id	sid	tid	token	lemma	pos
ajst-29623	1	1	academic	academic	ADJ
ajst-29623	1	2	journal	journal	NOUN
ajst-29623	1	3	of	of	ADP
ajst-29623	1	4	science	science	NOUN
ajst-29623	1	5	and	and	CCONJ
ajst-29623	1	6	technology	technology	NOUN
ajst-29623	1	7	issn	issn	NOUN
ajst-29623	1	8	:	:	PUNCT
ajst-29623	1	9	2771	2771	NUM
ajst-29623	1	10	-	-	SYM
ajst-29623	1	11	3032	3032	NUM
ajst-29623	1	12	|	|	NOUN
ajst-29623	1	13	vol	vol	NOUN
ajst-29623	1	14	.	.	PUNCT
ajst-29623	2	1	14	14	NUM
ajst-29623	2	2	,	,	PUNCT
ajst-29623	2	3	no	no	INTJ
ajst-29623	2	4	.	.	NOUN
ajst-29623	2	5	1	1	NUM
ajst-29623	2	6	,	,	PUNCT
ajst-29623	2	7	2025	2025	NUM
ajst-29623	2	8	294	294	NUM
ajst-29623	2	9	geometric	geometric	ADJ
ajst-29623	2	10	parameter	parameter	NOUN
ajst-29623	2	11	evaluation	evaluation	NOUN
ajst-29623	2	12	of	of	ADP
ajst-29623	2	13	oil	oil	NOUN
ajst-29623	2	14	and	and	CCONJ
ajst-29623	2	15	gas	gas	NOUN
ajst-29623	2	16	pipeline	pipeline	NOUN
ajst-29623	2	17	defects	defect	NOUN
ajst-29623	2	18	based	base	VERB
ajst-29623	2	19	on	on	ADP
ajst-29623	2	20	deep	deep	ADJ
ajst-29623	2	21	learning	learn	VERB
ajst-29623	2	22	neural	neural	ADJ
ajst-29623	2	23	networks	network	NOUN
ajst-29623	2	24	kaiqi	kaiqi	PROPN
ajst-29623	2	25	yan	yan	PROPN
ajst-29623	2	26	,	,	PUNCT
ajst-29623	2	27	wei	wei	PROPN
ajst-29623	2	28	wu	wu	PROPN
ajst-29623	2	29	,	,	PUNCT
ajst-29623	2	30	hangxin	hangxin	PROPN
ajst-29623	2	31	wei	wei	PROPN
ajst-29623	2	32	,	,	PUNCT
ajst-29623	2	33	jinghao	jinghao	PROPN
ajst-29623	2	34	sun	sun	PROPN
ajst-29623	2	35	school	school	PROPN
ajst-29623	2	36	of	of	ADP
ajst-29623	2	37	mechanical	mechanical	ADJ
ajst-29623	2	38	engineering	engineering	NOUN
ajst-29623	2	39	,	,	PUNCT
ajst-29623	2	40	xi’an	xi’an	PROPN
ajst-29623	2	41	shiyou	shiyou	PROPN
ajst-29623	2	42	university	university	PROPN
ajst-29623	2	43	,	,	PUNCT
ajst-29623	2	44	xi’an	xi’an	PROPN
ajst-29623	2	45	710065	710065	NUM
ajst-29623	2	46	,	,	PUNCT
ajst-29623	2	47	china	china	PROPN
ajst-29623	2	48	abstract	abstract	NOUN
ajst-29623	2	49	:	:	PUNCT
ajst-29623	2	50	pipeline	pipeline	PROPN
ajst-29623	2	51	magnetic	magnetic	PROPN
ajst-29623	2	52	flux	flux	PROPN
ajst-29623	2	53	leakage	leakage	PROPN
ajst-29623	2	54	(	(	PUNCT
ajst-29623	2	55	mfl	mfl	NOUN
ajst-29623	2	56	)	)	PUNCT
ajst-29623	2	57	detection	detection	NOUN
ajst-29623	2	58	technology	technology	NOUN
ajst-29623	2	59	has	have	AUX
ajst-29623	2	60	been	be	AUX
ajst-29623	2	61	widely	widely	ADV
ajst-29623	2	62	used	use	VERB
ajst-29623	2	63	in	in	ADP
ajst-29623	2	64	online	online	ADJ
ajst-29623	2	65	pipeline	pipeline	NOUN
ajst-29623	2	66	defect	defect	NOUN
ajst-29623	2	67	detection	detection	NOUN
ajst-29623	2	68	due	due	ADP
ajst-29623	2	69	to	to	ADP
ajst-29623	2	70	its	its	PRON
ajst-29623	2	71	advantages	advantage	NOUN
ajst-29623	2	72	of	of	ADP
ajst-29623	2	73	not	not	PART
ajst-29623	2	74	requiring	require	VERB
ajst-29623	2	75	coupling	couple	VERB
ajst-29623	2	76	agents	agent	NOUN
ajst-29623	2	77	and	and	CCONJ
ajst-29623	2	78	being	be	AUX
ajst-29623	2	79	easily	easily	ADV
ajst-29623	2	80	automated	automate	VERB
ajst-29623	2	81	.	.	PUNCT
ajst-29623	3	1	accurately	accurately	ADV
ajst-29623	3	2	predicting	predict	VERB
ajst-29623	3	3	defect	defect	NOUN
ajst-29623	3	4	sizes	size	NOUN
ajst-29623	3	5	based	base	VERB
ajst-29623	3	6	on	on	ADP
ajst-29623	3	7	detection	detection	NOUN
ajst-29623	3	8	data	datum	NOUN
ajst-29623	3	9	and	and	CCONJ
ajst-29623	3	10	conducting	conduct	VERB
ajst-29623	3	11	applicability	applicability	NOUN
ajst-29623	3	12	evaluations	evaluation	NOUN
ajst-29623	3	13	are	be	AUX
ajst-29623	3	14	crucial	crucial	ADJ
ajst-29623	3	15	for	for	ADP
ajst-29623	3	16	subsequent	subsequent	ADJ
ajst-29623	3	17	repair	repair	NOUN
ajst-29623	3	18	decisions	decision	NOUN
ajst-29623	3	19	.	.	PUNCT
ajst-29623	4	1	based	base	VERB
ajst-29623	4	2	on	on	ADP
ajst-29623	4	3	the	the	DET
ajst-29623	4	4	collected	collect	VERB
ajst-29623	4	5	mfl	mfl	PROPN
ajst-29623	4	6	data	data	PROPN
ajst-29623	4	7	,	,	PUNCT
ajst-29623	4	8	a	a	DET
ajst-29623	4	9	deep	deep	ADJ
ajst-29623	4	10	learning	learning	NOUN
ajst-29623	4	11	object	object	NOUN
ajst-29623	4	12	detection	detection	NOUN
ajst-29623	4	13	model	model	NOUN
ajst-29623	4	14	based	base	VERB
ajst-29623	4	15	on	on	ADP
ajst-29623	4	16	pp	pp	ADV
ajst-29623	4	17	-	-	PUNCT
ajst-29623	4	18	yoloe	yoloe	NOUN
ajst-29623	4	19	(	(	PUNCT
ajst-29623	4	20	paddle	paddle	NOUN
ajst-29623	4	21	paddle	paddle	NOUN
ajst-29623	4	22	you	you	PRON
ajst-29623	4	23	only	only	ADV
ajst-29623	4	24	look	look	VERB
ajst-29623	4	25	once	once	ADV
ajst-29623	4	26	evolved	evolve	VERB
ajst-29623	4	27	)	)	PUNCT
ajst-29623	4	28	is	be	AUX
ajst-29623	4	29	proposed	propose	VERB
ajst-29623	4	30	according	accord	VERB
ajst-29623	4	31	to	to	ADP
ajst-29623	4	32	the	the	DET
ajst-29623	4	33	characteristics	characteristic	NOUN
ajst-29623	4	34	of	of	ADP
ajst-29623	4	35	defect	defect	ADJ
ajst-29623	4	36	data	datum	NOUN
ajst-29623	4	37	.	.	PUNCT
ajst-29623	5	1	by	by	ADP
ajst-29623	5	2	converting	convert	VERB
ajst-29623	5	3	mfl	mfl	PROPN
ajst-29623	5	4	data	datum	NOUN
ajst-29623	5	5	into	into	ADP
ajst-29623	5	6	images	image	NOUN
ajst-29623	5	7	and	and	CCONJ
ajst-29623	5	8	feeding	feed	VERB
ajst-29623	5	9	them	they	PRON
ajst-29623	5	10	into	into	ADP
ajst-29623	5	11	the	the	DET
ajst-29623	5	12	model	model	NOUN
ajst-29623	5	13	for	for	ADP
ajst-29623	5	14	object	object	NOUN
ajst-29623	5	15	detection	detection	NOUN
ajst-29623	5	16	training	training	NOUN
ajst-29623	5	17	,	,	PUNCT
ajst-29623	5	18	defects	defect	NOUN
ajst-29623	5	19	can	can	AUX
ajst-29623	5	20	be	be	AUX
ajst-29623	5	21	quickly	quickly	ADV
ajst-29623	5	22	located	locate	VERB
ajst-29623	5	23	and	and	CCONJ
ajst-29623	5	24	data	datum	NOUN
ajst-29623	5	25	extracted	extract	VERB
ajst-29623	5	26	,	,	PUNCT
ajst-29623	5	27	providing	provide	VERB
ajst-29623	5	28	an	an	DET
ajst-29623	5	29	accurate	accurate	ADJ
ajst-29623	5	30	and	and	CCONJ
ajst-29623	5	31	reliable	reliable	ADJ
ajst-29623	5	32	dataset	dataset	NOUN
ajst-29623	5	33	for	for	ADP
ajst-29623	5	34	defect	defect	ADJ
ajst-29623	5	35	quantification	quantification	NOUN
ajst-29623	5	36	.	.	PUNCT
ajst-29623	6	1	the	the	DET
ajst-29623	6	2	axial	axial	ADJ
ajst-29623	6	3	and	and	CCONJ
ajst-29623	6	4	radial	radial	ADJ
ajst-29623	6	5	mfl	mfl	PROPN
ajst-29623	6	6	data	datum	NOUN
ajst-29623	6	7	of	of	ADP
ajst-29623	6	8	pipeline	pipeline	NOUN
ajst-29623	6	9	defects	defect	NOUN
ajst-29623	6	10	are	be	AUX
ajst-29623	6	11	used	use	VERB
ajst-29623	6	12	as	as	ADP
ajst-29623	6	13	inputs	input	NOUN
ajst-29623	6	14	to	to	ADP
ajst-29623	6	15	the	the	DET
ajst-29623	6	16	model	model	NOUN
ajst-29623	6	17	,	,	PUNCT
ajst-29623	6	18	and	and	CCONJ
ajst-29623	6	19	the	the	DET
ajst-29623	6	20	length	length	NOUN
ajst-29623	6	21	,	,	PUNCT
ajst-29623	6	22	width	width	ADJ
ajst-29623	6	23	,	,	PUNCT
ajst-29623	6	24	and	and	CCONJ
ajst-29623	6	25	depth	depth	NOUN
ajst-29623	6	26	of	of	ADP
ajst-29623	6	27	defects	defect	NOUN
ajst-29623	6	28	are	be	AUX
ajst-29623	6	29	output	output	NOUN
ajst-29623	6	30	in	in	ADP
ajst-29623	6	31	parallel	parallel	NOUN
ajst-29623	6	32	according	accord	VERB
ajst-29623	6	33	to	to	ADP
ajst-29623	6	34	the	the	DET
ajst-29623	6	35	characteristics	characteristic	NOUN
ajst-29623	6	36	of	of	ADP
ajst-29623	6	37	mfl	mfl	PROPN
ajst-29623	6	38	data	datum	NOUN
ajst-29623	6	39	,	,	PUNCT
ajst-29623	6	40	thus	thus	ADV
ajst-29623	6	41	achieving	achieve	VERB
ajst-29623	6	42	the	the	DET
ajst-29623	6	43	assessment	assessment	NOUN
ajst-29623	6	44	and	and	CCONJ
ajst-29623	6	45	prediction	prediction	NOUN
ajst-29623	6	46	of	of	ADP
ajst-29623	6	47	defect	defect	ADJ
ajst-29623	6	48	sizes	size	NOUN
ajst-29623	6	49	.	.	PUNCT
ajst-29623	7	1	keywords	keyword	NOUN
ajst-29623	7	2	:	:	PUNCT
ajst-29623	7	3	object	object	VERB
ajst-29623	7	4	detection	detection	NOUN
ajst-29623	7	5	;	;	PUNCT
ajst-29623	7	6	deep	deep	ADJ
ajst-29623	7	7	learning	learning	NOUN
ajst-29623	7	8	;	;	PUNCT
ajst-29623	7	9	magnetic	magnetic	ADJ
ajst-29623	7	10	flux	flux	NOUN
ajst-29623	7	11	leakage	leakage	PROPN
ajst-29623	7	12	(	(	PUNCT
ajst-29623	7	13	mfl	mfl	NOUN
ajst-29623	7	14	)	)	PUNCT
ajst-29623	7	15	detection	detection	NOUN
ajst-29623	7	16	;	;	PUNCT
ajst-29623	7	17	image	image	NOUN
ajst-29623	7	18	processing	processing	NOUN
ajst-29623	7	19	.	.	PUNCT
ajst-29623	8	1	1	1	X
ajst-29623	8	2	.	.	X
ajst-29623	8	3	introduction	introduction	NOUN
ajst-29623	8	4	due	due	ADP
ajst-29623	8	5	to	to	ADP
ajst-29623	8	6	the	the	DET
ajst-29623	8	7	long	long	ADJ
ajst-29623	8	8	pipeline	pipeline	NOUN
ajst-29623	8	9	route	route	NOUN
ajst-29623	8	10	and	and	CCONJ
ajst-29623	8	11	complex	complex	ADJ
ajst-29623	8	12	operating	operating	NOUN
ajst-29623	8	13	environment	environment	NOUN
ajst-29623	8	14	,	,	PUNCT
ajst-29623	8	15	the	the	DET
ajst-29623	8	16	oil	oil	NOUN
ajst-29623	8	17	and	and	CCONJ
ajst-29623	8	18	natural	natural	ADJ
ajst-29623	8	19	gas	gas	NOUN
ajst-29623	8	20	transported	transport	VERB
ajst-29623	8	21	inside	inside	ADP
ajst-29623	8	22	the	the	DET
ajst-29623	8	23	pipeline	pipeline	NOUN
ajst-29623	8	24	often	often	ADV
ajst-29623	8	25	contain	contain	VERB
ajst-29623	8	26	a	a	DET
ajst-29623	8	27	large	large	ADJ
ajst-29623	8	28	amount	amount	NOUN
ajst-29623	8	29	of	of	ADP
ajst-29623	8	30	corrosive	corrosive	ADJ
ajst-29623	8	31	substances	substance	NOUN
ajst-29623	8	32	,	,	PUNCT
ajst-29623	8	33	which	which	PRON
ajst-29623	8	34	are	be	AUX
ajst-29623	8	35	affected	affect	VERB
ajst-29623	8	36	by	by	ADP
ajst-29623	8	37	the	the	DET
ajst-29623	8	38	temperature	temperature	NOUN
ajst-29623	8	39	and	and	CCONJ
ajst-29623	8	40	pressure	pressure	NOUN
ajst-29623	8	41	of	of	ADP
ajst-29623	8	42	the	the	DET
ajst-29623	8	43	medium[1	medium[1	PROPN
ajst-29623	8	44	]	]	PUNCT
ajst-29623	8	45	.	.	PUNCT
ajst-29623	9	1	long	long	ADJ
ajst-29623	9	2	term	term	NOUN
ajst-29623	9	3	operation	operation	NOUN
ajst-29623	9	4	can	can	AUX
ajst-29623	9	5	cause	cause	VERB
ajst-29623	9	6	serious	serious	ADJ
ajst-29623	9	7	corrosion	corrosion	NOUN
ajst-29623	9	8	to	to	ADP
ajst-29623	9	9	the	the	DET
ajst-29623	9	10	inner	inner	ADJ
ajst-29623	9	11	wall	wall	NOUN
ajst-29623	9	12	of	of	ADP
ajst-29623	9	13	the	the	DET
ajst-29623	9	14	pipeline	pipeline	NOUN
ajst-29623	9	15	.	.	PUNCT
ajst-29623	10	1	magnetic	magnetic	ADJ
ajst-29623	10	2	flux	flux	PROPN
ajst-29623	10	3	leakage	leakage	PROPN
ajst-29623	10	4	(	(	PUNCT
ajst-29623	10	5	mfl	mfl	PROPN
ajst-29623	10	6	)	)	PUNCT
ajst-29623	10	7	is	be	AUX
ajst-29623	10	8	a	a	DET
ajst-29623	10	9	widely	widely	ADV
ajst-29623	10	10	used	use	VERB
ajst-29623	10	11	nondestructive	nondestructive	ADJ
ajst-29623	10	12	testing	testing	NOUN
ajst-29623	10	13	method	method	NOUN
ajst-29623	10	14	for	for	ADP
ajst-29623	10	15	detecting	detect	VERB
ajst-29623	10	16	defects	defect	NOUN
ajst-29623	10	17	in	in	ADP
ajst-29623	10	18	longdistance	longdistance	NOUN
ajst-29623	10	19	oil	oil	NOUN
ajst-29623	10	20	pipelines	pipeline	NOUN
ajst-29623	10	21	.	.	PUNCT
ajst-29623	11	1	by	by	ADP
ajst-29623	11	2	conducting	conduct	VERB
ajst-29623	11	3	mfl	mfl	NOUN
ajst-29623	11	4	tests	test	NOUN
ajst-29623	11	5	on	on	ADP
ajst-29623	11	6	pipeline	pipeline	NOUN
ajst-29623	11	7	defects	defect	NOUN
ajst-29623	11	8	,	,	PUNCT
ajst-29623	11	9	data	datum	NOUN
ajst-29623	11	10	is	be	AUX
ajst-29623	11	11	collected	collect	VERB
ajst-29623	11	12	to	to	PART
ajst-29623	11	13	provide	provide	VERB
ajst-29623	11	14	reliable	reliable	ADJ
ajst-29623	11	15	references	reference	NOUN
ajst-29623	11	16	for	for	ADP
ajst-29623	11	17	defect	defect	NOUN
ajst-29623	11	18	identification	identification	NOUN
ajst-29623	11	19	,	,	PUNCT
ajst-29623	11	20	maintenance	maintenance	NOUN
ajst-29623	11	21	,	,	PUNCT
ajst-29623	11	22	and	and	CCONJ
ajst-29623	11	23	repair[2	repair[2	ADP
ajst-29623	11	24	]	]	PUNCT
ajst-29623	11	25	.	.	PUNCT
ajst-29623	12	1	in	in	ADP
ajst-29623	12	2	the	the	DET
ajst-29623	12	3	field	field	NOUN
ajst-29623	12	4	of	of	ADP
ajst-29623	12	5	magnetic	magnetic	ADJ
ajst-29623	12	6	flux	flux	NOUN
ajst-29623	12	7	leakage	leakage	NOUN
ajst-29623	12	8	detection	detection	NOUN
ajst-29623	12	9	quantification	quantification	NOUN
ajst-29623	12	10	,	,	PUNCT
ajst-29623	12	11	single	single	ADJ
ajst-29623	12	12	task	task	NOUN
ajst-29623	12	13	models	model	NOUN
ajst-29623	12	14	based	base	VERB
ajst-29623	12	15	on	on	ADP
ajst-29623	12	16	deep	deep	ADJ
ajst-29623	12	17	learning	learning	NOUN
ajst-29623	12	18	are	be	AUX
ajst-29623	12	19	widely	widely	ADV
ajst-29623	12	20	used	use	VERB
ajst-29623	12	21	.	.	PUNCT
ajst-29623	13	1	these	these	DET
ajst-29623	13	2	models	model	NOUN
ajst-29623	13	3	typically	typically	ADV
ajst-29623	13	4	use	use	VERB
ajst-29623	13	5	convolutional	convolutional	ADJ
ajst-29623	13	6	neural	neural	ADJ
ajst-29623	13	7	networks	network	NOUN
ajst-29623	13	8	(	(	PUNCT
ajst-29623	13	9	cnn	cnn	PROPN
ajst-29623	13	10	)	)	PUNCT
ajst-29623	13	11	or	or	CCONJ
ajst-29623	13	12	recurrent	recurrent	ADJ
ajst-29623	13	13	neural	neural	ADJ
ajst-29623	13	14	networks	network	NOUN
ajst-29623	13	15	(	(	PUNCT
ajst-29623	13	16	rnn	rnn	PROPN
ajst-29623	13	17	)	)	PUNCT
ajst-29623	13	18	to	to	PART
ajst-29623	13	19	extract	extract	VERB
ajst-29623	13	20	features	feature	NOUN
ajst-29623	13	21	from	from	ADP
ajst-29623	13	22	magnetic	magnetic	ADJ
ajst-29623	13	23	flux	flux	NOUN
ajst-29623	13	24	leakage	leakage	NOUN
ajst-29623	13	25	data[3	data[3	ADV
ajst-29623	13	26	]	]	PUNCT
ajst-29623	13	27	,	,	PUNCT
ajst-29623	13	28	learn	learn	VERB
ajst-29623	13	29	and	and	CCONJ
ajst-29623	13	30	train	train	NOUN
ajst-29623	13	31	defects	defect	NOUN
ajst-29623	13	32	.	.	PUNCT
ajst-29623	14	1	traditional	traditional	ADJ
ajst-29623	14	2	single	single	ADJ
ajst-29623	14	3	task	task	NOUN
ajst-29623	14	4	models	model	NOUN
ajst-29623	14	5	are	be	AUX
ajst-29623	14	6	more	more	ADV
ajst-29623	14	7	suitable	suitable	ADJ
ajst-29623	14	8	for	for	ADP
ajst-29623	14	9	training	train	VERB
ajst-29623	14	10	scenarios	scenario	NOUN
ajst-29623	14	11	where	where	SCONJ
ajst-29623	14	12	task	task	NOUN
ajst-29623	14	13	objectives	objective	NOUN
ajst-29623	14	14	are	be	AUX
ajst-29623	14	15	independent	independent	ADJ
ajst-29623	14	16	of	of	ADP
ajst-29623	14	17	each	each	DET
ajst-29623	14	18	other	other	ADJ
ajst-29623	14	19	.	.	PUNCT
ajst-29623	15	1	however	however	ADV
ajst-29623	15	2	,	,	PUNCT
ajst-29623	15	3	due	due	ADP
ajst-29623	15	4	to	to	ADP
ajst-29623	15	5	the	the	DET
ajst-29623	15	6	correlation	correlation	NOUN
ajst-29623	15	7	between	between	ADP
ajst-29623	15	8	the	the	DET
ajst-29623	15	9	leakage	leakage	NOUN
ajst-29623	15	10	magnetic	magnetic	ADJ
ajst-29623	15	11	field	field	NOUN
ajst-29623	15	12	generated	generate	VERB
ajst-29623	15	13	by	by	ADP
ajst-29623	15	14	pipeline	pipeline	NOUN
ajst-29623	15	15	defects	defect	NOUN
ajst-29623	15	16	and	and	CCONJ
ajst-29623	15	17	the	the	DET
ajst-29623	15	18	various	various	ADJ
ajst-29623	15	19	size	size	NOUN
ajst-29623	15	20	parameters	parameter	NOUN
ajst-29623	15	21	of	of	ADP
ajst-29623	15	22	defects	defect	NOUN
ajst-29623	15	23	,	,	PUNCT
ajst-29623	15	24	it	it	PRON
ajst-29623	15	25	affects	affect	VERB
ajst-29623	15	26	the	the	DET
ajst-29623	15	27	learning	learning	NOUN
ajst-29623	15	28	efficiency	efficiency	NOUN
ajst-29623	15	29	of	of	ADP
ajst-29623	15	30	single	single	ADJ
ajst-29623	15	31	task	task	NOUN
ajst-29623	15	32	models	model	NOUN
ajst-29623	15	33	for	for	ADP
ajst-29623	15	34	defect	defect	VERB
ajst-29623	15	35	quantification	quantification	NOUN
ajst-29623	15	36	tasks	task	NOUN
ajst-29623	15	37	.	.	PUNCT
ajst-29623	16	1	in	in	ADP
ajst-29623	16	2	the	the	DET
ajst-29623	16	3	quantification	quantification	NOUN
ajst-29623	16	4	of	of	ADP
ajst-29623	16	5	pipeline	pipeline	NOUN
ajst-29623	16	6	defects	defect	NOUN
ajst-29623	16	7	,	,	PUNCT
ajst-29623	16	8	it	it	PRON
ajst-29623	16	9	is	be	AUX
ajst-29623	16	10	necessary	necessary	ADJ
ajst-29623	16	11	to	to	PART
ajst-29623	16	12	simultaneously	simultaneously	ADV
ajst-29623	16	13	output	output	VERB
ajst-29623	16	14	the	the	DET
ajst-29623	16	15	length	length	NOUN
ajst-29623	16	16	,	,	PUNCT
ajst-29623	16	17	width	width	ADJ
ajst-29623	16	18	,	,	PUNCT
ajst-29623	16	19	and	and	CCONJ
ajst-29623	16	20	depth	depth	NOUN
ajst-29623	16	21	dimensions	dimension	NOUN
ajst-29623	16	22	of	of	ADP
ajst-29623	16	23	the	the	DET
ajst-29623	16	24	defects	defect	NOUN
ajst-29623	16	25	,	,	PUNCT
ajst-29623	16	26	and	and	CCONJ
ajst-29623	16	27	these	these	DET
ajst-29623	16	28	size	size	NOUN
ajst-29623	16	29	parameters	parameter	NOUN
ajst-29623	16	30	will	will	AUX
ajst-29623	16	31	affect	affect	VERB
ajst-29623	16	32	the	the	DET
ajst-29623	16	33	characteristics	characteristic	NOUN
ajst-29623	16	34	of	of	ADP
ajst-29623	16	35	the	the	DET
ajst-29623	16	36	defect	defect	NOUN
ajst-29623	16	37	signal[4	signal[4	NOUN
ajst-29623	16	38	]	]	PUNCT
ajst-29623	16	39	.	.	PUNCT
ajst-29623	17	1	the	the	DET
ajst-29623	17	2	construction	construction	NOUN
ajst-29623	17	3	of	of	ADP
ajst-29623	17	4	a	a	DET
ajst-29623	17	5	pipeline	pipeline	NOUN
ajst-29623	17	6	corrosion	corrosion	NOUN
ajst-29623	17	7	defect	defect	NOUN
ajst-29623	17	8	dataset	dataset	NOUN
ajst-29623	17	9	was	be	AUX
ajst-29623	17	10	completed	complete	VERB
ajst-29623	17	11	through	through	ADP
ajst-29623	17	12	the	the	DET
ajst-29623	17	13	pp	pp	ADJ
ajst-29623	17	14	-	-	PUNCT
ajst-29623	17	15	yoloe	yoloe	NOUN
ajst-29623	17	16	model	model	NOUN
ajst-29623	17	17	,	,	PUNCT
ajst-29623	17	18	and	and	CCONJ
ajst-29623	17	19	the	the	DET
ajst-29623	17	20	mtl	mtl	PROPN
ajst-29623	17	21	model	model	NOUN
ajst-29623	17	22	was	be	AUX
ajst-29623	17	23	used	use	VERB
ajst-29623	17	24	to	to	PART
ajst-29623	17	25	train	train	VERB
ajst-29623	17	26	and	and	CCONJ
ajst-29623	17	27	quantify	quantify	VERB
ajst-29623	17	28	the	the	DET
ajst-29623	17	29	size	size	NOUN
ajst-29623	17	30	of	of	ADP
ajst-29623	17	31	corrosion	corrosion	NOUN
ajst-29623	17	32	defects[5	defects[5	NOUN
ajst-29623	17	33	]	]	PUNCT
ajst-29623	17	34	.	.	PUNCT
ajst-29623	18	1	optimization	optimization	NOUN
ajst-29623	18	2	was	be	AUX
ajst-29623	18	3	carried	carry	VERB
ajst-29623	18	4	out	out	ADP
ajst-29623	18	5	from	from	ADP
ajst-29623	18	6	the	the	DET
ajst-29623	18	7	aspects	aspect	NOUN
ajst-29623	18	8	of	of	ADP
ajst-29623	18	9	activation	activation	NOUN
ajst-29623	18	10	function	function	NOUN
ajst-29623	18	11	and	and	CCONJ
ajst-29623	18	12	optimization	optimization	NOUN
ajst-29623	18	13	algorithm	algorithm	NOUN
ajst-29623	18	14	,	,	PUNCT
ajst-29623	18	15	and	and	CCONJ
ajst-29623	18	16	the	the	DET
ajst-29623	18	17	optimal	optimal	ADJ
ajst-29623	18	18	strategy	strategy	NOUN
ajst-29623	18	19	was	be	AUX
ajst-29623	18	20	selected	select	VERB
ajst-29623	18	21	to	to	PART
ajst-29623	18	22	achieve	achieve	VERB
ajst-29623	18	23	accurate	accurate	ADJ
ajst-29623	18	24	prediction	prediction	NOUN
ajst-29623	18	25	of	of	ADP
ajst-29623	18	26	corrosion	corrosion	NOUN
ajst-29623	18	27	defect	defect	NOUN
ajst-29623	18	28	size	size	NOUN
ajst-29623	18	29	and	and	CCONJ
ajst-29623	18	30	improve	improve	VERB
ajst-29623	18	31	the	the	DET
ajst-29623	18	32	quality	quality	NOUN
ajst-29623	18	33	and	and	CCONJ
ajst-29623	18	34	efficiency	efficiency	NOUN
ajst-29623	18	35	of	of	ADP
ajst-29623	18	36	pipeline	pipeline	NOUN
ajst-29623	18	37	magnetic	magnetic	ADJ
ajst-29623	18	38	leakage	leakage	NOUN
ajst-29623	18	39	detection	detection	NOUN
ajst-29623	18	40	data	data	VERB
ajst-29623	18	41	analysis	analysis	NOUN
ajst-29623	18	42	.	.	PUNCT
ajst-29623	19	1	2	2	X
ajst-29623	19	2	.	.	X
ajst-29623	19	3	defect	defect	NOUN
ajst-29623	19	4	pipeline	pipeline	NOUN
ajst-29623	19	5	detection	detection	NOUN
ajst-29623	19	6	process	process	NOUN
ajst-29623	19	7	2.1	2.1	NUM
ajst-29623	19	8	.	.	PUNCT
ajst-29623	20	1	hydraulic	hydraulic	ADJ
ajst-29623	20	2	system	system	NOUN
ajst-29623	20	3	working	work	VERB
ajst-29623	20	4	principle	principle	NOUN
ajst-29623	20	5	the	the	DET
ajst-29623	20	6	mfl	mfl	PROPN
ajst-29623	20	7	data	data	PROPN
ajst-29623	20	8	consists	consist	VERB
ajst-29623	20	9	of	of	ADP
ajst-29623	20	10	data	datum	NOUN
ajst-29623	20	11	in	in	ADP
ajst-29623	20	12	three	three	NUM
ajst-29623	20	13	magnetic	magnetic	ADJ
ajst-29623	20	14	field	field	NOUN
ajst-29623	20	15	directions	direction	NOUN
ajst-29623	20	16	:	:	PUNCT
ajst-29623	20	17	radial	radial	ADJ
ajst-29623	20	18	,	,	PUNCT
ajst-29623	20	19	axial	axial	ADJ
ajst-29623	20	20	,	,	PUNCT
ajst-29623	20	21	and	and	CCONJ
ajst-29623	20	22	circumferential	circumferential	ADJ
ajst-29623	20	23	.	.	PUNCT
ajst-29623	21	1	different	different	ADJ
ajst-29623	21	2	magnetic	magnetic	ADJ
ajst-29623	21	3	field	field	NOUN
ajst-29623	21	4	direction	direction	NOUN
ajst-29623	21	5	data	datum	NOUN
ajst-29623	21	6	contains	contain	VERB
ajst-29623	21	7	different	different	ADJ
ajst-29623	21	8	mfl	mfl	PROPN
ajst-29623	21	9	signal	signal	NOUN
ajst-29623	21	10	characteristics[6	characteristics[6	X
ajst-29623	21	11	]	]	X
ajst-29623	21	12	.	.	PUNCT
ajst-29623	22	1	when	when	SCONJ
ajst-29623	22	2	the	the	DET
ajst-29623	22	3	circumferential	circumferential	ADJ
ajst-29623	22	4	component	component	NOUN
ajst-29623	22	5	of	of	ADP
ajst-29623	22	6	the	the	DET
ajst-29623	22	7	mfl	mfl	PROPN
ajst-29623	22	8	signal	signal	PROPN
ajst-29623	22	9	fluctuates	fluctuate	NOUN
ajst-29623	22	10	greatly	greatly	ADV
ajst-29623	22	11	and	and	CCONJ
ajst-29623	22	12	has	have	VERB
ajst-29623	22	13	no	no	DET
ajst-29623	22	14	obvious	obvious	ADJ
ajst-29623	22	15	pattern	pattern	NOUN
ajst-29623	22	16	,	,	PUNCT
ajst-29623	22	17	the	the	DET
ajst-29623	22	18	radial	radial	ADJ
ajst-29623	22	19	and	and	CCONJ
ajst-29623	22	20	axial	axial	ADJ
ajst-29623	22	21	components	component	NOUN
ajst-29623	22	22	are	be	AUX
ajst-29623	22	23	usually	usually	ADV
ajst-29623	22	24	selected	select	VERB
ajst-29623	22	25	to	to	PART
ajst-29623	22	26	analyze	analyze	VERB
ajst-29623	22	27	the	the	DET
ajst-29623	22	28	relationship	relationship	NOUN
ajst-29623	22	29	between	between	ADP
ajst-29623	22	30	the	the	DET
ajst-29623	22	31	defect	defect	NOUN
ajst-29623	22	32	size	size	NOUN
ajst-29623	22	33	and	and	CCONJ
ajst-29623	22	34	the	the	DET
ajst-29623	22	35	mfl	mfl	PROPN
ajst-29623	22	36	signal	signal	NOUN
ajst-29623	22	37	.	.	PUNCT
ajst-29623	23	1	the	the	DET
ajst-29623	23	2	pipeline	pipeline	NOUN
ajst-29623	23	3	mfl	mfl	PROPN
ajst-29623	23	4	signals	signal	NOUN
ajst-29623	23	5	involved	involve	VERB
ajst-29623	23	6	in	in	ADP
ajst-29623	23	7	this	this	DET
ajst-29623	23	8	experiment	experiment	NOUN
ajst-29623	23	9	include	include	VERB
ajst-29623	23	10	two	two	NUM
ajst-29623	23	11	types	type	NOUN
ajst-29623	23	12	of	of	ADP
ajst-29623	23	13	radial	radial	ADJ
ajst-29623	23	14	mfl	mfl	PROPN
ajst-29623	23	15	signals	signal	NOUN
ajst-29623	23	16	and	and	CCONJ
ajst-29623	23	17	axial	axial	ADJ
ajst-29623	23	18	mfl	mfl	PROPN
ajst-29623	23	19	signals	signal	NOUN
ajst-29623	23	20	.	.	PUNCT
ajst-29623	24	1	by	by	ADP
ajst-29623	24	2	changing	change	VERB
ajst-29623	24	3	the	the	DET
ajst-29623	24	4	defect	defect	ADJ
ajst-29623	24	5	size	size	NOUN
ajst-29623	24	6	,	,	PUNCT
ajst-29623	24	7	the	the	DET
ajst-29623	24	8	distribution	distribution	NOUN
ajst-29623	24	9	law	law	NOUN
ajst-29623	24	10	of	of	ADP
ajst-29623	24	11	the	the	DET
ajst-29623	24	12	mfl	mfl	PROPN
ajst-29623	24	13	field	field	NOUN
ajst-29623	24	14	of	of	ADP
ajst-29623	24	15	pipeline	pipeline	NOUN
ajst-29623	24	16	defects	defect	NOUN
ajst-29623	24	17	under	under	ADP
ajst-29623	24	18	different	different	ADJ
ajst-29623	24	19	sizes	size	NOUN
ajst-29623	24	20	is	be	AUX
ajst-29623	24	21	obtained	obtain	VERB
ajst-29623	24	22	.	.	PUNCT
ajst-29623	25	1	under	under	ADP
ajst-29623	25	2	the	the	DET
ajst-29623	25	3	condition	condition	NOUN
ajst-29623	25	4	of	of	ADP
ajst-29623	25	5	the	the	DET
ajst-29623	25	6	same	same	ADJ
ajst-29623	25	7	width	width	NOUN
ajst-29623	25	8	,	,	PUNCT
ajst-29623	25	9	as	as	ADP
ajst-29623	25	10	the	the	DET
ajst-29623	25	11	defect	defect	NOUN
ajst-29623	25	12	depth	depth	NOUN
ajst-29623	25	13	increases	increase	NOUN
ajst-29623	25	14	,	,	PUNCT
ajst-29623	25	15	the	the	DET
ajst-29623	25	16	peak	peak	NOUN
ajst-29623	25	17	values	value	NOUN
ajst-29623	25	18	of	of	ADP
ajst-29623	25	19	the	the	DET
ajst-29623	25	20	radial	radial	ADJ
ajst-29623	25	21	and	and	CCONJ
ajst-29623	25	22	axial	axial	ADJ
ajst-29623	25	23	mfl	mfl	PROPN
ajst-29623	25	24	components	component	NOUN
ajst-29623	25	25	of	of	ADP
ajst-29623	25	26	the	the	DET
ajst-29623	25	27	defect	defect	NOUN
ajst-29623	25	28	increase	increase	NOUN
ajst-29623	25	29	significantly	significantly	ADV
ajst-29623	25	30	.	.	PUNCT
ajst-29623	26	1	under	under	ADP
ajst-29623	26	2	the	the	DET
ajst-29623	26	3	same	same	ADJ
ajst-29623	26	4	depth	depth	NOUN
ajst-29623	26	5	,	,	PUNCT
ajst-29623	26	6	the	the	DET
ajst-29623	26	7	positive	positive	ADJ
ajst-29623	26	8	negative	negative	ADJ
ajst-29623	26	9	extreme	extreme	ADJ
ajst-29623	26	10	value	value	NOUN
ajst-29623	26	11	spacing	spacing	NOUN
ajst-29623	26	12	of	of	ADP
ajst-29623	26	13	the	the	DET
ajst-29623	26	14	radial	radial	ADJ
ajst-29623	26	15	mfl	mfl	PROPN
ajst-29623	26	16	component	component	NOUN
ajst-29623	26	17	increases	increase	VERB
ajst-29623	26	18	with	with	ADP
ajst-29623	26	19	the	the	DET
ajst-29623	26	20	increase	increase	NOUN
ajst-29623	26	21	of	of	ADP
ajst-29623	26	22	the	the	DET
ajst-29623	26	23	width	width	NOUN
ajst-29623	26	24	,	,	PUNCT
ajst-29623	26	25	and	and	CCONJ
ajst-29623	26	26	the	the	DET
ajst-29623	26	27	valley	valley	NOUN
ajst-29623	26	28	spacing	spacing	NOUN
ajst-29623	26	29	of	of	ADP
ajst-29623	26	30	the	the	DET
ajst-29623	26	31	axial	axial	PROPN
ajst-29623	26	32	mfl	mfl	PROPN
ajst-29623	26	33	component	component	NOUN
ajst-29623	26	34	increases	increase	VERB
ajst-29623	26	35	with	with	ADP
ajst-29623	26	36	the	the	DET
ajst-29623	26	37	increase	increase	NOUN
ajst-29623	26	38	of	of	ADP
ajst-29623	26	39	the	the	DET
ajst-29623	26	40	width	width	NOUN
ajst-29623	26	41	.	.	PUNCT
ajst-29623	27	1	in	in	ADP
ajst-29623	27	2	figures	figure	NOUN
ajst-29623	27	3	1	1	NUM
ajst-29623	27	4	and	and	CCONJ
ajst-29623	27	5	2	2	NUM
ajst-29623	27	6	,	,	PUNCT
ajst-29623	27	7	when	when	SCONJ
ajst-29623	27	8	the	the	DET
ajst-29623	27	9	length	length	NOUN
ajst-29623	27	10	and	and	CCONJ
ajst-29623	27	11	width	width	NOUN
ajst-29623	27	12	are	be	AUX
ajst-29623	27	13	kept	keep	VERB
ajst-29623	27	14	constant	constant	ADJ
ajst-29623	27	15	and	and	CCONJ
ajst-29623	27	16	only	only	ADV
ajst-29623	27	17	the	the	DET
ajst-29623	27	18	defect	defect	ADJ
ajst-29623	27	19	depth	depth	NOUN
ajst-29623	27	20	is	be	AUX
ajst-29623	27	21	changed	change	VERB
ajst-29623	27	22	,	,	PUNCT
ajst-29623	27	23	the	the	DET
ajst-29623	27	24	variation	variation	NOUN
ajst-29623	27	25	laws	law	NOUN
ajst-29623	27	26	of	of	ADP
ajst-29623	27	27	the	the	DET
ajst-29623	27	28	radial	radial	ADJ
ajst-29623	27	29	and	and	CCONJ
ajst-29623	27	30	axial	axial	ADJ
ajst-29623	27	31	mfl	mfl	PROPN
ajst-29623	27	32	components	component	NOUN
ajst-29623	27	33	are	be	AUX
ajst-29623	27	34	shown	show	VERB
ajst-29623	27	35	.	.	PUNCT
ajst-29623	28	1	2.2	2.2	NUM
ajst-29623	28	2	.	.	PUNCT
ajst-29623	28	3	object	object	NOUN
ajst-29623	28	4	detection	detection	NOUN
ajst-29623	28	5	model	model	NOUN
ajst-29623	28	6	establishment	establishment	NOUN
ajst-29623	28	7	after	after	SCONJ
ajst-29623	28	8	the	the	DET
ajst-29623	28	9	pre	pre	ADJ
ajst-29623	28	10	processed	process	VERB
ajst-29623	28	11	images	image	NOUN
ajst-29623	28	12	are	be	AUX
ajst-29623	28	13	input	input	VERB
ajst-29623	28	14	into	into	ADP
ajst-29623	28	15	the	the	DET
ajst-29623	28	16	object	object	NOUN
ajst-29623	28	17	detection	detection	NOUN
ajst-29623	28	18	model	model	NOUN
ajst-29623	28	19	,	,	PUNCT
ajst-29623	28	20	the	the	DET
ajst-29623	28	21	image	image	NOUN
ajst-29623	28	22	feature	feature	NOUN
ajst-29623	28	23	information	information	NOUN
ajst-29623	28	24	is	be	AUX
ajst-29623	28	25	first	first	ADV
ajst-29623	28	26	extracted	extract	VERB
ajst-29623	28	27	.	.	PUNCT
ajst-29623	29	1	subsequently	subsequently	ADV
ajst-29623	29	2	,	,	PUNCT
ajst-29623	29	3	the	the	DET
ajst-29623	29	4	extracted	extract	VERB
ajst-29623	29	5	target	target	NOUN
ajst-29623	29	6	features	feature	NOUN
ajst-29623	29	7	are	be	AUX
ajst-29623	29	8	fused	fuse	VERB
ajst-29623	29	9	,	,	PUNCT
ajst-29623	29	10	and	and	CCONJ
ajst-29623	29	11	the	the	DET
ajst-29623	29	12	fused	fuse	VERB
ajst-29623	29	13	images	image	NOUN
ajst-29623	29	14	are	be	AUX
ajst-29623	29	15	post	post	ADV
ajst-29623	29	16	processed	process	VERB
ajst-29623	29	17	and	and	CCONJ
ajst-29623	29	18	recognized	recognize	VERB
ajst-29623	29	19	through	through	ADP
ajst-29623	29	20	the	the	DET
ajst-29623	29	21	detection	detection	NOUN
ajst-29623	29	22	head[7	head[7	PROPN
ajst-29623	29	23	]	]	PUNCT
ajst-29623	29	24	,	,	PUNCT
ajst-29623	29	25	finally	finally	ADV
ajst-29623	29	26	achieving	achieve	VERB
ajst-29623	29	27	the	the	DET
ajst-29623	29	28	output	output	NOUN
ajst-29623	29	29	of	of	ADP
ajst-29623	29	30	the	the	DET
ajst-29623	29	31	object	object	NOUN
ajst-29623	29	32	detection	detection	NOUN
ajst-29623	29	33	task	task	NOUN
ajst-29623	29	34	.	.	PUNCT
ajst-29623	30	1	the	the	DET
ajst-29623	30	2	composition	composition	NOUN
ajst-29623	30	3	of	of	ADP
ajst-29623	30	4	the	the	DET
ajst-29623	30	5	pp	pp	PROPN
ajst-29623	30	6	yoloe	yoloe	NOUN
ajst-29623	30	7	model	model	NOUN
ajst-29623	30	8	(	(	PUNCT
ajst-29623	30	9	figure	figure	NOUN
ajst-29623	30	10	3	3	NUM
ajst-29623	30	11	)	)	PUNCT
ajst-29623	30	12	includes	include	VERB
ajst-29623	30	13	a	a	DET
ajst-29623	30	14	backbone	backbone	NOUN
ajst-29623	30	15	feature	feature	NOUN
ajst-29623	30	16	extraction	extraction	NOUN
ajst-29623	30	17	network	network	NOUN
ajst-29623	30	18	,	,	PUNCT
ajst-29623	30	19	a	a	DET
ajst-29623	30	20	path	path	NOUN
ajst-29623	30	21	aggregation	aggregation	NOUN
ajst-29623	30	22	network	network	NOUN
ajst-29623	30	23	,	,	PUNCT
ajst-29623	30	24	and	and	CCONJ
ajst-29623	30	25	a	a	DET
ajst-29623	30	26	detection	detection	NOUN
ajst-29623	30	27	head	head	NOUN
ajst-29623	30	28	.	.	PUNCT
ajst-29623	31	1	the	the	DET
ajst-29623	31	2	backbone	backbone	NOUN
ajst-29623	31	3	feature	feature	NOUN
ajst-29623	31	4	extraction	extraction	NOUN
ajst-29623	31	5	network	network	NOUN
ajst-29623	31	6	is	be	AUX
ajst-29623	31	7	used	use	VERB
ajst-29623	31	8	to	to	PART
ajst-29623	31	9	extract	extract	VERB
ajst-29623	31	10	different	different	ADJ
ajst-29623	31	11	types	type	NOUN
ajst-29623	31	12	of	of	ADP
ajst-29623	31	13	visual	visual	ADJ
ajst-29623	31	14	information	information	NOUN
ajst-29623	31	15	(	(	PUNCT
ajst-29623	31	16	c3	c3	PROPN
ajst-29623	31	17	,	,	PUNCT
ajst-29623	31	18	c4	c4	NOUN
ajst-29623	31	19	,	,	PUNCT
ajst-29623	31	20	c5	c5	PROPN
ajst-29623	31	21	)	)	PUNCT
ajst-29623	31	22	of	of	ADP
ajst-29623	31	23	the	the	DET
ajst-29623	31	24	input	input	NOUN
ajst-29623	31	25	image	image	NOUN
ajst-29623	31	26	,	,	PUNCT
ajst-29623	31	27	which	which	PRON
ajst-29623	31	28	is	be	AUX
ajst-29623	31	29	then	then	ADV
ajst-29623	31	30	sent	send	VERB
ajst-29623	31	31	to	to	ADP
ajst-29623	31	32	the	the	DET
ajst-29623	31	33	path	path	NOUN
ajst-29623	31	34	aggregation	aggregation	NOUN
ajst-29623	31	35	network	network	NOUN
ajst-29623	31	36	for	for	ADP
ajst-29623	31	37	target	target	NOUN
ajst-29623	31	38	feature	feature	NOUN
ajst-29623	31	39	aggregation	aggregation	NOUN
ajst-29623	31	40	.	.	PUNCT
ajst-29623	32	1	the	the	DET
ajst-29623	32	2	path	path	NOUN
ajst-29623	32	3	aggregation	aggregation	NOUN
ajst-29623	32	4	network	network	NOUN
ajst-29623	32	5	includes	include	VERB
ajst-29623	32	6	three	three	NUM
ajst-29623	32	7	levels	level	NOUN
ajst-29623	32	8	:	:	PUNCT
ajst-29623	32	9	p3	p3	PROPN
ajst-29623	32	10	,	,	PUNCT
ajst-29623	32	11	p4	p4	ADJ
ajst-29623	32	12	,	,	PUNCT
ajst-29623	32	13	and	and	CCONJ
ajst-29623	32	14	p5	p5	ADJ
ajst-29623	32	15	,	,	PUNCT
ajst-29623	32	16	and	and	CCONJ
ajst-29623	32	17	295	295	NUM
ajst-29623	32	18	each	each	DET
ajst-29623	32	19	level	level	NOUN
ajst-29623	32	20	is	be	AUX
ajst-29623	32	21	classified	classify	VERB
ajst-29623	32	22	and	and	CCONJ
ajst-29623	32	23	recognized	recognize	VERB
ajst-29623	32	24	through	through	ADP
ajst-29623	32	25	a	a	DET
ajst-29623	32	26	detection	detection	NOUN
ajst-29623	32	27	head	head	NOUN
ajst-29623	32	28	.	.	PUNCT
ajst-29623	33	1	when	when	SCONJ
ajst-29623	33	2	the	the	DET
ajst-29623	33	3	detection	detection	NOUN
ajst-29623	33	4	head	head	NOUN
ajst-29623	33	5	obtains	obtain	VERB
ajst-29623	33	6	the	the	DET
ajst-29623	33	7	transmitted	transmit	VERB
ajst-29623	33	8	features	feature	NOUN
ajst-29623	33	9	,	,	PUNCT
ajst-29623	33	10	it	it	PRON
ajst-29623	33	11	first	first	ADV
ajst-29623	33	12	enhances	enhance	VERB
ajst-29623	33	13	the	the	DET
ajst-29623	33	14	network	network	NOUN
ajst-29623	33	15	's	's	PART
ajst-29623	33	16	perception	perception	NOUN
ajst-29623	33	17	ability	ability	NOUN
ajst-29623	33	18	of	of	ADP
ajst-29623	33	19	the	the	DET
ajst-29623	33	20	target	target	NOUN
ajst-29623	33	21	edge	edge	NOUN
ajst-29623	33	22	features	feature	NOUN
ajst-29623	33	23	through	through	ADP
ajst-29623	33	24	the	the	DET
ajst-29623	33	25	feature	feature	NOUN
ajst-29623	33	26	enhancement	enhancement	NOUN
ajst-29623	33	27	module	module	NOUN
ajst-29623	33	28	,	,	PUNCT
ajst-29623	33	29	then	then	ADV
ajst-29623	33	30	integrates	integrate	VERB
ajst-29623	33	31	each	each	DET
ajst-29623	33	32	dimension	dimension	NOUN
ajst-29623	33	33	of	of	ADP
ajst-29623	33	34	the	the	DET
ajst-29623	33	35	image	image	NOUN
ajst-29623	33	36	,	,	PUNCT
ajst-29623	33	37	and	and	CCONJ
ajst-29623	33	38	uses	use	VERB
ajst-29623	33	39	convolutional	convolutional	ADJ
ajst-29623	33	40	processing	processing	NOUN
ajst-29623	33	41	to	to	PART
ajst-29623	33	42	perform	perform	VERB
ajst-29623	33	43	spatial	spatial	ADJ
ajst-29623	33	44	and	and	CCONJ
ajst-29623	33	45	channel	channel	NOUN
ajst-29623	33	46	transformations	transformation	NOUN
ajst-29623	33	47	on	on	ADP
ajst-29623	33	48	the	the	DET
ajst-29623	33	49	length	length	NOUN
ajst-29623	33	50	,	,	PUNCT
ajst-29623	33	51	width	width	ADJ
ajst-29623	33	52	,	,	PUNCT
ajst-29623	33	53	and	and	CCONJ
ajst-29623	33	54	dimension	dimension	NOUN
ajst-29623	33	55	of	of	ADP
ajst-29623	33	56	the	the	DET
ajst-29623	33	57	target	target	NOUN
ajst-29623	33	58	,	,	PUNCT
ajst-29623	33	59	realizing	realize	VERB
ajst-29623	33	60	accurate	accurate	ADJ
ajst-29623	33	61	prediction	prediction	NOUN
ajst-29623	33	62	of	of	ADP
ajst-29623	33	63	the	the	DET
ajst-29623	33	64	target	target	NOUN
ajst-29623	33	65	position	position	NOUN
ajst-29623	33	66	,	,	PUNCT
ajst-29623	33	67	category	category	NOUN
ajst-29623	33	68	,	,	PUNCT
ajst-29623	33	69	and	and	CCONJ
ajst-29623	33	70	confidence	confidence	NOUN
ajst-29623	33	71	.	.	PUNCT
ajst-29623	34	1	figure	figure	NOUN
ajst-29623	34	2	1	1	NUM
ajst-29623	34	3	.	.	PUNCT
ajst-29623	34	4	radial	radial	ADJ
ajst-29623	34	5	magnetic	magnetic	PROPN
ajst-29623	34	6	flux	flux	PROPN
ajst-29623	34	7	leakage	leakage	NOUN
ajst-29623	34	8	component	component	NOUN
ajst-29623	34	9	figure	figure	NOUN
ajst-29623	34	10	2	2	NUM
ajst-29623	34	11	.	.	PUNCT
ajst-29623	34	12	axial	axial	ADJ
ajst-29623	34	13	magnetic	magnetic	ADJ
ajst-29623	34	14	flux	flux	PROPN
ajst-29623	34	15	leakage	leakage	NOUN
ajst-29623	34	16	component	component	NOUN
ajst-29623	34	17	figure	figure	NOUN
ajst-29623	34	18	3	3	NUM
ajst-29623	34	19	.	.	PUNCT
ajst-29623	34	20	pp	pp	ADJ
ajst-29623	34	21	-	-	PUNCT
ajst-29623	34	22	yoloe	yoloe	NOUN
ajst-29623	34	23	model	model	NOUN
ajst-29623	34	24	architecture	architecture	NOUN
ajst-29623	34	25	the	the	DET
ajst-29623	34	26	model	model	NOUN
ajst-29623	34	27	architecture	architecture	NOUN
ajst-29623	34	28	of	of	ADP
ajst-29623	34	29	pp	pp	ADJ
ajst-29623	34	30	yoloe	yoloe	NOUN
ajst-29623	34	31	is	be	AUX
ajst-29623	34	32	proposed	propose	VERB
ajst-29623	34	33	.	.	PUNCT
ajst-29623	35	1	the	the	DET
ajst-29623	35	2	backbone	backbone	NOUN
ajst-29623	35	3	network	network	NOUN
ajst-29623	35	4	is	be	AUX
ajst-29623	35	5	csprep	csprep	NOUN
ajst-29623	35	6	resnet	resnet	PROPN
ajst-29623	35	7	,	,	PUNCT
ajst-29623	35	8	the	the	DET
ajst-29623	35	9	neck	neck	NOUN
ajst-29623	35	10	is	be	AUX
ajst-29623	35	11	the	the	DET
ajst-29623	35	12	path	path	NOUN
ajst-29623	35	13	aggregation	aggregation	NOUN
ajst-29623	35	14	network	network	NOUN
ajst-29623	35	15	(	(	PUNCT
ajst-29623	35	16	pan	pan	NOUN
ajst-29623	35	17	)	)	PUNCT
ajst-29623	35	18	,	,	PUNCT
ajst-29623	35	19	and	and	CCONJ
ajst-29623	35	20	the	the	DET
ajst-29623	35	21	head	head	NOUN
ajst-29623	35	22	is	be	AUX
ajst-29623	35	23	the	the	DET
ajst-29623	35	24	efficient	efficient	ADJ
ajst-29623	35	25	task	task	NOUN
ajst-29623	35	26	aligned	align	VERB
ajst-29623	35	27	head	head	NOUN
ajst-29623	35	28	(	(	PUNCT
ajst-29623	35	29	et	et	NOUN
ajst-29623	35	30	head	head	NOUN
ajst-29623	35	31	)	)	PUNCT
ajst-29623	35	32	.	.	PUNCT
ajst-29623	36	1	width	width	VERB
ajst-29623	36	2	multiplier	multipli	ADJ
ajst-29623	36	3	α	α	NOUN
ajst-29623	36	4	and	and	CCONJ
ajst-29623	36	5	depth	depth	NOUN
ajst-29623	36	6	multiplier	multipli	ADJ
ajst-29623	36	7	β	β	X
ajst-29623	36	8	are	be	AUX
ajst-29623	36	9	used	use	VERB
ajst-29623	36	10	to	to	PART
ajst-29623	36	11	obtain	obtain	VERB
ajst-29623	36	12	a	a	DET
ajst-29623	36	13	series	series	NOUN
ajst-29623	36	14	of	of	ADP
ajst-29623	36	15	detection	detection	NOUN
ajst-29623	36	16	networks	network	NOUN
ajst-29623	36	17	with	with	ADP
ajst-29623	36	18	different	different	ADJ
ajst-29623	36	19	parameters	parameter	NOUN
ajst-29623	36	20	and	and	CCONJ
ajst-29623	36	21	computational	computational	ADJ
ajst-29623	36	22	costs	cost	NOUN
ajst-29623	36	23	.	.	PUNCT
ajst-29623	37	1	the	the	DET
ajst-29623	37	2	width	width	NOUN
ajst-29623	37	3	of	of	ADP
ajst-29623	37	4	the	the	DET
ajst-29623	37	5	basic	basic	ADJ
ajst-29623	37	6	backbone	backbone	NOUN
ajst-29623	37	7	network	network	NOUN
ajst-29623	37	8	is	be	AUX
ajst-29623	37	9	set	set	VERB
ajst-29623	37	10	to	to	ADP
ajst-29623	37	11	[	[	X
ajst-29623	37	12	64	64	NUM
ajst-29623	37	13	,	,	PUNCT
ajst-29623	37	14	128	128	NUM
ajst-29623	37	15	,	,	PUNCT
ajst-29623	37	16	256	256	NUM
ajst-29623	37	17	,	,	PUNCT
ajst-29623	37	18	512	512	NUM
ajst-29623	37	19	,	,	PUNCT
ajst-29623	37	20	1024	1024	NUM
ajst-29623	37	21	]	]	PUNCT
ajst-29623	37	22	.	.	PUNCT
ajst-29623	38	1	except	except	SCONJ
ajst-29623	38	2	for	for	ADP
ajst-29623	38	3	the	the	DET
ajst-29623	38	4	stem	stem	NOUN
ajst-29623	38	5	,	,	PUNCT
ajst-29623	38	6	the	the	DET
ajst-29623	38	7	depth	depth	NOUN
ajst-29623	38	8	of	of	ADP
ajst-29623	38	9	the	the	DET
ajst-29623	38	10	basic	basic	ADJ
ajst-29623	38	11	backbone	backbone	NOUN
ajst-29623	38	12	network	network	NOUN
ajst-29623	38	13	is	be	AUX
ajst-29623	38	14	set	set	VERB
ajst-29623	38	15	to	to	ADP
ajst-29623	38	16	[	[	X
ajst-29623	38	17	3	3	NUM
ajst-29623	38	18	,	,	PUNCT
ajst-29623	38	19	6	6	NUM
ajst-29623	38	20	,	,	PUNCT
ajst-29623	38	21	6	6	NUM
ajst-29623	38	22	,	,	PUNCT
ajst-29623	38	23	3	3	NUM
ajst-29623	38	24	]	]	PUNCT
ajst-29623	38	25	.	.	PUNCT
ajst-29623	39	1	the	the	DET
ajst-29623	39	2	width	width	ADJ
ajst-29623	39	3	and	and	CCONJ
ajst-29623	39	4	depth	depth	NOUN
ajst-29623	39	5	of	of	ADP
ajst-29623	39	6	the	the	DET
ajst-29623	39	7	basic	basic	ADJ
ajst-29623	39	8	neck	neck	NOUN
ajst-29623	39	9	network	network	NOUN
ajst-29623	39	10	are	be	AUX
ajst-29623	39	11	set	set	VERB
ajst-29623	39	12	to	to	ADP
ajst-29623	39	13	[	[	X
ajst-29623	39	14	192	192	NUM
ajst-29623	39	15	,	,	PUNCT
ajst-29623	39	16	384	384	NUM
ajst-29623	39	17	,	,	PUNCT
ajst-29623	39	18	768	768	NUM
ajst-29623	39	19	]	]	PUNCT
ajst-29623	39	20	and	and	CCONJ
ajst-29623	39	21	3	3	NUM
ajst-29623	39	22	,	,	PUNCT
ajst-29623	39	23	respectively	respectively	ADV
ajst-29623	39	24	.	.	PUNCT
ajst-29623	40	1	296	296	NUM
ajst-29623	40	2	table	table	NOUN
ajst-29623	40	3	1	1	NUM
ajst-29623	40	4	shows	show	VERB
ajst-29623	40	5	the	the	DET
ajst-29623	40	6	specifications	specification	NOUN
ajst-29623	40	7	of	of	ADP
ajst-29623	40	8	the	the	DET
ajst-29623	40	9	width	width	ADJ
ajst-29623	40	10	multiplier	multipli	ADJ
ajst-29623	40	11	α	α	NOUN
ajst-29623	40	12	and	and	CCONJ
ajst-29623	40	13	depth	depth	NOUN
ajst-29623	40	14	multiplier	multipli	ADJ
ajst-29623	40	15	β	β	NOUN
ajst-29623	40	16	of	of	ADP
ajst-29623	40	17	different	different	ADJ
ajst-29623	40	18	models	model	NOUN
ajst-29623	40	19	.	.	PUNCT
ajst-29623	41	1	table	table	NOUN
ajst-29623	41	2	1	1	NUM
ajst-29623	41	3	.	.	PUNCT
ajst-29623	41	4	width	width	VERB
ajst-29623	41	5	multiplier	multipli	ADJ
ajst-29623	41	6	𝛼	𝛼	NOUN
ajst-29623	41	7	and	and	CCONJ
ajst-29623	41	8	depth	depth	NOUN
ajst-29623	41	9	multiplier	multipli	ADJ
ajst-29623	41	10	𝛽	𝛽	NOUN
ajst-29623	41	11	specification	specification	NOUN
ajst-29623	41	12	for	for	ADP
ajst-29623	41	13	a	a	DET
ajst-29623	41	14	series	series	NOUN
ajst-29623	41	15	of	of	ADP
ajst-29623	41	16	networks	network	NOUN
ajst-29623	41	17	width	width	VERB
ajst-29623	41	18	multiplier	multipli	ADJ
ajst-29623	42	1	𝛼	𝛼	NOUN
ajst-29623	42	2	depth	depth	NOUN
ajst-29623	42	3	multiplier	multipli	ADJ
ajst-29623	42	4	𝛽	𝛽	NOUN
ajst-29623	42	5	s	s	PROPN
ajst-29623	42	6	0.50	0.50	NUM
ajst-29623	42	7	0.33	0.33	NUM
ajst-29623	42	8	m	m	NUM
ajst-29623	42	9	0.75	0.75	NUM
ajst-29623	42	10	0.67	0.67	NUM
ajst-29623	42	11	l	l	NOUN
ajst-29623	42	12	1.00	1.00	NUM
ajst-29623	42	13	1.00	1.00	NUM
ajst-29623	42	14	x	x	SYM
ajst-29623	42	15	1.25	1.25	NUM
ajst-29623	42	16	1.33	1.33	NUM
ajst-29623	42	17	2.2.1	2.2.1	NUM
ajst-29623	42	18	.	.	PUNCT
ajst-29623	42	19	task	task	NOUN
ajst-29623	42	20	alignment	alignment	NOUN
ajst-29623	42	21	learning	learning	NOUN
ajst-29623	42	22	(	(	PUNCT
ajst-29623	42	23	tal	tal	X
ajst-29623	42	24	)	)	PUNCT
ajst-29623	42	25	to	to	PART
ajst-29623	42	26	further	far	ADV
ajst-29623	42	27	improve	improve	VERB
ajst-29623	42	28	the	the	DET
ajst-29623	42	29	accuracy	accuracy	NOUN
ajst-29623	42	30	,	,	PUNCT
ajst-29623	42	31	it	it	PRON
ajst-29623	42	32	consists	consist	VERB
ajst-29623	42	33	of	of	ADP
ajst-29623	42	34	dynamic	dynamic	ADJ
ajst-29623	42	35	label	label	NOUN
ajst-29623	42	36	assignment	assignment	NOUN
ajst-29623	42	37	and	and	CCONJ
ajst-29623	42	38	task	task	NOUN
ajst-29623	42	39	alignment	alignment	NOUN
ajst-29623	42	40	loss	loss	NOUN
ajst-29623	42	41	.	.	PUNCT
ajst-29623	43	1	dynamic	dynamic	ADJ
ajst-29623	43	2	label	label	NOUN
ajst-29623	43	3	assignment	assignment	NOUN
ajst-29623	43	4	means	mean	VERB
ajst-29623	43	5	prediction	prediction	NOUN
ajst-29623	43	6	/	/	SYM
ajst-29623	43	7	loss	loss	NOUN
ajst-29623	43	8	perception	perception	NOUN
ajst-29623	43	9	.	.	PUNCT
ajst-29623	44	1	according	accord	VERB
ajst-29623	44	2	to	to	ADP
ajst-29623	44	3	the	the	DET
ajst-29623	44	4	prediction	prediction	NOUN
ajst-29623	44	5	,	,	PUNCT
ajst-29623	44	6	it	it	PRON
ajst-29623	44	7	assigns	assign	VERB
ajst-29623	44	8	a	a	DET
ajst-29623	44	9	dynamic	dynamic	ADJ
ajst-29623	44	10	number	number	NOUN
ajst-29623	44	11	of	of	ADP
ajst-29623	44	12	positive	positive	ADJ
ajst-29623	44	13	anchors	anchor	NOUN
ajst-29623	44	14	to	to	ADP
ajst-29623	44	15	each	each	DET
ajst-29623	44	16	real	real	ADJ
ajst-29623	44	17	world	world	NOUN
ajst-29623	44	18	target[8	target[8	NUM
ajst-29623	44	19	]	]	PUNCT
ajst-29623	44	20	.	.	PUNCT
ajst-29623	45	1	by	by	ADP
ajst-29623	45	2	clearly	clearly	ADV
ajst-29623	45	3	aligning	align	VERB
ajst-29623	45	4	these	these	DET
ajst-29623	45	5	two	two	NUM
ajst-29623	45	6	tasks	task	NOUN
ajst-29623	45	7	,	,	PUNCT
ajst-29623	45	8	tal	tal	PROPN
ajst-29623	45	9	can	can	AUX
ajst-29623	45	10	obtain	obtain	VERB
ajst-29623	45	11	the	the	DET
ajst-29623	45	12	highest	high	ADJ
ajst-29623	45	13	classification	classification	NOUN
ajst-29623	45	14	score	score	NOUN
ajst-29623	45	15	and	and	CCONJ
ajst-29623	45	16	the	the	DET
ajst-29623	45	17	most	most	ADV
ajst-29623	45	18	accurate	accurate	ADJ
ajst-29623	45	19	bounding	bounding	NOUN
ajst-29623	45	20	box	box	NOUN
ajst-29623	45	21	at	at	ADP
ajst-29623	45	22	the	the	DET
ajst-29623	45	23	same	same	ADJ
ajst-29623	45	24	time	time	NOUN
ajst-29623	45	25	.	.	PUNCT
ajst-29623	46	1	for	for	ADP
ajst-29623	46	2	the	the	DET
ajst-29623	46	3	task	task	NOUN
ajst-29623	46	4	alignment	alignment	NOUN
ajst-29623	46	5	loss	loss	NOUN
ajst-29623	46	6	,	,	PUNCT
ajst-29623	46	7	the	the	DET
ajst-29623	46	8	normalized	normalize	VERB
ajst-29623	46	9	t	t	PROPN
ajst-29623	46	10	,	,	PUNCT
ajst-29623	46	11	�	�	PROPN
ajst-29623	46	12	̂	̂	VERB
ajst-29623	46	13	�	�	NOUN
ajst-29623	46	14	is	be	AUX
ajst-29623	46	15	used	use	VERB
ajst-29623	46	16	to	to	PART
ajst-29623	46	17	replace	replace	VERB
ajst-29623	46	18	the	the	DET
ajst-29623	46	19	target	target	NOUN
ajst-29623	46	20	in	in	ADP
ajst-29623	46	21	the	the	DET
ajst-29623	46	22	loss	loss	NOUN
ajst-29623	46	23	.	.	PUNCT
ajst-29623	47	1	the	the	DET
ajst-29623	47	2	maximum	maximum	ADJ
ajst-29623	47	3	iou	iou	NOUN
ajst-29623	47	4	is	be	AUX
ajst-29623	47	5	used	use	VERB
ajst-29623	47	6	for	for	ADP
ajst-29623	47	7	normalization	normalization	NOUN
ajst-29623	47	8	.	.	PUNCT
ajst-29623	48	1	the	the	DET
ajst-29623	48	2	binary	binary	PROPN
ajst-29623	48	3	cross	cross	PROPN
ajst-29623	48	4	entropy	entropy	PROPN
ajst-29623	48	5	(	(	PUNCT
ajst-29623	48	6	bce	bce	PROPN
ajst-29623	48	7	)	)	PUNCT
ajst-29623	48	8	for	for	ADP
ajst-29623	48	9	classification	classification	NOUN
ajst-29623	48	10	is	be	AUX
ajst-29623	48	11	:	:	PUNCT
ajst-29623	48	12	𝐿	𝐿	PROPN
ajst-29623	48	13	∑	∑	PROPN
ajst-29623	48	14	𝐵𝐶𝐸	𝐵𝐶𝐸	PROPN
ajst-29623	48	15	𝑃	𝑃	NOUN
ajst-29623	48	16	,	,	PUNCT
ajst-29623	48	17	�	�	PROPN
ajst-29623	48	18	̂	̂	SYM
ajst-29623	48	19	�	�	PROPN
ajst-29623	48	20	(	(	PUNCT
ajst-29623	48	21	1	1	NUM
ajst-29623	48	22	)	)	PUNCT
ajst-29623	48	23	2.2.2	2.2.2	NUM
ajst-29623	48	24	.	.	PUNCT
ajst-29623	49	1	efficient	efficient	ADJ
ajst-29623	49	2	task	task	PROPN
ajst-29623	49	3	aligned	align	VERB
ajst-29623	49	4	head	head	NOUN
ajst-29623	49	5	(	(	PUNCT
ajst-29623	49	6	et	et	NOUN
ajst-29623	49	7	head	head	NOUN
ajst-29623	49	8	)	)	PUNCT
ajst-29623	49	9	in	in	ADP
ajst-29623	49	10	object	object	NOUN
ajst-29623	49	11	detection	detection	NOUN
ajst-29623	49	12	,	,	PUNCT
ajst-29623	49	13	the	the	DET
ajst-29623	49	14	et	et	NOUN
ajst-29623	49	15	head	head	NOUN
ajst-29623	49	16	is	be	AUX
ajst-29623	49	17	proposed	propose	VERB
ajst-29623	49	18	.	.	PUNCT
ajst-29623	50	1	it	it	PRON
ajst-29623	50	2	uses	use	VERB
ajst-29623	50	3	ese	ese	NOUN
ajst-29623	50	4	to	to	PART
ajst-29623	50	5	replace	replace	VERB
ajst-29623	50	6	the	the	DET
ajst-29623	50	7	layer	layer	NOUN
ajst-29623	50	8	attention	attention	NOUN
ajst-29623	50	9	in	in	ADP
ajst-29623	50	10	tood	tood	PROPN
ajst-29623	50	11	,	,	PUNCT
ajst-29623	50	12	simplifies	simplify	VERB
ajst-29623	50	13	the	the	DET
ajst-29623	50	14	alignment	alignment	NOUN
ajst-29623	50	15	of	of	ADP
ajst-29623	50	16	the	the	DET
ajst-29623	50	17	classification	classification	NOUN
ajst-29623	50	18	branch	branch	NOUN
ajst-29623	50	19	to	to	ADP
ajst-29623	50	20	a	a	DET
ajst-29623	50	21	shortcut	shortcut	NOUN
ajst-29623	50	22	,	,	PUNCT
ajst-29623	50	23	and	and	CCONJ
ajst-29623	50	24	replaces	replace	VERB
ajst-29623	50	25	the	the	DET
ajst-29623	50	26	alignment	alignment	NOUN
ajst-29623	50	27	of	of	ADP
ajst-29623	50	28	the	the	DET
ajst-29623	50	29	regression	regression	NOUN
ajst-29623	50	30	branch	branch	NOUN
ajst-29623	50	31	with	with	ADP
ajst-29623	50	32	the	the	DET
ajst-29623	50	33	distribution	distribution	NOUN
ajst-29623	50	34	focal	focal	ADJ
ajst-29623	50	35	loss	loss	NOUN
ajst-29623	50	36	(	(	PUNCT
ajst-29623	50	37	dfl	dfl	PROPN
ajst-29623	50	38	)	)	PUNCT
ajst-29623	50	39	layer[9	layer[9	PROPN
ajst-29623	50	40	]	]	PUNCT
ajst-29623	50	41	.	.	PUNCT
ajst-29623	51	1	different	different	ADJ
ajst-29623	51	2	label	label	NOUN
ajst-29623	51	3	assignments	assignment	NOUN
ajst-29623	51	4	on	on	ADP
ajst-29623	51	5	the	the	DET
ajst-29623	51	6	basic	basic	ADJ
ajst-29623	51	7	model	model	NOUN
ajst-29623	51	8	.	.	PUNCT
ajst-29623	52	1	use	use	VERB
ajst-29623	52	2	csprep	csprep	NOUN
ajst-29623	52	3	resstage	resstage	NOUN
ajst-29623	52	4	as	as	ADP
ajst-29623	52	5	the	the	DET
ajst-29623	52	6	backbone	backbone	NOUN
ajst-29623	52	7	network	network	NOUN
ajst-29623	52	8	and	and	CCONJ
ajst-29623	52	9	neck	neck	NOUN
ajst-29623	52	10	,	,	PUNCT
ajst-29623	52	11	adopt	adopt	VERB
ajst-29623	52	12	a	a	DET
ajst-29623	52	13	1×1	1×1	ADJ
ajst-29623	52	14	convolutional	convolutional	ADJ
ajst-29623	52	15	layer	layer	NOUN
ajst-29623	52	16	as	as	ADP
ajst-29623	52	17	the	the	DET
ajst-29623	52	18	head	head	NOUN
ajst-29623	52	19	,	,	PUNCT
ajst-29623	52	20	and	and	CCONJ
ajst-29623	52	21	only	only	ADV
ajst-29623	52	22	train	train	VERB
ajst-29623	52	23	for	for	ADP
ajst-29623	52	24	36	36	NUM
ajst-29623	52	25	epochs	epoch	NOUN
ajst-29623	52	26	on	on	ADP
ajst-29623	52	27	coco	coco	PROPN
ajst-29623	52	28	train2017	train2017	PROPN
ajst-29623	52	29	.	.	PUNCT
ajst-29623	53	1	in	in	ADP
ajst-29623	53	2	the	the	DET
ajst-29623	53	3	learning	learning	NOUN
ajst-29623	53	4	of	of	ADP
ajst-29623	53	5	classification	classification	NOUN
ajst-29623	53	6	and	and	CCONJ
ajst-29623	53	7	localization	localization	NOUN
ajst-29623	53	8	tasks	task	NOUN
ajst-29623	53	9	,	,	PUNCT
ajst-29623	53	10	variational	variational	ADJ
ajst-29623	53	11	focal	focal	ADJ
ajst-29623	53	12	loss	loss	NOUN
ajst-29623	53	13	(	(	PUNCT
ajst-29623	53	14	vfl	vfl	NOUN
ajst-29623	53	15	)	)	PUNCT
ajst-29623	53	16	and	and	CCONJ
ajst-29623	53	17	distribution	distribution	NOUN
ajst-29623	53	18	focal	focal	ADJ
ajst-29623	53	19	l	l	PROPN
ajst-29623	53	20	oss	oss	NOUN
ajst-29623	53	21	(	(	PUNCT
ajst-29623	53	22	dfl	dfl	PROPN
ajst-29623	53	23	)	)	PUNCT
ajst-29623	53	24	are	be	AUX
ajst-29623	53	25	selected	select	VERB
ajst-29623	53	26	respectively[10	respectively[10	ADV
ajst-29623	53	27	]	]	PUNCT
ajst-29623	53	28	.	.	PUNCT
ajst-29623	54	1	applying	apply	VERB
ajst-29623	54	2	vfl	vfl	PROPN
ajst-29623	54	3	and	and	CCONJ
ajst-29623	54	4	dfl	dfl	PROPN
ajst-29623	54	5	to	to	ADP
ajst-29623	54	6	the	the	DET
ajst-29623	54	7	object	object	NOUN
ajst-29623	54	8	detector	detector	NOUN
ajst-29623	54	9	has	have	AUX
ajst-29623	54	10	achieved	achieve	VERB
ajst-29623	54	11	performan	performan	NOUN
ajst-29623	54	12	ce	ce	PROPN
ajst-29623	54	13	improvements	improvement	NOUN
ajst-29623	54	14	.	.	PUNCT
ajst-29623	55	1	for	for	ADP
ajst-29623	55	2	vfl	vfl	PROPN
ajst-29623	55	3	,	,	PUNCT
ajst-29623	55	4	different	different	ADJ
ajst-29623	55	5	from	from	ADP
ajst-29623	55	6	the	the	DET
ajst-29623	55	7	quality	quality	NOUN
ajst-29623	55	8	focal	focal	ADJ
ajst-29623	55	9	loss	loss	NOUN
ajst-29623	55	10	(	(	PUNCT
ajst-29623	55	11	qfl	qfl	NOUN
ajst-29623	55	12	)	)	PUNCT
ajst-29623	55	13	in	in	ADP
ajst-29623	55	14	[	[	X
ajst-29623	55	15	reference	reference	NOUN
ajst-29623	55	16	]	]	PUNCT
ajst-29623	55	17	,	,	PUNCT
ajst-29623	55	18	vfl	vfl	PROPN
ajst-29623	55	19	uses	use	VERB
ajst-29623	55	20	the	the	DET
ajst-29623	55	21	target	target	NOUN
ajst-29623	55	22	s	s	PART
ajst-29623	55	23	core	core	NOUN
ajst-29623	55	24	to	to	PART
ajst-29623	55	25	weight	weight	VERB
ajst-29623	55	26	the	the	DET
ajst-29623	55	27	loss	loss	NOUN
ajst-29623	55	28	of	of	ADP
ajst-29623	55	29	positive	positive	ADJ
ajst-29623	55	30	samples	sample	NOUN
ajst-29623	55	31	,	,	PUNCT
ajst-29623	55	32	making	make	VERB
ajst-29623	55	33	the	the	DET
ajst-29623	55	34	loss	loss	NOUN
ajst-29623	55	35	of	of	ADP
ajst-29623	55	36	positive	positive	ADJ
ajst-29623	55	37	samples	sample	NOUN
ajst-29623	55	38	with	with	ADP
ajst-29623	55	39	high	high	ADJ
ajst-29623	55	40	iou	iou	NOUN
ajst-29623	55	41	relatively	relatively	ADV
ajst-29623	55	42	large	large	ADJ
ajst-29623	55	43	.	.	PUNCT
ajst-29623	56	1	both	both	PRON
ajst-29623	56	2	use	use	VERB
ajst-29623	56	3	the	the	DET
ajst-29623	56	4	iou	iou	NOUN
ajst-29623	56	5	aware	aware	ADJ
ajst-29623	56	6	classification	classification	NOUN
ajst-29623	56	7	score	score	NOUN
ajst-29623	56	8	(	(	PUNCT
ajst-29623	56	9	iacs	iac	NOUN
ajst-29623	56	10	)	)	PUNCT
ajst-29623	56	11	as	as	ADP
ajst-29623	56	12	the	the	DET
ajst-29623	56	13	target	target	NOUN
ajst-29623	56	14	for	for	ADP
ajst-29623	56	15	prediction	prediction	NOUN
ajst-29623	56	16	,	,	PUNCT
ajst-29623	56	17	effectively	effectively	ADV
ajst-29623	56	18	learning	learn	VERB
ajst-29623	56	19	the	the	DET
ajst-29623	56	20	joint	joint	ADJ
ajst-29623	56	21	r	r	NOUN
ajst-29623	56	22	epresentation	epresentation	NOUN
ajst-29623	56	23	of	of	ADP
ajst-29623	56	24	the	the	DET
ajst-29623	56	25	classification	classification	NOUN
ajst-29623	56	26	score	score	NOUN
ajst-29623	56	27	and	and	CCONJ
ajst-29623	56	28	the	the	DET
ajst-29623	56	29	localizat	localizat	NOUN
ajst-29623	56	30	ion	ion	NOUN
ajst-29623	56	31	quality	quality	NOUN
ajst-29623	56	32	estimate	estimate	NOUN
ajst-29623	56	33	,	,	PUNCT
ajst-29623	56	34	thus	thus	ADV
ajst-29623	56	35	achieving	achieve	VERB
ajst-29623	56	36	a	a	DET
ajst-29623	56	37	high	high	ADJ
ajst-29623	56	38	degree	degree	NOUN
ajst-29623	56	39	of	of	ADP
ajst-29623	56	40	co	co	NOUN
ajst-29623	56	41	nsistency	nsistency	NOUN
ajst-29623	56	42	between	between	ADP
ajst-29623	56	43	training	training	NOUN
ajst-29623	56	44	and	and	CCONJ
ajst-29623	56	45	inference	inference	NOUN
ajst-29623	56	46	.	.	PUNCT
ajst-29623	57	1	for	for	ADP
ajst-29623	57	2	dfl	dfl	PROPN
ajst-29623	57	3	,	,	PUNCT
ajst-29623	57	4	to	to	AUX
ajst-29623	57	5	s	s	PRON
ajst-29623	57	6	olve	olve	VERB
ajst-29623	57	7	the	the	DET
ajst-29623	57	8	problem	problem	NOUN
ajst-29623	57	9	of	of	ADP
ajst-29623	57	10	inflexible	inflexible	ADJ
ajst-29623	57	11	bounding	bounding	NOUN
ajst-29623	57	12	box	box	NOUN
ajst-29623	57	13	represent	represent	VERB
ajst-29623	57	14	ation	ation	PROPN
ajst-29623	57	15	,	,	PUNCT
ajst-29623	57	16	using	use	VERB
ajst-29623	57	17	a	a	DET
ajst-29623	57	18	general	general	ADJ
ajst-29623	57	19	distribution	distribution	NOUN
ajst-29623	57	20	to	to	PART
ajst-29623	57	21	predict	predict	VERB
ajst-29623	57	22	the	the	DET
ajst-29623	57	23	boundi	boundi	PROPN
ajst-29623	57	24	ng	ng	PROPN
ajst-29623	57	25	box	box	PROPN
ajst-29623	57	26	is	be	AUX
ajst-29623	57	27	proposed	propose	VERB
ajst-29623	57	28	.	.	PUNCT
ajst-29623	58	1	the	the	DET
ajst-29623	58	2	model	model	NOUN
ajst-29623	58	3	is	be	AUX
ajst-29623	58	4	supervised	supervise	VERB
ajst-29623	58	5	by	by	ADP
ajst-29623	58	6	the	the	DET
ajst-29623	58	7	fol	fol	X
ajst-29623	58	8	lowing	low	VERB
ajst-29623	58	9	loss	loss	NOUN
ajst-29623	58	10	function	function	NOUN
ajst-29623	58	11	:	:	PUNCT
ajst-29623	58	12	𝐿𝑜𝑠𝑠	𝐿𝑜𝑠𝑠	PROPN
ajst-29623	58	13	∗	∗	NOUN
ajst-29623	58	14	∗	∗	NOUN
ajst-29623	58	15	∗	∗	NOUN
ajst-29623	58	16	∑	∑	PUNCT
ajst-29623	58	17	(	(	PUNCT
ajst-29623	58	18	2	2	NUM
ajst-29623	58	19	)	)	PUNCT
ajst-29623	58	20	where	where	SCONJ
ajst-29623	58	21	�	�	PROPN
ajst-29623	58	22	̂	̂	VERB
ajst-29623	58	23	�	�	PROPN
ajst-29623	58	24	represents	represent	VERB
ajst-29623	58	25	the	the	DET
ajst-29623	58	26	normalized	normalized	ADJ
ajst-29623	58	27	target	target	NOUN
ajst-29623	58	28	score	score	NOUN
ajst-29623	58	29	.	.	PUNCT
ajst-29623	59	1	obtained	obtain	VERB
ajst-29623	59	2	.	.	PUNCT
ajst-29623	60	1	3	3	X
ajst-29623	60	2	.	.	X
ajst-29623	60	3	improved	improve	VERB
ajst-29623	60	4	pp	pp	ADP
ajst-29623	60	5	yoloe	yoloe	NOUN
ajst-29623	60	6	model	model	NOUN
ajst-29623	60	7	the	the	DET
ajst-29623	60	8	improved	improved	ADJ
ajst-29623	60	9	pp	pp	ADP
ajst-29623	60	10	yoloe	yoloe	NOUN
ajst-29623	60	11	consists	consist	VERB
ajst-29623	60	12	of	of	ADP
ajst-29623	60	13	an	an	DET
ajst-29623	60	14	expandable	expandable	ADJ
ajst-29623	60	15	backbone	backbone	NOUN
ajst-29623	60	16	and	and	CCONJ
ajst-29623	60	17	neck	neck	NOUN
ajst-29623	60	18	,	,	PUNCT
ajst-29623	60	19	task	task	NOUN
ajst-29623	60	20	alignment	alignment	NOUN
ajst-29623	60	21	learning	learning	NOUN
ajst-29623	60	22	,	,	PUNCT
ajst-29623	60	23	an	an	DET
ajst-29623	60	24	efficient	efficient	ADJ
ajst-29623	60	25	task	task	NOUN
ajst-29623	60	26	aligned	align	VERB
ajst-29623	60	27	head	head	NOUN
ajst-29623	60	28	with	with	ADP
ajst-29623	60	29	dfl	dfl	PROPN
ajst-29623	60	30	and	and	CCONJ
ajst-29623	60	31	vfl	vfl	PROPN
ajst-29623	60	32	,	,	PUNCT
ajst-29623	60	33	and	and	CCONJ
ajst-29623	60	34	the	the	DET
ajst-29623	60	35	silu	silu	ADJ
ajst-29623	60	36	activation	activation	NOUN
ajst-29623	60	37	function	function	NOUN
ajst-29623	60	38	.	.	PUNCT
ajst-29623	61	1	3.1	3.1	NUM
ajst-29623	61	2	.	.	PUNCT
ajst-29623	61	3	backbone	backbone	NOUN
ajst-29623	61	4	optimization	optimization	NOUN
ajst-29623	61	5	the	the	DET
ajst-29623	61	6	backbone	backbone	NOUN
ajst-29623	61	7	of	of	ADP
ajst-29623	61	8	pp	pp	ADJ
ajst-29623	61	9	yoloe	yoloe	NOUN
ajst-29623	61	10	improves	improve	VERB
ajst-29623	61	11	resnet	resnet	NOUN
ajst-29623	61	12	using	use	VERB
ajst-29623	61	13	the	the	DET
ajst-29623	61	14	repvgg	repvgg	NOUN
ajst-29623	61	15	module	module	NOUN
ajst-29623	61	16	and	and	CCONJ
ajst-29623	61	17	the	the	DET
ajst-29623	61	18	model	model	NOUN
ajst-29623	61	19	concept	concept	NOUN
ajst-29623	61	20	of	of	ADP
ajst-29623	61	21	csp	csp	PROPN
ajst-29623	61	22	.	.	PUNCT
ajst-29623	62	1	it	it	PRON
ajst-29623	62	2	also	also	ADV
ajst-29623	62	3	uses	use	VERB
ajst-29623	62	4	modules	module	NOUN
ajst-29623	62	5	such	such	ADJ
ajst-29623	62	6	as	as	ADP
ajst-29623	62	7	the	the	DET
ajst-29623	62	8	silu	silu	ADJ
ajst-29623	62	9	activation	activation	NOUN
ajst-29623	62	10	function	function	NOUN
ajst-29623	62	11	and	and	CCONJ
ajst-29623	62	12	effitive	effitive	ADJ
ajst-29623	62	13	se	se	PROPN
ajst-29623	62	14	attention	attention	NOUN
ajst-29623	62	15	.	.	PUNCT
ajst-29623	63	1	repvgg	repvgg	PROPN
ajst-29623	63	2	is	be	AUX
ajst-29623	63	3	an	an	DET
ajst-29623	63	4	improvement	improvement	NOUN
ajst-29623	63	5	based	base	VERB
ajst-29623	63	6	on	on	ADP
ajst-29623	63	7	vgg[11	vgg[11	NOUN
ajst-29623	63	8	]	]	PUNCT
ajst-29623	63	9	.	.	PUNCT
ajst-29623	64	1	an	an	DET
ajst-29623	64	2	identity	identity	NOUN
ajst-29623	64	3	and	and	CCONJ
ajst-29623	64	4	a	a	DET
ajst-29623	64	5	residual	residual	ADJ
ajst-29623	64	6	branch	branch	NOUN
ajst-29623	64	7	are	be	AUX
ajst-29623	64	8	added	add	VERB
ajst-29623	64	9	to	to	ADP
ajst-29623	64	10	the	the	DET
ajst-29623	64	11	block	block	NOUN
ajst-29623	64	12	of	of	ADP
ajst-29623	64	13	the	the	DET
ajst-29623	64	14	vgg	vgg	PROPN
ajst-29623	64	15	network	network	NOUN
ajst-29623	64	16	.	.	PUNCT
ajst-29623	65	1	in	in	ADP
ajst-29623	65	2	the	the	DET
ajst-29623	65	3	model	model	NOUN
ajst-29623	65	4	inference	inference	NOUN
ajst-29623	65	5	stage	stage	NOUN
ajst-29623	65	6	,	,	PUNCT
ajst-29623	65	7	all	all	DET
ajst-29623	65	8	network	network	NOUN
ajst-29623	65	9	layers	layer	NOUN
ajst-29623	65	10	are	be	AUX
ajst-29623	65	11	converted	convert	VERB
ajst-29623	65	12	into	into	ADP
ajst-29623	65	13	3×3	3×3	NUM
ajst-29623	65	14	convolutions	convolution	NOUN
ajst-29623	65	15	through	through	ADP
ajst-29623	65	16	the	the	DET
ajst-29623	65	17	op	op	NOUN
ajst-29623	65	18	fusion	fusion	NOUN
ajst-29623	65	19	strategy	strategy	NOUN
ajst-29623	65	20	,	,	PUNCT
ajst-29623	65	21	facilitating	facilitate	VERB
ajst-29623	65	22	network	network	NOUN
ajst-29623	65	23	deployment	deployment	NOUN
ajst-29623	65	24	and	and	CCONJ
ajst-29623	65	25	acceleration	acceleration	NOUN
ajst-29623	65	26	.	.	PUNCT
ajst-29623	66	1	the	the	DET
ajst-29623	66	2	convolution	convolution	NOUN
ajst-29623	66	3	layers	layer	NOUN
ajst-29623	66	4	and	and	CCONJ
ajst-29623	66	5	bn	bn	ADP
ajst-29623	66	6	layers	layer	NOUN
ajst-29623	66	7	in	in	ADP
ajst-29623	66	8	the	the	DET
ajst-29623	66	9	residual	residual	ADJ
ajst-29623	66	10	block	block	NOUN
ajst-29623	66	11	are	be	AUX
ajst-29623	66	12	fused	fuse	VERB
ajst-29623	66	13	through	through	ADP
ajst-29623	66	14	the	the	DET
ajst-29623	66	15	following	follow	VERB
ajst-29623	66	16	equations	equation	NOUN
ajst-29623	66	17	:	:	PUNCT
ajst-29623	66	18	𝑊	𝑊	PROPN
ajst-29623	66	19	𝑊	𝑊	PROPN
ajst-29623	66	20	(	(	PUNCT
ajst-29623	66	21	3	3	NUM
ajst-29623	66	22	)	)	PUNCT
ajst-29623	66	23	𝑏	𝑏	NOUN
ajst-29623	66	24	∗	∗	NOUN
ajst-29623	66	25	𝛽	𝛽	NOUN
ajst-29623	66	26	(	(	PUNCT
ajst-29623	66	27	4	4	NUM
ajst-29623	66	28	)	)	PUNCT
ajst-29623	66	29	𝑏	𝑏	PROPN
ajst-29623	66	30	𝑀	𝑀	PROPN
ajst-29623	66	31	∗	∗	NOUN
ajst-29623	66	32	𝑊	𝑊	PROPN
ajst-29623	66	33	,	,	PUNCT
ajst-29623	66	34	𝜇	𝜇	ADP
ajst-29623	66	35	,	,	PUNCT
ajst-29623	66	36	𝜎	𝜎	PROPN
ajst-29623	66	37	,	,	PUNCT
ajst-29623	66	38	𝛾	𝛾	PROPN
ajst-29623	66	39	,	,	PUNCT
ajst-29623	66	40	𝛽	𝛽	PROPN
ajst-29623	66	41	𝑀	𝑀	PROPN
ajst-29623	66	42	∗	∗	VERB
ajst-29623	66	43	𝑊	𝑊	PROPN
ajst-29623	66	44	𝑏	𝑏	NOUN
ajst-29623	66	45	(	(	PUNCT
ajst-29623	66	46	5	5	NUM
ajst-29623	66	47	)	)	PUNCT
ajst-29623	66	48	𝑊	𝑊	NOUN
ajst-29623	66	49	and	and	CCONJ
ajst-29623	66	50	𝑏	𝑏	PROPN
ajst-29623	66	51	represent	represent	VERB
ajst-29623	66	52	the	the	DET
ajst-29623	66	53	weights	weight	NOUN
ajst-29623	66	54	and	and	CCONJ
ajst-29623	66	55	biases	bias	NOUN
ajst-29623	66	56	of	of	ADP
ajst-29623	66	57	the	the	DET
ajst-29623	66	58	fused	fuse	VERB
ajst-29623	66	59	convolution	convolution	NOUN
ajst-29623	66	60	,	,	PUNCT
ajst-29623	66	61	respectively	respectively	ADV
ajst-29623	66	62	.	.	PUNCT
ajst-29623	67	1	convolutions	convolution	NOUN
ajst-29623	67	2	with	with	ADP
ajst-29623	67	3	different	different	ADJ
ajst-29623	67	4	convolution	convolution	NOUN
ajst-29623	67	5	kernels	kernel	NOUN
ajst-29623	67	6	are	be	AUX
ajst-29623	67	7	converted	convert	VERB
ajst-29623	67	8	into	into	ADP
ajst-29623	67	9	convolutions	convolution	NOUN
ajst-29623	67	10	with	with	ADP
ajst-29623	67	11	3×3	3×3	NUM
ajst-29623	67	12	sized	sized	ADJ
ajst-29623	67	13	convolution	convolution	NOUN
ajst-29623	67	14	kernels	kernel	NOUN
ajst-29623	67	15	(	(	PUNCT
ajst-29623	67	16	figure	figure	NOUN
ajst-29623	67	17	4	4	NUM
ajst-29623	67	18	)	)	PUNCT
ajst-29623	67	19	.	.	PUNCT
ajst-29623	68	1	since	since	SCONJ
ajst-29623	68	2	the	the	DET
ajst-29623	68	3	entire	entire	ADJ
ajst-29623	68	4	residual	residual	ADJ
ajst-29623	68	5	block	block	NOUN
ajst-29623	68	6	may	may	AUX
ajst-29623	68	7	contain	contain	VERB
ajst-29623	68	8	a	a	DET
ajst-29623	68	9	1×1	1×1	NUM
ajst-29623	68	10	convolution	convolution	NOUN
ajst-29623	68	11	branch	branch	NOUN
ajst-29623	68	12	and	and	CCONJ
ajst-29623	68	13	an	an	DET
ajst-29623	68	14	identity	identity	NOUN
ajst-29623	68	15	branch	branch	NOUN
ajst-29623	68	16	,	,	PUNCT
ajst-29623	68	17	for	for	ADP
ajst-29623	68	18	the	the	DET
ajst-29623	68	19	1×1	1×1	ADJ
ajst-29623	68	20	convolution	convolution	NOUN
ajst-29623	68	21	branch	branch	NOUN
ajst-29623	68	22	,	,	PUNCT
ajst-29623	68	23	the	the	DET
ajst-29623	68	24	entire	entire	ADJ
ajst-29623	68	25	conversion	conversion	NOUN
ajst-29623	68	26	process	process	NOUN
ajst-29623	68	27	is	be	AUX
ajst-29623	68	28	to	to	PART
ajst-29623	68	29	replace	replace	VERB
ajst-29623	68	30	the	the	DET
ajst-29623	68	31	1×1	1×1	NUM
ajst-29623	68	32	convolution	convolution	NOUN
ajst-29623	68	33	kernel	kernel	NOUN
ajst-29623	68	34	with	with	ADP
ajst-29623	68	35	a	a	DET
ajst-29623	68	36	3×3	3×3	NUM
ajst-29623	68	37	convolution	convolution	NOUN
ajst-29623	68	38	kernel	kernel	NOUN
ajst-29623	68	39	and	and	CCONJ
ajst-29623	68	40	merge	merge	VERB
ajst-29623	68	41	the	the	DET
ajst-29623	68	42	3×3	3×3	NUM
ajst-29623	68	43	convolutions	convolution	NOUN
ajst-29623	68	44	in	in	ADP
ajst-29623	68	45	the	the	DET
ajst-29623	68	46	residual	residual	ADJ
ajst-29623	68	47	branch	branch	NOUN
ajst-29623	68	48	.	.	PUNCT
ajst-29623	69	1	the	the	DET
ajst-29623	69	2	weights	weight	NOUN
ajst-29623	69	3	and	and	CCONJ
ajst-29623	69	4	biases	bias	NOUN
ajst-29623	69	5	of	of	ADP
ajst-29623	69	6	all	all	DET
ajst-29623	69	7	branches	branch	NOUN
ajst-29623	69	8	are	be	AUX
ajst-29623	69	9	superimposed	superimpose	VERB
ajst-29623	69	10	to	to	PART
ajst-29623	69	11	obtain	obtain	VERB
ajst-29623	69	12	a	a	DET
ajst-29623	69	13	fused	fuse	VERB
ajst-29623	69	14	3×3	3×3	NUM
ajst-29623	69	15	convolutional	convolutional	ADJ
ajst-29623	69	16	layer	layer	NOUN
ajst-29623	69	17	.	.	PUNCT
ajst-29623	70	1	a	a	DET
ajst-29623	70	2	3×3	3×3	NUM
ajst-29623	70	3	convolution	convolution	NOUN
ajst-29623	70	4	kernel	kernel	NOUN
ajst-29623	70	5	is	be	AUX
ajst-29623	70	6	set	set	VERB
ajst-29623	70	7	,	,	PUNCT
ajst-29623	70	8	and	and	CCONJ
ajst-29623	70	9	the	the	DET
ajst-29623	70	10	weight	weight	NOUN
ajst-29623	70	11	values	value	NOUN
ajst-29623	70	12	at	at	ADP
ajst-29623	70	13	all	all	DET
ajst-29623	70	14	9	9	NUM
ajst-29623	70	15	positions	position	NOUN
ajst-29623	70	16	are	be	AUX
ajst-29623	70	17	set	set	VERB
ajst-29623	70	18	to	to	ADP
ajst-29623	70	19	1	1	NUM
ajst-29623	70	20	.	.	PUNCT
ajst-29623	71	1	after	after	ADP
ajst-29623	71	2	multiplying	multiply	VERB
ajst-29623	71	3	it	it	PRON
ajst-29623	71	4	with	with	ADP
ajst-29623	71	5	the	the	DET
ajst-29623	71	6	input	input	NOUN
ajst-29623	71	7	feature	feature	NOUN
ajst-29623	71	8	map	map	NOUN
ajst-29623	71	9	,	,	PUNCT
ajst-29623	71	10	the	the	DET
ajst-29623	71	11	original	original	ADJ
ajst-29623	71	12	values	value	NOUN
ajst-29623	71	13	are	be	AUX
ajst-29623	71	14	maintained	maintain	VERB
ajst-29623	71	15	.	.	PUNCT
ajst-29623	72	1	swish	swish	ADJ
ajst-29623	72	2	activation	activation	NOUN
ajst-29623	72	3	function	function	NOUN
ajst-29623	72	4	:	:	PUNCT
ajst-29623	72	5	swish	swish	ADJ
ajst-29623	72	6	contains	contain	VERB
ajst-29623	72	7	silu	silu	NOUN
ajst-29623	72	8	:	:	PUNCT
ajst-29623	72	9	𝑆𝑖𝐿𝑈	𝑆𝑖𝐿𝑈	VERB
ajst-29623	72	10	𝑥	𝑥	PART
ajst-29623	72	11	𝑥	𝑥	NOUN
ajst-29623	72	12	∗	∗	VERB
ajst-29623	72	13	𝑆𝑖𝑔𝑚𝑜𝑖𝑑	𝑆𝑖𝑔𝑚𝑜𝑖𝑑	PROPN
ajst-29623	72	14	𝑥	𝑥	PROPN
ajst-29623	72	15	(	(	PUNCT
ajst-29623	72	16	6	6	NUM
ajst-29623	72	17	)	)	PUNCT
ajst-29623	72	18	𝑆𝑤𝑖𝑠ℎ	𝑆𝑤𝑖𝑠ℎ	PROPN
ajst-29623	72	19	𝑥	𝑥	PRON
ajst-29623	72	20	𝑥	𝑥	NOUN
ajst-29623	72	21	∗	∗	VERB
ajst-29623	72	22	𝑆𝑖𝑔𝑚𝑜𝑖𝑑	𝑆𝑖𝑔𝑚𝑜𝑖𝑑	PROPN
ajst-29623	72	23	𝛽𝑥	𝛽𝑥	PROPN
ajst-29623	72	24	(	(	PUNCT
ajst-29623	72	25	7	7	NUM
ajst-29623	72	26	)	)	PUNCT
ajst-29623	72	27	𝛽	𝛽	NOUN
ajst-29623	72	28	is	be	AUX
ajst-29623	72	29	a	a	DET
ajst-29623	72	30	training	training	NOUN
ajst-29623	72	31	parameter	parameter	NOUN
ajst-29623	72	32	.	.	PUNCT
ajst-29623	73	1	figure	figure	NOUN
ajst-29623	73	2	4	4	NUM
ajst-29623	73	3	.	.	PUNCT
ajst-29623	74	1	the	the	DET
ajst-29623	74	2	structure	structure	NOUN
ajst-29623	74	3	of	of	ADP
ajst-29623	74	4	cbs	cbs	PROPN
ajst-29623	74	5	3.2	3.2	NUM
ajst-29623	74	6	.	.	PUNCT
ajst-29623	75	1	strengthening	strengthen	VERB
ajst-29623	75	2	the	the	DET
ajst-29623	75	3	cspnet	cspnet	ADJ
ajst-29623	75	4	structure	structure	NOUN
ajst-29623	75	5	design	design	VERB
ajst-29623	75	6	a	a	DET
ajst-29623	75	7	partial	partial	ADJ
ajst-29623	75	8	dense	dense	ADJ
ajst-29623	75	9	block	block	NOUN
ajst-29623	75	10	.	.	PUNCT
ajst-29623	76	1	through	through	ADP
ajst-29623	76	2	the	the	DET
ajst-29623	76	3	split	split	NOUN
ajst-29623	76	4	and	and	CCONJ
ajst-29623	76	5	merge	merge	VERB
ajst-29623	76	6	strategy	strategy	NOUN
ajst-29623	76	7	,	,	PUNCT
ajst-29623	76	8	the	the	DET
ajst-29623	76	9	number	number	NOUN
ajst-29623	76	10	of	of	ADP
ajst-29623	76	11	gradient	gradient	ADJ
ajst-29623	76	12	paths	path	NOUN
ajst-29623	76	13	is	be	AUX
ajst-29623	76	14	doubled	double	VERB
ajst-29623	76	15	.	.	PUNCT
ajst-29623	77	1	a	a	DET
ajst-29623	77	2	297	297	NUM
ajst-29623	77	3	cross	cross	NOUN
ajst-29623	77	4	stage	stage	NOUN
ajst-29623	77	5	strategy	strategy	NOUN
ajst-29623	77	6	is	be	AUX
ajst-29623	77	7	adopted	adopt	VERB
ajst-29623	77	8	to	to	PART
ajst-29623	77	9	reduce	reduce	VERB
ajst-29623	77	10	the	the	DET
ajst-29623	77	11	disadvantages	disadvantage	NOUN
ajst-29623	77	12	of	of	ADP
ajst-29623	77	13	using	use	VERB
ajst-29623	77	14	explicit	explicit	ADJ
ajst-29623	77	15	feature	feature	NOUN
ajst-29623	77	16	map	map	NOUN
ajst-29623	77	17	replication	replication	NOUN
ajst-29623	77	18	for	for	ADP
ajst-29623	77	19	connection	connection	NOUN
ajst-29623	77	20	.	.	PUNCT
ajst-29623	78	1	the	the	DET
ajst-29623	78	2	number	number	NOUN
ajst-29623	78	3	of	of	ADP
ajst-29623	78	4	channels	channel	NOUN
ajst-29623	78	5	at	at	ADP
ajst-29623	78	6	the	the	DET
ajst-29623	78	7	bottom	bottom	ADJ
ajst-29623	78	8	layer	layer	NOUN
ajst-29623	78	9	of	of	ADP
ajst-29623	78	10	densenet	densenet	NOUN
ajst-29623	78	11	is	be	AUX
ajst-29623	78	12	much	much	ADV
ajst-29623	78	13	larger	large	ADJ
ajst-29623	78	14	than	than	ADP
ajst-29623	78	15	the	the	DET
ajst-29623	78	16	growth	growth	NOUN
ajst-29623	78	17	rate	rate	NOUN
ajst-29623	78	18	.	.	PUNCT
ajst-29623	79	1	since	since	SCONJ
ajst-29623	79	2	the	the	DET
ajst-29623	79	3	bottom	bottom	ADJ
ajst-29623	79	4	layer	layer	NOUN
ajst-29623	79	5	channels	channel	NOUN
ajst-29623	79	6	involved	involve	VERB
ajst-29623	79	7	in	in	ADP
ajst-29623	79	8	the	the	DET
ajst-29623	79	9	dense	dense	ADJ
ajst-29623	79	10	layer	layer	NOUN
ajst-29623	79	11	operation	operation	NOUN
ajst-29623	79	12	in	in	ADP
ajst-29623	79	13	the	the	DET
ajst-29623	79	14	partial	partial	ADJ
ajst-29623	79	15	dense	dense	ADJ
ajst-29623	79	16	block	block	NOUN
ajst-29623	79	17	only	only	ADV
ajst-29623	79	18	account	account	VERB
ajst-29623	79	19	for	for	ADP
ajst-29623	79	20	half	half	NOUN
ajst-29623	79	21	of	of	ADP
ajst-29623	79	22	the	the	DET
ajst-29623	79	23	original	original	ADJ
ajst-29623	79	24	channels	channel	NOUN
ajst-29623	79	25	,	,	PUNCT
ajst-29623	79	26	it	it	PRON
ajst-29623	79	27	can	can	AUX
ajst-29623	79	28	effectively	effectively	ADV
ajst-29623	79	29	solve	solve	VERB
ajst-29623	79	30	nearly	nearly	ADV
ajst-29623	79	31	half	half	NOUN
ajst-29623	79	32	of	of	ADP
ajst-29623	79	33	the	the	DET
ajst-29623	79	34	computational	computational	ADJ
ajst-29623	79	35	bottleneck	bottleneck	NOUN
ajst-29623	79	36	(	(	PUNCT
ajst-29623	79	37	figure	figure	NOUN
ajst-29623	79	38	5	5	NUM
ajst-29623	79	39	)	)	PUNCT
ajst-29623	79	40	.	.	PUNCT
ajst-29623	80	1	assume	assume	VERB
ajst-29623	80	2	that	that	SCONJ
ajst-29623	80	3	the	the	DET
ajst-29623	80	4	basic	basic	ADJ
ajst-29623	80	5	feature	feature	NOUN
ajst-29623	80	6	map	map	NOUN
ajst-29623	80	7	size	size	NOUN
ajst-29623	80	8	of	of	ADP
ajst-29623	80	9	the	the	DET
ajst-29623	80	10	dense	dense	ADJ
ajst-29623	80	11	block	block	NOUN
ajst-29623	80	12	in	in	ADP
ajst-29623	80	13	densenet	densenet	NOUN
ajst-29623	80	14	is	be	AUX
ajst-29623	80	15	𝑤	𝑤	ADP
ajst-29623	80	16	∗	∗	NOUN
ajst-29623	80	17	ℎ	ℎ	ADP
ajst-29623	80	18	∗	∗	PROPN
ajst-29623	80	19	𝑐	𝑐	PROPN
ajst-29623	80	20	,	,	PUNCT
ajst-29623	80	21	the	the	DET
ajst-29623	80	22	growth	growth	NOUN
ajst-29623	80	23	rate	rate	NOUN
ajst-29623	80	24	is	be	AUX
ajst-29623	80	25	𝑑	𝑑	NOUN
ajst-29623	80	26	,	,	PUNCT
ajst-29623	80	27	and	and	CCONJ
ajst-29623	80	28	there	there	PRON
ajst-29623	80	29	are	be	VERB
ajst-29623	80	30	layers	layer	NOUN
ajst-29623	80	31	in	in	ADP
ajst-29623	80	32	total	total	NOUN
ajst-29623	80	33	.	.	PUNCT
ajst-29623	81	1	then	then	ADV
ajst-29623	81	2	,	,	PUNCT
ajst-29623	81	3	the	the	DET
ajst-29623	81	4	cio	cio	NOUN
ajst-29623	81	5	of	of	ADP
ajst-29623	81	6	this	this	DET
ajst-29623	81	7	dense	dense	ADJ
ajst-29623	81	8	block	block	NOUN
ajst-29623	81	9	is	be	AUX
ajst-29623	81	10	𝑐	𝑐	NOUN
ajst-29623	81	11	∗	∗	NOUN
ajst-29623	81	12	𝑚	𝑚	X
ajst-29623	81	13	𝑚	𝑚	NOUN
ajst-29623	81	14	𝑚	𝑚	NOUN
ajst-29623	81	15	∗	∗	NOUN
ajst-29623	81	16	𝑑	𝑑	PROPN
ajst-29623	81	17	2	2	NUM
ajst-29623	81	18	.	.	PUNCT
ajst-29623	82	1	𝑚	𝑚	NOUN
ajst-29623	82	2	and	and	CCONJ
ajst-29623	82	3	𝑑	𝑑	PRON
ajst-29623	82	4	are	be	AUX
ajst-29623	82	5	usually	usually	ADV
ajst-29623	82	6	much	much	ADV
ajst-29623	82	7	smaller	small	ADJ
ajst-29623	82	8	than	than	ADP
ajst-29623	82	9	𝑐	𝑐	PROPN
ajst-29623	82	10	,	,	PUNCT
ajst-29623	82	11	but	but	CCONJ
ajst-29623	82	12	the	the	DET
ajst-29623	82	13	partial	partial	ADJ
ajst-29623	82	14	dense	dense	ADJ
ajst-29623	82	15	block	block	NOUN
ajst-29623	82	16	can	can	AUX
ajst-29623	82	17	save	save	VERB
ajst-29623	82	18	up	up	ADP
ajst-29623	82	19	to	to	PART
ajst-29623	82	20	half	half	NOUN
ajst-29623	82	21	of	of	ADP
ajst-29623	82	22	the	the	DET
ajst-29623	82	23	network	network	NOUN
ajst-29623	82	24	memory	memory	NOUN
ajst-29623	82	25	traffic	traffic	NOUN
ajst-29623	82	26	.	.	PUNCT
ajst-29623	83	1	figure	figure	NOUN
ajst-29623	83	2	5	5	NUM
ajst-29623	83	3	.	.	PUNCT
ajst-29623	83	4	cspnet	cspnet	ADJ
ajst-29623	83	5	structure	structure	NOUN
ajst-29623	83	6	the	the	DET
ajst-29623	83	7	spp	spp	NOUN
ajst-29623	83	8	module	module	NOUN
ajst-29623	83	9	can	can	AUX
ajst-29623	83	10	use	use	VERB
ajst-29623	83	11	the	the	DET
ajst-29623	83	12	same	same	ADJ
ajst-29623	83	13	image	image	NOUN
ajst-29623	83	14	with	with	ADP
ajst-29623	83	15	different	different	ADJ
ajst-29623	83	16	sizes	size	NOUN
ajst-29623	83	17	(	(	PUNCT
ajst-29623	83	18	scales	scale	NOUN
ajst-29623	83	19	)	)	PUNCT
ajst-29623	83	20	as	as	ADP
ajst-29623	83	21	the	the	DET
ajst-29623	83	22	input	input	NOUN
ajst-29623	83	23	and	and	CCONJ
ajst-29623	83	24	obtain	obtain	VERB
ajst-29623	83	25	pooled	pool	VERB
ajst-29623	83	26	features	feature	NOUN
ajst-29623	83	27	of	of	ADP
ajst-29623	83	28	the	the	DET
ajst-29623	83	29	same	same	ADJ
ajst-29623	83	30	length[12	length[12	NOUN
ajst-29623	83	31	]	]	PUNCT
ajst-29623	83	32	.	.	PUNCT
ajst-29623	84	1	the	the	DET
ajst-29623	84	2	spp	spp	NOUN
ajst-29623	84	3	module	module	NOUN
ajst-29623	84	4	can	can	AUX
ajst-29623	84	5	also	also	ADV
ajst-29623	84	6	process	process	VERB
ajst-29623	84	7	images	image	NOUN
ajst-29623	84	8	with	with	ADP
ajst-29623	84	9	different	different	ADJ
ajst-29623	84	10	aspect	aspect	NOUN
ajst-29623	84	11	ratios	ratio	NOUN
ajst-29623	84	12	and	and	CCONJ
ajst-29623	84	13	sizes	size	NOUN
ajst-29623	84	14	,	,	PUNCT
ajst-29623	84	15	so	so	SCONJ
ajst-29623	84	16	it	it	PRON
ajst-29623	84	17	improves	improve	VERB
ajst-29623	84	18	the	the	DET
ajst-29623	84	19	scale	scale	NOUN
ajst-29623	84	20	invariance	invariance	NOUN
ajst-29623	84	21	of	of	ADP
ajst-29623	84	22	the	the	DET
ajst-29623	84	23	image	image	NOUN
ajst-29623	84	24	and	and	CCONJ
ajst-29623	84	25	reduces	reduce	VERB
ajst-29623	84	26	over	over	ADP
ajst-29623	84	27	fitting	fitting	ADJ
ajst-29623	84	28	.	.	PUNCT
ajst-29623	85	1	the	the	DET
ajst-29623	85	2	spp	spp	NOUN
ajst-29623	85	3	structure	structure	NOUN
ajst-29623	85	4	is	be	AUX
ajst-29623	85	5	independent	independent	ADJ
ajst-29623	85	6	of	of	ADP
ajst-29623	85	7	a	a	DET
ajst-29623	85	8	specific	specific	ADJ
ajst-29623	85	9	cnn	cnn	NOUN
ajst-29623	85	10	network	network	NOUN
ajst-29623	85	11	design	design	NOUN
ajst-29623	85	12	and	and	CCONJ
ajst-29623	85	13	structure	structure	NOUN
ajst-29623	85	14	.	.	PUNCT
ajst-29623	86	1	through	through	ADP
ajst-29623	86	2	the	the	DET
ajst-29623	86	3	spp	spp	NOUN
ajst-29623	86	4	module[13	module[13	NOUN
ajst-29623	86	5	]	]	PUNCT
ajst-29623	86	6	,	,	PUNCT
ajst-29623	86	7	the	the	DET
ajst-29623	86	8	fusion	fusion	NOUN
ajst-29623	86	9	of	of	ADP
ajst-29623	86	10	local	local	ADJ
ajst-29623	86	11	and	and	CCONJ
ajst-29623	86	12	global	global	ADJ
ajst-29623	86	13	features	feature	NOUN
ajst-29623	86	14	at	at	ADP
ajst-29623	86	15	the	the	DET
ajst-29623	86	16	feathermap	feathermap	ADJ
ajst-29623	86	17	level	level	NOUN
ajst-29623	86	18	is	be	AUX
ajst-29623	86	19	realized	realize	VERB
ajst-29623	86	20	,	,	PUNCT
ajst-29623	86	21	enriching	enrich	VERB
ajst-29623	86	22	the	the	DET
ajst-29623	86	23	expression	expression	NOUN
ajst-29623	86	24	ability	ability	NOUN
ajst-29623	86	25	of	of	ADP
ajst-29623	86	26	the	the	DET
ajst-29623	86	27	final	final	ADJ
ajst-29623	86	28	feature	feature	NOUN
ajst-29623	86	29	map	map	NOUN
ajst-29623	86	30	and	and	CCONJ
ajst-29623	86	31	thus	thus	ADV
ajst-29623	86	32	iproving	iprove	VERB
ajst-29623	86	33	the	the	DET
ajst-29623	86	34	map	map	NOUN
ajst-29623	86	35	.	.	PUNCT
ajst-29623	87	1	4	4	X
ajst-29623	87	2	.	.	X
ajst-29623	87	3	simulation	simulation	NOUN
ajst-29623	87	4	and	and	CCONJ
ajst-29623	87	5	experiments	experiment	NOUN
ajst-29623	87	6	4.1	4.1	NUM
ajst-29623	87	7	.	.	PUNCT
ajst-29623	88	1	data	datum	NOUN
ajst-29623	88	2	collection	collection	NOUN
ajst-29623	88	3	the	the	DET
ajst-29623	88	4	ansys	ansys	PROPN
ajst-29623	88	5	maxwell	maxwell	PROPN
ajst-29623	88	6	finite	finite	PROPN
ajst-29623	88	7	element	element	PROPN
ajst-29623	88	8	simulation	simulation	PROPN
ajst-29623	88	9	software	software	NOUN
ajst-29623	88	10	is	be	AUX
ajst-29623	88	11	used	use	VERB
ajst-29623	88	12	to	to	PART
ajst-29623	88	13	build	build	VERB
ajst-29623	88	14	a	a	DET
ajst-29623	88	15	pipeline	pipeline	NOUN
ajst-29623	88	16	defect	defect	NOUN
ajst-29623	88	17	mfl	mfl	PROPN
ajst-29623	88	18	detection	detection	NOUN
ajst-29623	88	19	model	model	NOUN
ajst-29623	88	20	,	,	PUNCT
ajst-29623	88	21	providing	provide	VERB
ajst-29623	88	22	data	datum	NOUN
ajst-29623	88	23	for	for	ADP
ajst-29623	88	24	the	the	DET
ajst-29623	88	25	subsequent	subsequent	ADJ
ajst-29623	88	26	design	design	NOUN
ajst-29623	88	27	of	of	ADP
ajst-29623	88	28	the	the	DET
ajst-29623	88	29	pipeline	pipeline	NOUN
ajst-29623	88	30	defect	defect	NOUN
ajst-29623	88	31	detection	detection	NOUN
ajst-29623	88	32	system	system	NOUN
ajst-29623	88	33	based	base	VERB
ajst-29623	88	34	on	on	ADP
ajst-29623	88	35	deep	deep	ADJ
ajst-29623	88	36	learning	learn	VERB
ajst-29623	88	37	neural	neural	ADJ
ajst-29623	88	38	networks	network	NOUN
ajst-29623	88	39	and	and	CCONJ
ajst-29623	88	40	the	the	DET
ajst-29623	88	41	prediction	prediction	NOUN
ajst-29623	88	42	of	of	ADP
ajst-29623	88	43	defect	defect	ADJ
ajst-29623	88	44	sizes	size	NOUN
ajst-29623	88	45	.	.	PUNCT
ajst-29623	89	1	to	to	PART
ajst-29623	89	2	further	far	ADV
ajst-29623	89	3	verify	verify	VERB
ajst-29623	89	4	the	the	DET
ajst-29623	89	5	effectiveness	effectiveness	NOUN
ajst-29623	89	6	of	of	ADP
ajst-29623	89	7	the	the	DET
ajst-29623	89	8	method	method	NOUN
ajst-29623	89	9	proposed	propose	VERB
ajst-29623	89	10	in	in	ADP
ajst-29623	89	11	this	this	DET
ajst-29623	89	12	paper	paper	NOUN
ajst-29623	89	13	in	in	ADP
ajst-29623	89	14	practical	practical	ADJ
ajst-29623	89	15	engineering	engineering	NOUN
ajst-29623	89	16	,	,	PUNCT
ajst-29623	89	17	the	the	DET
ajst-29623	89	18	on	on	ADP
ajst-29623	89	19	site	site	NOUN
ajst-29623	89	20	defect	defect	NOUN
ajst-29623	89	21	mfl	mfl	PROPN
ajst-29623	89	22	data	datum	NOUN
ajst-29623	89	23	is	be	AUX
ajst-29623	89	24	obtained	obtain	VERB
ajst-29623	89	25	using	use	VERB
ajst-29623	89	26	a	a	DET
ajst-29623	89	27	pipeline	pipeline	NOUN
ajst-29623	89	28	defect	defect	NOUN
ajst-29623	89	29	mfl	mfl	PROPN
ajst-29623	89	30	detection	detection	NOUN
ajst-29623	89	31	experimental	experimental	ADJ
ajst-29623	89	32	platform	platform	NOUN
ajst-29623	89	33	.	.	PUNCT
ajst-29623	90	1	a	a	DET
ajst-29623	90	2	pipeline	pipeline	NOUN
ajst-29623	90	3	with	with	ADP
ajst-29623	90	4	a	a	DET
ajst-29623	90	5	diameter	diameter	NOUN
ajst-29623	90	6	of	of	ADP
ajst-29623	90	7	210	210	NUM
ajst-29623	90	8	mm	mm	NOUN
ajst-29623	90	9	,	,	PUNCT
ajst-29623	90	10	a	a	DET
ajst-29623	90	11	wall	wall	NOUN
ajst-29623	90	12	thickness	thickness	NOUN
ajst-29623	90	13	of	of	ADP
ajst-29623	90	14	13	13	NUM
ajst-29623	90	15	mm	mm	NOUN
ajst-29623	90	16	,	,	PUNCT
ajst-29623	90	17	and	and	CCONJ
ajst-29623	90	18	a	a	DET
ajst-29623	90	19	length	length	NOUN
ajst-29623	90	20	of	of	ADP
ajst-29623	90	21	100	100	NUM
ajst-29623	90	22	mm	mm	NOUN
ajst-29623	90	23	is	be	AUX
ajst-29623	90	24	selected	select	VERB
ajst-29623	90	25	.	.	PUNCT
ajst-29623	91	1	pipeline	pipeline	NOUN
ajst-29623	91	2	defects	defect	NOUN
ajst-29623	91	3	are	be	AUX
ajst-29623	91	4	artificially	artificially	ADV
ajst-29623	91	5	made	make	VERB
ajst-29623	91	6	,	,	PUNCT
ajst-29623	91	7	and	and	CCONJ
ajst-29623	91	8	hall	hall	NOUN
ajst-29623	91	9	sensors	sensor	NOUN
ajst-29623	91	10	are	be	AUX
ajst-29623	91	11	used	use	VERB
ajst-29623	91	12	to	to	PART
ajst-29623	91	13	detect	detect	VERB
ajst-29623	91	14	the	the	DET
ajst-29623	91	15	mfl	mfl	PROPN
ajst-29623	91	16	data	data	PROPN
ajst-29623	91	17	.	.	PUNCT
ajst-29623	92	1	an	an	DET
ajst-29623	92	2	experimental	experimental	ADJ
ajst-29623	92	3	platform	platform	NOUN
ajst-29623	92	4	framework	framework	NOUN
ajst-29623	92	5	is	be	AUX
ajst-29623	92	6	built	build	VERB
ajst-29623	92	7	.	.	PUNCT
ajst-29623	93	1	finally	finally	ADV
ajst-29623	93	2	,	,	PUNCT
ajst-29623	93	3	defect	defect	VERB
ajst-29623	93	4	size	size	NOUN
ajst-29623	93	5	data	datum	NOUN
ajst-29623	93	6	in	in	ADP
ajst-29623	93	7	the	the	DET
ajst-29623	93	8	range	range	NOUN
ajst-29623	93	9	of	of	ADP
ajst-29623	93	10	10	10	NUM
ajst-29623	93	11	40	40	NUM
ajst-29623	93	12	mm	mm	NOUN
ajst-29623	93	13	in	in	ADP
ajst-29623	93	14	length	length	NOUN
ajst-29623	93	15	,	,	PUNCT
ajst-29623	93	16	10	10	NUM
ajst-29623	93	17	40	40	NUM
ajst-29623	93	18	mm	mm	NOUN
ajst-29623	93	19	in	in	ADP
ajst-29623	93	20	width	width	NOUN
ajst-29623	93	21	,	,	PUNCT
ajst-29623	93	22	and	and	CCONJ
ajst-29623	93	23	1	1	NUM
ajst-29623	93	24	9	9	NUM
ajst-29623	93	25	mm	mm	NOUN
ajst-29623	93	26	in	in	ADP
ajst-29623	93	27	depth	depth	NOUN
ajst-29623	93	28	are	be	AUX
ajst-29623	93	29	collected	collect	VERB
ajst-29623	93	30	.	.	PUNCT
ajst-29623	94	1	4.2	4.2	NUM
ajst-29623	94	2	.	.	PUNCT
ajst-29623	94	3	data	datum	NOUN
ajst-29623	94	4	pre	pre	VERB
ajst-29623	94	5	–	–	PUNCT
ajst-29623	94	6	processing	process	VERB
ajst-29623	94	7	various	various	ADJ
ajst-29623	94	8	types	type	NOUN
ajst-29623	94	9	of	of	ADP
ajst-29623	94	10	noise	noise	NOUN
ajst-29623	94	11	are	be	AUX
ajst-29623	94	12	added	add	VERB
ajst-29623	94	13	to	to	ADP
ajst-29623	94	14	the	the	DET
ajst-29623	94	15	mfl	mfl	PROPN
ajst-29623	94	16	data	data	PROPN
ajst-29623	94	17	,	,	PUNCT
ajst-29623	94	18	and	and	CCONJ
ajst-29623	94	19	data	datum	NOUN
ajst-29623	94	20	augmentation	augmentation	NOUN
ajst-29623	94	21	operations	operation	NOUN
ajst-29623	94	22	are	be	AUX
ajst-29623	94	23	performed	perform	VERB
ajst-29623	94	24	on	on	ADP
ajst-29623	94	25	the	the	DET
ajst-29623	94	26	dataset	dataset	NOUN
ajst-29623	94	27	to	to	PART
ajst-29623	94	28	expand	expand	VERB
ajst-29623	94	29	the	the	DET
ajst-29623	94	30	mfl	mfl	PROPN
ajst-29623	94	31	data	datum	NOUN
ajst-29623	94	32	to	to	ADP
ajst-29623	94	33	10,000	10,000	NUM
ajst-29623	94	34	samples	sample	NOUN
ajst-29623	94	35	.	.	PUNCT
ajst-29623	95	1	the	the	DET
ajst-29623	95	2	markov	markov	NOUN
ajst-29623	95	3	transformation	transformation	NOUN
ajst-29623	95	4	field	field	NOUN
ajst-29623	95	5	is	be	AUX
ajst-29623	95	6	used	use	VERB
ajst-29623	95	7	to	to	PART
ajst-29623	95	8	perform	perform	VERB
ajst-29623	95	9	a	a	DET
ajst-29623	95	10	two	two	NUM
ajst-29623	95	11	dimensional	dimensional	ADJ
ajst-29623	95	12	transformation	transformation	NOUN
ajst-29623	95	13	on	on	ADP
ajst-29623	95	14	the	the	DET
ajst-29623	95	15	one	one	NUM
ajst-29623	95	16	dimensional	dimensional	ADJ
ajst-29623	95	17	mfl	mfl	PROPN
ajst-29623	95	18	data	datum	NOUN
ajst-29623	95	19	.	.	PUNCT
ajst-29623	96	1	since	since	SCONJ
ajst-29623	96	2	the	the	DET
ajst-29623	96	3	radial	radial	ADJ
ajst-29623	96	4	and	and	CCONJ
ajst-29623	96	5	axial	axial	ADJ
ajst-29623	96	6	mfl	mfl	PROPN
ajst-29623	96	7	data	datum	NOUN
ajst-29623	96	8	need	need	VERB
ajst-29623	96	9	to	to	PART
ajst-29623	96	10	be	be	AUX
ajst-29623	96	11	input	input	NOUN
ajst-29623	96	12	simultaneously	simultaneously	ADV
ajst-29623	96	13	,	,	PUNCT
ajst-29623	96	14	to	to	PART
ajst-29623	96	15	improve	improve	VERB
ajst-29623	96	16	the	the	DET
ajst-29623	96	17	training	training	NOUN
ajst-29623	96	18	efficiency	efficiency	NOUN
ajst-29623	96	19	,	,	PUNCT
ajst-29623	96	20	the	the	DET
ajst-29623	96	21	two	two	NUM
ajst-29623	96	22	dimensional	dimensional	ADJ
ajst-29623	96	23	images	image	NOUN
ajst-29623	96	24	of	of	ADP
ajst-29623	96	25	the	the	DET
ajst-29623	96	26	radial	radial	ADJ
ajst-29623	96	27	and	and	CCONJ
ajst-29623	96	28	axial	axial	ADJ
ajst-29623	96	29	mfl	mfl	PROPN
ajst-29623	96	30	data	datum	NOUN
ajst-29623	96	31	are	be	AUX
ajst-29623	96	32	feature	feature	NOUN
ajst-29623	96	33	fused	fuse	VERB
ajst-29623	96	34	and	and	CCONJ
ajst-29623	96	35	set	set	VERB
ajst-29623	96	36	to	to	ADP
ajst-29623	96	37	a	a	DET
ajst-29623	96	38	size	size	NOUN
ajst-29623	96	39	of	of	ADP
ajst-29623	96	40	32×32	32×32	NUM
ajst-29623	96	41	pixels	pixel	NOUN
ajst-29623	96	42	,	,	PUNCT
ajst-29623	96	43	as	as	SCONJ
ajst-29623	96	44	shown	show	VERB
ajst-29623	96	45	in	in	ADP
ajst-29623	96	46	figure	figure	NOUN
ajst-29623	96	47	6	6	NUM
ajst-29623	96	48	.	.	PUNCT
ajst-29623	97	1	figure	figure	NOUN
ajst-29623	97	2	6	6	NUM
ajst-29623	97	3	.	.	PUNCT
ajst-29623	98	1	flow	flow	NOUN
ajst-29623	98	2	chart	chart	NOUN
ajst-29623	98	3	of	of	ADP
ajst-29623	98	4	markov	markov	NOUN
ajst-29623	98	5	transition	transition	NOUN
ajst-29623	98	6	field	field	NOUN
ajst-29623	98	7	a	a	DET
ajst-29623	98	8	pipeline	pipeline	NOUN
ajst-29623	98	9	mfl	mfl	PROPN
ajst-29623	98	10	curve	curve	PROPN
ajst-29623	98	11	image	image	NOUN
ajst-29623	98	12	dataset	dataset	NOUN
ajst-29623	98	13	based	base	VERB
ajst-29623	98	14	on	on	ADP
ajst-29623	98	15	a	a	DET
ajst-29623	98	16	convolutional	convolutional	ADJ
ajst-29623	98	17	neural	neural	ADJ
ajst-29623	98	18	network	network	NOUN
ajst-29623	98	19	is	be	AUX
ajst-29623	98	20	established	establish	VERB
ajst-29623	98	21	.	.	PUNCT
ajst-29623	99	1	the	the	DET
ajst-29623	99	2	pixel	pixel	PROPN
ajst-29623	99	3	size	size	NOUN
ajst-29623	99	4	of	of	ADP
ajst-29623	99	5	the	the	DET
ajst-29623	99	6	feature	feature	NOUN
ajst-29623	99	7	image	image	NOUN
ajst-29623	99	8	of	of	ADP
ajst-29623	99	9	each	each	DET
ajst-29623	99	10	defect	defect	NOUN
ajst-29623	99	11	is	be	AUX
ajst-29623	99	12	32×32	32×32	NUM
ajst-29623	99	13	.	.	PUNCT
ajst-29623	100	1	the	the	DET
ajst-29623	100	2	dataset	dataset	NOUN
ajst-29623	100	3	is	be	AUX
ajst-29623	100	4	randomly	randomly	ADV
ajst-29623	100	5	divided	divide	VERB
ajst-29623	100	6	into	into	ADP
ajst-29623	100	7	a	a	DET
ajst-29623	100	8	training	training	NOUN
ajst-29623	100	9	set	set	NOUN
ajst-29623	100	10	and	and	CCONJ
ajst-29623	100	11	a	a	DET
ajst-29623	100	12	test	test	NOUN
ajst-29623	100	13	set	set	VERB
ajst-29623	100	14	in	in	ADP
ajst-29623	100	15	a	a	DET
ajst-29623	100	16	9:1	9:1	NUM
ajst-29623	100	17	ratio	ratio	NOUN
ajst-29623	100	18	.	.	PUNCT
ajst-29623	101	1	figure	figure	NOUN
ajst-29623	101	2	7	7	NUM
ajst-29623	101	3	shows	show	VERB
ajst-29623	101	4	the	the	DET
ajst-29623	101	5	defect	defect	NOUN
ajst-29623	101	6	sizes	size	NOUN
ajst-29623	101	7	corresponding	correspond	VERB
ajst-29623	101	8	to	to	PART
ajst-29623	101	9	randomly	randomly	ADV
ajst-29623	101	10	selected	select	VERB
ajst-29623	101	11	different	different	ADJ
ajst-29623	101	12	feature	feature	NOUN
ajst-29623	101	13	maps	map	NOUN
ajst-29623	101	14	.	.	PUNCT
ajst-29623	102	1	298	298	NUM
ajst-29623	102	2	figure	figure	NOUN
ajst-29623	102	3	7	7	NUM
ajst-29623	102	4	.	.	PUNCT
ajst-29623	102	5	feature	feature	NOUN
ajst-29623	102	6	maps	map	NOUN
ajst-29623	102	7	corresponding	correspond	VERB
ajst-29623	102	8	to	to	ADP
ajst-29623	102	9	different	different	ADJ
ajst-29623	102	10	defect	defect	NOUN
ajst-29623	102	11	sizes	size	NOUN
ajst-29623	103	1	4.3	4.3	NUM
ajst-29623	103	2	.	.	PUNCT
ajst-29623	103	3	optimization	optimization	NOUN
ajst-29623	103	4	process	process	NOUN
ajst-29623	103	5	(	(	PUNCT
ajst-29623	103	6	1	1	X
ajst-29623	103	7	)	)	PUNCT
ajst-29623	103	8	select	select	VERB
ajst-29623	103	9	an	an	DET
ajst-29623	103	10	appropriate	appropriate	ADJ
ajst-29623	103	11	initial	initial	ADJ
ajst-29623	103	12	learning	learning	NOUN
ajst-29623	103	13	rate	rate	NOUN
ajst-29623	103	14	lr	lr	NOUN
ajst-29623	103	15	for	for	ADP
ajst-29623	103	16	the	the	DET
ajst-29623	103	17	network	network	NOUN
ajst-29623	103	18	.	.	PUNCT
ajst-29623	104	1	(	(	PUNCT
ajst-29623	104	2	2	2	X
ajst-29623	104	3	)	)	PUNCT
ajst-29623	104	4	select	select	VERB
ajst-29623	104	5	a	a	DET
ajst-29623	104	6	suitable	suitable	ADJ
ajst-29623	104	7	momentum	momentum	NOUN
ajst-29623	104	8	mt	mt	PROPN
ajst-29623	104	9	for	for	ADP
ajst-29623	104	10	stochastic	stochastic	ADJ
ajst-29623	104	11	gradient	gradient	ADJ
ajst-29623	104	12	descent	descent	NOUN
ajst-29623	104	13	.	.	PUNCT
ajst-29623	105	1	(	(	PUNCT
ajst-29623	105	2	3	3	X
ajst-29623	105	3	)	)	PUNCT
ajst-29623	105	4	select	select	VERB
ajst-29623	105	5	a	a	DET
ajst-29623	105	6	suitable	suitable	ADJ
ajst-29623	105	7	l2	l2	NOUN
ajst-29623	105	8	regularization	regularization	NOUN
ajst-29623	105	9	coefficient	coefficient	NOUN
ajst-29623	105	10	λ	λ	PROPN
ajst-29623	105	11	.	.	PUNCT
ajst-29623	106	1	the	the	DET
ajst-29623	106	2	above	above	ADJ
ajst-29623	106	3	three	three	NUM
ajst-29623	106	4	parameters	parameter	NOUN
ajst-29623	106	5	affect	affect	VERB
ajst-29623	106	6	each	each	DET
ajst-29623	106	7	other	other	ADJ
ajst-29623	106	8	.	.	PUNCT
ajst-29623	107	1	any	any	DET
ajst-29623	107	2	change	change	NOUN
ajst-29623	107	3	in	in	ADP
ajst-29623	107	4	one	one	NUM
ajst-29623	107	5	parameter	parameter	NOUN
ajst-29623	107	6	will	will	AUX
ajst-29623	107	7	cause	cause	VERB
ajst-29623	107	8	the	the	DET
ajst-29623	107	9	remaining	remain	VERB
ajst-29623	107	10	parameters	parameter	NOUN
ajst-29623	107	11	to	to	PART
ajst-29623	107	12	have	have	VERB
ajst-29623	107	13	an	an	DET
ajst-29623	107	14	impact	impact	NOUN
ajst-29623	107	15	on	on	ADP
ajst-29623	107	16	the	the	DET
ajst-29623	107	17	final	final	ADJ
ajst-29623	107	18	effect	effect	NOUN
ajst-29623	107	19	of	of	ADP
ajst-29623	107	20	pp	pp	ADP
ajst-29623	107	21	yoloe	yoloe	NOUN
ajst-29623	107	22	.	.	PUNCT
ajst-29623	108	1	therefore	therefore	ADV
ajst-29623	108	2	,	,	PUNCT
ajst-29623	108	3	it	it	PRON
ajst-29623	108	4	is	be	AUX
ajst-29623	108	5	necessary	necessary	ADJ
ajst-29623	108	6	to	to	PART
ajst-29623	108	7	find	find	VERB
ajst-29623	108	8	the	the	DET
ajst-29623	108	9	globally	globally	ADV
ajst-29623	108	10	optimal	optimal	ADJ
ajst-29623	108	11	parameter	parameter	NOUN
ajst-29623	108	12	combination	combination	NOUN
ajst-29623	108	13	.	.	PUNCT
ajst-29623	109	1	the	the	DET
ajst-29623	109	2	dataset	dataset	NOUN
ajst-29623	109	3	is	be	AUX
ajst-29623	109	4	input	input	VERB
ajst-29623	109	5	into	into	ADP
ajst-29623	109	6	the	the	DET
ajst-29623	109	7	pp	pp	PROPN
ajst-29623	109	8	yoloe	yoloe	NOUN
ajst-29623	109	9	model	model	NOUN
ajst-29623	109	10	,	,	PUNCT
ajst-29623	109	11	and	and	CCONJ
ajst-29623	109	12	an	an	DET
ajst-29623	109	13	optimization	optimization	NOUN
ajst-29623	109	14	algorithm	algorithm	NOUN
ajst-29623	109	15	is	be	AUX
ajst-29623	109	16	used	use	VERB
ajst-29623	109	17	to	to	PART
ajst-29623	109	18	select	select	VERB
ajst-29623	109	19	the	the	DET
ajst-29623	109	20	hyperparameters	hyperparameter	NOUN
ajst-29623	109	21	.	.	PUNCT
ajst-29623	110	1	the	the	DET
ajst-29623	110	2	maximum	maximum	ADJ
ajst-29623	110	3	number	number	NOUN
ajst-29623	110	4	of	of	ADP
ajst-29623	110	5	iterations	iteration	NOUN
ajst-29623	110	6	is	be	AUX
ajst-29623	110	7	set	set	VERB
ajst-29623	110	8	to	to	ADP
ajst-29623	110	9	20	20	NUM
ajst-29623	110	10	.	.	PUNCT
ajst-29623	111	1	as	as	SCONJ
ajst-29623	111	2	can	can	AUX
ajst-29623	111	3	be	be	AUX
ajst-29623	111	4	seen	see	VERB
ajst-29623	111	5	from	from	ADP
ajst-29623	111	6	table	table	NOUN
ajst-29623	111	7	3	3	NUM
ajst-29623	111	8	,	,	PUNCT
ajst-29623	111	9	after	after	ADP
ajst-29623	111	10	30	30	NUM
ajst-29623	111	11	iterations	iteration	NOUN
ajst-29623	111	12	,	,	PUNCT
ajst-29623	111	13	the	the	DET
ajst-29623	111	14	rmse	rmse	NOUN
ajst-29623	111	15	obtained	obtain	VERB
ajst-29623	111	16	by	by	ADP
ajst-29623	111	17	the	the	DET
ajst-29623	111	18	12th	12th	ADJ
ajst-29623	111	19	optimized	optimize	VERB
ajst-29623	111	20	model	model	NOUN
ajst-29623	111	21	is	be	AUX
ajst-29623	111	22	the	the	DET
ajst-29623	111	23	lowest	low	ADJ
ajst-29623	111	24	,	,	PUNCT
ajst-29623	111	25	which	which	PRON
ajst-29623	111	26	is	be	AUX
ajst-29623	111	27	0.0691	0.0691	NUM
ajst-29623	111	28	.	.	PUNCT
ajst-29623	112	1	finally	finally	ADV
ajst-29623	112	2	,	,	PUNCT
ajst-29623	112	3	the	the	DET
ajst-29623	112	4	initial	initial	ADJ
ajst-29623	112	5	learning	learning	NOUN
ajst-29623	112	6	rate	rate	NOUN
ajst-29623	112	7	of	of	ADP
ajst-29623	112	8	the	the	DET
ajst-29623	112	9	network	network	NOUN
ajst-29623	112	10	lr	lr	X
ajst-29623	112	11	=	=	NOUN
ajst-29623	112	12	0.0008	0.0008	NUM
ajst-29623	112	13	,	,	PUNCT
ajst-29623	112	14	the	the	DET
ajst-29623	112	15	momentum	momentum	NOUN
ajst-29623	112	16	of	of	ADP
ajst-29623	112	17	stochastic	stochastic	ADJ
ajst-29623	112	18	gradient	gradient	ADJ
ajst-29623	112	19	descent	descent	NOUN
ajst-29623	112	20	mt	mt	PROPN
ajst-29623	112	21	=	=	PROPN
ajst-29623	112	22	0.8224	0.8224	NUM
ajst-29623	112	23	,	,	PUNCT
ajst-29623	112	24	and	and	CCONJ
ajst-29623	112	25	the	the	DET
ajst-29623	112	26	l2	l2	NOUN
ajst-29623	112	27	regularization	regularization	NOUN
ajst-29623	112	28	coefficient	coefficient	NOUN
ajst-29623	112	29	λ	λ	X
ajst-29623	112	30	=	=	NOUN
ajst-29623	112	31	0.009	0.009	NUM
ajst-29623	112	32	are	be	AUX
ajst-29623	112	33	determined	determine	VERB
ajst-29623	112	34	.	.	PUNCT
ajst-29623	113	1	at	at	ADP
ajst-29623	113	2	this	this	DET
ajst-29623	113	3	time	time	NOUN
ajst-29623	113	4	,	,	PUNCT
ajst-29623	113	5	it	it	PRON
ajst-29623	113	6	indicates	indicate	VERB
ajst-29623	113	7	that	that	SCONJ
ajst-29623	113	8	the	the	DET
ajst-29623	113	9	optimal	optimal	ADJ
ajst-29623	113	10	iteration	iteration	NOUN
ajst-29623	113	11	has	have	AUX
ajst-29623	113	12	been	be	AUX
ajst-29623	113	13	reached	reach	VERB
ajst-29623	113	14	,	,	PUNCT
ajst-29623	113	15	and	and	CCONJ
ajst-29623	113	16	subsequent	subsequent	ADJ
ajst-29623	113	17	calculations	calculation	NOUN
ajst-29623	113	18	can	can	AUX
ajst-29623	113	19	no	no	ADV
ajst-29623	113	20	longer	long	ADV
ajst-29623	113	21	improve	improve	VERB
ajst-29623	113	22	the	the	DET
ajst-29623	113	23	objective	objective	ADJ
ajst-29623	113	24	function	function	NOUN
ajst-29623	113	25	,	,	PUNCT
ajst-29623	113	26	indicating	indicate	VERB
ajst-29623	113	27	that	that	SCONJ
ajst-29623	113	28	the	the	DET
ajst-29623	113	29	global	global	ADJ
ajst-29623	113	30	optimum	optimum	NOUN
ajst-29623	113	31	has	have	AUX
ajst-29623	113	32	been	be	AUX
ajst-29623	113	33	found	find	VERB
ajst-29623	113	34	.	.	PUNCT
ajst-29623	114	1	to	to	PART
ajst-29623	114	2	verify	verify	VERB
ajst-29623	114	3	the	the	DET
ajst-29623	114	4	impact	impact	NOUN
ajst-29623	114	5	of	of	ADP
ajst-29623	114	6	the	the	DET
ajst-29623	114	7	optimization	optimization	NOUN
ajst-29623	114	8	algorithm	algorithm	NOUN
ajst-29623	114	9	on	on	ADP
ajst-29623	114	10	the	the	DET
ajst-29623	114	11	results	result	NOUN
ajst-29623	114	12	of	of	ADP
ajst-29623	114	13	the	the	DET
ajst-29623	114	14	pp	pp	ADJ
ajst-29623	114	15	-	-	PUNCT
ajst-29623	114	16	yoloe	yoloe	NOUN
ajst-29623	114	17	model	model	NOUN
ajst-29623	114	18	,	,	PUNCT
ajst-29623	114	19	an	an	DET
ajst-29623	114	20	unoptimized	unoptimized	ADJ
ajst-29623	114	21	model	model	NOUN
ajst-29623	114	22	was	be	AUX
ajst-29623	114	23	established	establish	VERB
ajst-29623	114	24	,	,	PUNCT
ajst-29623	114	25	which	which	PRON
ajst-29623	114	26	maintained	maintain	VERB
ajst-29623	114	27	the	the	DET
ajst-29623	114	28	same	same	ADJ
ajst-29623	114	29	structure	structure	NOUN
ajst-29623	114	30	as	as	ADP
ajst-29623	114	31	the	the	DET
ajst-29623	114	32	optimized	optimize	VERB
ajst-29623	114	33	pp	pp	NOUN
ajst-29623	114	34	-	-	PUNCT
ajst-29623	114	35	yoloe	yoloe	NOUN
ajst-29623	114	36	.	.	PUNCT
ajst-29623	115	1	the	the	DET
ajst-29623	115	2	default	default	NOUN
ajst-29623	115	3	neural	neural	ADJ
ajst-29623	115	4	network	network	NOUN
ajst-29623	115	5	hyperparameters	hyperparameter	NOUN
ajst-29623	115	6	were	be	AUX
ajst-29623	115	7	selected	select	VERB
ajst-29623	115	8	,	,	PUNCT
ajst-29623	115	9	that	that	ADV
ajst-29623	115	10	is	is	ADV
ajst-29623	115	11	,	,	PUNCT
ajst-29623	115	12	learning	learn	VERB
ajst-29623	115	13	rate	rate	NOUN
ajst-29623	115	14	(	(	PUNCT
ajst-29623	115	15	lr	lr	NOUN
ajst-29623	115	16	)	)	PUNCT
ajst-29623	115	17	=	=	SYM
ajst-29623	115	18	0.01	0.01	NUM
ajst-29623	115	19	,	,	PUNCT
ajst-29623	115	20	momentum	momentum	NOUN
ajst-29623	115	21	(	(	PUNCT
ajst-29623	115	22	mt	mt	PROPN
ajst-29623	115	23	)	)	PUNCT
ajst-29623	115	24	=	=	PUNCT
ajst-29623	115	25	0.8	0.8	NUM
ajst-29623	115	26	,	,	PUNCT
ajst-29623	115	27	and	and	CCONJ
ajst-29623	115	28	weight	weight	NOUN
ajst-29623	115	29	decay	decay	NOUN
ajst-29623	115	30	(	(	PUNCT
ajst-29623	115	31	λ	λ	NOUN
ajst-29623	115	32	)	)	PUNCT
ajst-29623	115	33	=	=	SYM
ajst-29623	115	34	0.0001	0.0001	NUM
ajst-29623	115	35	.	.	PUNCT
ajst-29623	116	1	compared	compare	VERB
ajst-29623	116	2	with	with	ADP
ajst-29623	116	3	the	the	DET
ajst-29623	116	4	unoptimized	unoptimized	ADJ
ajst-29623	116	5	pp	pp	NOUN
ajst-29623	116	6	-	-	PUNCT
ajst-29623	116	7	yoloe	yoloe	NOUN
ajst-29623	116	8	,	,	PUNCT
ajst-29623	116	9	the	the	DET
ajst-29623	116	10	optimized	optimize	VERB
ajst-29623	116	11	pp	pp	ADJ
ajst-29623	116	12	-	-	PUNCT
ajst-29623	116	13	yoloe	yoloe	NOUN
ajst-29623	116	14	achieved	achieve	VERB
ajst-29623	116	15	more	more	ADV
ajst-29623	116	16	accurate	accurate	ADJ
ajst-29623	116	17	predictions	prediction	NOUN
ajst-29623	116	18	.	.	PUNCT
ajst-29623	117	1	4.4	4.4	NUM
ajst-29623	117	2	.	.	PUNCT
ajst-29623	118	1	experimental	experimental	ADJ
ajst-29623	118	2	results	result	NOUN
ajst-29623	118	3	to	to	PART
ajst-29623	118	4	verify	verify	VERB
ajst-29623	118	5	the	the	DET
ajst-29623	118	6	effectiveness	effectiveness	NOUN
ajst-29623	118	7	of	of	ADP
ajst-29623	118	8	the	the	DET
ajst-29623	118	9	algorithm	algorithm	NOUN
ajst-29623	118	10	in	in	ADP
ajst-29623	118	11	this	this	DET
ajst-29623	118	12	paper	paper	NOUN
ajst-29623	118	13	,	,	PUNCT
ajst-29623	118	14	the	the	DET
ajst-29623	118	15	optimized	optimize	VERB
ajst-29623	118	16	model	model	NOUN
ajst-29623	118	17	is	be	AUX
ajst-29623	118	18	compared	compare	VERB
ajst-29623	118	19	with	with	ADP
ajst-29623	118	20	traditional	traditional	ADJ
ajst-29623	118	21	neural	neural	ADJ
ajst-29623	118	22	networks	network	NOUN
ajst-29623	118	23	,	,	PUNCT
ajst-29623	118	24	as	as	SCONJ
ajst-29623	118	25	shown	show	VERB
ajst-29623	118	26	in	in	ADP
ajst-29623	118	27	table	table	NOUN
ajst-29623	118	28	2	2	NUM
ajst-29623	118	29	.	.	PUNCT
ajst-29623	119	1	it	it	PRON
ajst-29623	119	2	can	can	AUX
ajst-29623	119	3	be	be	AUX
ajst-29623	119	4	seen	see	VERB
ajst-29623	119	5	from	from	ADP
ajst-29623	119	6	the	the	DET
ajst-29623	119	7	table	table	NOUN
ajst-29623	119	8	that	that	SCONJ
ajst-29623	119	9	the	the	DET
ajst-29623	119	10	un	un	PROPN
ajst-29623	119	11	optimized	optimize	VERB
ajst-29623	119	12	pp	pp	ADP
ajst-29623	119	13	yoloe	yoloe	NOUN
ajst-29623	119	14	has	have	VERB
ajst-29623	119	15	a	a	DET
ajst-29623	119	16	large	large	ADJ
ajst-29623	119	17	defect	defect	NOUN
ajst-29623	119	18	size	size	NOUN
ajst-29623	119	19	prediction	prediction	NOUN
ajst-29623	119	20	error	error	NOUN
ajst-29623	119	21	,	,	PUNCT
ajst-29623	119	22	so	so	CCONJ
ajst-29623	119	23	it	it	PRON
ajst-29623	119	24	is	be	AUX
ajst-29623	119	25	necessary	necessary	ADJ
ajst-29623	119	26	to	to	PART
ajst-29623	119	27	optimize	optimize	VERB
ajst-29623	119	28	it	it	PRON
ajst-29623	119	29	.	.	PUNCT
ajst-29623	120	1	the	the	DET
ajst-29623	120	2	neural	neural	ADJ
ajst-29623	120	3	network	network	NOUN
ajst-29623	120	4	structure	structure	NOUN
ajst-29623	120	5	optimized	optimize	VERB
ajst-29623	120	6	by	by	ADP
ajst-29623	120	7	the	the	DET
ajst-29623	120	8	optimization	optimization	NOUN
ajst-29623	120	9	algorithm	algorithm	NOUN
ajst-29623	120	10	is	be	AUX
ajst-29623	120	11	more	more	ADV
ajst-29623	120	12	accurate	accurate	ADJ
ajst-29623	120	13	in	in	ADP
ajst-29623	120	14	predicting	predict	VERB
ajst-29623	120	15	pipeline	pipeline	NOUN
ajst-29623	120	16	defects	defect	NOUN
ajst-29623	120	17	and	and	CCONJ
ajst-29623	120	18	is	be	AUX
ajst-29623	120	19	significantly	significantly	ADV
ajst-29623	120	20	better	well	ADJ
ajst-29623	120	21	than	than	SCONJ
ajst-29623	120	22	the	the	DET
ajst-29623	120	23	un	un	PROPN
ajst-29623	120	24	optimized	optimize	VERB
ajst-29623	120	25	pp	pp	ADP
ajst-29623	120	26	yoloe	yoloe	PROPN
ajst-29623	120	27	model	model	PROPN
ajst-29623	120	28	.	.	PUNCT
ajst-29623	121	1	table	table	NOUN
ajst-29623	121	2	2	2	NUM
ajst-29623	121	3	.	.	PUNCT
ajst-29623	121	4	comparison	comparison	NOUN
ajst-29623	121	5	of	of	ADP
ajst-29623	121	6	mean	mean	ADJ
ajst-29623	121	7	squared	square	VERB
ajst-29623	121	8	errors	error	NOUN
ajst-29623	121	9	among	among	ADP
ajst-29623	121	10	different	different	ADJ
ajst-29623	121	11	network	network	NOUN
ajst-29623	121	12	models	model	NOUN
ajst-29623	121	13	network	network	NOUN
ajst-29623	121	14	model	model	NOUN
ajst-29623	121	15	training	train	VERB
ajst-29623	121	16	time（s	time（s	PROPN
ajst-29623	121	17	）	）	PROPN
ajst-29623	121	18	rmse	rmse	PROPN
ajst-29623	121	19	bp	bp	PROPN
ajst-29623	121	20	2.12	2.12	NUM
ajst-29623	121	21	0.73	0.73	NUM
ajst-29623	121	22	rbf	rbf	PROPN
ajst-29623	121	23	2.03	2.03	NUM
ajst-29623	121	24	0.75	0.75	NUM
ajst-29623	121	25	svm	svm	NOUN
ajst-29623	121	26	2.05	2.05	NUM
ajst-29623	121	27	0.70	0.70	NUM
ajst-29623	121	28	transfer	transfer	NOUN
ajst-29623	121	29	learning	learn	VERB
ajst-29623	121	30	2.27	2.27	NUM
ajst-29623	121	31	0.4005	0.4005	PRON
ajst-29623	121	32	pp	pp	ADV
ajst-29623	121	33	-	-	PUNCT
ajst-29623	121	34	yoloe	yoloe	NOUN
ajst-29623	121	35	3.12	3.12	NUM
ajst-29623	121	36	0.1389	0.1389	NUM
ajst-29623	121	37	optimal	optimal	ADJ
ajst-29623	121	38	pp	pp	ADV
ajst-29623	121	39	-	-	PUNCT
ajst-29623	121	40	yoloe	yoloe	NOUN
ajst-29623	121	41	3.01	3.01	NUM
ajst-29623	121	42	0.0619	0.0619	NUM
ajst-29623	121	43	compared	compare	VERB
ajst-29623	121	44	with	with	ADP
ajst-29623	121	45	traditional	traditional	ADJ
ajst-29623	121	46	convolutional	convolutional	ADJ
ajst-29623	121	47	neural	neural	ADJ
ajst-29623	121	48	networks	network	NOUN
ajst-29623	121	49	,	,	PUNCT
ajst-29623	121	50	the	the	DET
ajst-29623	121	51	pp	pp	ADJ
ajst-29623	121	52	yoloe	yoloe	NOUN
ajst-29623	121	53	method	method	NOUN
ajst-29623	121	54	has	have	VERB
ajst-29623	121	55	more	more	ADV
ajst-29623	121	56	accurate	accurate	ADJ
ajst-29623	121	57	pipeline	pipeline	NOUN
ajst-29623	121	58	defect	defect	NOUN
ajst-29623	121	59	size	size	NOUN
ajst-29623	121	60	prediction	prediction	NOUN
ajst-29623	121	61	ability	ability	NOUN
ajst-29623	121	62	and	and	CCONJ
ajst-29623	121	63	training	training	NOUN
ajst-29623	121	64	efficiency	efficiency	NOUN
ajst-29623	121	65	.	.	PUNCT
ajst-29623	122	1	the	the	DET
ajst-29623	122	2	results	result	NOUN
ajst-29623	122	3	show	show	VERB
ajst-29623	122	4	that	that	SCONJ
ajst-29623	122	5	the	the	DET
ajst-29623	122	6	method	method	NOUN
ajst-29623	122	7	proposed	propose	VERB
ajst-29623	122	8	in	in	ADP
ajst-29623	122	9	this	this	DET
ajst-29623	122	10	paper	paper	NOUN
ajst-29623	122	11	provides	provide	VERB
ajst-29623	122	12	a	a	DET
ajst-29623	122	13	better	well	ADV
ajst-29623	122	14	performing	perform	VERB
ajst-29623	122	15	comprehensive	comprehensive	ADJ
ajst-29623	122	16	idea	idea	NOUN
ajst-29623	122	17	for	for	ADP
ajst-29623	122	18	predicting	predict	VERB
ajst-29623	122	19	pipeline	pipeline	NOUN
ajst-29623	122	20	defect	defect	NOUN
ajst-29623	122	21	mfl	mfl	PROPN
ajst-29623	122	22	detection	detection	NOUN
ajst-29623	122	23	in	in	ADP
ajst-29623	122	24	oil	oil	NOUN
ajst-29623	122	25	and	and	CCONJ
ajst-29623	122	26	gas	gas	NOUN
ajst-29623	122	27	pipelines	pipeline	NOUN
ajst-29623	122	28	.	.	PUNCT
ajst-29623	123	1	references	reference	NOUN
ajst-29623	123	2	[	[	X
ajst-29623	123	3	1	1	X
ajst-29623	123	4	]	]	PUNCT
ajst-29623	123	5	huang	huang	PROPN
ajst-29623	123	6	biao	biao	PROPN
ajst-29623	123	7	.	.	PUNCT
ajst-29623	124	1	analysis	analysis	NOUN
ajst-29623	124	2	of	of	ADP
ajst-29623	124	3	the	the	DET
ajst-29623	124	4	current	current	ADJ
ajst-29623	124	5	situation	situation	NOUN
ajst-29623	124	6	of	of	ADP
ajst-29623	124	7	oil	oil	NOUN
ajst-29623	124	8	and	and	CCONJ
ajst-29623	124	9	gas	gas	NOUN
ajst-29623	124	10	pipeline	pipeline	NOUN
ajst-29623	124	11	protection	protection	NOUN
ajst-29623	124	12	and	and	CCONJ
ajst-29623	124	13	the	the	DET
ajst-29623	124	14	construction	construction	NOUN
ajst-29623	124	15	of	of	ADP
ajst-29623	124	16	a	a	DET
ajst-29623	124	17	long	long	ADJ
ajst-29623	124	18	term	term	NOUN
ajst-29623	124	19	mechanism[j	mechanism[j	PROPN
ajst-29623	124	20	]	]	PUNCT
ajst-29623	124	21	.	.	PUNCT
ajst-29623	125	1	petrochemical	petrochemical	NOUN
ajst-29623	125	2	industry	industry	NOUN
ajst-29623	125	3	technology	technology	NOUN
ajst-29623	125	4	,	,	PUNCT
ajst-29623	125	5	2022	2022	NUM
ajst-29623	125	6	,	,	PUNCT
ajst-29623	125	7	29(9	29(9	NOUN
ajst-29623	125	8	):	):	PUNCT
ajst-29623	125	9	176	176	NUM
ajst-29623	125	10	178	178	NUM
ajst-29623	125	11	.	.	PUNCT
ajst-29623	126	1	[	[	X
ajst-29623	126	2	2	2	NUM
ajst-29623	126	3	]	]	X
ajst-29623	126	4	li	li	PROPN
ajst-29623	126	5	qiuyang	qiuyang	PROPN
ajst-29623	126	6	,	,	PUNCT
ajst-29623	126	7	zhao	zhao	PROPN
ajst-29623	126	8	minghua	minghua	PROPN
ajst-29623	126	9	,	,	PUNCT
ajst-29623	126	10	ren	ren	PROPN
ajst-29623	126	11	xuejun	xuejun	PROPN
ajst-29623	126	12	,	,	PUNCT
ajst-29623	126	13	et	et	PROPN
ajst-29623	126	14	al	al	PROPN
ajst-29623	126	15	.	.	PUNCT
ajst-29623	126	16	current	current	ADJ
ajst-29623	126	17	situation	situation	NOUN
ajst-29623	126	18	and	and	CCONJ
ajst-29623	126	19	development	development	NOUN
ajst-29623	126	20	trend	trend	NOUN
ajst-29623	126	21	of	of	ADP
ajst-29623	126	22	oil	oil	NOUN
ajst-29623	126	23	and	and	CCONJ
ajst-29623	126	24	gas	gas	NOUN
ajst-29623	126	25	pipeline	pipeline	NOUN
ajst-29623	126	26	construction	construction	NOUN
ajst-29623	126	27	in	in	ADP
ajst-29623	126	28	china[j	china[j	PROPN
ajst-29623	126	29	]	]	PUNCT
ajst-29623	126	30	.	.	PUNCT
ajst-29623	127	1	oil	oil	NOUN
ajst-29623	127	2	gas	gas	NOUN
ajst-29623	127	3	field	field	NOUN
ajst-29623	127	4	surface	surface	NOUN
ajst-29623	127	5	engineering	engineering	NOUN
ajst-29623	127	6	,	,	PUNCT
ajst-29623	127	7	2019	2019	NUM
ajst-29623	127	8	,	,	PUNCT
ajst-29623	127	9	38(1	38(1	NUM
ajst-29623	127	10	):	):	PUNCT
ajst-29623	127	11	14	14	NUM
ajst-29623	127	12	17	17	NUM
ajst-29623	127	13	.	.	PUNCT
ajst-29623	128	1	[	[	X
ajst-29623	128	2	3	3	X
ajst-29623	128	3	]	]	X
ajst-29623	128	4	li	li	PROPN
ajst-29623	128	5	qiuyang	qiuyang	PROPN
ajst-29623	128	6	,	,	PUNCT
ajst-29623	128	7	zhao	zhao	PROPN
ajst-29623	128	8	minghua	minghua	PROPN
ajst-29623	128	9	,	,	PUNCT
ajst-29623	128	10	zhang	zhang	PROPN
ajst-29623	128	11	bin	bin	PROPN
ajst-29623	128	12	,	,	PUNCT
ajst-29623	128	13	et	et	PROPN
ajst-29623	128	14	al	al	PROPN
ajst-29623	128	15	.	.	PUNCT
ajst-29623	128	16	current	current	ADJ
ajst-29623	128	17	situation	situation	NOUN
ajst-29623	128	18	and	and	CCONJ
ajst-29623	128	19	development	development	NOUN
ajst-29623	128	20	trend	trend	NOUN
ajst-29623	128	21	of	of	ADP
ajst-29623	128	22	global	global	ADJ
ajst-29623	128	23	oil	oil	NOUN
ajst-29623	128	24	and	and	CCONJ
ajst-29623	128	25	gas	gas	NOUN
ajst-29623	128	26	pipeline	pipeline	NOUN
ajst-29623	128	27	construction	construction	NOUN
ajst-29623	128	28	in	in	ADP
ajst-29623	128	29	2020[j	2020[j	NUM
ajst-29623	128	30	]	]	PUNCT
ajst-29623	128	31	.	.	PUNCT
ajst-29623	129	1	oil	oil	NOUN
ajst-29623	129	2	&	&	CCONJ
ajst-29623	129	3	gas	gas	NOUN
ajst-29623	129	4	storage	storage	NOUN
ajst-29623	129	5	and	and	CCONJ
ajst-29623	129	6	transportation	transportation	NOUN
ajst-29623	129	7	,	,	PUNCT
ajst-29623	129	8	2021	2021	NUM
ajst-29623	129	9	,	,	PUNCT
ajst-29623	129	10	40(12	40(12	NUM
ajst-29623	129	11	):	):	PUNCT
ajst-29623	129	12	1330	1330	NUM
ajst-29623	129	13	1337	1337	NUM
ajst-29623	129	14	+	+	PROPN
ajst-29623	129	15	1348	1348	NUM
ajst-29623	129	16	..	..	PUNCT
ajst-29623	129	17	299	299	NUM
ajst-29623	130	1	[	[	X
ajst-29623	130	2	4	4	NUM
ajst-29623	130	3	]	]	PUNCT
ajst-29623	130	4	jiang	jiang	PROPN
ajst-29623	130	5	ke	ke	PROPN
ajst-29623	130	6	,	,	PUNCT
ajst-29623	130	7	wang	wang	PROPN
ajst-29623	130	8	dongyuan	dongyuan	PROPN
ajst-29623	130	9	,	,	PUNCT
ajst-29623	130	10	yu	yu	PROPN
ajst-29623	130	11	zhifeng	zhifeng	PROPN
ajst-29623	130	12	,	,	PUNCT
ajst-29623	130	13	et	et	PROPN
ajst-29623	130	14	al	al	PROPN
ajst-29623	130	15	.	.	PROPN
ajst-29623	131	1	failure	failure	NOUN
ajst-29623	131	2	analysis	analysis	NOUN
ajst-29623	131	3	of	of	ADP
ajst-29623	131	4	pipelines	pipeline	NOUN
ajst-29623	131	5	under	under	ADP
ajst-29623	131	6	the	the	DET
ajst-29623	131	7	action	action	NOUN
ajst-29623	131	8	of	of	ADP
ajst-29623	131	9	lateral	lateral	ADJ
ajst-29623	131	10	landslides	landslide	NOUN
ajst-29623	131	11	:	:	PUNCT
ajst-29623	131	12	taking	take	VERB
ajst-29623	131	13	the	the	DET
ajst-29623	131	14	two	two	NUM
ajst-29623	131	15	explosion	explosion	NOUN
ajst-29623	131	16	accidents	accident	NOUN
ajst-29623	131	17	of	of	ADP
ajst-29623	131	18	the	the	DET
ajst-29623	131	19	guizhou	guizhou	PROPN
ajst-29623	131	20	qinglong	qinglong	PROPN
ajst-29623	131	21	section	section	NOUN
ajst-29623	131	22	of	of	ADP
ajst-29623	131	23	the	the	DET
ajst-29623	131	24	china	china	PROPN
ajst-29623	131	25	myanmar	myanmar	PROPN
ajst-29623	131	26	pipeline	pipeline	PROPN
ajst-29623	131	27	as	as	ADP
ajst-29623	131	28	examples[j	examples[j	PROPN
ajst-29623	131	29	]	]	PUNCT
ajst-29623	131	30	.	.	PUNCT
ajst-29623	132	1	science	science	NOUN
ajst-29623	132	2	technology	technology	NOUN
ajst-29623	132	3	and	and	CCONJ
ajst-29623	132	4	engineering	engineering	NOUN
ajst-29623	132	5	,	,	PUNCT
ajst-29623	132	6	2023	2023	NUM
ajst-29623	132	7	,	,	PUNCT
ajst-29623	132	8	23(21	23(21	NUM
ajst-29623	132	9	):	):	PUNCT
ajst-29623	132	10	8988	8988	NUM
ajst-29623	132	11	8995	8995	NUM
ajst-29623	132	12	.	.	PUNCT
ajst-29623	133	1	[	[	X
ajst-29623	133	2	5	5	X
ajst-29623	133	3	]	]	X
ajst-29623	133	4	wang	wang	PROPN
ajst-29623	133	5	junling	junling	PROPN
ajst-29623	133	6	,	,	PUNCT
ajst-29623	133	7	deng	deng	PROPN
ajst-29623	133	8	yulian	yulian	PROPN
ajst-29623	133	9	,	,	PUNCT
ajst-29623	133	10	li	li	PROPN
ajst-29623	133	11	ying	ying	PROPN
ajst-29623	133	12	,	,	PUNCT
ajst-29623	133	13	et	et	PROPN
ajst-29623	133	14	al	al	PROPN
ajst-29623	133	15	.	.	PROPN
ajst-29623	133	16	review	review	NOUN
ajst-29623	133	17	of	of	ADP
ajst-29623	133	18	drainage	drainage	NOUN
ajst-29623	133	19	pipeline	pipeline	NOUN
ajst-29623	133	20	detection	detection	NOUN
ajst-29623	133	21	and	and	CCONJ
ajst-29623	133	22	defect	defect	VERB
ajst-29623	133	23	recognition	recognition	NOUN
ajst-29623	133	24	technologies[j	technologies[j	PROPN
ajst-29623	133	25	]	]	PUNCT
ajst-29623	133	26	.	.	PUNCT
ajst-29623	134	1	science	science	NOUN
ajst-29623	134	2	technology	technology	NOUN
ajst-29623	134	3	and	and	CCONJ
ajst-29623	134	4	engineering	engineering	NOUN
ajst-29623	134	5	,	,	PUNCT
ajst-29623	134	6	2020	2020	NUM
ajst-29623	134	7	,	,	PUNCT
ajst-29623	134	8	20(33	20(33	NUM
ajst-29623	134	9	):	):	PUNCT
ajst-29623	134	10	13520	13520	NUM
ajst-29623	134	11	13528	13528	NUM
ajst-29623	134	12	.	.	PUNCT
ajst-29623	135	1	[	[	X
ajst-29623	135	2	6	6	NUM
ajst-29623	135	3	]	]	X
ajst-29623	135	4	liu	liu	PROPN
ajst-29623	135	5	jinhai	jinhai	PROPN
ajst-29623	135	6	,	,	PUNCT
ajst-29623	135	7	zhao	zhao	PROPN
ajst-29623	135	8	zhen	zhen	PROPN
ajst-29623	135	9	,	,	PUNCT
ajst-29623	135	10	fu	fu	PROPN
ajst-29623	135	11	mingrui	mingrui	PROPN
ajst-29623	135	12	,	,	PUNCT
ajst-29623	135	13	et	et	PROPN
ajst-29623	135	14	al	al	PROPN
ajst-29623	135	15	.	.	PROPN
ajst-29623	135	16	pipeline	pipeline	PROPN
ajst-29623	135	17	weld	weld	NOUN
ajst-29623	135	18	defect	defect	NOUN
ajst-29623	135	19	detection	detection	NOUN
ajst-29623	135	20	method	method	NOUN
ajst-29623	135	21	based	base	VERB
ajst-29623	135	22	on	on	ADP
ajst-29623	135	23	active	active	ADJ
ajst-29623	135	24	small	small	ADJ
ajst-29623	135	25	sample	sample	NOUN
ajst-29623	135	26	learning[j	learning[j	NOUN
ajst-29623	135	27	]	]	PUNCT
ajst-29623	135	28	.	.	PUNCT
ajst-29623	136	1	chinese	chinese	ADJ
ajst-29623	136	2	journal	journal	PROPN
ajst-29623	136	3	of	of	ADP
ajst-29623	136	4	scientific	scientific	ADJ
ajst-29623	136	5	instrument	instrument	NOUN
ajst-29623	136	6	,	,	PUNCT
ajst-29623	136	7	2022	2022	NUM
ajst-29623	136	8	,	,	PUNCT
ajst-29623	136	9	43(11	43(11	NUM
ajst-29623	136	10	):	):	PUNCT
ajst-29623	136	11	252	252	NUM
ajst-29623	136	12	261	261	NUM
ajst-29623	136	13	.	.	PUNCT
ajst-29623	137	1	[	[	X
ajst-29623	137	2	7	7	X
ajst-29623	137	3	]	]	X
ajst-29623	137	4	lv	lv	PROPN
ajst-29623	137	5	mingxuan	mingxuan	PROPN
ajst-29623	137	6	,	,	PUNCT
ajst-29623	137	7	zhang	zhang	PROPN
ajst-29623	137	8	bin	bin	PROPN
ajst-29623	137	9	,	,	PUNCT
ajst-29623	137	10	zhou	zhou	PROPN
ajst-29623	137	11	chao	chao	PROPN
ajst-29623	137	12	.	.	PUNCT
ajst-29623	137	13	parameter	parameter	PROPN
ajst-29623	137	14	debugging	debugging	NOUN
ajst-29623	137	15	of	of	ADP
ajst-29623	137	16	the	the	DET
ajst-29623	137	17	spiral	spiral	ADJ
ajst-29623	137	18	weld	weld	NOUN
ajst-29623	137	19	ultrasonic	ultrasonic	NOUN
ajst-29623	137	20	phased	phase	VERB
ajst-29623	137	21	array	array	NOUN
ajst-29623	137	22	detection	detection	NOUN
ajst-29623	137	23	system[j	system[j	NOUN
ajst-29623	137	24	]	]	PUNCT
ajst-29623	137	25	.	.	PUNCT
ajst-29623	137	26	welded	weld	VERB
ajst-29623	137	27	pipe	pipe	NOUN
ajst-29623	137	28	and	and	CCONJ
ajst-29623	137	29	tube	tube	NOUN
ajst-29623	137	30	,	,	PUNCT
ajst-29623	137	31	2023	2023	NUM
ajst-29623	137	32	,	,	PUNCT
ajst-29623	137	33	46(1	46(1	NUM
ajst-29623	137	34	):	):	PUNCT
ajst-29623	137	35	37	37	NUM
ajst-29623	137	36	41	41	NUM
ajst-29623	137	37	.	.	PUNCT
ajst-29623	138	1	[	[	X
ajst-29623	138	2	8	8	NUM
ajst-29623	138	3	]	]	X
ajst-29623	138	4	tang	tang	PROPN
ajst-29623	138	5	donglin	donglin	PROPN
ajst-29623	138	6	,	,	PUNCT
ajst-29623	138	7	yuan	yuan	PROPN
ajst-29623	138	8	xiaohong	xiaohong	PROPN
ajst-29623	138	9	,	,	PUNCT
ajst-29623	138	10	zhao	zhao	PROPN
ajst-29623	138	11	jiang	jiang	PROPN
ajst-29623	138	12	,	,	PUNCT
ajst-29623	138	13	et	et	PROPN
ajst-29623	138	14	al	al	PROPN
ajst-29623	138	15	.	.	PUNCT
ajst-29623	139	1	design	design	NOUN
ajst-29623	139	2	of	of	ADP
ajst-29623	139	3	an	an	DET
ajst-29623	139	4	on	on	ADP
ajst-29623	139	5	line	line	NOUN
ajst-29623	139	6	ultrasonic	ultrasonic	ADJ
ajst-29623	139	7	detection	detection	NOUN
ajst-29623	139	8	robot	robot	NOUN
ajst-29623	139	9	for	for	ADP
ajst-29623	139	10	internal	internal	ADJ
ajst-29623	139	11	corrosion	corrosion	NOUN
ajst-29623	139	12	defects	defect	NOUN
ajst-29623	139	13	of	of	ADP
ajst-29623	139	14	pipelines[j	pipelines[j	NOUN
ajst-29623	139	15	]	]	PUNCT
ajst-29623	139	16	.	.	PUNCT
ajst-29623	140	1	measurement	measurement	PROPN
ajst-29623	140	2	&	&	CCONJ
ajst-29623	140	3	control	control	PROPN
ajst-29623	140	4	technology	technology	PROPN
ajst-29623	140	5	,	,	PUNCT
ajst-29623	140	6	2015	2015	NUM
ajst-29623	140	7	,	,	PUNCT
ajst-29623	140	8	34(7	34(7	NUM
ajst-29623	140	9	):	):	PUNCT
ajst-29623	140	10	117	117	NUM
ajst-29623	140	11	119	119	NUM
ajst-29623	140	12	+	+	NOUN
ajst-29623	140	13	124	124	NUM
ajst-29623	140	14	.	.	PUNCT
ajst-29623	141	1	[	[	X
ajst-29623	141	2	9	9	NUM
ajst-29623	141	3	]	]	X
ajst-29623	141	4	tian	tian	ADJ
ajst-29623	141	5	ye	ye	NOUN
ajst-29623	141	6	,	,	PUNCT
ajst-29623	141	7	gao	gao	PROPN
ajst-29623	141	8	tao	tao	PROPN
ajst-29623	141	9	,	,	PUNCT
ajst-29623	141	10	xu	xu	PROPN
ajst-29623	141	11	guangda	guangda	PROPN
ajst-29623	141	12	,	,	PUNCT
ajst-29623	141	13	et	et	PROPN
ajst-29623	141	14	al	al	PROPN
ajst-29623	141	15	.	.	PROPN
ajst-29623	141	16	research	research	NOUN
ajst-29623	141	17	on	on	ADP
ajst-29623	141	18	the	the	DET
ajst-29623	141	19	defect	defect	NOUN
ajst-29623	141	20	recognition	recognition	NOUN
ajst-29623	141	21	and	and	CCONJ
ajst-29623	141	22	quantification	quantification	NOUN
ajst-29623	141	23	technology	technology	NOUN
ajst-29623	141	24	of	of	ADP
ajst-29623	141	25	magnetic	magnetic	ADJ
ajst-29623	141	26	flux	flux	PROPN
ajst-29623	141	27	leakage	leakage	PROPN
ajst-29623	141	28	internal	internal	ADJ
ajst-29623	141	29	detection	detection	NOUN
ajst-29623	141	30	for	for	ADP
ajst-29623	141	31	long	long	ADJ
ajst-29623	141	32	distance	distance	NOUN
ajst-29623	141	33	pipelines[j	pipelines[j	PROPN
ajst-29623	141	34	]	]	PUNCT
ajst-29623	141	35	.	.	PUNCT
ajst-29623	142	1	oil	oil	NOUN
ajst-29623	142	2	gas	gas	NOUN
ajst-29623	142	3	field	field	NOUN
ajst-29623	142	4	surface	surface	NOUN
ajst-29623	142	5	engineering	engineering	NOUN
ajst-29623	142	6	,	,	PUNCT
ajst-29623	142	7	2018	2018	NUM
ajst-29623	142	8	,	,	PUNCT
ajst-29623	142	9	37(10	37(10	NUM
ajst-29623	142	10	):	):	PUNCT
ajst-29623	142	11	51	51	NUM
ajst-29623	142	12	54	54	NUM
ajst-29623	142	13	+	+	SYM
ajst-29623	142	14	59	59	NUM
ajst-29623	142	15	.	.	PUNCT
ajst-29623	143	1	[	[	X
ajst-29623	143	2	10	10	NUM
ajst-29623	143	3	]	]	X
ajst-29623	143	4	mo	mo	PROPN
ajst-29623	143	5	li	li	PROPN
ajst-29623	143	6	,	,	PUNCT
ajst-29623	143	7	yong	yong	PROPN
ajst-29623	143	8	hao	hao	PROPN
ajst-29623	143	9	,	,	PUNCT
ajst-29623	143	10	li	li	PROPN
ajst-29623	143	11	changjun	changjun	PROPN
ajst-29623	143	12	,	,	PUNCT
ajst-29623	143	13	et	et	PROPN
ajst-29623	143	14	al	al	PROPN
ajst-29623	143	15	.	.	PROPN
ajst-29623	143	16	numerical	numerical	PROPN
ajst-29623	143	17	simulation	simulation	PROPN
ajst-29623	143	18	study	study	PROPN
ajst-29623	143	19	on	on	ADP
ajst-29623	143	20	magnetic	magnetic	ADJ
ajst-29623	143	21	flux	flux	PROPN
ajst-29623	143	22	leakage	leakage	NOUN
ajst-29623	143	23	detection	detection	NOUN
ajst-29623	143	24	signals	signal	NOUN
ajst-29623	143	25	of	of	ADP
ajst-29623	143	26	combined	combined	ADJ
ajst-29623	143	27	defects	defect	NOUN
ajst-29623	143	28	in	in	ADP
ajst-29623	143	29	oil	oil	NOUN
ajst-29623	143	30	and	and	CCONJ
ajst-29623	143	31	gas	gas	NOUN
ajst-29623	143	32	pipelines[j	pipelines[j	PROPN
ajst-29623	143	33	]	]	PUNCT
ajst-29623	143	34	.	.	PUNCT
ajst-29623	144	1	journal	journal	PROPN
ajst-29623	144	2	of	of	ADP
ajst-29623	144	3	safety	safety	NOUN
ajst-29623	144	4	science	science	NOUN
ajst-29623	144	5	and	and	CCONJ
ajst-29623	144	6	technology	technology	NOUN
ajst-29623	144	7	in	in	ADP
ajst-29623	144	8	china	china	PROPN
ajst-29623	144	9	,	,	PUNCT
ajst-29623	144	10	2024	2024	NUM
ajst-29623	144	11	,	,	PUNCT
ajst-29623	144	12	20(1	20(1	NUM
ajst-29623	144	13	):	):	PUNCT
ajst-29623	144	14	5	5	NUM
ajst-29623	144	15	10	10	NUM
ajst-29623	144	16	.	.	PUNCT
ajst-29623	145	1	[	[	X
ajst-29623	145	2	11	11	NUM
ajst-29623	145	3	]	]	PUNCT
ajst-29623	145	4	cao	cao	PROPN
ajst-29623	145	5	jie	jie	PROPN
ajst-29623	145	6	,	,	PUNCT
ajst-29623	145	7	ma	ma	PROPN
ajst-29623	145	8	jialin	jialin	PROPN
ajst-29623	145	9	,	,	PUNCT
ajst-29623	145	10	huang	huang	PROPN
ajst-29623	145	11	dailin	dailin	PROPN
ajst-29623	145	12	,	,	PUNCT
ajst-29623	145	13	et	et	PROPN
ajst-29623	145	14	al	al	PROPN
ajst-29623	145	15	.	.	PUNCT
ajst-29623	146	1	a	a	DET
ajst-29623	146	2	fault	fault	NOUN
ajst-29623	146	3	diagnosis	diagnosis	NOUN
ajst-29623	146	4	method	method	NOUN
ajst-29623	146	5	based	base	VERB
ajst-29623	146	6	on	on	ADP
ajst-29623	146	7	multi	multi	ADJ
ajst-29623	146	8	channel	channel	NOUN
ajst-29623	146	9	markov	markov	PROPN
ajst-29623	146	10	transition	transition	NOUN
ajst-29623	146	11	fields[j	fields[j	PROPN
ajst-29623	146	12	]	]	PUNCT
ajst-29623	146	13	.	.	PUNCT
ajst-29623	147	1	journal	journal	PROPN
ajst-29623	147	2	of	of	ADP
ajst-29623	147	3	jilin	jilin	PROPN
ajst-29623	147	4	university	university	PROPN
ajst-29623	147	5	(	(	PUNCT
ajst-29623	147	6	engineering	engineering	NOUN
ajst-29623	147	7	and	and	CCONJ
ajst-29623	147	8	technology	technology	NOUN
ajst-29623	147	9	edition	edition	NOUN
ajst-29623	147	10	)	)	PUNCT
ajst-29623	147	11	,	,	PUNCT
ajst-29623	147	12	2022	2022	NUM
ajst-29623	147	13	,	,	PUNCT
ajst-29623	147	14	52(2	52(2	NUM
ajst-29623	147	15	):	):	PUNCT
ajst-29623	147	16	491	491	NUM
ajst-29623	147	17	496	496	NUM
ajst-29623	147	18	.	.	PUNCT
ajst-29623	148	1	[	[	X
ajst-29623	148	2	12	12	NUM
ajst-29623	148	3	]	]	X
ajst-29623	148	4	yee	yee	PROPN
ajst-29623	148	5	k	k	PROPN
ajst-29623	148	6	s.	s.	PROPN
ajst-29623	148	7	numerical	numerical	PROPN
ajst-29623	148	8	solution	solution	NOUN
ajst-29623	148	9	of	of	ADP
ajst-29623	148	10	initial	initial	ADJ
ajst-29623	148	11	boundary	boundary	ADJ
ajst-29623	148	12	value	value	NOUN
ajst-29623	148	13	problems	problem	NOUN
ajst-29623	148	14	involving	involve	VERB
ajst-29623	148	15	maxwell	maxwell	PROPN
ajst-29623	148	16	's	's	PART
ajst-29623	148	17	equations	equation	NOUN
ajst-29623	148	18	in	in	ADP
ajst-29623	148	19	isotropic	isotropic	NOUN
ajst-29623	148	20	media[j	media[j	ADJ
ajst-29623	148	21	]	]	PUNCT
ajst-29623	148	22	.	.	PUNCT
ajst-29623	149	1	ieee	ieee	NOUN
ajst-29623	149	2	transactions	transaction	NOUN
ajst-29623	149	3	on	on	ADP
ajst-29623	149	4	antennas	antenna	NOUN
ajst-29623	149	5	&	&	CCONJ
ajst-29623	149	6	propagation	propagation	NOUN
ajst-29623	149	7	,	,	PUNCT
ajst-29623	149	8	1966	1966	NUM
ajst-29623	149	9	,	,	PUNCT
ajst-29623	149	10	14(5	14(5	NUM
ajst-29623	149	11	):	):	PUNCT
ajst-29623	149	12	daniel	daniel	PROPN
ajst-29623	149	13	j	j	PROPN
ajst-29623	149	14	,	,	PUNCT
ajst-29623	149	15	paulin	paulin	PROPN
ajst-29623	149	16	j	j	PROPN
ajst-29623	149	17	,	,	PUNCT
ajst-29623	149	18	abudhahir	abudhahir	ADJ
ajst-29623	149	19	a.	a.	NOUN
ajst-29623	149	20	characterization	characterization	NOUN
ajst-29623	149	21	of	of	ADP
ajst-29623	149	22	defects	defect	NOUN
ajst-29623	149	23	in	in	ADP
ajst-29623	149	24	magnetic	magnetic	ADJ
ajst-29623	149	25	flux	flux	NOUN
ajst-29623	149	26	leakage	leakage	PROPN
ajst-29623	149	27	(	(	PUNCT
ajst-29623	149	28	mfl	mfl	NOUN
ajst-29623	149	29	)	)	PUNCT
ajst-29623	149	30	images	image	NOUN
ajst-29623	149	31	using	use	VERB
ajst-29623	149	32	wavelet	wavelet	NOUN
ajst-29623	149	33	transform	transform	NOUN
ajst-29623	149	34	and	and	CCONJ
ajst-29623	149	35	neural	neural	ADJ
ajst-29623	149	36	network[c	network[c	PROPN
ajst-29623	149	37	]	]	PUNCT
ajst-29623	149	38	.	.	PUNCT
ajst-29623	150	1	international	international	ADJ
ajst-29623	150	2	conference	conference	NOUN
ajst-29623	150	3	on	on	ADP
ajst-29623	150	4	electronics	electronic	NOUN
ajst-29623	150	5	and	and	CCONJ
ajst-29623	150	6	communication	communication	NOUN
ajst-29623	150	7	systems	system	NOUN
ajst-29623	150	8	.	.	PUNCT
ajst-29623	151	1	ieee	ieee	PROPN
ajst-29623	151	2	,	,	PUNCT
ajst-29623	151	3	2014	2014	NUM
ajst-29623	151	4	.	.	PUNCT
ajst-29623	152	1	[	[	X
ajst-29623	152	2	13	13	NUM
ajst-29623	152	3	]	]	X
ajst-29623	152	4	daniel	daniel	PROPN
ajst-29623	152	5	j	j	PROPN
ajst-29623	152	6	,	,	PUNCT
ajst-29623	152	7	paulin	paulin	PROPN
ajst-29623	152	8	j	j	PROPN
ajst-29623	152	9	,	,	PUNCT
ajst-29623	152	10	abudhahir	abudhahir	ADJ
ajst-29623	152	11	a.	a.	NOUN
ajst-29623	152	12	characterization	characterization	NOUN
ajst-29623	152	13	of	of	ADP
ajst-29623	152	14	defects	defect	NOUN
ajst-29623	152	15	in	in	ADP
ajst-29623	152	16	magnetic	magnetic	ADJ
ajst-29623	152	17	flux	flux	NOUN
ajst-29623	152	18	leakage	leakage	PROPN
ajst-29623	152	19	(	(	PUNCT
ajst-29623	152	20	mfl	mfl	NOUN
ajst-29623	152	21	)	)	PUNCT
ajst-29623	152	22	images	image	NOUN
ajst-29623	152	23	using	use	VERB
ajst-29623	152	24	wavelet	wavelet	NOUN
ajst-29623	152	25	transform	transform	NOUN
ajst-29623	152	26	and	and	CCONJ
ajst-29623	152	27	neural	neural	ADJ
ajst-29623	152	28	network[c	network[c	PROPN
ajst-29623	152	29	]	]	PUNCT
ajst-29623	152	30	.	.	PUNCT
ajst-29623	153	1	international	international	ADJ
ajst-29623	153	2	conference	conference	NOUN
ajst-29623	153	3	on	on	ADP
ajst-29623	153	4	electronics	electronic	NOUN
ajst-29623	153	5	and	and	CCONJ
ajst-29623	153	6	communication	communication	NOUN
ajst-29623	153	7	systems	system	NOUN
ajst-29623	153	8	.	.	PUNCT
ajst-29623	154	1	ieee	ieee	PROPN
ajst-29623	154	2	,	,	PUNCT
ajst-29623	154	3	2014	2014	NUM
ajst-29623	154	4	.	.	PUNCT