUN
ajst-25603	84	30	,	,	PUNCT
ajst-25603	84	31	improves	improve	VERB
ajst-25603	84	32	the	the	DET
ajst-25603	84	33	detection	detection	NOUN
ajst-25603	84	34	accuracy	accuracy	NOUN
ajst-25603	84	35	of	of	ADP
ajst-25603	84	36	the	the	DET
ajst-25603	84	37	model	model	NOUN
ajst-25603	84	38	,	,	PUNCT
ajst-25603	84	39	and	and	CCONJ
ajst-25603	84	40	has	have	VERB
ajst-25603	84	41	better	well	ADJ
ajst-25603	84	42	applicability	applicability	NOUN
ajst-25603	84	43	for	for	ADP
ajst-25603	84	44	the	the	DET
ajst-25603	84	45	target	target	NOUN
ajst-25603	84	46	detection	detection	NOUN
ajst-25603	84	47	task	task	NOUN
ajst-25603	84	48	in	in	ADP
ajst-25603	84	49	foggy	foggy	ADJ
ajst-25603	84	50	scenarios	scenario	NOUN
ajst-25603	84	51	.	.	PUNCT
ajst-25603	85	1	5	5	X
ajst-25603	85	2	.	.	X
ajst-25603	85	3	conclusion	conclusion	NOUN
ajst-25603	85	4	aiming	aim	VERB
ajst-25603	85	5	at	at	ADP
ajst-25603	85	6	the	the	DET
ajst-25603	85	7	problems	problem	NOUN
ajst-25603	85	8	existing	exist	VERB
ajst-25603	85	9	in	in	ADP
ajst-25603	85	10	the	the	DET
ajst-25603	85	11	current	current	ADJ
ajst-25603	85	12	target	target	NOUN
ajst-25603	85	13	detection	detection	NOUN
ajst-25603	85	14	method	method	NOUN
ajst-25603	85	15	on	on	ADP
ajst-25603	85	16	the	the	DET
ajst-25603	85	17	lane	lane	NOUN
ajst-25603	85	18	in	in	ADP
ajst-25603	85	19	foggy	foggy	ADJ
ajst-25603	85	20	scenarios	scenario	NOUN
ajst-25603	85	21	,	,	PUNCT
ajst-25603	85	22	this	this	DET
ajst-25603	85	23	paper	paper	NOUN
ajst-25603	85	24	proposes	propose	VERB
ajst-25603	85	25	a	a	DET
ajst-25603	85	26	foggy	foggy	ADJ
ajst-25603	85	27	target	target	NOUN
ajst-25603	85	28	detection	detection	NOUN
ajst-25603	85	29	algorithm	algorithm	NOUN
ajst-25603	85	30	based	base	VERB
ajst-25603	85	31	on	on	ADP
ajst-25603	85	32	improved	improved	ADJ
ajst-25603	85	33	yolov8	yolov8	NOUN
ajst-25603	85	34	:	:	PUNCT
ajst-25603	85	35	yolo_bis	yolo_bis	NUM
ajst-25603	85	36	introduced	introduce	VERB
ajst-25603	85	37	the	the	DET
ajst-25603	85	38	shuffleattention	shuffleattention	NOUN
ajst-25603	85	39	attention	attention	NOUN
ajst-25603	85	40	mechanism	mechanism	NOUN
ajst-25603	85	41	into	into	ADP
ajst-25603	85	42	the	the	DET
ajst-25603	85	43	neck	neck	NOUN
ajst-25603	85	44	network	network	NOUN
ajst-25603	85	45	to	to	PART
ajst-25603	85	46	enhance	enhance	VERB
ajst-25603	85	47	the	the	DET
ajst-25603	85	48	diversity	diversity	NOUN
ajst-25603	85	49	of	of	ADP
ajst-25603	85	50	input	input	NOUN
ajst-25603	85	51	sequences	sequence	NOUN
ajst-25603	85	52	so	so	SCONJ
ajst-25603	85	53	as	as	SCONJ
ajst-25603	85	54	to	to	PART
ajst-25603	85	55	improve	improve	VERB
ajst-25603	85	56	the	the	DET
ajst-25603	85	57	detection	detection	NOUN
ajst-25603	85	58	performance	performance	NOUN
ajst-25603	85	59	of	of	ADP
ajst-25603	85	60	the	the	DET
ajst-25603	85	61	model	model	NOUN
ajst-25603	85	62	.	.	PUNCT
ajst-25603	86	1	the	the	DET
ajst-25603	86	2	original	original	ADJ
ajst-25603	86	3	concat	concat	NOUN
ajst-25603	86	4	was	be	AUX
ajst-25603	86	5	replaced	replace	VERB
ajst-25603	86	6	by	by	ADP
ajst-25603	86	7	the	the	DET
ajst-25603	86	8	bifpn	bifpn	PROPN
ajst-25603	86	9	weighted	weight	VERB
ajst-25603	86	10	bidirectional	bidirectional	ADJ
ajst-25603	86	11	feature	feature	NOUN
ajst-25603	86	12	pyramid	pyramid	NOUN
ajst-25603	86	13	network	network	NOUN
ajst-25603	86	14	,	,	PUNCT
ajst-25603	86	15	allowing	allow	VERB
ajst-25603	86	16	the	the	DET
ajst-25603	86	17	model	model	NOUN
ajst-25603	86	18	to	to	PART
ajst-25603	86	19	adaptively	adaptively	ADV
ajst-25603	86	20	adjust	adjust	VERB
ajst-25603	86	21	the	the	DET
ajst-25603	86	22	fusion	fusion	NOUN
ajst-25603	86	23	mode	mode	NOUN
ajst-25603	86	24	according	accord	VERB
ajst-25603	86	25	to	to	ADP
ajst-25603	86	26	the	the	DET
ajst-25603	86	27	importance	importance	NOUN
ajst-25603	86	28	of	of	ADP
ajst-25603	86	29	different	different	ADJ
ajst-25603	86	30	features	feature	NOUN
ajst-25603	86	31	,	,	PUNCT
ajst-25603	86	32	so	so	SCONJ
ajst-25603	86	33	as	as	SCONJ
ajst-25603	86	34	to	to	PART
ajst-25603	86	35	improve	improve	VERB
ajst-25603	86	36	the	the	DET
ajst-25603	86	37	detection	detection	NOUN
ajst-25603	86	38	performance	performance	NOUN
ajst-25603	86	39	.	.	PUNCT
ajst-25603	87	1	compared	compare	VERB
ajst-25603	87	2	with	with	ADP
ajst-25603	87	3	the	the	DET
ajst-25603	87	4	traditional	traditional	ADJ
ajst-25603	87	5	yolo_bis	yolo_bis	NUM
ajst-25603	87	6	v8	v8	PROPN
ajst-25603	87	7	model	model	NOUN
ajst-25603	87	8	,	,	PUNCT
ajst-25603	87	9	the	the	DET
ajst-25603	87	10	performance	performance	NOUN
ajst-25603	87	11	of	of	ADP
ajst-25603	87	12	yolo_bis	yolo_bis	NUM
ajst-25603	87	13	model	model	NOUN
ajst-25603	87	14	has	have	AUX
ajst-25603	87	15	improved	improve	VERB
ajst-25603	87	16	in	in	ADP
ajst-25603	87	17	all	all	DET
ajst-25603	87	18	aspects	aspect	NOUN
ajst-25603	87	19	.	.	PUNCT
ajst-25603	88	1	map@0.5	map@0.5	NOUN
ajst-25603	88	2	has	have	AUX
ajst-25603	88	3	increased	increase	VERB
ajst-25603	88	4	by	by	ADP
ajst-25603	88	5	1.3	1.3	NUM
ajst-25603	88	6	%	%	NOUN
ajst-25603	88	7	to	to	ADP
ajst-25603	88	8	68.3	68.3	NUM
ajst-25603	88	9	%	%	NOUN
ajst-25603	88	10	,	,	PUNCT
ajst-25603	88	11	and	and	CCONJ
ajst-25603	88	12	map@0.5	map@0.5	VERB
ajst-25603	88	13	-	-	NOUN
ajst-25603	88	14	0.95	0.95	NUM
ajst-25603	88	15	has	have	AUX
ajst-25603	88	16	increased	increase	VERB
ajst-25603	88	17	by	by	ADP
ajst-25603	88	18	1.2	1.2	NUM
ajst-25603	88	19	%	%	NOUN
ajst-25603	88	20	to	to	ADP
ajst-25603	88	21	44.6	44.6	NUM
ajst-25603	88	22	%	%	NOUN
ajst-25603	88	23	.	.	PUNCT
ajst-25603	89	1	the	the	DET
ajst-25603	89	2	improved	improved	ADJ
ajst-25603	89	3	model	model	NOUN
ajst-25603	89	4	can	can	AUX
ajst-25603	89	5	more	more	ADV
ajst-25603	89	6	accurately	accurately	ADV
ajst-25603	89	7	identify	identify	VERB
ajst-25603	89	8	and	and	CCONJ
ajst-25603	89	9	detect	detect	VERB
ajst-25603	89	10	vehicles	vehicle	NOUN
ajst-25603	89	11	and	and	CCONJ
ajst-25603	89	12	people	people	NOUN
ajst-25603	89	13	in	in	ADP
ajst-25603	89	14	foggy	foggy	ADJ
ajst-25603	89	15	scenarios	scenario	NOUN
ajst-25603	89	16	.	.	PUNCT
ajst-25603	90	1	147	147	NUM
ajst-25603	90	2	(	(	PUNCT
ajst-25603	90	3	a)target	a)target	NOUN
ajst-25603	90	4	masking	masking	NOUN
ajst-25603	90	5	(	(	PUNCT
ajst-25603	90	6	b)dense	b)dense	NOUN
ajst-25603	90	7	fog	fog	NOUN
ajst-25603	90	8	conditions	condition	NOUN
ajst-25603	90	9	(	(	PUNCT
ajst-25603	90	10	c)proximity	c)proximity	NOUN
ajst-25603	90	11	figure	figure	NOUN
ajst-25603	90	12	6	6	NUM
ajst-25603	90	13	.	.	PUNCT
ajst-25603	90	14	comparison	comparison	NOUN
ajst-25603	90	15	of	of	ADP
ajst-25603	90	16	visualization	visualization	NOUN
ajst-25603	90	17	results	result	VERB
ajst-25603	90	18	references	reference	NOUN
ajst-25603	90	19	[	[	X
ajst-25603	90	20	1	1	NUM
ajst-25603	90	21	]	]	X
ajst-25603	90	22	yang	yang	PROPN
ajst-25603	90	23	lei	lei	PROPN
ajst-25603	90	24	,	,	PUNCT
ajst-25603	90	25	chen	chen	PROPN
ajst-25603	90	26	yanfei	yanfei	PROPN
ajst-25603	90	27	,	,	PUNCT
ajst-25603	90	28	li	li	PROPN
ajst-25603	90	29	haiming	haiming	PROPN
ajst-25603	90	30	,	,	PUNCT
ajst-25603	90	31	et	et	PROPN
ajst-25603	90	32	al	al	PROPN
ajst-25603	90	33	.	.	PROPN
ajst-25603	90	34	target	target	NOUN
ajst-25603	90	35	detection	detection	NOUN
ajst-25603	90	36	algorithm	algorithm	NOUN
ajst-25603	90	37	for	for	ADP
ajst-25603	90	38	autopilot	autopilot	NOUN
ajst-25603	90	39	scene	scene	NOUN
ajst-25603	90	40	based	base	VERB
ajst-25603	90	41	on	on	ADP
ajst-25603	90	42	improved	improved	ADJ
ajst-25603	90	43	yolov8	yolov8	NOUN
ajst-25603	91	1	[	[	X
ajst-25603	91	2	j	j	X
ajst-25603	91	3	/	/	SYM
ajst-25603	91	4	ol	ol	PROPN
ajst-25603	91	5	]	]	PUNCT
ajst-25603	91	6	.	.	PUNCT
ajst-25603	92	1	computer	computer	NOUN
ajst-25603	92	2	engineering	engineering	NOUN
ajst-25603	92	3	and	and	CCONJ
ajst-25603	92	4	application,1	application,1	PROPN
ajst-25603	92	5	-	-	PUNCT
ajst-25603	92	6	17[2024	17[2024	PROPN
ajst-25603	92	7	-	-	PUNCT
ajst-25603	92	8	0902	0902	NUM
ajst-25603	92	9	]	]	PUNCT
ajst-25603	92	10	.	.	PUNCT
ajst-25603	93	1	[	[	X
ajst-25603	93	2	2	2	NUM
ajst-25603	93	3	]	]	X
ajst-25603	93	4	su	su	PROPN
ajst-25603	93	5	tong	tong	PROPN
ajst-25603	93	6	,	,	PUNCT
ajst-25603	93	7	wang	wang	PROPN
ajst-25603	93	8	ying	ying	PROPN
ajst-25603	93	9	,	,	PUNCT
ajst-25603	93	10	deng	deng	PROPN
ajst-25603	93	11	qiyang	qiyang	PROPN
ajst-25603	93	12	,	,	PUNCT
ajst-25603	93	13	et	et	PROPN
ajst-25603	93	14	al	al	PROPN
ajst-25603	93	15	.	.	PUNCT
ajst-25603	94	1	the	the	DET
ajst-25603	94	2	improved	improved	ADJ
ajst-25603	94	3	pedestrian	pedestrian	NOUN
ajst-25603	94	4	and	and	CCONJ
ajst-25603	94	5	vehicle	vehicle	NOUN
ajst-25603	94	6	detection	detection	NOUN
ajst-25603	94	7	algorithm	algorithm	NOUN
ajst-25603	94	8	[	[	X
ajst-25603	94	9	j	j	X
ajst-25603	94	10	/	/	SYM
ajst-25603	94	11	ol	ol	PROPN
ajst-25603	94	12	]	]	PUNCT
ajst-25603	94	13	.	.	PUNCT
ajst-25603	95	1	journal	journal	PROPN
ajst-25603	95	2	of	of	ADP
ajst-25603	95	3	system	system	NOUN
ajst-25603	95	4	simulation	simulation	NOUN
ajst-25603	95	5	,	,	PUNCT
ajst-25603	95	6	2024:1	2024:1	NUM
ajst-25603	95	7	-	-	SYM
ajst-25603	95	8	11	11	NUM
ajst-25603	95	9	.	.	PUNCT
ajst-25603	96	1	[	[	X
ajst-25603	96	2	3	3	NUM
ajst-25603	96	3	]	]	X
ajst-25603	96	4	li	li	PROPN
ajst-25603	96	5	rensi	rensi	PROPN
ajst-25603	96	6	,	,	PUNCT
ajst-25603	96	7	shi	shi	PROPN
ajst-25603	96	8	yunyu	yunyu	PROPN
ajst-25603	96	9	,	,	PUNCT
ajst-25603	96	10	liu	liu	PROPN
ajst-25603	96	11	xiang	xiang	PROPN
ajst-25603	96	12	,	,	PUNCT
ajst-25603	96	13	et	et	PROPN
ajst-25603	96	14	al	al	PROPN
ajst-25603	96	15	.	.	PUNCT
ajst-25603	96	16	image	image	NOUN
ajst-25603	96	17	object	object	NOUN
ajst-25603	96	18	detection	detection	NOUN
ajst-25603	96	19	based	base	VERB
ajst-25603	96	20	on	on	ADP
ajst-25603	96	21	double	double	ADJ
ajst-25603	96	22	-	-	PUNCT
ajst-25603	96	23	head	head	NOUN
ajst-25603	96	24	[	[	X
ajst-25603	96	25	j	j	X
ajst-25603	96	26	]	]	X
ajst-25603	96	27	.	.	PUNCT
ajst-25603	97	1	liquid	liquid	ADJ
ajst-25603	97	2	crystal	crystal	NOUN
ajst-25603	97	3	crystal	crystal	NOUN
ajst-25603	97	4	and	and	CCONJ
ajst-25603	97	5	display	display	NOUN
ajst-25603	97	6	,	,	PUNCT
ajst-25603	97	7	2023	2023	NUM
ajst-25603	97	8	,	,	PUNCT
ajst-25603	97	9	38	38	NUM
ajst-25603	97	10	(	(	PUNCT
ajst-25603	97	11	12	12	NUM
ajst-25603	97	12	):	):	PUNCT
ajst-25603	97	13	1717	1717	NUM
ajst-25603	97	14	-	-	SYM
ajst-25603	97	15	1727	1727	NUM
ajst-25603	97	16	.	.	PUNCT
ajst-25603	98	1	[	[	X
ajst-25603	98	2	4	4	NUM
ajst-25603	98	3	]	]	PUNCT
ajst-25603	98	4	qiang	qiang	PROPN
ajst-25603	98	5	zhang	zhang	PROPN
ajst-25603	98	6	,	,	PUNCT
ajst-25603	98	7	xiaojian	xiaojian	PROPN
ajst-25603	98	8	hu	hu	PROPN
ajst-25603	98	9	.	.	PUNCT
ajst-25603	99	1	msffa	msffa	PROPN
ajst-25603	99	2	-	-	PUNCT
ajst-25603	99	3	yolo	yolo	ADJ
ajst-25603	99	4	network	network	NOUN
ajst-25603	99	5	:	:	PUNCT
ajst-25603	99	6	multiclass	multiclass	ADJ
ajst-25603	99	7	object	object	NOUN
ajst-25603	99	8	detection	detection	NOUN
ajst-25603	99	9	for	for	ADP
ajst-25603	99	10	traffic	traffic	NOUN
ajst-25603	99	11	investigations	investigation	NOUN
ajst-25603	99	12	in	in	ADP
ajst-25603	99	13	foggy	foggy	ADJ
ajst-25603	99	14	weather.ieee	weather.ieee	NOUN
ajst-25603	99	15	t.	t.	NOUN
ajst-25603	99	16	instrumentation	instrumentation	NOUN
ajst-25603	99	17	and	and	CCONJ
ajst-25603	99	18	measurement	measurement	NOUN
ajst-25603	99	19	,	,	PUNCT
ajst-25603	99	20	2023	2023	NUM
ajst-25603	99	21	,	,	PUNCT
ajst-25603	99	22	72	72	NUM
ajst-25603	99	23	:	:	SYM
ajst-25603	99	24	1	1	NUM
ajst-25603	99	25	-	-	SYM
ajst-25603	99	26	12	12	NUM
ajst-25603	99	27	.	.	PUNCT
ajst-25603	100	1	[	[	X
ajst-25603	100	2	5	5	X
ajst-25603	100	3	]	]	PUNCT
ajst-25603	100	4	wei	wei	PROPN
ajst-25603	100	5	liu	liu	PROPN
ajst-25603	100	6	-	-	PUNCT
ajst-25603	100	7	mei	mei	PROPN
ajst-25603	100	8	,	,	PUNCT
ajst-25603	100	9	luo	luo	PROPN
ajst-25603	100	10	xue	xue	PROPN
ajst-25603	100	11	-	-	PUNCT
ajst-25603	100	12	mei	mei	PROPN
ajst-25603	100	13	,	,	PUNCT
ajst-25603	100	14	kang	kang	PROPN
ajst-25603	100	15	jian	jian	PROPN
ajst-25603	100	16	.	.	PROPN
ajst-25603	100	17	improved	improve	VERB
ajst-25603	100	18	small	small	ADJ
ajst-25603	100	19	target	target	NOUN
ajst-25603	100	20	detection	detection	NOUN
ajst-25603	100	21	algorithm	algorithm	NOUN
ajst-25603	100	22	for	for	ADP
ajst-25603	100	23	aerial	aerial	ADJ
ajst-25603	100	24	images	image	NOUN
ajst-25603	100	25	with	with	ADP
ajst-25603	100	26	yolov8	yolov8	NOUN
ajst-25603	101	1	[	[	X
ajst-25603	101	2	j/	j/	VERB
ajst-25603	101	3	ol	ol	ADJ
ajst-25603	101	4	]	]	PUNCT
ajst-25603	101	5	.	.	PUNCT
ajst-25603	102	1	computer	computer	NOUN
ajst-25603	102	2	engineering	engineering	NOUN
ajst-25603	102	3	and	and	CCONJ
ajst-25603	102	4	science,1	science,1	NOUN
ajst-25603	102	5	-	-	PUNCT
ajst-25603	102	6	13[2024	13[2024	NUM
ajst-25603	102	7	-	-	PUNCT
ajst-25603	102	8	09	09	NUM
ajst-25603	102	9	-	-	PUNCT
ajst-25603	102	10	02	02	NUM
ajst-25603	102	11	]	]	PUNCT
ajst-25603	102	12	.	.	PUNCT
ajst-25603	103	1	[	[	X
ajst-25603	103	2	6	6	NUM
ajst-25603	103	3	]	]	PUNCT
ajst-25603	103	4	m.	m.	NOUN
ajst-25603	103	5	tan	tan	PROPN
ajst-25603	103	6	,	,	PUNCT
ajst-25603	103	7	r.	r.	PROPN
ajst-25603	103	8	pang	pang	PROPN
ajst-25603	103	9	and	and	CCONJ
ajst-25603	103	10	q.	q.	PROPN
ajst-25603	103	11	v.	v.	PROPN
ajst-25603	103	12	le	le	PROPN
ajst-25603	103	13	,	,	PUNCT
ajst-25603	103	14	efficientdet	efficientdet	PROPN
ajst-25603	103	15	:	:	PUNCT
ajst-25603	103	16	scalable	scalable	ADJ
ajst-25603	103	17	and	and	CCONJ
ajst-25603	103	18	efficient	efficient	ADJ
ajst-25603	103	19	object	object	NOUN
ajst-25603	103	20	detection	detection	NOUN
ajst-25603	103	21	,	,	PUNCT
ajst-25603	103	22	2020	2020	NUM
ajst-25603	103	23	ieee	ieee	NOUN
ajst-25603	103	24	/	/	SYM
ajst-25603	103	25	cvf	cvf	NOUN
ajst-25603	103	26	conference	conference	NOUN
ajst-25603	103	27	on	on	ADP
ajst-25603	103	28	computer	computer	NOUN
ajst-25603	103	29	vision	vision	NOUN
ajst-25603	103	30	and	and	CCONJ
ajst-25603	103	31	pattern	pattern	NOUN
ajst-25603	103	32	recognition	recognition	NOUN
ajst-25603	103	33	(	(	PUNCT
ajst-25603	103	34	cvpr	cvpr	NOUN
ajst-25603	103	35	)	)	PUNCT
ajst-25603	103	36	,	,	PUNCT
ajst-25603	103	37	seattle	seattle	PROPN
ajst-25603	103	38	,	,	PUNCT
ajst-25603	103	39	wa	wa	PROPN
ajst-25603	103	40	,	,	PUNCT
ajst-25603	103	41	usa	usa	PROPN
ajst-25603	103	42	,	,	PUNCT
ajst-25603	103	43	2020	2020	NUM
ajst-25603	103	44	,	,	PUNCT
ajst-25603	103	45	pp	pp	ADV
ajst-25603	103	46	.	.	PUNCT
ajst-25603	104	1	10778	10778	NUM
ajst-25603	104	2	-	-	SYM
ajst-25603	104	3	10787	10787	NUM
ajst-25603	104	4	,	,	PUNCT
ajst-25603	104	5	doi	doi	NOUN
ajst-25603	104	6	:	:	PUNCT
ajst-25603	104	7	10.1109/	10.1109/	NUM
ajst-25603	104	8	cvpr	cvpr	NOUN
ajst-25603	104	9	42600	42600	NUM
ajst-25603	104	10	.	.	PUNCT
ajst-25603	105	1	2020.01079	2020.01079	NUM
ajst-25603	105	2	.	.	PUNCT
ajst-25603	106	1	[	[	X
ajst-25603	106	2	7	7	X
ajst-25603	106	3	]	]	X
ajst-25603	106	4	liu	liu	PROPN
ajst-25603	106	5	,	,	PUNCT
ajst-25603	106	6	lingzhi	lingzhi	PROPN
ajst-25603	106	7	.	.	PUNCT
ajst-25603	106	8	research	research	NOUN
ajst-25603	106	9	on	on	ADP
ajst-25603	106	10	target	target	NOUN
ajst-25603	106	11	detection	detection	NOUN
ajst-25603	106	12	algorithm	algorithm	NOUN
ajst-25603	106	13	based	base	VERB
ajst-25603	106	14	on	on	ADP
ajst-25603	106	15	multi	multi	ADJ
ajst-25603	106	16	-	-	ADJ
ajst-25603	106	17	scale	scale	ADJ
ajst-25603	106	18	attention	attention	NOUN
ajst-25603	106	19	and	and	CCONJ
ajst-25603	106	20	dense	dense	ADJ
ajst-25603	106	21	connected	connected	ADJ
ajst-25603	106	22	network[d	network[d	NOUN
ajst-25603	106	23	]	]	PUNCT
ajst-25603	106	24	.	.	PUNCT
ajst-25603	107	1	guilin	guilin	PROPN
ajst-25603	107	2	university	university	PROPN
ajst-25603	107	3	of	of	ADP
ajst-25603	107	4	electronic	electronic	ADJ
ajst-25603	107	5	science	science	NOUN
ajst-25603	107	6	and	and	CCONJ
ajst-25603	107	7	technology	technology	NOUN
ajst-25603	107	8	,	,	PUNCT
ajst-25603	107	9	2023	2023	NUM
ajst-25603	107	10	.	.	PUNCT
ajst-25603	108	1	doi:10	doi:10	PROPN
ajst-25603	108	2	.	.	PUNCT
ajst-25603	109	1	27049	27049	NUM
ajst-25603	109	2	/	/	SYM
ajst-25603	109	3	d.cnki.ggldc.2023.001546	d.cnki.ggldc.2023.001546	ADJ
ajst-25603	109	4	.	.	PUNCT
ajst-25603	110	1	[	[	X
ajst-25603	110	2	8	8	NUM
ajst-25603	110	3	]	]	PUNCT
ajst-25603	110	4	q.	q.	NOUN
ajst-25603	110	5	-l	-l	PROPN
ajst-25603	110	6	.	.	PUNCT
ajst-25603	111	1	zhang	zhang	PROPN
ajst-25603	111	2	and	and	CCONJ
ajst-25603	111	3	y.	y.	PROPN
ajst-25603	111	4	-b	-b	PROPN
ajst-25603	111	5	.	.	PUNCT
ajst-25603	112	1	yang	yang	PROPN
ajst-25603	112	2	,	,	PUNCT
ajst-25603	112	3	sa	sa	PROPN
ajst-25603	112	4	-	-	PUNCT
ajst-25603	112	5	net	net	NOUN
ajst-25603	112	6	:	:	PUNCT
ajst-25603	112	7	shuffle	shuffle	VERB
ajst-25603	112	8	attention	attention	NOUN
ajst-25603	112	9	for	for	ADP
ajst-25603	112	10	deep	deep	ADJ
ajst-25603	112	11	convolutional	convolutional	ADJ
ajst-25603	112	12	neural	neural	ADJ
ajst-25603	112	13	networks	network	NOUN
ajst-25603	112	14	,	,	PUNCT
ajst-25603	112	15	icassp	icassp	ADJ
ajst-25603	112	16	2021	2021	NUM
ajst-25603	112	17	2021	2021	NUM
ajst-25603	112	18	ieee	ieee	PROPN
ajst-25603	112	19	international	international	ADJ
ajst-25603	112	20	conference	conference	NOUN
ajst-25603	112	21	on	on	ADP
ajst-25603	112	22	acoustics	acoustic	NOUN
ajst-25603	112	23	,	,	PUNCT
ajst-25603	112	24	speech	speech	NOUN
ajst-25603	112	25	and	and	CCONJ
ajst-25603	112	26	signal	signal	NOUN
ajst-25603	112	27	processing	processing	NOUN
ajst-25603	112	28	(	(	PUNCT
ajst-25603	112	29	icassp	icassp	PROPN
ajst-25603	112	30	)	)	PUNCT
ajst-25603	112	31	,	,	PUNCT
ajst-25603	112	32	toronto	toronto	PROPN
ajst-25603	112	33	,	,	PUNCT
ajst-25603	112	34	on	on	ADP
ajst-25603	112	35	,	,	PUNCT
ajst-25603	112	36	canada	canada	PROPN
ajst-25603	112	37	,	,	PUNCT
ajst-25603	112	38	2021	2021	NUM
ajst-25603	112	39	,	,	PUNCT
ajst-25603	112	40	pp	pp	ADJ
ajst-25603	112	41	.	.	PUNCT
ajst-25603	113	1	2235	2235	NUM
ajst-25603	113	2	-	-	SYM
ajst-25603	113	3	2239	2239	NUM
ajst-25603	113	4	,	,	PUNCT
ajst-25603	113	5	doi	doi	NOUN
ajst-25603	113	6	:	:	PUNCT
ajst-25603	113	7	10.1109	10.1109	NUM
ajst-25603	113	8	/	/	SYM
ajst-25603	113	9	icassp39728.2021.9414568	icassp39728.2021.9414568	PROPN
ajst-25603	113	10	.	.	PUNCT
ajst-25603	114	1	[	[	X
ajst-25603	114	2	9	9	NUM
ajst-25603	114	3	]	]	SYM
ajst-25603	114	4	yuxi	yuxi	NOUN
ajst-25603	114	5	cheng	cheng	PROPN
ajst-25603	114	6	.	.	PUNCT
ajst-25603	115	1	research	research	NOUN
ajst-25603	115	2	on	on	ADP
ajst-25603	115	3	traffic	traffic	NOUN
ajst-25603	115	4	sign	sign	NOUN
ajst-25603	115	5	recognition	recognition	NOUN
ajst-25603	115	6	in	in	ADP
ajst-25603	115	7	foggy	foggy	ADJ
ajst-25603	115	8	weather	weather	NOUN
ajst-25603	115	9	combining	combine	VERB
ajst-25603	115	10	gan	gan	NOUN
ajst-25603	115	11	and	and	CCONJ
ajst-25603	115	12	yolov7[d	yolov7[d	NOUN
ajst-25603	115	13	]	]	X
ajst-25603	115	14	.	.	PUNCT
ajst-25603	116	1	liaoning	liaoning	PROPN
ajst-25603	116	2	university	university	PROPN
ajst-25603	116	3	of	of	ADP
ajst-25603	116	4	engineering	engineering	NOUN
ajst-25603	116	5	and	and	CCONJ
ajst-25603	116	6	technology	technology	NOUN
ajst-25603	116	7	,	,	PUNCT
ajst-25603	116	8	2023.doi	2023.doi	NUM
ajst-25603	116	9	:	:	PUNCT
ajst-25603	116	10	10	10	NUM
ajst-25603	116	11	.	.	X
ajst-25603	117	1	27210/	27210/	NUM
ajst-25603	117	2	d.cnki.glnju.2023.000096	d.cnki.glnju.2023.000096	NOUN
ajst-25603	117	3	.	.	PUNCT
ajst-25603	118	1	[	[	X
ajst-25603	118	2	10	10	NUM
ajst-25603	118	3	]	]	X
ajst-25603	118	4	zhu	zhu	PROPN
ajst-25603	118	5	yan	yan	PROPN
ajst-25603	118	6	,	,	PUNCT
ajst-25603	118	7	zhang	zhang	PROPN
ajst-25603	118	8	yuexia	yuexia	PROPN
ajst-25603	118	9	.	.	PUNCT
ajst-25603	119	1	sep	sep	PROPN
ajst-25603	119	2	-	-	PUNCT
ajst-25603	119	3	yolo	yolo	PROPN
ajst-25603	119	4	:	:	PUNCT
ajst-25603	119	5	improved	improved	ADJ
ajst-25603	119	6	road	road	NOUN
ajst-25603	119	7	target	target	NOUN
ajst-25603	119	8	detection	detection	NOUN
ajst-25603	119	9	algorithm	algorithm	NOUN
ajst-25603	119	10	based	base	VERB
ajst-25603	119	11	on	on	ADP
ajst-25603	119	12	yolov8[j	yolov8[j	PROPN
ajst-25603	119	13	/	/	SYM
ajst-25603	119	14	ol	ol	PROPN
ajst-25603	119	15	]	]	PUNCT
ajst-25603	119	16	.	.	PUNCT
ajst-25603	120	1	computer	computer	NOUN
ajst-25603	120	2	applications	application	NOUN
ajst-25603	120	3	and	and	CCONJ
ajst-25603	120	4	software,1	software,1	NOUN
ajst-25603	120	5	-	-	PUNCT
ajst-25603	120	6	8[2024	8[2024	NUM
ajst-25603	120	7	-	-	ADJ
ajst-25603	120	8	09	09	NUM
ajst-25603	120	9	-	-	PUNCT
ajst-25603	120	10	02	02	NUM
ajst-25603	120	11	]	]	PUNCT
ajst-25603	120	12	.	.	PUNCT