id	sid	tid	token	lemma	pos
ajst-24468	1	1	academic	academic	ADJ
ajst-24468	1	2	journal	journal	NOUN
ajst-24468	1	3	of	of	ADP
ajst-24468	1	4	science	science	NOUN
ajst-24468	1	5	and	and	CCONJ
ajst-24468	1	6	technology	technology	NOUN
ajst-24468	1	7	issn	issn	NOUN
ajst-24468	1	8	:	:	PUNCT
ajst-24468	1	9	2771	2771	NUM
ajst-24468	1	10	-	-	SYM
ajst-24468	1	11	3032	3032	NUM
ajst-24468	1	12	|	|	NOUN
ajst-24468	1	13	vol	vol	NOUN
ajst-24468	1	14	.	.	PROPN
ajst-24468	2	1	12	12	NUM
ajst-24468	2	2	,	,	PUNCT
ajst-24468	2	3	no	no	INTJ
ajst-24468	2	4	.	.	NOUN
ajst-24468	2	5	1	1	NUM
ajst-24468	2	6	,	,	PUNCT
ajst-24468	2	7	2024	2024	NUM
ajst-24468	2	8	45	45	NUM
ajst-24468	2	9	research	research	NOUN
ajst-24468	2	10	on	on	ADP
ajst-24468	2	11	a	a	DET
ajst-24468	2	12	road	road	NOUN
ajst-24468	2	13	defect	defect	NOUN
ajst-24468	2	14	detection	detection	NOUN
ajst-24468	2	15	method	method	NOUN
ajst-24468	2	16	based	base	VERB
ajst-24468	2	17	on	on	ADP
ajst-24468	2	18	improved	improved	ADJ
ajst-24468	2	19	yolov8	yolov8	NOUN
ajst-24468	2	20	yanyan	yanyan	PROPN
ajst-24468	2	21	wang	wang	PROPN
ajst-24468	2	22	,	,	PUNCT
ajst-24468	2	23	menghao	menghao	PROPN
ajst-24468	2	24	zhou	zhou	PROPN
ajst-24468	2	25	,	,	PUNCT
ajst-24468	2	26	haojie	haojie	NOUN
ajst-24468	2	27	chai	chai	NOUN
ajst-24468	2	28	and	and	CCONJ
ajst-24468	2	29	ran	run	VERB
ajst-24468	2	30	xue	xue	PROPN
ajst-24468	2	31	school	school	PROPN
ajst-24468	2	32	of	of	ADP
ajst-24468	2	33	artificial	artificial	ADJ
ajst-24468	2	34	intelligence	intelligence	NOUN
ajst-24468	2	35	,	,	PUNCT
ajst-24468	2	36	henan	henan	PROPN
ajst-24468	2	37	institute	institute	PROPN
ajst-24468	2	38	of	of	ADP
ajst-24468	2	39	science	science	PROPN
ajst-24468	2	40	and	and	CCONJ
ajst-24468	2	41	technology	technology	NOUN
ajst-24468	2	42	,	,	PUNCT
ajst-24468	2	43	xinxiang	xinxiang	PROPN
ajst-24468	2	44	453003	453003	NUM
ajst-24468	2	45	,	,	PUNCT
ajst-24468	2	46	china	china	PROPN
ajst-24468	2	47	abstract	abstract	NOUN
ajst-24468	2	48	:	:	PUNCT
ajst-24468	2	49	traditional	traditional	ADJ
ajst-24468	2	50	road	road	NOUN
ajst-24468	2	51	defect	defect	NOUN
ajst-24468	2	52	detection	detection	NOUN
ajst-24468	2	53	methods	method	NOUN
ajst-24468	2	54	mainly	mainly	ADV
ajst-24468	2	55	rely	rely	VERB
ajst-24468	2	56	on	on	ADP
ajst-24468	2	57	manual	manual	ADJ
ajst-24468	2	58	detection	detection	NOUN
ajst-24468	2	59	,	,	PUNCT
ajst-24468	2	60	which	which	PRON
ajst-24468	2	61	is	be	AUX
ajst-24468	2	62	inefficient	inefficient	ADJ
ajst-24468	2	63	and	and	CCONJ
ajst-24468	2	64	has	have	VERB
ajst-24468	2	65	many	many	ADJ
ajst-24468	2	66	limitations	limitation	NOUN
ajst-24468	2	67	.	.	PUNCT
ajst-24468	3	1	in	in	ADP
ajst-24468	3	2	order	order	NOUN
ajst-24468	3	3	to	to	PART
ajst-24468	3	4	detect	detect	VERB
ajst-24468	3	5	road	road	NOUN
ajst-24468	3	6	defects	defect	NOUN
ajst-24468	3	7	more	more	ADV
ajst-24468	3	8	accurately	accurately	ADV
ajst-24468	3	9	and	and	CCONJ
ajst-24468	3	10	quickly	quickly	ADV
ajst-24468	3	11	,	,	PUNCT
ajst-24468	3	12	this	this	DET
ajst-24468	3	13	paper	paper	NOUN
ajst-24468	3	14	proposes	propose	VERB
ajst-24468	3	15	a	a	DET
ajst-24468	3	16	road	road	NOUN
ajst-24468	3	17	defect	defect	NOUN
ajst-24468	3	18	detection	detection	NOUN
ajst-24468	3	19	method	method	NOUN
ajst-24468	3	20	based	base	VERB
ajst-24468	3	21	on	on	ADP
ajst-24468	3	22	improved	improved	ADJ
ajst-24468	3	23	yolov8	yolov8	NOUN
ajst-24468	3	24	.	.	PUNCT
ajst-24468	4	1	in	in	ADP
ajst-24468	4	2	this	this	DET
ajst-24468	4	3	paper	paper	NOUN
ajst-24468	4	4	,	,	PUNCT
ajst-24468	4	5	a	a	DET
ajst-24468	4	6	large	large	ADJ
ajst-24468	4	7	number	number	NOUN
ajst-24468	4	8	of	of	ADP
ajst-24468	4	9	image	image	NOUN
ajst-24468	4	10	datasets	dataset	NOUN
ajst-24468	4	11	containing	contain	VERB
ajst-24468	4	12	road	road	NOUN
ajst-24468	4	13	defects	defect	NOUN
ajst-24468	4	14	are	be	AUX
ajst-24468	4	15	collected	collect	VERB
ajst-24468	4	16	and	and	CCONJ
ajst-24468	4	17	annotated	annotate	VERB
ajst-24468	4	18	,	,	PUNCT
ajst-24468	4	19	and	and	CCONJ
ajst-24468	4	20	the	the	DET
ajst-24468	4	21	yolov8	yolov8	NOUN
ajst-24468	4	22	algorithm	algorithm	PROPN
ajst-24468	4	23	is	be	AUX
ajst-24468	4	24	used	use	VERB
ajst-24468	4	25	to	to	PART
ajst-24468	4	26	train	train	VERB
ajst-24468	4	27	the	the	DET
ajst-24468	4	28	model	model	NOUN
ajst-24468	4	29	on	on	ADP
ajst-24468	4	30	the	the	DET
ajst-24468	4	31	dataset	dataset	NOUN
ajst-24468	4	32	,	,	PUNCT
ajst-24468	4	33	one	one	NUM
ajst-24468	4	34	group	group	NOUN
ajst-24468	4	35	uses	use	VERB
ajst-24468	4	36	the	the	DET
ajst-24468	4	37	original	original	ADJ
ajst-24468	4	38	model	model	NOUN
ajst-24468	4	39	,	,	PUNCT
ajst-24468	4	40	one	one	NUM
ajst-24468	4	41	group	group	NOUN
ajst-24468	4	42	replaces	replace	VERB
ajst-24468	4	43	the	the	DET
ajst-24468	4	44	convolutional	convolutional	ADJ
ajst-24468	4	45	kernel	kernel	NOUN
ajst-24468	4	46	(	(	PUNCT
ajst-24468	4	47	dcnv2	dcnv2	PROPN
ajst-24468	4	48	)	)	PUNCT
ajst-24468	4	49	,	,	PUNCT
ajst-24468	4	50	and	and	CCONJ
ajst-24468	4	51	one	one	NUM
ajst-24468	4	52	group	group	NOUN
ajst-24468	4	53	adds	add	VERB
ajst-24468	4	54	the	the	DET
ajst-24468	4	55	attention	attention	NOUN
ajst-24468	4	56	mechanism	mechanism	NOUN
ajst-24468	4	57	(	(	PUNCT
ajst-24468	4	58	cbam	cbam	NOUN
ajst-24468	4	59	)	)	PUNCT
ajst-24468	4	60	,	,	PUNCT
ajst-24468	4	61	and	and	CCONJ
ajst-24468	4	62	then	then	ADV
ajst-24468	4	63	a	a	DET
ajst-24468	4	64	comparative	comparative	ADJ
ajst-24468	4	65	evaluation	evaluation	NOUN
ajst-24468	4	66	is	be	AUX
ajst-24468	4	67	performed	perform	VERB
ajst-24468	4	68	based	base	VERB
ajst-24468	4	69	on	on	ADP
ajst-24468	4	70	the	the	DET
ajst-24468	4	71	training	training	NOUN
ajst-24468	4	72	results	result	NOUN
ajst-24468	4	73	of	of	ADP
ajst-24468	4	74	the	the	DET
ajst-24468	4	75	models	model	NOUN
ajst-24468	4	76	in	in	ADP
ajst-24468	4	77	each	each	DET
ajst-24468	4	78	group	group	NOUN
ajst-24468	4	79	.	.	PUNCT
ajst-24468	5	1	the	the	DET
ajst-24468	5	2	experimental	experimental	ADJ
ajst-24468	5	3	results	result	NOUN
ajst-24468	5	4	show	show	VERB
ajst-24468	5	5	that	that	SCONJ
ajst-24468	5	6	the	the	DET
ajst-24468	5	7	improved	improved	ADJ
ajst-24468	5	8	model	model	NOUN
ajst-24468	5	9	reflects	reflect	VERB
ajst-24468	5	10	higher	high	ADJ
ajst-24468	5	11	accuracy	accuracy	NOUN
ajst-24468	5	12	in	in	ADP
ajst-24468	5	13	road	road	NOUN
ajst-24468	5	14	defect	defect	NOUN
ajst-24468	5	15	detection	detection	NOUN
ajst-24468	5	16	compared	compare	VERB
ajst-24468	5	17	to	to	ADP
ajst-24468	5	18	the	the	DET
ajst-24468	5	19	untuned	untuned	ADJ
ajst-24468	5	20	model	model	NOUN
ajst-24468	5	21	.	.	PUNCT
ajst-24468	6	1	the	the	DET
ajst-24468	6	2	model	model	NOUN
ajst-24468	6	3	with	with	ADP
ajst-24468	6	4	the	the	DET
ajst-24468	6	5	replaced	replace	VERB
ajst-24468	6	6	convolutional	convolutional	ADJ
ajst-24468	6	7	kernel	kernel	NOUN
ajst-24468	6	8	improved	improve	VERB
ajst-24468	6	9	about	about	ADV
ajst-24468	6	10	2	2	NUM
ajst-24468	6	11	%	%	NOUN
ajst-24468	6	12	compared	compare	VERB
ajst-24468	6	13	to	to	ADP
ajst-24468	6	14	the	the	DET
ajst-24468	6	15	original	original	ADJ
ajst-24468	6	16	model	model	NOUN
ajst-24468	6	17	mean	mean	ADJ
ajst-24468	6	18	average	average	ADJ
ajst-24468	6	19	precision	precision	NOUN
ajst-24468	6	20	(	(	PUNCT
ajst-24468	6	21	map	map	NOUN
ajst-24468	6	22	)	)	PUNCT
ajst-24468	6	23	,	,	PUNCT
ajst-24468	6	24	and	and	CCONJ
ajst-24468	6	25	the	the	DET
ajst-24468	6	26	model	model	NOUN
ajst-24468	6	27	with	with	ADP
ajst-24468	6	28	the	the	DET
ajst-24468	6	29	added	add	VERB
ajst-24468	6	30	attention	attention	NOUN
ajst-24468	6	31	mechanism	mechanism	NOUN
ajst-24468	6	32	improved	improve	VERB
ajst-24468	6	33	about	about	ADP
ajst-24468	6	34	3.5	3.5	NUM
ajst-24468	6	35	%	%	NOUN
ajst-24468	6	36	compared	compare	VERB
ajst-24468	6	37	to	to	ADP
ajst-24468	6	38	the	the	DET
ajst-24468	6	39	original	original	ADJ
ajst-24468	6	40	model	model	NOUN
ajst-24468	6	41	mpa	mpa	PROPN
ajst-24468	6	42	.	.	PUNCT
ajst-24468	7	1	this	this	DET
ajst-24468	7	2	study	study	NOUN
ajst-24468	7	3	can	can	AUX
ajst-24468	7	4	efficiently	efficiently	ADV
ajst-24468	7	5	identify	identify	VERB
ajst-24468	7	6	different	different	ADJ
ajst-24468	7	7	types	type	NOUN
ajst-24468	7	8	of	of	ADP
ajst-24468	7	9	road	road	NOUN
ajst-24468	7	10	defects	defect	NOUN
ajst-24468	7	11	,	,	PUNCT
ajst-24468	7	12	which	which	PRON
ajst-24468	7	13	can	can	AUX
ajst-24468	7	14	help	help	VERB
ajst-24468	7	15	road	road	NOUN
ajst-24468	7	16	maintenance	maintenance	NOUN
ajst-24468	7	17	work	work	NOUN
ajst-24468	7	18	to	to	PART
ajst-24468	7	19	be	be	AUX
ajst-24468	7	20	carried	carry	VERB
ajst-24468	7	21	out	out	ADP
ajst-24468	7	22	more	more	ADV
ajst-24468	7	23	scientifically	scientifically	ADV
ajst-24468	7	24	and	and	CCONJ
ajst-24468	7	25	efficiently	efficiently	ADV
ajst-24468	7	26	,	,	PUNCT
ajst-24468	7	27	and	and	CCONJ
ajst-24468	7	28	provide	provide	VERB
ajst-24468	7	29	important	important	ADJ
ajst-24468	7	30	support	support	NOUN
ajst-24468	7	31	for	for	ADP
ajst-24468	7	32	improving	improve	VERB
ajst-24468	7	33	road	road	NOUN
ajst-24468	7	34	safety	safety	NOUN
ajst-24468	7	35	and	and	CCONJ
ajst-24468	7	36	maintenance	maintenance	NOUN
ajst-24468	7	37	efficiency	efficiency	NOUN
ajst-24468	7	38	.	.	PUNCT
ajst-24468	8	1	keywords	keyword	NOUN
ajst-24468	8	2	:	:	PUNCT
ajst-24468	8	3	road	road	NOUN
ajst-24468	8	4	defect	defect	NOUN
ajst-24468	8	5	detection	detection	NOUN
ajst-24468	8	6	;	;	PUNCT
ajst-24468	8	7	yolov8	yolov8	NOUN
ajst-24468	8	8	;	;	PUNCT
ajst-24468	8	9	neural	neural	ADJ
ajst-24468	8	10	network	network	NOUN
ajst-24468	8	11	;	;	PUNCT
ajst-24468	8	12	deep	deep	ADJ
ajst-24468	8	13	learning	learning	NOUN
ajst-24468	8	14	.	.	PUNCT
ajst-24468	9	1	1	1	X
ajst-24468	9	2	.	.	X
ajst-24468	9	3	introduction	introduction	NOUN
ajst-24468	9	4	with	with	ADP
ajst-24468	9	5	the	the	DET
ajst-24468	9	6	acceleration	acceleration	NOUN
ajst-24468	9	7	of	of	ADP
ajst-24468	9	8	urbanization	urbanization	NOUN
ajst-24468	9	9	and	and	CCONJ
ajst-24468	9	10	the	the	DET
ajst-24468	9	11	continuous	continuous	ADJ
ajst-24468	9	12	growth	growth	NOUN
ajst-24468	9	13	of	of	ADP
ajst-24468	9	14	the	the	DET
ajst-24468	9	15	number	number	NOUN
ajst-24468	9	16	of	of	ADP
ajst-24468	9	17	vehicles	vehicle	NOUN
ajst-24468	9	18	,	,	PUNCT
ajst-24468	9	19	the	the	DET
ajst-24468	9	20	safety	safety	NOUN
ajst-24468	9	21	and	and	CCONJ
ajst-24468	9	22	accessibility	accessibility	NOUN
ajst-24468	9	23	of	of	ADP
ajst-24468	9	24	roads	road	NOUN
ajst-24468	9	25	are	be	AUX
ajst-24468	9	26	of	of	ADP
ajst-24468	9	27	great	great	ADJ
ajst-24468	9	28	significance	significance	NOUN
ajst-24468	9	29	to	to	ADP
ajst-24468	9	30	the	the	DET
ajst-24468	9	31	normal	normal	ADJ
ajst-24468	9	32	operation	operation	NOUN
ajst-24468	9	33	of	of	ADP
ajst-24468	9	34	urban	urban	ADJ
ajst-24468	9	35	traffic	traffic	NOUN
ajst-24468	9	36	.	.	PUNCT
ajst-24468	10	1	however	however	ADV
ajst-24468	10	2	,	,	PUNCT
ajst-24468	10	3	various	various	ADJ
ajst-24468	10	4	defects	defect	NOUN
ajst-24468	10	5	,	,	PUNCT
ajst-24468	10	6	such	such	ADJ
ajst-24468	10	7	as	as	ADP
ajst-24468	10	8	cracks	crack	NOUN
ajst-24468	10	9	,	,	PUNCT
ajst-24468	10	10	potholes	pothole	NOUN
ajst-24468	10	11	,	,	PUNCT
ajst-24468	10	12	and	and	CCONJ
ajst-24468	10	13	damages	damage	NOUN
ajst-24468	10	14	,	,	PUNCT
ajst-24468	10	15	will	will	AUX
ajst-24468	10	16	appear	appear	VERB
ajst-24468	10	17	on	on	ADP
ajst-24468	10	18	roads	road	NOUN
ajst-24468	10	19	in	in	ADP
ajst-24468	10	20	the	the	DET
ajst-24468	10	21	process	process	NOUN
ajst-24468	10	22	of	of	ADP
ajst-24468	10	23	use	use	NOUN
ajst-24468	10	24	,	,	PUNCT
ajst-24468	10	25	which	which	PRON
ajst-24468	10	26	will	will	AUX
ajst-24468	10	27	bring	bring	VERB
ajst-24468	10	28	safety	safety	NOUN
ajst-24468	10	29	hazards	hazard	NOUN
ajst-24468	10	30	to	to	ADP
ajst-24468	10	31	drivers	driver	NOUN
ajst-24468	10	32	and	and	CCONJ
ajst-24468	10	33	pedestrians	pedestrian	NOUN
ajst-24468	10	34	,	,	PUNCT
ajst-24468	10	35	and	and	CCONJ
ajst-24468	10	36	even	even	ADV
ajst-24468	10	37	cause	cause	VERB
ajst-24468	10	38	damages	damage	NOUN
ajst-24468	10	39	to	to	ADP
ajst-24468	10	40	vehicles	vehicle	NOUN
ajst-24468	10	41	.	.	PUNCT
ajst-24468	11	1	therefore	therefore	ADV
ajst-24468	11	2	,	,	PUNCT
ajst-24468	11	3	timely	timely	ADJ
ajst-24468	11	4	detection	detection	NOUN
ajst-24468	11	5	and	and	CCONJ
ajst-24468	11	6	repair	repair	NOUN
ajst-24468	11	7	of	of	ADP
ajst-24468	11	8	road	road	NOUN
ajst-24468	11	9	defects	defect	NOUN
ajst-24468	11	10	are	be	AUX
ajst-24468	11	11	very	very	ADV
ajst-24468	11	12	important	important	ADJ
ajst-24468	11	13	to	to	PART
ajst-24468	11	14	ensure	ensure	VERB
ajst-24468	11	15	the	the	DET
ajst-24468	11	16	safety	safety	NOUN
ajst-24468	11	17	and	and	CCONJ
ajst-24468	11	18	accessibility	accessibility	NOUN
ajst-24468	11	19	of	of	ADP
ajst-24468	11	20	roads	road	NOUN
ajst-24468	11	21	[	[	X
ajst-24468	11	22	1	1	NUM
ajst-24468	11	23	]	]	PUNCT
ajst-24468	11	24	.	.	PUNCT
ajst-24468	12	1	current	current	ADJ
ajst-24468	12	2	road	road	NOUN
ajst-24468	12	3	defect	defect	NOUN
ajst-24468	12	4	detection	detection	NOUN
ajst-24468	12	5	methods	method	NOUN
ajst-24468	12	6	mainly	mainly	ADV
ajst-24468	12	7	rely	rely	VERB
ajst-24468	12	8	on	on	ADP
ajst-24468	12	9	manual	manual	ADJ
ajst-24468	12	10	inspection	inspection	NOUN
ajst-24468	12	11	,	,	PUNCT
ajst-24468	12	12	and	and	CCONJ
ajst-24468	12	13	there	there	PRON
ajst-24468	12	14	are	be	VERB
ajst-24468	12	15	always	always	ADV
ajst-24468	12	16	differences	difference	NOUN
ajst-24468	12	17	in	in	ADP
ajst-24468	12	18	monitoring	monitoring	NOUN
ajst-24468	12	19	and	and	CCONJ
ajst-24468	12	20	evaluation	evaluation	NOUN
ajst-24468	12	21	standards	standard	NOUN
ajst-24468	12	22	,	,	PUNCT
ajst-24468	12	23	and	and	CCONJ
ajst-24468	12	24	they	they	PRON
ajst-24468	12	25	are	be	AUX
ajst-24468	12	26	also	also	ADV
ajst-24468	12	27	prone	prone	ADJ
ajst-24468	12	28	to	to	ADP
ajst-24468	12	29	the	the	DET
ajst-24468	12	30	limitations	limitation	NOUN
ajst-24468	12	31	of	of	ADP
ajst-24468	12	32	weather	weather	NOUN
ajst-24468	12	33	,	,	PUNCT
ajst-24468	12	34	light	light	NOUN
ajst-24468	12	35	and	and	CCONJ
ajst-24468	12	36	other	other	ADJ
ajst-24468	12	37	natural	natural	ADJ
ajst-24468	12	38	conditions	condition	NOUN
ajst-24468	12	39	,	,	PUNCT
ajst-24468	12	40	resulting	result	VERB
ajst-24468	12	41	in	in	ADP
ajst-24468	12	42	omissions	omission	NOUN
ajst-24468	12	43	and	and	CCONJ
ajst-24468	12	44	misdetections	misdetection	NOUN
ajst-24468	12	45	.	.	PUNCT
ajst-24468	13	1	therefore	therefore	ADV
ajst-24468	13	2	,	,	PUNCT
ajst-24468	13	3	finding	find	VERB
ajst-24468	13	4	an	an	DET
ajst-24468	13	5	efficient	efficient	ADJ
ajst-24468	13	6	and	and	CCONJ
ajst-24468	13	7	accurate	accurate	ADJ
ajst-24468	13	8	road	road	NOUN
ajst-24468	13	9	defect	defect	NOUN
ajst-24468	13	10	detection	detection	NOUN
ajst-24468	13	11	method	method	NOUN
ajst-24468	13	12	is	be	AUX
ajst-24468	13	13	of	of	ADP
ajst-24468	13	14	great	great	ADJ
ajst-24468	13	15	significance	significance	NOUN
ajst-24468	13	16	to	to	PART
ajst-24468	13	17	improve	improve	VERB
ajst-24468	13	18	the	the	DET
ajst-24468	13	19	efficiency	efficiency	NOUN
ajst-24468	13	20	of	of	ADP
ajst-24468	13	21	road	road	NOUN
ajst-24468	13	22	maintenance	maintenance	NOUN
ajst-24468	13	23	and	and	CCONJ
ajst-24468	13	24	ensure	ensure	VERB
ajst-24468	13	25	the	the	DET
ajst-24468	13	26	safety	safety	NOUN
ajst-24468	13	27	of	of	ADP
ajst-24468	13	28	people	people	NOUN
ajst-24468	13	29	traveling	travel	VERB
ajst-24468	13	30	[	[	X
ajst-24468	13	31	2	2	NUM
ajst-24468	13	32	,	,	PUNCT
ajst-24468	13	33	3	3	NUM
ajst-24468	13	34	]	]	PUNCT
ajst-24468	13	35	.	.	PUNCT
ajst-24468	14	1	at	at	ADP
ajst-24468	14	2	present	present	ADJ
ajst-24468	14	3	,	,	PUNCT
ajst-24468	14	4	the	the	DET
ajst-24468	14	5	defect	defect	NOUN
ajst-24468	14	6	detection	detection	NOUN
ajst-24468	14	7	method	method	NOUN
ajst-24468	14	8	based	base	VERB
ajst-24468	14	9	on	on	ADP
ajst-24468	14	10	deep	deep	ADJ
ajst-24468	14	11	learning	learning	NOUN
ajst-24468	14	12	has	have	AUX
ajst-24468	14	13	gradually	gradually	ADV
ajst-24468	14	14	become	become	VERB
ajst-24468	14	15	a	a	DET
ajst-24468	14	16	hot	hot	ADJ
ajst-24468	14	17	spot	spot	NOUN
ajst-24468	14	18	of	of	ADP
ajst-24468	14	19	research	research	NOUN
ajst-24468	14	20	[	[	X
ajst-24468	14	21	4	4	NUM
ajst-24468	14	22	-	-	SYM
ajst-24468	14	23	6	6	NUM
ajst-24468	14	24	]	]	PUNCT
ajst-24468	14	25	.	.	PUNCT
ajst-24468	15	1	sun	sun	PROPN
ajst-24468	15	2	et	et	PROPN
ajst-24468	15	3	al	al	PROPN
ajst-24468	16	1	[	[	X
ajst-24468	16	2	4	4	NUM
ajst-24468	16	3	]	]	PUNCT
ajst-24468	16	4	optimized	optimize	VERB
ajst-24468	16	5	the	the	DET
ajst-24468	16	6	aspect	aspect	NOUN
ajst-24468	16	7	ratio	ratio	NOUN
ajst-24468	16	8	of	of	ADP
ajst-24468	16	9	crack	crack	NOUN
ajst-24468	16	10	candidate	candidate	NOUN
ajst-24468	16	11	frames	frame	NOUN
ajst-24468	16	12	by	by	ADP
ajst-24468	16	13	fusing	fuse	VERB
ajst-24468	16	14	vgg16	vgg16	NOUN
ajst-24468	16	15	network	network	NOUN
ajst-24468	16	16	and	and	CCONJ
ajst-24468	16	17	faster	fast	ADJ
ajst-24468	16	18	r	r	NOUN
ajst-24468	16	19	-	-	PUNCT
ajst-24468	16	20	cnn	cnn	PROPN
ajst-24468	16	21	,	,	PUNCT
ajst-24468	16	22	which	which	PRON
ajst-24468	16	23	further	far	ADV
ajst-24468	16	24	improved	improve	VERB
ajst-24468	16	25	the	the	DET
ajst-24468	16	26	detection	detection	NOUN
ajst-24468	16	27	accuracy	accuracy	NOUN
ajst-24468	16	28	despite	despite	SCONJ
ajst-24468	16	29	sacrificing	sacrifice	VERB
ajst-24468	16	30	part	part	NOUN
ajst-24468	16	31	of	of	ADP
ajst-24468	16	32	the	the	DET
ajst-24468	16	33	detection	detection	NOUN
ajst-24468	16	34	speed	speed	NOUN
ajst-24468	16	35	.	.	PUNCT
ajst-24468	17	1	jiang	jiang	PROPN
ajst-24468	17	2	et	et	PROPN
ajst-24468	17	3	al	al	PROPN
ajst-24468	18	1	[	[	X
ajst-24468	18	2	5	5	NUM
ajst-24468	18	3	]	]	PUNCT
ajst-24468	18	4	improved	improved	ADJ
ajst-24468	18	5	yolov5s	yolov5s	PROPN
ajst-24468	18	6	by	by	ADP
ajst-24468	18	7	introducing	introduce	VERB
ajst-24468	18	8	an	an	DET
ajst-24468	18	9	attention	attention	NOUN
ajst-24468	18	10	mechanism	mechanism	NOUN
ajst-24468	18	11	and	and	CCONJ
ajst-24468	18	12	an	an	DET
ajst-24468	18	13	adaptive	adaptive	ADJ
ajst-24468	18	14	feature	feature	NOUN
ajst-24468	18	15	fusion	fusion	NOUN
ajst-24468	18	16	mechanism	mechanism	NOUN
ajst-24468	18	17	to	to	PART
ajst-24468	18	18	optimize	optimize	VERB
ajst-24468	18	19	the	the	DET
ajst-24468	18	20	accuracy	accuracy	NOUN
ajst-24468	18	21	rate	rate	NOUN
ajst-24468	18	22	of	of	ADP
ajst-24468	18	23	road	road	NOUN
ajst-24468	18	24	detection	detection	NOUN
ajst-24468	18	25	,	,	PUNCT
ajst-24468	18	26	but	but	CCONJ
ajst-24468	18	27	the	the	DET
ajst-24468	18	28	method	method	NOUN
ajst-24468	18	29	still	still	ADV
ajst-24468	18	30	has	have	VERB
ajst-24468	18	31	low	low	ADJ
ajst-24468	18	32	detection	detection	NOUN
ajst-24468	18	33	accuracy	accuracy	NOUN
ajst-24468	18	34	for	for	ADP
ajst-24468	18	35	transverse	transverse	NOUN
ajst-24468	18	36	cracks	crack	NOUN
ajst-24468	18	37	.	.	PUNCT
ajst-24468	19	1	yan	yan	PROPN
ajst-24468	19	2	et	et	PROPN
ajst-24468	19	3	al	al	PROPN
ajst-24468	20	1	[	[	X
ajst-24468	20	2	6	6	NUM
ajst-24468	20	3	]	]	PUNCT
ajst-24468	20	4	proposed	propose	VERB
ajst-24468	20	5	a	a	DET
ajst-24468	20	6	road	road	NOUN
ajst-24468	20	7	crack	crack	NOUN
ajst-24468	20	8	detection	detection	NOUN
ajst-24468	20	9	method	method	NOUN
ajst-24468	20	10	based	base	VERB
ajst-24468	20	11	on	on	ADP
ajst-24468	20	12	faster	fast	ADJ
ajst-24468	20	13	r	r	NOUN
ajst-24468	20	14	-	-	PUNCT
ajst-24468	20	15	cnn	cnn	PROPN
ajst-24468	20	16	,	,	PUNCT
ajst-24468	20	17	which	which	PRON
ajst-24468	20	18	is	be	AUX
ajst-24468	20	19	able	able	ADJ
ajst-24468	20	20	to	to	PART
ajst-24468	20	21	extract	extract	VERB
ajst-24468	20	22	the	the	DET
ajst-24468	20	23	crack	crack	NOUN
ajst-24468	20	24	morphology	morphology	NOUN
ajst-24468	20	25	and	and	CCONJ
ajst-24468	20	26	size	size	NOUN
ajst-24468	20	27	,	,	PUNCT
ajst-24468	20	28	but	but	CCONJ
ajst-24468	20	29	the	the	DET
ajst-24468	20	30	misdetection	misdetection	NOUN
ajst-24468	20	31	rate	rate	NOUN
ajst-24468	20	32	is	be	AUX
ajst-24468	20	33	on	on	ADP
ajst-24468	20	34	the	the	DET
ajst-24468	20	35	high	high	ADJ
ajst-24468	20	36	side	side	NOUN
ajst-24468	20	37	,	,	PUNCT
ajst-24468	20	38	which	which	PRON
ajst-24468	20	39	makes	make	VERB
ajst-24468	20	40	it	it	PRON
ajst-24468	20	41	difficult	difficult	ADJ
ajst-24468	20	42	to	to	PART
ajst-24468	20	43	be	be	AUX
ajst-24468	20	44	applied	apply	VERB
ajst-24468	20	45	in	in	ADP
ajst-24468	20	46	practice	practice	NOUN
ajst-24468	20	47	.	.	PUNCT
ajst-24468	21	1	sun	sun	PROPN
ajst-24468	21	2	et	et	PROPN
ajst-24468	21	3	al	al	PROPN
ajst-24468	22	1	[	[	X
ajst-24468	22	2	7	7	NUM
ajst-24468	22	3	]	]	PUNCT
ajst-24468	22	4	achieved	achieve	VERB
ajst-24468	22	5	intelligent	intelligent	ADJ
ajst-24468	22	6	identification	identification	NOUN
ajst-24468	22	7	of	of	ADP
ajst-24468	22	8	road	road	NOUN
ajst-24468	22	9	marking	mark	VERB
ajst-24468	22	10	defects	defect	NOUN
ajst-24468	22	11	through	through	ADP
ajst-24468	22	12	deep	deep	ADJ
ajst-24468	22	13	learning	learning	NOUN
ajst-24468	22	14	,	,	PUNCT
ajst-24468	22	15	using	use	VERB
ajst-24468	22	16	data	datum	NOUN
ajst-24468	22	17	augmentation	augmentation	NOUN
ajst-24468	22	18	,	,	PUNCT
ajst-24468	22	19	modular	modular	ADJ
ajst-24468	22	20	optimization	optimization	NOUN
ajst-24468	22	21	,	,	PUNCT
ajst-24468	22	22	and	and	CCONJ
ajst-24468	22	23	anchor	anchor	NOUN
ajst-24468	22	24	point	point	NOUN
ajst-24468	22	25	redesign	redesign	NOUN
ajst-24468	22	26	,	,	PUNCT
ajst-24468	22	27	to	to	PART
ajst-24468	22	28	locate	locate	VERB
ajst-24468	22	29	the	the	DET
ajst-24468	22	30	road	road	NOUN
ajst-24468	22	31	markings	marking	NOUN
ajst-24468	22	32	and	and	CCONJ
ajst-24468	22	33	classify	classify	VERB
ajst-24468	22	34	their	their	PRON
ajst-24468	22	35	defects	defect	NOUN
ajst-24468	22	36	.	.	PUNCT
ajst-24468	23	1	wang	wang	PROPN
ajst-24468	23	2	et	et	PROPN
ajst-24468	23	3	al	al	PROPN
ajst-24468	24	1	[	[	X
ajst-24468	24	2	8	8	NUM
ajst-24468	24	3	]	]	PUNCT
ajst-24468	24	4	proposed	propose	VERB
ajst-24468	24	5	a	a	DET
ajst-24468	24	6	lightweight	lightweight	ADJ
ajst-24468	24	7	ssd	ssd	NOUN
ajst-24468	24	8	-	-	PUNCT
ajst-24468	24	9	based	base	VERB
ajst-24468	24	10	road	road	NOUN
ajst-24468	24	11	crack	crack	NOUN
ajst-24468	24	12	detection	detection	NOUN
ajst-24468	24	13	method	method	NOUN
ajst-24468	24	14	,	,	PUNCT
ajst-24468	24	15	which	which	PRON
ajst-24468	24	16	improved	improve	VERB
ajst-24468	24	17	the	the	DET
ajst-24468	24	18	detection	detection	NOUN
ajst-24468	24	19	speed	speed	NOUN
ajst-24468	24	20	and	and	CCONJ
ajst-24468	24	21	accuracy	accuracy	NOUN
ajst-24468	24	22	,	,	PUNCT
ajst-24468	24	23	but	but	CCONJ
ajst-24468	24	24	the	the	DET
ajst-24468	24	25	accuracy	accuracy	NOUN
ajst-24468	24	26	was	be	AUX
ajst-24468	24	27	reduced	reduce	VERB
ajst-24468	24	28	for	for	ADP
ajst-24468	24	29	some	some	DET
ajst-24468	24	30	types	type	NOUN
ajst-24468	24	31	of	of	ADP
ajst-24468	24	32	detection	detection	NOUN
ajst-24468	24	33	.	.	PUNCT
ajst-24468	25	1	although	although	SCONJ
ajst-24468	25	2	many	many	ADJ
ajst-24468	25	3	research	research	NOUN
ajst-24468	25	4	results	result	NOUN
ajst-24468	25	5	have	have	AUX
ajst-24468	25	6	been	be	AUX
ajst-24468	25	7	achieved	achieve	VERB
ajst-24468	25	8	[	[	PUNCT
ajst-24468	25	9	9	9	NUM
ajst-24468	25	10	-	-	SYM
ajst-24468	25	11	12	12	NUM
ajst-24468	25	12	]	]	PUNCT
ajst-24468	25	13	,	,	PUNCT
ajst-24468	25	14	there	there	PRON
ajst-24468	25	15	are	be	VERB
ajst-24468	25	16	still	still	ADV
ajst-24468	25	17	shortcomings	shortcoming	NOUN
ajst-24468	25	18	such	such	ADJ
ajst-24468	25	19	as	as	ADP
ajst-24468	25	20	high	high	ADJ
ajst-24468	25	21	detection	detection	NOUN
ajst-24468	25	22	difficulty	difficulty	NOUN
ajst-24468	25	23	and	and	CCONJ
ajst-24468	25	24	poor	poor	ADJ
ajst-24468	25	25	generalization	generalization	NOUN
ajst-24468	25	26	ability	ability	NOUN
ajst-24468	25	27	,	,	PUNCT
ajst-24468	25	28	and	and	CCONJ
ajst-24468	25	29	there	there	PRON
ajst-24468	25	30	is	be	VERB
ajst-24468	25	31	still	still	ADV
ajst-24468	25	32	room	room	NOUN
ajst-24468	25	33	for	for	ADP
ajst-24468	25	34	progress	progress	NOUN
ajst-24468	25	35	in	in	ADP
ajst-24468	25	36	the	the	DET
ajst-24468	25	37	accuracy	accuracy	NOUN
ajst-24468	25	38	of	of	ADP
ajst-24468	25	39	detection	detection	NOUN
ajst-24468	25	40	.	.	PUNCT
ajst-24468	26	1	therefore	therefore	ADV
ajst-24468	26	2	,	,	PUNCT
ajst-24468	26	3	by	by	ADP
ajst-24468	26	4	analyzing	analyze	VERB
ajst-24468	26	5	the	the	DET
ajst-24468	26	6	existing	exist	VERB
ajst-24468	26	7	methods	method	NOUN
ajst-24468	26	8	,	,	PUNCT
ajst-24468	26	9	this	this	DET
ajst-24468	26	10	paper	paper	NOUN
ajst-24468	26	11	proposes	propose	VERB
ajst-24468	26	12	a	a	DET
ajst-24468	26	13	road	road	NOUN
ajst-24468	26	14	defect	defect	NOUN
ajst-24468	26	15	detection	detection	NOUN
ajst-24468	26	16	method	method	NOUN
ajst-24468	26	17	based	base	VERB
ajst-24468	26	18	on	on	ADP
ajst-24468	26	19	improved	improved	ADJ
ajst-24468	26	20	yolov8	yolov8	NOUN
ajst-24468	26	21	using	use	VERB
ajst-24468	26	22	deep	deep	ADJ
ajst-24468	26	23	learning	learning	NOUN
ajst-24468	26	24	technology	technology	NOUN
ajst-24468	26	25	.	.	PUNCT
ajst-24468	27	1	the	the	DET
ajst-24468	27	2	original	original	ADJ
ajst-24468	27	3	yolov8	yolov8	NOUN
ajst-24468	28	1	[	[	X
ajst-24468	28	2	5	5	NUM
ajst-24468	28	3	]	]	PUNCT
ajst-24468	28	4	target	target	NOUN
ajst-24468	28	5	detection	detection	NOUN
ajst-24468	28	6	algorithm	algorithm	NOUN
ajst-24468	28	7	model	model	NOUN
ajst-24468	28	8	is	be	AUX
ajst-24468	28	9	improved	improve	VERB
ajst-24468	28	10	by	by	ADP
ajst-24468	28	11	adding	add	VERB
ajst-24468	28	12	a	a	DET
ajst-24468	28	13	variable	variable	ADJ
ajst-24468	28	14	convolutional	convolutional	ADJ
ajst-24468	28	15	kernel	kernel	NOUN
ajst-24468	28	16	(	(	PUNCT
ajst-24468	28	17	dcnv2	dcnv2	PROPN
ajst-24468	28	18	)	)	PUNCT
ajst-24468	28	19	with	with	ADP
ajst-24468	28	20	an	an	DET
ajst-24468	28	21	attention	attention	NOUN
ajst-24468	28	22	mechanism	mechanism	NOUN
ajst-24468	28	23	(	(	PUNCT
ajst-24468	28	24	convolutional	convolutional	ADJ
ajst-24468	28	25	block	block	NOUN
ajst-24468	28	26	attention	attention	NOUN
ajst-24468	28	27	module	module	NOUN
ajst-24468	28	28	(	(	PUNCT
ajst-24468	28	29	cbam	cbam	NOUN
ajst-24468	28	30	)	)	PUNCT
ajst-24468	28	31	)	)	PUNCT
ajst-24468	28	32	,	,	PUNCT
ajst-24468	28	33	and	and	CCONJ
ajst-24468	28	34	then	then	ADV
ajst-24468	28	35	these	these	DET
ajst-24468	28	36	three	three	NUM
ajst-24468	28	37	models	model	NOUN
ajst-24468	28	38	are	be	AUX
ajst-24468	28	39	trained	train	VERB
ajst-24468	28	40	on	on	ADP
ajst-24468	28	41	the	the	DET
ajst-24468	28	42	dataset	dataset	NOUN
ajst-24468	28	43	separately	separately	ADV
ajst-24468	28	44	,	,	PUNCT
ajst-24468	28	45	i.e.	i.e.	X
ajst-24468	28	46	,	,	PUNCT
ajst-24468	28	47	one	one	NUM
ajst-24468	28	48	group	group	NOUN
ajst-24468	28	49	uses	use	VERB
ajst-24468	28	50	the	the	DET
ajst-24468	28	51	original	original	ADJ
ajst-24468	28	52	model	model	NOUN
ajst-24468	28	53	,	,	PUNCT
ajst-24468	28	54	one	one	NUM
ajst-24468	28	55	group	group	NOUN
ajst-24468	28	56	replaces	replace	VERB
ajst-24468	28	57	the	the	DET
ajst-24468	28	58	convolutional	convolutional	ADJ
ajst-24468	28	59	kernel	kernel	NOUN
ajst-24468	28	60	(	(	PUNCT
ajst-24468	28	61	dcnv2	dcnv2	PROPN
ajst-24468	28	62	)	)	PUNCT
ajst-24468	28	63	and	and	CCONJ
ajst-24468	28	64	one	one	NUM
ajst-24468	28	65	group	group	NOUN
ajst-24468	28	66	adds	add	VERB
ajst-24468	28	67	the	the	DET
ajst-24468	28	68	attention	attention	NOUN
ajst-24468	28	69	mechanism	mechanism	NOUN
ajst-24468	28	70	(	(	PUNCT
ajst-24468	28	71	cbam	cbam	NOUN
ajst-24468	28	72	)	)	PUNCT
ajst-24468	28	73	,	,	PUNCT
ajst-24468	28	74	and	and	CCONJ
ajst-24468	28	75	the	the	DET
ajst-24468	28	76	training	training	NOUN
ajst-24468	28	77	results	result	NOUN
ajst-24468	28	78	of	of	ADP
ajst-24468	28	79	different	different	ADJ
ajst-24468	28	80	models	model	NOUN
ajst-24468	28	81	are	be	AUX
ajst-24468	28	82	recorded	record	VERB
ajst-24468	28	83	separately	separately	ADV
ajst-24468	28	84	.	.	PUNCT
ajst-24468	29	1	then	then	ADV
ajst-24468	29	2	the	the	DET
ajst-24468	29	3	best	good	ADJ
ajst-24468	29	4	modeling	modeling	NOUN
ajst-24468	29	5	scheme	scheme	NOUN
ajst-24468	29	6	is	be	AUX
ajst-24468	29	7	selected	select	VERB
ajst-24468	29	8	based	base	VERB
ajst-24468	29	9	on	on	ADP
ajst-24468	29	10	the	the	DET
ajst-24468	29	11	comparative	comparative	ADJ
ajst-24468	29	12	evaluation	evaluation	NOUN
ajst-24468	29	13	of	of	ADP
ajst-24468	29	14	the	the	DET
ajst-24468	29	15	model	model	NOUN
ajst-24468	29	16	training	training	NOUN
ajst-24468	29	17	results	result	NOUN
ajst-24468	29	18	of	of	ADP
ajst-24468	29	19	each	each	DET
ajst-24468	29	20	group	group	NOUN
ajst-24468	29	21	to	to	PART
ajst-24468	29	22	improve	improve	VERB
ajst-24468	29	23	the	the	DET
ajst-24468	29	24	accuracy	accuracy	NOUN
ajst-24468	29	25	of	of	ADP
ajst-24468	29	26	road	road	NOUN
ajst-24468	29	27	defect	defect	NOUN
ajst-24468	29	28	detection	detection	NOUN
ajst-24468	29	29	.	.	PUNCT
ajst-24468	30	1	2	2	X
ajst-24468	30	2	.	.	NUM
ajst-24468	30	3	related	relate	VERB
ajst-24468	30	4	theories	theory	NOUN
ajst-24468	30	5	2.1	2.1	NUM
ajst-24468	30	6	.	.	PUNCT
ajst-24468	31	1	yolov8	yolov8	NOUN
ajst-24468	31	2	model	model	PROPN
ajst-24468	31	3	yolov8	yolov8	PROPN
ajst-24468	31	4	is	be	AUX
ajst-24468	31	5	an	an	DET
ajst-24468	31	6	efficient	efficient	ADJ
ajst-24468	31	7	,	,	PUNCT
ajst-24468	31	8	accurate	accurate	ADJ
ajst-24468	31	9	and	and	CCONJ
ajst-24468	31	10	easy	easy	ADJ
ajst-24468	31	11	-	-	PUNCT
ajst-24468	31	12	to	to	ADP
ajst-24468	31	13	-	-	PUNCT
ajst-24468	31	14	use	use	VERB
ajst-24468	31	15	target	target	NOUN
ajst-24468	31	16	detection	detection	NOUN
ajst-24468	31	17	algorithm	algorithm	NOUN
ajst-24468	31	18	,	,	PUNCT
ajst-24468	31	19	and	and	CCONJ
ajst-24468	31	20	its	its	PRON
ajst-24468	31	21	model	model	NOUN
ajst-24468	31	22	structure	structure	NOUN
ajst-24468	31	23	is	be	AUX
ajst-24468	31	24	an	an	DET
ajst-24468	31	25	important	important	ADJ
ajst-24468	31	26	milestone	milestone	NOUN
ajst-24468	31	27	in	in	ADP
ajst-24468	31	28	the	the	DET
ajst-24468	31	29	field	field	NOUN
ajst-24468	31	30	of	of	ADP
ajst-24468	31	31	target	target	NOUN
ajst-24468	31	32	detection	detection	NOUN
ajst-24468	31	33	,	,	PUNCT
ajst-24468	31	34	which	which	PRON
ajst-24468	31	35	has	have	AUX
ajst-24468	31	36	been	be	AUX
ajst-24468	31	37	improved	improve	VERB
ajst-24468	31	38	and	and	CCONJ
ajst-24468	31	39	optimized	optimize	VERB
ajst-24468	31	40	in	in	ADP
ajst-24468	31	41	several	several	ADJ
ajst-24468	31	42	ways	way	NOUN
ajst-24468	31	43	on	on	ADP
ajst-24468	31	44	the	the	DET
ajst-24468	31	45	basis	basis	NOUN
ajst-24468	31	46	of	of	ADP
ajst-24468	31	47	the	the	DET
ajst-24468	31	48	yolo	yolo	ADJ
ajst-24468	31	49	series	series	NOUN
ajst-24468	31	50	of	of	ADP
ajst-24468	31	51	models	model	NOUN
ajst-24468	31	52	,	,	PUNCT
ajst-24468	31	53	and	and	CCONJ
ajst-24468	31	54	its	its	PRON
ajst-24468	31	55	network	network	NOUN
ajst-24468	31	56	structure	structure	NOUN
ajst-24468	31	57	is	be	AUX
ajst-24468	31	58	shown	show	VERB
ajst-24468	31	59	in	in	ADP
ajst-24468	31	60	figure	figure	NOUN
ajst-24468	31	61	1	1	NUM
ajst-24468	31	62	.	.	PUNCT
ajst-24468	32	1	the	the	DET
ajst-24468	32	2	following	follow	VERB
ajst-24468	32	3	is	be	AUX
ajst-24468	32	4	a	a	DET
ajst-24468	32	5	brief	brief	ADJ
ajst-24468	32	6	description	description	NOUN
ajst-24468	32	7	of	of	ADP
ajst-24468	32	8	the	the	DET
ajst-24468	32	9	structure	structure	NOUN
ajst-24468	32	10	and	and	CCONJ
ajst-24468	32	11	role	role	NOUN
ajst-24468	32	12	function	function	NOUN
ajst-24468	32	13	of	of	ADP
ajst-24468	32	14	each	each	DET
ajst-24468	32	15	layer	layer	NOUN
ajst-24468	32	16	of	of	ADP
ajst-24468	32	17	the	the	DET
ajst-24468	32	18	model	model	NOUN
ajst-24468	32	19	:	:	PUNCT
ajst-24468	32	20	(	(	PUNCT
ajst-24468	32	21	1	1	X
ajst-24468	32	22	)	)	PUNCT
ajst-24468	32	23	input	input	NOUN
ajst-24468	32	24	the	the	DET
ajst-24468	32	25	input	input	NOUN
ajst-24468	32	26	side	side	NOUN
ajst-24468	32	27	of	of	ADP
ajst-24468	32	28	yolov8	yolov8	NOUN
ajst-24468	32	29	is	be	AUX
ajst-24468	32	30	similar	similar	ADJ
ajst-24468	32	31	to	to	ADP
ajst-24468	32	32	the	the	DET
ajst-24468	32	33	traditional	traditional	ADJ
ajst-24468	32	34	approach	approach	NOUN
ajst-24468	32	35	,	,	PUNCT
ajst-24468	32	36	accepting	accept	VERB
ajst-24468	32	37	images	image	NOUN
ajst-24468	32	38	as	as	ADP
ajst-24468	32	39	input	input	NOUN
ajst-24468	32	40	and	and	CCONJ
ajst-24468	32	41	performing	perform	VERB
ajst-24468	32	42	the	the	DET
ajst-24468	32	43	necessary	necessary	ADJ
ajst-24468	32	44	preprocessing	preprocessing	NOUN
ajst-24468	32	45	such	such	ADJ
ajst-24468	32	46	as	as	ADP
ajst-24468	32	47	scaling	scaling	NOUN
ajst-24468	32	48	and	and	CCONJ
ajst-24468	32	49	normalization	normalization	NOUN
ajst-24468	32	50	.	.	PUNCT
ajst-24468	33	1	(	(	PUNCT
ajst-24468	33	2	2	2	X
ajst-24468	33	3	)	)	PUNCT
ajst-24468	33	4	backbone	backbone	NOUN
ajst-24468	33	5	the	the	DET
ajst-24468	33	6	backbone	backbone	NOUN
ajst-24468	33	7	part	part	NOUN
ajst-24468	33	8	is	be	AUX
ajst-24468	33	9	responsible	responsible	ADJ
ajst-24468	33	10	for	for	ADP
ajst-24468	33	11	extracting	extract	VERB
ajst-24468	33	12	the	the	DET
ajst-24468	33	13	basic	basic	ADJ
ajst-24468	33	14	46	46	NUM
ajst-24468	33	15	features	feature	NOUN
ajst-24468	33	16	of	of	ADP
ajst-24468	33	17	the	the	DET
ajst-24468	33	18	image	image	NOUN
ajst-24468	33	19	.	.	PUNCT
ajst-24468	34	1	yolov8	yolov8	PROPN
ajst-24468	34	2	uses	use	VERB
ajst-24468	34	3	an	an	DET
ajst-24468	34	4	improved	improved	ADJ
ajst-24468	34	5	version	version	NOUN
ajst-24468	34	6	based	base	VERB
ajst-24468	34	7	on	on	ADP
ajst-24468	34	8	darknet	darknet	NOUN
ajst-24468	34	9	as	as	ADP
ajst-24468	34	10	the	the	DET
ajst-24468	34	11	base	base	NOUN
ajst-24468	34	12	network	network	NOUN
ajst-24468	34	13	,	,	PUNCT
ajst-24468	34	14	which	which	PRON
ajst-24468	34	15	is	be	AUX
ajst-24468	34	16	a	a	DET
ajst-24468	34	17	lightweight	lightweight	ADJ
ajst-24468	34	18	convolutional	convolutional	ADJ
ajst-24468	34	19	neural	neural	ADJ
ajst-24468	34	20	network	network	NOUN
ajst-24468	34	21	structure	structure	NOUN
ajst-24468	34	22	with	with	ADP
ajst-24468	34	23	better	well	ADJ
ajst-24468	34	24	feature	feature	NOUN
ajst-24468	34	25	extraction	extraction	NOUN
ajst-24468	34	26	capability	capability	NOUN
ajst-24468	34	27	and	and	CCONJ
ajst-24468	34	28	computational	computational	ADJ
ajst-24468	34	29	efficiency	efficiency	NOUN
ajst-24468	34	30	.	.	PUNCT
ajst-24468	35	1	fig	fig	NOUN
ajst-24468	35	2	1	1	NUM
ajst-24468	35	3	.	.	PUNCT
ajst-24468	36	1	yolov8	yolov8	NOUN
ajst-24468	36	2	infrastructure	infrastructure	PROPN
ajst-24468	36	3	diagram	diagram	PROPN
ajst-24468	36	4	(	(	PUNCT
ajst-24468	36	5	3	3	X
ajst-24468	36	6	)	)	PUNCT
ajst-24468	36	7	neck	neck	NOUN
ajst-24468	36	8	the	the	DET
ajst-24468	36	9	neck	neck	NOUN
ajst-24468	36	10	part	part	NOUN
ajst-24468	36	11	connects	connect	VERB
ajst-24468	36	12	the	the	DET
ajst-24468	36	13	backbone	backbone	NOUN
ajst-24468	36	14	and	and	CCONJ
ajst-24468	36	15	head	head	NOUN
ajst-24468	36	16	parts	part	NOUN
ajst-24468	36	17	.	.	PUNCT
ajst-24468	37	1	it	it	PRON
ajst-24468	37	2	effectively	effectively	ADV
ajst-24468	37	3	integrates	integrate	VERB
ajst-24468	37	4	feature	feature	NOUN
ajst-24468	37	5	maps	map	NOUN
ajst-24468	37	6	of	of	ADP
ajst-24468	37	7	different	different	ADJ
ajst-24468	37	8	scales	scale	NOUN
ajst-24468	37	9	through	through	ADP
ajst-24468	37	10	operations	operation	NOUN
ajst-24468	37	11	such	such	ADJ
ajst-24468	37	12	as	as	ADP
ajst-24468	37	13	up	up	ADV
ajst-24468	37	14	-	-	PUNCT
ajst-24468	37	15	sampling	sample	VERB
ajst-24468	37	16	,	,	PUNCT
ajst-24468	37	17	down	down	ADV
ajst-24468	37	18	-	-	PUNCT
ajst-24468	37	19	sampling	sample	VERB
ajst-24468	37	20	and	and	CCONJ
ajst-24468	37	21	feature	feature	NOUN
ajst-24468	37	22	fusion	fusion	NOUN
ajst-24468	37	23	.	.	PUNCT
ajst-24468	38	1	in	in	ADP
ajst-24468	38	2	yolov8	yolov8	PROPN
ajst-24468	38	3	,	,	PUNCT
ajst-24468	38	4	the	the	DET
ajst-24468	38	5	neck	neck	NOUN
ajst-24468	38	6	part	part	NOUN
ajst-24468	38	7	may	may	AUX
ajst-24468	38	8	adopt	adopt	VERB
ajst-24468	38	9	the	the	DET
ajst-24468	38	10	structure	structure	NOUN
ajst-24468	38	11	of	of	ADP
ajst-24468	38	12	pan	pan	PROPN
ajst-24468	38	13	(	(	PUNCT
ajst-24468	38	14	path	path	NOUN
ajst-24468	38	15	aggregation	aggregation	NOUN
ajst-24468	38	16	network	network	NOUN
ajst-24468	38	17	)	)	PUNCT
ajst-24468	38	18	,	,	PUNCT
ajst-24468	38	19	but	but	CCONJ
ajst-24468	38	20	the	the	DET
ajst-24468	38	21	convolution	convolution	NOUN
ajst-24468	38	22	structure	structure	NOUN
ajst-24468	38	23	is	be	AUX
ajst-24468	38	24	deleted	delete	VERB
ajst-24468	38	25	in	in	ADP
ajst-24468	38	26	the	the	DET
ajst-24468	38	27	up	up	ADV
ajst-24468	38	28	-	-	PUNCT
ajst-24468	38	29	sampling	sample	VERB
ajst-24468	38	30	stage	stage	NOUN
ajst-24468	38	31	and	and	CCONJ
ajst-24468	38	32	the	the	DET
ajst-24468	38	33	c3	c3	PROPN
ajst-24468	38	34	module	module	NOUN
ajst-24468	38	35	is	be	AUX
ajst-24468	38	36	replaced	replace	VERB
ajst-24468	38	37	by	by	ADP
ajst-24468	38	38	the	the	DET
ajst-24468	38	39	c2f	c2f	NOUN
ajst-24468	38	40	module	module	NOUN
ajst-24468	38	41	to	to	PART
ajst-24468	38	42	reduce	reduce	VERB
ajst-24468	38	43	the	the	DET
ajst-24468	38	44	amount	amount	NOUN
ajst-24468	38	45	of	of	ADP
ajst-24468	38	46	computation	computation	NOUN
ajst-24468	38	47	and	and	CCONJ
ajst-24468	38	48	improve	improve	VERB
ajst-24468	38	49	the	the	DET
ajst-24468	38	50	efficiency	efficiency	NOUN
ajst-24468	38	51	of	of	ADP
ajst-24468	38	52	feature	feature	NOUN
ajst-24468	38	53	transfer	transfer	NOUN
ajst-24468	38	54	.	.	PUNCT
ajst-24468	39	1	(	(	PUNCT
ajst-24468	39	2	4	4	X
ajst-24468	39	3	)	)	PUNCT
ajst-24468	39	4	head	head	NOUN
ajst-24468	39	5	the	the	DET
ajst-24468	39	6	head	head	NOUN
ajst-24468	39	7	part	part	NOUN
ajst-24468	39	8	is	be	AUX
ajst-24468	39	9	the	the	DET
ajst-24468	39	10	last	last	ADJ
ajst-24468	39	11	layer	layer	NOUN
ajst-24468	39	12	of	of	ADP
ajst-24468	39	13	the	the	DET
ajst-24468	39	14	model	model	NOUN
ajst-24468	39	15	,	,	PUNCT
ajst-24468	39	16	which	which	PRON
ajst-24468	39	17	is	be	AUX
ajst-24468	39	18	responsible	responsible	ADJ
ajst-24468	39	19	for	for	ADP
ajst-24468	39	20	predicting	predict	VERB
ajst-24468	39	21	information	information	NOUN
ajst-24468	39	22	such	such	ADJ
ajst-24468	39	23	as	as	ADP
ajst-24468	39	24	the	the	DET
ajst-24468	39	25	location	location	NOUN
ajst-24468	39	26	,	,	PUNCT
ajst-24468	39	27	size	size	NOUN
ajst-24468	39	28	and	and	CCONJ
ajst-24468	39	29	category	category	NOUN
ajst-24468	39	30	of	of	ADP
ajst-24468	39	31	the	the	DET
ajst-24468	39	32	target	target	NOUN
ajst-24468	39	33	.	.	PUNCT
ajst-24468	40	1	yolov8	yolov8	PROPN
ajst-24468	40	2	adopts	adopt	VERB
ajst-24468	40	3	the	the	DET
ajst-24468	40	4	design	design	NOUN
ajst-24468	40	5	idea	idea	NOUN
ajst-24468	40	6	of	of	ADP
ajst-24468	40	7	decoupled	decouple	VERB
ajst-24468	40	8	-	-	PUNCT
ajst-24468	40	9	head	head	NOUN
ajst-24468	40	10	,	,	PUNCT
ajst-24468	40	11	i.e.	i.e.	X
ajst-24468	40	12	,	,	PUNCT
ajst-24468	40	13	there	there	PRON
ajst-24468	40	14	are	be	VERB
ajst-24468	40	15	independent	independent	ADJ
ajst-24468	40	16	detectors	detector	NOUN
ajst-24468	40	17	for	for	ADP
ajst-24468	40	18	each	each	DET
ajst-24468	40	19	scale	scale	NOUN
ajst-24468	40	20	.	.	PUNCT
ajst-24468	41	1	each	each	DET
ajst-24468	41	2	detector	detector	NOUN
ajst-24468	41	3	consists	consist	VERB
ajst-24468	41	4	of	of	ADP
ajst-24468	41	5	a	a	DET
ajst-24468	41	6	set	set	NOUN
ajst-24468	41	7	of	of	ADP
ajst-24468	41	8	convolutional	convolutional	ADJ
ajst-24468	41	9	and	and	CCONJ
ajst-24468	41	10	fully	fully	ADV
ajst-24468	41	11	-	-	PUNCT
ajst-24468	41	12	connected	connect	VERB
ajst-24468	41	13	layers	layer	NOUN
ajst-24468	41	14	that	that	PRON
ajst-24468	41	15	are	be	AUX
ajst-24468	41	16	used	use	VERB
ajst-24468	41	17	to	to	PART
ajst-24468	41	18	predict	predict	VERB
ajst-24468	41	19	the	the	DET
ajst-24468	41	20	bounding	bounding	NOUN
ajst-24468	41	21	box	box	NOUN
ajst-24468	41	22	at	at	ADP
ajst-24468	41	23	that	that	DET
ajst-24468	41	24	scale	scale	NOUN
ajst-24468	41	25	.	.	PUNCT
ajst-24468	42	1	this	this	DET
ajst-24468	42	2	design	design	NOUN
ajst-24468	42	3	allows	allow	VERB
ajst-24468	42	4	the	the	DET
ajst-24468	42	5	model	model	NOUN
ajst-24468	42	6	to	to	PART
ajst-24468	42	7	perform	perform	VERB
ajst-24468	42	8	target	target	NOUN
ajst-24468	42	9	detection	detection	NOUN
ajst-24468	42	10	in	in	ADP
ajst-24468	42	11	parallel	parallel	NOUN
ajst-24468	42	12	at	at	ADP
ajst-24468	42	13	different	different	ADJ
ajst-24468	42	14	scales	scale	NOUN
ajst-24468	42	15	,	,	PUNCT
ajst-24468	42	16	improving	improve	VERB
ajst-24468	42	17	the	the	DET
ajst-24468	42	18	efficiency	efficiency	NOUN
ajst-24468	42	19	of	of	ADP
ajst-24468	42	20	training	training	NOUN
ajst-24468	42	21	and	and	CCONJ
ajst-24468	42	22	inference	inference	NOUN
ajst-24468	42	23	.	.	PUNCT
ajst-24468	43	1	2.2	2.2	NUM
ajst-24468	43	2	.	.	PUNCT
ajst-24468	43	3	attention	attention	NOUN
ajst-24468	43	4	mechanism	mechanism	NOUN
ajst-24468	43	5	cbam	cbam	NOUN
ajst-24468	43	6	cbam	cbam	NOUN
ajst-24468	43	7	attention	attention	NOUN
ajst-24468	43	8	mechanism	mechanism	NOUN
ajst-24468	43	9	[	[	X
ajst-24468	43	10	13	13	NUM
ajst-24468	43	11	,	,	PUNCT
ajst-24468	43	12	14	14	NUM
ajst-24468	43	13	]	]	PUNCT
ajst-24468	43	14	is	be	AUX
ajst-24468	43	15	an	an	DET
ajst-24468	43	16	attention	attention	NOUN
ajst-24468	43	17	mechanism	mechanism	NOUN
ajst-24468	43	18	introduced	introduce	VERB
ajst-24468	43	19	in	in	ADP
ajst-24468	43	20	convolutional	convolutional	ADJ
ajst-24468	43	21	neural	neural	ADJ
ajst-24468	43	22	networks	network	NOUN
ajst-24468	43	23	(	(	PUNCT
ajst-24468	43	24	cnns	cnns	PROPN
ajst-24468	43	25	)	)	PUNCT
ajst-24468	43	26	to	to	PART
ajst-24468	43	27	improve	improve	VERB
ajst-24468	43	28	the	the	DET
ajst-24468	43	29	perceptual	perceptual	ADJ
ajst-24468	43	30	capabilities	capability	NOUN
ajst-24468	43	31	of	of	ADP
ajst-24468	43	32	the	the	DET
ajst-24468	43	33	model	model	NOUN
ajst-24468	43	34	and	and	CCONJ
ajst-24468	43	35	thus	thus	ADV
ajst-24468	43	36	improve	improve	VERB
ajst-24468	43	37	performance	performance	NOUN
ajst-24468	43	38	without	without	ADP
ajst-24468	43	39	increasing	increase	VERB
ajst-24468	43	40	network	network	NOUN
ajst-24468	43	41	complexity	complexity	NOUN
ajst-24468	43	42	.	.	PUNCT
ajst-24468	44	1	it	it	PRON
ajst-24468	44	2	improves	improve	VERB
ajst-24468	44	3	the	the	DET
ajst-24468	44	4	accuracy	accuracy	NOUN
ajst-24468	44	5	of	of	ADP
ajst-24468	44	6	feature	feature	NOUN
ajst-24468	44	7	extraction	extraction	NOUN
ajst-24468	44	8	by	by	ADP
ajst-24468	44	9	learning	learn	VERB
ajst-24468	44	10	the	the	DET
ajst-24468	44	11	attentional	attentional	ADJ
ajst-24468	44	12	weights	weight	NOUN
ajst-24468	44	13	for	for	ADP
ajst-24468	44	14	each	each	DET
ajst-24468	44	15	channel	channel	NOUN
ajst-24468	44	16	and	and	CCONJ
ajst-24468	44	17	applying	apply	VERB
ajst-24468	44	18	these	these	DET
ajst-24468	44	19	weights	weight	NOUN
ajst-24468	44	20	to	to	ADP
ajst-24468	44	21	each	each	DET
ajst-24468	44	22	channel	channel	NOUN
ajst-24468	44	23	of	of	ADP
ajst-24468	44	24	the	the	DET
ajst-24468	44	25	original	original	ADJ
ajst-24468	44	26	feature	feature	NOUN
ajst-24468	44	27	map	map	NOUN
ajst-24468	44	28	,	,	PUNCT
ajst-24468	44	29	thus	thus	ADV
ajst-24468	44	30	paying	pay	VERB
ajst-24468	44	31	more	more	ADJ
ajst-24468	44	32	attention	attention	NOUN
ajst-24468	44	33	to	to	ADP
ajst-24468	44	34	the	the	DET
ajst-24468	44	35	channels	channel	NOUN
ajst-24468	44	36	that	that	PRON
ajst-24468	44	37	are	be	AUX
ajst-24468	44	38	helpful	helpful	ADJ
ajst-24468	44	39	for	for	ADP
ajst-24468	44	40	the	the	DET
ajst-24468	44	41	task	task	NOUN
ajst-24468	44	42	at	at	ADP
ajst-24468	44	43	hand	hand	NOUN
ajst-24468	44	44	.	.	PUNCT
ajst-24468	45	1	the	the	DET
ajst-24468	45	2	spatial	spatial	ADJ
ajst-24468	45	3	attention	attention	NOUN
ajst-24468	45	4	module	module	NOUN
ajst-24468	45	5	,	,	PUNCT
ajst-24468	45	6	on	on	ADP
ajst-24468	45	7	the	the	DET
ajst-24468	45	8	other	other	ADJ
ajst-24468	45	9	hand	hand	NOUN
ajst-24468	45	10	,	,	PUNCT
ajst-24468	45	11	adjusts	adjust	VERB
ajst-24468	45	12	the	the	DET
ajst-24468	45	13	spatial	spatial	ADJ
ajst-24468	45	14	dimensions	dimension	NOUN
ajst-24468	45	15	of	of	ADP
ajst-24468	45	16	the	the	DET
ajst-24468	45	17	feature	feature	NOUN
ajst-24468	45	18	map	map	NOUN
ajst-24468	45	19	to	to	PART
ajst-24468	45	20	achieve	achieve	VERB
ajst-24468	45	21	fine	fine	ADJ
ajst-24468	45	22	attention	attention	NOUN
ajst-24468	45	23	to	to	ADP
ajst-24468	45	24	different	different	ADJ
ajst-24468	45	25	regions	region	NOUN
ajst-24468	45	26	.	.	PUNCT
ajst-24468	46	1	it	it	PRON
ajst-24468	46	2	enables	enable	VERB
ajst-24468	46	3	the	the	DET
ajst-24468	46	4	model	model	NOUN
ajst-24468	46	5	to	to	PART
ajst-24468	46	6	pay	pay	VERB
ajst-24468	46	7	more	more	ADJ
ajst-24468	46	8	attention	attention	NOUN
ajst-24468	46	9	to	to	ADP
ajst-24468	46	10	important	important	ADJ
ajst-24468	46	11	spatial	spatial	ADJ
ajst-24468	46	12	locations	location	NOUN
ajst-24468	46	13	by	by	ADP
ajst-24468	46	14	weighting	weight	VERB
ajst-24468	46	15	different	different	ADJ
ajst-24468	46	16	spatial	spatial	ADJ
ajst-24468	46	17	locations	location	NOUN
ajst-24468	46	18	of	of	ADP
ajst-24468	46	19	the	the	DET
ajst-24468	46	20	input	input	NOUN
ajst-24468	46	21	feature	feature	NOUN
ajst-24468	46	22	maps	map	NOUN
ajst-24468	46	23	,	,	PUNCT
ajst-24468	46	24	which	which	PRON
ajst-24468	46	25	improves	improve	VERB
ajst-24468	46	26	the	the	DET
ajst-24468	46	27	performance	performance	NOUN
ajst-24468	46	28	of	of	ADP
ajst-24468	46	29	the	the	DET
ajst-24468	46	30	model	model	NOUN
ajst-24468	46	31	.	.	PUNCT
ajst-24468	47	1	cbam	cbam	NOUN
ajst-24468	47	2	enhances	enhance	VERB
ajst-24468	47	3	the	the	DET
ajst-24468	47	4	feature	feature	NOUN
ajst-24468	47	5	representation	representation	NOUN
ajst-24468	47	6	by	by	ADP
ajst-24468	47	7	combining	combine	VERB
ajst-24468	47	8	channel	channel	NOUN
ajst-24468	47	9	attention	attention	NOUN
ajst-24468	47	10	and	and	CCONJ
ajst-24468	47	11	spatial	spatial	ADJ
ajst-24468	47	12	attention	attention	NOUN
ajst-24468	47	13	,	,	PUNCT
ajst-24468	47	14	which	which	PRON
ajst-24468	47	15	enables	enable	VERB
ajst-24468	47	16	the	the	DET
ajst-24468	47	17	model	model	NOUN
ajst-24468	47	18	to	to	PART
ajst-24468	47	19	pay	pay	VERB
ajst-24468	47	20	more	more	ADJ
ajst-24468	47	21	attention	attention	NOUN
ajst-24468	47	22	to	to	ADP
ajst-24468	47	23	important	important	ADJ
ajst-24468	47	24	feature	feature	NOUN
ajst-24468	47	25	information	information	NOUN
ajst-24468	47	26	.	.	PUNCT
ajst-24468	48	1	2.3	2.3	NUM
ajst-24468	48	2	.	.	PUNCT
ajst-24468	48	3	deformable	deformable	ADJ
ajst-24468	48	4	convolution	convolution	NOUN
ajst-24468	48	5	kernel	kernel	NOUN
ajst-24468	48	6	deformable	deformable	ADJ
ajst-24468	48	7	convolution	convolution	NOUN
ajst-24468	48	8	[	[	X
ajst-24468	48	9	15	15	NUM
ajst-24468	48	10	]	]	PUNCT
ajst-24468	48	11	is	be	AUX
ajst-24468	48	12	an	an	DET
ajst-24468	48	13	improved	improved	ADJ
ajst-24468	48	14	convolution	convolution	NOUN
ajst-24468	48	15	operation	operation	NOUN
ajst-24468	48	16	that	that	PRON
ajst-24468	48	17	enables	enable	VERB
ajst-24468	48	18	the	the	DET
ajst-24468	48	19	convolution	convolution	NOUN
ajst-24468	48	20	kernel	kernel	NOUN
ajst-24468	48	21	to	to	PART
ajst-24468	48	22	adaptively	adaptively	ADV
ajst-24468	48	23	adjust	adjust	VERB
ajst-24468	48	24	the	the	DET
ajst-24468	48	25	sampling	sampling	NOUN
ajst-24468	48	26	position	position	NOUN
ajst-24468	48	27	and	and	CCONJ
ajst-24468	48	28	size	size	NOUN
ajst-24468	48	29	according	accord	VERB
ajst-24468	48	30	to	to	ADP
ajst-24468	48	31	the	the	DET
ajst-24468	48	32	shape	shape	NOUN
ajst-24468	48	33	and	and	CCONJ
ajst-24468	48	34	size	size	NOUN
ajst-24468	48	35	of	of	ADP
ajst-24468	48	36	the	the	DET
ajst-24468	48	37	object	object	NOUN
ajst-24468	48	38	during	during	ADP
ajst-24468	48	39	training	training	NOUN
ajst-24468	48	40	by	by	ADP
ajst-24468	48	41	adding	add	VERB
ajst-24468	48	42	an	an	DET
ajst-24468	48	43	offset	offset	NOUN
ajst-24468	48	44	to	to	ADP
ajst-24468	48	45	the	the	DET
ajst-24468	48	46	standard	standard	ADJ
ajst-24468	48	47	convolution	convolution	NOUN
ajst-24468	48	48	operation	operation	NOUN
ajst-24468	48	49	.	.	PUNCT
ajst-24468	49	1	in	in	ADP
ajst-24468	49	2	this	this	DET
ajst-24468	49	3	way	way	NOUN
ajst-24468	49	4	,	,	PUNCT
ajst-24468	49	5	the	the	DET
ajst-24468	49	6	deformable	deformable	ADJ
ajst-24468	49	7	convolution	convolution	NOUN
ajst-24468	49	8	is	be	AUX
ajst-24468	49	9	able	able	ADJ
ajst-24468	49	10	to	to	PART
ajst-24468	49	11	better	well	ADV
ajst-24468	49	12	capture	capture	VERB
ajst-24468	49	13	the	the	DET
ajst-24468	49	14	feature	feature	NOUN
ajst-24468	49	15	information	information	NOUN
ajst-24468	49	16	of	of	ADP
ajst-24468	49	17	the	the	DET
ajst-24468	49	18	object	object	NOUN
ajst-24468	49	19	,	,	PUNCT
ajst-24468	49	20	especially	especially	ADV
ajst-24468	49	21	when	when	SCONJ
ajst-24468	49	22	the	the	DET
ajst-24468	49	23	object	object	NOUN
ajst-24468	49	24	's	's	PART
ajst-24468	49	25	position	position	NOUN
ajst-24468	49	26	,	,	PUNCT
ajst-24468	49	27	size	size	NOUN
ajst-24468	49	28	,	,	PUNCT
ajst-24468	49	29	angle	angle	NOUN
ajst-24468	49	30	,	,	PUNCT
ajst-24468	49	31	etc	etc	X
ajst-24468	49	32	.	.	X
ajst-24468	50	1	in	in	ADP
ajst-24468	50	2	the	the	DET
ajst-24468	50	3	image	image	NOUN
ajst-24468	50	4	changes	change	NOUN
ajst-24468	50	5	.	.	PUNCT
ajst-24468	51	1	in	in	ADP
ajst-24468	51	2	the	the	DET
ajst-24468	51	3	yolov8	yolov8	NOUN
ajst-24468	51	4	model	model	NOUN
ajst-24468	51	5	,	,	PUNCT
ajst-24468	51	6	modifying	modify	VERB
ajst-24468	51	7	the	the	DET
ajst-24468	51	8	deformable	deformable	ADJ
ajst-24468	51	9	convolution	convolution	NOUN
ajst-24468	51	10	serves	serve	VERB
ajst-24468	51	11	the	the	DET
ajst-24468	51	12	following	follow	VERB
ajst-24468	51	13	purposes	purpose	NOUN
ajst-24468	51	14	:	:	PUNCT
ajst-24468	51	15	(	(	PUNCT
ajst-24468	51	16	1	1	X
ajst-24468	51	17	)	)	PUNCT
ajst-24468	51	18	improve	improve	VERB
ajst-24468	51	19	the	the	DET
ajst-24468	51	20	adaptability	adaptability	NOUN
ajst-24468	51	21	of	of	ADP
ajst-24468	51	22	the	the	DET
ajst-24468	51	23	model	model	NOUN
ajst-24468	51	24	:	:	PUNCT
ajst-24468	51	25	by	by	ADP
ajst-24468	51	26	introducing	introduce	VERB
ajst-24468	51	27	deformable	deformable	ADJ
ajst-24468	51	28	convolution	convolution	NOUN
ajst-24468	51	29	,	,	PUNCT
ajst-24468	51	30	the	the	DET
ajst-24468	51	31	yolov8	yolov8	NOUN
ajst-24468	51	32	model	model	NOUN
ajst-24468	51	33	can	can	AUX
ajst-24468	51	34	better	well	ADV
ajst-24468	51	35	adapt	adapt	VERB
ajst-24468	51	36	to	to	ADP
ajst-24468	51	37	different	different	ADJ
ajst-24468	51	38	detection	detection	NOUN
ajst-24468	51	39	targets	target	NOUN
ajst-24468	51	40	,	,	PUNCT
ajst-24468	51	41	thus	thus	ADV
ajst-24468	51	42	improving	improve	VERB
ajst-24468	51	43	the	the	DET
ajst-24468	51	44	accuracy	accuracy	NOUN
ajst-24468	51	45	of	of	ADP
ajst-24468	51	46	target	target	NOUN
ajst-24468	51	47	detection	detection	NOUN
ajst-24468	51	48	.	.	PUNCT
ajst-24468	52	1	especially	especially	ADV
ajst-24468	52	2	for	for	ADP
ajst-24468	52	3	objects	object	NOUN
ajst-24468	52	4	or	or	CCONJ
ajst-24468	52	5	features	feature	NOUN
ajst-24468	52	6	with	with	ADP
ajst-24468	52	7	irregular	irregular	ADJ
ajst-24468	52	8	shapes	shape	NOUN
ajst-24468	52	9	or	or	CCONJ
ajst-24468	52	10	large	large	ADJ
ajst-24468	52	11	deformation	deformation	NOUN
ajst-24468	52	12	,	,	PUNCT
ajst-24468	52	13	deformable	deformable	ADJ
ajst-24468	52	14	convolution	convolution	NOUN
ajst-24468	52	15	can	can	AUX
ajst-24468	52	16	better	well	ADV
ajst-24468	52	17	capture	capture	VERB
ajst-24468	52	18	their	their	PRON
ajst-24468	52	19	feature	feature	NOUN
ajst-24468	52	20	information	information	NOUN
ajst-24468	52	21	and	and	CCONJ
ajst-24468	52	22	improve	improve	VERB
ajst-24468	52	23	the	the	DET
ajst-24468	52	24	robustness	robustness	NOUN
ajst-24468	52	25	of	of	ADP
ajst-24468	52	26	detection	detection	NOUN
ajst-24468	52	27	.	.	PUNCT
ajst-24468	53	1	(	(	PUNCT
ajst-24468	53	2	2	2	X
ajst-24468	53	3	)	)	PUNCT
ajst-24468	53	4	expanding	expand	VERB
ajst-24468	53	5	the	the	DET
ajst-24468	53	6	sensing	sense	VERB
ajst-24468	53	7	field	field	NOUN
ajst-24468	53	8	:	:	PUNCT
ajst-24468	53	9	deformable	deformable	ADJ
ajst-24468	53	10	convolution	convolution	NOUN
ajst-24468	53	11	allows	allow	VERB
ajst-24468	53	12	the	the	DET
ajst-24468	53	13	convolution	convolution	NOUN
ajst-24468	53	14	kernel	kernel	NOUN
ajst-24468	53	15	to	to	PART
ajst-24468	53	16	be	be	AUX
ajst-24468	53	17	expanded	expand	VERB
ajst-24468	53	18	to	to	ADP
ajst-24468	53	19	a	a	DET
ajst-24468	53	20	larger	large	ADJ
ajst-24468	53	21	range	range	NOUN
ajst-24468	53	22	during	during	ADP
ajst-24468	53	23	the	the	DET
ajst-24468	53	24	training	training	NOUN
ajst-24468	53	25	process	process	NOUN
ajst-24468	53	26	,	,	PUNCT
ajst-24468	53	27	thus	thus	ADV
ajst-24468	53	28	increasing	increase	VERB
ajst-24468	53	29	the	the	DET
ajst-24468	53	30	model	model	NOUN
ajst-24468	53	31	's	's	PART
ajst-24468	53	32	ability	ability	NOUN
ajst-24468	53	33	to	to	PART
ajst-24468	53	34	sense	sense	VERB
ajst-24468	53	35	different	different	ADJ
ajst-24468	53	36	regions	region	NOUN
ajst-24468	53	37	and	and	CCONJ
ajst-24468	53	38	scales	scale	NOUN
ajst-24468	53	39	in	in	ADP
ajst-24468	53	40	the	the	DET
ajst-24468	53	41	image	image	NOUN
ajst-24468	53	42	.	.	PUNCT
ajst-24468	54	1	this	this	PRON
ajst-24468	54	2	helps	help	VERB
ajst-24468	54	3	the	the	DET
ajst-24468	54	4	model	model	NOUN
ajst-24468	54	5	to	to	PART
ajst-24468	54	6	better	well	ADV
ajst-24468	54	7	capture	capture	VERB
ajst-24468	54	8	contextual	contextual	ADJ
ajst-24468	54	9	information	information	NOUN
ajst-24468	54	10	in	in	ADP
ajst-24468	54	11	the	the	DET
ajst-24468	54	12	image	image	NOUN
ajst-24468	54	13	and	and	CCONJ
ajst-24468	54	14	improve	improve	VERB
ajst-24468	54	15	the	the	DET
ajst-24468	54	16	accuracy	accuracy	NOUN
ajst-24468	54	17	of	of	ADP
ajst-24468	54	18	target	target	NOUN
ajst-24468	54	19	detection	detection	NOUN
ajst-24468	54	20	.	.	PUNCT
ajst-24468	55	1	(	(	PUNCT
ajst-24468	55	2	3	3	X
ajst-24468	55	3	)	)	PUNCT
ajst-24468	55	4	flexibility	flexibility	NOUN
ajst-24468	55	5	:	:	PUNCT
ajst-24468	55	6	the	the	DET
ajst-24468	55	7	offset	offset	NOUN
ajst-24468	55	8	of	of	ADP
ajst-24468	55	9	the	the	DET
ajst-24468	55	10	deformable	deformable	ADJ
ajst-24468	55	11	convolution	convolution	NOUN
ajst-24468	55	12	is	be	AUX
ajst-24468	55	13	obtained	obtain	VERB
ajst-24468	55	14	through	through	ADP
ajst-24468	55	15	learning	learning	NOUN
ajst-24468	55	16	,	,	PUNCT
ajst-24468	55	17	which	which	PRON
ajst-24468	55	18	means	mean	VERB
ajst-24468	55	19	that	that	SCONJ
ajst-24468	55	20	the	the	DET
ajst-24468	55	21	model	model	NOUN
ajst-24468	55	22	can	can	AUX
ajst-24468	55	23	adaptively	adaptively	ADV
ajst-24468	55	24	adjust	adjust	VERB
ajst-24468	55	25	the	the	DET
ajst-24468	55	26	shape	shape	NOUN
ajst-24468	55	27	and	and	CCONJ
ajst-24468	55	28	size	size	NOUN
ajst-24468	55	29	of	of	ADP
ajst-24468	55	30	the	the	DET
ajst-24468	55	31	convolution	convolution	NOUN
ajst-24468	55	32	kernel	kernel	NOUN
ajst-24468	55	33	according	accord	VERB
ajst-24468	55	34	to	to	ADP
ajst-24468	55	35	the	the	DET
ajst-24468	55	36	characteristics	characteristic	NOUN
ajst-24468	55	37	of	of	ADP
ajst-24468	55	38	the	the	DET
ajst-24468	55	39	input	input	NOUN
ajst-24468	55	40	data	datum	NOUN
ajst-24468	55	41	,	,	PUNCT
ajst-24468	55	42	and	and	CCONJ
ajst-24468	55	43	it	it	PRON
ajst-24468	55	44	allows	allow	VERB
ajst-24468	55	45	the	the	DET
ajst-24468	55	46	yolov8	yolov8	PROPN
ajst-24468	55	47	model	model	NOUN
ajst-24468	55	48	to	to	PART
ajst-24468	55	49	better	well	ADV
ajst-24468	55	50	adapt	adapt	VERB
ajst-24468	55	51	to	to	ADP
ajst-24468	55	52	different	different	ADJ
ajst-24468	55	53	application	application	NOUN
ajst-24468	55	54	scenarios	scenario	NOUN
ajst-24468	55	55	and	and	CCONJ
ajst-24468	55	56	datasets	dataset	NOUN
ajst-24468	55	57	.	.	PUNCT
ajst-24468	56	1	3	3	X
ajst-24468	56	2	.	.	X
ajst-24468	56	3	system	system	NOUN
ajst-24468	56	4	model	model	NOUN
ajst-24468	56	5	3.1	3.1	NUM
ajst-24468	56	6	.	.	PUNCT
ajst-24468	57	1	yolov8	yolov8	NOUN
ajst-24468	57	2	model	model	PROPN
ajst-24468	57	3	optimization	optimization	NOUN
ajst-24468	57	4	(	(	PUNCT
ajst-24468	57	5	1	1	X
ajst-24468	57	6	)	)	PUNCT
ajst-24468	57	7	adding	add	VERB
ajst-24468	57	8	the	the	DET
ajst-24468	57	9	cbam	cbam	NOUN
ajst-24468	57	10	attention	attention	NOUN
ajst-24468	57	11	mechanism	mechanism	NOUN
ajst-24468	57	12	to	to	ADP
ajst-24468	57	13	the	the	DET
ajst-24468	57	14	yolov8	yolov8	NOUN
ajst-24468	57	15	model	model	NOUN
ajst-24468	57	16	can	can	AUX
ajst-24468	57	17	effectively	effectively	ADV
ajst-24468	57	18	improve	improve	VERB
ajst-24468	57	19	the	the	DET
ajst-24468	57	20	performance	performance	NOUN
ajst-24468	57	21	of	of	ADP
ajst-24468	57	22	the	the	DET
ajst-24468	57	23	model	model	NOUN
ajst-24468	57	24	,	,	PUNCT
ajst-24468	57	25	especially	especially	ADV
ajst-24468	57	26	in	in	ADP
ajst-24468	57	27	the	the	DET
ajst-24468	57	28	target	target	NOUN
ajst-24468	57	29	detection	detection	NOUN
ajst-24468	57	30	task	task	NOUN
ajst-24468	57	31	.	.	PUNCT
ajst-24468	58	1	by	by	ADP
ajst-24468	58	2	adding	add	VERB
ajst-24468	58	3	the	the	DET
ajst-24468	58	4	cbam	cbam	NOUN
ajst-24468	58	5	attention	attention	NOUN
ajst-24468	58	6	mechanism	mechanism	NOUN
ajst-24468	58	7	,	,	PUNCT
ajst-24468	58	8	the	the	DET
ajst-24468	58	9	yolov8	yolov8	NOUN
ajst-24468	58	10	model	model	NOUN
ajst-24468	58	11	can	can	AUX
ajst-24468	58	12	automatically	automatically	ADV
ajst-24468	58	13	evaluate	evaluate	VERB
ajst-24468	58	14	and	and	CCONJ
ajst-24468	58	15	learn	learn	VERB
ajst-24468	58	16	the	the	DET
ajst-24468	58	17	importance	importance	NOUN
ajst-24468	58	18	of	of	ADP
ajst-24468	58	19	each	each	DET
ajst-24468	58	20	channel	channel	NOUN
ajst-24468	58	21	,	,	PUNCT
ajst-24468	58	22	so	so	SCONJ
ajst-24468	58	23	as	as	SCONJ
ajst-24468	58	24	to	to	PART
ajst-24468	58	25	better	well	ADV
ajst-24468	58	26	distinguish	distinguish	VERB
ajst-24468	58	27	between	between	ADP
ajst-24468	58	28	foreground	foreground	NOUN
ajst-24468	58	29	and	and	CCONJ
ajst-24468	58	30	background	background	NOUN
ajst-24468	58	31	,	,	PUNCT
ajst-24468	58	32	and	and	CCONJ
ajst-24468	58	33	improve	improve	VERB
ajst-24468	58	34	the	the	DET
ajst-24468	58	35	accuracy	accuracy	NOUN
ajst-24468	58	36	and	and	CCONJ
ajst-24468	58	37	reliability	reliability	NOUN
ajst-24468	58	38	of	of	ADP
ajst-24468	58	39	target	target	NOUN
ajst-24468	58	40	detection	detection	NOUN
ajst-24468	58	41	.	.	PUNCT
ajst-24468	59	1	(	(	PUNCT
ajst-24468	59	2	2	2	X
ajst-24468	59	3	)	)	PUNCT
ajst-24468	59	4	modifying	modify	VERB
ajst-24468	59	5	the	the	DET
ajst-24468	59	6	deformable	deformable	ADJ
ajst-24468	59	7	convolution	convolution	NOUN
ajst-24468	59	8	in	in	ADP
ajst-24468	59	9	the	the	DET
ajst-24468	59	10	yolov8	yolov8	NOUN
ajst-24468	59	11	model	model	NOUN
ajst-24468	59	12	can	can	AUX
ajst-24468	59	13	improve	improve	VERB
ajst-24468	59	14	the	the	DET
ajst-24468	59	15	model	model	NOUN
ajst-24468	59	16	's	's	PART
ajst-24468	59	17	adaptability	adaptability	NOUN
ajst-24468	59	18	to	to	ADP
ajst-24468	59	19	changes	change	NOUN
ajst-24468	59	20	in	in	ADP
ajst-24468	59	21	the	the	DET
ajst-24468	59	22	shape	shape	NOUN
ajst-24468	59	23	and	and	CCONJ
ajst-24468	59	24	size	size	NOUN
ajst-24468	59	25	of	of	ADP
ajst-24468	59	26	objects	object	NOUN
ajst-24468	59	27	,	,	PUNCT
ajst-24468	59	28	thus	thus	ADV
ajst-24468	59	29	enhancing	enhance	VERB
ajst-24468	59	30	the	the	DET
ajst-24468	59	31	robustness	robustness	NOUN
ajst-24468	59	32	and	and	CCONJ
ajst-24468	59	33	accuracy	accuracy	NOUN
ajst-24468	59	34	of	of	ADP
ajst-24468	59	35	target	target	NOUN
ajst-24468	59	36	detection	detection	NOUN
ajst-24468	59	37	.	.	PUNCT
ajst-24468	60	1	in	in	ADP
ajst-24468	60	2	this	this	DET
ajst-24468	60	3	study	study	NOUN
ajst-24468	60	4	,	,	PUNCT
ajst-24468	60	5	the	the	DET
ajst-24468	60	6	dysnake	dysnake	NOUN
ajst-24468	60	7	-	-	PUNCT
ajst-24468	60	8	trunk	trunk	NOUN
ajst-24468	60	9	c3	c3	NOUN
ajst-24468	60	10	is	be	AUX
ajst-24468	60	11	replaced	replace	VERB
ajst-24468	60	12	with	with	ADP
ajst-24468	60	13	the	the	DET
ajst-24468	60	14	dcnv2	dcnv2	NOUN
ajst-24468	60	15	convolutional	convolutional	ADJ
ajst-24468	60	16	kernel	kernel	NOUN
ajst-24468	60	17	.	.	PUNCT
ajst-24468	61	1	by	by	ADP
ajst-24468	61	2	modifying	modify	VERB
ajst-24468	61	3	the	the	DET
ajst-24468	61	4	deformable	deformable	ADJ
ajst-24468	61	5	convolution	convolution	NOUN
ajst-24468	61	6	in	in	ADP
ajst-24468	61	7	the	the	DET
ajst-24468	61	8	yolov8	yolov8	NOUN
ajst-24468	61	9	model	model	NOUN
ajst-24468	61	10	,	,	PUNCT
ajst-24468	61	11	the	the	DET
ajst-24468	61	12	adaptability	adaptability	NOUN
ajst-24468	61	13	of	of	ADP
ajst-24468	61	14	the	the	DET
ajst-24468	61	15	model	model	NOUN
ajst-24468	61	16	,	,	PUNCT
ajst-24468	61	17	expanding	expand	VERB
ajst-24468	61	18	the	the	DET
ajst-24468	61	19	receptive	receptive	ADJ
ajst-24468	61	20	field	field	NOUN
ajst-24468	61	21	,	,	PUNCT
ajst-24468	61	22	and	and	CCONJ
ajst-24468	61	23	increasing	increase	VERB
ajst-24468	61	24	the	the	DET
ajst-24468	61	25	flexibility	flexibility	NOUN
ajst-24468	61	26	can	can	AUX
ajst-24468	61	27	be	be	AUX
ajst-24468	61	28	improved	improve	VERB
ajst-24468	61	29	,	,	PUNCT
ajst-24468	61	30	thus	thus	ADV
ajst-24468	61	31	enhancing	enhance	VERB
ajst-24468	61	32	the	the	DET
ajst-24468	61	33	accuracy	accuracy	NOUN
ajst-24468	61	34	and	and	CCONJ
ajst-24468	61	35	robustness	robustness	NOUN
ajst-24468	61	36	of	of	ADP
ajst-24468	61	37	target	target	NOUN
ajst-24468	61	38	detection	detection	NOUN
ajst-24468	61	39	.	.	PUNCT
ajst-24468	62	1	this	this	DET
ajst-24468	62	2	improvement	improvement	NOUN
ajst-24468	62	3	is	be	AUX
ajst-24468	62	4	especially	especially	ADV
ajst-24468	62	5	important	important	ADJ
ajst-24468	62	6	in	in	ADP
ajst-24468	62	7	target	target	NOUN
ajst-24468	62	8	detection	detection	NOUN
ajst-24468	62	9	tasks	task	NOUN
ajst-24468	62	10	in	in	ADP
ajst-24468	62	11	complex	complex	ADJ
ajst-24468	62	12	scenes	scene	NOUN
ajst-24468	62	13	and	and	CCONJ
ajst-24468	62	14	can	can	AUX
ajst-24468	62	15	help	help	VERB
ajst-24468	62	16	the	the	DET
ajst-24468	62	17	model	model	NOUN
ajst-24468	62	18	better	well	ADV
ajst-24468	62	19	cope	cope	VERB
ajst-24468	62	20	with	with	ADP
ajst-24468	62	21	various	various	ADJ
ajst-24468	62	22	challenges	challenge	NOUN
ajst-24468	62	23	.	.	PUNCT
ajst-24468	63	1	3.2	3.2	NUM
ajst-24468	63	2	.	.	PUNCT
ajst-24468	63	3	introduction	introduction	NOUN
ajst-24468	63	4	of	of	ADP
ajst-24468	63	5	the	the	DET
ajst-24468	63	6	dataset	dataset	NOUN
ajst-24468	63	7	the	the	DET
ajst-24468	63	8	source	source	NOUN
ajst-24468	63	9	of	of	ADP
ajst-24468	63	10	the	the	DET
ajst-24468	63	11	dataset	dataset	NOUN
ajst-24468	63	12	in	in	ADP
ajst-24468	63	13	this	this	DET
ajst-24468	63	14	paper	paper	NOUN
ajst-24468	63	15	's	's	PART
ajst-24468	63	16	experiments	experiment	NOUN
ajst-24468	63	17	is	be	AUX
ajst-24468	63	18	partly	partly	ADV
ajst-24468	63	19	the	the	DET
ajst-24468	63	20	road	road	NOUN
ajst-24468	63	21	defects	defect	NOUN
ajst-24468	63	22	voc	voc	NOUN
ajst-24468	63	23	dataset	dataset	VERB
ajst-24468	63	24	from	from	ADP
ajst-24468	63	25	the	the	DET
ajst-24468	63	26	public	public	ADJ
ajst-24468	63	27	dataset	dataset	NOUN
ajst-24468	63	28	on	on	ADP
ajst-24468	63	29	the	the	DET
ajst-24468	63	30	flying	fly	VERB
ajst-24468	63	31	paddle	paddle	NOUN
ajst-24468	63	32	(	(	PUNCT
ajst-24468	63	33	ai	ai	NOUN
ajst-24468	63	34	studio	studio	NOUN
ajst-24468	63	35	)	)	PUNCT
ajst-24468	63	36	community	community	NOUN
ajst-24468	63	37	,	,	PUNCT
ajst-24468	63	38	and	and	CCONJ
ajst-24468	63	39	partly	partly	ADV
ajst-24468	63	40	the	the	DET
ajst-24468	63	41	actual	actual	ADJ
ajst-24468	63	42	collected	collect	VERB
ajst-24468	63	43	images	image	NOUN
ajst-24468	63	44	.	.	PUNCT
ajst-24468	64	1	in	in	ADP
ajst-24468	64	2	this	this	DET
ajst-24468	64	3	road	road	NOUN
ajst-24468	64	4	surface	surface	NOUN
ajst-24468	64	5	defects	defect	NOUN
ajst-24468	64	6	dataset	dataset	VERB
ajst-24468	64	7	,	,	PUNCT
ajst-24468	64	8	four	four	NUM
ajst-24468	64	9	typical	typical	ADJ
ajst-24468	64	10	road	road	NOUN
ajst-24468	64	11	surface	surface	NOUN
ajst-24468	64	12	defects	defect	NOUN
ajst-24468	64	13	are	be	AUX
ajst-24468	64	14	collected	collect	VERB
ajst-24468	64	15	,	,	PUNCT
ajst-24468	64	16	namely	namely	ADV
ajst-24468	64	17	longitudinal	longitudinal	ADJ
ajst-24468	64	18	cracks	crack	NOUN
ajst-24468	64	19	,	,	PUNCT
ajst-24468	64	20	transverse	transverse	NOUN
ajst-24468	64	21	crack	crack	NOUN
ajst-24468	64	22	,	,	PUNCT
ajst-24468	64	23	alligator	alligator	NOUN
ajst-24468	64	24	crack	crack	NOUN
ajst-24468	64	25	and	and	CCONJ
ajst-24468	64	26	potholes	pothole	NOUN
ajst-24468	64	27	,	,	PUNCT
ajst-24468	64	28	each	each	PRON
ajst-24468	64	29	of	of	ADP
ajst-24468	64	30	which	which	PRON
ajst-24468	64	31	has	have	VERB
ajst-24468	64	32	a	a	DET
ajst-24468	64	33	certain	certain	ADJ
ajst-24468	64	34	number	number	NOUN
ajst-24468	64	35	of	of	ADP
ajst-24468	64	36	samples	sample	NOUN
ajst-24468	64	37	covering	cover	VERB
ajst-24468	64	38	defects	defect	NOUN
ajst-24468	64	39	of	of	ADP
ajst-24468	64	40	different	different	ADJ
ajst-24468	64	41	sizes	size	NOUN
ajst-24468	64	42	,	,	PUNCT
ajst-24468	64	43	shapes	shape	NOUN
ajst-24468	64	44	,	,	PUNCT
ajst-24468	64	45	and	and	CCONJ
ajst-24468	64	46	severity	severity	NOUN
ajst-24468	64	47	of	of	ADP
ajst-24468	64	48	defects	defect	NOUN
ajst-24468	64	49	.	.	PUNCT
ajst-24468	65	1	there	there	PRON
ajst-24468	65	2	are	be	VERB
ajst-24468	65	3	a	a	DET
ajst-24468	65	4	total	total	NOUN
ajst-24468	65	5	of	of	ADP
ajst-24468	65	6	3321	3321	NUM
ajst-24468	65	7	road	road	NOUN
ajst-24468	65	8	defect	defect	NOUN
ajst-24468	65	9	images	image	NOUN
ajst-24468	65	10	in	in	ADP
ajst-24468	65	11	the	the	DET
ajst-24468	65	12	dataset	dataset	NOUN
ajst-24468	65	13	,	,	PUNCT
ajst-24468	65	14	and	and	CCONJ
ajst-24468	65	15	in	in	ADP
ajst-24468	65	16	this	this	DET
ajst-24468	65	17	paper	paper	NOUN
ajst-24468	65	18	,	,	PUNCT
ajst-24468	65	19	the	the	DET
ajst-24468	65	20	dataset	dataset	NOUN
ajst-24468	65	21	is	be	AUX
ajst-24468	65	22	divided	divide	VERB
ajst-24468	65	23	into	into	ADP
ajst-24468	65	24	a	a	DET
ajst-24468	65	25	training	training	NOUN
ajst-24468	65	26	47	47	NUM
ajst-24468	65	27	set	set	NOUN
ajst-24468	65	28	,	,	PUNCT
ajst-24468	65	29	a	a	DET
ajst-24468	65	30	test	test	NOUN
ajst-24468	65	31	set	set	NOUN
ajst-24468	65	32	,	,	PUNCT
ajst-24468	65	33	and	and	CCONJ
ajst-24468	65	34	a	a	DET
ajst-24468	65	35	validation	validation	NOUN
ajst-24468	65	36	set	set	NOUN
ajst-24468	65	37	according	accord	VERB
ajst-24468	65	38	to	to	ADP
ajst-24468	65	39	the	the	DET
ajst-24468	65	40	ratio	ratio	NOUN
ajst-24468	65	41	of	of	ADP
ajst-24468	65	42	8:1:1	8:1:1	PROPN
ajst-24468	65	43	.	.	PUNCT
ajst-24468	66	1	the	the	DET
ajst-24468	66	2	four	four	NUM
ajst-24468	66	3	typical	typical	ADJ
ajst-24468	66	4	road	road	NOUN
ajst-24468	66	5	surface	surface	NOUN
ajst-24468	66	6	defects	defect	NOUN
ajst-24468	66	7	are	be	AUX
ajst-24468	66	8	shown	show	VERB
ajst-24468	66	9	in	in	ADP
ajst-24468	66	10	figure	figure	NOUN
ajst-24468	66	11	2	2	NUM
ajst-24468	66	12	.	.	PUNCT
ajst-24468	67	1	(	(	PUNCT
ajst-24468	67	2	a)longitudinal	a)longitudinal	ADJ
ajst-24468	67	3	cracks	crack	NOUN
ajst-24468	67	4	(	(	PUNCT
ajst-24468	67	5	b)transverse	b)transverse	NOUN
ajst-24468	67	6	cracks	crack	NOUN
ajst-24468	67	7	(	(	PUNCT
ajst-24468	67	8	c)alligator	c)alligator	NOUN
ajst-24468	67	9	crack	crack	NOUN
ajst-24468	67	10	(	(	PUNCT
ajst-24468	67	11	d)potholes	d)pothole	NOUN
ajst-24468	67	12	fig	fig	NOUN
ajst-24468	67	13	2	2	NUM
ajst-24468	67	14	.	.	NOUN
ajst-24468	67	15	four	four	NUM
ajst-24468	67	16	typical	typical	ADJ
ajst-24468	67	17	road	road	NOUN
ajst-24468	67	18	surface	surface	NOUN
ajst-24468	67	19	defects	defect	NOUN
ajst-24468	67	20	4	4	NUM
ajst-24468	67	21	.	.	PUNCT
ajst-24468	67	22	experimentation	experimentation	NOUN
ajst-24468	67	23	and	and	CCONJ
ajst-24468	67	24	analysis	analysis	NOUN
ajst-24468	67	25	4.1	4.1	NUM
ajst-24468	67	26	.	.	PUNCT
ajst-24468	68	1	environment	environment	NOUN
ajst-24468	68	2	configuration	configuration	NOUN
ajst-24468	68	3	prior	prior	ADV
ajst-24468	68	4	to	to	ADP
ajst-24468	68	5	model	model	NOUN
ajst-24468	68	6	training	training	NOUN
ajst-24468	68	7	,	,	PUNCT
ajst-24468	68	8	a	a	DET
ajst-24468	68	9	series	series	NOUN
ajst-24468	68	10	of	of	ADP
ajst-24468	68	11	preparations	preparation	NOUN
ajst-24468	68	12	are	be	AUX
ajst-24468	68	13	required	require	VERB
ajst-24468	68	14	to	to	PART
ajst-24468	68	15	ensure	ensure	VERB
ajst-24468	68	16	that	that	SCONJ
ajst-24468	68	17	the	the	DET
ajst-24468	68	18	training	training	NOUN
ajst-24468	68	19	process	process	NOUN
ajst-24468	68	20	runs	run	VERB
ajst-24468	68	21	smoothly	smoothly	ADV
ajst-24468	68	22	.	.	PUNCT
ajst-24468	69	1	a	a	DET
ajst-24468	69	2	brief	brief	ADJ
ajst-24468	69	3	description	description	NOUN
ajst-24468	69	4	of	of	ADP
ajst-24468	69	5	the	the	DET
ajst-24468	69	6	preparations	preparation	NOUN
ajst-24468	69	7	before	before	ADP
ajst-24468	69	8	training	train	VERB
ajst-24468	69	9	the	the	DET
ajst-24468	69	10	yolov8	yolov8	PROPN
ajst-24468	69	11	model	model	PROPN
ajst-24468	69	12	:	:	PUNCT
ajst-24468	69	13	environment	environment	NOUN
ajst-24468	69	14	configuration	configuration	NOUN
ajst-24468	69	15	:	:	PUNCT
ajst-24468	69	16	install	install	VERB
ajst-24468	69	17	python	python	NOUN
ajst-24468	69	18	and	and	CCONJ
ajst-24468	69	19	its	its	PRON
ajst-24468	69	20	related	related	ADJ
ajst-24468	69	21	libraries	library	NOUN
ajst-24468	69	22	,	,	PUNCT
ajst-24468	69	23	including	include	VERB
ajst-24468	69	24	pytorch	pytorch	NOUN
ajst-24468	69	25	and	and	CCONJ
ajst-24468	69	26	yolov8	yolov8	PROPN
ajst-24468	69	27	.	.	PUNCT
ajst-24468	70	1	install	install	PROPN
ajst-24468	70	2	cuda	cuda	NOUN
ajst-24468	70	3	and	and	CCONJ
ajst-24468	70	4	cudnn	cudnn	NOUN
ajst-24468	70	5	as	as	SCONJ
ajst-24468	70	6	needed	need	VERB
ajst-24468	70	7	,	,	PUNCT
ajst-24468	70	8	and	and	CCONJ
ajst-24468	70	9	ensure	ensure	VERB
ajst-24468	70	10	that	that	SCONJ
ajst-24468	70	11	the	the	DET
ajst-24468	70	12	python	python	NOUN
ajst-24468	70	13	environment	environment	NOUN
ajst-24468	70	14	is	be	AUX
ajst-24468	70	15	configured	configure	VERB
ajst-24468	70	16	correctly	correctly	ADV
ajst-24468	70	17	,	,	PUNCT
ajst-24468	70	18	including	include	VERB
ajst-24468	70	19	python	python	PROPN
ajst-24468	70	20	version	version	NOUN
ajst-24468	70	21	and	and	CCONJ
ajst-24468	70	22	library	library	NOUN
ajst-24468	70	23	dependencies	dependency	NOUN
ajst-24468	71	1	[	[	X
ajst-24468	71	2	16	16	NUM
ajst-24468	71	3	]	]	PUNCT
ajst-24468	71	4	.	.	PUNCT
ajst-24468	72	1	the	the	DET
ajst-24468	72	2	main	main	ADJ
ajst-24468	72	3	experimental	experimental	ADJ
ajst-24468	72	4	environment	environment	NOUN
ajst-24468	72	5	configuration	configuration	NOUN
ajst-24468	72	6	is	be	AUX
ajst-24468	72	7	shown	show	VERB
ajst-24468	72	8	in	in	ADP
ajst-24468	72	9	table	table	NOUN
ajst-24468	72	10	1	1	NUM
ajst-24468	72	11	.	.	PUNCT
ajst-24468	72	12	table	table	NOUN
ajst-24468	72	13	1	1	NUM
ajst-24468	72	14	.	.	PUNCT
ajst-24468	72	15	experimental	experimental	ADJ
ajst-24468	72	16	environment	environment	NOUN
ajst-24468	72	17	parameters	parameter	NOUN
ajst-24468	72	18	configuration	configuration	NOUN
ajst-24468	72	19	operating	operating	NOUN
ajst-24468	72	20	system	system	NOUN
ajst-24468	72	21	windows	window	VERB
ajst-24468	72	22	11	11	NUM
ajst-24468	72	23	cpu	cpu	NOUN
ajst-24468	72	24	amd	amd	NOUN
ajst-24468	72	25	ryzen	ryzen	ADJ
ajst-24468	72	26	5	5	NUM
ajst-24468	72	27	4600h	4600h	NUM
ajst-24468	72	28	with	with	ADP
ajst-24468	72	29	radeon	radeon	NOUN
ajst-24468	72	30	graphics	graphic	NOUN
ajst-24468	72	31	memory	memory	PROPN
ajst-24468	72	32	python	python	PROPN
ajst-24468	72	33	environment	environment	NOUN
ajst-24468	72	34	deep	deep	ADJ
ajst-24468	72	35	learning	learning	NOUN
ajst-24468	72	36	framework	framework	NOUN
ajst-24468	72	37	16	16	NUM
ajst-24468	72	38	g	g	PROPN
ajst-24468	72	39	3.8	3.8	NUM
ajst-24468	72	40	torch1.9.0+cu111	torch1.9.0+cu111	NOUN
ajst-24468	72	41	dataset	dataset	NOUN
ajst-24468	72	42	preparation	preparation	NOUN
ajst-24468	72	43	:	:	PUNCT
ajst-24468	72	44	prepare	prepare	VERB
ajst-24468	72	45	the	the	DET
ajst-24468	72	46	dataset	dataset	NOUN
ajst-24468	72	47	for	for	ADP
ajst-24468	72	48	road	road	NOUN
ajst-24468	72	49	defect	defect	NOUN
ajst-24468	72	50	detection	detection	NOUN
ajst-24468	72	51	,	,	PUNCT
ajst-24468	72	52	ensuring	ensure	VERB
ajst-24468	72	53	that	that	SCONJ
ajst-24468	72	54	the	the	DET
ajst-24468	72	55	dataset	dataset	NOUN
ajst-24468	72	56	contains	contain	VERB
ajst-24468	72	57	sufficient	sufficient	ADJ
ajst-24468	72	58	samples	sample	NOUN
ajst-24468	72	59	and	and	CCONJ
ajst-24468	72	60	diversity	diversity	NOUN
ajst-24468	72	61	.	.	PUNCT
ajst-24468	73	1	and	and	CCONJ
ajst-24468	73	2	annotate	annotate	VERB
ajst-24468	73	3	the	the	DET
ajst-24468	73	4	dataset	dataset	NOUN
ajst-24468	73	5	to	to	PART
ajst-24468	73	6	generate	generate	VERB
ajst-24468	73	7	annotation	annotation	NOUN
ajst-24468	73	8	files	file	NOUN
ajst-24468	73	9	compatible	compatible	ADJ
ajst-24468	73	10	with	with	ADP
ajst-24468	73	11	yolov8	yolov8	PROPN
ajst-24468	73	12	.	.	PUNCT
ajst-24468	74	1	configuration	configuration	NOUN
ajst-24468	74	2	file	file	NOUN
ajst-24468	74	3	preparation	preparation	NOUN
ajst-24468	74	4	:	:	PUNCT
ajst-24468	74	5	prepare	prepare	VERB
ajst-24468	74	6	model	model	NOUN
ajst-24468	74	7	configuration	configuration	NOUN
ajst-24468	74	8	files	file	NOUN
ajst-24468	74	9	,	,	PUNCT
ajst-24468	74	10	which	which	PRON
ajst-24468	74	11	define	define	VERB
ajst-24468	74	12	the	the	DET
ajst-24468	74	13	model	model	NOUN
ajst-24468	74	14	architecture	architecture	NOUN
ajst-24468	74	15	,	,	PUNCT
ajst-24468	74	16	training	training	NOUN
ajst-24468	74	17	parameters	parameter	NOUN
ajst-24468	74	18	,	,	PUNCT
ajst-24468	74	19	etc	etc	X
ajst-24468	74	20	.	.	X
ajst-24468	74	21	prepare	prepare	VERB
ajst-24468	74	22	dataset	dataset	NOUN
ajst-24468	74	23	configuration	configuration	NOUN
ajst-24468	74	24	files	file	NOUN
ajst-24468	74	25	,	,	PUNCT
ajst-24468	74	26	which	which	PRON
ajst-24468	74	27	specify	specify	VERB
ajst-24468	74	28	the	the	DET
ajst-24468	74	29	path	path	NOUN
ajst-24468	74	30	,	,	PUNCT
ajst-24468	74	31	category	category	NOUN
ajst-24468	74	32	,	,	PUNCT
ajst-24468	74	33	and	and	CCONJ
ajst-24468	74	34	other	other	ADJ
ajst-24468	74	35	information	information	NOUN
ajst-24468	74	36	of	of	ADP
ajst-24468	74	37	the	the	DET
ajst-24468	74	38	dataset	dataset	NOUN
ajst-24468	74	39	.	.	PUNCT
ajst-24468	75	1	adjust	adjust	VERB
ajst-24468	75	2	the	the	DET
ajst-24468	75	3	default	default	NOUN
ajst-24468	75	4	configuration	configuration	NOUN
ajst-24468	75	5	file	file	NOUN
ajst-24468	75	6	(	(	PUNCT
ajst-24468	75	7	default.yaml	default.yaml	PROPN
ajst-24468	75	8	)	)	PUNCT
ajst-24468	75	9	as	as	SCONJ
ajst-24468	75	10	needed	need	VERB
ajst-24468	75	11	to	to	PART
ajst-24468	75	12	fit	fit	VERB
ajst-24468	75	13	specific	specific	ADJ
ajst-24468	75	14	training	training	NOUN
ajst-24468	75	15	needs	need	NOUN
ajst-24468	75	16	.	.	PUNCT
ajst-24468	76	1	after	after	SCONJ
ajst-24468	76	2	all	all	DET
ajst-24468	76	3	preparations	preparation	NOUN
ajst-24468	76	4	are	be	AUX
ajst-24468	76	5	complete	complete	ADJ
ajst-24468	76	6	,	,	PUNCT
ajst-24468	76	7	start	start	VERB
ajst-24468	76	8	training	train	VERB
ajst-24468	76	9	the	the	DET
ajst-24468	76	10	model	model	NOUN
ajst-24468	76	11	.	.	PUNCT
ajst-24468	77	1	4.2	4.2	NUM
ajst-24468	77	2	.	.	PUNCT
ajst-24468	77	3	model	model	NOUN
ajst-24468	77	4	training	training	NOUN
ajst-24468	77	5	epochs	epoch	NOUN
ajst-24468	77	6	(	(	PUNCT
ajst-24468	77	7	number	number	NOUN
ajst-24468	77	8	of	of	ADP
ajst-24468	77	9	rounds	round	NOUN
ajst-24468	77	10	):	):	PUNCT
ajst-24468	77	11	the	the	DET
ajst-24468	77	12	complete	complete	ADJ
ajst-24468	77	13	number	number	NOUN
ajst-24468	77	14	of	of	ADP
ajst-24468	77	15	times	time	NOUN
ajst-24468	77	16	the	the	DET
ajst-24468	77	17	entire	entire	ADJ
ajst-24468	77	18	dataset	dataset	NOUN
ajst-24468	77	19	will	will	AUX
ajst-24468	77	20	be	be	AUX
ajst-24468	77	21	traversed	traverse	VERB
ajst-24468	77	22	.	.	PUNCT
ajst-24468	78	1	in	in	ADP
ajst-24468	78	2	this	this	DET
ajst-24468	78	3	paper	paper	NOUN
ajst-24468	78	4	,	,	PUNCT
ajst-24468	78	5	the	the	DET
ajst-24468	78	6	model	model	NOUN
ajst-24468	78	7	will	will	AUX
ajst-24468	78	8	traverse	traverse	VERB
ajst-24468	78	9	the	the	DET
ajst-24468	78	10	entire	entire	ADJ
ajst-24468	78	11	dataset	dataset	NOUN
ajst-24468	78	12	200	200	NUM
ajst-24468	78	13	times	time	NOUN
ajst-24468	78	14	.	.	PUNCT
ajst-24468	79	1	patience	patience	NOUN
ajst-24468	79	2	(	(	PUNCT
ajst-24468	79	3	number	number	NOUN
ajst-24468	79	4	of	of	ADP
ajst-24468	79	5	rounds	round	NOUN
ajst-24468	79	6	to	to	PART
ajst-24468	79	7	wait	wait	VERB
ajst-24468	79	8	for	for	ADP
ajst-24468	79	9	early	early	ADJ
ajst-24468	79	10	-	-	PUNCT
ajst-24468	79	11	stop	stop	NOUN
ajst-24468	79	12	training	training	NOUN
ajst-24468	79	13	):	):	PUNCT
ajst-24468	79	14	when	when	SCONJ
ajst-24468	79	15	using	use	VERB
ajst-24468	79	16	the	the	DET
ajst-24468	79	17	early	early	ADJ
ajst-24468	79	18	-	-	PUNCT
ajst-24468	79	19	stop	stop	NOUN
ajst-24468	79	20	strategy	strategy	NOUN
ajst-24468	79	21	,	,	PUNCT
ajst-24468	79	22	this	this	DET
ajst-24468	79	23	parameter	parameter	NOUN
ajst-24468	79	24	defines	define	VERB
ajst-24468	79	25	how	how	SCONJ
ajst-24468	79	26	many	many	ADJ
ajst-24468	79	27	epochs	epoch	NOUN
ajst-24468	79	28	the	the	DET
ajst-24468	79	29	model	model	NOUN
ajst-24468	79	30	needs	need	VERB
ajst-24468	79	31	to	to	PART
ajst-24468	79	32	wait	wait	VERB
ajst-24468	79	33	before	before	ADP
ajst-24468	79	34	stopping	stop	VERB
ajst-24468	79	35	training	training	NOUN
ajst-24468	79	36	when	when	SCONJ
ajst-24468	79	37	its	its	PRON
ajst-24468	79	38	performance	performance	NOUN
ajst-24468	79	39	on	on	ADP
ajst-24468	79	40	the	the	DET
ajst-24468	79	41	validation	validation	NOUN
ajst-24468	79	42	set	set	NOUN
ajst-24468	79	43	is	be	AUX
ajst-24468	79	44	no	no	ADV
ajst-24468	79	45	longer	long	ADV
ajst-24468	79	46	improving	improve	VERB
ajst-24468	79	47	.	.	PUNCT
ajst-24468	80	1	batch	batch	NOUN
ajst-24468	80	2	(	(	PUNCT
ajst-24468	80	3	number	number	NOUN
ajst-24468	80	4	of	of	ADP
ajst-24468	80	5	images	image	NOUN
ajst-24468	80	6	per	per	ADP
ajst-24468	80	7	batch	batch	NOUN
ajst-24468	80	8	):	):	PUNCT
ajst-24468	80	9	the	the	DET
ajst-24468	80	10	number	number	NOUN
ajst-24468	80	11	of	of	ADP
ajst-24468	80	12	samples	sample	NOUN
ajst-24468	80	13	used	use	VERB
ajst-24468	80	14	in	in	ADP
ajst-24468	80	15	a	a	DET
ajst-24468	80	16	weight	weight	NOUN
ajst-24468	80	17	update	update	NOUN
ajst-24468	80	18	.	.	PUNCT
ajst-24468	81	1	lr0	lr0	NOUN
ajst-24468	81	2	(	(	PUNCT
ajst-24468	81	3	initial	initial	ADJ
ajst-24468	81	4	learning	learning	NOUN
ajst-24468	81	5	rate	rate	NOUN
ajst-24468	81	6	):	):	PUNCT
ajst-24468	81	7	the	the	DET
ajst-24468	81	8	learning	learning	NOUN
ajst-24468	81	9	rate	rate	NOUN
ajst-24468	81	10	determines	determine	VERB
ajst-24468	81	11	the	the	DET
ajst-24468	81	12	step	step	NOUN
ajst-24468	81	13	size	size	NOUN
ajst-24468	81	14	of	of	ADP
ajst-24468	81	15	the	the	DET
ajst-24468	81	16	weight	weight	NOUN
ajst-24468	81	17	update	update	NOUN
ajst-24468	81	18	during	during	ADP
ajst-24468	81	19	training	training	NOUN
ajst-24468	81	20	of	of	ADP
ajst-24468	81	21	the	the	DET
ajst-24468	81	22	model	model	NOUN
ajst-24468	81	23	.	.	PUNCT
ajst-24468	82	1	lrf	lrf	NOUN
ajst-24468	82	2	(	(	PUNCT
ajst-24468	82	3	final	final	ADJ
ajst-24468	82	4	learning	learning	NOUN
ajst-24468	82	5	rate	rate	NOUN
ajst-24468	82	6	):	):	PUNCT
ajst-24468	82	7	this	this	DET
ajst-24468	82	8	parameter	parameter	NOUN
ajst-24468	82	9	defines	define	VERB
ajst-24468	82	10	the	the	DET
ajst-24468	82	11	minimum	minimum	ADJ
ajst-24468	82	12	value	value	NOUN
ajst-24468	82	13	that	that	PRON
ajst-24468	82	14	the	the	DET
ajst-24468	82	15	learning	learning	NOUN
ajst-24468	82	16	rate	rate	NOUN
ajst-24468	82	17	decays	decay	NOUN
ajst-24468	82	18	to	to	ADP
ajst-24468	82	19	.	.	PUNCT
ajst-24468	83	1	the	the	DET
ajst-24468	83	2	specific	specific	ADJ
ajst-24468	83	3	model	model	NOUN
ajst-24468	83	4	training	training	NOUN
ajst-24468	83	5	parameters	parameter	NOUN
ajst-24468	83	6	are	be	AUX
ajst-24468	83	7	shown	show	VERB
ajst-24468	83	8	in	in	ADP
ajst-24468	83	9	table	table	NOUN
ajst-24468	83	10	2	2	NUM
ajst-24468	83	11	.	.	PUNCT
ajst-24468	83	12	table	table	NOUN
ajst-24468	83	13	2	2	NUM
ajst-24468	83	14	.	.	PUNCT
ajst-24468	83	15	model	model	NOUN
ajst-24468	83	16	training	training	NOUN
ajst-24468	83	17	parameters	parameter	NOUN
ajst-24468	83	18	key	key	ADJ
ajst-24468	83	19	value	value	NOUN
ajst-24468	83	20	description	description	NOUN
ajst-24468	83	21	epochs	epoch	VERB
ajst-24468	83	22	200	200	NUM
ajst-24468	83	23	number	number	NOUN
ajst-24468	83	24	of	of	ADP
ajst-24468	83	25	training	training	NOUN
ajst-24468	83	26	rounds	round	VERB
ajst-24468	83	27	patience	patience	NOUN
ajst-24468	83	28	50	50	NUM
ajst-24468	83	29	number	number	NOUN
ajst-24468	83	30	of	of	ADP
ajst-24468	83	31	waiting	waiting	NOUN
ajst-24468	83	32	rounds	round	NOUN
ajst-24468	83	33	for	for	ADP
ajst-24468	83	34	early	early	ADJ
ajst-24468	83	35	stop	stop	NOUN
ajst-24468	83	36	training	training	NOUN
ajst-24468	83	37	batch	batch	NOUN
ajst-24468	83	38	16	16	NUM
ajst-24468	83	39	number	number	NOUN
ajst-24468	83	40	of	of	ADP
ajst-24468	83	41	images	image	NOUN
ajst-24468	83	42	per	per	ADP
ajst-24468	83	43	batch	batch	NOUN
ajst-24468	83	44	lr0	lr0	NOUN
ajst-24468	83	45	0.01	0.01	NUM
ajst-24468	83	46	initial	initial	ADJ
ajst-24468	83	47	learning	learning	NOUN
ajst-24468	83	48	rate	rate	NOUN
ajst-24468	83	49	lrf	lrf	NOUN
ajst-24468	83	50	0.01	0.01	NUM
ajst-24468	83	51	final	final	ADJ
ajst-24468	83	52	learning	learning	NOUN
ajst-24468	83	53	rate	rate	NOUN
ajst-24468	83	54	4.3	4.3	NUM
ajst-24468	83	55	.	.	PUNCT
ajst-24468	83	56	model	model	NOUN
ajst-24468	83	57	training	training	NOUN
ajst-24468	83	58	results	result	VERB
ajst-24468	83	59	the	the	DET
ajst-24468	83	60	model	model	NOUN
ajst-24468	83	61	training	training	NOUN
ajst-24468	83	62	results	result	NOUN
ajst-24468	83	63	of	of	ADP
ajst-24468	83	64	this	this	DET
ajst-24468	83	65	paper	paper	NOUN
ajst-24468	83	66	are	be	AUX
ajst-24468	83	67	shown	show	VERB
ajst-24468	83	68	in	in	ADP
ajst-24468	83	69	figure	figure	NOUN
ajst-24468	83	70	3	3	NUM
ajst-24468	83	71	.	.	PUNCT
ajst-24468	84	1	(	(	PUNCT
ajst-24468	84	2	1	1	X
ajst-24468	84	3	)	)	PUNCT
ajst-24468	84	4	weights	weight	NOUN
ajst-24468	84	5	file	file	VERB
ajst-24468	84	6	this	this	DET
ajst-24468	84	7	directory	directory	NOUN
ajst-24468	84	8	holds	hold	VERB
ajst-24468	84	9	two	two	NUM
ajst-24468	84	10	weights	weight	NOUN
ajst-24468	84	11	saved	save	VERB
ajst-24468	84	12	during	during	ADP
ajst-24468	84	13	training	training	NOUN
ajst-24468	84	14	,	,	PUNCT
ajst-24468	84	15	respectively	respectively	ADV
ajst-24468	84	16	:	:	PUNCT
ajst-24468	84	17	last.pt	last.pt	PROPN
ajst-24468	84	18	:	:	PUNCT
ajst-24468	84	19	"	"	PUNCT
ajst-24468	84	20	last.pt	last.pt	PROPN
ajst-24468	84	21	"	"	PUNCT
ajst-24468	84	22	usually	usually	ADV
ajst-24468	84	23	refers	refer	VERB
ajst-24468	84	24	to	to	ADP
ajst-24468	84	25	the	the	DET
ajst-24468	84	26	last	last	ADJ
ajst-24468	84	27	weights	weight	NOUN
ajst-24468	84	28	file	file	NOUN
ajst-24468	84	29	saved	save	VERB
ajst-24468	84	30	during	during	ADP
ajst-24468	84	31	model	model	NOUN
ajst-24468	84	32	training	training	NOUN
ajst-24468	84	33	.	.	PUNCT
ajst-24468	85	1	best.pt	best.pt	NOUN
ajst-24468	85	2	:	:	PUNCT
ajst-24468	85	3	"	"	PUNCT
ajst-24468	85	4	best.pt	best.pt	NOUN
ajst-24468	85	5	"	"	PUNCT
ajst-24468	85	6	usually	usually	ADV
ajst-24468	85	7	refers	refer	VERB
ajst-24468	85	8	to	to	ADP
ajst-24468	85	9	the	the	DET
ajst-24468	85	10	weight	weight	NOUN
ajst-24468	85	11	file	file	NOUN
ajst-24468	85	12	of	of	ADP
ajst-24468	85	13	the	the	DET
ajst-24468	85	14	model	model	NOUN
ajst-24468	85	15	that	that	PRON
ajst-24468	85	16	performs	perform	VERB
ajst-24468	85	17	best	well	ADV
ajst-24468	85	18	on	on	ADP
ajst-24468	85	19	the	the	DET
ajst-24468	85	20	validation	validation	NOUN
ajst-24468	85	21	set	set	NOUN
ajst-24468	85	22	or	or	CCONJ
ajst-24468	85	23	test	test	NOUN
ajst-24468	85	24	set	set	VERB
ajst-24468	85	25	.	.	PUNCT
ajst-24468	86	1	(	(	PUNCT
ajst-24468	86	2	2	2	X
ajst-24468	86	3	)	)	PUNCT
ajst-24468	86	4	confusion	confusion	NOUN
ajst-24468	86	5	matrix	matrix	NOUN
ajst-24468	86	6	the	the	DET
ajst-24468	86	7	confusion	confusion	NOUN
ajst-24468	86	8	matrix	matrix	NOUN
ajst-24468	86	9	is	be	AUX
ajst-24468	86	10	used	use	VERB
ajst-24468	86	11	to	to	PART
ajst-24468	86	12	evaluate	evaluate	VERB
ajst-24468	86	13	the	the	DET
ajst-24468	86	14	performance	performance	NOUN
ajst-24468	86	15	of	of	ADP
ajst-24468	86	16	the	the	DET
ajst-24468	86	17	model	model	NOUN
ajst-24468	86	18	,	,	PUNCT
ajst-24468	86	19	which	which	PRON
ajst-24468	86	20	can	can	AUX
ajst-24468	86	21	visualize	visualize	VERB
ajst-24468	86	22	the	the	DET
ajst-24468	86	23	classification	classification	NOUN
ajst-24468	86	24	of	of	ADP
ajst-24468	86	25	the	the	DET
ajst-24468	86	26	model	model	NOUN
ajst-24468	86	27	on	on	ADP
ajst-24468	86	28	each	each	DET
ajst-24468	86	29	category	category	NOUN
ajst-24468	86	30	.	.	PUNCT
ajst-24468	87	1	the	the	DET
ajst-24468	87	2	rows	row	NOUN
ajst-24468	87	3	of	of	ADP
ajst-24468	87	4	the	the	DET
ajst-24468	87	5	matrix	matrix	NOUN
ajst-24468	87	6	represent	represent	VERB
ajst-24468	87	7	the	the	DET
ajst-24468	87	8	actual	actual	ADJ
ajst-24468	87	9	categories	category	NOUN
ajst-24468	87	10	and	and	CCONJ
ajst-24468	87	11	the	the	DET
ajst-24468	87	12	columns	column	NOUN
ajst-24468	87	13	represent	represent	VERB
ajst-24468	87	14	the	the	DET
ajst-24468	87	15	predicted	predict	VERB
ajst-24468	87	16	categories	category	NOUN
ajst-24468	87	17	,	,	PUNCT
ajst-24468	87	18	and	and	CCONJ
ajst-24468	87	19	the	the	DET
ajst-24468	87	20	number	number	NOUN
ajst-24468	87	21	of	of	ADP
ajst-24468	87	22	different	different	ADJ
ajst-24468	87	23	category	category	NOUN
ajst-24468	87	24	combinations	combination	NOUN
ajst-24468	87	25	is	be	AUX
ajst-24468	87	26	obtained	obtain	VERB
ajst-24468	87	27	by	by	ADP
ajst-24468	87	28	comparing	compare	VERB
ajst-24468	87	29	and	and	CCONJ
ajst-24468	87	30	counting	count	VERB
ajst-24468	87	31	the	the	DET
ajst-24468	87	32	real	real	ADJ
ajst-24468	87	33	categories	category	NOUN
ajst-24468	87	34	of	of	ADP
ajst-24468	87	35	the	the	DET
ajst-24468	87	36	samples	sample	NOUN
ajst-24468	87	37	and	and	CCONJ
ajst-24468	87	38	the	the	DET
ajst-24468	87	39	categories	category	NOUN
ajst-24468	87	40	predicted	predict	VERB
ajst-24468	87	41	by	by	ADP
ajst-24468	87	42	the	the	DET
ajst-24468	87	43	model	model	NOUN
ajst-24468	87	44	.	.	PUNCT
ajst-24468	88	1	precision	precision	NOUN
ajst-24468	88	2	is	be	AUX
ajst-24468	88	3	the	the	DET
ajst-24468	88	4	proportion	proportion	NOUN
ajst-24468	88	5	of	of	ADP
ajst-24468	88	6	all	all	DET
ajst-24468	88	7	samples	sample	NOUN
ajst-24468	88	8	predicted	predict	VERB
ajst-24468	88	9	as	as	ADP
ajst-24468	88	10	positive	positive	ADJ
ajst-24468	88	11	by	by	ADP
ajst-24468	88	12	the	the	DET
ajst-24468	88	13	model	model	NOUN
ajst-24468	88	14	that	that	PRON
ajst-24468	88	15	are	be	AUX
ajst-24468	88	16	actually	actually	ADV
ajst-24468	88	17	positive	positive	ADJ
ajst-24468	88	18	,	,	PUNCT
ajst-24468	88	19	which	which	PRON
ajst-24468	88	20	measures	measure	VERB
ajst-24468	88	21	the	the	DET
ajst-24468	88	22	accuracy	accuracy	NOUN
ajst-24468	88	23	of	of	ADP
ajst-24468	88	24	the	the	DET
ajst-24468	88	25	model	model	NOUN
ajst-24468	88	26	in	in	ADP
ajst-24468	88	27	positive	positive	ADJ
ajst-24468	88	28	prediction	prediction	NOUN
ajst-24468	88	29	,	,	PUNCT
ajst-24468	88	30	and	and	CCONJ
ajst-24468	88	31	is	be	AUX
ajst-24468	88	32	given	give	VERB
ajst-24468	88	33	by	by	ADP
ajst-24468	88	34	the	the	DET
ajst-24468	88	35	formula	formula	NOUN
ajst-24468	88	36	precision	precision	NOUN
ajst-24468	89	1	=	=	SYM
ajst-24468	89	2	tp/(tp	tp/(tp	PROPN
ajst-24468	89	3	+	+	CCONJ
ajst-24468	89	4	fp	fp	X
ajst-24468	89	5	)	)	PUNCT
ajst-24468	89	6	(	(	PUNCT
ajst-24468	89	7	1	1	X
ajst-24468	89	8	)	)	PUNCT
ajst-24468	89	9	recall	recall	NOUN
ajst-24468	89	10	is	be	AUX
ajst-24468	89	11	the	the	DET
ajst-24468	89	12	proportion	proportion	NOUN
ajst-24468	89	13	of	of	ADP
ajst-24468	89	14	all	all	DET
ajst-24468	89	15	samples	sample	NOUN
ajst-24468	89	16	that	that	PRON
ajst-24468	89	17	are	be	AUX
ajst-24468	89	18	actually	actually	ADV
ajst-24468	89	19	positive	positive	ADJ
ajst-24468	89	20	cases	case	NOUN
ajst-24468	89	21	that	that	PRON
ajst-24468	89	22	are	be	AUX
ajst-24468	89	23	successfully	successfully	ADV
ajst-24468	89	24	predicted	predict	VERB
ajst-24468	89	25	to	to	PART
ajst-24468	89	26	be	be	AUX
ajst-24468	89	27	positive	positive	ADJ
ajst-24468	89	28	by	by	ADP
ajst-24468	89	29	the	the	DET
ajst-24468	89	30	model	model	NOUN
ajst-24468	89	31	,	,	PUNCT
ajst-24468	89	32	and	and	CCONJ
ajst-24468	89	33	it	it	PRON
ajst-24468	89	34	measures	measure	VERB
ajst-24468	89	35	the	the	DET
ajst-24468	89	36	model	model	NOUN
ajst-24468	89	37	's	's	PART
ajst-24468	89	38	ability	ability	NOUN
ajst-24468	89	39	to	to	PART
ajst-24468	89	40	recognize	recognize	VERB
ajst-24468	89	41	positive	positive	ADJ
ajst-24468	89	42	cases	case	NOUN
ajst-24468	89	43	,	,	PUNCT
ajst-24468	89	44	which	which	PRON
ajst-24468	89	45	is	be	AUX
ajst-24468	89	46	given	give	VERB
ajst-24468	89	47	by	by	ADP
ajst-24468	89	48	the	the	DET
ajst-24468	89	49	formula	formula	NOUN
ajst-24468	89	50	recall	recall	NOUN
ajst-24468	89	51	=	=	VERB
ajst-24468	89	52	tp	tp	NOUN
ajst-24468	89	53	/	/	PUNCT
ajst-24468	89	54	(	(	PUNCT
ajst-24468	89	55	tp	tp	X
ajst-24468	89	56	+	+	CCONJ
ajst-24468	89	57	fn	fn	NOUN
ajst-24468	89	58	)	)	PUNCT
ajst-24468	89	59	(	(	PUNCT
ajst-24468	89	60	2	2	X
ajst-24468	89	61	)	)	PUNCT
ajst-24468	89	62	48	48	NUM
ajst-24468	89	63	fig	fig	NOUN
ajst-24468	89	64	3	3	NUM
ajst-24468	89	65	.	.	PUNCT
ajst-24468	89	66	model	model	NOUN
ajst-24468	89	67	training	training	NOUN
ajst-24468	89	68	results	result	NOUN
ajst-24468	89	69	based	base	VERB
ajst-24468	89	70	on	on	ADP
ajst-24468	89	71	the	the	DET
ajst-24468	89	72	confusion	confusion	NOUN
ajst-24468	89	73	matrix	matrix	NOUN
ajst-24468	89	74	,	,	PUNCT
ajst-24468	89	75	we	we	PRON
ajst-24468	89	76	can	can	AUX
ajst-24468	89	77	make	make	VERB
ajst-24468	89	78	a	a	DET
ajst-24468	89	79	preliminary	preliminary	ADJ
ajst-24468	89	80	evaluation	evaluation	NOUN
ajst-24468	89	81	of	of	ADP
ajst-24468	89	82	the	the	DET
ajst-24468	89	83	performance	performance	NOUN
ajst-24468	89	84	of	of	ADP
ajst-24468	89	85	the	the	DET
ajst-24468	89	86	classifier	classifier	NOUN
ajst-24468	89	87	,	,	PUNCT
ajst-24468	89	88	and	and	CCONJ
ajst-24468	89	89	the	the	DET
ajst-24468	89	90	confusion	confusion	NOUN
ajst-24468	89	91	matrix	matrix	NOUN
ajst-24468	89	92	of	of	ADP
ajst-24468	89	93	the	the	DET
ajst-24468	89	94	model	model	NOUN
ajst-24468	89	95	trained	train	VERB
ajst-24468	89	96	in	in	ADP
ajst-24468	89	97	this	this	DET
ajst-24468	89	98	paper	paper	NOUN
ajst-24468	89	99	is	be	AUX
ajst-24468	89	100	shown	show	VERB
ajst-24468	89	101	in	in	ADP
ajst-24468	89	102	fig	fig	NOUN
ajst-24468	89	103	.	.	PUNCT
ajst-24468	90	1	4	4	X
ajst-24468	90	2	.	.	X
ajst-24468	90	3	fig	fig	NOUN
ajst-24468	90	4	4	4	NUM
ajst-24468	90	5	.	.	PUNCT
ajst-24468	90	6	confusion	confusion	NOUN
ajst-24468	90	7	matrix	matrix	NOUN
ajst-24468	90	8	(	(	PUNCT
ajst-24468	90	9	3	3	NUM
ajst-24468	90	10	)	)	PUNCT
ajst-24468	90	11	f1	f1	NOUN
ajst-24468	90	12	curve	curve	NOUN
ajst-24468	90	13	the	the	DET
ajst-24468	90	14	f1	f1	PROPN
ajst-24468	90	15	curve	curve	NOUN
ajst-24468	90	16	is	be	AUX
ajst-24468	90	17	a	a	DET
ajst-24468	90	18	performance	performance	NOUN
ajst-24468	90	19	evaluation	evaluation	NOUN
ajst-24468	90	20	tool	tool	NOUN
ajst-24468	90	21	commonly	commonly	ADV
ajst-24468	90	22	used	use	VERB
ajst-24468	90	23	in	in	ADP
ajst-24468	90	24	multicategorization	multicategorization	NOUN
ajst-24468	90	25	problems	problem	NOUN
ajst-24468	90	26	.	.	PUNCT
ajst-24468	91	1	it	it	PRON
ajst-24468	91	2	is	be	AUX
ajst-24468	91	3	based	base	VERB
ajst-24468	91	4	on	on	ADP
ajst-24468	91	5	the	the	DET
ajst-24468	91	6	f1	f1	NOUN
ajst-24468	91	7	score	score	NOUN
ajst-24468	91	8	,	,	PUNCT
ajst-24468	91	9	which	which	PRON
ajst-24468	91	10	is	be	AUX
ajst-24468	91	11	the	the	DET
ajst-24468	91	12	harmonic	harmonic	ADJ
ajst-24468	91	13	mean	mean	NOUN
ajst-24468	91	14	of	of	ADP
ajst-24468	91	15	precision	precision	NOUN
ajst-24468	91	16	and	and	CCONJ
ajst-24468	91	17	recall	recall	NOUN
ajst-24468	91	18	,	,	PUNCT
ajst-24468	91	19	and	and	CCONJ
ajst-24468	91	20	takes	take	VERB
ajst-24468	91	21	values	value	NOUN
ajst-24468	91	22	between	between	ADP
ajst-24468	91	23	0	0	NUM
ajst-24468	91	24	and	and	CCONJ
ajst-24468	91	25	1	1	NUM
ajst-24468	91	26	.	.	PUNCT
ajst-24468	92	1	the	the	DET
ajst-24468	92	2	f1	f1	PROPN
ajst-24468	92	3	curve	curve	NOUN
ajst-24468	92	4	is	be	AUX
ajst-24468	92	5	defined	define	VERB
ajst-24468	92	6	as	as	ADP
ajst-24468	92	7	the	the	DET
ajst-24468	92	8	value	value	NOUN
ajst-24468	92	9	of	of	ADP
ajst-24468	92	10	f1	f1	NOUN
ajst-24468	92	11	that	that	PRON
ajst-24468	92	12	is	be	AUX
ajst-24468	92	13	closer	close	ADJ
ajst-24468	92	14	to	to	ADP
ajst-24468	92	15	1	1	NUM
ajst-24468	92	16	than	than	ADP
ajst-24468	92	17	the	the	DET
ajst-24468	92	18	f1	f1	PROPN
ajst-24468	92	19	curve	curve	NOUN
ajst-24468	92	20	.	.	PUNCT
ajst-24468	93	1	the	the	PRON
ajst-24468	93	2	closer	close	ADJ
ajst-24468	93	3	the	the	DET
ajst-24468	93	4	value	value	NOUN
ajst-24468	93	5	of	of	ADP
ajst-24468	93	6	f1	f1	NOUN
ajst-24468	93	7	is	be	AUX
ajst-24468	93	8	to	to	ADP
ajst-24468	93	9	1	1	NUM
ajst-24468	93	10	,	,	PUNCT
ajst-24468	93	11	the	the	PRON
ajst-24468	93	12	better	well	ADJ
ajst-24468	93	13	the	the	DET
ajst-24468	93	14	model	model	NOUN
ajst-24468	93	15	performance	performance	NOUN
ajst-24468	93	16	is	be	AUX
ajst-24468	93	17	,	,	PUNCT
ajst-24468	93	18	and	and	CCONJ
ajst-24468	93	19	f1	f1	NOUN
ajst-24468	93	20	is	be	AUX
ajst-24468	93	21	defined	define	VERB
ajst-24468	93	22	as	as	ADP
ajst-24468	93	23	∗	∗	NOUN
ajst-24468	93	24	∗	∗	NOUN
ajst-24468	93	25	(	(	PUNCT
ajst-24468	93	26	3	3	NUM
ajst-24468	93	27	)	)	PUNCT
ajst-24468	93	28	the	the	DET
ajst-24468	93	29	f1	f1	NOUN
ajst-24468	93	30	curves	curve	NOUN
ajst-24468	93	31	for	for	ADP
ajst-24468	93	32	the	the	DET
ajst-24468	93	33	models	model	NOUN
ajst-24468	93	34	trained	train	VERB
ajst-24468	93	35	in	in	ADP
ajst-24468	93	36	this	this	DET
ajst-24468	93	37	paper	paper	NOUN
ajst-24468	93	38	are	be	AUX
ajst-24468	93	39	shown	show	VERB
ajst-24468	93	40	below	below	ADP
ajst-24468	93	41	.	.	PUNCT
ajst-24468	94	1	fig	fig	NOUN
ajst-24468	94	2	5	5	NUM
ajst-24468	94	3	.	.	PUNCT
ajst-24468	95	1	f1_curve	f1_curve	NOUN
ajst-24468	95	2	(	(	PUNCT
ajst-24468	95	3	4	4	X
ajst-24468	95	4	)	)	PUNCT
ajst-24468	95	5	labels	label	NOUN
ajst-24468	95	6	(	(	PUNCT
ajst-24468	95	7	labeled	label	VERB
ajst-24468	95	8	statistical	statistical	ADJ
ajst-24468	95	9	charts	chart	NOUN
ajst-24468	95	10	)	)	PUNCT
ajst-24468	95	11	fig	fig	NOUN
ajst-24468	95	12	6	6	NUM
ajst-24468	95	13	.	.	PUNCT
ajst-24468	95	14	labels	label	NOUN
ajst-24468	95	15	49	49	NUM
ajst-24468	95	16	in	in	ADP
ajst-24468	95	17	figure	figure	NOUN
ajst-24468	95	18	6	6	NUM
ajst-24468	95	19	,	,	PUNCT
ajst-24468	95	20	they	they	PRON
ajst-24468	95	21	are	be	AUX
ajst-24468	95	22	listed	list	VERB
ajst-24468	95	23	in	in	ADP
ajst-24468	95	24	order	order	NOUN
ajst-24468	95	25	from	from	ADP
ajst-24468	95	26	left	leave	VERB
ajst-24468	95	27	to	to	ADP
ajst-24468	95	28	right	right	NOUN
ajst-24468	95	29	:	:	PUNCT
ajst-24468	95	30	palace	palace	NOUN
ajst-24468	95	31	1	1	NUM
ajst-24468	95	32	:	:	PUNCT
ajst-24468	95	33	the	the	DET
ajst-24468	95	34	amount	amount	NOUN
ajst-24468	95	35	of	of	ADP
ajst-24468	95	36	data	datum	NOUN
ajst-24468	95	37	in	in	ADP
ajst-24468	95	38	the	the	DET
ajst-24468	95	39	training	training	NOUN
ajst-24468	95	40	set	set	NOUN
ajst-24468	95	41	,	,	PUNCT
ajst-24468	95	42	showing	show	VERB
ajst-24468	95	43	the	the	DET
ajst-24468	95	44	number	number	NOUN
ajst-24468	95	45	of	of	ADP
ajst-24468	95	46	samples	sample	NOUN
ajst-24468	95	47	contained	contain	VERB
ajst-24468	95	48	in	in	ADP
ajst-24468	95	49	each	each	DET
ajst-24468	95	50	category	category	NOUN
ajst-24468	95	51	.	.	PUNCT
ajst-24468	96	1	palace	palace	NOUN
ajst-24468	96	2	2	2	NUM
ajst-24468	96	3	:	:	PUNCT
ajst-24468	96	4	size	size	NOUN
ajst-24468	96	5	and	and	CCONJ
ajst-24468	96	6	number	number	NOUN
ajst-24468	96	7	of	of	ADP
ajst-24468	96	8	boxes	box	NOUN
ajst-24468	96	9	,	,	PUNCT
ajst-24468	96	10	showing	show	VERB
ajst-24468	96	11	the	the	DET
ajst-24468	96	12	size	size	NOUN
ajst-24468	96	13	distribution	distribution	NOUN
ajst-24468	96	14	and	and	CCONJ
ajst-24468	96	15	corresponding	corresponding	ADJ
ajst-24468	96	16	number	number	NOUN
ajst-24468	96	17	of	of	ADP
ajst-24468	96	18	bounding	bound	VERB
ajst-24468	96	19	boxes	box	NOUN
ajst-24468	96	20	in	in	ADP
ajst-24468	96	21	the	the	DET
ajst-24468	96	22	training	training	NOUN
ajst-24468	96	23	set	set	NOUN
ajst-24468	96	24	.	.	PUNCT
ajst-24468	97	1	palace	palace	NOUN
ajst-24468	97	2	3	3	NUM
ajst-24468	97	3	:	:	PUNCT
ajst-24468	97	4	position	position	NOUN
ajst-24468	97	5	of	of	ADP
ajst-24468	97	6	the	the	DET
ajst-24468	97	7	center	center	NOUN
ajst-24468	97	8	point	point	NOUN
ajst-24468	97	9	relative	relative	ADJ
ajst-24468	97	10	to	to	ADP
ajst-24468	97	11	the	the	DET
ajst-24468	97	12	whole	whole	ADJ
ajst-24468	97	13	image	image	NOUN
ajst-24468	97	14	,	,	PUNCT
ajst-24468	97	15	describing	describe	VERB
ajst-24468	97	16	the	the	DET
ajst-24468	97	17	distribution	distribution	NOUN
ajst-24468	97	18	of	of	ADP
ajst-24468	97	19	the	the	DET
ajst-24468	97	20	position	position	NOUN
ajst-24468	97	21	of	of	ADP
ajst-24468	97	22	the	the	DET
ajst-24468	97	23	bounding	bounding	NOUN
ajst-24468	97	24	box	box	NOUN
ajst-24468	97	25	center	center	NOUN
ajst-24468	97	26	point	point	NOUN
ajst-24468	97	27	in	in	ADP
ajst-24468	97	28	the	the	DET
ajst-24468	97	29	image	image	NOUN
ajst-24468	97	30	.	.	PUNCT
ajst-24468	98	1	grid	grid	NOUN
ajst-24468	98	2	4	4	NUM
ajst-24468	98	3	:	:	PUNCT
ajst-24468	98	4	the	the	DET
ajst-24468	98	5	height	height	NOUN
ajst-24468	98	6	-	-	PUNCT
ajst-24468	98	7	to	to	ADP
ajst-24468	98	8	-	-	PUNCT
ajst-24468	98	9	width	width	NOUN
ajst-24468	98	10	ratio	ratio	NOUN
ajst-24468	98	11	of	of	ADP
ajst-24468	98	12	the	the	DET
ajst-24468	98	13	target	target	NOUN
ajst-24468	98	14	in	in	ADP
ajst-24468	98	15	the	the	DET
ajst-24468	98	16	image	image	NOUN
ajst-24468	98	17	relative	relative	ADJ
ajst-24468	98	18	to	to	ADP
ajst-24468	98	19	the	the	DET
ajst-24468	98	20	whole	whole	ADJ
ajst-24468	98	21	image	image	NOUN
ajst-24468	98	22	,	,	PUNCT
ajst-24468	98	23	reflecting	reflect	VERB
ajst-24468	98	24	the	the	DET
ajst-24468	98	25	distribution	distribution	NOUN
ajst-24468	98	26	of	of	ADP
ajst-24468	98	27	the	the	DET
ajst-24468	98	28	height	height	NOUN
ajst-24468	98	29	-	-	PUNCT
ajst-24468	98	30	to	to	ADP
ajst-24468	98	31	-	-	PUNCT
ajst-24468	98	32	width	width	NOUN
ajst-24468	98	33	ratio	ratio	NOUN
ajst-24468	98	34	of	of	ADP
ajst-24468	98	35	the	the	DET
ajst-24468	98	36	target	target	NOUN
ajst-24468	98	37	in	in	ADP
ajst-24468	98	38	the	the	DET
ajst-24468	98	39	training	training	NOUN
ajst-24468	98	40	set	set	NOUN
ajst-24468	98	41	.	.	PUNCT
ajst-24468	99	1	(	(	PUNCT
ajst-24468	99	2	5	5	X
ajst-24468	99	3	)	)	PUNCT
ajst-24468	99	4	labels_correlogram	labels_correlogram	NOUN
ajst-24468	99	5	(	(	PUNCT
ajst-24468	99	6	label	label	NOUN
ajst-24468	99	7	correlation	correlation	NOUN
ajst-24468	99	8	graph	graph	NOUN
ajst-24468	99	9	)	)	PUNCT
ajst-24468	99	10	the	the	DET
ajst-24468	99	11	results	result	NOUN
ajst-24468	99	12	of	of	ADP
ajst-24468	99	13	this	this	DET
ajst-24468	99	14	model	model	NOUN
ajst-24468	99	15	training	training	NOUN
ajst-24468	99	16	show	show	VERB
ajst-24468	99	17	the	the	DET
ajst-24468	99	18	distribution	distribution	NOUN
ajst-24468	99	19	of	of	ADP
ajst-24468	99	20	data	datum	NOUN
ajst-24468	99	21	with	with	ADP
ajst-24468	99	22	different	different	ADJ
ajst-24468	99	23	heights	height	NOUN
ajst-24468	99	24	and	and	CCONJ
ajst-24468	99	25	widths	width	NOUN
ajst-24468	99	26	,	,	PUNCT
ajst-24468	99	27	as	as	SCONJ
ajst-24468	99	28	shown	show	VERB
ajst-24468	99	29	in	in	ADP
ajst-24468	99	30	figure	figure	NOUN
ajst-24468	99	31	7	7	NUM
ajst-24468	99	32	.	.	PUNCT
ajst-24468	100	1	the	the	DET
ajst-24468	100	2	scatterplot	scatterplot	NOUN
ajst-24468	100	3	and	and	CCONJ
ajst-24468	100	4	histogram	histogram	NOUN
ajst-24468	100	5	on	on	ADP
ajst-24468	100	6	the	the	DET
ajst-24468	100	7	left	left	ADJ
ajst-24468	100	8	focus	focus	NOUN
ajst-24468	100	9	on	on	ADP
ajst-24468	100	10	the	the	DET
ajst-24468	100	11	distribution	distribution	NOUN
ajst-24468	100	12	of	of	ADP
ajst-24468	100	13	"	"	PUNCT
ajst-24468	100	14	height	height	NOUN
ajst-24468	100	15	"	"	PUNCT
ajst-24468	100	16	,	,	PUNCT
ajst-24468	100	17	while	while	SCONJ
ajst-24468	100	18	the	the	DET
ajst-24468	100	19	graph	graph	NOUN
ajst-24468	100	20	on	on	ADP
ajst-24468	100	21	the	the	DET
ajst-24468	100	22	right	right	NOUN
ajst-24468	100	23	focuses	focus	VERB
ajst-24468	100	24	on	on	ADP
ajst-24468	100	25	the	the	DET
ajst-24468	100	26	distribution	distribution	NOUN
ajst-24468	100	27	of	of	ADP
ajst-24468	100	28	"	"	PUNCT
ajst-24468	100	29	width	width	NOUN
ajst-24468	100	30	"	"	PUNCT
ajst-24468	100	31	.	.	PUNCT
ajst-24468	101	1	by	by	ADP
ajst-24468	101	2	further	far	ADV
ajst-24468	101	3	analyzing	analyze	VERB
ajst-24468	101	4	these	these	DET
ajst-24468	101	5	data	datum	NOUN
ajst-24468	101	6	,	,	PUNCT
ajst-24468	101	7	we	we	PRON
ajst-24468	101	8	can	can	AUX
ajst-24468	101	9	optimize	optimize	VERB
ajst-24468	101	10	the	the	DET
ajst-24468	101	11	model	model	NOUN
ajst-24468	101	12	parameters	parameter	NOUN
ajst-24468	101	13	and	and	CCONJ
ajst-24468	101	14	improve	improve	VERB
ajst-24468	101	15	the	the	DET
ajst-24468	101	16	prediction	prediction	NOUN
ajst-24468	101	17	accuracy	accuracy	NOUN
ajst-24468	101	18	and	and	CCONJ
ajst-24468	101	19	generalization	generalization	NOUN
ajst-24468	101	20	ability	ability	NOUN
ajst-24468	101	21	of	of	ADP
ajst-24468	101	22	the	the	DET
ajst-24468	101	23	model	model	NOUN
ajst-24468	101	24	.	.	PUNCT
ajst-24468	102	1	fig	fig	PROPN
ajst-24468	102	2	7	7	NUM
ajst-24468	102	3	.	.	PUNCT
ajst-24468	103	1	labels_correlogram	labels_correlogram	PROPN
ajst-24468	103	2	(	(	PUNCT
ajst-24468	103	3	6	6	NUM
ajst-24468	103	4	)	)	PUNCT
ajst-24468	103	5	p_curve	p_curve	NOUN
ajst-24468	103	6	(	(	PUNCT
ajst-24468	103	7	precision	precision	NOUN
ajst-24468	103	8	-	-	PUNCT
ajst-24468	103	9	confidence	confidence	NOUN
ajst-24468	103	10	curve	curve	NOUN
ajst-24468	103	11	)	)	PUNCT
ajst-24468	103	12	the	the	DET
ajst-24468	103	13	precision	precision	NOUN
ajst-24468	103	14	-	-	PUNCT
ajst-24468	103	15	confidence	confidence	NOUN
ajst-24468	103	16	curve	curve	NOUN
ajst-24468	103	17	(	(	PUNCT
ajst-24468	103	18	p_curve	p_curve	NOUN
ajst-24468	103	19	)	)	PUNCT
ajst-24468	103	20	plot	plot	NOUN
ajst-24468	103	21	of	of	ADP
ajst-24468	103	22	the	the	DET
ajst-24468	103	23	model	model	NOUN
ajst-24468	103	24	trained	train	VERB
ajst-24468	103	25	in	in	ADP
ajst-24468	103	26	this	this	DET
ajst-24468	103	27	paper	paper	NOUN
ajst-24468	103	28	is	be	AUX
ajst-24468	103	29	shown	show	VERB
ajst-24468	103	30	in	in	ADP
ajst-24468	103	31	fig	fig	NOUN
ajst-24468	103	32	.	.	PUNCT
ajst-24468	104	1	8	8	NUM
ajst-24468	104	2	,	,	PUNCT
ajst-24468	104	3	where	where	SCONJ
ajst-24468	104	4	the	the	DET
ajst-24468	104	5	horizontal	horizontal	ADJ
ajst-24468	104	6	coordinate	coordinate	NOUN
ajst-24468	104	7	represents	represent	VERB
ajst-24468	104	8	the	the	DET
ajst-24468	104	9	confidence	confidence	NOUN
ajst-24468	104	10	level	level	NOUN
ajst-24468	104	11	of	of	ADP
ajst-24468	104	12	the	the	DET
ajst-24468	104	13	detector	detector	NOUN
ajst-24468	104	14	and	and	CCONJ
ajst-24468	104	15	the	the	DET
ajst-24468	104	16	vertical	vertical	ADJ
ajst-24468	104	17	coordinate	coordinate	NOUN
ajst-24468	104	18	represents	represent	VERB
ajst-24468	104	19	the	the	DET
ajst-24468	104	20	precision	precision	NOUN
ajst-24468	104	21	(	(	PUNCT
ajst-24468	104	22	or	or	CCONJ
ajst-24468	104	23	recall	recall	NOUN
ajst-24468	104	24	)	)	PUNCT
ajst-24468	104	25	.	.	PUNCT
ajst-24468	105	1	the	the	DET
ajst-24468	105	2	shape	shape	NOUN
ajst-24468	105	3	and	and	CCONJ
ajst-24468	105	4	position	position	NOUN
ajst-24468	105	5	of	of	ADP
ajst-24468	105	6	the	the	DET
ajst-24468	105	7	curve	curve	NOUN
ajst-24468	105	8	reflect	reflect	VERB
ajst-24468	105	9	the	the	DET
ajst-24468	105	10	performance	performance	NOUN
ajst-24468	105	11	of	of	ADP
ajst-24468	105	12	the	the	DET
ajst-24468	105	13	detector	detector	NOUN
ajst-24468	105	14	at	at	ADP
ajst-24468	105	15	different	different	ADJ
ajst-24468	105	16	confidence	confidence	NOUN
ajst-24468	105	17	levels	level	NOUN
ajst-24468	105	18	.	.	PUNCT
ajst-24468	106	1	fig	fig	NOUN
ajst-24468	106	2	8	8	NUM
ajst-24468	106	3	.	.	PUNCT
ajst-24468	107	1	p_curve	p_curve	NOUN
ajst-24468	107	2	this	this	DET
ajst-24468	107	3	result	result	NOUN
ajst-24468	107	4	reflects	reflect	VERB
ajst-24468	107	5	the	the	DET
ajst-24468	107	6	efficiency	efficiency	NOUN
ajst-24468	107	7	and	and	CCONJ
ajst-24468	107	8	accuracy	accuracy	NOUN
ajst-24468	107	9	of	of	ADP
ajst-24468	107	10	the	the	DET
ajst-24468	107	11	model	model	NOUN
ajst-24468	107	12	on	on	ADP
ajst-24468	107	13	the	the	DET
ajst-24468	107	14	crack	crack	NOUN
ajst-24468	107	15	detection	detection	NOUN
ajst-24468	107	16	task	task	NOUN
ajst-24468	107	17	,	,	PUNCT
ajst-24468	107	18	especially	especially	ADV
ajst-24468	107	19	in	in	ADP
ajst-24468	107	20	the	the	DET
ajst-24468	107	21	high	high	ADJ
ajst-24468	107	22	confidence	confidence	NOUN
ajst-24468	107	23	interval	interval	NOUN
ajst-24468	107	24	,	,	PUNCT
ajst-24468	107	25	the	the	DET
ajst-24468	107	26	model	model	NOUN
ajst-24468	107	27	performs	perform	VERB
ajst-24468	107	28	well	well	ADV
ajst-24468	107	29	and	and	CCONJ
ajst-24468	107	30	provides	provide	VERB
ajst-24468	107	31	a	a	DET
ajst-24468	107	32	strong	strong	ADJ
ajst-24468	107	33	support	support	NOUN
ajst-24468	107	34	for	for	ADP
ajst-24468	107	35	the	the	DET
ajst-24468	107	36	automatic	automatic	ADJ
ajst-24468	107	37	identification	identification	NOUN
ajst-24468	107	38	and	and	CCONJ
ajst-24468	107	39	assessment	assessment	NOUN
ajst-24468	107	40	of	of	ADP
ajst-24468	107	41	road	road	NOUN
ajst-24468	107	42	cracks	crack	NOUN
ajst-24468	107	43	.	.	PUNCT
ajst-24468	108	1	(	(	PUNCT
ajst-24468	108	2	7	7	X
ajst-24468	108	3	)	)	PUNCT
ajst-24468	108	4	pr_curve	pr_curve	NOUN
ajst-24468	108	5	(	(	PUNCT
ajst-24468	108	6	precision	precision	NOUN
ajst-24468	108	7	-	-	PUNCT
ajst-24468	108	8	recall	recall	NOUN
ajst-24468	108	9	curve	curve	NOUN
ajst-24468	108	10	)	)	PUNCT
ajst-24468	108	11	the	the	DET
ajst-24468	108	12	precision	precision	NOUN
ajst-24468	108	13	-	-	PUNCT
ajst-24468	108	14	recall	recall	NOUN
ajst-24468	108	15	curve	curve	NOUN
ajst-24468	108	16	plot	plot	NOUN
ajst-24468	108	17	of	of	ADP
ajst-24468	108	18	this	this	DET
ajst-24468	108	19	model	model	NOUN
ajst-24468	108	20	training	training	NOUN
ajst-24468	108	21	result	result	NOUN
ajst-24468	108	22	demonstrates	demonstrate	VERB
ajst-24468	108	23	the	the	DET
ajst-24468	108	24	performance	performance	NOUN
ajst-24468	108	25	of	of	ADP
ajst-24468	108	26	the	the	DET
ajst-24468	108	27	model	model	NOUN
ajst-24468	108	28	in	in	ADP
ajst-24468	108	29	detecting	detect	VERB
ajst-24468	108	30	four	four	NUM
ajst-24468	108	31	different	different	ADJ
ajst-24468	108	32	types	type	NOUN
ajst-24468	108	33	of	of	ADP
ajst-24468	108	34	cracks	crack	NOUN
ajst-24468	108	35	(	(	PUNCT
ajst-24468	108	36	pothole	pothole	NOUN
ajst-24468	108	37	,	,	PUNCT
ajst-24468	108	38	alligator	alligator	NOUN
ajst-24468	108	39	cracking	cracking	NOUN
ajst-24468	108	40	,	,	PUNCT
ajst-24468	108	41	lateral	lateral	ADJ
ajst-24468	108	42	cracking	cracking	NOUN
ajst-24468	108	43	,	,	PUNCT
ajst-24468	108	44	and	and	CCONJ
ajst-24468	108	45	longitudinal	longitudinal	ADJ
ajst-24468	108	46	cracking	cracking	NOUN
ajst-24468	108	47	)	)	PUNCT
ajst-24468	108	48	,	,	PUNCT
ajst-24468	108	49	and	and	CCONJ
ajst-24468	108	50	the	the	DET
ajst-24468	108	51	results	result	NOUN
ajst-24468	108	52	are	be	AUX
ajst-24468	108	53	shown	show	VERB
ajst-24468	108	54	in	in	ADP
ajst-24468	108	55	fig	fig	NOUN
ajst-24468	108	56	.	.	PUNCT
ajst-24468	109	1	9	9	X
ajst-24468	109	2	.	.	X
ajst-24468	109	3	fig	fig	NOUN
ajst-24468	109	4	9	9	NUM
ajst-24468	109	5	.	.	PUNCT
ajst-24468	110	1	pr_curve	pr_curve	VERB
ajst-24468	110	2	(	(	PUNCT
ajst-24468	110	3	8)	8)	NUM
ajst-24468	110	4	r_curve	r_curve	NOUN
ajst-24468	110	5	(	(	PUNCT
ajst-24468	110	6	recall	recall	NOUN
ajst-24468	110	7	-	-	PUNCT
ajst-24468	110	8	confidence	confidence	NOUN
ajst-24468	110	9	curve	curve	NOUN
ajst-24468	110	10	)	)	PUNCT
ajst-24468	110	11	recall	recall	NOUN
ajst-24468	110	12	indicates	indicate	VERB
ajst-24468	110	13	the	the	DET
ajst-24468	110	14	proportion	proportion	NOUN
ajst-24468	110	15	of	of	ADP
ajst-24468	110	16	all	all	DET
ajst-24468	110	17	positive	positive	ADJ
ajst-24468	110	18	examples	example	NOUN
ajst-24468	110	19	that	that	PRON
ajst-24468	110	20	are	be	AUX
ajst-24468	110	21	correctly	correctly	ADV
ajst-24468	110	22	predicted	predict	VERB
ajst-24468	110	23	as	as	ADP
ajst-24468	110	24	positive	positive	ADJ
ajst-24468	110	25	,	,	PUNCT
ajst-24468	110	26	while	while	SCONJ
ajst-24468	110	27	confidence	confidence	NOUN
ajst-24468	110	28	reflects	reflect	VERB
ajst-24468	110	29	how	how	SCONJ
ajst-24468	110	30	certain	certain	ADJ
ajst-24468	110	31	the	the	DET
ajst-24468	110	32	model	model	NOUN
ajst-24468	110	33	is	be	AUX
ajst-24468	110	34	about	about	ADP
ajst-24468	110	35	its	its	PRON
ajst-24468	110	36	predictions	prediction	NOUN
ajst-24468	110	37	.	.	PUNCT
ajst-24468	111	1	with	with	ADP
ajst-24468	111	2	figure	figure	NOUN
ajst-24468	111	3	10	10	NUM
ajst-24468	111	4	,	,	PUNCT
ajst-24468	111	5	we	we	PRON
ajst-24468	111	6	can	can	AUX
ajst-24468	111	7	observe	observe	VERB
ajst-24468	111	8	that	that	SCONJ
ajst-24468	111	9	the	the	DET
ajst-24468	111	10	recall	recall	NOUN
ajst-24468	111	11	is	be	AUX
ajst-24468	111	12	decreasing	decrease	VERB
ajst-24468	111	13	as	as	ADP
ajst-24468	111	14	the	the	DET
ajst-24468	111	15	model	model	NOUN
ajst-24468	111	16	's	's	PART
ajst-24468	111	17	confidence	confidence	NOUN
ajst-24468	111	18	in	in	ADP
ajst-24468	111	19	its	its	PRON
ajst-24468	111	20	predictions	prediction	NOUN
ajst-24468	111	21	increases	increase	NOUN
ajst-24468	111	22	.	.	PUNCT
ajst-24468	112	1	fig	fig	NOUN
ajst-24468	112	2	10	10	NUM
ajst-24468	112	3	.	.	PUNCT
ajst-24468	113	1	r_curve	r_curve	NOUN
ajst-24468	113	2	(	(	PUNCT
ajst-24468	113	3	9	9	NUM
ajst-24468	113	4	)	)	PUNCT
ajst-24468	113	5	results	result	NOUN
ajst-24468	113	6	(	(	PUNCT
ajst-24468	113	7	loss	loss	NOUN
ajst-24468	113	8	function	function	NOUN
ajst-24468	113	9	)	)	PUNCT
ajst-24468	113	10	as	as	SCONJ
ajst-24468	113	11	can	can	AUX
ajst-24468	113	12	be	be	AUX
ajst-24468	113	13	seen	see	VERB
ajst-24468	113	14	in	in	ADP
ajst-24468	113	15	fig	fig	NOUN
ajst-24468	113	16	.	.	PUNCT
ajst-24468	114	1	11	11	NUM
ajst-24468	114	2	,	,	PUNCT
ajst-24468	114	3	the	the	DET
ajst-24468	114	4	training	training	NOUN
ajst-24468	114	5	process	process	NOUN
ajst-24468	114	6	involves	involve	VERB
ajst-24468	114	7	losses	loss	NOUN
ajst-24468	114	8	in	in	ADP
ajst-24468	114	9	several	several	ADJ
ajst-24468	114	10	aspects	aspect	NOUN
ajst-24468	114	11	,	,	PUNCT
ajst-24468	114	12	which	which	PRON
ajst-24468	114	13	gradually	gradually	ADV
ajst-24468	114	14	decrease	decrease	VERB
ajst-24468	114	15	with	with	ADP
ajst-24468	114	16	the	the	DET
ajst-24468	114	17	increase	increase	NOUN
ajst-24468	114	18	of	of	ADP
ajst-24468	114	19	training	training	NOUN
ajst-24468	114	20	data	datum	NOUN
ajst-24468	114	21	points	point	NOUN
ajst-24468	114	22	,	,	PUNCT
ajst-24468	114	23	indicating	indicate	VERB
ajst-24468	114	24	that	that	SCONJ
ajst-24468	114	25	the	the	DET
ajst-24468	114	26	model	model	NOUN
ajst-24468	114	27	is	be	AUX
ajst-24468	114	28	being	be	AUX
ajst-24468	114	29	gradually	gradually	ADV
ajst-24468	114	30	optimized	optimize	VERB
ajst-24468	114	31	.	.	PUNCT
ajst-24468	115	1	at	at	ADP
ajst-24468	115	2	the	the	DET
ajst-24468	115	3	same	same	ADJ
ajst-24468	115	4	time	time	NOUN
ajst-24468	115	5	,	,	PUNCT
ajst-24468	115	6	the	the	DET
ajst-24468	115	7	precision	precision	NOUN
ajst-24468	115	8	and	and	CCONJ
ajst-24468	115	9	recall	recall	NOUN
ajst-24468	115	10	of	of	ADP
ajst-24468	115	11	the	the	DET
ajst-24468	115	12	model	model	NOUN
ajst-24468	115	13	on	on	ADP
ajst-24468	115	14	the	the	DET
ajst-24468	115	15	training	training	NOUN
ajst-24468	115	16	set	set	NOUN
ajst-24468	115	17	are	be	AUX
ajst-24468	115	18	gradually	gradually	ADV
ajst-24468	115	19	improving	improve	VERB
ajst-24468	115	20	,	,	PUNCT
ajst-24468	115	21	which	which	PRON
ajst-24468	115	22	indicates	indicate	VERB
ajst-24468	115	23	that	that	SCONJ
ajst-24468	115	24	the	the	DET
ajst-24468	115	25	accuracy	accuracy	NOUN
ajst-24468	115	26	of	of	ADP
ajst-24468	115	27	the	the	DET
ajst-24468	115	28	model	model	NOUN
ajst-24468	115	29	in	in	ADP
ajst-24468	115	30	recognizing	recognize	VERB
ajst-24468	115	31	the	the	DET
ajst-24468	115	32	target	target	NOUN
ajst-24468	115	33	is	be	AUX
ajst-24468	115	34	improving	improve	VERB
ajst-24468	115	35	.	.	PUNCT
ajst-24468	116	1	the	the	DET
ajst-24468	116	2	improvement	improvement	NOUN
ajst-24468	116	3	of	of	ADP
ajst-24468	116	4	these	these	DET
ajst-24468	116	5	two	two	NUM
ajst-24468	116	6	metrics	metric	NOUN
ajst-24468	116	7	indicates	indicate	VERB
ajst-24468	116	8	that	that	SCONJ
ajst-24468	116	9	the	the	DET
ajst-24468	116	10	model	model	NOUN
ajst-24468	116	11	is	be	AUX
ajst-24468	116	12	performing	perform	VERB
ajst-24468	116	13	better	well	ADV
ajst-24468	116	14	and	and	CCONJ
ajst-24468	116	15	better	well	ADJ
ajst-24468	116	16	on	on	ADP
ajst-24468	116	17	the	the	DET
ajst-24468	116	18	classification	classification	NOUN
ajst-24468	116	19	task	task	NOUN
ajst-24468	116	20	.	.	PUNCT
ajst-24468	117	1	4.4	4.4	NUM
ajst-24468	117	2	.	.	PUNCT
ajst-24468	118	1	model	model	NOUN
ajst-24468	118	2	performance	performance	NOUN
ajst-24468	118	3	comparison	comparison	NOUN
ajst-24468	118	4	in	in	ADP
ajst-24468	118	5	this	this	DET
ajst-24468	118	6	paper	paper	NOUN
ajst-24468	118	7	,	,	PUNCT
ajst-24468	118	8	the	the	DET
ajst-24468	118	9	original	original	ADJ
ajst-24468	118	10	yolov8	yolov8	NOUN
ajst-24468	118	11	model	model	NOUN
ajst-24468	118	12	is	be	AUX
ajst-24468	118	13	compared	compare	VERB
ajst-24468	118	14	with	with	ADP
ajst-24468	118	15	the	the	DET
ajst-24468	118	16	model	model	NOUN
ajst-24468	118	17	of	of	ADP
ajst-24468	118	18	yolov8	yolov8	NOUN
ajst-24468	118	19	with	with	ADP
ajst-24468	118	20	added	add	VERB
ajst-24468	118	21	cbam	cbam	NOUN
ajst-24468	118	22	attention	attention	NOUN
ajst-24468	118	23	mechanism	mechanism	NOUN
ajst-24468	118	24	and	and	CCONJ
ajst-24468	118	25	replaced	replace	VERB
ajst-24468	118	26	convolutional	convolutional	ADJ
ajst-24468	118	27	kernel	kernel	NOUN
ajst-24468	118	28	,	,	PUNCT
ajst-24468	118	29	and	and	CCONJ
ajst-24468	118	30	its	its	PRON
ajst-24468	118	31	training	training	NOUN
ajst-24468	118	32	results	result	NOUN
ajst-24468	118	33	are	be	AUX
ajst-24468	118	34	shown	show	VERB
ajst-24468	118	35	in	in	ADP
ajst-24468	118	36	table	table	NOUN
ajst-24468	118	37	3	3	NUM
ajst-24468	118	38	.	.	PUNCT
ajst-24468	119	1	the	the	DET
ajst-24468	119	2	results	result	NOUN
ajst-24468	119	3	show	show	VERB
ajst-24468	119	4	that	that	SCONJ
ajst-24468	119	5	the	the	DET
ajst-24468	119	6	50	50	NUM
ajst-24468	119	7	yolov8	yolov8	NOUN
ajst-24468	119	8	model	model	NOUN
ajst-24468	119	9	with	with	ADP
ajst-24468	119	10	added	add	VERB
ajst-24468	119	11	attention	attention	NOUN
ajst-24468	119	12	mechanism	mechanism	NOUN
ajst-24468	119	13	is	be	AUX
ajst-24468	119	14	better	well	ADJ
ajst-24468	119	15	than	than	ADP
ajst-24468	119	16	the	the	DET
ajst-24468	119	17	yolov8	yolov8	NOUN
ajst-24468	119	18	model	model	NOUN
ajst-24468	119	19	with	with	ADP
ajst-24468	119	20	replaced	replace	VERB
ajst-24468	119	21	convolutional	convolutional	ADJ
ajst-24468	119	22	kernel	kernel	NOUN
ajst-24468	119	23	and	and	CCONJ
ajst-24468	119	24	the	the	DET
ajst-24468	119	25	original	original	ADJ
ajst-24468	119	26	yolov8	yolov8	NOUN
ajst-24468	119	27	model	model	NOUN
ajst-24468	119	28	in	in	ADP
ajst-24468	119	29	terms	term	NOUN
ajst-24468	119	30	of	of	ADP
ajst-24468	119	31	real	real	ADJ
ajst-24468	119	32	-	-	PUNCT
ajst-24468	119	33	time	time	NOUN
ajst-24468	119	34	performance	performance	NOUN
ajst-24468	119	35	,	,	PUNCT
ajst-24468	119	36	accuracy	accuracy	NOUN
ajst-24468	119	37	,	,	PUNCT
ajst-24468	119	38	and	and	CCONJ
ajst-24468	119	39	training	training	NOUN
ajst-24468	119	40	stability	stability	NOUN
ajst-24468	119	41	.	.	PUNCT
ajst-24468	120	1	fig	fig	NOUN
ajst-24468	120	2	11	11	NUM
ajst-24468	120	3	.	.	PUNCT
ajst-24468	121	1	results	result	NOUN
ajst-24468	121	2	(	(	PUNCT
ajst-24468	121	3	loss	loss	NOUN
ajst-24468	121	4	function	function	NOUN
ajst-24468	121	5	)	)	PUNCT
ajst-24468	121	6	table	table	NOUN
ajst-24468	121	7	3	3	NUM
ajst-24468	121	8	.	.	PUNCT
ajst-24468	121	9	model	model	NOUN
ajst-24468	121	10	performance	performance	NOUN
ajst-24468	121	11	comparison	comparison	NOUN
ajst-24468	121	12	weighting	weight	VERB
ajst-24468	121	13	model	model	NOUN
ajst-24468	121	14	number	number	NOUN
ajst-24468	121	15	of	of	ADP
ajst-24468	121	16	iterations	iteration	NOUN
ajst-24468	121	17	training	training	NOUN
ajst-24468	121	18	batches	batch	NOUN
ajst-24468	121	19	p(%	p(%	NOUN
ajst-24468	121	20	)	)	PUNCT
ajst-24468	121	21	r(%	r(%	NOUN
ajst-24468	121	22	)	)	PUNCT
ajst-24468	121	23	map(%	map(%	NOUN
ajst-24468	121	24	)	)	PUNCT
ajst-24468	121	25	yolov8	yolov8	NOUN
ajst-24468	121	26	model	model	NOUN
ajst-24468	121	27	200	200	NUM
ajst-24468	121	28	16	16	NUM
ajst-24468	121	29	0.937	0.937	NUM
ajst-24468	121	30	0.80	0.80	NUM
ajst-24468	121	31	0.652	0.652	NUM
ajst-24468	121	32	replacement	replacement	NOUN
ajst-24468	121	33	of	of	ADP
ajst-24468	121	34	convolution	convolution	NOUN
ajst-24468	121	35	kernel	kernel	PROPN
ajst-24468	121	36	200	200	NUM
ajst-24468	121	37	16	16	NUM
ajst-24468	121	38	0.945	0.945	NUM
ajst-24468	121	39	0.79	0.79	NUM
ajst-24468	121	40	0.665	0.665	NUM
ajst-24468	121	41	add	add	VERB
ajst-24468	121	42	cbam	cbam	NOUN
ajst-24468	121	43	attention	attention	NOUN
ajst-24468	121	44	mechanism	mechanism	NOUN
ajst-24468	121	45	200	200	NUM
ajst-24468	121	46	16	16	NUM
ajst-24468	121	47	0.952	0.952	NUM
ajst-24468	121	48	0.81	0.81	NUM
ajst-24468	121	49	0.671	0.671	NUM
ajst-24468	121	50	5	5	NUM
ajst-24468	121	51	.	.	PUNCT
ajst-24468	122	1	summary	summary	NOUN
ajst-24468	122	2	in	in	ADP
ajst-24468	122	3	order	order	NOUN
ajst-24468	122	4	to	to	PART
ajst-24468	122	5	detect	detect	VERB
ajst-24468	122	6	road	road	NOUN
ajst-24468	122	7	defects	defect	NOUN
ajst-24468	122	8	more	more	ADV
ajst-24468	122	9	accurately	accurately	ADV
ajst-24468	122	10	and	and	CCONJ
ajst-24468	122	11	efficiently	efficiently	ADV
ajst-24468	122	12	,	,	PUNCT
ajst-24468	122	13	this	this	DET
ajst-24468	122	14	paper	paper	NOUN
ajst-24468	122	15	proposes	propose	VERB
ajst-24468	122	16	a	a	DET
ajst-24468	122	17	road	road	NOUN
ajst-24468	122	18	defect	defect	NOUN
ajst-24468	122	19	detection	detection	NOUN
ajst-24468	122	20	method	method	NOUN
ajst-24468	122	21	based	base	VERB
ajst-24468	122	22	on	on	ADP
ajst-24468	122	23	improved	improved	ADJ
ajst-24468	122	24	yolov8	yolov8	NOUN
ajst-24468	122	25	.	.	PUNCT
ajst-24468	123	1	the	the	DET
ajst-24468	123	2	original	original	ADJ
ajst-24468	123	3	yolov8	yolov8	NOUN
ajst-24468	123	4	model	model	NOUN
ajst-24468	123	5	is	be	AUX
ajst-24468	123	6	improved	improve	VERB
ajst-24468	123	7	by	by	ADP
ajst-24468	123	8	introducing	introduce	VERB
ajst-24468	123	9	the	the	DET
ajst-24468	123	10	cbam	cbam	NOUN
ajst-24468	123	11	attention	attention	NOUN
ajst-24468	123	12	mechanism	mechanism	NOUN
ajst-24468	123	13	and	and	CCONJ
ajst-24468	123	14	replacing	replace	VERB
ajst-24468	123	15	the	the	DET
ajst-24468	123	16	convolutional	convolutional	ADJ
ajst-24468	123	17	kernel	kernel	NOUN
ajst-24468	123	18	,	,	PUNCT
ajst-24468	123	19	and	and	CCONJ
ajst-24468	123	20	the	the	DET
ajst-24468	123	21	two	two	NUM
ajst-24468	123	22	improved	improved	ADJ
ajst-24468	123	23	models	model	NOUN
ajst-24468	123	24	and	and	CCONJ
ajst-24468	123	25	the	the	DET
ajst-24468	123	26	original	original	ADJ
ajst-24468	123	27	yolov8	yolov8	NOUN
ajst-24468	123	28	model	model	NOUN
ajst-24468	123	29	are	be	AUX
ajst-24468	123	30	trained	train	VERB
ajst-24468	123	31	on	on	ADP
ajst-24468	123	32	welllabeled	welllabele	VERB
ajst-24468	123	33	road	road	NOUN
ajst-24468	123	34	defects	defect	NOUN
ajst-24468	123	35	dataset	dataset	VERB
ajst-24468	123	36	respectively	respectively	ADV
ajst-24468	123	37	.	.	PUNCT
ajst-24468	124	1	finally	finally	ADV
ajst-24468	124	2	,	,	PUNCT
ajst-24468	124	3	the	the	DET
ajst-24468	124	4	training	training	NOUN
ajst-24468	124	5	results	result	NOUN
ajst-24468	124	6	of	of	ADP
ajst-24468	124	7	each	each	DET
ajst-24468	124	8	group	group	NOUN
ajst-24468	124	9	of	of	ADP
ajst-24468	124	10	models	model	NOUN
ajst-24468	124	11	are	be	AUX
ajst-24468	124	12	then	then	ADV
ajst-24468	124	13	compared	compare	VERB
ajst-24468	124	14	.	.	PUNCT
ajst-24468	125	1	the	the	DET
ajst-24468	125	2	results	result	NOUN
ajst-24468	125	3	show	show	VERB
ajst-24468	125	4	that	that	SCONJ
ajst-24468	125	5	the	the	DET
ajst-24468	125	6	improved	improved	ADJ
ajst-24468	125	7	models	model	NOUN
ajst-24468	125	8	reflect	reflect	VERB
ajst-24468	125	9	higher	high	ADJ
ajst-24468	125	10	accuracy	accuracy	NOUN
ajst-24468	125	11	in	in	ADP
ajst-24468	125	12	road	road	NOUN
ajst-24468	125	13	defect	defect	NOUN
ajst-24468	125	14	detection	detection	NOUN
ajst-24468	125	15	compared	compare	VERB
ajst-24468	125	16	with	with	ADP
ajst-24468	125	17	the	the	DET
ajst-24468	125	18	original	original	ADJ
ajst-24468	125	19	model	model	NOUN
ajst-24468	125	20	.	.	PUNCT
ajst-24468	126	1	among	among	ADP
ajst-24468	126	2	them	they	PRON
ajst-24468	126	3	,	,	PUNCT
ajst-24468	126	4	the	the	DET
ajst-24468	126	5	yolov8	yolov8	NOUN
ajst-24468	126	6	model	model	NOUN
ajst-24468	126	7	with	with	ADP
ajst-24468	126	8	the	the	DET
ajst-24468	126	9	added	add	VERB
ajst-24468	126	10	attention	attention	NOUN
ajst-24468	126	11	mechanism	mechanism	NOUN
ajst-24468	126	12	outperforms	outperform	VERB
ajst-24468	126	13	the	the	DET
ajst-24468	126	14	other	other	ADJ
ajst-24468	126	15	two	two	NUM
ajst-24468	126	16	models	model	NOUN
ajst-24468	126	17	in	in	ADP
ajst-24468	126	18	terms	term	NOUN
ajst-24468	126	19	of	of	ADP
ajst-24468	126	20	real	real	ADJ
ajst-24468	126	21	-	-	PUNCT
ajst-24468	126	22	time	time	NOUN
ajst-24468	126	23	performance	performance	NOUN
ajst-24468	126	24	,	,	PUNCT
ajst-24468	126	25	accuracy	accuracy	NOUN
ajst-24468	126	26	,	,	PUNCT
ajst-24468	126	27	and	and	CCONJ
ajst-24468	126	28	training	training	NOUN
ajst-24468	126	29	stability	stability	NOUN
ajst-24468	126	30	.	.	PUNCT
ajst-24468	127	1	the	the	DET
ajst-24468	127	2	method	method	NOUN
ajst-24468	127	3	proposed	propose	VERB
ajst-24468	127	4	in	in	ADP
ajst-24468	127	5	this	this	DET
ajst-24468	127	6	paper	paper	NOUN
ajst-24468	127	7	can	can	AUX
ajst-24468	127	8	identify	identify	VERB
ajst-24468	127	9	and	and	CCONJ
ajst-24468	127	10	detect	detect	VERB
ajst-24468	127	11	road	road	NOUN
ajst-24468	127	12	defects	defect	NOUN
ajst-24468	127	13	efficiently	efficiently	ADV
ajst-24468	127	14	,	,	PUNCT
ajst-24468	127	15	which	which	PRON
ajst-24468	127	16	is	be	AUX
ajst-24468	127	17	of	of	ADP
ajst-24468	127	18	great	great	ADJ
ajst-24468	127	19	significance	significance	NOUN
ajst-24468	127	20	in	in	ADP
ajst-24468	127	21	ensuring	ensure	VERB
ajst-24468	127	22	public	public	ADJ
ajst-24468	127	23	travel	travel	NOUN
ajst-24468	127	24	safety	safety	NOUN
ajst-24468	127	25	,	,	PUNCT
ajst-24468	127	26	optimizing	optimize	VERB
ajst-24468	127	27	traffic	traffic	NOUN
ajst-24468	127	28	operation	operation	NOUN
ajst-24468	127	29	,	,	PUNCT
ajst-24468	127	30	and	and	CCONJ
ajst-24468	127	31	saving	save	VERB
ajst-24468	127	32	resources	resource	NOUN
ajst-24468	127	33	.	.	PUNCT
ajst-24468	128	1	acknowledgments	acknowledgment	NOUN
ajst-24468	128	2	science	science	NOUN
ajst-24468	128	3	and	and	CCONJ
ajst-24468	128	4	technology	technology	NOUN
ajst-24468	128	5	research	research	NOUN
ajst-24468	128	6	project	project	NOUN
ajst-24468	128	7	of	of	ADP
ajst-24468	128	8	henan	henan	PROPN
ajst-24468	128	9	province	province	PROPN
ajst-24468	128	10	,	,	PUNCT
ajst-24468	128	11	research	research	NOUN
ajst-24468	128	12	on	on	ADP
ajst-24468	128	13	key	key	ADJ
ajst-24468	128	14	technologies	technology	NOUN
ajst-24468	128	15	of	of	ADP
ajst-24468	128	16	agricultural	agricultural	ADJ
ajst-24468	128	17	robot	robot	NOUN
ajst-24468	128	18	edge	edge	NOUN
ajst-24468	128	19	cloud	cloud	NOUN
ajst-24468	128	20	computing	computing	NOUN
ajst-24468	128	21	unloading	unloading	NOUN
ajst-24468	128	22	based	base	VERB
ajst-24468	128	23	on	on	ADP
ajst-24468	128	24	d2d	d2d	NOUN
ajst-24468	128	25	collaboration	collaboration	NOUN
ajst-24468	128	26	,	,	PUNCT
ajst-24468	128	27	242102210050	242102210050	NUM
ajst-24468	128	28	,	,	PUNCT
ajst-24468	128	29	yanyan	yanyan	ADJ
ajst-24468	128	30	wang	wang	PROPN
ajst-24468	128	31	(	(	PUNCT
ajst-24468	128	32	host	host	NOUN
ajst-24468	128	33	)	)	PUNCT
ajst-24468	128	34	;	;	PUNCT
ajst-24468	128	35	henan	henan	PROPN
ajst-24468	128	36	province	province	PROPN
ajst-24468	128	37	science	science	PROPN
ajst-24468	128	38	and	and	CCONJ
ajst-24468	128	39	technology	technology	NOUN
ajst-24468	128	40	research	research	NOUN
ajst-24468	128	41	project	project	NOUN
ajst-24468	128	42	,	,	PUNCT
ajst-24468	128	43	research	research	NOUN
ajst-24468	128	44	on	on	ADP
ajst-24468	128	45	key	key	ADJ
ajst-24468	128	46	technologies	technology	NOUN
ajst-24468	128	47	of	of	ADP
ajst-24468	128	48	abnormal	abnormal	ADJ
ajst-24468	128	49	behavior	behavior	NOUN
ajst-24468	128	50	detection	detection	NOUN
ajst-24468	128	51	for	for	ADP
ajst-24468	128	52	university	university	NOUN
ajst-24468	128	53	security	security	NOUN
ajst-24468	128	54	surveillance	surveillance	NOUN
ajst-24468	128	55	video	video	NOUN
ajst-24468	128	56	,	,	PUNCT
ajst-24468	128	57	2421	2421	NUM
ajst-24468	128	58	02210057	02210057	NUM
ajst-24468	128	59	,	,	PUNCT
ajst-24468	128	60	xue	xue	PROPN
ajst-24468	128	61	ran	run	VERB
ajst-24468	128	62	(	(	PUNCT
ajst-24468	128	63	host	host	NOUN
ajst-24468	128	64	)	)	PUNCT
ajst-24468	128	65	.	.	PUNCT
ajst-24468	129	1	references	reference	NOUN
ajst-24468	129	2	[	[	X
ajst-24468	129	3	1	1	NUM
ajst-24468	129	4	]	]	X
ajst-24468	129	5	li	li	PROPN
ajst-24468	129	6	wenwen	wenwen	PROPN
ajst-24468	129	7	.	.	PUNCT
ajst-24468	130	1	design	design	NOUN
ajst-24468	130	2	of	of	ADP
ajst-24468	130	3	road	road	NOUN
ajst-24468	130	4	defect	defect	NOUN
ajst-24468	130	5	detection	detection	NOUN
ajst-24468	130	6	and	and	CCONJ
ajst-24468	130	7	recognition	recognition	NOUN
ajst-24468	130	8	system	system	NOUN
ajst-24468	130	9	based	base	VERB
ajst-24468	130	10	on	on	ADP
ajst-24468	130	11	machine	machine	NOUN
ajst-24468	130	12	vision	vision	NOUN
ajst-24468	131	1	[	[	X
ajst-24468	131	2	d	d	X
ajst-24468	131	3	]	]	X
ajst-24468	131	4	.	.	PUNCT
ajst-24468	132	1	fujian	fujian	PROPN
ajst-24468	132	2	university	university	PROPN
ajst-24468	132	3	of	of	ADP
ajst-24468	132	4	technology	technology	NOUN
ajst-24468	132	5	,	,	PUNCT
ajst-24468	132	6	2023	2023	NUM
ajst-24468	132	7	.	.	PUNCT
ajst-24468	133	1	[	[	X
ajst-24468	133	2	2	2	NUM
ajst-24468	133	3	]	]	X
ajst-24468	133	4	feng	feng	PROPN
ajst-24468	133	5	shujie	shujie	PROPN
ajst-24468	133	6	.	.	PUNCT
ajst-24468	134	1	research	research	NOUN
ajst-24468	134	2	on	on	ADP
ajst-24468	134	3	pavement	pavement	NOUN
ajst-24468	134	4	defect	defect	NOUN
ajst-24468	134	5	detection	detection	NOUN
ajst-24468	134	6	algorithm	algorithm	NOUN
ajst-24468	134	7	based	base	VERB
ajst-24468	134	8	on	on	ADP
ajst-24468	134	9	deep	deep	ADJ
ajst-24468	134	10	learning	learning	NOUN
ajst-24468	135	1	[	[	X
ajst-24468	135	2	d	d	X
ajst-24468	135	3	]	]	X
ajst-24468	135	4	.	.	PUNCT
ajst-24468	136	1	shaanxi	shaanxi	PROPN
ajst-24468	136	2	university	university	PROPN
ajst-24468	136	3	of	of	ADP
ajst-24468	136	4	science	science	NOUN
ajst-24468	136	5	and	and	CCONJ
ajst-24468	136	6	technology,2023	technology,2023	NOUN
ajst-24468	136	7	.	.	PUNCT
ajst-24468	137	1	[	[	X
ajst-24468	137	2	3	3	NUM
ajst-24468	137	3	]	]	X
ajst-24468	137	4	munish	munish	NOUN
ajst-24468	137	5	r	r	NOUN
ajst-24468	137	6	,	,	PUNCT
ajst-24468	137	7	boris	boris	PROPN
ajst-24468	137	8	b	b	PROPN
ajst-24468	137	9	,	,	PUNCT
ajst-24468	137	10	maryam	maryam	PROPN
ajst-24468	137	11	d.	d.	PROPN
ajst-24468	137	12	automated	automate	VERB
ajst-24468	137	13	road	road	NOUN
ajst-24468	137	14	defect	defect	NOUN
ajst-24468	137	15	and	and	CCONJ
ajst-24468	137	16	anomaly	anomaly	NOUN
ajst-24468	137	17	detection	detection	NOUN
ajst-24468	137	18	for	for	ADP
ajst-24468	137	19	traffic	traffic	NOUN
ajst-24468	137	20	safety	safety	NOUN
ajst-24468	137	21	:	:	PUNCT
ajst-24468	137	22	a	a	DET
ajst-24468	137	23	systematic	systematic	ADJ
ajst-24468	137	24	review	review	NOUN
ajst-24468	137	25	.	.	PUNCT
ajst-24468	138	1	[	[	X
ajst-24468	138	2	j	j	X
ajst-24468	138	3	]	]	X
ajst-24468	138	4	.	.	PUNCT
ajst-24468	139	1	sensors	sensor	NOUN
ajst-24468	139	2	(	(	PUNCT
ajst-24468	139	3	basel	basel	PROPN
ajst-24468	139	4	,	,	PUNCT
ajst-24468	139	5	switzerland),2023,23(12	switzerland),2023,23(12	PROPN
ajst-24468	139	6	):	):	PUNCT
ajst-24468	139	7	[	[	X
ajst-24468	139	8	4	4	X
ajst-24468	139	9	]	]	X
ajst-24468	139	10	sun	sun	NOUN
ajst-24468	139	11	chaoyun	chaoyun	NOUN
ajst-24468	139	12	,	,	PUNCT
ajst-24468	139	13	pei	pei	PROPN
ajst-24468	139	14	lili	lili	PROPN
ajst-24468	139	15	,	,	PUNCT
ajst-24468	139	16	li	li	PROPN
ajst-24468	139	17	wei	wei	PROPN
ajst-24468	139	18	,	,	PUNCT
ajst-24468	139	19	et	et	PROPN
ajst-24468	139	20	al	al	PROPN
ajst-24468	139	21	.	.	PUNCT
ajst-24468	140	1	pavement	pavement	NOUN
ajst-24468	140	2	filling	fill	VERB
ajst-24468	140	3	crack	crack	NOUN
ajst-24468	140	4	detection	detection	NOUN
ajst-24468	140	5	method	method	NOUN
ajst-24468	140	6	based	base	VERB
ajst-24468	140	7	on	on	ADP
ajst-24468	140	8	improved	improved	ADJ
ajst-24468	140	9	faster	fast	ADV
ajst-24468	140	10	r	r	NOUN
ajst-24468	140	11	-	-	PUNCT
ajst-24468	140	12	cnn	cnn	NOUN
ajst-24468	141	1	[	[	X
ajst-24468	141	2	j	j	X
ajst-24468	141	3	]	]	X
ajst-24468	141	4	.	.	PUNCT
ajst-24468	142	1	journal	journal	PROPN
ajst-24468	142	2	of	of	ADP
ajst-24468	142	3	south	south	PROPN
ajst-24468	142	4	china	china	PROPN
ajst-24468	142	5	university	university	PROPN
ajst-24468	142	6	of	of	ADP
ajst-24468	142	7	technology	technology	NOUN
ajst-24468	142	8	(	(	PUNCT
ajst-24468	142	9	natural	natural	ADJ
ajst-24468	142	10	science	science	NOUN
ajst-24468	142	11	edition),20,48(02	edition),20,48(02	NOUN
ajst-24468	142	12	)	)	PUNCT
ajst-24468	142	13	;	;	PUNCT
ajst-24468	142	14	84	84	NUM
ajst-24468	142	15	-	-	SYM
ajst-24468	142	16	93	93	NUM
ajst-24468	142	17	.	.	PUNCT
ajst-24468	143	1	[	[	X
ajst-24468	143	2	5	5	NUM
ajst-24468	143	3	]	]	X
ajst-24468	143	4	jiang	jiang	PROPN
ajst-24468	143	5	dawei	dawei	PROPN
ajst-24468	143	6	,	,	PUNCT
ajst-24468	143	7	wu	wu	PROPN
ajst-24468	143	8	zhengping	zhengping	PROPN
ajst-24468	143	9	,	,	PUNCT
ajst-24468	143	10	jing	je	VERB
ajst-24468	143	11	siwei	siwei	NOUN
ajst-24468	143	12	.	.	PUNCT
ajst-24468	144	1	research	research	NOUN
ajst-24468	144	2	on	on	ADP
ajst-24468	144	3	road	road	NOUN
ajst-24468	144	4	defect	defect	NOUN
ajst-24468	144	5	detection	detection	NOUN
ajst-24468	144	6	and	and	CCONJ
ajst-24468	144	7	classification	classification	NOUN
ajst-24468	144	8	based	base	VERB
ajst-24468	144	9	on	on	ADP
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ajst-24468	144	11	yolov5	yolov5	NOUN
ajst-24468	145	1	[	[	X
ajst-24468	145	2	j	j	X
ajst-24468	145	3	]	]	X
ajst-24468	145	4	.	.	PUNCT
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ajst-24468	146	2	technology	technology	NOUN
ajst-24468	146	3	and	and	CCONJ
ajst-24468	146	4	informatization	informatization	NOUN
ajst-24468	146	5	,	,	PUNCT
ajst-24468	146	6	2024	2024	NUM
ajst-24468	146	7	(	(	PUNCT
ajst-24468	146	8	02	02	NUM
ajst-24468	146	9	)	)	PUNCT
ajst-24468	146	10	;	;	PUNCT
ajst-24468	146	11	31	31	NUM
ajst-24468	146	12	-	-	SYM
ajst-24468	146	13	34	34	NUM
ajst-24468	146	14	.	.	PUNCT
ajst-24468	147	1	[	[	X
ajst-24468	147	2	6	6	NUM
ajst-24468	147	3	]	]	PUNCT
ajst-24468	147	4	yan	yan	PROPN
ajst-24468	147	5	banfu	banfu	PROPN
ajst-24468	147	6	,	,	PUNCT
ajst-24468	147	7	xu	xu	PROPN
ajst-24468	147	8	guanya	guanya	PROPN
ajst-24468	147	9	,	,	PUNCT
ajst-24468	147	10	luan	luan	PROPN
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ajst-24468	147	15	.	.	PROPN
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ajst-24468	147	17	distress	distress	NOUN
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ajst-24468	147	19	based	base	VERB
ajst-24468	147	20	on	on	ADP
ajst-24468	147	21	faster	fast	ADJ
ajst-24468	147	22	r	r	NOUN
ajst-24468	147	23	-	-	PUNCT
ajst-24468	147	24	cnn	cnn	PROPN
ajst-24468	147	25	and	and	CCONJ
ajst-24468	147	26	morphological	morphological	ADJ
ajst-24468	147	27	method	method	NOUN
ajst-24468	147	28	[	[	X
ajst-24468	147	29	j	j	X
ajst-24468	147	30	]	]	X
ajst-24468	147	31	.	.	PUNCT
ajst-24468	148	1	china	china	PROPN
ajst-24468	148	2	journal	journal	PROPN
ajst-24468	148	3	of	of	ADP
ajst-24468	148	4	highway	highway	NOUN
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ajst-24468	148	6	transportation	transportation	NOUN
ajst-24468	148	7	,	,	PUNCT
ajst-24468	148	8	2021	2021	NUM
ajst-24468	148	9	,	,	PUNCT
ajst-24468	148	10	34(9):181	34(9):181	NUM
ajst-24468	148	11	-	-	SYM
ajst-24468	148	12	193	193	NUM
ajst-24468	148	13	.	.	PUNCT
ajst-24468	149	1	[	[	X
ajst-24468	149	2	7	7	X
ajst-24468	149	3	]	]	PUNCT
ajst-24468	149	4	yazhen	yazhen	PROPN
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ajst-24468	149	14	of	of	ADP
ajst-24468	149	15	pavement	pavement	NOUN
ajst-24468	149	16	marking	mark	VERB
ajst-24468	149	17	defects	defect	NOUN
ajst-24468	149	18	in	in	ADP
ajst-24468	149	19	road	road	NOUN
ajst-24468	149	20	inspection	inspection	NOUN
ajst-24468	149	21	images	image	NOUN
ajst-24468	149	22	using	use	VERB
ajst-24468	149	23	deep	deep	ADJ
ajst-24468	149	24	learning	learning	NOUN
ajst-24468	150	1	[	[	X
ajst-24468	150	2	j	j	X
ajst-24468	150	3	]	]	X
ajst-24468	150	4	.	.	PUNCT
ajst-24468	151	1	journal	journal	PROPN
ajst-24468	151	2	of	of	ADP
ajst-24468	151	3	performance	performance	NOUN
ajst-24468	151	4	of	of	ADP
ajst-24468	151	5	constructed	construct	VERB
ajst-24468	151	6	facilities	facility	NOUN
ajst-24468	151	7	,	,	PUNCT
ajst-24468	151	8	2024,38(2	2024,38(2	NUM
ajst-24468	151	9	)	)	PUNCT
ajst-24468	151	10	.	.	PUNCT
ajst-24468	152	1	[	[	X
ajst-24468	152	2	8	8	NUM
ajst-24468	152	3	]	]	X
ajst-24468	152	4	wang	wang	PROPN
ajst-24468	152	5	bo	bo	PROPN
ajst-24468	152	6	,	,	PUNCT
ajst-24468	152	7	li	li	PROPN
ajst-24468	152	8	qi	qi	PROPN
ajst-24468	152	9	,	,	PUNCT
ajst-24468	152	10	liu	liu	PROPN
ajst-24468	152	11	jiao	jiao	PROPN
ajst-24468	152	12	.	.	PUNCT
ajst-24468	153	1	a	a	DET
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ajst-24468	153	3	ssd	ssd	NOUN
ajst-24468	153	4	road	road	NOUN
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ajst-24468	154	1	[	[	X
ajst-24468	154	2	j	j	X
ajst-24468	154	3	]	]	X
ajst-24468	154	4	.	.	PUNCT
ajst-24468	155	1	journal	journal	PROPN
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ajst-24468	155	6	,	,	PUNCT
ajst-24468	155	7	36	36	NUM
ajst-24468	155	8	(	(	PUNCT
ajst-24468	155	9	4	4	NUM
ajst-24468	155	10	):	):	PUNCT
ajst-24468	155	11	83	83	NUM
ajst-24468	155	12	-	-	SYM
ajst-24468	155	13	90	90	NUM
ajst-24468	155	14	.	.	PUNCT
ajst-24468	156	1	[	[	X
ajst-24468	156	2	9	9	NUM
ajst-24468	156	3	]	]	X
ajst-24468	156	4	wang	wang	PROPN
ajst-24468	156	5	xueqiu	xueqiu	PROPN
ajst-24468	156	6	,	,	PUNCT
ajst-24468	156	7	gao	gao	PROPN
ajst-24468	156	8	huan	huan	PROPN
ajst-24468	156	9	bing	bing	PROPN
ajst-24468	156	10	,	,	PUNCT
ajst-24468	156	11	jia	jia	PROPN
ajst-24468	156	12	zemeng	zemeng	PROPN
ajst-24468	156	13	.	.	PUNCT
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ajst-24468	157	2	road	road	NOUN
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ajst-24468	158	1	[	[	X
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ajst-24468	158	5	]	]	PUNCT
ajst-24468	158	6	.	.	PUNCT
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ajst-24468	159	2	engineering	engineering	NOUN
ajst-24468	159	3	and	and	CCONJ
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ajst-24468	159	5	-	-	PUNCT
ajst-24468	159	6	16[2024	16[2024	NUM
ajst-24468	159	7	-	-	PUNCT
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ajst-24468	159	9	-	-	SYM
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ajst-24468	159	11	]	]	PUNCT
ajst-24468	159	12	.	.	PUNCT
ajst-24468	160	1	[	[	X
ajst-24468	160	2	10	10	NUM
ajst-24468	160	3	]	]	X
ajst-24468	160	4	cheng	cheng	PROPN
ajst-24468	160	5	z	z	PROPN
ajst-24468	160	6	,	,	PUNCT
ajst-24468	160	7	gang	gang	PROPN
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ajst-24468	160	9	,	,	PUNCT
ajst-24468	160	10	zekai	zekai	PROPN
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ajst-24468	160	12	,	,	PUNCT
ajst-24468	160	13	et	et	PROPN
ajst-24468	160	14	al.aal	al.aal	NOUN
ajst-24468	160	15	-	-	PUNCT
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ajst-24468	160	17	:	:	PUNCT
ajst-24468	160	18	a	a	DET
ajst-24468	160	19	lightweight	lightweight	ADJ
ajst-24468	160	20	detection	detection	NOUN
ajst-24468	160	21	method	method	NOUN
ajst-24468	160	22	for	for	ADP
ajst-24468	160	23	road	road	NOUN
ajst-24468	160	24	surface	surface	NOUN
ajst-24468	160	25	defects	defect	NOUN
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ajst-24468	160	28	attention	attention	NOUN
ajst-24468	160	29	and	and	CCONJ
ajst-24468	160	30	data	datum	NOUN
ajst-24468	160	31	augmentation[j	augmentation[j	PROPN
ajst-24468	160	32	]	]	PUNCT
ajst-24468	160	33	.	.	PUNCT
ajst-24468	161	1	applied	apply	VERB
ajst-24468	161	2	sciences	science	NOUN
ajst-24468	161	3	,	,	PUNCT
ajst-24468	161	4	2023	2023	NUM
ajst-24468	161	5	,	,	PUNCT
ajst-24468	161	6	13	13	NUM
ajst-24468	161	7	(	(	PUNCT
ajst-24468	161	8	3):1435	3):1435	NOUN
ajst-24468	161	9	-	-	PUNCT
ajst-24468	161	10	1435	1435	NUM
ajst-24468	161	11	.	.	PUNCT
ajst-24468	162	1	[	[	X
ajst-24468	162	2	11	11	NUM
ajst-24468	162	3	]	]	X
ajst-24468	162	4	li	li	PROPN
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ajst-24468	162	6	,	,	PUNCT
ajst-24468	162	7	li	li	PROPN
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ajst-24468	162	9	,	,	PUNCT
ajst-24468	162	10	chen	chen	PROPN
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ajst-24468	162	12	.	.	PUNCT
ajst-24468	163	1	road	road	NOUN
ajst-24468	163	2	defect	defect	NOUN
ajst-24468	163	3	detection	detection	NOUN
ajst-24468	163	4	based	base	VERB
ajst-24468	163	5	on	on	ADP
ajst-24468	163	6	image	image	NOUN
ajst-24468	163	7	point	point	NOUN
ajst-24468	163	8	cloud	cloud	NOUN
ajst-24468	164	1	[	[	X
ajst-24468	164	2	j	j	X
ajst-24468	164	3	]	]	X
ajst-24468	164	4	.	.	PUNCT
ajst-24468	165	1	application	application	NOUN
ajst-24468	165	2	of	of	ADP
ajst-24468	165	3	computer	computer	NOUN
ajst-24468	165	4	systems	system	NOUN
ajst-24468	165	5	,	,	PUNCT
ajst-24468	165	6	2019,33(03):220	2019,33(03):220	NOUN
ajst-24468	165	7	-	-	SYM
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ajst-24468	165	9	.	.	PUNCT
ajst-24468	166	1	(	(	PUNCT
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ajst-24468	166	3	chinese	chinese	PROPN
ajst-24468	166	4	)	)	PUNCT
ajst-24468	166	5	51	51	NUM
ajst-24468	167	1	[	[	X
ajst-24468	167	2	12	12	NUM
ajst-24468	167	3	]	]	PUNCT
ajst-24468	167	4	xu	xu	PROPN
ajst-24468	167	5	tiefeng	tiefeng	PROPN
ajst-24468	167	6	,	,	PUNCT
ajst-24468	167	7	huang	huang	PROPN
ajst-24468	167	8	he	he	PROPN
ajst-24468	167	9	,	,	PUNCT
ajst-24468	167	10	zhang	zhang	PROPN
ajst-24468	167	11	hongmin	hongmin	PROPN
ajst-24468	167	12	,	,	PUNCT
ajst-24468	167	13	et	et	PROPN
ajst-24468	167	14	al	al	PROPN
ajst-24468	167	15	.	.	PUNCT
ajst-24468	168	1	lightweight	lightweight	ADJ
ajst-24468	168	2	road	road	NOUN
ajst-24468	168	3	disease	disease	NOUN
ajst-24468	168	4	detection	detection	NOUN
ajst-24468	168	5	method	method	NOUN
ajst-24468	168	6	based	base	VERB
ajst-24468	168	7	on	on	ADP
ajst-24468	168	8	improved	improved	ADJ
ajst-24468	168	9	yolov8	yolov8	NOUN
ajst-24468	169	1	[	[	X
ajst-24468	169	2	j	j	X
ajst-24468	169	3	]	]	X
ajst-24468	169	4	.	.	PUNCT
ajst-24468	170	1	computer	computer	NOUN
ajst-24468	170	2	engineering	engineering	NOUN
ajst-24468	170	3	and	and	CCONJ
ajst-24468	170	4	application	application	NOUN
ajst-24468	170	5	;	;	PUNCT
ajst-24468	170	6	2024	2024	NUM
ajst-24468	170	7	,	,	PUNCT
ajst-24468	170	8	1	1	NUM
ajst-24468	170	9	-	-	SYM
ajst-24468	170	10	16	16	NUM
ajst-24468	170	11	.	.	PUNCT
ajst-24468	171	1	[	[	X
ajst-24468	171	2	13	13	NUM
ajst-24468	171	3	]	]	X
ajst-24468	171	4	woo	woo	PROPN
ajst-24468	171	5	,	,	PUNCT
ajst-24468	171	6	s.	s.	PROPN
ajst-24468	171	7	,	,	PUNCT
ajst-24468	171	8	park	park	PROPN
ajst-24468	171	9	,	,	PUNCT
ajst-24468	171	10	j.	j.	PROPN
ajst-24468	171	11	,	,	PUNCT
ajst-24468	171	12	lee	lee	PROPN
ajst-24468	171	13	,	,	PUNCT
ajst-24468	171	14	j.	j.	PROPN
ajst-24468	171	15	y.	y.	PROPN
ajst-24468	171	16	,	,	PUNCT
ajst-24468	171	17	&	&	CCONJ
ajst-24468	171	18	kweon	kweon	PROPN
ajst-24468	171	19	,	,	PUNCT
ajst-24468	171	20	i.	i.	PROPN
ajst-24468	171	21	s.	s.	PROPN
ajst-24468	171	22	(	(	PUNCT
ajst-24468	171	23	2018	2018	NUM
ajst-24468	171	24	)	)	PUNCT
ajst-24468	171	25	.	.	PUNCT
ajst-24468	172	1	cbam	cbam	NOUN
ajst-24468	172	2	:	:	PUNCT
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ajst-24468	172	4	block	block	NOUN
ajst-24468	172	5	attention	attention	NOUN
ajst-24468	172	6	module	module	NOUN
ajst-24468	172	7	.	.	PUNCT
ajst-24468	173	1	in	in	ADP
ajst-24468	173	2	proceedings	proceeding	NOUN
ajst-24468	173	3	of	of	ADP
ajst-24468	173	4	the	the	DET
ajst-24468	173	5	ieee	ieee	NOUN
ajst-24468	173	6	conference	conference	NOUN
ajst-24468	173	7	on	on	ADP
ajst-24468	173	8	computer	computer	NOUN
ajst-24468	173	9	vision	vision	NOUN
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ajst-24468	173	12	recognition	recognition	NOUN
ajst-24468	173	13	(	(	PUNCT
ajst-24468	173	14	cvpr	cvpr	NOUN
ajst-24468	173	15	)	)	PUNCT
ajst-24468	173	16	.	.	PUNCT
ajst-24468	174	1	[	[	X
ajst-24468	174	2	14	14	NUM
ajst-24468	174	3	]	]	PUNCT
ajst-24468	174	4	anghyun	anghyun	NOUN
ajst-24468	174	5	,	,	PUNCT
ajst-24468	174	6	woo	woo	PROPN
ajst-24468	174	7	.	.	PUNCT
ajst-24468	174	8	,	,	PUNCT
ajst-24468	174	9	jongchan	jongchan	PROPN
ajst-24468	174	10	,	,	PUNCT
ajst-24468	174	11	park	park	PROPN
ajst-24468	174	12	.	.	PUNCT
ajst-24468	174	13	,	,	PUNCT
ajst-24468	175	1	joon	joon	PROPN
ajst-24468	175	2	-	-	PUNCT
ajst-24468	175	3	young	young	PROPN
ajst-24468	175	4	,	,	PUNCT
ajst-24468	175	5	lee	lee	PROPN
ajst-24468	175	6	.	.	PUNCT
ajst-24468	175	7	cbam	cbam	PROPN
ajst-24468	175	8	:	:	PUNCT
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ajst-24468	175	10	block	block	NOUN
ajst-24468	175	11	attention	attention	NOUN
ajst-24468	175	12	module[j	module[j	NOUN
ajst-24468	175	13	]	]	PUNCT
ajst-24468	175	14	.	.	PUNCT
ajst-24468	176	1	arxiv	arxiv	NOUN
ajst-24468	176	2	:	:	PUNCT
ajst-24468	176	3	1807.06521	1807.06521	NUM
ajst-24468	176	4	[	[	X
ajst-24468	176	5	15	15	NUM
ajst-24468	176	6	]	]	X
ajst-24468	176	7	jifeng	jifeng	PROPN
ajst-24468	176	8	dai	dai	PROPN
ajst-24468	176	9	,	,	PUNCT
ajst-24468	176	10	haozhi	haozhi	PROPN
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ajst-24468	176	14	xiong	xiong	PROPN
ajst-24468	176	15	.	.	PUNCT
ajst-24468	177	1	(	(	PUNCT
ajst-24468	177	2	2019	2019	NUM
ajst-24468	177	3	)	)	PUNCT
ajst-24468	177	4	.	.	PUNCT
ajst-24468	178	1	deformable	deformable	ADJ
ajst-24468	178	2	convnets	convnet	NOUN
ajst-24468	178	3	v2	v2	NOUN
ajst-24468	178	4	:	:	PUNCT
ajst-24468	178	5	more	more	ADV
ajst-24468	178	6	deformable	deformable	ADJ
ajst-24468	178	7	,	,	PUNCT
ajst-24468	178	8	better	well	ADJ
ajst-24468	178	9	results	result	NOUN
ajst-24468	178	10	.	.	PUNCT
ajst-24468	179	1	arxiv	arxiv	PROPN
ajst-24468	179	2	preprint	preprint	PROPN
ajst-24468	179	3	arxiv	arxiv	PROPN
ajst-24468	179	4	;	;	PUNCT
ajst-24468	179	5	1908.01891	1908.01891	NUM
ajst-24468	179	6	.	.	PUNCT
ajst-24468	180	1	[	[	X
ajst-24468	180	2	16	16	NUM
ajst-24468	180	3	]	]	X
ajst-24468	180	4	deng	deng	PROPN
ajst-24468	180	5	huan	huan	PROPN
ajst-24468	180	6	.	.	PUNCT
ajst-24468	181	1	steel	steel	NOUN
ajst-24468	181	2	surface	surface	NOUN
ajst-24468	181	3	defect	defect	NOUN
ajst-24468	181	4	detection	detection	NOUN
ajst-24468	181	5	method	method	NOUN
ajst-24468	181	6	based	base	VERB
ajst-24468	181	7	on	on	ADP
ajst-24468	181	8	regularized	regularize	VERB
ajst-24468	181	9	yolo	yolo	NOUN
ajst-24468	182	1	[	[	X
ajst-24468	182	2	j	j	X
ajst-24468	182	3	]	]	X
ajst-24468	182	4	.	.	PUNCT
ajst-24468	183	1	science	science	NOUN
ajst-24468	183	2	and	and	CCONJ
ajst-24468	183	3	technology	technology	NOUN
ajst-24468	183	4	innovation	innovation	NOUN
ajst-24468	183	5	and	and	CCONJ
ajst-24468	183	6	application	application	NOUN
ajst-24468	183	7	,	,	PUNCT
ajst-24468	183	8	2019,14(11	2019,14(11	NUM
ajst-24468	183	9	)	)	PUNCT
ajst-24468	183	10	;	;	PUNCT
ajst-24468	183	11	168	168	NUM
ajst-24468	183	12	-	-	SYM
ajst-24468	183	13	172	172	NUM
ajst-24468	183	14	.	.	PUNCT