id	sid	tid	token	lemma	pos
ajst-18332	1	1	academic	academic	ADJ
ajst-18332	1	2	journal	journal	NOUN
ajst-18332	1	3	of	of	ADP
ajst-18332	1	4	science	science	NOUN
ajst-18332	1	5	and	and	CCONJ
ajst-18332	1	6	technology	technology	NOUN
ajst-18332	1	7	issn	issn	NOUN
ajst-18332	1	8	:	:	PUNCT
ajst-18332	1	9	2771	2771	NUM
ajst-18332	1	10	-	-	SYM
ajst-18332	1	11	3032	3032	NUM
ajst-18332	1	12	|	|	NOUN
ajst-18332	1	13	vol	vol	NOUN
ajst-18332	1	14	.	.	PROPN
ajst-18332	2	1	9	9	NUM
ajst-18332	2	2	,	,	PUNCT
ajst-18332	2	3	no	no	INTJ
ajst-18332	2	4	.	.	NOUN
ajst-18332	2	5	3	3	NUM
ajst-18332	2	6	,	,	PUNCT
ajst-18332	2	7	2024	2024	NUM
ajst-18332	2	8	91	91	NUM
ajst-18332	2	9	end	end	NOUN
ajst-18332	2	10	face	face	NOUN
ajst-18332	2	11	detection	detection	NOUN
ajst-18332	2	12	of	of	ADP
ajst-18332	2	13	rebar	rebar	NOUN
ajst-18332	2	14	based	base	VERB
ajst-18332	2	15	on	on	ADP
ajst-18332	2	16	improved	improve	VERB
ajst-18332	2	17	pp	pp	ADP
ajst-18332	2	18	yolo	yolo	ADJ
ajst-18332	2	19	kunning	kunning	PROPN
ajst-18332	2	20	lai1	lai1	PROPN
ajst-18332	2	21	,	,	PUNCT
ajst-18332	2	22	sibo	sibo	PROPN
ajst-18332	2	23	huang1	huang1	PROPN
ajst-18332	2	24	,	,	PUNCT
ajst-18332	2	25	han	han	PROPN
ajst-18332	2	26	cui2	cui2	PROPN
ajst-18332	2	27	,	,	PUNCT
ajst-18332	2	28	yong	yong	PROPN
ajst-18332	2	29	cheng3	cheng3	PROPN
ajst-18332	2	30	and	and	CCONJ
ajst-18332	2	31	wei	wei	PROPN
ajst-18332	2	32	luo1	luo1	PROPN
ajst-18332	2	33	,	,	PUNCT
ajst-18332	2	34	*	*	PUNCT
ajst-18332	2	35	1modern	1modern	NUM
ajst-18332	2	36	education	education	NOUN
ajst-18332	2	37	technology	technology	NOUN
ajst-18332	2	38	center	center	NOUN
ajst-18332	2	39	,	,	PUNCT
ajst-18332	2	40	huizhou	huizhou	PROPN
ajst-18332	2	41	university	university	PROPN
ajst-18332	2	42	,	,	PUNCT
ajst-18332	2	43	huizhou	huizhou	NOUN
ajst-18332	2	44	,	,	PUNCT
ajst-18332	2	45	china	china	PROPN
ajst-18332	2	46	2school	2school	NUM
ajst-18332	2	47	of	of	ADP
ajst-18332	2	48	electronic	electronic	ADJ
ajst-18332	2	49	information	information	NOUN
ajst-18332	2	50	and	and	CCONJ
ajst-18332	2	51	electrical	electrical	ADJ
ajst-18332	2	52	engineering	engineering	NOUN
ajst-18332	2	53	,	,	PUNCT
ajst-18332	2	54	huizhou	huizhou	PROPN
ajst-18332	2	55	university	university	PROPN
ajst-18332	2	56	,	,	PUNCT
ajst-18332	2	57	huizhou	huizhou	NOUN
ajst-18332	2	58	,	,	PUNCT
ajst-18332	2	59	china	china	PROPN
ajst-18332	2	60	3beijng	3beijng	NUM
ajst-18332	2	61	qingtai	qingtai	PROPN
ajst-18332	2	62	data	data	PROPN
ajst-18332	2	63	technology	technology	NOUN
ajst-18332	2	64	company	company	NOUN
ajst-18332	2	65	limited	limit	VERB
ajst-18332	2	66	,	,	PUNCT
ajst-18332	2	67	beijing	beijing	PROPN
ajst-18332	2	68	,	,	PUNCT
ajst-18332	2	69	china	china	PROPN
ajst-18332	2	70	*	*	PUNCT
ajst-18332	2	71	corresponding	correspond	VERB
ajst-18332	2	72	author	author	NOUN
ajst-18332	2	73	:	:	PUNCT
ajst-18332	2	74	luowei@hzu.edu.cn	luowei@hzu.edu.cn	PROPN
ajst-18332	2	75	abstract	abstract	NOUN
ajst-18332	2	76	:	:	PUNCT
ajst-18332	2	77	this	this	DET
ajst-18332	2	78	paper	paper	NOUN
ajst-18332	2	79	introduces	introduce	VERB
ajst-18332	2	80	an	an	DET
ajst-18332	2	81	improved	improved	ADJ
ajst-18332	2	82	pp	pp	ADJ
ajst-18332	2	83	-	-	PUNCT
ajst-18332	2	84	yolo	yolo	ADJ
ajst-18332	2	85	network	network	NOUN
ajst-18332	2	86	method	method	NOUN
ajst-18332	2	87	to	to	PART
ajst-18332	2	88	enhance	enhance	VERB
ajst-18332	2	89	the	the	DET
ajst-18332	2	90	accuracy	accuracy	NOUN
ajst-18332	2	91	of	of	ADP
ajst-18332	2	92	rebar	rebar	NOUN
ajst-18332	2	93	end	end	NOUN
ajst-18332	2	94	identification	identification	NOUN
ajst-18332	2	95	and	and	CCONJ
ajst-18332	2	96	counting	counting	NOUN
ajst-18332	2	97	in	in	ADP
ajst-18332	2	98	construction	construction	NOUN
ajst-18332	2	99	engineering	engineering	NOUN
ajst-18332	2	100	.	.	PUNCT
ajst-18332	3	1	the	the	DET
ajst-18332	3	2	network	network	NOUN
ajst-18332	3	3	is	be	AUX
ajst-18332	3	4	optimized	optimize	VERB
ajst-18332	3	5	for	for	ADP
ajst-18332	3	6	the	the	DET
ajst-18332	3	7	specific	specific	ADJ
ajst-18332	3	8	characteristics	characteristic	NOUN
ajst-18332	3	9	of	of	ADP
ajst-18332	3	10	rebar	rebar	ADJ
ajst-18332	3	11	images	image	NOUN
ajst-18332	3	12	,	,	PUNCT
ajst-18332	3	13	enhancing	enhance	VERB
ajst-18332	3	14	its	its	PRON
ajst-18332	3	15	recognition	recognition	NOUN
ajst-18332	3	16	capabilities	capability	NOUN
ajst-18332	3	17	for	for	ADP
ajst-18332	3	18	rebars	rebar	NOUN
ajst-18332	3	19	of	of	ADP
ajst-18332	3	20	various	various	ADJ
ajst-18332	3	21	sizes	size	NOUN
ajst-18332	3	22	and	and	CCONJ
ajst-18332	3	23	shapes	shape	NOUN
ajst-18332	3	24	.	.	PUNCT
ajst-18332	4	1	notably	notably	ADV
ajst-18332	4	2	,	,	PUNCT
ajst-18332	4	3	the	the	DET
ajst-18332	4	4	network	network	NOUN
ajst-18332	4	5	structure	structure	NOUN
ajst-18332	4	6	introduces	introduce	VERB
ajst-18332	4	7	new	new	ADJ
ajst-18332	4	8	pathways	pathway	NOUN
ajst-18332	4	9	in	in	ADP
ajst-18332	4	10	the	the	DET
ajst-18332	4	11	4th	4th	ADJ
ajst-18332	4	12	and	and	CCONJ
ajst-18332	4	13	5th	5th	ADJ
ajst-18332	4	14	layers	layer	NOUN
ajst-18332	4	15	,	,	PUNCT
ajst-18332	4	16	enabling	enable	VERB
ajst-18332	4	17	more	more	ADV
ajst-18332	4	18	effective	effective	ADJ
ajst-18332	4	19	learning	learning	NOUN
ajst-18332	4	20	and	and	CCONJ
ajst-18332	4	21	identification	identification	NOUN
ajst-18332	4	22	of	of	ADP
ajst-18332	4	23	rebar	rebar	NOUN
ajst-18332	4	24	features	feature	NOUN
ajst-18332	4	25	from	from	ADP
ajst-18332	4	26	low	low	ADJ
ajst-18332	4	27	-	-	PUNCT
ajst-18332	4	28	level	level	NOUN
ajst-18332	4	29	characteristics	characteristic	NOUN
ajst-18332	4	30	,	,	PUNCT
ajst-18332	4	31	thus	thus	ADV
ajst-18332	4	32	improving	improve	VERB
ajst-18332	4	33	overall	overall	ADJ
ajst-18332	4	34	recognition	recognition	NOUN
ajst-18332	4	35	and	and	CCONJ
ajst-18332	4	36	counting	counting	NOUN
ajst-18332	4	37	accuracy	accuracy	NOUN
ajst-18332	4	38	.	.	PUNCT
ajst-18332	5	1	additionally	additionally	ADV
ajst-18332	5	2	,	,	PUNCT
ajst-18332	5	3	an	an	DET
ajst-18332	5	4	optimized	optimize	VERB
ajst-18332	5	5	data	data	NOUN
ajst-18332	5	6	augmentation	augmentation	NOUN
ajst-18332	5	7	strategy	strategy	NOUN
ajst-18332	5	8	,	,	PUNCT
ajst-18332	5	9	tailored	tailor	VERB
ajst-18332	5	10	to	to	ADP
ajst-18332	5	11	the	the	DET
ajst-18332	5	12	unique	unique	ADJ
ajst-18332	5	13	features	feature	NOUN
ajst-18332	5	14	of	of	ADP
ajst-18332	5	15	rebar	rebar	ADJ
ajst-18332	5	16	images	image	NOUN
ajst-18332	5	17	,	,	PUNCT
ajst-18332	5	18	replaces	replace	VERB
ajst-18332	5	19	the	the	DET
ajst-18332	5	20	traditional	traditional	ADJ
ajst-18332	5	21	mixup	mixup	NOUN
ajst-18332	5	22	method	method	NOUN
ajst-18332	5	23	.	.	PUNCT
ajst-18332	6	1	specific	specific	ADJ
ajst-18332	6	2	algorithms	algorithm	NOUN
ajst-18332	6	3	are	be	AUX
ajst-18332	6	4	introduced	introduce	VERB
ajst-18332	6	5	to	to	PART
ajst-18332	6	6	enhance	enhance	VERB
ajst-18332	6	7	the	the	DET
ajst-18332	6	8	network	network	NOUN
ajst-18332	6	9	's	's	PART
ajst-18332	6	10	efficiency	efficiency	NOUN
ajst-18332	6	11	in	in	ADP
ajst-18332	6	12	learning	learn	VERB
ajst-18332	6	13	rebar	rebar	ADJ
ajst-18332	6	14	image	image	NOUN
ajst-18332	6	15	characteristics	characteristic	NOUN
ajst-18332	6	16	.	.	PUNCT
ajst-18332	7	1	these	these	DET
ajst-18332	7	2	improvements	improvement	NOUN
ajst-18332	7	3	led	lead	VERB
ajst-18332	7	4	to	to	ADP
ajst-18332	7	5	excellent	excellent	ADJ
ajst-18332	7	6	performance	performance	NOUN
ajst-18332	7	7	in	in	ADP
ajst-18332	7	8	rebar	rebar	ADJ
ajst-18332	7	9	end	end	NOUN
ajst-18332	7	10	face	face	NOUN
ajst-18332	7	11	recognition	recognition	NOUN
ajst-18332	7	12	tests	test	NOUN
ajst-18332	7	13	,	,	PUNCT
ajst-18332	7	14	achieving	achieve	VERB
ajst-18332	7	15	an	an	DET
ajst-18332	7	16	average	average	ADJ
ajst-18332	7	17	precision	precision	NOUN
ajst-18332	7	18	(	(	PUNCT
ajst-18332	7	19	ap	ap	PROPN
ajst-18332	7	20	)	)	PUNCT
ajst-18332	7	21	of	of	ADP
ajst-18332	7	22	96.23	96.23	NUM
ajst-18332	7	23	%	%	NOUN
ajst-18332	7	24	,	,	PUNCT
ajst-18332	7	25	a	a	DET
ajst-18332	7	26	1.07	1.07	NUM
ajst-18332	7	27	%	%	NOUN
ajst-18332	7	28	increase	increase	NOUN
ajst-18332	7	29	compared	compare	VERB
ajst-18332	7	30	to	to	ADP
ajst-18332	7	31	the	the	DET
ajst-18332	7	32	original	original	ADJ
ajst-18332	7	33	model	model	NOUN
ajst-18332	7	34	.	.	PUNCT
ajst-18332	8	1	this	this	DET
ajst-18332	8	2	significant	significant	ADJ
ajst-18332	8	3	performance	performance	NOUN
ajst-18332	8	4	improvement	improvement	NOUN
ajst-18332	8	5	confirms	confirm	VERB
ajst-18332	8	6	the	the	DET
ajst-18332	8	7	effectiveness	effectiveness	NOUN
ajst-18332	8	8	of	of	ADP
ajst-18332	8	9	our	our	PRON
ajst-18332	8	10	proposed	propose	VERB
ajst-18332	8	11	improvements	improvement	NOUN
ajst-18332	8	12	in	in	ADP
ajst-18332	8	13	practical	practical	ADJ
ajst-18332	8	14	applications	application	NOUN
ajst-18332	8	15	,	,	PUNCT
ajst-18332	8	16	offering	offer	VERB
ajst-18332	8	17	new	new	ADJ
ajst-18332	8	18	perspectives	perspective	NOUN
ajst-18332	8	19	for	for	ADP
ajst-18332	8	20	the	the	DET
ajst-18332	8	21	development	development	NOUN
ajst-18332	8	22	of	of	ADP
ajst-18332	8	23	rebar	rebar	ADJ
ajst-18332	8	24	detection	detection	NOUN
ajst-18332	8	25	technology	technology	NOUN
ajst-18332	8	26	in	in	ADP
ajst-18332	8	27	the	the	DET
ajst-18332	8	28	construction	construction	NOUN
ajst-18332	8	29	industry	industry	NOUN
ajst-18332	8	30	.	.	PUNCT
ajst-18332	9	1	keywords	keyword	NOUN
ajst-18332	9	2	:	:	PUNCT
ajst-18332	9	3	deep	deep	ADJ
ajst-18332	9	4	learning	learning	NOUN
ajst-18332	9	5	;	;	PUNCT
ajst-18332	9	6	object	object	VERB
ajst-18332	9	7	detection	detection	NOUN
ajst-18332	9	8	;	;	PUNCT
ajst-18332	9	9	pp	pp	NUM
ajst-18332	9	10	-	-	PUNCT
ajst-18332	9	11	yolo	yolo	NOUN
ajst-18332	9	12	;	;	PUNCT
ajst-18332	9	13	rebar	rebar	NOUN
ajst-18332	9	14	.	.	PUNCT
ajst-18332	10	1	1	1	X
ajst-18332	10	2	.	.	X
ajst-18332	10	3	introduction	introduction	NOUN
ajst-18332	10	4	in	in	ADP
ajst-18332	10	5	construction	construction	NOUN
ajst-18332	10	6	engineering	engineering	NOUN
ajst-18332	10	7	,	,	PUNCT
ajst-18332	10	8	rebar	rebar	NOUN
ajst-18332	10	9	is	be	AUX
ajst-18332	10	10	a	a	DET
ajst-18332	10	11	critical	critical	ADJ
ajst-18332	10	12	supporting	support	VERB
ajst-18332	10	13	material	material	NOUN
ajst-18332	10	14	,	,	PUNCT
ajst-18332	10	15	and	and	CCONJ
ajst-18332	10	16	its	its	PRON
ajst-18332	10	17	precise	precise	ADJ
ajst-18332	10	18	use	use	NOUN
ajst-18332	10	19	is	be	AUX
ajst-18332	10	20	crucial	crucial	ADJ
ajst-18332	10	21	for	for	ADP
ajst-18332	10	22	efficiency	efficiency	NOUN
ajst-18332	10	23	and	and	CCONJ
ajst-18332	10	24	structural	structural	ADJ
ajst-18332	10	25	integrity	integrity	NOUN
ajst-18332	10	26	[	[	X
ajst-18332	10	27	1][2][3	1][2][3	NUM
ajst-18332	10	28	]	]	PUNCT
ajst-18332	10	29	.	.	PUNCT
ajst-18332	11	1	accurate	accurate	ADJ
ajst-18332	11	2	identification	identification	NOUN
ajst-18332	11	3	and	and	CCONJ
ajst-18332	11	4	counting	counting	NOUN
ajst-18332	11	5	of	of	ADP
ajst-18332	11	6	rebar	rebar	NOUN
ajst-18332	11	7	ends	end	NOUN
ajst-18332	11	8	are	be	AUX
ajst-18332	11	9	vital	vital	ADJ
ajst-18332	11	10	for	for	ADP
ajst-18332	11	11	the	the	DET
ajst-18332	11	12	stability	stability	NOUN
ajst-18332	11	13	and	and	CCONJ
ajst-18332	11	14	safety	safety	NOUN
ajst-18332	11	15	of	of	ADP
ajst-18332	11	16	structures	structure	NOUN
ajst-18332	11	17	.	.	PUNCT
ajst-18332	12	1	the	the	DET
ajst-18332	12	2	varied	varied	ADJ
ajst-18332	12	3	sizes	size	NOUN
ajst-18332	12	4	,	,	PUNCT
ajst-18332	12	5	shapes	shape	NOUN
ajst-18332	12	6	,	,	PUNCT
ajst-18332	12	7	and	and	CCONJ
ajst-18332	12	8	configurations	configuration	NOUN
ajst-18332	12	9	of	of	ADP
ajst-18332	12	10	rebar	rebar	NOUN
ajst-18332	12	11	in	in	ADP
ajst-18332	12	12	images	image	NOUN
ajst-18332	12	13	complicate	complicate	VERB
ajst-18332	12	14	automated	automate	VERB
ajst-18332	12	15	identification	identification	NOUN
ajst-18332	12	16	and	and	CCONJ
ajst-18332	12	17	counting	counting	NOUN
ajst-18332	12	18	.	.	PUNCT
ajst-18332	13	1	developing	develop	VERB
ajst-18332	13	2	a	a	DET
ajst-18332	13	3	method	method	NOUN
ajst-18332	13	4	for	for	ADP
ajst-18332	13	5	rapid	rapid	ADJ
ajst-18332	13	6	and	and	CCONJ
ajst-18332	13	7	precise	precise	ADJ
ajst-18332	13	8	identification	identification	NOUN
ajst-18332	13	9	and	and	CCONJ
ajst-18332	13	10	counting	counting	NOUN
ajst-18332	13	11	of	of	ADP
ajst-18332	13	12	rebar	rebar	NOUN
ajst-18332	13	13	ends	end	NOUN
ajst-18332	13	14	is	be	AUX
ajst-18332	13	15	therefore	therefore	ADV
ajst-18332	13	16	a	a	DET
ajst-18332	13	17	significant	significant	ADJ
ajst-18332	13	18	research	research	NOUN
ajst-18332	13	19	focus	focus	NOUN
ajst-18332	13	20	in	in	ADP
ajst-18332	13	21	construction	construction	NOUN
ajst-18332	13	22	engineering	engineering	NOUN
ajst-18332	13	23	.	.	PUNCT
ajst-18332	14	1	[	[	X
ajst-18332	14	2	4	4	X
ajst-18332	14	3	]	]	PUNCT
ajst-18332	14	4	this	this	DET
ajst-18332	14	5	paper	paper	NOUN
ajst-18332	14	6	enhances	enhance	VERB
ajst-18332	14	7	the	the	DET
ajst-18332	14	8	pp	pp	ADV
ajst-18332	14	9	-	-	PUNCT
ajst-18332	14	10	yolo	yolo	ADJ
ajst-18332	14	11	network	network	NOUN
ajst-18332	14	12	[	[	X
ajst-18332	14	13	5	5	NUM
ajst-18332	14	14	]	]	PUNCT
ajst-18332	14	15	,	,	PUNCT
ajst-18332	14	16	renowned	renowne	VERB
ajst-18332	14	17	for	for	ADP
ajst-18332	14	18	its	its	PRON
ajst-18332	14	19	effectiveness	effectiveness	NOUN
ajst-18332	14	20	in	in	ADP
ajst-18332	14	21	natural	natural	ADJ
ajst-18332	14	22	image	image	NOUN
ajst-18332	14	23	object	object	NOUN
ajst-18332	14	24	detection	detection	NOUN
ajst-18332	14	25	,	,	PUNCT
ajst-18332	14	26	to	to	AUX
ajst-18332	14	27	better	well	ADV
ajst-18332	14	28	suit	suit	VERB
ajst-18332	14	29	rebar	rebar	NOUN
ajst-18332	14	30	end	end	NOUN
ajst-18332	14	31	identification	identification	NOUN
ajst-18332	14	32	and	and	CCONJ
ajst-18332	14	33	counting	counting	NOUN
ajst-18332	14	34	.	.	PUNCT
ajst-18332	15	1	traditional	traditional	ADJ
ajst-18332	15	2	rebar	rebar	ADJ
ajst-18332	15	3	identification	identification	NOUN
ajst-18332	15	4	methods	method	NOUN
ajst-18332	15	5	,	,	PUNCT
ajst-18332	15	6	mostly	mostly	ADV
ajst-18332	15	7	based	base	VERB
ajst-18332	15	8	on	on	ADP
ajst-18332	15	9	manual	manual	ADJ
ajst-18332	15	10	inspection	inspection	NOUN
ajst-18332	15	11	,	,	PUNCT
ajst-18332	15	12	are	be	AUX
ajst-18332	15	13	not	not	PART
ajst-18332	15	14	only	only	ADV
ajst-18332	15	15	inefficient	inefficient	ADJ
ajst-18332	15	16	but	but	CCONJ
ajst-18332	15	17	also	also	ADV
ajst-18332	15	18	require	require	VERB
ajst-18332	15	19	substantial	substantial	ADJ
ajst-18332	15	20	expertise	expertise	NOUN
ajst-18332	15	21	.	.	PUNCT
ajst-18332	16	1	the	the	DET
ajst-18332	16	2	incorporation	incorporation	NOUN
ajst-18332	16	3	of	of	ADP
ajst-18332	16	4	deep	deep	ADJ
ajst-18332	16	5	learning	learning	NOUN
ajst-18332	16	6	,	,	PUNCT
ajst-18332	16	7	particularly	particularly	ADV
ajst-18332	16	8	in	in	ADP
ajst-18332	16	9	object	object	NOUN
ajst-18332	16	10	detection	detection	NOUN
ajst-18332	16	11	,	,	PUNCT
ajst-18332	16	12	has	have	AUX
ajst-18332	16	13	demonstrated	demonstrate	VERB
ajst-18332	16	14	exceptional	exceptional	ADJ
ajst-18332	16	15	effectiveness	effectiveness	NOUN
ajst-18332	16	16	and	and	CCONJ
ajst-18332	16	17	practicality	practicality	NOUN
ajst-18332	16	18	globally	globally	ADV
ajst-18332	16	19	.	.	PUNCT
ajst-18332	17	1	this	this	DET
ajst-18332	17	2	research	research	NOUN
ajst-18332	17	3	aims	aim	VERB
ajst-18332	17	4	to	to	PART
ajst-18332	17	5	improve	improve	VERB
ajst-18332	17	6	the	the	DET
ajst-18332	17	7	accuracy	accuracy	NOUN
ajst-18332	17	8	and	and	CCONJ
ajst-18332	17	9	efficiency	efficiency	NOUN
ajst-18332	17	10	of	of	ADP
ajst-18332	17	11	automated	automate	VERB
ajst-18332	17	12	rebar	rebar	NOUN
ajst-18332	17	13	end	end	NOUN
ajst-18332	17	14	face	face	NOUN
ajst-18332	17	15	identification	identification	NOUN
ajst-18332	17	16	and	and	CCONJ
ajst-18332	17	17	counting	counting	NOUN
ajst-18332	17	18	by	by	ADP
ajst-18332	17	19	refining	refine	VERB
ajst-18332	17	20	the	the	DET
ajst-18332	17	21	pp	pp	ADJ
ajst-18332	17	22	-	-	PUNCT
ajst-18332	17	23	yolo	yolo	ADJ
ajst-18332	17	24	network	network	NOUN
ajst-18332	17	25	.	.	PUNCT
ajst-18332	18	1	major	major	ADJ
ajst-18332	18	2	advancements	advancement	NOUN
ajst-18332	18	3	in	in	ADP
ajst-18332	18	4	image	image	NOUN
ajst-18332	18	5	processing	processing	NOUN
ajst-18332	18	6	within	within	ADP
ajst-18332	18	7	deep	deep	ADJ
ajst-18332	18	8	learning	learning	NOUN
ajst-18332	18	9	began	begin	VERB
ajst-18332	18	10	with	with	ADP
ajst-18332	18	11	the	the	DET
ajst-18332	18	12	2012	2012	NUM
ajst-18332	18	13	computer	computer	NOUN
ajst-18332	18	14	vision	vision	NOUN
ajst-18332	18	15	competition	competition	NOUN
ajst-18332	18	16	,	,	PUNCT
ajst-18332	18	17	where	where	SCONJ
ajst-18332	18	18	krizhevsky[6	krizhevsky[6	X
ajst-18332	18	19	]	]	PUNCT
ajst-18332	18	20	and	and	CCONJ
ajst-18332	18	21	others	other	NOUN
ajst-18332	18	22	'	'	PART
ajst-18332	18	23	modification	modification	NOUN
ajst-18332	18	24	of	of	ADP
ajst-18332	18	25	the	the	DET
ajst-18332	18	26	alexnet[7	alexnet[7	PROPN
ajst-18332	18	27	]	]	PUNCT
ajst-18332	18	28	network	network	NOUN
ajst-18332	18	29	marked	mark	VERB
ajst-18332	18	30	a	a	DET
ajst-18332	18	31	significant	significant	ADJ
ajst-18332	18	32	milestone	milestone	NOUN
ajst-18332	18	33	,	,	PUNCT
ajst-18332	18	34	achieving	achieve	VERB
ajst-18332	18	35	unparalleled	unparalleled	ADJ
ajst-18332	18	36	accuracy	accuracy	NOUN
ajst-18332	18	37	in	in	ADP
ajst-18332	18	38	image	image	NOUN
ajst-18332	18	39	classification	classification	NOUN
ajst-18332	18	40	tasks	task	NOUN
ajst-18332	18	41	and	and	CCONJ
ajst-18332	18	42	advancing	advance	VERB
ajst-18332	18	43	the	the	DET
ajst-18332	18	44	use	use	NOUN
ajst-18332	18	45	of	of	ADP
ajst-18332	18	46	convolutional	convolutional	ADJ
ajst-18332	18	47	neural	neural	ADJ
ajst-18332	18	48	networks	network	NOUN
ajst-18332	18	49	(	(	PUNCT
ajst-18332	18	50	cnns	cnns	PROPN
ajst-18332	18	51	)	)	PUNCT
ajst-18332	18	52	in	in	ADP
ajst-18332	18	53	image	image	NOUN
ajst-18332	18	54	recognition	recognition	NOUN
ajst-18332	18	55	.	.	PUNCT
ajst-18332	19	1	with	with	ADP
ajst-18332	19	2	rapid	rapid	ADJ
ajst-18332	19	3	advancements	advancement	NOUN
ajst-18332	19	4	in	in	ADP
ajst-18332	19	5	hardware	hardware	NOUN
ajst-18332	19	6	technology	technology	NOUN
ajst-18332	19	7	,	,	PUNCT
ajst-18332	19	8	more	more	ADV
ajst-18332	19	9	robust	robust	ADJ
ajst-18332	19	10	and	and	CCONJ
ajst-18332	19	11	intricate	intricate	ADJ
ajst-18332	19	12	deep	deep	ADJ
ajst-18332	19	13	learning	learning	NOUN
ajst-18332	19	14	network	network	NOUN
ajst-18332	19	15	architectures	architecture	NOUN
ajst-18332	19	16	have	have	AUX
ajst-18332	19	17	evolved	evolve	VERB
ajst-18332	19	18	,	,	PUNCT
ajst-18332	19	19	greatly	greatly	ADV
ajst-18332	19	20	expanding	expand	VERB
ajst-18332	19	21	the	the	DET
ajst-18332	19	22	scope	scope	NOUN
ajst-18332	19	23	of	of	ADP
ajst-18332	19	24	computer	computer	NOUN
ajst-18332	19	25	vision	vision	NOUN
ajst-18332	19	26	.	.	PUNCT
ajst-18332	20	1	these	these	DET
ajst-18332	20	2	developments	development	NOUN
ajst-18332	20	3	offer	offer	VERB
ajst-18332	20	4	robust	robust	ADJ
ajst-18332	20	5	technical	technical	ADJ
ajst-18332	20	6	support	support	NOUN
ajst-18332	20	7	for	for	ADP
ajst-18332	20	8	image	image	NOUN
ajst-18332	20	9	recognition	recognition	NOUN
ajst-18332	20	10	tasks	task	NOUN
ajst-18332	20	11	in	in	ADP
ajst-18332	20	12	specialized	specialized	ADJ
ajst-18332	20	13	areas	area	NOUN
ajst-18332	20	14	like	like	ADP
ajst-18332	20	15	rebar	rebar	NOUN
ajst-18332	20	16	end	end	NOUN
ajst-18332	20	17	detection	detection	NOUN
ajst-18332	20	18	and	and	CCONJ
ajst-18332	20	19	counting	counting	NOUN
ajst-18332	20	20	,	,	PUNCT
ajst-18332	20	21	enabling	enable	VERB
ajst-18332	20	22	precise	precise	ADJ
ajst-18332	20	23	identification	identification	NOUN
ajst-18332	20	24	in	in	ADP
ajst-18332	20	25	complex	complex	ADJ
ajst-18332	20	26	scenarios	scenario	NOUN
ajst-18332	20	27	.	.	PUNCT
ajst-18332	21	1	for	for	ADP
ajst-18332	21	2	example	example	NOUN
ajst-18332	21	3	,	,	PUNCT
ajst-18332	21	4	the	the	DET
ajst-18332	21	5	regionbased	regionbase	VERB
ajst-18332	21	6	convolutional	convolutional	ADJ
ajst-18332	21	7	neural	neural	ADJ
ajst-18332	21	8	network	network	NOUN
ajst-18332	21	9	(	(	PUNCT
ajst-18332	21	10	r	r	NOUN
ajst-18332	21	11	-	-	PUNCT
ajst-18332	21	12	cnn	cnn	NOUN
ajst-18332	21	13	)	)	PUNCT
ajst-18332	21	14	algorithm	algorithm	NOUN
ajst-18332	22	1	[	[	X
ajst-18332	22	2	8	8	NUM
ajst-18332	22	3	]	]	PUNCT
ajst-18332	22	4	,	,	PUNCT
ajst-18332	22	5	proposed	propose	VERB
ajst-18332	22	6	by	by	ADP
ajst-18332	22	7	girshick	girshick	PROPN
ajst-18332	22	8	et	et	PROPN
ajst-18332	22	9	al	al	PROPN
ajst-18332	22	10	.	.	PUNCT
ajst-18332	23	1	[	[	X
ajst-18332	23	2	9	9	NUM
ajst-18332	23	3	]	]	PUNCT
ajst-18332	23	4	in	in	ADP
ajst-18332	23	5	2014	2014	NUM
ajst-18332	23	6	and	and	CCONJ
ajst-18332	23	7	applied	apply	VERB
ajst-18332	23	8	in	in	ADP
ajst-18332	23	9	rebar	rebar	ADJ
ajst-18332	23	10	end	end	NOUN
ajst-18332	23	11	face	face	NOUN
ajst-18332	23	12	recognition	recognition	NOUN
ajst-18332	23	13	,	,	PUNCT
ajst-18332	23	14	markedly	markedly	ADV
ajst-18332	23	15	improved	improve	VERB
ajst-18332	23	16	recognition	recognition	NOUN
ajst-18332	23	17	accuracy	accuracy	NOUN
ajst-18332	23	18	by	by	ADP
ajst-18332	23	19	segmenting	segment	VERB
ajst-18332	23	20	the	the	DET
ajst-18332	23	21	task	task	NOUN
ajst-18332	23	22	into	into	ADP
ajst-18332	23	23	classification	classification	NOUN
ajst-18332	23	24	and	and	CCONJ
ajst-18332	23	25	localization	localization	NOUN
ajst-18332	23	26	.	.	PUNCT
ajst-18332	24	1	later	later	ADJ
ajst-18332	24	2	algorithms	algorithm	NOUN
ajst-18332	24	3	,	,	PUNCT
ajst-18332	24	4	such	such	ADJ
ajst-18332	24	5	as	as	ADP
ajst-18332	24	6	fast	fast	ADJ
ajst-18332	24	7	r	r	NOUN
ajst-18332	24	8	-	-	PUNCT
ajst-18332	24	9	cnn	cnn	NOUN
ajst-18332	24	10	[	[	X
ajst-18332	24	11	10	10	NUM
ajst-18332	24	12	]	]	PUNCT
ajst-18332	24	13	and	and	CCONJ
ajst-18332	24	14	faster	fast	ADJ
ajst-18332	24	15	r	r	NOUN
ajst-18332	24	16	-	-	PUNCT
ajst-18332	24	17	cnn	cnn	PROPN
ajst-18332	24	18	,	,	PUNCT
ajst-18332	24	19	showed	show	VERB
ajst-18332	24	20	more	more	ADV
ajst-18332	24	21	efficient	efficient	ADJ
ajst-18332	24	22	rebar	rebar	NOUN
ajst-18332	24	23	image	image	NOUN
ajst-18332	24	24	processing	processing	NOUN
ajst-18332	24	25	.	.	PUNCT
ajst-18332	25	1	the	the	DET
ajst-18332	25	2	introduction	introduction	NOUN
ajst-18332	25	3	of	of	ADP
ajst-18332	25	4	the	the	DET
ajst-18332	25	5	yolo	yolo	ADJ
ajst-18332	25	6	series	series	PROPN
ajst-18332	25	7	algorithms	algorithm	NOUN
ajst-18332	25	8	in	in	ADP
ajst-18332	25	9	2017	2017	NUM
ajst-18332	25	10	marked	mark	VERB
ajst-18332	25	11	a	a	DET
ajst-18332	25	12	new	new	ADJ
ajst-18332	25	13	era	era	NOUN
ajst-18332	25	14	for	for	ADP
ajst-18332	25	15	quick	quick	ADJ
ajst-18332	25	16	and	and	CCONJ
ajst-18332	25	17	accurate	accurate	ADJ
ajst-18332	25	18	rebar	rebar	NOUN
ajst-18332	25	19	end	end	NOUN
ajst-18332	25	20	counting	counting	NOUN
ajst-18332	25	21	,	,	PUNCT
ajst-18332	25	22	merging	merge	VERB
ajst-18332	25	23	target	target	NOUN
ajst-18332	25	24	bounding	bounding	NOUN
ajst-18332	25	25	box	box	NOUN
ajst-18332	25	26	localization	localization	NOUN
ajst-18332	25	27	and	and	CCONJ
ajst-18332	25	28	classification	classification	NOUN
ajst-18332	25	29	,	,	PUNCT
ajst-18332	25	30	thereby	thereby	ADV
ajst-18332	25	31	significantly	significantly	ADV
ajst-18332	25	32	boosting	boost	VERB
ajst-18332	25	33	detection	detection	NOUN
ajst-18332	25	34	speed	speed	NOUN
ajst-18332	25	35	and	and	CCONJ
ajst-18332	25	36	accuracy	accuracy	NOUN
ajst-18332	25	37	.	.	PUNCT
ajst-18332	26	1	that	that	DET
ajst-18332	26	2	same	same	ADJ
ajst-18332	26	3	year	year	NOUN
ajst-18332	26	4	,	,	PUNCT
ajst-18332	26	5	lin	lin	PROPN
ajst-18332	27	1	[	[	X
ajst-18332	27	2	11	11	NUM
ajst-18332	27	3	]	]	PUNCT
ajst-18332	27	4	and	and	CCONJ
ajst-18332	27	5	others	other	NOUN
ajst-18332	27	6	introduced	introduce	VERB
ajst-18332	27	7	a	a	DET
ajst-18332	27	8	bottom	bottom	NOUN
ajst-18332	27	9	-	-	PUNCT
ajst-18332	27	10	up	up	NOUN
ajst-18332	27	11	and	and	CCONJ
ajst-18332	27	12	top	top	ADJ
ajst-18332	27	13	-	-	PUNCT
ajst-18332	27	14	down	down	ADP
ajst-18332	27	15	structure	structure	NOUN
ajst-18332	27	16	in	in	ADP
ajst-18332	27	17	the	the	DET
ajst-18332	27	18	feature	feature	NOUN
ajst-18332	27	19	pyramid	pyramid	NOUN
ajst-18332	27	20	network	network	NOUN
ajst-18332	27	21	(	(	PUNCT
ajst-18332	27	22	fpn	fpn	PROPN
ajst-18332	27	23	)	)	PUNCT
ajst-18332	27	24	,	,	PUNCT
ajst-18332	27	25	enhancing	enhance	VERB
ajst-18332	27	26	small	small	ADJ
ajst-18332	27	27	rebar	rebar	NOUN
ajst-18332	27	28	end	end	NOUN
ajst-18332	27	29	face	face	NOUN
ajst-18332	27	30	recognition	recognition	NOUN
ajst-18332	27	31	accuracy	accuracy	NOUN
ajst-18332	27	32	.	.	PUNCT
ajst-18332	28	1	in	in	ADP
ajst-18332	28	2	2019	2019	NUM
ajst-18332	28	3	,	,	PUNCT
ajst-18332	28	4	the	the	DET
ajst-18332	28	5	introduction	introduction	NOUN
ajst-18332	28	6	of	of	ADP
ajst-18332	28	7	the	the	DET
ajst-18332	28	8	cornernet	cornernet	NOUN
ajst-18332	28	9	[	[	X
ajst-18332	28	10	12	12	NUM
ajst-18332	28	11	]	]	X
ajst-18332	28	12	algorithm	algorithm	NOUN
ajst-18332	28	13	addressed	address	VERB
ajst-18332	28	14	the	the	DET
ajst-18332	28	15	inefficiencies	inefficiency	NOUN
ajst-18332	28	16	of	of	ADP
ajst-18332	28	17	traditional	traditional	ADJ
ajst-18332	28	18	anchor	anchor	NOUN
ajst-18332	28	19	-	-	PUNCT
ajst-18332	28	20	based	base	VERB
ajst-18332	28	21	methods	method	NOUN
ajst-18332	28	22	,	,	PUNCT
ajst-18332	28	23	contributing	contribute	VERB
ajst-18332	28	24	to	to	PART
ajst-18332	28	25	progress	progress	VERB
ajst-18332	28	26	in	in	ADP
ajst-18332	28	27	efficient	efficient	ADJ
ajst-18332	28	28	recognition	recognition	NOUN
ajst-18332	28	29	and	and	CCONJ
ajst-18332	28	30	counting	counting	NOUN
ajst-18332	28	31	of	of	ADP
ajst-18332	28	32	rebar	rebar	NOUN
ajst-18332	28	33	ends.[13	ends.[13	PROPN
ajst-18332	28	34	]	]	X
ajst-18332	29	1	[	[	X
ajst-18332	29	2	14	14	NUM
ajst-18332	29	3	]	]	X
ajst-18332	29	4	[	[	X
ajst-18332	29	5	15	15	NUM
ajst-18332	29	6	]	]	X
ajst-18332	29	7	the	the	DET
ajst-18332	29	8	pp	pp	PROPN
ajst-18332	29	9	-	-	PUNCT
ajst-18332	29	10	yolo	yolo	ADJ
ajst-18332	29	11	network	network	NOUN
ajst-18332	29	12	,	,	PUNCT
ajst-18332	29	13	developed	develop	VERB
ajst-18332	29	14	on	on	ADP
ajst-18332	29	15	baidu	baidu	PROPN
ajst-18332	29	16	's	's	PART
ajst-18332	29	17	deep	deep	ADJ
ajst-18332	29	18	learning	learning	NOUN
ajst-18332	29	19	framework	framework	NOUN
ajst-18332	29	20	paddlepaddle	paddlepaddle	NOUN
ajst-18332	29	21	,	,	PUNCT
ajst-18332	29	22	is	be	AUX
ajst-18332	29	23	particularly	particularly	ADV
ajst-18332	29	24	apt	apt	ADJ
ajst-18332	29	25	for	for	ADP
ajst-18332	29	26	complex	complex	ADJ
ajst-18332	29	27	image	image	NOUN
ajst-18332	29	28	object	object	NOUN
ajst-18332	29	29	detection	detection	NOUN
ajst-18332	29	30	.	.	PUNCT
ajst-18332	30	1	2	2	X
ajst-18332	30	2	.	.	NUM
ajst-18332	30	3	improved	improve	VERB
ajst-18332	30	4	pp	pp	PROPN
ajst-18332	30	5	-	-	PUNCT
ajst-18332	30	6	yolo	yolo	ADJ
ajst-18332	30	7	network	network	NOUN
ajst-18332	30	8	for	for	ADP
ajst-18332	30	9	rebar	rebar	NOUN
ajst-18332	30	10	end	end	NOUN
ajst-18332	30	11	face	face	NOUN
ajst-18332	30	12	image	image	NOUN
ajst-18332	30	13	detection	detection	NOUN
ajst-18332	30	14	the	the	DET
ajst-18332	30	15	pp	pp	ADV
ajst-18332	30	16	-	-	PUNCT
ajst-18332	30	17	yolo	yolo	ADJ
ajst-18332	30	18	network	network	NOUN
ajst-18332	30	19	is	be	AUX
ajst-18332	30	20	a	a	DET
ajst-18332	30	21	typical	typical	ADJ
ajst-18332	30	22	application	application	NOUN
ajst-18332	30	23	of	of	ADP
ajst-18332	30	24	a	a	DET
ajst-18332	30	25	singlestage	singlestage	NOUN
ajst-18332	30	26	object	object	NOUN
ajst-18332	30	27	detection	detection	NOUN
ajst-18332	30	28	algorithm	algorithm	NOUN
ajst-18332	30	29	,	,	PUNCT
ajst-18332	30	30	which	which	PRON
ajst-18332	30	31	includes	include	VERB
ajst-18332	30	32	a	a	DET
ajst-18332	30	33	backbone	backbone	NOUN
ajst-18332	30	34	network	network	NOUN
ajst-18332	30	35	,	,	PUNCT
ajst-18332	30	36	a	a	DET
ajst-18332	30	37	detection	detection	NOUN
ajst-18332	30	38	neck	neck	NOUN
ajst-18332	30	39	(	(	PUNCT
ajst-18332	30	40	usually	usually	ADV
ajst-18332	30	41	a	a	DET
ajst-18332	30	42	feature	feature	NOUN
ajst-18332	30	43	pyramid	pyramid	NOUN
ajst-18332	30	44	network	network	NOUN
ajst-18332	30	45	)	)	PUNCT
ajst-18332	30	46	,	,	PUNCT
ajst-18332	30	47	and	and	CCONJ
ajst-18332	30	48	a	a	DET
ajst-18332	30	49	detection	detection	NOUN
ajst-18332	30	50	head	head	NOUN
ajst-18332	30	51	(	(	PUNCT
ajst-18332	30	52	for	for	ADP
ajst-18332	30	53	classification	classification	NOUN
ajst-18332	30	54	and	and	CCONJ
ajst-18332	30	55	localization	localization	NOUN
ajst-18332	30	56	)	)	PUNCT
ajst-18332	30	57	.	.	PUNCT
ajst-18332	31	1	in	in	ADP
ajst-18332	31	2	this	this	DET
ajst-18332	31	3	study	study	NOUN
ajst-18332	31	4	,	,	PUNCT
ajst-18332	31	5	we	we	PRON
ajst-18332	31	6	made	make	VERB
ajst-18332	31	7	improvements	improvement	NOUN
ajst-18332	31	8	to	to	ADP
ajst-18332	31	9	these	these	DET
ajst-18332	31	10	parts	part	NOUN
ajst-18332	31	11	,	,	PUNCT
ajst-18332	31	12	especially	especially	ADV
ajst-18332	31	13	optimizing	optimize	VERB
ajst-18332	31	14	them	they	PRON
ajst-18332	31	15	for	for	ADP
ajst-18332	31	16	the	the	DET
ajst-18332	31	17	identification	identification	NOUN
ajst-18332	31	18	and	and	CCONJ
ajst-18332	31	19	counting	counting	NOUN
ajst-18332	31	20	of	of	ADP
ajst-18332	31	21	rebar	rebar	NOUN
ajst-18332	31	22	ends	end	NOUN
ajst-18332	31	23	.	.	PUNCT
ajst-18332	32	1	through	through	ADP
ajst-18332	32	2	these	these	DET
ajst-18332	32	3	improvements	improvement	NOUN
ajst-18332	32	4	,	,	PUNCT
ajst-18332	32	5	we	we	PRON
ajst-18332	32	6	aim	aim	VERB
ajst-18332	32	7	to	to	PART
ajst-18332	32	8	enhance	enhance	VERB
ajst-18332	32	9	the	the	DET
ajst-18332	32	10	efficiency	efficiency	NOUN
ajst-18332	32	11	and	and	CCONJ
ajst-18332	32	12	accuracy	accuracy	NOUN
ajst-18332	32	13	of	of	ADP
ajst-18332	32	14	rebar	rebar	ADJ
ajst-18332	32	15	image	image	NOUN
ajst-18332	32	16	target	target	NOUN
ajst-18332	32	17	detection	detection	NOUN
ajst-18332	32	18	.	.	PUNCT
ajst-18332	33	1	2.1	2.1	NUM
ajst-18332	33	2	.	.	PUNCT
ajst-18332	33	3	improvement	improvement	NOUN
ajst-18332	33	4	of	of	ADP
ajst-18332	33	5	the	the	DET
ajst-18332	33	6	detection	detection	NOUN
ajst-18332	33	7	neck	neck	NOUN
ajst-18332	33	8	improvement	improvement	NOUN
ajst-18332	33	9	of	of	ADP
ajst-18332	33	10	the	the	DET
ajst-18332	33	11	detection	detection	NOUN
ajst-18332	33	12	neck	neck	NOUN
ajst-18332	33	13	:	:	PUNCT
ajst-18332	33	14	this	this	DET
ajst-18332	33	15	paper	paper	NOUN
ajst-18332	33	16	improves	improve	VERB
ajst-18332	33	17	the	the	DET
ajst-18332	33	18	pp	pp	ADJ
ajst-18332	33	19	-	-	PUNCT
ajst-18332	33	20	yolo	yolo	ADJ
ajst-18332	33	21	network	network	NOUN
ajst-18332	33	22	,	,	PUNCT
ajst-18332	33	23	a	a	DET
ajst-18332	33	24	typical	typical	ADJ
ajst-18332	33	25	application	application	NOUN
ajst-18332	33	26	of	of	ADP
ajst-18332	33	27	a	a	DET
ajst-18332	33	28	single	single	ADJ
ajst-18332	33	29	-	-	PUNCT
ajst-18332	33	30	stage	stage	NOUN
ajst-18332	33	31	object	object	NOUN
ajst-18332	33	32	detection	detection	NOUN
ajst-18332	33	33	algorithm	algorithm	NOUN
ajst-18332	33	34	,	,	PUNCT
ajst-18332	33	35	focusing	focus	VERB
ajst-18332	33	36	on	on	ADP
ajst-18332	33	37	enhancing	enhance	VERB
ajst-18332	33	38	performance	performance	NOUN
ajst-18332	33	39	for	for	ADP
ajst-18332	33	40	rebar	rebar	ADJ
ajst-18332	33	41	end	end	NOUN
ajst-18332	33	42	identification	identification	NOUN
ajst-18332	33	43	and	and	CCONJ
ajst-18332	33	44	counting	counting	NOUN
ajst-18332	33	45	.	.	PUNCT
ajst-18332	34	1	we	we	PRON
ajst-18332	34	2	made	make	VERB
ajst-18332	34	3	targeted	target	VERB
ajst-18332	34	4	improvements	improvement	NOUN
ajst-18332	34	5	to	to	ADP
ajst-18332	34	6	the	the	DET
ajst-18332	34	7	network	network	NOUN
ajst-18332	34	8	's	's	PART
ajst-18332	34	9	three	three	NUM
ajst-18332	34	10	main	main	ADJ
ajst-18332	34	11	parts	part	NOUN
ajst-18332	34	12	92	92	NUM
ajst-18332	34	13	the	the	DET
ajst-18332	34	14	backbone	backbone	NOUN
ajst-18332	34	15	network	network	NOUN
ajst-18332	34	16	,	,	PUNCT
ajst-18332	34	17	the	the	DET
ajst-18332	34	18	detection	detection	NOUN
ajst-18332	34	19	neck	neck	NOUN
ajst-18332	34	20	(	(	PUNCT
ajst-18332	34	21	usually	usually	ADV
ajst-18332	34	22	a	a	DET
ajst-18332	34	23	feature	feature	NOUN
ajst-18332	34	24	pyramid	pyramid	NOUN
ajst-18332	34	25	network	network	NOUN
ajst-18332	34	26	)	)	PUNCT
ajst-18332	34	27	,	,	PUNCT
ajst-18332	34	28	and	and	CCONJ
ajst-18332	34	29	the	the	DET
ajst-18332	34	30	detection	detection	NOUN
ajst-18332	34	31	head	head	NOUN
ajst-18332	34	32	(	(	PUNCT
ajst-18332	34	33	for	for	ADP
ajst-18332	34	34	classification	classification	NOUN
ajst-18332	34	35	and	and	CCONJ
ajst-18332	34	36	localization	localization	NOUN
ajst-18332	34	37	)	)	PUNCT
ajst-18332	34	38	to	to	PART
ajst-18332	34	39	enhance	enhance	VERB
ajst-18332	34	40	the	the	DET
ajst-18332	34	41	efficiency	efficiency	NOUN
ajst-18332	34	42	and	and	CCONJ
ajst-18332	34	43	accuracy	accuracy	NOUN
ajst-18332	34	44	of	of	ADP
ajst-18332	34	45	rebar	rebar	ADJ
ajst-18332	34	46	image	image	NOUN
ajst-18332	34	47	target	target	NOUN
ajst-18332	34	48	detection	detection	NOUN
ajst-18332	34	49	.	.	PUNCT
ajst-18332	35	1	in	in	ADP
ajst-18332	35	2	terms	term	NOUN
ajst-18332	35	3	of	of	ADP
ajst-18332	35	4	improvements	improvement	NOUN
ajst-18332	35	5	to	to	ADP
ajst-18332	35	6	the	the	DET
ajst-18332	35	7	detection	detection	NOUN
ajst-18332	35	8	neck	neck	NOUN
ajst-18332	35	9	,	,	PUNCT
ajst-18332	35	10	for	for	ADP
ajst-18332	35	11	rebar	rebar	NOUN
ajst-18332	35	12	end	end	NOUN
ajst-18332	35	13	identification	identification	NOUN
ajst-18332	35	14	applications	application	NOUN
ajst-18332	35	15	,	,	PUNCT
ajst-18332	35	16	we	we	PRON
ajst-18332	35	17	reconstructed	reconstruct	VERB
ajst-18332	35	18	the	the	DET
ajst-18332	35	19	feature	feature	NOUN
ajst-18332	35	20	pyramid	pyramid	NOUN
ajst-18332	35	21	network	network	NOUN
ajst-18332	35	22	,	,	PUNCT
ajst-18332	35	23	adding	add	VERB
ajst-18332	35	24	lateral	lateral	ADJ
ajst-18332	35	25	connection	connection	NOUN
ajst-18332	35	26	structures	structure	NOUN
ajst-18332	35	27	.	.	PUNCT
ajst-18332	36	1	moreover	moreover	ADV
ajst-18332	36	2	,	,	PUNCT
ajst-18332	36	3	we	we	PRON
ajst-18332	36	4	introduced	introduce	VERB
ajst-18332	36	5	dilated	dilated	ADJ
ajst-18332	36	6	convolutions	convolution	NOUN
ajst-18332	36	7	to	to	PART
ajst-18332	36	8	expand	expand	VERB
ajst-18332	36	9	the	the	DET
ajst-18332	36	10	receptive	receptive	ADJ
ajst-18332	36	11	field	field	NOUN
ajst-18332	36	12	and	and	CCONJ
ajst-18332	36	13	capture	capture	VERB
ajst-18332	36	14	more	more	ADV
ajst-18332	36	15	extensive	extensive	ADJ
ajst-18332	36	16	contextual	contextual	ADJ
ajst-18332	36	17	information	information	NOUN
ajst-18332	36	18	,	,	PUNCT
ajst-18332	36	19	thus	thus	ADV
ajst-18332	36	20	improving	improve	VERB
ajst-18332	36	21	the	the	DET
ajst-18332	36	22	recognition	recognition	NOUN
ajst-18332	36	23	ability	ability	NOUN
ajst-18332	36	24	of	of	ADP
ajst-18332	36	25	rebar	rebar	ADJ
ajst-18332	36	26	end	end	NOUN
ajst-18332	36	27	features	feature	NOUN
ajst-18332	36	28	.	.	PUNCT
ajst-18332	37	1	we	we	PRON
ajst-18332	37	2	also	also	ADV
ajst-18332	37	3	integrated	integrate	VERB
ajst-18332	37	4	a	a	DET
ajst-18332	37	5	self	self	NOUN
ajst-18332	37	6	-	-	PUNCT
ajst-18332	37	7	attention	attention	NOUN
ajst-18332	37	8	mechanism[16	mechanism[16	NOUN
ajst-18332	37	9	]	]	X
ajst-18332	37	10	,	,	PUNCT
ajst-18332	37	11	especially	especially	ADV
ajst-18332	37	12	in	in	ADP
ajst-18332	37	13	the	the	DET
ajst-18332	37	14	4th	4th	ADJ
ajst-18332	37	15	and	and	CCONJ
ajst-18332	37	16	5th	5th	ADJ
ajst-18332	37	17	layer	layer	NOUN
ajst-18332	37	18	feature	feature	NOUN
ajst-18332	37	19	mappings	mapping	NOUN
ajst-18332	37	20	(	(	PUNCT
ajst-18332	37	21	c4	c4	NOUN
ajst-18332	37	22	and	and	CCONJ
ajst-18332	37	23	c5	c5	PROPN
ajst-18332	37	24	)	)	PUNCT
ajst-18332	37	25	,	,	PUNCT
ajst-18332	37	26	allowing	allow	VERB
ajst-18332	37	27	the	the	DET
ajst-18332	37	28	network	network	NOUN
ajst-18332	37	29	to	to	PART
ajst-18332	37	30	focus	focus	VERB
ajst-18332	37	31	more	more	ADV
ajst-18332	37	32	on	on	ADP
ajst-18332	37	33	the	the	DET
ajst-18332	37	34	critical	critical	ADJ
ajst-18332	37	35	parts	part	NOUN
ajst-18332	37	36	of	of	ADP
ajst-18332	37	37	the	the	DET
ajst-18332	37	38	rebar	rebar	NOUN
ajst-18332	37	39	ends	end	VERB
ajst-18332	37	40	and	and	CCONJ
ajst-18332	37	41	improve	improve	VERB
ajst-18332	37	42	feature	feature	NOUN
ajst-18332	37	43	discrimination	discrimination	NOUN
ajst-18332	37	44	.	.	PUNCT
ajst-18332	38	1	in	in	ADP
ajst-18332	38	2	terms	term	NOUN
ajst-18332	38	3	of	of	ADP
ajst-18332	38	4	network	network	NOUN
ajst-18332	38	5	architecture	architecture	NOUN
ajst-18332	38	6	adjustment	adjustment	NOUN
ajst-18332	38	7	,	,	PUNCT
ajst-18332	38	8	we	we	PRON
ajst-18332	38	9	retained	retain	VERB
ajst-18332	38	10	the	the	DET
ajst-18332	38	11	basic	basic	ADJ
ajst-18332	38	12	framework	framework	NOUN
ajst-18332	38	13	of	of	ADP
ajst-18332	38	14	the	the	DET
ajst-18332	38	15	last	last	ADJ
ajst-18332	38	16	three	three	NUM
ajst-18332	38	17	layers	layer	NOUN
ajst-18332	38	18	of	of	ADP
ajst-18332	38	19	feature	feature	NOUN
ajst-18332	38	20	mappings	mapping	NOUN
ajst-18332	38	21	(	(	PUNCT
ajst-18332	38	22	c3	c3	NOUN
ajst-18332	38	23	,	,	PUNCT
ajst-18332	38	24	c4	c4	NOUN
ajst-18332	38	25	,	,	PUNCT
ajst-18332	38	26	and	and	CCONJ
ajst-18332	38	27	c5	c5	PROPN
ajst-18332	38	28	)	)	PUNCT
ajst-18332	38	29	in	in	ADP
ajst-18332	38	30	the	the	DET
ajst-18332	38	31	backbone	backbone	NOUN
ajst-18332	38	32	network	network	NOUN
ajst-18332	38	33	and	and	CCONJ
ajst-18332	38	34	strengthened	strengthen	VERB
ajst-18332	38	35	the	the	DET
ajst-18332	38	36	feature	feature	NOUN
ajst-18332	38	37	fusion	fusion	NOUN
ajst-18332	38	38	and	and	CCONJ
ajst-18332	38	39	information	information	NOUN
ajst-18332	38	40	transmission	transmission	NOUN
ajst-18332	38	41	between	between	ADP
ajst-18332	38	42	these	these	DET
ajst-18332	38	43	layers	layer	NOUN
ajst-18332	38	44	.	.	PUNCT
ajst-18332	39	1	especially	especially	ADV
ajst-18332	39	2	between	between	ADP
ajst-18332	39	3	the	the	DET
ajst-18332	39	4	c4	c4	NOUN
ajst-18332	39	5	and	and	CCONJ
ajst-18332	39	6	c5	c5	PROPN
ajst-18332	39	7	layers	layer	NOUN
ajst-18332	39	8	,	,	PUNCT
ajst-18332	39	9	the	the	DET
ajst-18332	39	10	combination	combination	NOUN
ajst-18332	39	11	of	of	ADP
ajst-18332	39	12	self	self	NOUN
ajst-18332	39	13	-	-	PUNCT
ajst-18332	39	14	attention	attention	NOUN
ajst-18332	39	15	mechanisms	mechanism	NOUN
ajst-18332	39	16	and	and	CCONJ
ajst-18332	39	17	dilated	dilate	VERB
ajst-18332	39	18	convolutions	convolution	NOUN
ajst-18332	39	19	effectively	effectively	ADV
ajst-18332	39	20	transmitted	transmit	VERB
ajst-18332	39	21	more	more	ADV
ajst-18332	39	22	abundant	abundant	ADJ
ajst-18332	39	23	semantic	semantic	ADJ
ajst-18332	39	24	information	information	NOUN
ajst-18332	39	25	from	from	ADP
ajst-18332	39	26	lower	low	ADJ
ajst-18332	39	27	to	to	ADP
ajst-18332	39	28	higher	high	ADJ
ajst-18332	39	29	layers	layer	NOUN
ajst-18332	39	30	.	.	PUNCT
ajst-18332	40	1	we	we	PRON
ajst-18332	40	2	defined	define	VERB
ajst-18332	40	3	the	the	DET
ajst-18332	40	4	feature	feature	NOUN
ajst-18332	40	5	mapping	mapping	NOUN
ajst-18332	40	6	of	of	ADP
ajst-18332	40	7	the	the	DET
ajst-18332	40	8	l	l	NOUN
ajst-18332	40	9	-	-	PUNCT
ajst-18332	40	10	th	th	VERB
ajst-18332	40	11	layer	layer	NOUN
ajst-18332	40	12	output	output	NOUN
ajst-18332	40	13	of	of	ADP
ajst-18332	40	14	the	the	DET
ajst-18332	40	15	feature	feature	NOUN
ajst-18332	40	16	pyramid	pyramid	NOUN
ajst-18332	40	17	as	as	ADP
ajst-18332	40	18	pl	pl	PROPN
ajst-18332	40	19	,	,	PUNCT
ajst-18332	40	20	and	and	CCONJ
ajst-18332	40	21	in	in	ADP
ajst-18332	40	22	this	this	DET
ajst-18332	40	23	paper	paper	NOUN
ajst-18332	40	24	's	's	PART
ajst-18332	40	25	experiments	experiment	NOUN
ajst-18332	40	26	,	,	PUNCT
ajst-18332	40	27	l	l	NOUN
ajst-18332	40	28	=	=	SYM
ajst-18332	40	29	3	3	NUM
ajst-18332	40	30	,	,	PUNCT
ajst-18332	40	31	4	4	NUM
ajst-18332	40	32	,	,	PUNCT
ajst-18332	40	33	5	5	NUM
ajst-18332	40	34	.	.	PUNCT
ajst-18332	40	35	when	when	SCONJ
ajst-18332	40	36	the	the	DET
ajst-18332	40	37	input	input	NOUN
ajst-18332	40	38	image	image	NOUN
ajst-18332	40	39	size	size	NOUN
ajst-18332	40	40	is	be	AUX
ajst-18332	40	41	w	w	PROPN
ajst-18332	40	42	×	×	PROPN
ajst-18332	40	43	h	h	NOUN
ajst-18332	40	44	,	,	PUNCT
ajst-18332	40	45	the	the	DET
ajst-18332	40	46	output	output	NOUN
ajst-18332	40	47	feature	feature	NOUN
ajst-18332	40	48	mapping	mapping	NOUN
ajst-18332	40	49	pl	pl	X
ajst-18332	40	50	is	be	AUX
ajst-18332	40	51	:	:	PUNCT
ajst-18332	40	52	𝑃	𝑃	NOUN
ajst-18332	40	53	,	,	PUNCT
ajst-18332	40	54	𝑙	𝑙	PRON
ajst-18332	40	55	3,4,5	3,4,5	NUM
ajst-18332	40	56	(	(	PUNCT
ajst-18332	40	57	1	1	NUM
ajst-18332	40	58	)	)	PUNCT
ajst-18332	40	59	in	in	ADP
ajst-18332	40	60	key	key	ADJ
ajst-18332	40	61	applications	application	NOUN
ajst-18332	40	62	for	for	ADP
ajst-18332	40	63	rebar	rebar	ADJ
ajst-18332	40	64	end	end	NOUN
ajst-18332	40	65	face	face	NOUN
ajst-18332	40	66	recognition	recognition	NOUN
ajst-18332	40	67	,	,	PUNCT
ajst-18332	40	68	we	we	PRON
ajst-18332	40	69	made	make	VERB
ajst-18332	40	70	systematic	systematic	ADJ
ajst-18332	40	71	improvements	improvement	NOUN
ajst-18332	40	72	to	to	ADP
ajst-18332	40	73	the	the	DET
ajst-18332	40	74	detection	detection	NOUN
ajst-18332	40	75	neck	neck	NOUN
ajst-18332	40	76	of	of	ADP
ajst-18332	40	77	the	the	DET
ajst-18332	40	78	ppyolo	ppyolo	PROPN
ajst-18332	40	79	network	network	NOUN
ajst-18332	40	80	,	,	PUNCT
ajst-18332	40	81	especially	especially	ADV
ajst-18332	40	82	by	by	ADP
ajst-18332	40	83	adding	add	VERB
ajst-18332	40	84	dilated	dilated	ADJ
ajst-18332	40	85	convolutions	convolution	NOUN
ajst-18332	40	86	and	and	CCONJ
ajst-18332	40	87	attention	attention	NOUN
ajst-18332	40	88	mechanisms	mechanism	NOUN
ajst-18332	40	89	to	to	PART
ajst-18332	40	90	enhance	enhance	VERB
ajst-18332	40	91	the	the	DET
ajst-18332	40	92	model	model	NOUN
ajst-18332	40	93	's	's	PART
ajst-18332	40	94	performance	performance	NOUN
ajst-18332	40	95	.	.	PUNCT
ajst-18332	41	1	these	these	DET
ajst-18332	41	2	improvements	improvement	NOUN
ajst-18332	41	3	were	be	AUX
ajst-18332	41	4	based	base	VERB
ajst-18332	41	5	on	on	ADP
ajst-18332	41	6	an	an	DET
ajst-18332	41	7	analysis	analysis	NOUN
ajst-18332	41	8	of	of	ADP
ajst-18332	41	9	the	the	DET
ajst-18332	41	10	original	original	ADJ
ajst-18332	41	11	pp	pp	ADJ
ajst-18332	41	12	-	-	PUNCT
ajst-18332	41	13	yolo	yolo	ADJ
ajst-18332	41	14	network	network	NOUN
ajst-18332	41	15	,	,	PUNCT
ajst-18332	41	16	identifying	identify	VERB
ajst-18332	41	17	its	its	PRON
ajst-18332	41	18	limitations	limitation	NOUN
ajst-18332	41	19	in	in	ADP
ajst-18332	41	20	information	information	NOUN
ajst-18332	41	21	transmission	transmission	NOUN
ajst-18332	41	22	:	:	PUNCT
ajst-18332	41	23	although	although	SCONJ
ajst-18332	41	24	the	the	DET
ajst-18332	41	25	network	network	NOUN
ajst-18332	41	26	could	could	AUX
ajst-18332	41	27	transmit	transmit	VERB
ajst-18332	41	28	low	low	ADJ
ajst-18332	41	29	-	-	PUNCT
ajst-18332	41	30	level	level	NOUN
ajst-18332	41	31	semantic	semantic	ADJ
ajst-18332	41	32	information	information	NOUN
ajst-18332	41	33	to	to	ADP
ajst-18332	41	34	higher	high	ADJ
ajst-18332	41	35	levels	level	NOUN
ajst-18332	41	36	,	,	PUNCT
ajst-18332	41	37	it	it	PRON
ajst-18332	41	38	failed	fail	VERB
ajst-18332	41	39	to	to	PART
ajst-18332	41	40	fully	fully	ADV
ajst-18332	41	41	utilize	utilize	VERB
ajst-18332	41	42	the	the	DET
ajst-18332	41	43	rich	rich	ADJ
ajst-18332	41	44	spatial	spatial	ADJ
ajst-18332	41	45	features	feature	NOUN
ajst-18332	41	46	of	of	ADP
ajst-18332	41	47	the	the	DET
ajst-18332	41	48	lower	low	ADJ
ajst-18332	41	49	levels	level	NOUN
ajst-18332	41	50	.	.	PUNCT
ajst-18332	42	1	to	to	PART
ajst-18332	42	2	address	address	VERB
ajst-18332	42	3	this	this	DET
ajst-18332	42	4	issue	issue	NOUN
ajst-18332	42	5	,	,	PUNCT
ajst-18332	42	6	we	we	PRON
ajst-18332	42	7	introduced	introduce	VERB
ajst-18332	42	8	dilated	dilated	ADJ
ajst-18332	42	9	convolutions	convolution	NOUN
ajst-18332	42	10	in	in	ADP
ajst-18332	42	11	the	the	DET
ajst-18332	42	12	c3	c3	PROPN
ajst-18332	42	13	,	,	PUNCT
ajst-18332	42	14	c4	c4	NOUN
ajst-18332	42	15	,	,	PUNCT
ajst-18332	42	16	and	and	CCONJ
ajst-18332	42	17	c5	c5	PROPN
ajst-18332	42	18	layers	layer	NOUN
ajst-18332	42	19	of	of	ADP
ajst-18332	42	20	the	the	DET
ajst-18332	42	21	feature	feature	NOUN
ajst-18332	42	22	pyramid	pyramid	NOUN
ajst-18332	42	23	.	.	PUNCT
ajst-18332	43	1	this	this	DET
ajst-18332	43	2	convolution	convolution	NOUN
ajst-18332	43	3	form	form	NOUN
ajst-18332	43	4	expands	expand	VERB
ajst-18332	43	5	the	the	DET
ajst-18332	43	6	receptive	receptive	ADJ
ajst-18332	43	7	field	field	NOUN
ajst-18332	43	8	by	by	ADP
ajst-18332	43	9	introducing	introduce	VERB
ajst-18332	43	10	spatial	spatial	ADJ
ajst-18332	43	11	intervals	interval	NOUN
ajst-18332	43	12	in	in	ADP
ajst-18332	43	13	standard	standard	ADJ
ajst-18332	43	14	convolution	convolution	NOUN
ajst-18332	43	15	kernels	kernel	NOUN
ajst-18332	43	16	,	,	PUNCT
ajst-18332	43	17	maintaining	maintain	VERB
ajst-18332	43	18	a	a	DET
ajst-18332	43	19	balance	balance	NOUN
ajst-18332	43	20	between	between	ADP
ajst-18332	43	21	parameter	parameter	NOUN
ajst-18332	43	22	count	count	NOUN
ajst-18332	43	23	and	and	CCONJ
ajst-18332	43	24	computational	computational	ADJ
ajst-18332	43	25	complexity	complexity	NOUN
ajst-18332	43	26	,	,	PUNCT
ajst-18332	43	27	while	while	SCONJ
ajst-18332	43	28	also	also	ADV
ajst-18332	43	29	enhancing	enhance	VERB
ajst-18332	43	30	the	the	DET
ajst-18332	43	31	model	model	NOUN
ajst-18332	43	32	's	's	PART
ajst-18332	43	33	ability	ability	NOUN
ajst-18332	43	34	to	to	PART
ajst-18332	43	35	capture	capture	VERB
ajst-18332	43	36	spatial	spatial	ADJ
ajst-18332	43	37	information	information	NOUN
ajst-18332	43	38	.	.	PUNCT
ajst-18332	44	1	this	this	PRON
ajst-18332	44	2	is	be	AUX
ajst-18332	44	3	particularly	particularly	ADV
ajst-18332	44	4	beneficial	beneficial	ADJ
ajst-18332	44	5	before	before	ADP
ajst-18332	44	6	the	the	DET
ajst-18332	44	7	upsampling	upsampling	NOUN
ajst-18332	44	8	block	block	NOUN
ajst-18332	44	9	,	,	PUNCT
ajst-18332	44	10	as	as	SCONJ
ajst-18332	44	11	dilated	dilated	ADJ
ajst-18332	44	12	convolutions	convolution	NOUN
ajst-18332	44	13	help	help	VERB
ajst-18332	44	14	maintain	maintain	VERB
ajst-18332	44	15	details	detail	NOUN
ajst-18332	44	16	that	that	PRON
ajst-18332	44	17	may	may	AUX
ajst-18332	44	18	be	be	AUX
ajst-18332	44	19	lost	lose	VERB
ajst-18332	44	20	during	during	ADP
ajst-18332	44	21	downsampling	downsample	VERB
ajst-18332	44	22	.	.	PUNCT
ajst-18332	45	1	furthermore	furthermore	ADV
ajst-18332	45	2	,	,	PUNCT
ajst-18332	45	3	we	we	PRON
ajst-18332	45	4	integrated	integrate	VERB
ajst-18332	45	5	a	a	DET
ajst-18332	45	6	self	self	NOUN
ajst-18332	45	7	-	-	PUNCT
ajst-18332	45	8	attention	attention	NOUN
ajst-18332	45	9	mechanism	mechanism	NOUN
ajst-18332	45	10	in	in	ADP
ajst-18332	45	11	the	the	DET
ajst-18332	45	12	p4	p4	ADJ
ajst-18332	45	13	and	and	CCONJ
ajst-18332	45	14	p5	p5	ADJ
ajst-18332	45	15	feature	feature	NOUN
ajst-18332	45	16	mappings	mapping	NOUN
ajst-18332	45	17	,	,	PUNCT
ajst-18332	45	18	allowing	allow	VERB
ajst-18332	45	19	the	the	DET
ajst-18332	45	20	network	network	NOUN
ajst-18332	45	21	to	to	PART
ajst-18332	45	22	focus	focus	VERB
ajst-18332	45	23	on	on	ADP
ajst-18332	45	24	key	key	ADJ
ajst-18332	45	25	areas	area	NOUN
ajst-18332	45	26	containing	contain	VERB
ajst-18332	45	27	more	more	ADJ
ajst-18332	45	28	target	target	NOUN
ajst-18332	45	29	information	information	NOUN
ajst-18332	45	30	.	.	PUNCT
ajst-18332	46	1	at	at	ADP
ajst-18332	46	2	the	the	DET
ajst-18332	46	3	output	output	NOUN
ajst-18332	46	4	end	end	NOUN
ajst-18332	46	5	of	of	ADP
ajst-18332	46	6	the	the	DET
ajst-18332	46	7	convolution	convolution	NOUN
ajst-18332	46	8	block	block	NOUN
ajst-18332	46	9	,	,	PUNCT
ajst-18332	46	10	especially	especially	ADV
ajst-18332	46	11	at	at	ADP
ajst-18332	46	12	the	the	DET
ajst-18332	46	13	nodes	node	NOUN
ajst-18332	46	14	where	where	SCONJ
ajst-18332	46	15	upsampling	upsample	VERB
ajst-18332	46	16	and	and	CCONJ
ajst-18332	46	17	downsampling	downsample	VERB
ajst-18332	46	18	information	information	NOUN
ajst-18332	46	19	are	be	AUX
ajst-18332	46	20	merged	merge	VERB
ajst-18332	46	21	,	,	PUNCT
ajst-18332	46	22	we	we	PRON
ajst-18332	46	23	also	also	ADV
ajst-18332	46	24	applied	apply	VERB
ajst-18332	46	25	attention	attention	NOUN
ajst-18332	46	26	modules	module	NOUN
ajst-18332	46	27	.	.	PUNCT
ajst-18332	47	1	this	this	DET
ajst-18332	47	2	design	design	NOUN
ajst-18332	47	3	further	far	ADV
ajst-18332	47	4	enhanced	enhance	VERB
ajst-18332	47	5	feature	feature	NOUN
ajst-18332	47	6	discrimination	discrimination	NOUN
ajst-18332	47	7	and	and	CCONJ
ajst-18332	47	8	promoted	promote	VERB
ajst-18332	47	9	the	the	DET
ajst-18332	47	10	fine	fine	ADJ
ajst-18332	47	11	fusion	fusion	NOUN
ajst-18332	47	12	of	of	ADP
ajst-18332	47	13	features	feature	NOUN
ajst-18332	47	14	at	at	ADP
ajst-18332	47	15	different	different	ADJ
ajst-18332	47	16	levels	level	NOUN
ajst-18332	47	17	.	.	PUNCT
ajst-18332	48	1	with	with	ADP
ajst-18332	48	2	these	these	DET
ajst-18332	48	3	structural	structural	ADJ
ajst-18332	48	4	improvements	improvement	NOUN
ajst-18332	48	5	,	,	PUNCT
ajst-18332	48	6	especially	especially	ADV
ajst-18332	48	7	through	through	ADP
ajst-18332	48	8	the	the	DET
ajst-18332	48	9	downsample	downsample	PROPN
ajst-18332	48	10	block	block	NOUN
ajst-18332	48	11	module	module	NOUN
ajst-18332	48	12	,	,	PUNCT
ajst-18332	48	13	we	we	PRON
ajst-18332	48	14	established	establish	VERB
ajst-18332	48	15	a	a	DET
ajst-18332	48	16	new	new	ADJ
ajst-18332	48	17	pathway	pathway	NOUN
ajst-18332	48	18	for	for	ADP
ajst-18332	48	19	effective	effective	ADJ
ajst-18332	48	20	information	information	NOUN
ajst-18332	48	21	transmission	transmission	NOUN
ajst-18332	48	22	from	from	ADP
ajst-18332	48	23	lower	low	ADJ
ajst-18332	48	24	to	to	ADP
ajst-18332	48	25	higher	high	ADJ
ajst-18332	48	26	layers	layer	NOUN
ajst-18332	48	27	.	.	PUNCT
ajst-18332	49	1	this	this	DET
ajst-18332	49	2	improvement	improvement	NOUN
ajst-18332	49	3	not	not	PART
ajst-18332	49	4	only	only	ADV
ajst-18332	49	5	enhanced	enhance	VERB
ajst-18332	49	6	the	the	DET
ajst-18332	49	7	efficiency	efficiency	NOUN
ajst-18332	49	8	of	of	ADP
ajst-18332	49	9	converting	convert	VERB
ajst-18332	49	10	low	low	ADJ
ajst-18332	49	11	-	-	PUNCT
ajst-18332	49	12	level	level	NOUN
ajst-18332	49	13	spatial	spatial	ADJ
ajst-18332	49	14	features	feature	NOUN
ajst-18332	49	15	to	to	ADP
ajst-18332	49	16	high	high	ADJ
ajst-18332	49	17	-	-	PUNCT
ajst-18332	49	18	level	level	NOUN
ajst-18332	49	19	semantic	semantic	ADJ
ajst-18332	49	20	information	information	NOUN
ajst-18332	49	21	but	but	CCONJ
ajst-18332	49	22	also	also	ADV
ajst-18332	49	23	significantly	significantly	ADV
ajst-18332	49	24	improved	improve	VERB
ajst-18332	49	25	the	the	DET
ajst-18332	49	26	model	model	NOUN
ajst-18332	49	27	's	's	PART
ajst-18332	49	28	accuracy	accuracy	NOUN
ajst-18332	49	29	in	in	ADP
ajst-18332	49	30	rebar	rebar	ADJ
ajst-18332	49	31	end	end	NOUN
ajst-18332	49	32	face	face	NOUN
ajst-18332	49	33	recognition	recognition	NOUN
ajst-18332	49	34	tasks	task	NOUN
ajst-18332	49	35	.	.	PUNCT
ajst-18332	50	1	experimental	experimental	ADJ
ajst-18332	50	2	results	result	NOUN
ajst-18332	50	3	validated	validate	VERB
ajst-18332	50	4	the	the	DET
ajst-18332	50	5	effectiveness	effectiveness	NOUN
ajst-18332	50	6	of	of	ADP
ajst-18332	50	7	this	this	DET
ajst-18332	50	8	design	design	NOUN
ajst-18332	50	9	,	,	PUNCT
ajst-18332	50	10	as	as	SCONJ
ajst-18332	50	11	shown	show	VERB
ajst-18332	50	12	in	in	ADP
ajst-18332	50	13	figure	figure	NOUN
ajst-18332	50	14	2	2	NUM
ajst-18332	50	15	,	,	PUNCT
ajst-18332	50	16	where	where	SCONJ
ajst-18332	50	17	the	the	DET
ajst-18332	50	18	improved	improved	ADJ
ajst-18332	50	19	detection	detection	NOUN
ajst-18332	50	20	neck	neck	NOUN
ajst-18332	50	21	structure	structure	NOUN
ajst-18332	50	22	exhibited	exhibit	VERB
ajst-18332	50	23	exceptional	exceptional	ADJ
ajst-18332	50	24	performance	performance	NOUN
ajst-18332	50	25	in	in	ADP
ajst-18332	50	26	rebar	rebar	ADJ
ajst-18332	50	27	end	end	NOUN
ajst-18332	50	28	face	face	NOUN
ajst-18332	50	29	recognition	recognition	NOUN
ajst-18332	50	30	,	,	PUNCT
ajst-18332	50	31	indeed	indeed	ADV
ajst-18332	50	32	improving	improve	VERB
ajst-18332	50	33	recognition	recognition	NOUN
ajst-18332	50	34	accuracy	accuracy	NOUN
ajst-18332	50	35	.	.	PUNCT
ajst-18332	51	1	figure	figure	NOUN
ajst-18332	51	2	1	1	NUM
ajst-18332	51	3	.	.	PUNCT
ajst-18332	51	4	detailed	detailed	ADJ
ajst-18332	51	5	structure	structure	NOUN
ajst-18332	51	6	of	of	ADP
ajst-18332	51	7	the	the	DET
ajst-18332	51	8	ppyolo	ppyolo	NOUN
ajst-18332	51	9	feature	feature	VERB
ajst-18332	51	10	pyramid	pyramid	NOUN
ajst-18332	51	11	figure	figure	NOUN
ajst-18332	51	12	2	2	NUM
ajst-18332	51	13	.	.	PUNCT
ajst-18332	51	14	block	block	NOUN
ajst-18332	51	15	diagram	diagram	NOUN
ajst-18332	51	16	of	of	ADP
ajst-18332	51	17	detection	detection	NOUN
ajst-18332	51	18	neck	neck	NOUN
ajst-18332	51	19	optimisation	optimisation	NOUN
ajst-18332	51	20	strategy	strategy	NOUN
ajst-18332	51	21	93	93	NUM
ajst-18332	51	22	2.2	2.2	NUM
ajst-18332	51	23	.	.	PUNCT
ajst-18332	52	1	detection	detection	NOUN
ajst-18332	52	2	head	head	NOUN
ajst-18332	52	3	in	in	ADP
ajst-18332	52	4	the	the	DET
ajst-18332	52	5	pp	pp	ADJ
ajst-18332	52	6	-	-	PUNCT
ajst-18332	52	7	yolo	yolo	ADJ
ajst-18332	52	8	network	network	NOUN
ajst-18332	52	9	for	for	ADP
ajst-18332	52	10	rebar	rebar	NOUN
ajst-18332	52	11	end	end	NOUN
ajst-18332	52	12	face	face	VERB
ajst-18332	52	13	image	image	NOUN
ajst-18332	52	14	identification	identification	NOUN
ajst-18332	52	15	,	,	PUNCT
ajst-18332	52	16	the	the	DET
ajst-18332	52	17	detection	detection	NOUN
ajst-18332	52	18	head	head	NOUN
ajst-18332	52	19	uses	use	VERB
ajst-18332	52	20	3×3	3×3	NUM
ajst-18332	52	21	and	and	CCONJ
ajst-18332	52	22	1×1	1×1	NUM
ajst-18332	52	23	convolution	convolution	NOUN
ajst-18332	52	24	layers	layer	NOUN
ajst-18332	52	25	for	for	ADP
ajst-18332	52	26	final	final	ADJ
ajst-18332	52	27	predictions	prediction	NOUN
ajst-18332	52	28	.	.	PUNCT
ajst-18332	53	1	these	these	DET
ajst-18332	53	2	convolution	convolution	NOUN
ajst-18332	53	3	layers	layer	NOUN
ajst-18332	53	4	are	be	AUX
ajst-18332	53	5	meticulously	meticulously	ADV
ajst-18332	53	6	designed	design	VERB
ajst-18332	53	7	to	to	PART
ajst-18332	53	8	include	include	VERB
ajst-18332	53	9	necessary	necessary	ADJ
ajst-18332	53	10	batch	batch	NOUN
ajst-18332	53	11	normalization	normalization	NOUN
ajst-18332	53	12	and	and	CCONJ
ajst-18332	53	13	activation	activation	NOUN
ajst-18332	53	14	functions	function	NOUN
ajst-18332	53	15	,	,	PUNCT
ajst-18332	53	16	effectively	effectively	ADV
ajst-18332	53	17	processing	process	VERB
ajst-18332	53	18	the	the	DET
ajst-18332	53	19	features	feature	NOUN
ajst-18332	53	20	of	of	ADP
ajst-18332	53	21	rebar	rebar	ADJ
ajst-18332	53	22	end	end	NOUN
ajst-18332	53	23	faces	face	VERB
ajst-18332	53	24	.	.	PUNCT
ajst-18332	54	1	for	for	ADP
ajst-18332	54	2	each	each	DET
ajst-18332	54	3	input	input	NOUN
ajst-18332	54	4	image	image	NOUN
ajst-18332	54	5	of	of	ADP
ajst-18332	54	6	rebar	rebar	NOUN
ajst-18332	54	7	ends	end	NOUN
ajst-18332	54	8	,	,	PUNCT
ajst-18332	54	9	the	the	DET
ajst-18332	54	10	prediction	prediction	NOUN
ajst-18332	54	11	results	result	NOUN
ajst-18332	54	12	are	be	AUX
ajst-18332	54	13	associated	associate	VERB
ajst-18332	54	14	with	with	ADP
ajst-18332	54	15	specially	specially	ADV
ajst-18332	54	16	designed	design	VERB
ajst-18332	54	17	anchor	anchor	NOUN
ajst-18332	54	18	boxes	box	NOUN
ajst-18332	54	19	,	,	PUNCT
ajst-18332	54	20	which	which	PRON
ajst-18332	54	21	are	be	AUX
ajst-18332	54	22	adjusted	adjust	VERB
ajst-18332	54	23	to	to	PART
ajst-18332	54	24	fit	fit	VERB
ajst-18332	54	25	the	the	DET
ajst-18332	54	26	unique	unique	ADJ
ajst-18332	54	27	shape	shape	NOUN
ajst-18332	54	28	and	and	CCONJ
ajst-18332	54	29	size	size	NOUN
ajst-18332	54	30	of	of	ADP
ajst-18332	54	31	rebars	rebar	NOUN
ajst-18332	54	32	,	,	PUNCT
ajst-18332	54	33	optimizing	optimize	VERB
ajst-18332	54	34	the	the	DET
ajst-18332	54	35	accuracy	accuracy	NOUN
ajst-18332	54	36	of	of	ADP
ajst-18332	54	37	predicted	predict	VERB
ajst-18332	54	38	probabilities	probability	NOUN
ajst-18332	54	39	and	and	CCONJ
ajst-18332	54	40	location	location	NOUN
ajst-18332	54	41	coordinates	coordinate	NOUN
ajst-18332	54	42	.	.	PUNCT
ajst-18332	55	1	to	to	PART
ajst-18332	55	2	further	far	ADV
ajst-18332	55	3	optimize	optimize	VERB
ajst-18332	55	4	the	the	DET
ajst-18332	55	5	accurate	accurate	ADJ
ajst-18332	55	6	identification	identification	NOUN
ajst-18332	55	7	and	and	CCONJ
ajst-18332	55	8	precise	precise	ADJ
ajst-18332	55	9	localization	localization	NOUN
ajst-18332	55	10	of	of	ADP
ajst-18332	55	11	rebar	rebar	NOUN
ajst-18332	55	12	ends	end	NOUN
ajst-18332	55	13	,	,	PUNCT
ajst-18332	55	14	we	we	PRON
ajst-18332	55	15	employed	employ	VERB
ajst-18332	55	16	an	an	DET
ajst-18332	55	17	improved	improved	ADJ
ajst-18332	55	18	loss	loss	NOUN
ajst-18332	55	19	function	function	NOUN
ajst-18332	55	20	strategy	strategy	NOUN
ajst-18332	55	21	.	.	PUNCT
ajst-18332	56	1	the	the	DET
ajst-18332	56	2	classification	classification	NOUN
ajst-18332	56	3	task	task	NOUN
ajst-18332	56	4	uses	use	VERB
ajst-18332	56	5	cross	cross	ADJ
ajst-18332	56	6	-	-	ADJ
ajst-18332	56	7	entropy	entropy	ADJ
ajst-18332	56	8	loss	loss	NOUN
ajst-18332	56	9	,	,	PUNCT
ajst-18332	56	10	but	but	CCONJ
ajst-18332	56	11	we	we	PRON
ajst-18332	56	12	introduced	introduce	VERB
ajst-18332	56	13	label	label	NOUN
ajst-18332	56	14	smoothing	smooth	VERB
ajst-18332	56	15	technology	technology	NOUN
ajst-18332	56	16	to	to	PART
ajst-18332	56	17	reduce	reduce	VERB
ajst-18332	56	18	the	the	DET
ajst-18332	56	19	model	model	NOUN
ajst-18332	56	20	's	's	PART
ajst-18332	56	21	overconfidence	overconfidence	NOUN
ajst-18332	56	22	in	in	ADP
ajst-18332	56	23	a	a	DET
ajst-18332	56	24	single	single	ADJ
ajst-18332	56	25	category	category	NOUN
ajst-18332	56	26	,	,	PUNCT
ajst-18332	56	27	thereby	thereby	ADV
ajst-18332	56	28	improving	improve	VERB
ajst-18332	56	29	its	its	PRON
ajst-18332	56	30	generalization	generalization	NOUN
ajst-18332	56	31	ability	ability	NOUN
ajst-18332	56	32	.	.	PUNCT
ajst-18332	57	1	for	for	ADP
ajst-18332	57	2	localization	localization	NOUN
ajst-18332	57	3	tasks	task	NOUN
ajst-18332	57	4	,	,	PUNCT
ajst-18332	57	5	we	we	PRON
ajst-18332	57	6	used	use	VERB
ajst-18332	57	7	ciou	ciou	NOUN
ajst-18332	57	8	loss	loss	NOUN
ajst-18332	57	9	[	[	X
ajst-18332	57	10	18	18	NUM
ajst-18332	57	11	]	]	X
ajst-18332	57	12	,	,	PUNCT
ajst-18332	57	13	an	an	DET
ajst-18332	57	14	iou	iou	NOUN
ajst-18332	57	15	-	-	PUNCT
ajst-18332	57	16	based	base	VERB
ajst-18332	57	17	loss	loss	NOUN
ajst-18332	57	18	function	function	NOUN
ajst-18332	57	19	that	that	PRON
ajst-18332	57	20	considers	consider	VERB
ajst-18332	57	21	not	not	PART
ajst-18332	57	22	only	only	ADV
ajst-18332	57	23	the	the	DET
ajst-18332	57	24	overlap	overlap	NOUN
ajst-18332	57	25	area	area	NOUN
ajst-18332	57	26	between	between	ADP
ajst-18332	57	27	the	the	DET
ajst-18332	57	28	prediction	prediction	NOUN
ajst-18332	57	29	box	box	NOUN
ajst-18332	57	30	and	and	CCONJ
ajst-18332	57	31	the	the	DET
ajst-18332	57	32	true	true	ADJ
ajst-18332	57	33	box	box	PROPN
ajst-18332	57	34	but	but	CCONJ
ajst-18332	57	35	also	also	ADV
ajst-18332	57	36	the	the	DET
ajst-18332	57	37	distance	distance	NOUN
ajst-18332	57	38	and	and	CCONJ
ajst-18332	57	39	shape	shape	NOUN
ajst-18332	57	40	of	of	ADP
ajst-18332	57	41	their	their	PRON
ajst-18332	57	42	centers	center	NOUN
ajst-18332	57	43	.	.	PUNCT
ajst-18332	58	1	this	this	PRON
ajst-18332	58	2	is	be	AUX
ajst-18332	58	3	very	very	ADV
ajst-18332	58	4	effective	effective	ADJ
ajst-18332	58	5	in	in	ADP
ajst-18332	58	6	improving	improve	VERB
ajst-18332	58	7	the	the	DET
ajst-18332	58	8	accuracy	accuracy	NOUN
ajst-18332	58	9	of	of	ADP
ajst-18332	58	10	bounding	bound	VERB
ajst-18332	58	11	box	box	NOUN
ajst-18332	58	12	localization	localization	NOUN
ajst-18332	58	13	.	.	PUNCT
ajst-18332	59	1	the	the	DET
ajst-18332	59	2	calculation	calculation	NOUN
ajst-18332	59	3	formula	formula	NOUN
ajst-18332	59	4	for	for	ADP
ajst-18332	59	5	ciou	ciou	NOUN
ajst-18332	59	6	loss	loss	NOUN
ajst-18332	59	7	is	be	AUX
ajst-18332	59	8	as	as	SCONJ
ajst-18332	59	9	follows	follow	VERB
ajst-18332	59	10	:	:	PUNCT
ajst-18332	59	11	𝐶𝐼𝑂𝑈	𝐶𝐼𝑂𝑈	VERB
ajst-18332	59	12	𝐿𝑂𝑆𝑆	𝐿𝑂𝑆𝑆	PROPN
ajst-18332	59	13	1	1	NUM
ajst-18332	59	14	𝐼𝑂𝑈	𝐼𝑂𝑈	PROPN
ajst-18332	59	15	,	,	PUNCT
ajst-18332	59	16	𝛼	𝛼	VERB
ajst-18332	59	17	𝑣	𝑣	X
ajst-18332	59	18	(	(	PUNCT
ajst-18332	59	19	2	2	NUM
ajst-18332	59	20	)	)	PUNCT
ajst-18332	59	21	where	where	SCONJ
ajst-18332	59	22	iou	iou	PROPN
ajst-18332	59	23	denotes	denote	VERB
ajst-18332	59	24	the	the	DET
ajst-18332	59	25	intersection	intersection	NOUN
ajst-18332	59	26	over	over	ADP
ajst-18332	59	27	union	union	NOUN
ajst-18332	59	28	between	between	ADP
ajst-18332	59	29	the	the	DET
ajst-18332	59	30	predicted	predict	VERB
ajst-18332	59	31	and	and	CCONJ
ajst-18332	59	32	actual	actual	ADJ
ajst-18332	59	33	boxes	box	NOUN
ajst-18332	59	34	.	.	PUNCT
ajst-18332	60	1	the	the	DET
ajst-18332	60	2	term	term	NOUN
ajst-18332	60	3	'	'	PUNCT
ajst-18332	60	4	pbbgt	pbbgt	NOUN
ajst-18332	60	5	'	'	PUNCT
ajst-18332	60	6	refers	refer	NOUN
ajst-18332	60	7	to	to	ADP
ajst-18332	60	8	the	the	DET
ajst-18332	60	9	euclidean	euclidean	ADJ
ajst-18332	60	10	distance	distance	NOUN
ajst-18332	60	11	between	between	ADP
ajst-18332	60	12	the	the	DET
ajst-18332	60	13	centers	center	NOUN
ajst-18332	60	14	of	of	ADP
ajst-18332	60	15	these	these	DET
ajst-18332	60	16	boxes	box	NOUN
ajst-18332	60	17	.	.	PUNCT
ajst-18332	61	1	'	'	PUNCT
ajst-18332	61	2	c	c	X
ajst-18332	61	3	'	'	PUNCT
ajst-18332	61	4	represents	represent	VERB
ajst-18332	61	5	the	the	DET
ajst-18332	61	6	diagonal	diagonal	ADJ
ajst-18332	61	7	length	length	NOUN
ajst-18332	61	8	of	of	ADP
ajst-18332	61	9	the	the	DET
ajst-18332	61	10	smallest	small	ADJ
ajst-18332	61	11	area	area	NOUN
ajst-18332	61	12	enclosing	enclose	VERB
ajst-18332	61	13	both	both	DET
ajst-18332	61	14	boxes	box	NOUN
ajst-18332	61	15	.	.	PUNCT
ajst-18332	62	1	the	the	DET
ajst-18332	62	2	parameter	parameter	NOUN
ajst-18332	62	3	'	'	PUNCT
ajst-18332	62	4	α	α	X
ajst-18332	62	5	'	'	PUNCT
ajst-18332	62	6	serves	serve	VERB
ajst-18332	62	7	as	as	ADP
ajst-18332	62	8	a	a	DET
ajst-18332	62	9	weight	weight	NOUN
ajst-18332	62	10	,	,	PUNCT
ajst-18332	62	11	while	while	SCONJ
ajst-18332	62	12	'	'	PUNCT
ajst-18332	62	13	v	v	NOUN
ajst-18332	62	14	'	'	PUNCT
ajst-18332	62	15	quantifies	quantifie	NOUN
ajst-18332	62	16	the	the	DET
ajst-18332	62	17	aspect	aspect	NOUN
ajst-18332	62	18	ratio	ratio	NOUN
ajst-18332	62	19	consistency	consistency	NOUN
ajst-18332	62	20	between	between	ADP
ajst-18332	62	21	the	the	DET
ajst-18332	62	22	predicted	predict	VERB
ajst-18332	62	23	and	and	CCONJ
ajst-18332	62	24	actual	actual	ADJ
ajst-18332	62	25	boxes	box	NOUN
ajst-18332	62	26	.	.	PUNCT
ajst-18332	63	1	in	in	ADP
ajst-18332	63	2	the	the	DET
ajst-18332	63	3	structural	structural	ADJ
ajst-18332	63	4	design	design	NOUN
ajst-18332	63	5	of	of	ADP
ajst-18332	63	6	the	the	DET
ajst-18332	63	7	detection	detection	NOUN
ajst-18332	63	8	head	head	NOUN
ajst-18332	63	9	,	,	PUNCT
ajst-18332	63	10	we	we	PRON
ajst-18332	63	11	paid	pay	VERB
ajst-18332	63	12	special	special	ADJ
ajst-18332	63	13	attention	attention	NOUN
ajst-18332	63	14	to	to	ADP
ajst-18332	63	15	the	the	DET
ajst-18332	63	16	final	final	ADJ
ajst-18332	63	17	output	output	NOUN
ajst-18332	63	18	configuration	configuration	NOUN
ajst-18332	63	19	of	of	ADP
ajst-18332	63	20	each	each	DET
ajst-18332	63	21	output	output	NOUN
ajst-18332	63	22	channel	channel	NOUN
ajst-18332	63	23	.	.	PUNCT
ajst-18332	64	1	considering	consider	VERB
ajst-18332	64	2	that	that	DET
ajst-18332	64	3	rebar	rebar	NOUN
ajst-18332	64	4	end	end	NOUN
ajst-18332	64	5	face	face	NOUN
ajst-18332	64	6	identification	identification	NOUN
ajst-18332	64	7	usually	usually	ADV
ajst-18332	64	8	involves	involve	VERB
ajst-18332	64	9	more	more	ADV
ajst-18332	64	10	specific	specific	ADJ
ajst-18332	64	11	categories	category	NOUN
ajst-18332	64	12	,	,	PUNCT
ajst-18332	64	13	we	we	PRON
ajst-18332	64	14	adjusted	adjust	VERB
ajst-18332	64	15	the	the	DET
ajst-18332	64	16	final	final	ADJ
ajst-18332	64	17	output	output	NOUN
ajst-18332	64	18	of	of	ADP
ajst-18332	64	19	each	each	DET
ajst-18332	64	20	output	output	NOUN
ajst-18332	64	21	channel	channel	NOUN
ajst-18332	64	22	to	to	ADP
ajst-18332	64	23	3×(1	3×(1	NUM
ajst-18332	64	24	+	+	CCONJ
ajst-18332	64	25	4	4	NUM
ajst-18332	64	26	)	)	PUNCT
ajst-18332	64	27	,	,	PUNCT
ajst-18332	64	28	that	that	ADV
ajst-18332	64	29	is	is	ADV
ajst-18332	64	30	,	,	PUNCT
ajst-18332	64	31	one	one	NUM
ajst-18332	64	32	rebar	rebar	NOUN
ajst-18332	64	33	end	end	NOUN
ajst-18332	64	34	face	face	NOUN
ajst-18332	64	35	category	category	NOUN
ajst-18332	64	36	plus	plus	CCONJ
ajst-18332	64	37	four	four	NUM
ajst-18332	64	38	positional	positional	ADJ
ajst-18332	64	39	coordinate	coordinate	NOUN
ajst-18332	64	40	parameters	parameter	NOUN
ajst-18332	64	41	.	.	PUNCT
ajst-18332	65	1	this	this	DET
ajst-18332	65	2	configuration	configuration	NOUN
ajst-18332	65	3	ensures	ensure	VERB
ajst-18332	65	4	that	that	SCONJ
ajst-18332	65	5	the	the	DET
ajst-18332	65	6	network	network	NOUN
ajst-18332	65	7	can	can	AUX
ajst-18332	65	8	effectively	effectively	ADV
ajst-18332	65	9	classify	classify	VERB
ajst-18332	65	10	and	and	CCONJ
ajst-18332	65	11	locate	locate	ADJ
ajst-18332	65	12	rebar	rebar	NOUN
ajst-18332	65	13	end	end	NOUN
ajst-18332	65	14	faces	face	VERB
ajst-18332	65	15	.	.	PUNCT
ajst-18332	66	1	we	we	PRON
ajst-18332	66	2	also	also	ADV
ajst-18332	66	3	adjusted	adjust	VERB
ajst-18332	66	4	the	the	DET
ajst-18332	66	5	weights	weight	NOUN
ajst-18332	66	6	of	of	ADP
ajst-18332	66	7	the	the	DET
ajst-18332	66	8	cross	cross	ADJ
ajst-18332	66	9	-	-	ADJ
ajst-18332	66	10	entropy	entropy	ADJ
ajst-18332	66	11	loss	loss	NOUN
ajst-18332	66	12	function	function	NOUN
ajst-18332	66	13	.	.	PUNCT
ajst-18332	67	1	this	this	DET
ajst-18332	67	2	adjustment	adjustment	NOUN
ajst-18332	67	3	is	be	AUX
ajst-18332	67	4	based	base	VERB
ajst-18332	67	5	on	on	ADP
ajst-18332	67	6	the	the	DET
ajst-18332	67	7	characteristics	characteristic	NOUN
ajst-18332	67	8	of	of	ADP
ajst-18332	67	9	rebar	rebar	NOUN
ajst-18332	67	10	features	feature	NOUN
ajst-18332	67	11	,	,	PUNCT
ajst-18332	67	12	such	such	ADJ
ajst-18332	67	13	as	as	ADP
ajst-18332	67	14	the	the	DET
ajst-18332	67	15	size	size	NOUN
ajst-18332	67	16	,	,	PUNCT
ajst-18332	67	17	shape	shape	NOUN
ajst-18332	67	18	,	,	PUNCT
ajst-18332	67	19	and	and	CCONJ
ajst-18332	67	20	frequency	frequency	NOUN
ajst-18332	67	21	of	of	ADP
ajst-18332	67	22	appearance	appearance	NOUN
ajst-18332	67	23	in	in	ADP
ajst-18332	67	24	images	image	NOUN
ajst-18332	67	25	.	.	PUNCT
ajst-18332	68	1	in	in	ADP
ajst-18332	68	2	this	this	DET
ajst-18332	68	3	way	way	NOUN
ajst-18332	68	4	,	,	PUNCT
ajst-18332	68	5	we	we	PRON
ajst-18332	68	6	can	can	AUX
ajst-18332	68	7	optimize	optimize	VERB
ajst-18332	68	8	the	the	DET
ajst-18332	68	9	classification	classification	NOUN
ajst-18332	68	10	training	training	NOUN
ajst-18332	68	11	effect	effect	NOUN
ajst-18332	68	12	for	for	ADP
ajst-18332	68	13	rebar	rebar	NOUN
ajst-18332	68	14	ends	end	NOUN
ajst-18332	68	15	and	and	CCONJ
ajst-18332	68	16	improve	improve	VERB
ajst-18332	68	17	the	the	DET
ajst-18332	68	18	model	model	NOUN
ajst-18332	68	19	's	's	PART
ajst-18332	68	20	recognition	recognition	NOUN
ajst-18332	68	21	ability	ability	NOUN
ajst-18332	68	22	for	for	ADP
ajst-18332	68	23	different	different	ADJ
ajst-18332	68	24	categories	category	NOUN
ajst-18332	68	25	of	of	ADP
ajst-18332	68	26	rebars	rebar	NOUN
ajst-18332	68	27	.	.	PUNCT
ajst-18332	69	1	figure	figure	NOUN
ajst-18332	69	2	3	3	NUM
ajst-18332	69	3	in	in	ADP
ajst-18332	69	4	this	this	DET
ajst-18332	69	5	paper	paper	NOUN
ajst-18332	69	6	details	detail	NOUN
ajst-18332	69	7	the	the	DET
ajst-18332	69	8	structure	structure	NOUN
ajst-18332	69	9	of	of	ADP
ajst-18332	69	10	the	the	DET
ajst-18332	69	11	detection	detection	NOUN
ajst-18332	69	12	head	head	NOUN
ajst-18332	69	13	and	and	CCONJ
ajst-18332	69	14	its	its	PRON
ajst-18332	69	15	coordinated	coordinate	VERB
ajst-18332	69	16	use	use	NOUN
ajst-18332	69	17	with	with	ADP
ajst-18332	69	18	the	the	DET
ajst-18332	69	19	loss	loss	NOUN
ajst-18332	69	20	function	function	NOUN
ajst-18332	69	21	.	.	PUNCT
ajst-18332	70	1	specifically	specifically	ADV
ajst-18332	70	2	,	,	PUNCT
ajst-18332	70	3	the	the	DET
ajst-18332	70	4	introduction	introduction	NOUN
ajst-18332	70	5	of	of	ADP
ajst-18332	70	6	ciou	ciou	NOUN
ajst-18332	70	7	loss	loss	NOUN
ajst-18332	70	8	is	be	AUX
ajst-18332	70	9	explained	explain	VERB
ajst-18332	70	10	in	in	ADP
ajst-18332	70	11	detail	detail	NOUN
ajst-18332	70	12	,	,	PUNCT
ajst-18332	70	13	showing	show	VERB
ajst-18332	70	14	how	how	SCONJ
ajst-18332	70	15	it	it	PRON
ajst-18332	70	16	optimizes	optimize	VERB
ajst-18332	70	17	localization	localization	NOUN
ajst-18332	70	18	tasks	task	NOUN
ajst-18332	70	19	.	.	PUNCT
ajst-18332	71	1	the	the	DET
ajst-18332	71	2	weight	weight	NOUN
ajst-18332	71	3	adjustment	adjustment	NOUN
ajst-18332	71	4	of	of	ADP
ajst-18332	71	5	the	the	DET
ajst-18332	71	6	cross	cross	ADJ
ajst-18332	71	7	-	-	ADJ
ajst-18332	71	8	entropy	entropy	ADJ
ajst-18332	71	9	loss	loss	NOUN
ajst-18332	71	10	function	function	NOUN
ajst-18332	71	11	is	be	AUX
ajst-18332	71	12	also	also	ADV
ajst-18332	71	13	elucidated	elucidate	VERB
ajst-18332	71	14	,	,	PUNCT
ajst-18332	71	15	including	include	VERB
ajst-18332	71	16	its	its	PRON
ajst-18332	71	17	definition	definition	NOUN
ajst-18332	71	18	and	and	CCONJ
ajst-18332	71	19	calculation	calculation	NOUN
ajst-18332	71	20	method	method	NOUN
ajst-18332	71	21	,	,	PUNCT
ajst-18332	71	22	as	as	ADV
ajst-18332	71	23	well	well	ADV
ajst-18332	71	24	as	as	ADP
ajst-18332	71	25	examples	example	NOUN
ajst-18332	71	26	of	of	ADP
ajst-18332	71	27	how	how	SCONJ
ajst-18332	71	28	to	to	PART
ajst-18332	71	29	adjust	adjust	VERB
ajst-18332	71	30	weights	weight	NOUN
ajst-18332	71	31	based	base	VERB
ajst-18332	71	32	on	on	ADP
ajst-18332	71	33	rebar	rebar	NOUN
ajst-18332	71	34	features	feature	NOUN
ajst-18332	71	35	.	.	PUNCT
ajst-18332	72	1	with	with	ADP
ajst-18332	72	2	these	these	DET
ajst-18332	72	3	improvements	improvement	NOUN
ajst-18332	72	4	,	,	PUNCT
ajst-18332	72	5	our	our	PRON
ajst-18332	72	6	pp	pp	ADJ
ajst-18332	72	7	-	-	PUNCT
ajst-18332	72	8	yolo	yolo	ADJ
ajst-18332	72	9	network	network	NOUN
ajst-18332	72	10	not	not	PART
ajst-18332	72	11	only	only	ADV
ajst-18332	72	12	excels	excel	NOUN
ajst-18332	72	13	in	in	ADP
ajst-18332	72	14	the	the	DET
ajst-18332	72	15	detection	detection	NOUN
ajst-18332	72	16	of	of	ADP
ajst-18332	72	17	rebar	rebar	ADJ
ajst-18332	72	18	end	end	NOUN
ajst-18332	72	19	faces	face	NOUN
ajst-18332	72	20	but	but	CCONJ
ajst-18332	72	21	also	also	ADV
ajst-18332	72	22	demonstrates	demonstrate	VERB
ajst-18332	72	23	high	high	ADJ
ajst-18332	72	24	accuracy	accuracy	NOUN
ajst-18332	72	25	and	and	CCONJ
ajst-18332	72	26	efficiency	efficiency	NOUN
ajst-18332	72	27	in	in	ADP
ajst-18332	72	28	processing	processing	NOUN
ajst-18332	72	29	end	end	NOUN
ajst-18332	72	30	face	face	VERB
ajst-18332	72	31	images	image	NOUN
ajst-18332	72	32	in	in	ADP
ajst-18332	72	33	complex	complex	ADJ
ajst-18332	72	34	environments	environment	NOUN
ajst-18332	72	35	.	.	PUNCT
ajst-18332	73	1	the	the	DET
ajst-18332	73	2	definition	definition	NOUN
ajst-18332	73	3	of	of	ADP
ajst-18332	73	4	the	the	DET
ajst-18332	73	5	cross	cross	ADJ
ajst-18332	73	6	-	-	ADJ
ajst-18332	73	7	entropy	entropy	ADJ
ajst-18332	73	8	loss	loss	NOUN
ajst-18332	73	9	function	function	NOUN
ajst-18332	73	10	is	be	AUX
ajst-18332	73	11	as	as	SCONJ
ajst-18332	73	12	shown	show	VERB
ajst-18332	73	13	in	in	ADP
ajst-18332	73	14	formula	formula	NOUN
ajst-18332	73	15	(	(	PUNCT
ajst-18332	73	16	3).where	3).where	NUM
ajst-18332	73	17	weight	weight	NOUN
ajst-18332	73	18	is	be	AUX
ajst-18332	73	19	the	the	DET
ajst-18332	73	20	specified	specified	ADJ
ajst-18332	73	21	weight	weight	NOUN
ajst-18332	73	22	for	for	ADP
ajst-18332	73	23	each	each	DET
ajst-18332	73	24	category	category	NOUN
ajst-18332	73	25	,	,	PUNCT
ajst-18332	73	26	and	and	CCONJ
ajst-18332	73	27	input	input	NOUN
ajst-18332	73	28	is	be	AUX
ajst-18332	73	29	the	the	DET
ajst-18332	73	30	input	input	NOUN
ajst-18332	73	31	for	for	ADP
ajst-18332	73	32	the	the	DET
ajst-18332	73	33	specified	specified	ADJ
ajst-18332	73	34	category	category	NOUN
ajst-18332	73	35	.	.	PUNCT
ajst-18332	74	1	𝑙𝑜𝑠𝑠	𝑙𝑜𝑠𝑠	PROPN
ajst-18332	74	2	𝑤𝑒𝑖𝑔ℎ𝑡	𝑤𝑒𝑖𝑔ℎ𝑡	PROPN
ajst-18332	74	3	𝑐𝑙𝑎𝑠𝑠	𝑐𝑙𝑎𝑠𝑠	PROPN
ajst-18332	74	4	𝑖𝑛𝑝𝑢𝑡	𝑖𝑛𝑝𝑢𝑡	PROPN
ajst-18332	74	5	𝑐𝑙𝑎𝑠𝑠	𝑐𝑙𝑎𝑠𝑠	PROPN
ajst-18332	74	6	log	log	PROPN
ajst-18332	74	7	∑	∑	PUNCT
ajst-18332	74	8	exp	exp	X
ajst-18332	74	9	𝑖𝑛𝑝𝑢𝑡	𝑖𝑛𝑝𝑢𝑡	X
ajst-18332	74	10	𝑗	𝑗	PROPN
ajst-18332	74	11	1,2	1,2	NUM
ajst-18332	74	12	,	,	PUNCT
ajst-18332	74	13	…	…	PUNCT
ajst-18332	74	14	,	,	PUNCT
ajst-18332	74	15	𝐾	𝐾	PROPN
ajst-18332	74	16	(	(	PUNCT
ajst-18332	74	17	3	3	NUM
ajst-18332	74	18	)	)	PUNCT
ajst-18332	74	19	figure	figure	NOUN
ajst-18332	74	20	3	3	NUM
ajst-18332	74	21	.	.	PUNCT
ajst-18332	74	22	block	block	NOUN
ajst-18332	74	23	diagram	diagram	NOUN
ajst-18332	74	24	of	of	ADP
ajst-18332	74	25	the	the	DET
ajst-18332	74	26	improved	improve	VERB
ajst-18332	74	27	data	data	NOUN
ajst-18332	74	28	enhancement	enhancement	NOUN
ajst-18332	74	29	strategy	strategy	NOUN
ajst-18332	74	30	2.3	2.3	NUM
ajst-18332	74	31	.	.	PUNCT
ajst-18332	75	1	improvements	improvement	NOUN
ajst-18332	75	2	in	in	ADP
ajst-18332	75	3	data	data	NOUN
ajst-18332	75	4	enhancement	enhancement	NOUN
ajst-18332	75	5	optimisation	optimisation	NOUN
ajst-18332	75	6	strategies	strategy	NOUN
ajst-18332	75	7	in	in	ADP
ajst-18332	75	8	order	order	NOUN
ajst-18332	75	9	to	to	PART
ajst-18332	75	10	further	far	ADV
ajst-18332	75	11	improve	improve	VERB
ajst-18332	75	12	the	the	DET
ajst-18332	75	13	generalisation	generalisation	NOUN
ajst-18332	75	14	performance	performance	NOUN
ajst-18332	75	15	of	of	ADP
ajst-18332	75	16	the	the	DET
ajst-18332	75	17	rebar	rebar	NOUN
ajst-18332	75	18	end	end	NOUN
ajst-18332	75	19	face	face	NOUN
ajst-18332	75	20	recognition	recognition	NOUN
ajst-18332	75	21	model	model	NOUN
ajst-18332	75	22	and	and	CCONJ
ajst-18332	75	23	to	to	PART
ajst-18332	75	24	avoid	avoid	VERB
ajst-18332	75	25	overfitting	overfitte	VERB
ajst-18332	75	26	phenomena	phenomenon	NOUN
ajst-18332	75	27	,	,	PUNCT
ajst-18332	75	28	we	we	PRON
ajst-18332	75	29	optimised	optimise	VERB
ajst-18332	75	30	our	our	PRON
ajst-18332	75	31	data	data	NOUN
ajst-18332	75	32	enhancement	enhancement	NOUN
ajst-18332	75	33	strategy	strategy	NOUN
ajst-18332	75	34	.	.	PUNCT
ajst-18332	76	1	although	although	SCONJ
ajst-18332	76	2	initially	initially	ADV
ajst-18332	76	3	,	,	PUNCT
ajst-18332	76	4	our	our	PRON
ajst-18332	76	5	pp	pp	ADJ
ajst-18332	76	6	-	-	PUNCT
ajst-18332	76	7	yolo	yolo	ADJ
ajst-18332	76	8	model	model	NOUN
ajst-18332	76	9	used	use	VERB
ajst-18332	76	10	the	the	DET
ajst-18332	76	11	image	image	NOUN
ajst-18332	76	12	mixup	mixup	NOUN
ajst-18332	76	13	algorithm	algorithm	NOUN
ajst-18332	76	14	in	in	ADP
ajst-18332	76	15	the	the	DET
ajst-18332	76	16	expectation	expectation	NOUN
ajst-18332	76	17	of	of	ADP
ajst-18332	76	18	enhancing	enhance	VERB
ajst-18332	76	19	the	the	DET
ajst-18332	76	20	dataset	dataset	NOUN
ajst-18332	76	21	through	through	ADP
ajst-18332	76	22	weighted	weight	VERB
ajst-18332	76	23	mixing	mixing	NOUN
ajst-18332	76	24	between	between	ADP
ajst-18332	76	25	images	image	NOUN
ajst-18332	76	26	,	,	PUNCT
ajst-18332	76	27	we	we	PRON
ajst-18332	76	28	observed	observe	VERB
ajst-18332	76	29	that	that	SCONJ
ajst-18332	76	30	this	this	DET
ajst-18332	76	31	method	method	NOUN
ajst-18332	76	32	may	may	AUX
ajst-18332	76	33	produce	produce	VERB
ajst-18332	76	34	undesirable	undesirable	ADJ
ajst-18332	76	35	mixing	mixing	NOUN
ajst-18332	76	36	effects	effect	NOUN
ajst-18332	76	37	on	on	ADP
ajst-18332	76	38	rebar	rebar	ADJ
ajst-18332	76	39	images	image	NOUN
ajst-18332	76	40	,	,	PUNCT
ajst-18332	76	41	which	which	PRON
ajst-18332	76	42	in	in	ADP
ajst-18332	76	43	turn	turn	NOUN
ajst-18332	76	44	may	may	AUX
ajst-18332	76	45	adversely	adversely	ADV
ajst-18332	76	46	affect	affect	VERB
ajst-18332	76	47	the	the	DET
ajst-18332	76	48	training	training	NOUN
ajst-18332	76	49	efficiency	efficiency	NOUN
ajst-18332	76	50	of	of	ADP
ajst-18332	76	51	the	the	DET
ajst-18332	76	52	model	model	NOUN
ajst-18332	76	53	.	.	PUNCT
ajst-18332	77	1	to	to	PART
ajst-18332	77	2	overcome	overcome	VERB
ajst-18332	77	3	these	these	DET
ajst-18332	77	4	limitations	limitation	NOUN
ajst-18332	77	5	,	,	PUNCT
ajst-18332	77	6	we	we	PRON
ajst-18332	77	7	turned	turn	VERB
ajst-18332	77	8	to	to	ADP
ajst-18332	77	9	the	the	DET
ajst-18332	77	10	cutmix	cutmix	NOUN
ajst-18332	77	11	algorithm	algorithm	NOUN
ajst-18332	77	12	,	,	PUNCT
ajst-18332	77	13	which	which	PRON
ajst-18332	77	14	generates	generate	VERB
ajst-18332	77	15	training	training	NOUN
ajst-18332	77	16	samples	sample	NOUN
ajst-18332	77	17	that	that	PRON
ajst-18332	77	18	are	be	AUX
ajst-18332	77	19	better	well	ADV
ajst-18332	77	20	suited	suited	ADJ
ajst-18332	77	21	to	to	ADP
ajst-18332	77	22	the	the	DET
ajst-18332	77	23	characteristics	characteristic	NOUN
ajst-18332	77	24	of	of	ADP
ajst-18332	77	25	the	the	DET
ajst-18332	77	26	rebar	rebar	NOUN
ajst-18332	77	27	by	by	ADP
ajst-18332	77	28	replacing	replace	VERB
ajst-18332	77	29	certain	certain	ADJ
ajst-18332	77	30	portions	portion	NOUN
ajst-18332	77	31	of	of	ADP
ajst-18332	77	32	the	the	DET
ajst-18332	77	33	image	image	NOUN
ajst-18332	77	34	in	in	ADP
ajst-18332	77	35	order	order	NOUN
ajst-18332	77	36	to	to	PART
ajst-18332	77	37	introduce	introduce	VERB
ajst-18332	77	38	a	a	DET
ajst-18332	77	39	more	more	ADV
ajst-18332	77	40	diverse	diverse	ADJ
ajst-18332	77	41	set	set	NOUN
ajst-18332	77	42	of	of	ADP
ajst-18332	77	43	image	image	NOUN
ajst-18332	77	44	features.cutmix	features.cutmix	PROPN
ajst-18332	77	45	creates	create	VERB
ajst-18332	77	46	a	a	DET
ajst-18332	77	47	new	new	ADJ
ajst-18332	77	48	combination	combination	NOUN
ajst-18332	77	49	of	of	ADP
ajst-18332	77	50	images	image	NOUN
ajst-18332	77	51	by	by	ADP
ajst-18332	77	52	randomly	randomly	ADV
ajst-18332	77	53	cropping	crop	VERB
ajst-18332	77	54	blocks	block	NOUN
ajst-18332	77	55	of	of	ADP
ajst-18332	77	56	images	image	NOUN
ajst-18332	77	57	and	and	CCONJ
ajst-18332	77	58	stitching	stitch	VERB
ajst-18332	77	59	them	they	PRON
ajst-18332	77	60	together	together	ADV
ajst-18332	77	61	with	with	ADP
ajst-18332	77	62	other	other	ADJ
ajst-18332	77	63	blocks	block	NOUN
ajst-18332	77	64	of	of	ADP
ajst-18332	77	65	images.this	images.this	PRON
ajst-18332	77	66	combination	combination	NOUN
ajst-18332	77	67	preserves	preserve	VERB
ajst-18332	77	68	the	the	DET
ajst-18332	77	69	key	key	ADJ
ajst-18332	77	70	feature	feature	NOUN
ajst-18332	77	71	information	information	NOUN
ajst-18332	77	72	of	of	ADP
ajst-18332	77	73	the	the	DET
ajst-18332	77	74	rebar	rebar	NOUN
ajst-18332	77	75	while	while	SCONJ
ajst-18332	77	76	increasing	increase	VERB
ajst-18332	77	77	the	the	DET
ajst-18332	77	78	diversity	diversity	NOUN
ajst-18332	77	79	of	of	ADP
ajst-18332	77	80	backgrounds	background	NOUN
ajst-18332	77	81	and	and	CCONJ
ajst-18332	77	82	environments	environment	NOUN
ajst-18332	77	83	.	.	PUNCT
ajst-18332	78	1	in	in	ADP
ajst-18332	78	2	addition	addition	NOUN
ajst-18332	78	3	,	,	PUNCT
ajst-18332	78	4	we	we	PRON
ajst-18332	78	5	introduced	introduce	VERB
ajst-18332	78	6	a	a	DET
ajst-18332	78	7	series	series	NOUN
ajst-18332	78	8	of	of	ADP
ajst-18332	78	9	direct	direct	ADJ
ajst-18332	78	10	data	datum	NOUN
ajst-18332	78	11	enhancement	enhancement	NOUN
ajst-18332	78	12	techniques	technique	NOUN
ajst-18332	78	13	,	,	PUNCT
ajst-18332	78	14	including	include	VERB
ajst-18332	78	15	basic	basic	ADJ
ajst-18332	78	16	geometric	geometric	ADJ
ajst-18332	78	17	transformations	transformation	NOUN
ajst-18332	78	18	(	(	PUNCT
ajst-18332	78	19	e.g.	e.g.	ADV
ajst-18332	78	20	,	,	PUNCT
ajst-18332	78	21	rotation	rotation	NOUN
ajst-18332	78	22	and	and	CCONJ
ajst-18332	78	23	scaling	scaling	NOUN
ajst-18332	78	24	)	)	PUNCT
ajst-18332	78	25	,	,	PUNCT
ajst-18332	78	26	lighting	lighting	NOUN
ajst-18332	78	27	and	and	CCONJ
ajst-18332	78	28	94	94	NUM
ajst-18332	78	29	contrast	contrast	NOUN
ajst-18332	78	30	adjustments	adjustment	NOUN
ajst-18332	78	31	,	,	PUNCT
ajst-18332	78	32	noise	noise	NOUN
ajst-18332	78	33	injection	injection	NOUN
ajst-18332	78	34	,	,	PUNCT
ajst-18332	78	35	and	and	CCONJ
ajst-18332	78	36	blurring	blur	VERB
ajst-18332	78	37	and	and	CCONJ
ajst-18332	78	38	sharpening	sharpen	VERB
ajst-18332	78	39	treatments	treatment	NOUN
ajst-18332	78	40	,	,	PUNCT
ajst-18332	78	41	all	all	PRON
ajst-18332	78	42	designed	design	VERB
ajst-18332	78	43	to	to	PART
ajst-18332	78	44	simulate	simulate	VERB
ajst-18332	78	45	a	a	DET
ajst-18332	78	46	wide	wide	ADJ
ajst-18332	78	47	range	range	NOUN
ajst-18332	78	48	of	of	ADP
ajst-18332	78	49	conditions	condition	NOUN
ajst-18332	78	50	likely	likely	ADJ
ajst-18332	78	51	to	to	PART
ajst-18332	78	52	be	be	AUX
ajst-18332	78	53	encountered	encounter	VERB
ajst-18332	78	54	in	in	ADP
ajst-18332	78	55	the	the	DET
ajst-18332	78	56	real	real	ADJ
ajst-18332	78	57	world	world	NOUN
ajst-18332	78	58	.	.	PUNCT
ajst-18332	79	1	with	with	ADP
ajst-18332	79	2	these	these	DET
ajst-18332	79	3	combined	combine	VERB
ajst-18332	79	4	data	datum	NOUN
ajst-18332	79	5	enhancements	enhancement	NOUN
ajst-18332	79	6	,	,	PUNCT
ajst-18332	79	7	the	the	DET
ajst-18332	79	8	model	model	NOUN
ajst-18332	79	9	is	be	AUX
ajst-18332	79	10	able	able	ADJ
ajst-18332	79	11	to	to	PART
ajst-18332	79	12	recognise	recognise	VERB
ajst-18332	79	13	rebar	rebar	ADJ
ajst-18332	79	14	end	end	NOUN
ajst-18332	79	15	faces	face	NOUN
ajst-18332	79	16	in	in	ADP
ajst-18332	79	17	a	a	DET
ajst-18332	79	18	variety	variety	NOUN
ajst-18332	79	19	of	of	ADP
ajst-18332	79	20	changing	change	VERB
ajst-18332	79	21	environments	environment	NOUN
ajst-18332	79	22	,	,	PUNCT
ajst-18332	79	23	thus	thus	ADV
ajst-18332	79	24	improving	improve	VERB
ajst-18332	79	25	the	the	DET
ajst-18332	79	26	robustness	robustness	NOUN
ajst-18332	79	27	and	and	CCONJ
ajst-18332	79	28	accuracy	accuracy	NOUN
ajst-18332	79	29	of	of	ADP
ajst-18332	79	30	the	the	DET
ajst-18332	79	31	model	model	NOUN
ajst-18332	79	32	in	in	ADP
ajst-18332	79	33	real	real	ADJ
ajst-18332	79	34	-	-	PUNCT
ajst-18332	79	35	world	world	NOUN
ajst-18332	79	36	application	application	NOUN
ajst-18332	79	37	scenarios	scenario	NOUN
ajst-18332	79	38	.	.	PUNCT
ajst-18332	80	1	the	the	DET
ajst-18332	80	2	improved	improve	VERB
ajst-18332	80	3	data	data	NOUN
ajst-18332	80	4	augmentation	augmentation	NOUN
ajst-18332	80	5	process	process	NOUN
ajst-18332	80	6	is	be	AUX
ajst-18332	80	7	demonstrated	demonstrate	VERB
ajst-18332	80	8	in	in	ADP
ajst-18332	80	9	fig	fig	NOUN
ajst-18332	80	10	.	.	PUNCT
ajst-18332	81	1	4	4	NUM
ajst-18332	81	2	,	,	PUNCT
ajst-18332	81	3	which	which	PRON
ajst-18332	81	4	not	not	PART
ajst-18332	81	5	only	only	ADV
ajst-18332	81	6	effectively	effectively	ADV
ajst-18332	81	7	enhances	enhance	VERB
ajst-18332	81	8	the	the	DET
ajst-18332	81	9	quality	quality	NOUN
ajst-18332	81	10	and	and	CCONJ
ajst-18332	81	11	diversity	diversity	NOUN
ajst-18332	81	12	of	of	ADP
ajst-18332	81	13	the	the	DET
ajst-18332	81	14	training	training	NOUN
ajst-18332	81	15	samples	sample	NOUN
ajst-18332	81	16	,	,	PUNCT
ajst-18332	81	17	but	but	CCONJ
ajst-18332	81	18	also	also	ADV
ajst-18332	81	19	ensures	ensure	VERB
ajst-18332	81	20	that	that	SCONJ
ajst-18332	81	21	the	the	DET
ajst-18332	81	22	additional	additional	ADJ
ajst-18332	81	23	data	data	NOUN
ajst-18332	81	24	processing	processing	NOUN
ajst-18332	81	25	does	do	AUX
ajst-18332	81	26	not	not	PART
ajst-18332	81	27	significantly	significantly	ADV
ajst-18332	81	28	increase	increase	VERB
ajst-18332	81	29	the	the	DET
ajst-18332	81	30	computational	computational	ADJ
ajst-18332	81	31	burden	burden	NOUN
ajst-18332	81	32	during	during	ADP
ajst-18332	81	33	training	training	NOUN
ajst-18332	81	34	and	and	CCONJ
ajst-18332	81	35	inference	inference	NOUN
ajst-18332	81	36	.	.	PUNCT
ajst-18332	82	1	with	with	ADP
ajst-18332	82	2	this	this	DET
ajst-18332	82	3	well	well	ADV
ajst-18332	82	4	-	-	PUNCT
ajst-18332	82	5	designed	design	VERB
ajst-18332	82	6	data	datum	NOUN
ajst-18332	82	7	augmentation	augmentation	NOUN
ajst-18332	82	8	scheme	scheme	NOUN
ajst-18332	82	9	,	,	PUNCT
ajst-18332	82	10	our	our	PRON
ajst-18332	82	11	model	model	NOUN
ajst-18332	82	12	is	be	AUX
ajst-18332	82	13	significantly	significantly	ADV
ajst-18332	82	14	improved	improve	VERB
ajst-18332	82	15	in	in	ADP
ajst-18332	82	16	both	both	DET
ajst-18332	82	17	recognition	recognition	NOUN
ajst-18332	82	18	accuracy	accuracy	NOUN
ajst-18332	82	19	and	and	CCONJ
ajst-18332	82	20	efficiency	efficiency	NOUN
ajst-18332	82	21	.	.	PUNCT
ajst-18332	83	1	figure	figure	NOUN
ajst-18332	83	2	4	4	NUM
ajst-18332	83	3	.	.	PUNCT
ajst-18332	83	4	block	block	NOUN
ajst-18332	83	5	diagram	diagram	NOUN
ajst-18332	83	6	of	of	ADP
ajst-18332	83	7	the	the	DET
ajst-18332	83	8	improved	improve	VERB
ajst-18332	83	9	data	data	NOUN
ajst-18332	83	10	enhancement	enhancement	NOUN
ajst-18332	83	11	strategy	strategy	NOUN
ajst-18332	83	12	2.4	2.4	NUM
ajst-18332	83	13	.	.	PUNCT
ajst-18332	84	1	optimisation	optimisation	NOUN
ajst-18332	84	2	of	of	ADP
ajst-18332	84	3	training	training	NOUN
ajst-18332	84	4	strategies	strategy	NOUN
ajst-18332	84	5	the	the	DET
ajst-18332	84	6	experimental	experimental	ADJ
ajst-18332	84	7	datasets	dataset	NOUN
ajst-18332	84	8	in	in	ADP
ajst-18332	84	9	this	this	DET
ajst-18332	84	10	paper	paper	NOUN
ajst-18332	84	11	focus	focus	NOUN
ajst-18332	84	12	on	on	ADP
ajst-18332	84	13	rebar	rebar	ADJ
ajst-18332	84	14	endface	endface	NOUN
ajst-18332	84	15	recognition	recognition	NOUN
ajst-18332	84	16	,	,	PUNCT
ajst-18332	84	17	and	and	CCONJ
ajst-18332	84	18	therefore	therefore	ADV
ajst-18332	84	19	specialised	specialised	ADJ
ajst-18332	84	20	datasets	dataset	NOUN
ajst-18332	84	21	containing	contain	VERB
ajst-18332	84	22	a	a	DET
ajst-18332	84	23	wide	wide	ADJ
ajst-18332	84	24	range	range	NOUN
ajst-18332	84	25	of	of	ADP
ajst-18332	84	26	rebar	rebar	ADJ
ajst-18332	84	27	types	type	NOUN
ajst-18332	84	28	and	and	CCONJ
ajst-18332	84	29	configurations	configuration	NOUN
ajst-18332	84	30	were	be	AUX
ajst-18332	84	31	chosen	choose	VERB
ajst-18332	84	32	.	.	PUNCT
ajst-18332	85	1	due	due	ADP
ajst-18332	85	2	to	to	ADP
ajst-18332	85	3	the	the	DET
ajst-18332	85	4	uneven	uneven	ADJ
ajst-18332	85	5	distribution	distribution	NOUN
ajst-18332	85	6	of	of	ADP
ajst-18332	85	7	rebar	rebar	ADJ
ajst-18332	85	8	types	type	NOUN
ajst-18332	85	9	in	in	ADP
ajst-18332	85	10	these	these	DET
ajst-18332	85	11	datasets	dataset	NOUN
ajst-18332	85	12	(	(	PUNCT
ajst-18332	85	13	e.g.	e.g.	ADV
ajst-18332	85	14	,	,	PUNCT
ajst-18332	85	15	some	some	DET
ajst-18332	85	16	types	type	NOUN
ajst-18332	85	17	have	have	VERB
ajst-18332	85	18	far	far	ADV
ajst-18332	85	19	more	more	ADJ
ajst-18332	85	20	rebar	rebar	NOUN
ajst-18332	85	21	images	image	NOUN
ajst-18332	85	22	than	than	ADP
ajst-18332	85	23	others	other	NOUN
ajst-18332	85	24	)	)	PUNCT
ajst-18332	85	25	,	,	PUNCT
ajst-18332	85	26	this	this	PRON
ajst-18332	85	27	may	may	AUX
ajst-18332	85	28	result	result	VERB
ajst-18332	85	29	in	in	ADP
ajst-18332	85	30	the	the	DET
ajst-18332	85	31	model	model	NOUN
ajst-18332	85	32	recognising	recognise	VERB
ajst-18332	85	33	some	some	DET
ajst-18332	85	34	common	common	ADJ
ajst-18332	85	35	types	type	NOUN
ajst-18332	85	36	with	with	ADP
ajst-18332	85	37	higher	high	ADJ
ajst-18332	85	38	accuracy	accuracy	NOUN
ajst-18332	85	39	than	than	ADP
ajst-18332	85	40	less	less	ADV
ajst-18332	85	41	common	common	ADJ
ajst-18332	85	42	types	type	NOUN
ajst-18332	85	43	during	during	ADP
ajst-18332	85	44	model	model	NOUN
ajst-18332	85	45	training	training	NOUN
ajst-18332	85	46	.	.	PUNCT
ajst-18332	86	1	in	in	ADP
ajst-18332	86	2	addition	addition	NOUN
ajst-18332	86	3	,	,	PUNCT
ajst-18332	86	4	there	there	PRON
ajst-18332	86	5	may	may	AUX
ajst-18332	86	6	be	be	AUX
ajst-18332	86	7	visual	visual	ADJ
ajst-18332	86	8	similarities	similarity	NOUN
ajst-18332	86	9	between	between	ADP
ajst-18332	86	10	different	different	ADJ
ajst-18332	86	11	types	type	NOUN
ajst-18332	86	12	of	of	ADP
ajst-18332	86	13	rebar	rebar	NOUN
ajst-18332	86	14	,	,	PUNCT
ajst-18332	86	15	which	which	PRON
ajst-18332	86	16	may	may	AUX
ajst-18332	86	17	lead	lead	VERB
ajst-18332	86	18	to	to	ADP
ajst-18332	86	19	mutual	mutual	ADJ
ajst-18332	86	20	inhibition	inhibition	NOUN
ajst-18332	86	21	of	of	ADP
ajst-18332	86	22	the	the	DET
ajst-18332	86	23	models	model	NOUN
ajst-18332	86	24	during	during	ADP
ajst-18332	86	25	learning	learn	VERB
ajst-18332	86	26	.	.	PUNCT
ajst-18332	87	1	to	to	PART
ajst-18332	87	2	address	address	VERB
ajst-18332	87	3	these	these	DET
ajst-18332	87	4	challenges	challenge	NOUN
ajst-18332	87	5	and	and	CCONJ
ajst-18332	87	6	optimise	optimise	VERB
ajst-18332	87	7	the	the	DET
ajst-18332	87	8	training	training	NOUN
ajst-18332	87	9	process	process	NOUN
ajst-18332	87	10	,	,	PUNCT
ajst-18332	87	11	we	we	PRON
ajst-18332	87	12	performed	perform	VERB
ajst-18332	87	13	specific	specific	ADJ
ajst-18332	87	14	processing	processing	NOUN
ajst-18332	87	15	on	on	ADP
ajst-18332	87	16	the	the	DET
ajst-18332	87	17	dataset	dataset	NOUN
ajst-18332	87	18	.	.	PUNCT
ajst-18332	88	1	first	first	ADV
ajst-18332	88	2	,	,	PUNCT
ajst-18332	88	3	we	we	PRON
ajst-18332	88	4	analysed	analyse	VERB
ajst-18332	88	5	the	the	DET
ajst-18332	88	6	rebar	rebar	NOUN
ajst-18332	88	7	dataset	dataset	NOUN
ajst-18332	88	8	to	to	PART
ajst-18332	88	9	pick	pick	VERB
ajst-18332	88	10	out	out	ADP
ajst-18332	88	11	difficult	difficult	ADJ
ajst-18332	88	12	-	-	PUNCT
ajst-18332	88	13	to	to	ADP
ajst-18332	88	14	-	-	PUNCT
ajst-18332	88	15	recognise	recognise	VERB
ajst-18332	88	16	rebar	rebar	ADJ
ajst-18332	88	17	types	type	NOUN
ajst-18332	88	18	and	and	CCONJ
ajst-18332	88	19	mixed	mix	VERB
ajst-18332	88	20	these	these	PRON
ajst-18332	88	21	with	with	ADP
ajst-18332	88	22	data	datum	NOUN
ajst-18332	88	23	of	of	ADP
ajst-18332	88	24	common	common	ADJ
ajst-18332	88	25	types	type	NOUN
ajst-18332	88	26	to	to	PART
ajst-18332	88	27	achieve	achieve	VERB
ajst-18332	88	28	a	a	DET
ajst-18332	88	29	more	more	ADV
ajst-18332	88	30	balanced	balanced	ADJ
ajst-18332	88	31	training	training	NOUN
ajst-18332	88	32	set	set	NOUN
ajst-18332	88	33	.	.	PUNCT
ajst-18332	89	1	in	in	ADP
ajst-18332	89	2	addition	addition	NOUN
ajst-18332	89	3	,	,	PUNCT
ajst-18332	89	4	we	we	PRON
ajst-18332	89	5	developed	develop	VERB
ajst-18332	89	6	a	a	DET
ajst-18332	89	7	training	training	NOUN
ajst-18332	89	8	strategy	strategy	NOUN
ajst-18332	89	9	optimisation	optimisation	NOUN
ajst-18332	89	10	procedure	procedure	NOUN
ajst-18332	89	11	to	to	PART
ajst-18332	89	12	improve	improve	VERB
ajst-18332	89	13	the	the	DET
ajst-18332	89	14	performance	performance	NOUN
ajst-18332	89	15	of	of	ADP
ajst-18332	89	16	the	the	DET
ajst-18332	89	17	model	model	NOUN
ajst-18332	89	18	in	in	ADP
ajst-18332	89	19	identifying	identify	VERB
ajst-18332	89	20	various	various	ADJ
ajst-18332	89	21	types	type	NOUN
ajst-18332	89	22	of	of	ADP
ajst-18332	89	23	rebar	rebar	NOUN
ajst-18332	89	24	.	.	PUNCT
ajst-18332	90	1	we	we	PRON
ajst-18332	90	2	used	use	VERB
ajst-18332	90	3	a	a	DET
ajst-18332	90	4	method	method	NOUN
ajst-18332	90	5	of	of	ADP
ajst-18332	90	6	dynamically	dynamically	ADV
ajst-18332	90	7	adjusting	adjust	VERB
ajst-18332	90	8	the	the	DET
ajst-18332	90	9	training	training	NOUN
ajst-18332	90	10	set	set	VERB
ajst-18332	90	11	by	by	ADP
ajst-18332	90	12	adjusting	adjust	VERB
ajst-18332	90	13	the	the	DET
ajst-18332	90	14	composition	composition	NOUN
ajst-18332	90	15	of	of	ADP
ajst-18332	90	16	the	the	DET
ajst-18332	90	17	dataset	dataset	NOUN
ajst-18332	90	18	based	base	VERB
ajst-18332	90	19	on	on	ADP
ajst-18332	90	20	the	the	DET
ajst-18332	90	21	model	model	NOUN
ajst-18332	90	22	's	's	PART
ajst-18332	90	23	performance	performance	NOUN
ajst-18332	90	24	at	at	ADP
ajst-18332	90	25	different	different	ADJ
ajst-18332	90	26	stages	stage	NOUN
ajst-18332	90	27	,	,	PUNCT
ajst-18332	90	28	focusing	focus	VERB
ajst-18332	90	29	on	on	ADP
ajst-18332	90	30	training	train	VERB
ajst-18332	90	31	those	those	DET
ajst-18332	90	32	rebar	rebar	ADJ
ajst-18332	90	33	types	type	NOUN
ajst-18332	90	34	that	that	PRON
ajst-18332	90	35	the	the	DET
ajst-18332	90	36	model	model	NOUN
ajst-18332	90	37	has	have	VERB
ajst-18332	90	38	poorer	poor	ADJ
ajst-18332	90	39	recognition	recognition	NOUN
ajst-18332	90	40	performance	performance	NOUN
ajst-18332	90	41	.	.	PUNCT
ajst-18332	91	1	in	in	ADP
ajst-18332	91	2	this	this	DET
ajst-18332	91	3	way	way	NOUN
ajst-18332	91	4	,	,	PUNCT
ajst-18332	91	5	we	we	PRON
ajst-18332	91	6	ensure	ensure	VERB
ajst-18332	91	7	that	that	SCONJ
ajst-18332	91	8	the	the	DET
ajst-18332	91	9	model	model	NOUN
ajst-18332	91	10	is	be	AUX
ajst-18332	91	11	able	able	ADJ
ajst-18332	91	12	to	to	PART
ajst-18332	91	13	learn	learn	VERB
ajst-18332	91	14	various	various	ADJ
ajst-18332	91	15	types	type	NOUN
ajst-18332	91	16	of	of	ADP
ajst-18332	91	17	rebar	rebar	NOUN
ajst-18332	91	18	effectively	effectively	ADV
ajst-18332	91	19	,	,	PUNCT
ajst-18332	91	20	while	while	SCONJ
ajst-18332	91	21	avoiding	avoid	VERB
ajst-18332	91	22	the	the	DET
ajst-18332	91	23	problem	problem	NOUN
ajst-18332	91	24	of	of	ADP
ajst-18332	91	25	uneven	uneven	ADJ
ajst-18332	91	26	recognition	recognition	NOUN
ajst-18332	91	27	accuracy	accuracy	NOUN
ajst-18332	91	28	due	due	ADP
ajst-18332	91	29	to	to	ADP
ajst-18332	91	30	dataset	dataset	NOUN
ajst-18332	91	31	bias	bias	NOUN
ajst-18332	91	32	.	.	PUNCT
ajst-18332	92	1	3	3	X
ajst-18332	92	2	.	.	X
ajst-18332	92	3	experimental	experimental	ADJ
ajst-18332	92	4	results	result	NOUN
ajst-18332	92	5	and	and	CCONJ
ajst-18332	92	6	analysis	analysis	NOUN
ajst-18332	92	7	3.1	3.1	NUM
ajst-18332	92	8	.	.	PUNCT
ajst-18332	93	1	dataset	dataset	NOUN
ajst-18332	93	2	and	and	CCONJ
ajst-18332	93	3	experimental	experimental	ADJ
ajst-18332	93	4	environment	environment	NOUN
ajst-18332	93	5	in	in	ADP
ajst-18332	93	6	this	this	DET
ajst-18332	93	7	study	study	NOUN
ajst-18332	93	8	,	,	PUNCT
ajst-18332	93	9	we	we	PRON
ajst-18332	93	10	primarily	primarily	ADV
ajst-18332	93	11	utilized	utilize	VERB
ajst-18332	93	12	a	a	DET
ajst-18332	93	13	publicly	publicly	ADV
ajst-18332	93	14	available	available	ADJ
ajst-18332	93	15	rebar	rebar	NOUN
ajst-18332	93	16	end	end	NOUN
ajst-18332	93	17	face	face	NOUN
ajst-18332	93	18	dataset	dataset	NOUN
ajst-18332	93	19	provided	provide	VERB
ajst-18332	93	20	by	by	ADP
ajst-18332	93	21	baidu	baidu	NOUN
ajst-18332	93	22	for	for	ADP
ajst-18332	93	23	our	our	PRON
ajst-18332	93	24	experiments	experiment	NOUN
ajst-18332	93	25	in	in	ADP
ajst-18332	93	26	rebar	rebar	ADJ
ajst-18332	93	27	end	end	NOUN
ajst-18332	93	28	face	face	NOUN
ajst-18332	93	29	recognition	recognition	NOUN
ajst-18332	93	30	and	and	CCONJ
ajst-18332	93	31	counting	counting	NOUN
ajst-18332	93	32	.	.	PUNCT
ajst-18332	94	1	to	to	PART
ajst-18332	94	2	enhance	enhance	VERB
ajst-18332	94	3	the	the	DET
ajst-18332	94	4	efficacy	efficacy	NOUN
ajst-18332	94	5	and	and	CCONJ
ajst-18332	94	6	accuracy	accuracy	NOUN
ajst-18332	94	7	of	of	ADP
ajst-18332	94	8	the	the	DET
ajst-18332	94	9	experiments	experiment	NOUN
ajst-18332	94	10	,	,	PUNCT
ajst-18332	94	11	we	we	PRON
ajst-18332	94	12	normalized	normalize	VERB
ajst-18332	94	13	the	the	DET
ajst-18332	94	14	dataset	dataset	NOUN
ajst-18332	94	15	and	and	CCONJ
ajst-18332	94	16	applied	apply	VERB
ajst-18332	94	17	standardized	standardized	ADJ
ajst-18332	94	18	sizing	sizing	NOUN
ajst-18332	94	19	to	to	PART
ajst-18332	94	20	minimize	minimize	VERB
ajst-18332	94	21	the	the	DET
ajst-18332	94	22	impact	impact	NOUN
ajst-18332	94	23	of	of	ADP
ajst-18332	94	24	factors	factor	NOUN
ajst-18332	94	25	such	such	ADJ
ajst-18332	94	26	as	as	ADP
ajst-18332	94	27	lighting	lighting	NOUN
ajst-18332	94	28	and	and	CCONJ
ajst-18332	94	29	background	background	NOUN
ajst-18332	94	30	,	,	PUNCT
ajst-18332	94	31	ensuring	ensure	VERB
ajst-18332	94	32	that	that	SCONJ
ajst-18332	94	33	the	the	DET
ajst-18332	94	34	images	image	NOUN
ajst-18332	94	35	fed	feed	VERB
ajst-18332	94	36	into	into	ADP
ajst-18332	94	37	the	the	DET
ajst-18332	94	38	network	network	NOUN
ajst-18332	94	39	had	have	VERB
ajst-18332	94	40	uniform	uniform	ADJ
ajst-18332	94	41	dimensions	dimension	NOUN
ajst-18332	94	42	and	and	CCONJ
ajst-18332	94	43	proportions	proportion	NOUN
ajst-18332	94	44	.	.	PUNCT
ajst-18332	95	1	additionally	additionally	ADV
ajst-18332	95	2	,	,	PUNCT
ajst-18332	95	3	to	to	PART
ajst-18332	95	4	augment	augment	VERB
ajst-18332	95	5	the	the	DET
ajst-18332	95	6	model	model	NOUN
ajst-18332	95	7	's	's	PART
ajst-18332	95	8	ability	ability	NOUN
ajst-18332	95	9	to	to	PART
ajst-18332	95	10	recognize	recognize	VERB
ajst-18332	95	11	rebar	rebar	NOUN
ajst-18332	95	12	at	at	ADP
ajst-18332	95	13	various	various	ADJ
ajst-18332	95	14	angles	angle	NOUN
ajst-18332	95	15	and	and	CCONJ
ajst-18332	95	16	scales	scale	NOUN
ajst-18332	95	17	,	,	PUNCT
ajst-18332	95	18	we	we	PRON
ajst-18332	95	19	employed	employ	VERB
ajst-18332	95	20	data	datum	NOUN
ajst-18332	95	21	augmentation	augmentation	NOUN
ajst-18332	95	22	techniques	technique	NOUN
ajst-18332	95	23	like	like	ADP
ajst-18332	95	24	rotation	rotation	NOUN
ajst-18332	95	25	,	,	PUNCT
ajst-18332	95	26	scaling	scaling	NOUN
ajst-18332	95	27	,	,	PUNCT
ajst-18332	95	28	and	and	CCONJ
ajst-18332	95	29	flipping	flip	VERB
ajst-18332	95	30	during	during	ADP
ajst-18332	95	31	the	the	DET
ajst-18332	95	32	data	datum	NOUN
ajst-18332	95	33	preprocessing	preprocessing	NOUN
ajst-18332	95	34	stage	stage	NOUN
ajst-18332	95	35	.	.	PUNCT
ajst-18332	96	1	furthermore	furthermore	ADV
ajst-18332	96	2	,	,	PUNCT
ajst-18332	96	3	to	to	PART
ajst-18332	96	4	improve	improve	VERB
ajst-18332	96	5	the	the	DET
ajst-18332	96	6	model	model	NOUN
ajst-18332	96	7	's	's	PART
ajst-18332	96	8	adaptability	adaptability	NOUN
ajst-18332	96	9	to	to	ADP
ajst-18332	96	10	variations	variation	NOUN
ajst-18332	96	11	in	in	ADP
ajst-18332	96	12	image	image	NOUN
ajst-18332	96	13	quality	quality	NOUN
ajst-18332	96	14	in	in	ADP
ajst-18332	96	15	actual	actual	ADJ
ajst-18332	96	16	engineering	engineering	NOUN
ajst-18332	96	17	environments	environment	NOUN
ajst-18332	96	18	,	,	PUNCT
ajst-18332	96	19	we	we	PRON
ajst-18332	96	20	incorporated	incorporate	VERB
ajst-18332	96	21	random	random	ADJ
ajst-18332	96	22	noise	noise	NOUN
ajst-18332	96	23	and	and	CCONJ
ajst-18332	96	24	blurring	blurring	NOUN
ajst-18332	96	25	processes	process	NOUN
ajst-18332	96	26	.	.	PUNCT
ajst-18332	97	1	in	in	ADP
ajst-18332	97	2	terms	term	NOUN
ajst-18332	97	3	of	of	ADP
ajst-18332	97	4	dataset	dataset	NOUN
ajst-18332	97	5	division	division	NOUN
ajst-18332	97	6	,	,	PUNCT
ajst-18332	97	7	we	we	PRON
ajst-18332	97	8	used	use	VERB
ajst-18332	97	9	80	80	NUM
ajst-18332	97	10	%	%	NOUN
ajst-18332	97	11	of	of	ADP
ajst-18332	97	12	the	the	DET
ajst-18332	97	13	images	image	NOUN
ajst-18332	97	14	as	as	ADP
ajst-18332	97	15	the	the	DET
ajst-18332	97	16	training	training	NOUN
ajst-18332	97	17	set	set	VERB
ajst-18332	97	18	for	for	ADP
ajst-18332	97	19	model	model	NOUN
ajst-18332	97	20	learning	learning	NOUN
ajst-18332	97	21	and	and	CCONJ
ajst-18332	97	22	optimization	optimization	NOUN
ajst-18332	97	23	,	,	PUNCT
ajst-18332	97	24	while	while	SCONJ
ajst-18332	97	25	the	the	DET
ajst-18332	97	26	remaining	remain	VERB
ajst-18332	97	27	20	20	NUM
ajst-18332	97	28	%	%	NOUN
ajst-18332	97	29	served	serve	VERB
ajst-18332	97	30	as	as	ADP
ajst-18332	97	31	the	the	DET
ajst-18332	97	32	test	test	NOUN
ajst-18332	97	33	set	set	VERB
ajst-18332	97	34	for	for	ADP
ajst-18332	97	35	evaluating	evaluate	VERB
ajst-18332	97	36	the	the	DET
ajst-18332	97	37	model	model	NOUN
ajst-18332	97	38	's	's	PART
ajst-18332	97	39	performance	performance	NOUN
ajst-18332	97	40	.	.	PUNCT
ajst-18332	98	1	the	the	DET
ajst-18332	98	2	deep	deep	ADJ
ajst-18332	98	3	learning	learning	NOUN
ajst-18332	98	4	architecture	architecture	NOUN
ajst-18332	98	5	used	use	VERB
ajst-18332	98	6	for	for	ADP
ajst-18332	98	7	the	the	DET
ajst-18332	98	8	experiment	experiment	NOUN
ajst-18332	98	9	is	be	AUX
ajst-18332	98	10	baidu	baidu	NOUN
ajst-18332	98	11	paddlepaddle2.2.2	paddlepaddle2.2.2	NOUN
ajst-18332	98	12	,	,	PUNCT
ajst-18332	98	13	python	python	NOUN
ajst-18332	98	14	version	version	NOUN
ajst-18332	98	15	3.7	3.7	NUM
ajst-18332	98	16	,	,	PUNCT
ajst-18332	98	17	paddlex	paddlex	NOUN
ajst-18332	98	18	version	version	PROPN
ajst-18332	98	19	2.0.0	2.0.0	NUM
ajst-18332	98	20	.	.	PUNCT
ajst-18332	99	1	the	the	DET
ajst-18332	99	2	running	run	VERB
ajst-18332	99	3	environment	environment	NOUN
ajst-18332	99	4	is	be	AUX
ajst-18332	99	5	gpu，nvidia	gpu，nvidia	PROPN
ajst-18332	99	6	tesla	tesla	PROPN
ajst-18332	99	7	v100	v100	PROPN
ajst-18332	99	8	,	,	PUNCT
ajst-18332	99	9	with	with	ADP
ajst-18332	99	10	32	32	NUM
ajst-18332	99	11	gb	gb	NOUN
ajst-18332	99	12	of	of	ADP
ajst-18332	99	13	video	video	NOUN
ajst-18332	99	14	memory	memory	NOUN
ajst-18332	99	15	,	,	PUNCT
ajst-18332	99	16	a	a	DET
ajst-18332	99	17	4	4	NUM
ajst-18332	99	18	-	-	PUNCT
ajst-18332	99	19	core	core	NOUN
ajst-18332	99	20	cpu	cpu	NOUN
ajst-18332	99	21	,	,	PUNCT
ajst-18332	99	22	32	32	NUM
ajst-18332	99	23	gb	gb	NOUN
ajst-18332	99	24	of	of	ADP
ajst-18332	99	25	cpu	cpu	NOUN
ajst-18332	99	26	ram	ram	NOUN
ajst-18332	99	27	,	,	PUNCT
ajst-18332	99	28	and	and	CCONJ
ajst-18332	99	29	a	a	DET
ajst-18332	99	30	100	100	NUM
ajst-18332	99	31	g	g	NOUN
ajst-18332	99	32	hard	hard	ADJ
ajst-18332	99	33	drive	drive	NOUN
ajst-18332	99	34	.	.	PUNCT
ajst-18332	100	1	3.2	3.2	NUM
ajst-18332	100	2	.	.	PUNCT
ajst-18332	100	3	experimental	experimental	ADJ
ajst-18332	100	4	parameter	parameter	NOUN
ajst-18332	100	5	setup	setup	NOUN
ajst-18332	100	6	and	and	CCONJ
ajst-18332	100	7	evaluation	evaluation	NOUN
ajst-18332	100	8	metrics	metric	NOUN
ajst-18332	100	9	in	in	ADP
ajst-18332	100	10	the	the	DET
ajst-18332	100	11	experimental	experimental	ADJ
ajst-18332	100	12	part	part	NOUN
ajst-18332	100	13	of	of	ADP
ajst-18332	100	14	this	this	DET
ajst-18332	100	15	study	study	NOUN
ajst-18332	100	16	,	,	PUNCT
ajst-18332	100	17	we	we	PRON
ajst-18332	100	18	meticulously	meticulously	ADV
ajst-18332	100	19	set	set	VERB
ajst-18332	100	20	the	the	DET
ajst-18332	100	21	experimental	experimental	ADJ
ajst-18332	100	22	parameters	parameter	NOUN
ajst-18332	100	23	to	to	PART
ajst-18332	100	24	ensure	ensure	VERB
ajst-18332	100	25	accuracy	accuracy	NOUN
ajst-18332	100	26	and	and	CCONJ
ajst-18332	100	27	repeatability	repeatability	NOUN
ajst-18332	100	28	.	.	PUNCT
ajst-18332	101	1	the	the	DET
ajst-18332	101	2	primary	primary	ADJ
ajst-18332	101	3	objective	objective	NOUN
ajst-18332	101	4	of	of	ADP
ajst-18332	101	5	the	the	DET
ajst-18332	101	6	experiment	experiment	NOUN
ajst-18332	101	7	was	be	AUX
ajst-18332	101	8	to	to	PART
ajst-18332	101	9	compare	compare	VERB
ajst-18332	101	10	the	the	DET
ajst-18332	101	11	performance	performance	NOUN
ajst-18332	101	12	differences	difference	NOUN
ajst-18332	101	13	between	between	ADP
ajst-18332	101	14	the	the	DET
ajst-18332	101	15	improved	improved	ADJ
ajst-18332	101	16	pp	pp	PROPN
ajst-18332	101	17	-	-	PUNCT
ajst-18332	101	18	yolo	yolo	ADJ
ajst-18332	101	19	network	network	NOUN
ajst-18332	101	20	and	and	CCONJ
ajst-18332	101	21	the	the	DET
ajst-18332	101	22	classic	classic	ADJ
ajst-18332	101	23	yolov3	yolov3	PROPN
ajst-18332	101	24	,	,	PUNCT
ajst-18332	101	25	as	as	ADV
ajst-18332	101	26	well	well	ADV
ajst-18332	101	27	as	as	ADP
ajst-18332	101	28	the	the	DET
ajst-18332	101	29	latest	late	ADJ
ajst-18332	101	30	yolov5	yolov5	NOUN
ajst-18332	101	31	algorithms	algorithm	NOUN
ajst-18332	101	32	in	in	ADP
ajst-18332	101	33	the	the	DET
ajst-18332	101	34	task	task	NOUN
ajst-18332	101	35	of	of	ADP
ajst-18332	101	36	rebar	rebar	NOUN
ajst-18332	101	37	end	end	NOUN
ajst-18332	101	38	face	face	NOUN
ajst-18332	101	39	recognition	recognition	NOUN
ajst-18332	101	40	.	.	PUNCT
ajst-18332	102	1	to	to	PART
ajst-18332	102	2	ensure	ensure	VERB
ajst-18332	102	3	a	a	DET
ajst-18332	102	4	fair	fair	ADJ
ajst-18332	102	5	comparison	comparison	NOUN
ajst-18332	102	6	,	,	PUNCT
ajst-18332	102	7	all	all	DET
ajst-18332	102	8	models	model	NOUN
ajst-18332	102	9	were	be	AUX
ajst-18332	102	10	trained	train	VERB
ajst-18332	102	11	and	and	CCONJ
ajst-18332	102	12	evaluated	evaluate	VERB
ajst-18332	102	13	in	in	ADP
ajst-18332	102	14	the	the	DET
ajst-18332	102	15	same	same	ADJ
ajst-18332	102	16	hardware	hardware	NOUN
ajst-18332	102	17	and	and	CCONJ
ajst-18332	102	18	software	software	NOUN
ajst-18332	102	19	environments	environment	NOUN
ajst-18332	102	20	.	.	PUNCT
ajst-18332	103	1	the	the	DET
ajst-18332	103	2	main	main	ADJ
ajst-18332	103	3	steps	step	NOUN
ajst-18332	103	4	of	of	ADP
ajst-18332	103	5	the	the	DET
ajst-18332	103	6	experimental	experimental	ADJ
ajst-18332	103	7	process	process	NOUN
ajst-18332	103	8	can	can	AUX
ajst-18332	103	9	be	be	AUX
ajst-18332	103	10	summarized	summarize	VERB
ajst-18332	103	11	as	as	SCONJ
ajst-18332	103	12	follows	follow	VERB
ajst-18332	103	13	:	:	PUNCT
ajst-18332	103	14	initially	initially	ADV
ajst-18332	103	15	setting	set	VERB
ajst-18332	103	16	the	the	DET
ajst-18332	103	17	training	training	NOUN
ajst-18332	103	18	model	model	NOUN
ajst-18332	103	19	path	path	NOUN
ajst-18332	103	20	and	and	CCONJ
ajst-18332	103	21	evaluation	evaluation	NOUN
ajst-18332	103	22	model	model	NOUN
ajst-18332	103	23	path	path	NOUN
ajst-18332	103	24	,	,	PUNCT
ajst-18332	103	25	sequentially	sequentially	ADV
ajst-18332	103	26	setting	set	VERB
ajst-18332	103	27	the	the	DET
ajst-18332	103	28	model	model	NOUN
ajst-18332	103	29	's	's	PART
ajst-18332	103	30	backbone	backbone	NOUN
ajst-18332	103	31	,	,	PUNCT
ajst-18332	103	32	detection	detection	NOUN
ajst-18332	103	33	neck	neck	NOUN
ajst-18332	103	34	,	,	PUNCT
ajst-18332	103	35	detection	detection	NOUN
ajst-18332	103	36	head	head	NOUN
ajst-18332	103	37	,	,	PUNCT
ajst-18332	103	38	loss	loss	NOUN
ajst-18332	103	39	function	function	NOUN
ajst-18332	103	40	(	(	PUNCT
ajst-18332	103	41	learning	learn	VERB
ajst-18332	103	42	rate	rate	NOUN
ajst-18332	103	43	set	set	VERB
ajst-18332	103	44	to	to	ADP
ajst-18332	103	45	0.0003	0.0003	NUM
ajst-18332	103	46	)	)	PUNCT
ajst-18332	103	47	;	;	PUNCT
ajst-18332	103	48	then	then	ADV
ajst-18332	103	49	proceeding	proceed	VERB
ajst-18332	103	50	with	with	ADP
ajst-18332	103	51	training	training	NOUN
ajst-18332	103	52	data	datum	NOUN
ajst-18332	103	53	augmentation	augmentation	NOUN
ajst-18332	103	54	and	and	CCONJ
ajst-18332	103	55	optimization	optimization	NOUN
ajst-18332	103	56	;	;	PUNCT
ajst-18332	103	57	and	and	CCONJ
ajst-18332	103	58	finally	finally	ADV
ajst-18332	103	59	,	,	PUNCT
ajst-18332	103	60	setting	set	VERB
ajst-18332	103	61	the	the	DET
ajst-18332	103	62	parameters	parameter	NOUN
ajst-18332	103	63	for	for	ADP
ajst-18332	103	64	the	the	DET
ajst-18332	103	65	prediction	prediction	NOUN
ajst-18332	103	66	model	model	NOUN
ajst-18332	103	67	.	.	PUNCT
ajst-18332	104	1	in	in	ADP
ajst-18332	104	2	the	the	DET
ajst-18332	104	3	rebar	rebar	NOUN
ajst-18332	104	4	end	end	NOUN
ajst-18332	104	5	face	face	NOUN
ajst-18332	104	6	recognition	recognition	NOUN
ajst-18332	104	7	experiments	experiment	NOUN
ajst-18332	104	8	for	for	ADP
ajst-18332	104	9	a	a	DET
ajst-18332	104	10	single	single	ADJ
ajst-18332	104	11	category	category	NOUN
ajst-18332	104	12	,	,	PUNCT
ajst-18332	104	13	average	average	ADJ
ajst-18332	104	14	precision	precision	NOUN
ajst-18332	104	15	(	(	PUNCT
ajst-18332	104	16	ap	ap	PROPN
ajst-18332	104	17	)	)	PUNCT
ajst-18332	104	18	is	be	AUX
ajst-18332	104	19	used	use	VERB
ajst-18332	104	20	as	as	ADP
ajst-18332	104	21	an	an	DET
ajst-18332	104	22	evaluation	evaluation	NOUN
ajst-18332	104	23	metric	metric	NOUN
ajst-18332	104	24	since	since	SCONJ
ajst-18332	104	25	we	we	PRON
ajst-18332	104	26	are	be	AUX
ajst-18332	104	27	only	only	ADV
ajst-18332	104	28	concerned	concerned	ADJ
ajst-18332	104	29	with	with	ADP
ajst-18332	104	30	the	the	DET
ajst-18332	104	31	recognition	recognition	NOUN
ajst-18332	104	32	accuracy	accuracy	NOUN
ajst-18332	104	33	of	of	ADP
ajst-18332	104	34	a	a	DET
ajst-18332	104	35	specific	specific	ADJ
ajst-18332	104	36	category.ap	category.ap	NUM
ajst-18332	104	37	is	be	AUX
ajst-18332	104	38	an	an	DET
ajst-18332	104	39	important	important	ADJ
ajst-18332	104	40	performance	performance	NOUN
ajst-18332	104	41	metric	metric	ADJ
ajst-18332	104	42	to	to	PART
ajst-18332	104	43	measure	measure	VERB
ajst-18332	104	44	the	the	DET
ajst-18332	104	45	performance	performance	NOUN
ajst-18332	104	46	of	of	ADP
ajst-18332	104	47	a	a	DET
ajst-18332	104	48	model	model	NOUN
ajst-18332	104	49	on	on	ADP
ajst-18332	104	50	a	a	DET
ajst-18332	104	51	single	single	ADJ
ajst-18332	104	52	category	category	NOUN
ajst-18332	104	53	.	.	PUNCT
ajst-18332	105	1	in	in	ADP
ajst-18332	105	2	this	this	DET
ajst-18332	105	3	case	case	NOUN
ajst-18332	105	4	,	,	PUNCT
ajst-18332	105	5	map	map	NOUN
ajst-18332	105	6	(	(	PUNCT
ajst-18332	105	7	mean	mean	VERB
ajst-18332	105	8	average	average	ADJ
ajst-18332	105	9	precision	precision	NOUN
ajst-18332	105	10	)	)	PUNCT
ajst-18332	105	11	is	be	AUX
ajst-18332	105	12	not	not	PART
ajst-18332	105	13	applicable	applicable	ADJ
ajst-18332	105	14	because	because	SCONJ
ajst-18332	105	15	we	we	PRON
ajst-18332	105	16	only	only	ADV
ajst-18332	105	17	focus	focus	VERB
ajst-18332	105	18	on	on	ADP
ajst-18332	105	19	the	the	DET
ajst-18332	105	20	recognition	recognition	NOUN
ajst-18332	105	21	results	result	NOUN
ajst-18332	105	22	of	of	ADP
ajst-18332	105	23	a	a	DET
ajst-18332	105	24	single	single	ADJ
ajst-18332	105	25	category.the	category.the	ADJ
ajst-18332	105	26	formula	formula	NOUN
ajst-18332	105	27	for	for	ADP
ajst-18332	105	28	ap	ap	PROPN
ajst-18332	105	29	is	be	AUX
ajst-18332	105	30	:	:	PUNCT
ajst-18332	105	31	𝐴𝑃	𝐴𝑃	X
ajst-18332	105	32	∑	∑	PUNCT
ajst-18332	105	33	𝑃	𝑃	VERB
ajst-18332	105	34	𝑘	𝑘	PRON
ajst-18332	105	35	∆𝑟	∆𝑟	PROPN
ajst-18332	105	36	𝑘	𝑘	PROPN
ajst-18332	105	37	(	(	PUNCT
ajst-18332	105	38	4	4	NUM
ajst-18332	105	39	)	)	PUNCT
ajst-18332	105	40	in	in	ADP
ajst-18332	105	41	this	this	DET
ajst-18332	105	42	equation	equation	NOUN
ajst-18332	105	43	,	,	PUNCT
ajst-18332	105	44	p(k	p(k	NOUN
ajst-18332	105	45	)	)	PUNCT
ajst-18332	105	46	is	be	AUX
ajst-18332	105	47	the	the	DET
ajst-18332	105	48	percent	percent	NOUN
ajst-18332	105	49	detection	detection	NOUN
ajst-18332	105	50	at	at	ADP
ajst-18332	105	51	the	the	DET
ajst-18332	105	52	kth	kth	PROPN
ajst-18332	105	53	threshold	threshold	NOUN
ajst-18332	105	54	,	,	PUNCT
ajst-18332	105	55	∆r(k	∆r(k	PROPN
ajst-18332	105	56	)	)	PUNCT
ajst-18332	105	57	is	be	AUX
ajst-18332	105	58	the	the	DET
ajst-18332	105	59	amount	amount	NOUN
ajst-18332	105	60	of	of	ADP
ajst-18332	105	61	change	change	NOUN
ajst-18332	105	62	in	in	ADP
ajst-18332	105	63	percent	percent	NOUN
ajst-18332	105	64	detection	detection	NOUN
ajst-18332	105	65	at	at	ADP
ajst-18332	105	66	the	the	DET
ajst-18332	105	67	kth	kth	PROPN
ajst-18332	105	68	threshold	threshold	NOUN
ajst-18332	105	69	,	,	PUNCT
ajst-18332	105	70	and	and	CCONJ
ajst-18332	105	71	n	n	PRON
ajst-18332	105	72	is	be	AUX
ajst-18332	105	73	the	the	DET
ajst-18332	105	74	number	number	NOUN
ajst-18332	105	75	of	of	ADP
ajst-18332	105	76	thresholds	threshold	NOUN
ajst-18332	105	77	.	.	PUNCT
ajst-18332	106	1	in	in	ADP
ajst-18332	106	2	95	95	NUM
ajst-18332	106	3	essence	essence	NOUN
ajst-18332	106	4	,	,	PUNCT
ajst-18332	106	5	this	this	DET
ajst-18332	106	6	formula	formula	NOUN
ajst-18332	106	7	calculates	calculate	VERB
ajst-18332	106	8	the	the	DET
ajst-18332	106	9	area	area	NOUN
ajst-18332	106	10	under	under	ADP
ajst-18332	106	11	the	the	DET
ajst-18332	106	12	entire	entire	ADJ
ajst-18332	106	13	p	p	NOUN
ajst-18332	106	14	-	-	PUNCT
ajst-18332	106	15	r	r	NOUN
ajst-18332	106	16	curve	curve	NOUN
ajst-18332	106	17	by	by	ADP
ajst-18332	106	18	integrating	integrate	VERB
ajst-18332	106	19	the	the	DET
ajst-18332	106	20	check	check	NOUN
ajst-18332	106	21	-	-	PUNCT
ajst-18332	106	22	accuracy	accuracy	NOUN
ajst-18332	106	23	and	and	CCONJ
ajst-18332	106	24	checkcompleteness	checkcompleteness	NOUN
ajst-18332	106	25	at	at	ADP
ajst-18332	106	26	different	different	ADJ
ajst-18332	106	27	thresholds	threshold	NOUN
ajst-18332	106	28	.	.	PUNCT
ajst-18332	107	1	by	by	ADP
ajst-18332	107	2	calculating	calculate	VERB
ajst-18332	107	3	the	the	DET
ajst-18332	107	4	ap	ap	PROPN
ajst-18332	107	5	,	,	PUNCT
ajst-18332	107	6	we	we	PRON
ajst-18332	107	7	are	be	AUX
ajst-18332	107	8	able	able	ADJ
ajst-18332	107	9	to	to	PART
ajst-18332	107	10	fully	fully	ADV
ajst-18332	107	11	evaluate	evaluate	VERB
ajst-18332	107	12	the	the	DET
ajst-18332	107	13	performance	performance	NOUN
ajst-18332	107	14	of	of	ADP
ajst-18332	107	15	the	the	DET
ajst-18332	107	16	model	model	NOUN
ajst-18332	107	17	on	on	ADP
ajst-18332	107	18	the	the	DET
ajst-18332	107	19	rebar	rebar	NOUN
ajst-18332	107	20	end	end	NOUN
ajst-18332	107	21	face	face	NOUN
ajst-18332	107	22	recognition	recognition	NOUN
ajst-18332	107	23	task	task	NOUN
ajst-18332	107	24	.	.	PUNCT
ajst-18332	108	1	a	a	DET
ajst-18332	108	2	high	high	ADJ
ajst-18332	108	3	ap	ap	PROPN
ajst-18332	108	4	value	value	NOUN
ajst-18332	108	5	indicates	indicate	VERB
ajst-18332	108	6	that	that	SCONJ
ajst-18332	108	7	the	the	DET
ajst-18332	108	8	model	model	NOUN
ajst-18332	108	9	performs	perform	VERB
ajst-18332	108	10	well	well	ADV
ajst-18332	108	11	in	in	ADP
ajst-18332	108	12	terms	term	NOUN
ajst-18332	108	13	of	of	ADP
ajst-18332	108	14	both	both	DET
ajst-18332	108	15	recognition	recognition	NOUN
ajst-18332	108	16	accuracy	accuracy	NOUN
ajst-18332	108	17	and	and	CCONJ
ajst-18332	108	18	coverage	coverage	NOUN
ajst-18332	108	19	,	,	PUNCT
ajst-18332	108	20	while	while	SCONJ
ajst-18332	108	21	a	a	DET
ajst-18332	108	22	low	low	ADJ
ajst-18332	108	23	ap	ap	PROPN
ajst-18332	108	24	value	value	NOUN
ajst-18332	108	25	suggests	suggest	VERB
ajst-18332	108	26	that	that	SCONJ
ajst-18332	108	27	the	the	DET
ajst-18332	108	28	model	model	NOUN
ajst-18332	108	29	needs	need	VERB
ajst-18332	108	30	to	to	PART
ajst-18332	108	31	be	be	AUX
ajst-18332	108	32	further	far	ADV
ajst-18332	108	33	optimised	optimise	VERB
ajst-18332	108	34	.	.	PUNCT
ajst-18332	109	1	furthermore	furthermore	ADV
ajst-18332	109	2	,	,	PUNCT
ajst-18332	109	3	in	in	ADP
ajst-18332	109	4	the	the	DET
ajst-18332	109	5	rebar	rebar	ADJ
ajst-18332	109	6	endface	endface	NOUN
ajst-18332	109	7	recognition	recognition	NOUN
ajst-18332	109	8	experiments	experiment	NOUN
ajst-18332	109	9	of	of	ADP
ajst-18332	109	10	this	this	DET
ajst-18332	109	11	study	study	NOUN
ajst-18332	109	12	,	,	PUNCT
ajst-18332	109	13	we	we	PRON
ajst-18332	109	14	pay	pay	VERB
ajst-18332	109	15	special	special	ADJ
ajst-18332	109	16	attention	attention	NOUN
ajst-18332	109	17	to	to	ADP
ajst-18332	109	18	the	the	DET
ajst-18332	109	19	detection	detection	NOUN
ajst-18332	109	20	time	time	NOUN
ajst-18332	109	21	of	of	ADP
ajst-18332	109	22	the	the	DET
ajst-18332	109	23	model	model	NOUN
ajst-18332	109	24	,	,	PUNCT
ajst-18332	109	25	i.e.	i.e.	X
ajst-18332	109	26	,	,	PUNCT
ajst-18332	109	27	the	the	DET
ajst-18332	109	28	time	time	NOUN
ajst-18332	109	29	required	require	VERB
ajst-18332	109	30	for	for	ADP
ajst-18332	109	31	the	the	DET
ajst-18332	109	32	model	model	NOUN
ajst-18332	109	33	to	to	PART
ajst-18332	109	34	process	process	VERB
ajst-18332	109	35	a	a	DET
ajst-18332	109	36	single	single	ADJ
ajst-18332	109	37	image	image	NOUN
ajst-18332	109	38	.	.	PUNCT
ajst-18332	110	1	this	this	PRON
ajst-18332	110	2	is	be	AUX
ajst-18332	110	3	because	because	SCONJ
ajst-18332	110	4	in	in	ADP
ajst-18332	110	5	real	real	ADJ
ajst-18332	110	6	-	-	PUNCT
ajst-18332	110	7	world	world	NOUN
ajst-18332	110	8	applications	application	NOUN
ajst-18332	110	9	,	,	PUNCT
ajst-18332	110	10	such	such	ADJ
ajst-18332	110	11	as	as	ADP
ajst-18332	110	12	construction	construction	NOUN
ajst-18332	110	13	sites	site	NOUN
ajst-18332	110	14	or	or	CCONJ
ajst-18332	110	15	manufacturing	manufacturing	NOUN
ajst-18332	110	16	environments	environment	NOUN
ajst-18332	110	17	,	,	PUNCT
ajst-18332	110	18	fast	fast	ADJ
ajst-18332	110	19	and	and	CCONJ
ajst-18332	110	20	accurate	accurate	ADJ
ajst-18332	110	21	identification	identification	NOUN
ajst-18332	110	22	of	of	ADP
ajst-18332	110	23	rebar	rebar	ADJ
ajst-18332	110	24	end	end	NOUN
ajst-18332	110	25	faces	face	VERB
ajst-18332	110	26	is	be	AUX
ajst-18332	110	27	crucial	crucial	ADJ
ajst-18332	110	28	for	for	ADP
ajst-18332	110	29	improving	improve	VERB
ajst-18332	110	30	efficiency	efficiency	NOUN
ajst-18332	110	31	and	and	CCONJ
ajst-18332	110	32	ensuring	ensure	VERB
ajst-18332	110	33	quality	quality	NOUN
ajst-18332	110	34	control	control	NOUN
ajst-18332	110	35	.	.	PUNCT
ajst-18332	111	1	the	the	DET
ajst-18332	111	2	detection	detection	NOUN
ajst-18332	111	3	time	time	NOUN
ajst-18332	111	4	has	have	VERB
ajst-18332	111	5	a	a	DET
ajst-18332	111	6	direct	direct	ADJ
ajst-18332	111	7	impact	impact	NOUN
ajst-18332	111	8	on	on	ADP
ajst-18332	111	9	the	the	DET
ajst-18332	111	10	responsiveness	responsiveness	NOUN
ajst-18332	111	11	and	and	CCONJ
ajst-18332	111	12	real	real	ADJ
ajst-18332	111	13	-	-	PUNCT
ajst-18332	111	14	time	time	NOUN
ajst-18332	111	15	performance	performance	NOUN
ajst-18332	111	16	of	of	ADP
ajst-18332	111	17	the	the	DET
ajst-18332	111	18	overall	overall	ADJ
ajst-18332	111	19	system	system	NOUN
ajst-18332	111	20	.	.	PUNCT
ajst-18332	112	1	3.3	3.3	NUM
ajst-18332	112	2	.	.	PUNCT
ajst-18332	113	1	experimental	experimental	ADJ
ajst-18332	113	2	results	result	NOUN
ajst-18332	113	3	and	and	CCONJ
ajst-18332	113	4	analysis	analysis	NOUN
ajst-18332	113	5	in	in	ADP
ajst-18332	113	6	the	the	DET
ajst-18332	113	7	experiments	experiment	NOUN
ajst-18332	113	8	on	on	ADP
ajst-18332	113	9	rebar	rebar	ADJ
ajst-18332	113	10	end	end	NOUN
ajst-18332	113	11	-	-	PUNCT
ajst-18332	113	12	face	face	NOUN
ajst-18332	113	13	detection	detection	NOUN
ajst-18332	113	14	,	,	PUNCT
ajst-18332	113	15	we	we	PRON
ajst-18332	113	16	conducted	conduct	VERB
ajst-18332	113	17	separate	separate	ADJ
ajst-18332	113	18	tests	test	NOUN
ajst-18332	113	19	for	for	ADP
ajst-18332	113	20	each	each	DET
ajst-18332	113	21	improvement	improvement	NOUN
ajst-18332	113	22	point	point	NOUN
ajst-18332	113	23	of	of	ADP
ajst-18332	113	24	the	the	DET
ajst-18332	113	25	improved	improved	ADJ
ajst-18332	113	26	pp	pp	PROPN
ajst-18332	113	27	-	-	PUNCT
ajst-18332	113	28	yolo	yolo	ADJ
ajst-18332	113	29	network	network	NOUN
ajst-18332	113	30	proposed	propose	VERB
ajst-18332	113	31	in	in	ADP
ajst-18332	113	32	this	this	DET
ajst-18332	113	33	paper	paper	NOUN
ajst-18332	113	34	to	to	PART
ajst-18332	113	35	ensure	ensure	VERB
ajst-18332	113	36	the	the	DET
ajst-18332	113	37	accuracy	accuracy	NOUN
ajst-18332	113	38	and	and	CCONJ
ajst-18332	113	39	reliability	reliability	NOUN
ajst-18332	113	40	of	of	ADP
ajst-18332	113	41	the	the	DET
ajst-18332	113	42	experimental	experimental	ADJ
ajst-18332	113	43	results	result	NOUN
ajst-18332	113	44	.	.	PUNCT
ajst-18332	114	1	these	these	DET
ajst-18332	114	2	tests	test	NOUN
ajst-18332	114	3	were	be	AUX
ajst-18332	114	4	conducted	conduct	VERB
ajst-18332	114	5	in	in	ADP
ajst-18332	114	6	a	a	DET
ajst-18332	114	7	separate	separate	ADJ
ajst-18332	114	8	experimental	experimental	ADJ
ajst-18332	114	9	environment	environment	NOUN
ajst-18332	114	10	to	to	PART
ajst-18332	114	11	avoid	avoid	VERB
ajst-18332	114	12	interference	interference	NOUN
ajst-18332	114	13	from	from	ADP
ajst-18332	114	14	non	non	ADJ
ajst-18332	114	15	-	-	ADJ
ajst-18332	114	16	relevant	relevant	ADJ
ajst-18332	114	17	factors	factor	NOUN
ajst-18332	114	18	.	.	PUNCT
ajst-18332	115	1	specifically	specifically	ADV
ajst-18332	115	2	,	,	PUNCT
ajst-18332	115	3	the	the	DET
ajst-18332	115	4	experiments	experiment	NOUN
ajst-18332	115	5	involved	involve	VERB
ajst-18332	115	6	a	a	DET
ajst-18332	115	7	number	number	NOUN
ajst-18332	115	8	of	of	ADP
ajst-18332	115	9	different	different	ADJ
ajst-18332	115	10	network	network	NOUN
ajst-18332	115	11	configurations	configuration	NOUN
ajst-18332	115	12	,	,	PUNCT
ajst-18332	115	13	including	include	VERB
ajst-18332	115	14	the	the	DET
ajst-18332	115	15	use	use	NOUN
ajst-18332	115	16	of	of	ADP
ajst-18332	115	17	different	different	ADJ
ajst-18332	115	18	backbone	backbone	NOUN
ajst-18332	115	19	networks	network	NOUN
ajst-18332	115	20	and	and	CCONJ
ajst-18332	115	21	the	the	DET
ajst-18332	115	22	detection	detection	NOUN
ajst-18332	115	23	of	of	ADP
ajst-18332	115	24	improvements	improvement	NOUN
ajst-18332	115	25	to	to	ADP
ajst-18332	115	26	the	the	DET
ajst-18332	115	27	neck	neck	NOUN
ajst-18332	115	28	structure	structure	NOUN
ajst-18332	115	29	.	.	PUNCT
ajst-18332	116	1	one	one	NUM
ajst-18332	116	2	of	of	ADP
ajst-18332	116	3	the	the	DET
ajst-18332	116	4	key	key	ADJ
ajst-18332	116	5	configurations	configuration	NOUN
ajst-18332	116	6	in	in	ADP
ajst-18332	116	7	the	the	DET
ajst-18332	116	8	experiment	experiment	NOUN
ajst-18332	116	9	was	be	AUX
ajst-18332	116	10	to	to	PART
ajst-18332	116	11	base	base	VERB
ajst-18332	116	12	the	the	DET
ajst-18332	116	13	improved	improved	ADJ
ajst-18332	116	14	network	network	NOUN
ajst-18332	116	15	on	on	ADP
ajst-18332	116	16	the	the	DET
ajst-18332	116	17	overall	overall	ADJ
ajst-18332	116	18	architecture	architecture	NOUN
ajst-18332	116	19	of	of	ADP
ajst-18332	116	20	the	the	DET
ajst-18332	116	21	resnet101	resnet101	PROPN
ajst-18332	116	22	-	-	PUNCT
ajst-18332	116	23	vd	vd	NOUN
ajst-18332	116	24	-	-	PUNCT
ajst-18332	116	25	dcn	dcn	PROPN
ajst-18332	116	26	backbone	backbone	NOUN
ajst-18332	116	27	network	network	NOUN
ajst-18332	116	28	,	,	PUNCT
ajst-18332	116	29	and	and	CCONJ
ajst-18332	116	30	this	this	DET
ajst-18332	116	31	experiment	experiment	NOUN
ajst-18332	116	32	was	be	AUX
ajst-18332	116	33	designated	designate	VERB
ajst-18332	116	34	as	as	ADP
ajst-18332	116	35	method	method	NOUN
ajst-18332	116	36	1	1	NUM
ajst-18332	116	37	.	.	PUNCT
ajst-18332	117	1	in	in	ADP
ajst-18332	117	2	addition	addition	NOUN
ajst-18332	117	3	,	,	PUNCT
ajst-18332	117	4	we	we	PRON
ajst-18332	117	5	explored	explore	VERB
ajst-18332	117	6	the	the	DET
ajst-18332	117	7	optimisation	optimisation	NOUN
ajst-18332	117	8	of	of	ADP
ajst-18332	117	9	the	the	DET
ajst-18332	117	10	training	training	NOUN
ajst-18332	117	11	strategy	strategy	NOUN
ajst-18332	117	12	based	base	VERB
ajst-18332	117	13	on	on	ADP
ajst-18332	117	14	the	the	DET
ajst-18332	117	15	resnet50vd	resnet50vd	NOUN
ajst-18332	117	16	-	-	PUNCT
ajst-18332	117	17	dcn	dcn	PROPN
ajst-18332	117	18	and	and	CCONJ
ajst-18332	117	19	resnet101	resnet101	PROPN
ajst-18332	117	20	-	-	PUNCT
ajst-18332	117	21	vd	vd	NOUN
ajst-18332	117	22	-	-	PUNCT
ajst-18332	117	23	dcn	dcn	PROPN
ajst-18332	117	24	backbone	backbone	NOUN
ajst-18332	117	25	networks	network	NOUN
ajst-18332	117	26	,	,	PUNCT
ajst-18332	117	27	and	and	CCONJ
ajst-18332	117	28	these	these	PRON
ajst-18332	117	29	were	be	AUX
ajst-18332	117	30	designated	designate	VERB
ajst-18332	117	31	as	as	ADP
ajst-18332	117	32	method	method	NOUN
ajst-18332	117	33	2	2	NUM
ajst-18332	117	34	and	and	CCONJ
ajst-18332	117	35	method	method	NOUN
ajst-18332	117	36	3	3	NUM
ajst-18332	117	37	,	,	PUNCT
ajst-18332	117	38	respectively	respectively	ADV
ajst-18332	117	39	.	.	PUNCT
ajst-18332	118	1	in	in	ADP
ajst-18332	118	2	particular	particular	ADJ
ajst-18332	118	3	,	,	PUNCT
ajst-18332	118	4	the	the	DET
ajst-18332	118	5	experiment	experiment	NOUN
ajst-18332	118	6	for	for	ADP
ajst-18332	118	7	the	the	DET
ajst-18332	118	8	optimised	optimise	VERB
ajst-18332	118	9	detection	detection	NOUN
ajst-18332	118	10	of	of	ADP
ajst-18332	118	11	neck	neck	NOUN
ajst-18332	118	12	structures	structure	NOUN
ajst-18332	118	13	in	in	ADP
ajst-18332	118	14	the	the	DET
ajst-18332	118	15	resnet50	resnet50	NOUN
ajst-18332	118	16	-	-	PUNCT
ajst-18332	118	17	vd	vd	NOUN
ajst-18332	118	18	-	-	PUNCT
ajst-18332	118	19	dcn	dcn	PROPN
ajst-18332	118	20	backbone	backbone	NOUN
ajst-18332	118	21	network	network	NOUN
ajst-18332	118	22	was	be	AUX
ajst-18332	118	23	designated	designate	VERB
ajst-18332	118	24	as	as	ADP
ajst-18332	118	25	method	method	NOUN
ajst-18332	118	26	4	4	NUM
ajst-18332	118	27	.	.	PUNCT
ajst-18332	119	1	the	the	DET
ajst-18332	119	2	experiment	experiment	NOUN
ajst-18332	119	3	for	for	ADP
ajst-18332	119	4	detecting	detect	VERB
ajst-18332	119	5	neck	neck	NOUN
ajst-18332	119	6	structures	structure	NOUN
ajst-18332	119	7	optimised	optimise	VERB
ajst-18332	119	8	in	in	ADP
ajst-18332	119	9	the	the	DET
ajst-18332	119	10	resnet50	resnet50	NOUN
ajst-18332	119	11	-	-	PUNCT
ajst-18332	119	12	vd	vd	NOUN
ajst-18332	119	13	-	-	PUNCT
ajst-18332	119	14	dcn	dcn	PROPN
ajst-18332	119	15	backbone	backbone	NOUN
ajst-18332	119	16	network	network	NOUN
ajst-18332	119	17	is	be	AUX
ajst-18332	119	18	designated	designate	VERB
ajst-18332	119	19	as	as	ADP
ajst-18332	119	20	method	method	NOUN
ajst-18332	119	21	4	4	NUM
ajst-18332	119	22	.	.	PUNCT
ajst-18332	120	1	the	the	DET
ajst-18332	120	2	results	result	NOUN
ajst-18332	120	3	of	of	ADP
ajst-18332	120	4	the	the	DET
ajst-18332	120	5	yolov3	yolov3	NOUN
ajst-18332	120	6	,	,	PUNCT
ajst-18332	120	7	yolov5,pp	yolov5,pp	PROPN
ajst-18332	120	8	yolo	yolo	ADJ
ajst-18332	120	9	model	model	NOUN
ajst-18332	120	10	training	training	NOUN
ajst-18332	120	11	for	for	ADP
ajst-18332	120	12	this	this	DET
ajst-18332	120	13	experiment	experiment	NOUN
ajst-18332	120	14	are	be	AUX
ajst-18332	120	15	shown	show	VERB
ajst-18332	120	16	in	in	ADP
ajst-18332	120	17	figures	figure	NOUN
ajst-18332	120	18	8	8	NUM
ajst-18332	120	19	-	-	SYM
ajst-18332	120	20	11	11	NUM
ajst-18332	120	21	.	.	PUNCT
ajst-18332	121	1	the	the	DET
ajst-18332	121	2	results	result	NOUN
ajst-18332	121	3	of	of	ADP
ajst-18332	121	4	the	the	DET
ajst-18332	121	5	comparison	comparison	NOUN
ajst-18332	121	6	experiment	experiment	NOUN
ajst-18332	121	7	are	be	AUX
ajst-18332	121	8	shown	show	VERB
ajst-18332	121	9	in	in	ADP
ajst-18332	121	10	table	table	NOUN
ajst-18332	121	11	1	1	NUM
ajst-18332	121	12	.	.	PUNCT
ajst-18332	122	1	the	the	DET
ajst-18332	122	2	experimental	experimental	ADJ
ajst-18332	122	3	results	result	NOUN
ajst-18332	122	4	show	show	VERB
ajst-18332	122	5	that	that	SCONJ
ajst-18332	122	6	the	the	DET
ajst-18332	122	7	optimised	optimise	VERB
ajst-18332	122	8	training	training	NOUN
ajst-18332	122	9	strategy	strategy	NOUN
ajst-18332	122	10	significantly	significantly	ADV
ajst-18332	122	11	improves	improve	VERB
ajst-18332	122	12	the	the	DET
ajst-18332	122	13	performance	performance	NOUN
ajst-18332	122	14	of	of	ADP
ajst-18332	122	15	the	the	DET
ajst-18332	122	16	network	network	NOUN
ajst-18332	122	17	in	in	ADP
ajst-18332	122	18	rebar	rebar	ADJ
ajst-18332	122	19	end	end	NOUN
ajst-18332	122	20	face	face	NOUN
ajst-18332	122	21	detection	detection	NOUN
ajst-18332	122	22	.	.	PUNCT
ajst-18332	123	1	in	in	ADP
ajst-18332	123	2	particular	particular	ADJ
ajst-18332	123	3	,	,	PUNCT
ajst-18332	123	4	when	when	SCONJ
ajst-18332	123	5	using	use	VERB
ajst-18332	123	6	method	method	NOUN
ajst-18332	123	7	4	4	NUM
ajst-18332	123	8	,	,	PUNCT
ajst-18332	123	9	we	we	PRON
ajst-18332	123	10	observed	observe	VERB
ajst-18332	123	11	a	a	DET
ajst-18332	123	12	1.07	1.07	NUM
ajst-18332	123	13	%	%	NOUN
ajst-18332	123	14	improvement	improvement	NOUN
ajst-18332	123	15	in	in	ADP
ajst-18332	123	16	the	the	DET
ajst-18332	123	17	ap	ap	PROPN
ajst-18332	123	18	value	value	NOUN
ajst-18332	123	19	compared	compare	VERB
ajst-18332	123	20	to	to	PART
ajst-18332	123	21	method	method	NOUN
ajst-18332	123	22	1	1	NUM
ajst-18332	123	23	,	,	PUNCT
ajst-18332	123	24	which	which	PRON
ajst-18332	123	25	demonstrates	demonstrate	VERB
ajst-18332	123	26	the	the	DET
ajst-18332	123	27	effectiveness	effectiveness	NOUN
ajst-18332	123	28	of	of	ADP
ajst-18332	123	29	the	the	DET
ajst-18332	123	30	optimised	optimise	VERB
ajst-18332	123	31	detection	detection	NOUN
ajst-18332	123	32	neck	neck	NOUN
ajst-18332	123	33	structure	structure	NOUN
ajst-18332	123	34	in	in	ADP
ajst-18332	123	35	improving	improve	VERB
ajst-18332	123	36	the	the	DET
ajst-18332	123	37	model	model	NOUN
ajst-18332	123	38	accuracy	accuracy	NOUN
ajst-18332	123	39	.	.	PUNCT
ajst-18332	124	1	although	although	SCONJ
ajst-18332	124	2	this	this	DET
ajst-18332	124	3	modification	modification	NOUN
ajst-18332	124	4	may	may	AUX
ajst-18332	124	5	have	have	AUX
ajst-18332	124	6	slightly	slightly	ADV
ajst-18332	124	7	increased	increase	VERB
ajst-18332	124	8	the	the	DET
ajst-18332	124	9	number	number	NOUN
ajst-18332	124	10	of	of	ADP
ajst-18332	124	11	parameters	parameter	NOUN
ajst-18332	124	12	of	of	ADP
ajst-18332	124	13	the	the	DET
ajst-18332	124	14	network	network	NOUN
ajst-18332	124	15	and	and	CCONJ
ajst-18332	124	16	may	may	AUX
ajst-18332	124	17	have	have	AUX
ajst-18332	124	18	led	lead	VERB
ajst-18332	124	19	to	to	ADP
ajst-18332	124	20	a	a	DET
ajst-18332	124	21	reduction	reduction	NOUN
ajst-18332	124	22	in	in	ADP
ajst-18332	124	23	the	the	DET
ajst-18332	124	24	speed	speed	NOUN
ajst-18332	124	25	of	of	ADP
ajst-18332	124	26	inference	inference	NOUN
ajst-18332	124	27	,	,	PUNCT
ajst-18332	124	28	the	the	DET
ajst-18332	124	29	significant	significant	ADJ
ajst-18332	124	30	effect	effect	NOUN
ajst-18332	124	31	in	in	ADP
ajst-18332	124	32	terms	term	NOUN
ajst-18332	124	33	of	of	ADP
ajst-18332	124	34	accuracy	accuracy	NOUN
ajst-18332	124	35	improvement	improvement	NOUN
ajst-18332	124	36	makes	make	VERB
ajst-18332	124	37	it	it	PRON
ajst-18332	124	38	a	a	DET
ajst-18332	124	39	worthwhile	worthwhile	ADJ
ajst-18332	124	40	improvement	improvement	NOUN
ajst-18332	124	41	to	to	PART
ajst-18332	124	42	adopt	adopt	VERB
ajst-18332	124	43	.	.	PUNCT
ajst-18332	125	1	through	through	ADP
ajst-18332	125	2	comparative	comparative	ADJ
ajst-18332	125	3	analysis	analysis	NOUN
ajst-18332	125	4	,	,	PUNCT
ajst-18332	125	5	we	we	PRON
ajst-18332	125	6	found	find	VERB
ajst-18332	125	7	that	that	SCONJ
ajst-18332	125	8	the	the	DET
ajst-18332	125	9	network	network	NOUN
ajst-18332	125	10	,	,	PUNCT
ajst-18332	125	11	while	while	SCONJ
ajst-18332	125	12	performing	perform	VERB
ajst-18332	125	13	well	well	ADV
ajst-18332	125	14	in	in	ADP
ajst-18332	125	15	learning	learn	VERB
ajst-18332	125	16	features	feature	NOUN
ajst-18332	125	17	for	for	ADP
ajst-18332	125	18	small	small	ADJ
ajst-18332	125	19	and	and	CCONJ
ajst-18332	125	20	very	very	ADV
ajst-18332	125	21	small	small	ADJ
ajst-18332	125	22	rebar	rebar	NOUN
ajst-18332	125	23	targets	target	NOUN
ajst-18332	125	24	,	,	PUNCT
ajst-18332	125	25	requires	require	VERB
ajst-18332	125	26	further	further	ADJ
ajst-18332	125	27	optimisation	optimisation	NOUN
ajst-18332	125	28	when	when	SCONJ
ajst-18332	125	29	dealing	deal	VERB
ajst-18332	125	30	with	with	ADP
ajst-18332	125	31	medium	medium	ADJ
ajst-18332	125	32	or	or	CCONJ
ajst-18332	125	33	larger	large	ADJ
ajst-18332	125	34	rebar	rebar	NOUN
ajst-18332	125	35	targets	target	NOUN
ajst-18332	125	36	.	.	PUNCT
ajst-18332	126	1	this	this	DET
ajst-18332	126	2	finding	finding	NOUN
ajst-18332	126	3	provides	provide	VERB
ajst-18332	126	4	valuable	valuable	ADJ
ajst-18332	126	5	guidance	guidance	NOUN
ajst-18332	126	6	on	on	ADP
ajst-18332	126	7	the	the	DET
ajst-18332	126	8	direction	direction	NOUN
ajst-18332	126	9	of	of	ADP
ajst-18332	126	10	future	future	ADJ
ajst-18332	126	11	improvements	improvement	NOUN
ajst-18332	126	12	to	to	ADP
ajst-18332	126	13	the	the	DET
ajst-18332	126	14	rebar	rebar	ADJ
ajst-18332	126	15	end	end	NOUN
ajst-18332	126	16	-	-	PUNCT
ajst-18332	126	17	face	face	NOUN
ajst-18332	126	18	detection	detection	NOUN
ajst-18332	126	19	network	network	NOUN
ajst-18332	126	20	.	.	PUNCT
ajst-18332	127	1	in	in	ADP
ajst-18332	127	2	this	this	DET
ajst-18332	127	3	rebar	rebar	ADJ
ajst-18332	127	4	end	end	NOUN
ajst-18332	127	5	-	-	PUNCT
ajst-18332	127	6	face	face	NOUN
ajst-18332	127	7	detection	detection	NOUN
ajst-18332	127	8	experiment	experiment	NOUN
ajst-18332	127	9	,	,	PUNCT
ajst-18332	127	10	a	a	DET
ajst-18332	127	11	detailed	detailed	ADJ
ajst-18332	127	12	loss	loss	NOUN
ajst-18332	127	13	function	function	NOUN
ajst-18332	127	14	convergence	convergence	NOUN
ajst-18332	127	15	analysis	analysis	NOUN
ajst-18332	127	16	is	be	AUX
ajst-18332	127	17	performed	perform	VERB
ajst-18332	127	18	in	in	ADP
ajst-18332	127	19	order	order	NOUN
ajst-18332	127	20	to	to	PART
ajst-18332	127	21	evaluate	evaluate	VERB
ajst-18332	127	22	the	the	DET
ajst-18332	127	23	training	training	NOUN
ajst-18332	127	24	efficiency	efficiency	NOUN
ajst-18332	127	25	and	and	CCONJ
ajst-18332	127	26	stability	stability	NOUN
ajst-18332	127	27	of	of	ADP
ajst-18332	127	28	different	different	ADJ
ajst-18332	127	29	models	model	NOUN
ajst-18332	127	30	.	.	PUNCT
ajst-18332	128	1	this	this	DET
ajst-18332	128	2	analysis	analysis	NOUN
ajst-18332	128	3	compares	compare	VERB
ajst-18332	128	4	the	the	DET
ajst-18332	128	5	convergence	convergence	NOUN
ajst-18332	128	6	performance	performance	NOUN
ajst-18332	128	7	of	of	ADP
ajst-18332	128	8	the	the	DET
ajst-18332	128	9	classic	classic	ADJ
ajst-18332	128	10	yolov3	yolov3	PROPN
ajst-18332	128	11	,	,	PUNCT
ajst-18332	128	12	the	the	DET
ajst-18332	128	13	latest	late	ADJ
ajst-18332	128	14	yolov5	yolov5	NOUN
ajst-18332	128	15	,	,	PUNCT
ajst-18332	128	16	and	and	CCONJ
ajst-18332	128	17	our	our	PRON
ajst-18332	128	18	improved	improved	ADJ
ajst-18332	128	19	pp	pp	PROPN
ajst-18332	128	20	-	-	PUNCT
ajst-18332	128	21	yolo	yolo	ADJ
ajst-18332	128	22	network	network	NOUN
ajst-18332	128	23	on	on	ADP
ajst-18332	128	24	the	the	DET
ajst-18332	128	25	loss	loss	NOUN
ajst-18332	128	26	function	function	NOUN
ajst-18332	128	27	.	.	PUNCT
ajst-18332	129	1	as	as	ADP
ajst-18332	129	2	the	the	DET
ajst-18332	129	3	main	main	ADJ
ajst-18332	129	4	objective	objective	NOUN
ajst-18332	129	5	of	of	ADP
ajst-18332	129	6	optimisation	optimisation	NOUN
ajst-18332	129	7	during	during	ADP
ajst-18332	129	8	model	model	NOUN
ajst-18332	129	9	training	training	NOUN
ajst-18332	129	10	,	,	PUNCT
ajst-18332	129	11	the	the	DET
ajst-18332	129	12	convergence	convergence	NOUN
ajst-18332	129	13	speed	speed	NOUN
ajst-18332	129	14	and	and	CCONJ
ajst-18332	129	15	stability	stability	NOUN
ajst-18332	129	16	of	of	ADP
ajst-18332	129	17	the	the	DET
ajst-18332	129	18	loss	loss	NOUN
ajst-18332	129	19	function	function	NOUN
ajst-18332	129	20	are	be	AUX
ajst-18332	129	21	key	key	ADJ
ajst-18332	129	22	indicators	indicator	NOUN
ajst-18332	129	23	to	to	PART
ajst-18332	129	24	assess	assess	VERB
ajst-18332	129	25	the	the	DET
ajst-18332	129	26	effectiveness	effectiveness	NOUN
ajst-18332	129	27	of	of	ADP
ajst-18332	129	28	model	model	NOUN
ajst-18332	129	29	training	training	NOUN
ajst-18332	129	30	.	.	PUNCT
ajst-18332	130	1	during	during	ADP
ajst-18332	130	2	the	the	DET
ajst-18332	130	3	experiments	experiment	NOUN
ajst-18332	130	4	,	,	PUNCT
ajst-18332	130	5	we	we	PRON
ajst-18332	130	6	monitored	monitor	VERB
ajst-18332	130	7	the	the	DET
ajst-18332	130	8	change	change	NOUN
ajst-18332	130	9	of	of	ADP
ajst-18332	130	10	the	the	DET
ajst-18332	130	11	loss	loss	NOUN
ajst-18332	130	12	value	value	NOUN
ajst-18332	130	13	of	of	ADP
ajst-18332	130	14	each	each	DET
ajst-18332	130	15	model	model	NOUN
ajst-18332	130	16	throughout	throughout	ADP
ajst-18332	130	17	the	the	DET
ajst-18332	130	18	training	training	NOUN
ajst-18332	130	19	cycle	cycle	NOUN
ajst-18332	130	20	.	.	PUNCT
ajst-18332	131	1	by	by	ADP
ajst-18332	131	2	recording	record	VERB
ajst-18332	131	3	the	the	DET
ajst-18332	131	4	loss	loss	NOUN
ajst-18332	131	5	values	value	NOUN
ajst-18332	131	6	at	at	ADP
ajst-18332	131	7	each	each	DET
ajst-18332	131	8	iteration	iteration	NOUN
ajst-18332	131	9	step	step	NOUN
ajst-18332	131	10	,	,	PUNCT
ajst-18332	131	11	we	we	PRON
ajst-18332	131	12	were	be	AUX
ajst-18332	131	13	able	able	ADJ
ajst-18332	131	14	to	to	PART
ajst-18332	131	15	observe	observe	VERB
ajst-18332	131	16	the	the	DET
ajst-18332	131	17	performance	performance	NOUN
ajst-18332	131	18	changes	change	NOUN
ajst-18332	131	19	during	during	ADP
ajst-18332	131	20	model	model	NOUN
ajst-18332	131	21	learning	learning	NOUN
ajst-18332	131	22	.	.	PUNCT
ajst-18332	132	1	the	the	DET
ajst-18332	132	2	fast	fast	ADJ
ajst-18332	132	3	and	and	CCONJ
ajst-18332	132	4	steady	steady	ADJ
ajst-18332	132	5	decrease	decrease	NOUN
ajst-18332	132	6	of	of	ADP
ajst-18332	132	7	the	the	DET
ajst-18332	132	8	loss	loss	NOUN
ajst-18332	132	9	function	function	NOUN
ajst-18332	132	10	indicates	indicate	VERB
ajst-18332	132	11	that	that	SCONJ
ajst-18332	132	12	the	the	DET
ajst-18332	132	13	model	model	NOUN
ajst-18332	132	14	is	be	AUX
ajst-18332	132	15	able	able	ADJ
ajst-18332	132	16	to	to	PART
ajst-18332	132	17	learn	learn	VERB
ajst-18332	132	18	effectively	effectively	ADV
ajst-18332	132	19	from	from	ADP
ajst-18332	132	20	the	the	DET
ajst-18332	132	21	training	training	NOUN
ajst-18332	132	22	data	datum	NOUN
ajst-18332	132	23	and	and	CCONJ
ajst-18332	132	24	has	have	VERB
ajst-18332	132	25	better	well	ADJ
ajst-18332	132	26	generalisation	generalisation	NOUN
ajst-18332	132	27	ability	ability	NOUN
ajst-18332	132	28	.	.	PUNCT
ajst-18332	133	1	the	the	DET
ajst-18332	133	2	loss	loss	NOUN
ajst-18332	133	3	function	function	NOUN
ajst-18332	133	4	convergence	convergence	NOUN
ajst-18332	133	5	plot	plot	NOUN
ajst-18332	133	6	for	for	ADP
ajst-18332	133	7	the	the	DET
ajst-18332	133	8	experimental	experimental	ADJ
ajst-18332	133	9	yolov3	yolov3	PROPN
ajst-18332	133	10	,	,	PUNCT
ajst-18332	133	11	yolov5	yolov5	NOUN
ajst-18332	133	12	,	,	PUNCT
ajst-18332	133	13	pp	pp	ADP
ajst-18332	133	14	yolo	yolo	ADJ
ajst-18332	133	15	model	model	NOUN
ajst-18332	133	16	is	be	AUX
ajst-18332	133	17	shown	show	VERB
ajst-18332	133	18	in	in	ADP
ajst-18332	133	19	figure	figure	NOUN
ajst-18332	133	20	5	5	NUM
ajst-18332	133	21	-	-	SYM
ajst-18332	133	22	7	7	NUM
ajst-18332	133	23	.	.	PUNCT
ajst-18332	134	1	the	the	DET
ajst-18332	134	2	experimental	experimental	ADJ
ajst-18332	134	3	results	result	NOUN
ajst-18332	134	4	show	show	VERB
ajst-18332	134	5	that	that	SCONJ
ajst-18332	134	6	the	the	DET
ajst-18332	134	7	improved	improve	VERB
ajst-18332	134	8	ppyolo	ppyolo	NOUN
ajst-18332	134	9	network	network	NOUN
ajst-18332	134	10	outperforms	outperform	NOUN
ajst-18332	134	11	yolov3	yolov3	PROPN
ajst-18332	134	12	and	and	CCONJ
ajst-18332	134	13	yolov5	yolov5	NOUN
ajst-18332	134	14	in	in	ADP
ajst-18332	134	15	terms	term	NOUN
ajst-18332	134	16	of	of	ADP
ajst-18332	134	17	the	the	DET
ajst-18332	134	18	convergence	convergence	NOUN
ajst-18332	134	19	speed	speed	NOUN
ajst-18332	134	20	and	and	CCONJ
ajst-18332	134	21	stability	stability	NOUN
ajst-18332	134	22	of	of	ADP
ajst-18332	134	23	the	the	DET
ajst-18332	134	24	loss	loss	NOUN
ajst-18332	134	25	function.specifically	function.specifically	ADV
ajst-18332	134	26	,	,	PUNCT
ajst-18332	134	27	pp	pp	ADV
ajst-18332	134	28	-	-	PUNCT
ajst-18332	134	29	yolo	yolo	PROPN
ajst-18332	134	30	shows	show	VERB
ajst-18332	134	31	a	a	DET
ajst-18332	134	32	faster	fast	ADJ
ajst-18332	134	33	loss	loss	NOUN
ajst-18332	134	34	decline	decline	NOUN
ajst-18332	134	35	in	in	ADP
ajst-18332	134	36	the	the	DET
ajst-18332	134	37	early	early	ADJ
ajst-18332	134	38	stages	stage	NOUN
ajst-18332	134	39	of	of	ADP
ajst-18332	134	40	training	training	NOUN
ajst-18332	134	41	and	and	CCONJ
ajst-18332	134	42	maintains	maintain	VERB
ajst-18332	134	43	low	low	ADJ
ajst-18332	134	44	loss	loss	NOUN
ajst-18332	134	45	fluctuations	fluctuation	NOUN
ajst-18332	134	46	throughout	throughout	ADP
ajst-18332	134	47	the	the	DET
ajst-18332	134	48	training	training	NOUN
ajst-18332	134	49	process	process	NOUN
ajst-18332	134	50	,	,	PUNCT
ajst-18332	134	51	which	which	PRON
ajst-18332	134	52	demonstrates	demonstrate	VERB
ajst-18332	134	53	the	the	DET
ajst-18332	134	54	effectiveness	effectiveness	NOUN
ajst-18332	134	55	of	of	ADP
ajst-18332	134	56	the	the	DET
ajst-18332	134	57	optimised	optimise	VERB
ajst-18332	134	58	algorithms	algorithm	NOUN
ajst-18332	134	59	and	and	CCONJ
ajst-18332	134	60	network	network	NOUN
ajst-18332	134	61	structure	structure	NOUN
ajst-18332	134	62	.	.	PUNCT
ajst-18332	135	1	in	in	ADP
ajst-18332	135	2	contrast	contrast	NOUN
ajst-18332	135	3	,	,	PUNCT
ajst-18332	135	4	yolov3	yolov3	PROPN
ajst-18332	135	5	and	and	CCONJ
ajst-18332	135	6	yolov5	yolov5	PROPN
ajst-18332	135	7	,	,	PUNCT
ajst-18332	135	8	while	while	SCONJ
ajst-18332	135	9	also	also	ADV
ajst-18332	135	10	demonstrating	demonstrate	VERB
ajst-18332	135	11	a	a	DET
ajst-18332	135	12	decrease	decrease	NOUN
ajst-18332	135	13	in	in	ADP
ajst-18332	135	14	loss	loss	NOUN
ajst-18332	135	15	values	value	NOUN
ajst-18332	135	16	,	,	PUNCT
ajst-18332	135	17	showed	show	VERB
ajst-18332	135	18	large	large	ADJ
ajst-18332	135	19	fluctuations	fluctuation	NOUN
ajst-18332	135	20	during	during	ADP
ajst-18332	135	21	certain	certain	ADJ
ajst-18332	135	22	training	training	NOUN
ajst-18332	135	23	phases	phase	NOUN
ajst-18332	135	24	,	,	PUNCT
ajst-18332	135	25	which	which	PRON
ajst-18332	135	26	may	may	AUX
ajst-18332	135	27	indicate	indicate	VERB
ajst-18332	135	28	that	that	SCONJ
ajst-18332	135	29	the	the	DET
ajst-18332	135	30	training	training	NOUN
ajst-18332	135	31	stability	stability	NOUN
ajst-18332	135	32	of	of	ADP
ajst-18332	135	33	these	these	DET
ajst-18332	135	34	models	model	NOUN
ajst-18332	135	35	in	in	ADP
ajst-18332	135	36	specific	specific	ADJ
ajst-18332	135	37	scenarios	scenario	NOUN
ajst-18332	135	38	could	could	AUX
ajst-18332	135	39	be	be	AUX
ajst-18332	135	40	improved	improve	VERB
ajst-18332	135	41	.	.	PUNCT
ajst-18332	136	1	figure	figure	NOUN
ajst-18332	136	2	5	5	NUM
ajst-18332	136	3	.	.	PUNCT
ajst-18332	136	4	loss	loss	NOUN
ajst-18332	136	5	function	function	NOUN
ajst-18332	136	6	variation	variation	NOUN
ajst-18332	136	7	curve	curve	NOUN
ajst-18332	136	8	for	for	ADP
ajst-18332	136	9	pp	pp	ADV
ajst-18332	136	10	-	-	PUNCT
ajst-18332	136	11	yolo	yolo	ADJ
ajst-18332	136	12	96	96	NUM
ajst-18332	136	13	figure	figure	NOUN
ajst-18332	136	14	6	6	NUM
ajst-18332	136	15	.	.	PUNCT
ajst-18332	136	16	convergence	convergence	NOUN
ajst-18332	136	17	curve	curve	NOUN
ajst-18332	136	18	of	of	ADP
ajst-18332	136	19	the	the	DET
ajst-18332	136	20	loss	loss	NOUN
ajst-18332	136	21	function	function	NOUN
ajst-18332	136	22	for	for	ADP
ajst-18332	136	23	yolov3	yolov3	PROPN
ajst-18332	136	24	figure	figure	NOUN
ajst-18332	136	25	7	7	NUM
ajst-18332	136	26	.	.	PUNCT
ajst-18332	136	27	convergence	convergence	NOUN
ajst-18332	136	28	curve	curve	NOUN
ajst-18332	136	29	of	of	ADP
ajst-18332	136	30	the	the	DET
ajst-18332	136	31	loss	loss	NOUN
ajst-18332	136	32	function	function	NOUN
ajst-18332	136	33	for	for	ADP
ajst-18332	136	34	yolov5	yolov5	NOUN
ajst-18332	136	35	figure	figure	NOUN
ajst-18332	136	36	8	8	NUM
ajst-18332	136	37	.	.	PUNCT
ajst-18332	137	1	visualization	visualization	NOUN
ajst-18332	137	2	of	of	ADP
ajst-18332	137	3	training	training	NOUN
ajst-18332	137	4	strategy	strategy	NOUN
ajst-18332	137	5	optimization	optimization	NOUN
ajst-18332	137	6	results	result	VERB
ajst-18332	137	7	for	for	ADP
ajst-18332	137	8	pp	pp	ADV
ajst-18332	137	9	-	-	PUNCT
ajst-18332	137	10	yolo	yolo	ADJ
ajst-18332	137	11	figure	figure	NOUN
ajst-18332	137	12	9	9	NUM
ajst-18332	137	13	.	.	PUNCT
ajst-18332	137	14	visualization	visualization	NOUN
ajst-18332	137	15	of	of	ADP
ajst-18332	137	16	bottleneck	bottleneck	NOUN
ajst-18332	137	17	optimization	optimization	NOUN
ajst-18332	137	18	results	result	VERB
ajst-18332	137	19	for	for	ADP
ajst-18332	137	20	pp	pp	ADV
ajst-18332	137	21	-	-	PUNCT
ajst-18332	137	22	yolo	yolo	ADJ
ajst-18332	137	23	detection	detection	NOUN
ajst-18332	137	24	97	97	NUM
ajst-18332	137	25	figure	figure	NOUN
ajst-18332	137	26	10	10	NUM
ajst-18332	137	27	.	.	PUNCT
ajst-18332	138	1	visualization	visualization	NOUN
ajst-18332	138	2	of	of	ADP
ajst-18332	138	3	unoptimized	unoptimized	ADJ
ajst-18332	138	4	results	result	NOUN
ajst-18332	138	5	for	for	ADP
ajst-18332	138	6	pp	pp	ADV
ajst-18332	138	7	-	-	PUNCT
ajst-18332	138	8	yolo	yolo	ADJ
ajst-18332	138	9	figure	figure	NOUN
ajst-18332	138	10	11	11	NUM
ajst-18332	138	11	.	.	PUNCT
ajst-18332	139	1	visualization	visualization	NOUN
ajst-18332	139	2	of	of	ADP
ajst-18332	139	3	optimization	optimization	NOUN
ajst-18332	139	4	strategy	strategy	NOUN
ajst-18332	139	5	results	result	VERB
ajst-18332	139	6	for	for	ADP
ajst-18332	139	7	pp	pp	ADV
ajst-18332	139	8	-	-	PUNCT
ajst-18332	139	9	yolo	yolo	ADJ
ajst-18332	139	10	table	table	NOUN
ajst-18332	139	11	1	1	NUM
ajst-18332	139	12	.	.	PUNCT
ajst-18332	139	13	detection	detection	NOUN
ajst-18332	139	14	performance	performance	NOUN
ajst-18332	139	15	of	of	ADP
ajst-18332	139	16	various	various	ADJ
ajst-18332	139	17	improvement	improvement	NOUN
ajst-18332	139	18	methods	method	NOUN
ajst-18332	139	19	on	on	ADP
ajst-18332	139	20	public	public	ADJ
ajst-18332	139	21	datasets	dataset	NOUN
ajst-18332	139	22	model	model	NOUN
ajst-18332	139	23	optimization	optimization	NOUN
ajst-18332	139	24	ap	ap	PROPN
ajst-18332	139	25	detect	detect	VERB
ajst-18332	139	26	time	time	NOUN
ajst-18332	139	27	yolov3	yolov3	PROPN
ajst-18332	139	28	unoptimized	unoptimized	ADJ
ajst-18332	139	29	93.20	93.20	NUM
ajst-18332	139	30	%	%	NOUN
ajst-18332	139	31	45.4ms	45.4ms	NUM
ajst-18332	139	32	yolov5	yolov5	NOUN
ajst-18332	139	33	unoptimized	unoptimized	ADJ
ajst-18332	139	34	94.23	94.23	NUM
ajst-18332	139	35	%	%	NOUN
ajst-18332	139	36	44.0ms	44.0ms	NUM
ajst-18332	139	37	pp	pp	ADJ
ajst-18332	139	38	-	-	PUNCT
ajst-18332	139	39	yolo	yolo	NOUN
ajst-18332	139	40	unoptimized	unoptimized	ADJ
ajst-18332	139	41	95.16	95.16	NUM
ajst-18332	139	42	%	%	NOUN
ajst-18332	139	43	23.3ms	23.3ms	NUM
ajst-18332	139	44	pp	pp	ADJ
ajst-18332	139	45	-	-	PUNCT
ajst-18332	139	46	yolo	yolo	ADJ
ajst-18332	139	47	training	training	NOUN
ajst-18332	139	48	strategy	strategy	NOUN
ajst-18332	139	49	optimization	optimization	NOUN
ajst-18332	139	50	96.17	96.17	NUM
ajst-18332	139	51	%	%	NOUN
ajst-18332	139	52	23.1ms	23.1ms	NUM
ajst-18332	139	53	pp	pp	ADJ
ajst-18332	139	54	-	-	PUNCT
ajst-18332	139	55	yolo	yolo	ADJ
ajst-18332	139	56	data	datum	NOUN
ajst-18332	139	57	augmentation	augmentation	NOUN
ajst-18332	139	58	optimization	optimization	NOUN
ajst-18332	139	59	96.20	96.20	NUM
ajst-18332	139	60	%	%	NOUN
ajst-18332	139	61	21.2ms	21.2ms	NUM
ajst-18332	139	62	pp	pp	ADJ
ajst-18332	139	63	-	-	PUNCT
ajst-18332	139	64	yolo	yolo	NOUN
ajst-18332	139	65	detecting	detect	VERB
ajst-18332	139	66	bottleneck	bottleneck	NOUN
ajst-18332	139	67	optimization	optimization	NOUN
ajst-18332	139	68	96.23	96.23	NUM
ajst-18332	139	69	%	%	NOUN
ajst-18332	139	70	22.3ms	22.3ms	NUM
ajst-18332	139	71	4	4	NUM
ajst-18332	139	72	.	.	PUNCT
ajst-18332	139	73	conclusions	conclusion	NOUN
ajst-18332	139	74	in	in	ADP
ajst-18332	139	75	this	this	DET
ajst-18332	139	76	paper	paper	NOUN
ajst-18332	139	77	,	,	PUNCT
ajst-18332	139	78	we	we	PRON
ajst-18332	139	79	propose	propose	VERB
ajst-18332	139	80	an	an	DET
ajst-18332	139	81	improved	improved	ADJ
ajst-18332	139	82	pp	pp	ADJ
ajst-18332	139	83	-	-	PUNCT
ajst-18332	139	84	yolo	yolo	ADJ
ajst-18332	139	85	network	network	NOUN
ajst-18332	139	86	method	method	NOUN
ajst-18332	139	87	dedicated	dedicate	VERB
ajst-18332	139	88	to	to	ADP
ajst-18332	139	89	the	the	DET
ajst-18332	139	90	identification	identification	NOUN
ajst-18332	139	91	and	and	CCONJ
ajst-18332	139	92	counting	counting	NOUN
ajst-18332	139	93	of	of	ADP
ajst-18332	139	94	rebar	rebar	NOUN
ajst-18332	139	95	end	end	NOUN
ajst-18332	139	96	face	face	NOUN
ajst-18332	139	97	images	image	NOUN
ajst-18332	139	98	.	.	PUNCT
ajst-18332	140	1	we	we	PRON
ajst-18332	140	2	perform	perform	VERB
ajst-18332	140	3	targeted	target	VERB
ajst-18332	140	4	optimization	optimization	NOUN
ajst-18332	140	5	of	of	ADP
ajst-18332	140	6	the	the	DET
ajst-18332	140	7	training	training	NOUN
ajst-18332	140	8	strategy	strategy	NOUN
ajst-18332	140	9	and	and	CCONJ
ajst-18332	140	10	the	the	DET
ajst-18332	140	11	detection	detection	NOUN
ajst-18332	140	12	of	of	ADP
ajst-18332	140	13	the	the	DET
ajst-18332	140	14	neck	neck	NOUN
ajst-18332	140	15	part	part	NOUN
ajst-18332	140	16	.	.	PUNCT
ajst-18332	141	1	the	the	DET
ajst-18332	141	2	optimization	optimization	NOUN
ajst-18332	141	3	of	of	ADP
ajst-18332	141	4	the	the	DET
ajst-18332	141	5	training	training	NOUN
ajst-18332	141	6	method	method	NOUN
ajst-18332	141	7	is	be	AUX
ajst-18332	141	8	based	base	VERB
ajst-18332	141	9	on	on	ADP
ajst-18332	141	10	the	the	DET
ajst-18332	141	11	characteristics	characteristic	NOUN
ajst-18332	141	12	of	of	ADP
ajst-18332	141	13	rebar	rebar	ADJ
ajst-18332	141	14	images	image	NOUN
ajst-18332	141	15	,	,	PUNCT
ajst-18332	141	16	we	we	PRON
ajst-18332	141	17	adopt	adopt	VERB
ajst-18332	141	18	the	the	DET
ajst-18332	141	19	cutmix	cutmix	NOUN
ajst-18332	141	20	data	datum	NOUN
ajst-18332	141	21	enhancement	enhancement	NOUN
ajst-18332	141	22	algorithm	algorithm	NOUN
ajst-18332	141	23	,	,	PUNCT
ajst-18332	141	24	which	which	PRON
ajst-18332	141	25	is	be	AUX
ajst-18332	141	26	suitable	suitable	ADJ
ajst-18332	141	27	for	for	ADP
ajst-18332	141	28	the	the	DET
ajst-18332	141	29	characteristics	characteristic	NOUN
ajst-18332	141	30	of	of	ADP
ajst-18332	141	31	rebar	rebar	ADJ
ajst-18332	141	32	images	image	NOUN
ajst-18332	141	33	,	,	PUNCT
ajst-18332	141	34	to	to	PART
ajst-18332	141	35	replace	replace	VERB
ajst-18332	141	36	the	the	DET
ajst-18332	141	37	traditional	traditional	ADJ
ajst-18332	141	38	mixup	mixup	NOUN
ajst-18332	141	39	data	datum	NOUN
ajst-18332	141	40	enhancement	enhancement	NOUN
ajst-18332	141	41	method	method	NOUN
ajst-18332	141	42	,	,	PUNCT
ajst-18332	141	43	and	and	CCONJ
ajst-18332	141	44	introduce	introduce	VERB
ajst-18332	141	45	the	the	DET
ajst-18332	141	46	gridmask	gridmask	NOUN
ajst-18332	141	47	algorithm	algorithm	NOUN
ajst-18332	141	48	to	to	PART
ajst-18332	141	49	enhance	enhance	VERB
ajst-18332	141	50	the	the	DET
ajst-18332	141	51	network	network	NOUN
ajst-18332	141	52	's	's	PART
ajst-18332	141	53	ability	ability	NOUN
ajst-18332	141	54	to	to	PART
ajst-18332	141	55	learn	learn	VERB
ajst-18332	141	56	rebar	rebar	NOUN
ajst-18332	141	57	features	feature	NOUN
ajst-18332	141	58	.	.	PUNCT
ajst-18332	142	1	these	these	DET
ajst-18332	142	2	optimizations	optimization	NOUN
ajst-18332	142	3	enable	enable	VERB
ajst-18332	142	4	the	the	DET
ajst-18332	142	5	model	model	NOUN
ajst-18332	142	6	to	to	AUX
ajst-18332	142	7	more	more	ADV
ajst-18332	142	8	fully	fully	ADV
ajst-18332	142	9	and	and	CCONJ
ajst-18332	142	10	evenly	evenly	ADV
ajst-18332	142	11	learn	learn	VERB
ajst-18332	142	12	the	the	DET
ajst-18332	142	13	various	various	ADJ
ajst-18332	142	14	features	feature	NOUN
ajst-18332	142	15	of	of	ADP
ajst-18332	142	16	rebar	rebar	ADJ
ajst-18332	142	17	end	end	NOUN
ajst-18332	142	18	faces	face	NOUN
ajst-18332	142	19	,	,	PUNCT
ajst-18332	142	20	which	which	PRON
ajst-18332	142	21	effectively	effectively	ADV
ajst-18332	142	22	improves	improve	VERB
ajst-18332	142	23	the	the	DET
ajst-18332	142	24	recognition	recognition	NOUN
ajst-18332	142	25	accuracy	accuracy	NOUN
ajst-18332	142	26	of	of	ADP
ajst-18332	142	27	various	various	ADJ
ajst-18332	142	28	types	type	NOUN
ajst-18332	142	29	of	of	ADP
ajst-18332	142	30	rebars	rebar	NOUN
ajst-18332	142	31	.	.	PUNCT
ajst-18332	143	1	our	our	PRON
ajst-18332	143	2	training	training	NOUN
ajst-18332	143	3	strategy	strategy	NOUN
ajst-18332	143	4	optimization	optimization	NOUN
ajst-18332	143	5	solves	solve	VERB
ajst-18332	143	6	the	the	DET
ajst-18332	143	7	problems	problem	NOUN
ajst-18332	143	8	of	of	ADP
ajst-18332	143	9	large	large	ADJ
ajst-18332	143	10	size	size	NOUN
ajst-18332	143	11	variation	variation	NOUN
ajst-18332	143	12	and	and	CCONJ
ajst-18332	143	13	inter	inter	ADJ
ajst-18332	143	14	-	-	ADJ
ajst-18332	143	15	class	class	ADJ
ajst-18332	143	16	similarity	similarity	NOUN
ajst-18332	143	17	in	in	ADP
ajst-18332	143	18	rebar	rebar	ADJ
ajst-18332	143	19	images	image	NOUN
ajst-18332	143	20	,	,	PUNCT
ajst-18332	143	21	and	and	CCONJ
ajst-18332	143	22	achieves	achieve	VERB
ajst-18332	143	23	a	a	DET
ajst-18332	143	24	more	more	ADV
ajst-18332	143	25	balanced	balanced	ADJ
ajst-18332	143	26	recognition	recognition	NOUN
ajst-18332	143	27	accuracy	accuracy	NOUN
ajst-18332	143	28	of	of	ADP
ajst-18332	143	29	different	different	ADJ
ajst-18332	143	30	types	type	NOUN
ajst-18332	143	31	of	of	ADP
ajst-18332	143	32	rebars	rebar	NOUN
ajst-18332	143	33	,	,	PUNCT
ajst-18332	143	34	with	with	ADP
ajst-18332	143	35	a	a	DET
ajst-18332	143	36	maximum	maximum	NOUN
ajst-18332	143	37	of	of	ADP
ajst-18332	143	38	96.23	96.23	NUM
ajst-18332	143	39	%	%	NOUN
ajst-18332	143	40	ap	ap	PROPN
ajst-18332	143	41	.	.	PUNCT
ajst-18332	144	1	in	in	ADP
ajst-18332	144	2	the	the	DET
ajst-18332	144	3	improvement	improvement	NOUN
ajst-18332	144	4	of	of	ADP
ajst-18332	144	5	the	the	DET
ajst-18332	144	6	detection	detection	NOUN
ajst-18332	144	7	neck	neck	NOUN
ajst-18332	144	8	part	part	NOUN
ajst-18332	144	9	,	,	PUNCT
ajst-18332	144	10	we	we	PRON
ajst-18332	144	11	retain	retain	VERB
ajst-18332	144	12	the	the	DET
ajst-18332	144	13	basic	basic	ADJ
ajst-18332	144	14	architecture	architecture	NOUN
ajst-18332	144	15	of	of	ADP
ajst-18332	144	16	the	the	DET
ajst-18332	144	17	original	original	ADJ
ajst-18332	144	18	network	network	NOUN
ajst-18332	144	19	,	,	PUNCT
ajst-18332	144	20	and	and	CCONJ
ajst-18332	144	21	at	at	ADP
ajst-18332	144	22	the	the	DET
ajst-18332	144	23	same	same	ADJ
ajst-18332	144	24	time	time	NOUN
ajst-18332	144	25	,	,	PUNCT
ajst-18332	144	26	we	we	PRON
ajst-18332	144	27	add	add	VERB
ajst-18332	144	28	the	the	DET
ajst-18332	144	29	information	information	NOUN
ajst-18332	144	30	transfer	transfer	NOUN
ajst-18332	144	31	pathway	pathway	NOUN
ajst-18332	144	32	from	from	ADP
ajst-18332	144	33	the	the	DET
ajst-18332	144	34	low	low	ADJ
ajst-18332	144	35	-	-	PUNCT
ajst-18332	144	36	layer	layer	NOUN
ajst-18332	144	37	to	to	ADP
ajst-18332	144	38	the	the	DET
ajst-18332	144	39	high	high	ADJ
ajst-18332	144	40	-	-	PUNCT
ajst-18332	144	41	layer	layer	NOUN
ajst-18332	144	42	network	network	NOUN
ajst-18332	144	43	in	in	ADP
ajst-18332	144	44	layers	layer	NOUN
ajst-18332	144	45	4	4	NUM
ajst-18332	144	46	and	and	CCONJ
ajst-18332	144	47	5	5	NUM
ajst-18332	144	48	.	.	PUNCT
ajst-18332	145	1	this	this	DET
ajst-18332	145	2	improvement	improvement	NOUN
ajst-18332	145	3	not	not	PART
ajst-18332	145	4	only	only	ADV
ajst-18332	145	5	allows	allow	VERB
ajst-18332	145	6	the	the	DET
ajst-18332	145	7	lowlevel	lowlevel	NOUN
ajst-18332	145	8	network	network	NOUN
ajst-18332	145	9	to	to	PART
ajst-18332	145	10	learn	learn	VERB
ajst-18332	145	11	the	the	DET
ajst-18332	145	12	feature	feature	NOUN
ajst-18332	145	13	information	information	NOUN
ajst-18332	145	14	of	of	ADP
ajst-18332	145	15	the	the	DET
ajst-18332	145	16	highlevel	highlevel	ADJ
ajst-18332	145	17	network	network	NOUN
ajst-18332	145	18	,	,	PUNCT
ajst-18332	145	19	but	but	CCONJ
ajst-18332	145	20	also	also	ADV
ajst-18332	145	21	enhances	enhance	VERB
ajst-18332	145	22	the	the	DET
ajst-18332	145	23	ability	ability	NOUN
ajst-18332	145	24	of	of	ADP
ajst-18332	145	25	the	the	DET
ajst-18332	145	26	high	high	ADJ
ajst-18332	145	27	-	-	PUNCT
ajst-18332	145	28	level	level	NOUN
ajst-18332	145	29	network	network	NOUN
ajst-18332	145	30	to	to	PART
ajst-18332	145	31	learn	learn	VERB
ajst-18332	145	32	the	the	DET
ajst-18332	145	33	features	feature	NOUN
ajst-18332	145	34	of	of	ADP
ajst-18332	145	35	the	the	DET
ajst-18332	145	36	rebar	rebar	NOUN
ajst-18332	145	37	.	.	PUNCT
ajst-18332	146	1	this	this	DET
ajst-18332	146	2	optimized	optimize	VERB
ajst-18332	146	3	method	method	NOUN
ajst-18332	146	4	of	of	ADP
ajst-18332	146	5	detecting	detect	VERB
ajst-18332	146	6	necks	neck	NOUN
ajst-18332	146	7	significantly	significantly	ADV
ajst-18332	146	8	improves	improve	VERB
ajst-18332	146	9	the	the	DET
ajst-18332	146	10	accuracy	accuracy	NOUN
ajst-18332	146	11	of	of	ADP
ajst-18332	146	12	rebar	rebar	NOUN
ajst-18332	146	13	end	end	NOUN
ajst-18332	146	14	face	face	NOUN
ajst-18332	146	15	recognition	recognition	NOUN
ajst-18332	146	16	and	and	CCONJ
ajst-18332	146	17	ensures	ensure	VERB
ajst-18332	146	18	that	that	SCONJ
ajst-18332	146	19	the	the	DET
ajst-18332	146	20	model	model	NOUN
ajst-18332	146	21	has	have	VERB
ajst-18332	146	22	excellent	excellent	ADJ
ajst-18332	146	23	generalization	generalization	NOUN
ajst-18332	146	24	ability	ability	NOUN
ajst-18332	146	25	in	in	ADP
ajst-18332	146	26	the	the	DET
ajst-18332	146	27	face	face	NOUN
ajst-18332	146	28	of	of	ADP
ajst-18332	146	29	different	different	ADJ
ajst-18332	146	30	datasets	dataset	NOUN
ajst-18332	146	31	,	,	PUNCT
ajst-18332	146	32	and	and	CCONJ
ajst-18332	146	33	its	its	PRON
ajst-18332	146	34	ap	ap	PROPN
ajst-18332	146	35	is	be	AUX
ajst-18332	146	36	improved	improve	VERB
ajst-18332	146	37	by	by	ADP
ajst-18332	146	38	1.07	1.07	NUM
ajst-18332	146	39	%	%	NOUN
ajst-18332	146	40	compared	compare	VERB
ajst-18332	146	41	to	to	ADP
ajst-18332	146	42	the	the	DET
ajst-18332	146	43	unimproved	unimproved	ADJ
ajst-18332	146	44	pp	pp	PROPN
ajst-18332	146	45	-	-	PUNCT
ajst-18332	146	46	yolo	yolo	ADJ
ajst-18332	146	47	network	network	NOUN
ajst-18332	146	48	.	.	PUNCT
ajst-18332	147	1	acknowledgment	acknowledgment	NOUN
ajst-18332	147	2	this	this	DET
ajst-18332	147	3	work	work	NOUN
ajst-18332	147	4	was	be	AUX
ajst-18332	147	5	supported	support	VERB
ajst-18332	147	6	in	in	ADP
ajst-18332	147	7	part	part	NOUN
ajst-18332	147	8	by	by	ADP
ajst-18332	147	9	the	the	DET
ajst-18332	147	10	‘	'	PUNCT
ajst-18332	147	11	qingtai	qingtai	ADJ
ajst-18332	147	12	digital	digital	ADJ
ajst-18332	147	13	intelligence	intelligence	NOUN
ajst-18332	147	14	integration	integration	NOUN
ajst-18332	147	15	’	'	PUNCT
ajst-18332	147	16	collaborative	collaborative	ADJ
ajst-18332	147	17	innovation	innovation	NOUN
ajst-18332	147	18	project	project	NOUN
ajst-18332	147	19	of	of	ADP
ajst-18332	147	20	the	the	DET
ajst-18332	147	21	science	science	NOUN
ajst-18332	147	22	and	and	CCONJ
ajst-18332	147	23	technology	technology	NOUN
ajst-18332	147	24	development	development	NOUN
ajst-18332	147	25	center	center	NOUN
ajst-18332	147	26	of	of	ADP
ajst-18332	147	27	the	the	DET
ajst-18332	147	28	ministry	ministry	PROPN
ajst-18332	147	29	of	of	ADP
ajst-18332	147	30	education	education	PROPN
ajst-18332	147	31	under	under	ADP
ajst-18332	147	32	grant	grant	PROPN
ajst-18332	147	33	2020qt16	2020qt16	NUM
ajst-18332	147	34	,	,	PUNCT
ajst-18332	147	35	in	in	ADP
ajst-18332	147	36	part	part	NOUN
ajst-18332	147	37	by	by	ADP
ajst-18332	147	38	the	the	DET
ajst-18332	147	39	featured	feature	VERB
ajst-18332	147	40	innovative	innovative	ADJ
ajst-18332	147	41	projects	project	NOUN
ajst-18332	147	42	of	of	ADP
ajst-18332	147	43	department	department	NOUN
ajst-18332	147	44	of	of	ADP
ajst-18332	147	45	education	education	NOUN
ajst-18332	147	46	of	of	ADP
ajst-18332	147	47	guangdong	guangdong	PROPN
ajst-18332	147	48	province	province	PROPN
ajst-18332	147	49	(	(	PUNCT
ajst-18332	147	50	no.2019ktscx175	no.2019ktscx175	PROPN
ajst-18332	147	51	,	,	PUNCT
ajst-18332	147	52	no.2023ktscx144	no.2023ktscx144	ADJ
ajst-18332	147	53	)	)	PUNCT
ajst-18332	147	54	,	,	PUNCT
ajst-18332	147	55	and	and	CCONJ
ajst-18332	147	56	part	part	NOUN
ajst-18332	147	57	by	by	ADP
ajst-18332	147	58	the	the	DET
ajst-18332	147	59	guangdong	guangdong	PROPN
ajst-18332	147	60	basic	basic	ADJ
ajst-18332	147	61	and	and	CCONJ
ajst-18332	147	62	applied	apply	VERB
ajst-18332	147	63	basic	basic	ADJ
ajst-18332	147	64	research	research	NOUN
ajst-18332	147	65	foundation	foundation	NOUN
ajst-18332	147	66	(	(	PUNCT
ajst-18332	147	67	no.2022a1515140120	no.2022a1515140120	ADJ
ajst-18332	147	68	)	)	PUNCT
ajst-18332	147	69	.	.	PUNCT
ajst-18332	148	1	98	98	NUM
ajst-18332	148	2	references	reference	NOUN
ajst-18332	148	3	[	[	X
ajst-18332	148	4	1	1	NUM
ajst-18332	148	5	]	]	X
ajst-18332	148	6	zhao	zhao	X
ajst-18332	148	7	,	,	PUNCT
ajst-18332	148	8	w.	w.	PROPN
ajst-18332	148	9	q.	q.	PROPN
ajst-18332	148	10	,	,	PUNCT
ajst-18332	148	11	kong	kong	PROPN
ajst-18332	148	12	,	,	PUNCT
ajst-18332	148	13	z.	z.	PROPN
ajst-18332	148	14	x.	x.	PROPN
ajst-18332	148	15	,	,	PUNCT
ajst-18332	148	16	zhou	zhou	PROPN
ajst-18332	148	17	,	,	PUNCT
ajst-18332	148	18	z.	z.	PROPN
ajst-18332	148	19	d.	d.	PROPN
ajst-18332	148	20	,	,	PUNCT
ajst-18332	148	21	et	et	PROPN
ajst-18332	148	22	al	al	PROPN
ajst-18332	148	23	.	.	PROPN
ajst-18332	148	24	(	(	PUNCT
ajst-18332	148	25	2021	2021	NUM
ajst-18332	148	26	)	)	PUNCT
ajst-18332	148	27	.	.	PUNCT
ajst-18332	149	1	enhanced	enhance	VERB
ajst-18332	149	2	feature	feature	NOUN
ajst-18332	149	3	detection	detection	NOUN
ajst-18332	149	4	of	of	ADP
ajst-18332	149	5	small	small	ADJ
ajst-18332	149	6	targets	target	NOUN
ajst-18332	149	7	in	in	ADP
ajst-18332	149	8	aerial	aerial	ADJ
ajst-18332	149	9	remote	remote	ADJ
ajst-18332	149	10	sensing	sensing	NOUN
ajst-18332	149	11	.	.	PUNCT
ajst-18332	150	1	journal	journal	PROPN
ajst-18332	150	2	of	of	ADP
ajst-18332	150	3	china	china	PROPN
ajst-18332	150	4	image	image	PROPN
ajst-18332	150	5	and	and	CCONJ
ajst-18332	150	6	graphics	graphic	NOUN
ajst-18332	150	7	,	,	PUNCT
ajst-18332	150	8	26(3	26(3	NUM
ajst-18332	150	9	)	)	PUNCT
ajst-18332	150	10	,	,	PUNCT
ajst-18332	150	11	644	644	NUM
ajst-18332	150	12	-	-	SYM
ajst-18332	150	13	653	653	NUM
ajst-18332	150	14	.	.	PUNCT
ajst-18332	151	1	[	[	X
ajst-18332	151	2	2	2	NUM
ajst-18332	151	3	]	]	PUNCT
ajst-18332	151	4	krizhevsky	krizhevsky	NOUN
ajst-18332	151	5	,	,	PUNCT
ajst-18332	151	6	a.	a.	NOUN
ajst-18332	151	7	,	,	PUNCT
ajst-18332	151	8	sutskever	sutskever	PROPN
ajst-18332	151	9	,	,	PUNCT
ajst-18332	151	10	i.	i.	PROPN
ajst-18332	151	11	,	,	PUNCT
ajst-18332	151	12	&	&	CCONJ
ajst-18332	151	13	hinton	hinton	PROPN
ajst-18332	151	14	,	,	PUNCT
ajst-18332	151	15	g.	g.	PROPN
ajst-18332	151	16	(	(	PUNCT
ajst-18332	151	17	2012	2012	NUM
ajst-18332	151	18	)	)	PUNCT
ajst-18332	151	19	.	.	PUNCT
ajst-18332	152	1	imagenet	imagenet	PROPN
ajst-18332	152	2	classification	classification	NOUN
ajst-18332	152	3	with	with	ADP
ajst-18332	152	4	deep	deep	ADJ
ajst-18332	152	5	convolutional	convolutional	ADJ
ajst-18332	152	6	neural	neural	ADJ
ajst-18332	152	7	networks	network	NOUN
ajst-18332	152	8	.	.	PUNCT
ajst-18332	153	1	in	in	ADP
ajst-18332	153	2	advances	advance	NOUN
ajst-18332	153	3	in	in	ADP
ajst-18332	153	4	neural	neural	ADJ
ajst-18332	153	5	information	information	NOUN
ajst-18332	153	6	processing	processing	NOUN
ajst-18332	153	7	systems	system	NOUN
ajst-18332	153	8	(	(	PUNCT
ajst-18332	153	9	pp	pp	ADJ
ajst-18332	153	10	.	.	PUNCT
ajst-18332	154	1	1097	1097	NUM
ajst-18332	154	2	-	-	SYM
ajst-18332	154	3	1105	1105	NUM
ajst-18332	154	4	)	)	PUNCT
ajst-18332	154	5	.	.	PUNCT
ajst-18332	155	1	new	new	PROPN
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ajst-18332	155	5	.	.	PUNCT
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ajst-18332	156	5	,	,	PUNCT
ajst-18332	156	6	r.	r.	PROPN
ajst-18332	156	7	,	,	PUNCT
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ajst-18332	156	9	,	,	PUNCT
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ajst-18332	156	13	,	,	PUNCT
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ajst-18332	156	15	,	,	PUNCT
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ajst-18332	156	18	.	.	PUNCT
ajst-18332	157	1	(	(	PUNCT
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ajst-18332	157	3	)	)	PUNCT
ajst-18332	157	4	.	.	PUNCT
ajst-18332	158	1	rich	rich	ADJ
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ajst-18332	158	3	hierarchies	hierarchy	NOUN
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ajst-18332	158	5	accurate	accurate	ADJ
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ajst-18332	158	8	and	and	CCONJ
ajst-18332	158	9	semantic	semantic	ADJ
ajst-18332	158	10	segmentation	segmentation	NOUN
ajst-18332	158	11	.	.	PUNCT
ajst-18332	159	1	in	in	ADP
ajst-18332	159	2	proceedings	proceeding	NOUN
ajst-18332	159	3	of	of	ADP
ajst-18332	159	4	the	the	DET
ajst-18332	159	5	ieee	ieee	NOUN
ajst-18332	159	6	conference	conference	NOUN
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ajst-18332	159	11	pattern	pattern	NOUN
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ajst-18332	159	13	(	(	PUNCT
ajst-18332	159	14	pp	pp	ADJ
ajst-18332	159	15	.	.	PUNCT
ajst-18332	160	1	580	580	NUM
ajst-18332	160	2	-	-	SYM
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ajst-18332	160	4	)	)	PUNCT
ajst-18332	160	5	.	.	PUNCT
ajst-18332	161	1	piscataway	piscataway	PROPN
ajst-18332	161	2	,	,	PUNCT
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ajst-18332	161	4	.	.	PUNCT
ajst-18332	162	1	[	[	X
ajst-18332	162	2	4	4	NUM
ajst-18332	162	3	]	]	X
ajst-18332	162	4	girshick	girshick	PROPN
ajst-18332	162	5	,	,	PUNCT
ajst-18332	162	6	r.	r.	PROPN
ajst-18332	162	7	b.	b.	PROPN
ajst-18332	162	8	(	(	PUNCT
ajst-18332	162	9	2015	2015	NUM
ajst-18332	162	10	)	)	PUNCT
ajst-18332	162	11	.	.	PUNCT
ajst-18332	163	1	fast	fast	ADJ
ajst-18332	163	2	r	r	NOUN
ajst-18332	163	3	-	-	PUNCT
ajst-18332	163	4	cnn	cnn	NOUN
ajst-18332	163	5	.	.	PUNCT
ajst-18332	164	1	in	in	ADP
ajst-18332	164	2	proceedings	proceeding	NOUN
ajst-18332	164	3	of	of	ADP
ajst-18332	164	4	the	the	DET
ajst-18332	164	5	ieee	ieee	NOUN
ajst-18332	164	6	international	international	PROPN
ajst-18332	164	7	conference	conference	NOUN
ajst-18332	164	8	on	on	ADP
ajst-18332	164	9	computer	computer	NOUN
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ajst-18332	164	11	(	(	PUNCT
ajst-18332	164	12	pp	pp	ADJ
ajst-18332	164	13	.	.	PUNCT
ajst-18332	164	14	14401448	14401448	NUM
ajst-18332	164	15	)	)	PUNCT
ajst-18332	164	16	.	.	PUNCT
ajst-18332	165	1	piscataway	piscataway	PROPN
ajst-18332	165	2	,	,	PUNCT
ajst-18332	165	3	usa	usa	PROPN
ajst-18332	165	4	.	.	PUNCT
ajst-18332	166	1	[	[	X
ajst-18332	166	2	5	5	NUM
ajst-18332	166	3	]	]	SYM
ajst-18332	166	4	ren	ren	PROPN
ajst-18332	166	5	,	,	PUNCT
ajst-18332	166	6	s.	s.	PROPN
ajst-18332	166	7	,	,	PUNCT
ajst-18332	166	8	he	he	PRON
ajst-18332	166	9	,	,	PUNCT
ajst-18332	166	10	k.	k.	PROPN
ajst-18332	166	11	,	,	PUNCT
ajst-18332	166	12	girshiek	girshiek	PROPN
ajst-18332	166	13	,	,	PUNCT
ajst-18332	166	14	r.	r.	PROPN
ajst-18332	166	15	b.	b.	PROPN
ajst-18332	166	16	,	,	PUNCT
ajst-18332	166	17	et	et	PROPN
ajst-18332	166	18	al	al	PROPN
ajst-18332	166	19	.	.	PUNCT
ajst-18332	167	1	(	(	PUNCT
ajst-18332	167	2	2017	2017	NUM
ajst-18332	167	3	)	)	PUNCT
ajst-18332	167	4	.	.	PUNCT
ajst-18332	168	1	faster	fast	ADV
ajst-18332	168	2	r	r	X
ajst-18332	168	3	-	-	PUNCT
ajst-18332	168	4	cnn	cnn	NOUN
ajst-18332	168	5	:	:	PUNCT
ajst-18332	168	6	towards	towards	ADP
ajst-18332	168	7	real	real	ADJ
ajst-18332	168	8	-	-	PUNCT
ajst-18332	168	9	time	time	NOUN
ajst-18332	168	10	object	object	NOUN
ajst-18332	168	11	detection	detection	NOUN
ajst-18332	168	12	with	with	ADP
ajst-18332	168	13	region	region	NOUN
ajst-18332	168	14	proposal	proposal	NOUN
ajst-18332	168	15	networks	network	NOUN
ajst-18332	168	16	.	.	PUNCT
ajst-18332	169	1	ieee	ieee	NOUN
ajst-18332	169	2	transactions	transaction	NOUN
ajst-18332	169	3	on	on	ADP
ajst-18332	169	4	pattern	pattern	NOUN
ajst-18332	169	5	analysis	analysis	NOUN
ajst-18332	169	6	and	and	CCONJ
ajst-18332	169	7	machine	machine	NOUN
ajst-18332	169	8	intelligence	intelligence	NOUN
ajst-18332	169	9	,	,	PUNCT
ajst-18332	169	10	39(6	39(6	NOUN
ajst-18332	169	11	)	)	PUNCT
ajst-18332	169	12	,	,	PUNCT
ajst-18332	169	13	91	91	NUM
ajst-18332	169	14	-	-	SYM
ajst-18332	169	15	99	99	NUM
ajst-18332	169	16	.	.	PUNCT
ajst-18332	170	1	[	[	X
ajst-18332	170	2	6	6	NUM
ajst-18332	170	3	]	]	PUNCT
ajst-18332	170	4	he	he	PRON
ajst-18332	170	5	,	,	PUNCT
ajst-18332	170	6	k.	k.	PROPN
ajst-18332	170	7	,	,	PUNCT
ajst-18332	170	8	gkioxari	gkioxari	NOUN
ajst-18332	170	9	,	,	PUNCT
ajst-18332	170	10	g.	g.	NOUN
ajst-18332	170	11	,	,	PUNCT
ajst-18332	170	12	dollar	dollar	NOUN
ajst-18332	170	13	,	,	PUNCT
ajst-18332	170	14	p.	p.	PROPN
ajst-18332	170	15	,	,	PUNCT
ajst-18332	170	16	et	et	PROPN
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ajst-18332	170	18	.	.	PUNCT
ajst-18332	170	19	(	(	PUNCT
ajst-18332	170	20	2017	2017	NUM
ajst-18332	170	21	)	)	PUNCT
ajst-18332	170	22	.	.	PUNCT
ajst-18332	171	1	mask	mask	VERB
ajst-18332	171	2	r	r	NOUN
ajst-18332	171	3	-	-	PUNCT
ajst-18332	171	4	cnn	cnn	PROPN
ajst-18332	171	5	.	.	PUNCT
ajst-18332	172	1	in	in	ADP
ajst-18332	172	2	proceedings	proceeding	NOUN
ajst-18332	172	3	of	of	ADP
ajst-18332	172	4	the	the	DET
ajst-18332	172	5	ieee	ieee	NOUN
ajst-18332	172	6	international	international	PROPN
ajst-18332	172	7	conference	conference	NOUN
ajst-18332	172	8	on	on	ADP
ajst-18332	172	9	computer	computer	NOUN
ajst-18332	172	10	vision	vision	NOUN
ajst-18332	172	11	(	(	PUNCT
ajst-18332	172	12	pp	pp	ADJ
ajst-18332	172	13	.	.	PUNCT
ajst-18332	173	1	2961	2961	NUM
ajst-18332	173	2	-	-	SYM
ajst-18332	173	3	2969	2969	NUM
ajst-18332	173	4	)	)	PUNCT
ajst-18332	173	5	.	.	PUNCT
ajst-18332	174	1	piscataway	piscataway	PROPN
ajst-18332	174	2	,	,	PUNCT
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ajst-18332	174	4	.	.	PUNCT
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ajst-18332	175	2	7	7	NUM
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ajst-18332	175	5	,	,	PUNCT
ajst-18332	175	6	r.	r.	PROPN
ajst-18332	175	7	,	,	PUNCT
ajst-18332	175	8	santosh	santosh	PROPN
ajst-18332	175	9	,	,	PUNCT
ajst-18332	175	10	d.	d.	PROPN
ajst-18332	175	11	,	,	PUNCT
ajst-18332	175	12	ross	ross	PROPN
ajst-18332	175	13	,	,	PUNCT
ajst-18332	175	14	g.	g.	PROPN
ajst-18332	175	15	,	,	PUNCT
ajst-18332	175	16	et	et	PROPN
ajst-18332	175	17	al	al	PROPN
ajst-18332	175	18	.	.	PUNCT
ajst-18332	176	1	(	(	PUNCT
ajst-18332	176	2	2016	2016	NUM
ajst-18332	176	3	)	)	PUNCT
ajst-18332	176	4	.	.	PUNCT
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ajst-18332	177	9	-	-	PUNCT
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ajst-18332	177	13	.	.	PUNCT
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ajst-18332	178	4	on	on	ADP
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ajst-18332	178	10	(	(	PUNCT
ajst-18332	178	11	pp	pp	ADJ
ajst-18332	178	12	.	.	PUNCT
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ajst-18332	179	2	-	-	SYM
ajst-18332	179	3	788	788	NUM
ajst-18332	179	4	)	)	PUNCT
ajst-18332	179	5	.	.	PUNCT
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ajst-18332	181	1	[	[	X
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ajst-18332	181	4	wei	wei	PROPN
ajst-18332	181	5	,	,	PUNCT
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ajst-18332	181	16	)	)	PUNCT
ajst-18332	181	17	.	.	PUNCT
ajst-18332	182	1	ratio	ratio	NOUN
ajst-18332	182	2	-	-	PUNCT
ajst-18332	182	3	and	and	CCONJ
ajst-18332	182	4	-	-	PUNCT
ajst-18332	182	5	scale	scale	NOUN
ajst-18332	182	6	-	-	PUNCT
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ajst-18332	182	8	yolo	yolo	NOUN
ajst-18332	182	9	for	for	ADP
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ajst-18332	182	11	detection	detection	NOUN
ajst-18332	182	12	.	.	PUNCT
ajst-18332	183	1	ieee	ieee	NOUN
ajst-18332	183	2	transactions	transaction	NOUN
ajst-18332	183	3	on	on	ADP
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ajst-18332	183	5	processing	processing	NOUN
ajst-18332	183	6	,	,	PUNCT
ajst-18332	183	7	30	30	NUM
ajst-18332	183	8	,	,	PUNCT
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ajst-18332	183	10	-	-	SYM
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ajst-18332	183	12	.	.	PUNCT
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ajst-18332	184	2	9	9	NUM
ajst-18332	184	3	]	]	X
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ajst-18332	184	5	,	,	PUNCT
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ajst-18332	184	7	a.	a.	PROPN
ajst-18332	184	8	,	,	PUNCT
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ajst-18332	184	10	,	,	PUNCT
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ajst-18332	185	8	printed	print	VERB
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ajst-18332	185	11	via	via	ADP
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ajst-18332	185	17	-	-	PUNCT
ajst-18332	185	18	only	only	ADV
ajst-18332	185	19	-	-	PUNCT
ajst-18332	185	20	look	look	NOUN
ajst-18332	185	21	-	-	PUNCT
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ajst-18332	185	23	.	.	PUNCT
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ajst-18332	186	2	biosciences	bioscience	NOUN
ajst-18332	186	3	and	and	CCONJ
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ajst-18332	186	5	,	,	PUNCT
ajst-18332	186	6	18(4	18(4	NUM
ajst-18332	186	7	)	)	PUNCT
ajst-18332	186	8	,	,	PUNCT
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ajst-18332	186	10	-	-	SYM
ajst-18332	186	11	4428	4428	NUM
ajst-18332	186	12	.	.	PUNCT
ajst-18332	187	1	[	[	X
ajst-18332	187	2	10	10	NUM
ajst-18332	187	3	]	]	X
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ajst-18332	187	6	t.	t.	PROPN
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ajst-18332	187	8	,	,	PUNCT
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ajst-18332	187	10	,	,	PUNCT
ajst-18332	187	11	p.	p.	NOUN
ajst-18332	187	12	,	,	PUNCT
ajst-18332	187	13	girshick	girshick	PROPN
ajst-18332	187	14	,	,	PUNCT
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ajst-18332	187	16	,	,	PUNCT
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ajst-18332	187	20	(	(	PUNCT
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ajst-18332	187	23	.	.	PUNCT
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ajst-18332	189	1	in	in	ADP
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ajst-18332	189	13	(	(	PUNCT
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ajst-18332	189	15	.	.	PUNCT
ajst-18332	190	1	2117	2117	NUM
ajst-18332	190	2	-	-	SYM
ajst-18332	190	3	2125	2125	NUM
ajst-18332	190	4	)	)	PUNCT
ajst-18332	190	5	.	.	PUNCT
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ajst-18332	192	7	,	,	PUNCT
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ajst-18332	192	11	j.	j.	PROPN
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ajst-18332	192	14	)	)	PUNCT
ajst-18332	192	15	.	.	PUNCT
ajst-18332	193	1	cornernet	cornernet	PROPN
ajst-18332	193	2	:	:	PUNCT
ajst-18332	193	3	detecting	detect	VERB
ajst-18332	193	4	objects	object	NOUN
ajst-18332	193	5	as	as	ADP
ajst-18332	193	6	paired	pair	VERB
ajst-18332	193	7	keypoints	keypoint	NOUN
ajst-18332	193	8	[	[	X
ajst-18332	193	9	eb	eb	X
ajst-18332	193	10	/	/	SYM
ajst-18332	193	11	ol	ol	PROPN
ajst-18332	193	12	]	]	PUNCT
ajst-18332	193	13	.	.	PUNCT
ajst-18332	194	1	available	available	ADJ
ajst-18332	194	2	at	at	ADP
ajst-18332	194	3	https://arxiv.org/abs/1808.01244	https://arxiv.org/abs/1808.01244	PROPN
ajst-18332	194	4	.	.	PUNCT
ajst-18332	195	1	arxiv	arxiv	PROPN
ajst-18332	195	2	preprint	preprint	VERB
ajst-18332	195	3	arxiv:1808.01244	arxiv:1808.01244	ADV
ajst-18332	195	4	.	.	PUNCT
ajst-18332	196	1	[	[	X
ajst-18332	196	2	12	12	NUM
ajst-18332	196	3	]	]	X
ajst-18332	196	4	long	long	ADV
ajst-18332	196	5	,	,	PUNCT
ajst-18332	196	6	x.	x.	PROPN
ajst-18332	196	7	,	,	PUNCT
ajst-18332	196	8	deng	deng	PROPN
ajst-18332	196	9	,	,	PUNCT
ajst-18332	196	10	k.	k.	PROPN
ajst-18332	196	11	,	,	PUNCT
ajst-18332	196	12	wang	wang	PROPN
ajst-18332	196	13	,	,	PUNCT
ajst-18332	196	14	g.	g.	PROPN
ajst-18332	196	15	,	,	PUNCT
ajst-18332	196	16	et	et	PROPN
ajst-18332	196	17	al	al	PROPN
ajst-18332	196	18	.	.	PROPN
ajst-18332	197	1	(	(	PUNCT
ajst-18332	197	2	2020	2020	NUM
ajst-18332	197	3	)	)	PUNCT
ajst-18332	197	4	.	.	PUNCT
ajst-18332	198	1	pp	pp	PROPN
ajst-18332	198	2	-	-	PUNCT
ajst-18332	198	3	yolo	yolo	NOUN
ajst-18332	198	4	:	:	PUNCT
ajst-18332	198	5	an	an	DET
ajst-18332	198	6	effective	effective	ADJ
ajst-18332	198	7	and	and	CCONJ
ajst-18332	198	8	efficient	efficient	ADJ
ajst-18332	198	9	implementation	implementation	NOUN
ajst-18332	198	10	of	of	ADP
ajst-18332	198	11	object	object	NOUN
ajst-18332	198	12	detector	detector	NOUN
ajst-18332	199	1	[	[	X
ajst-18332	199	2	eb	eb	PROPN
ajst-18332	199	3	/	/	SYM
ajst-18332	199	4	ol	ol	PROPN
ajst-18332	199	5	]	]	PUNCT
ajst-18332	199	6	.	.	PUNCT
ajst-18332	200	1	available	available	ADJ
ajst-18332	200	2	at	at	ADP
ajst-18332	200	3	https://arxiv.org/abs/2007.12099	https://arxiv.org/abs/2007.12099	PROPN
ajst-18332	200	4	.	.	PUNCT
ajst-18332	201	1	arxiv	arxiv	PROPN
ajst-18332	201	2	preprint	preprint	PROPN
ajst-18332	201	3	arxiv:2007.12099	arxiv:2007.12099	NUM
ajst-18332	201	4	.	.	PUNCT
ajst-18332	202	1	[	[	X
ajst-18332	202	2	13	13	NUM
ajst-18332	202	3	]	]	SYM
ajst-18332	202	4	ma	ma	PROPN
ajst-18332	202	5	,	,	PUNCT
ajst-18332	202	6	j.	j.	PROPN
ajst-18332	202	7	,	,	PUNCT
ajst-18332	202	8	yao	yao	PROPN
ajst-18332	202	9	,	,	PUNCT
ajst-18332	202	10	z.	z.	PROPN
ajst-18332	202	11	,	,	PUNCT
ajst-18332	202	12	xu	xu	PROPN
ajst-18332	202	13	,	,	PUNCT
ajst-18332	202	14	c.	c.	PROPN
ajst-18332	202	15	f.	f.	PROPN
ajst-18332	202	16	,	,	PUNCT
ajst-18332	202	17	et	et	PROPN
ajst-18332	202	18	al	al	PROPN
ajst-18332	202	19	.	.	PROPN
ajst-18332	202	20	(	(	PUNCT
ajst-18332	202	21	2021	2021	NUM
ajst-18332	202	22	)	)	PUNCT
ajst-18332	202	23	.	.	PUNCT
ajst-18332	203	1	a	a	DET
ajst-18332	203	2	real	real	ADJ
ajst-18332	203	3	-	-	PUNCT
ajst-18332	203	4	time	time	NOUN
ajst-18332	203	5	tracking	tracking	NOUN
ajst-18332	203	6	algorithm	algorithm	NOUN
ajst-18332	203	7	for	for	ADP
ajst-18332	203	8	multiple	multiple	ADJ
ajst-18332	203	9	drones	drone	NOUN
ajst-18332	203	10	based	base	VERB
ajst-18332	203	11	on	on	ADP
ajst-18332	203	12	improved	improved	ADJ
ajst-18332	203	13	pp	pp	PROPN
ajst-18332	203	14	-	-	PUNCT
ajst-18332	203	15	yolo	yolo	ADJ
ajst-18332	203	16	and	and	CCONJ
ajst-18332	203	17	deep	deep	ADJ
ajst-18332	203	18	sort	sort	NOUN
ajst-18332	203	19	.	.	PUNCT
ajst-18332	204	1	computer	computer	NOUN
ajst-18332	204	2	applications	application	NOUN
ajst-18332	204	3	.	.	PUNCT
ajst-18332	205	1	doi:10.11772	doi:10.11772	NOUN
ajst-18332	205	2	/	/	SYM
ajst-18332	205	3	j.issn.1001	j.issn.1001	NOUN
ajst-18332	205	4	-	-	PUNCT
ajst-18332	205	5	9081.2021071146	9081.2021071146	NUM
ajst-18332	205	6	.	.	PUNCT
ajst-18332	206	1	[	[	X
ajst-18332	206	2	14	14	NUM
ajst-18332	206	3	]	]	X
ajst-18332	206	4	liu	liu	PROPN
ajst-18332	206	5	,	,	PUNCT
ajst-18332	206	6	r.	r.	PROPN
ajst-18332	206	7	,	,	PUNCT
ajst-18332	206	8	lehman	lehman	PROPN
ajst-18332	206	9	,	,	PUNCT
ajst-18332	206	10	j.	j.	PROPN
ajst-18332	206	11	,	,	PUNCT
ajst-18332	206	12	molino	molino	PROPN
ajst-18332	206	13	,	,	PUNCT
ajst-18332	206	14	p.	p.	PROPN
ajst-18332	206	15	,	,	PUNCT
ajst-18332	206	16	et	et	PROPN
ajst-18332	206	17	al	al	PROPN
ajst-18332	206	18	.	.	PUNCT
ajst-18332	206	19	(	(	PUNCT
ajst-18332	206	20	2018	2018	NUM
ajst-18332	206	21	)	)	PUNCT
ajst-18332	206	22	.	.	PUNCT
ajst-18332	207	1	an	an	DET
ajst-18332	207	2	intriguing	intriguing	ADJ
ajst-18332	207	3	failing	failing	NOUN
ajst-18332	207	4	of	of	ADP
ajst-18332	207	5	convolutional	convolutional	ADJ
ajst-18332	207	6	neural	neural	ADJ
ajst-18332	207	7	networks	network	NOUN
ajst-18332	207	8	and	and	CCONJ
ajst-18332	207	9	the	the	DET
ajst-18332	207	10	coordconv	coordconv	NOUN
ajst-18332	207	11	solution	solution	NOUN
ajst-18332	207	12	[	[	X
ajst-18332	207	13	eb	eb	PROPN
ajst-18332	207	14	/	/	SYM
ajst-18332	207	15	ol	ol	PROPN
ajst-18332	207	16	]	]	PUNCT
ajst-18332	207	17	.	.	PUNCT
ajst-18332	208	1	available	available	ADJ
ajst-18332	208	2	at	at	ADP
ajst-18332	208	3	https://arxiv.org/abs/1807.03247	https://arxiv.org/abs/1807.03247	PROPN
ajst-18332	208	4	.	.	PUNCT
ajst-18332	209	1	arxiv	arxiv	PROPN
ajst-18332	209	2	preprint	preprint	NOUN
ajst-18332	209	3	arxiv:1807.03247	arxiv:1807.03247	PROPN
ajst-18332	209	4	.	.	PUNCT
ajst-18332	210	1	[	[	X
ajst-18332	210	2	15	15	NUM
ajst-18332	210	3	]	]	X
ajst-18332	210	4	wang	wang	PROPN
ajst-18332	210	5	,	,	PUNCT
ajst-18332	210	6	y.	y.	PROPN
ajst-18332	210	7	,	,	PUNCT
ajst-18332	210	8	zhen	zhen	PROPN
ajst-18332	210	9	,	,	PUNCT
ajst-18332	210	10	p.	p.	PROPN
ajst-18332	210	11	b.	b.	PROPN
ajst-18332	210	12	,	,	PUNCT
ajst-18332	210	13	hou	hou	PROPN
ajst-18332	210	14	,	,	PUNCT
ajst-18332	210	15	j.	j.	PROPN
ajst-18332	210	16	h.	h.	PROPN
ajst-18332	210	17	,	,	PUNCT
ajst-18332	210	18	et	et	PROPN
ajst-18332	210	19	al	al	PROPN
ajst-18332	210	20	.	.	PROPN
ajst-18332	210	21	(	(	PUNCT
ajst-18332	210	22	2021	2021	NUM
ajst-18332	210	23	)	)	PUNCT
ajst-18332	210	24	.	.	PUNCT
ajst-18332	211	1	convolutional	convolutional	ADJ
ajst-18332	211	2	neural	neural	ADJ
ajst-18332	211	3	networks	network	NOUN
ajst-18332	211	4	with	with	ADP
ajst-18332	211	5	dynamic	dynamic	ADJ
ajst-18332	211	6	regularization	regularization	NOUN
ajst-18332	211	7	.	.	PUNCT
ajst-18332	212	1	ieee	ieee	NOUN
ajst-18332	212	2	transactions	transaction	NOUN
ajst-18332	212	3	on	on	ADP
ajst-18332	212	4	neural	neural	ADJ
ajst-18332	212	5	networks	network	NOUN
ajst-18332	212	6	and	and	CCONJ
ajst-18332	212	7	learning	learning	NOUN
ajst-18332	212	8	systems	system	NOUN
ajst-18332	212	9	,	,	PUNCT
ajst-18332	212	10	32(5	32(5	NOUN
ajst-18332	212	11	)	)	PUNCT
ajst-18332	212	12	,	,	PUNCT
ajst-18332	212	13	2299	2299	NUM
ajst-18332	212	14	-	-	SYM
ajst-18332	212	15	2304	2304	NUM
ajst-18332	212	16	.	.	PUNCT
ajst-18332	213	1	[	[	X
ajst-18332	213	2	16	16	NUM
ajst-18332	213	3	]	]	X
ajst-18332	213	4	liu	liu	PROPN
ajst-18332	213	5	,	,	PUNCT
ajst-18332	213	6	z.	z.	PROPN
ajst-18332	213	7	h.	h.	PROPN
ajst-18332	213	8	,	,	PUNCT
ajst-18332	213	9	zhang	zhang	PROPN
ajst-18332	213	10	,	,	PUNCT
ajst-18332	213	11	y.	y.	PROPN
ajst-18332	213	12	d.	d.	PROPN
ajst-18332	213	13	,	,	PUNCT
ajst-18332	213	14	chen	chen	PROPN
ajst-18332	213	15	,	,	PUNCT
ajst-18332	213	16	y.	y.	PROPN
ajst-18332	213	17	z.	z.	PROPN
ajst-18332	213	18	,	,	PUNCT
ajst-18332	213	19	et	et	PROPN
ajst-18332	213	20	al	al	PROPN
ajst-18332	213	21	.	.	PROPN
ajst-18332	213	22	(	(	PUNCT
ajst-18332	213	23	2020	2020	NUM
ajst-18332	213	24	)	)	PUNCT
ajst-18332	213	25	.	.	PUNCT
ajst-18332	214	1	detection	detection	NOUN
ajst-18332	214	2	of	of	ADP
ajst-18332	214	3	algorithmically	algorithmically	ADV
ajst-18332	214	4	generated	generate	VERB
ajst-18332	214	5	domain	domain	NOUN
ajst-18332	214	6	names	name	NOUN
ajst-18332	214	7	using	use	VERB
ajst-18332	214	8	the	the	DET
ajst-18332	214	9	recurrent	recurrent	ADJ
ajst-18332	214	10	convolutional	convolutional	ADJ
ajst-18332	214	11	neural	neural	ADJ
ajst-18332	214	12	network	network	NOUN
ajst-18332	214	13	with	with	ADP
ajst-18332	214	14	spatial	spatial	ADJ
ajst-18332	214	15	pyramid	pyramid	NOUN
ajst-18332	214	16	pooling	pooling	NOUN
ajst-18332	214	17	.	.	PUNCT
ajst-18332	215	1	entropy	entropy	PROPN
ajst-18332	215	2	,	,	PUNCT
ajst-18332	215	3	22(9	22(9	NUM
ajst-18332	215	4	)	)	PUNCT
ajst-18332	215	5	,	,	PUNCT
ajst-18332	215	6	1	1	NUM
ajst-18332	215	7	-	-	SYM
ajst-18332	215	8	20	20	NUM
ajst-18332	215	9	.	.	PUNCT
ajst-18332	216	1	[	[	X
ajst-18332	216	2	17	17	NUM
ajst-18332	216	3	]	]	SYM
ajst-18332	216	4	yun	yun	PROPN
ajst-18332	216	5	,	,	PUNCT
ajst-18332	216	6	s.	s.	PROPN
ajst-18332	216	7	,	,	PUNCT
ajst-18332	216	8	han	han	PROPN
ajst-18332	216	9	,	,	PUNCT
ajst-18332	216	10	d.	d.	PROPN
ajst-18332	216	11	,	,	PUNCT
ajst-18332	216	12	oh	oh	INTJ
ajst-18332	216	13	,	,	PUNCT
ajst-18332	216	14	j.	j.	PROPN
ajst-18332	216	15	s.	s.	PROPN
ajst-18332	216	16	,	,	PUNCT
ajst-18332	216	17	et	et	PROPN
ajst-18332	216	18	al	al	PROPN
ajst-18332	216	19	.	.	PROPN
ajst-18332	217	1	(	(	PUNCT
ajst-18332	217	2	2019	2019	NUM
ajst-18332	217	3	)	)	PUNCT
ajst-18332	217	4	.	.	PUNCT
ajst-18332	218	1	cutmix	cutmix	PROPN
ajst-18332	218	2	:	:	PUNCT
ajst-18332	218	3	regularization	regularization	NOUN
ajst-18332	218	4	strategy	strategy	NOUN
ajst-18332	218	5	to	to	PART
ajst-18332	218	6	train	train	VERB
ajst-18332	218	7	strong	strong	ADJ
ajst-18332	218	8	classifiers	classifier	NOUN
ajst-18332	218	9	with	with	ADP
ajst-18332	218	10	localizable	localizable	ADJ
ajst-18332	218	11	features	feature	NOUN
ajst-18332	218	12	.	.	PUNCT
ajst-18332	219	1	in	in	ADP
ajst-18332	219	2	2019	2019	NUM
ajst-18332	219	3	ieee	ieee	NOUN
ajst-18332	219	4	/	/	SYM
ajst-18332	219	5	cvf	cvf	NOUN
ajst-18332	219	6	international	international	ADJ
ajst-18332	219	7	conference	conference	NOUN
ajst-18332	219	8	on	on	ADP
ajst-18332	219	9	computer	computer	NOUN
ajst-18332	219	10	vision	vision	NOUN
ajst-18332	219	11	(	(	PUNCT
ajst-18332	219	12	iccv	iccv	PROPN
ajst-18332	219	13	)	)	PUNCT
ajst-18332	219	14	(	(	PUNCT
ajst-18332	219	15	pp	pp	ADJ
ajst-18332	219	16	.	.	PUNCT
ajst-18332	219	17	6022	6022	NUM
ajst-18332	219	18	-	-	SYM
ajst-18332	219	19	6031	6031	NUM
ajst-18332	219	20	)	)	PUNCT
ajst-18332	219	21	.	.	PUNCT
ajst-18332	220	1	piscataway	piscataway	PROPN
ajst-18332	220	2	,	,	PUNCT
ajst-18332	220	3	usa	usa	PROPN
ajst-18332	220	4	.	.	PUNCT
ajst-18332	221	1	[	[	X
ajst-18332	221	2	18	18	NUM
ajst-18332	221	3	]	]	SYM
ajst-18332	221	4	li	li	PROPN
ajst-18332	221	5	,	,	PUNCT
ajst-18332	221	6	k.	k.	PROPN
ajst-18332	221	7	q.	q.	PROPN
ajst-18332	221	8	,	,	PUNCT
ajst-18332	221	9	chen	chen	PROPN
ajst-18332	221	10	,	,	PUNCT
ajst-18332	221	11	y.	y.	PROPN
ajst-18332	221	12	,	,	PUNCT
ajst-18332	221	13	liu	liu	PROPN
ajst-18332	221	14	,	,	PUNCT
ajst-18332	221	15	j.	j.	PROPN
ajst-18332	221	16	c.	c.	PROPN
ajst-18332	221	17	,	,	PUNCT
ajst-18332	221	18	et	et	PROPN
ajst-18332	221	19	al	al	PROPN
ajst-18332	221	20	.	.	PUNCT
ajst-18332	222	1	a	a	DET
ajst-18332	222	2	review	review	NOUN
ajst-18332	222	3	of	of	ADP
ajst-18332	222	4	object	object	NOUN
ajst-18332	222	5	detection	detection	NOUN
ajst-18332	222	6	algorithms	algorithm	NOUN
ajst-18332	222	7	based	base	VERB
ajst-18332	222	8	on	on	ADP
ajst-18332	222	9	deep	deep	ADJ
ajst-18332	222	10	learning	learning	NOUN
ajst-18332	222	11	.	.	PUNCT
ajst-18332	223	1	computer	computer	NOUN
ajst-18332	223	2	engineering	engineering	NOUN
ajst-18332	223	3	.	.	PUNCT
ajst-18332	224	1	doi	doi	NOUN
ajst-18332	224	2	:	:	PUNCT
ajst-18332	224	3	10.19678	10.19678	NUM
ajst-18332	224	4	/	/	SYM
ajst-18332	224	5	j.issn.1000	j.issn.1000	PROPN
ajst-18332	224	6	-	-	PUNCT
ajst-18332	224	7	3428.0062	3428.0062	PROPN
ajst-18332	224	8	.	.	PUNCT