id	sid	tid	token	lemma	pos
fcis-6353	1	1	frontiers	frontier	NOUN
fcis-6353	1	2	in	in	ADP
fcis-6353	1	3	computing	computing	NOUN
fcis-6353	1	4	and	and	CCONJ
fcis-6353	1	5	intelligent	intelligent	ADJ
fcis-6353	1	6	systems	system	NOUN
fcis-6353	1	7	issn	issn	VERB
fcis-6353	1	8	:	:	PUNCT
fcis-6353	1	9	2832	2832	NUM
fcis-6353	1	10	-	-	SYM
fcis-6353	1	11	6024	6024	NUM
fcis-6353	1	12	|	|	NOUN
fcis-6353	1	13	vol	vol	NOUN
fcis-6353	1	14	.	.	PROPN
fcis-6353	2	1	3	3	NUM
fcis-6353	2	2	,	,	PUNCT
fcis-6353	2	3	no	no	INTJ
fcis-6353	2	4	.	.	NOUN
fcis-6353	2	5	1	1	NUM
fcis-6353	2	6	,	,	PUNCT
fcis-6353	2	7	2023	2023	NUM
fcis-6353	2	8	154	154	NUM
fcis-6353	2	9	research	research	NOUN
fcis-6353	2	10	on	on	ADP
fcis-6353	2	11	water	water	NOUN
fcis-6353	2	12	garbage	garbage	NOUN
fcis-6353	2	13	detection	detection	NOUN
fcis-6353	2	14	algorithm	algorithm	NOUN
fcis-6353	2	15	based	base	VERB
fcis-6353	2	16	on	on	ADP
fcis-6353	2	17	gfl	gfl	PROPN
fcis-6353	2	18	network	network	NOUN
fcis-6353	2	19	nengmin	nengmin	VERB
fcis-6353	2	20	yi	yi	PROPN
fcis-6353	2	21	*	*	PUNCT
fcis-6353	2	22	,	,	PUNCT
fcis-6353	2	23	weilin	weilin	PROPN
fcis-6353	2	24	luo	luo	PROPN
fcis-6353	2	25	southwest	southwest	PROPN
fcis-6353	2	26	minzu	minzu	PROPN
fcis-6353	2	27	university	university	PROPN
fcis-6353	2	28	,	,	PUNCT
fcis-6353	2	29	chengdu	chengdu	PROPN
fcis-6353	2	30	610200	610200	NUM
fcis-6353	2	31	,	,	PUNCT
fcis-6353	2	32	china	china	PROPN
fcis-6353	2	33	*	*	PUNCT
fcis-6353	2	34	corresponding	correspond	VERB
fcis-6353	2	35	author	author	NOUN
fcis-6353	2	36	:	:	PUNCT
fcis-6353	2	37	nengmin	nengmin	NOUN
fcis-6353	2	38	yi	yi	PROPN
fcis-6353	2	39	abstract	abstract	NOUN
fcis-6353	2	40	:	:	PUNCT
fcis-6353	2	41	aiming	aim	VERB
fcis-6353	2	42	at	at	ADP
fcis-6353	2	43	the	the	DET
fcis-6353	2	44	problem	problem	NOUN
fcis-6353	2	45	that	that	SCONJ
fcis-6353	2	46	existing	exist	VERB
fcis-6353	2	47	water	water	NOUN
fcis-6353	2	48	surface	surface	NOUN
fcis-6353	2	49	garbage	garbage	NOUN
fcis-6353	2	50	detection	detection	NOUN
fcis-6353	2	51	algorithms	algorithm	NOUN
fcis-6353	2	52	can	can	AUX
fcis-6353	2	53	not	not	PART
fcis-6353	2	54	meet	meet	VERB
fcis-6353	2	55	the	the	DET
fcis-6353	2	56	tradeoff	tradeoff	NOUN
fcis-6353	2	57	between	between	ADP
fcis-6353	2	58	real	real	ADJ
fcis-6353	2	59	-	-	PUNCT
fcis-6353	2	60	time	time	NOUN
fcis-6353	2	61	and	and	CCONJ
fcis-6353	2	62	accuracy	accuracy	NOUN
fcis-6353	2	63	in	in	ADP
fcis-6353	2	64	natural	natural	ADJ
fcis-6353	2	65	scenes	scene	NOUN
fcis-6353	2	66	,	,	PUNCT
fcis-6353	2	67	we	we	PRON
fcis-6353	2	68	propose	propose	VERB
fcis-6353	2	69	a	a	DET
fcis-6353	2	70	gfl	gfl	NOUN
fcis-6353	2	71	(	(	PUNCT
fcis-6353	2	72	generalized	generalize	VERB
fcis-6353	2	73	focal	focal	ADJ
fcis-6353	2	74	loss	loss	NOUN
fcis-6353	2	75	)	)	PUNCT
fcis-6353	2	76	based	base	VERB
fcis-6353	2	77	gfl	gfl	PROPN
fcis-6353	2	78	_	_	PUNCT
fcis-6353	2	79	ham	ham	NOUN
fcis-6353	2	80	algorithm	algorithm	NOUN
fcis-6353	2	81	to	to	PART
fcis-6353	2	82	improve	improve	VERB
fcis-6353	2	83	the	the	DET
fcis-6353	2	84	accuracy	accuracy	NOUN
fcis-6353	2	85	of	of	ADP
fcis-6353	2	86	water	water	NOUN
fcis-6353	2	87	surface	surface	NOUN
fcis-6353	2	88	garbage	garbage	NOUN
fcis-6353	2	89	detection	detection	NOUN
fcis-6353	2	90	,	,	PUNCT
fcis-6353	2	91	and	and	CCONJ
fcis-6353	2	92	better	well	ADV
fcis-6353	2	93	apply	apply	VERB
fcis-6353	2	94	to	to	ADP
fcis-6353	2	95	water	water	NOUN
fcis-6353	2	96	surface	surface	NOUN
fcis-6353	2	97	garbage	garbage	NOUN
fcis-6353	2	98	detection	detection	NOUN
fcis-6353	2	99	application	application	NOUN
fcis-6353	2	100	scenarios	scenario	NOUN
fcis-6353	2	101	.	.	PUNCT
fcis-6353	3	1	we	we	PRON
fcis-6353	3	2	have	have	AUX
fcis-6353	3	3	added	add	VERB
fcis-6353	3	4	the	the	DET
fcis-6353	3	5	ham	ham	NOUN
fcis-6353	3	6	(	(	PUNCT
fcis-6353	3	7	hybrid	hybrid	ADJ
fcis-6353	3	8	attention	attention	NOUN
fcis-6353	3	9	model	model	NOUN
fcis-6353	3	10	)	)	PUNCT
fcis-6353	3	11	module	module	NOUN
fcis-6353	3	12	to	to	ADP
fcis-6353	3	13	the	the	DET
fcis-6353	3	14	feature	feature	NOUN
fcis-6353	3	15	extraction	extraction	NOUN
fcis-6353	3	16	module	module	NOUN
fcis-6353	3	17	(	(	PUNCT
fcis-6353	3	18	resnet50	resnet50	NOUN
fcis-6353	3	19	)	)	PUNCT
fcis-6353	3	20	of	of	ADP
fcis-6353	3	21	the	the	DET
fcis-6353	3	22	gfl	gfl	PROPN
fcis-6353	3	23	network	network	NOUN
fcis-6353	3	24	to	to	PART
fcis-6353	3	25	improve	improve	VERB
fcis-6353	3	26	the	the	DET
fcis-6353	3	27	feature	feature	NOUN
fcis-6353	3	28	extraction	extraction	NOUN
fcis-6353	3	29	capability	capability	NOUN
fcis-6353	3	30	of	of	ADP
fcis-6353	3	31	the	the	DET
fcis-6353	3	32	backbone	backbone	NOUN
fcis-6353	3	33	network	network	NOUN
fcis-6353	3	34	and	and	CCONJ
fcis-6353	3	35	the	the	DET
fcis-6353	3	36	network	network	NOUN
fcis-6353	3	37	representation	representation	NOUN
fcis-6353	3	38	capability	capability	NOUN
fcis-6353	3	39	.	.	PUNCT
fcis-6353	4	1	and	and	CCONJ
fcis-6353	4	2	use	use	VERB
fcis-6353	4	3	the	the	DET
fcis-6353	4	4	selfconstructed	selfconstructe	VERB
fcis-6353	4	5	garbage	garbage	NOUN
fcis-6353	4	6	detection	detection	NOUN
fcis-6353	4	7	datasets	dataset	NOUN
fcis-6353	4	8	under	under	ADP
fcis-6353	4	9	natural	natural	ADJ
fcis-6353	4	10	scenes	scene	NOUN
fcis-6353	4	11	for	for	ADP
fcis-6353	4	12	training	training	NOUN
fcis-6353	4	13	and	and	CCONJ
fcis-6353	4	14	testing	testing	NOUN
fcis-6353	4	15	.	.	PUNCT
fcis-6353	5	1	after	after	ADP
fcis-6353	5	2	analyzing	analyze	VERB
fcis-6353	5	3	the	the	DET
fcis-6353	5	4	training	training	NOUN
fcis-6353	5	5	process	process	NOUN
fcis-6353	5	6	and	and	CCONJ
fcis-6353	5	7	test	test	NOUN
fcis-6353	5	8	results	result	NOUN
fcis-6353	5	9	,	,	PUNCT
fcis-6353	5	10	our	our	PRON
fcis-6353	5	11	network	network	NOUN
fcis-6353	5	12	has	have	VERB
fcis-6353	5	13	higher	high	ADJ
fcis-6353	5	14	reliability	reliability	NOUN
fcis-6353	5	15	compared	compare	VERB
fcis-6353	5	16	to	to	ADP
fcis-6353	5	17	the	the	DET
fcis-6353	5	18	current	current	ADJ
fcis-6353	5	19	mainstream	mainstream	ADJ
fcis-6353	5	20	single	single	ADJ
fcis-6353	5	21	stage	stage	NOUN
fcis-6353	5	22	network	network	NOUN
fcis-6353	5	23	.	.	PUNCT
fcis-6353	6	1	our	our	PRON
fcis-6353	6	2	network	network	NOUN
fcis-6353	6	3	can	can	AUX
fcis-6353	6	4	achieve	achieve	VERB
fcis-6353	6	5	60.12	60.12	NUM
fcis-6353	6	6	%	%	NOUN
fcis-6353	6	7	map	map	NOUN
fcis-6353	6	8	on	on	ADP
fcis-6353	6	9	the	the	DET
fcis-6353	6	10	water	water	NOUN
fcis-6353	6	11	surface	surface	NOUN
fcis-6353	6	12	garbage	garbage	NOUN
fcis-6353	6	13	detection	detection	NOUN
fcis-6353	6	14	datasets	dataset	NOUN
fcis-6353	6	15	,	,	PUNCT
fcis-6353	6	16	which	which	PRON
fcis-6353	6	17	is	be	AUX
fcis-6353	6	18	1.09	1.09	NUM
fcis-6353	6	19	%	%	NOUN
fcis-6353	6	20	,	,	PUNCT
fcis-6353	6	21	1.36	1.36	NUM
fcis-6353	6	22	%	%	NOUN
fcis-6353	6	23	,	,	PUNCT
fcis-6353	6	24	and	and	CCONJ
fcis-6353	6	25	1.61	1.61	NUM
fcis-6353	6	26	%	%	NOUN
fcis-6353	6	27	higher	high	ADJ
fcis-6353	6	28	than	than	ADP
fcis-6353	6	29	yolov3	yolov3	PROPN
fcis-6353	6	30	,	,	PUNCT
fcis-6353	6	31	ssd	ssd	PROPN
fcis-6353	6	32	,	,	PUNCT
fcis-6353	6	33	and	and	CCONJ
fcis-6353	6	34	gfl	gfl	PROPN
fcis-6353	6	35	,	,	PUNCT
fcis-6353	6	36	respectively	respectively	ADV
fcis-6353	6	37	.	.	PUNCT
fcis-6353	7	1	keywords	keyword	NOUN
fcis-6353	7	2	:	:	PUNCT
fcis-6353	7	3	garbage	garbage	NOUN
fcis-6353	7	4	detection	detection	NOUN
fcis-6353	7	5	;	;	PUNCT
fcis-6353	7	6	ham	ham	NOUN
fcis-6353	7	7	attention	attention	NOUN
fcis-6353	7	8	mechanism	mechanism	NOUN
fcis-6353	7	9	;	;	PUNCT
fcis-6353	7	10	gfl	gfl	PROPN
fcis-6353	7	11	(	(	PUNCT
fcis-6353	7	12	generalized	generalize	VERB
fcis-6353	7	13	focal	focal	ADJ
fcis-6353	7	14	loss	loss	NOUN
fcis-6353	7	15	)	)	PUNCT
fcis-6353	7	16	.	.	PUNCT
fcis-6353	8	1	1	1	X
fcis-6353	8	2	.	.	X
fcis-6353	8	3	introduction	introduction	NOUN
fcis-6353	8	4	with	with	ADP
fcis-6353	8	5	the	the	DET
fcis-6353	8	6	development	development	NOUN
fcis-6353	8	7	of	of	ADP
fcis-6353	8	8	economy	economy	NOUN
fcis-6353	8	9	and	and	CCONJ
fcis-6353	8	10	society	society	NOUN
fcis-6353	8	11	,	,	PUNCT
fcis-6353	8	12	people	people	NOUN
fcis-6353	8	13	's	's	PART
fcis-6353	8	14	living	living	NOUN
fcis-6353	8	15	standards	standard	NOUN
fcis-6353	8	16	are	be	AUX
fcis-6353	8	17	increasingly	increasingly	ADV
fcis-6353	8	18	improving	improve	VERB
fcis-6353	8	19	,	,	PUNCT
fcis-6353	8	20	and	and	CCONJ
fcis-6353	8	21	intelligent	intelligent	ADJ
fcis-6353	8	22	and	and	CCONJ
fcis-6353	8	23	modern	modern	ADJ
fcis-6353	8	24	lifestyles	lifestyle	NOUN
fcis-6353	8	25	have	have	AUX
fcis-6353	8	26	replaced	replace	VERB
fcis-6353	8	27	traditional	traditional	ADJ
fcis-6353	8	28	production	production	NOUN
fcis-6353	8	29	and	and	CCONJ
fcis-6353	8	30	life	life	NOUN
fcis-6353	8	31	.	.	PUNCT
fcis-6353	9	1	diversified	diversify	VERB
fcis-6353	9	2	production	production	NOUN
fcis-6353	9	3	and	and	CCONJ
fcis-6353	9	4	lifestyle	lifestyle	NOUN
fcis-6353	9	5	is	be	AUX
fcis-6353	9	6	a	a	DET
fcis-6353	9	7	doubleedged	doubleedge	VERB
fcis-6353	9	8	sword	sword	NOUN
fcis-6353	9	9	,	,	PUNCT
fcis-6353	9	10	and	and	CCONJ
fcis-6353	9	11	the	the	DET
fcis-6353	9	12	rising	rise	VERB
fcis-6353	9	13	quality	quality	NOUN
fcis-6353	9	14	of	of	ADP
fcis-6353	9	15	life	life	NOUN
fcis-6353	9	16	has	have	AUX
fcis-6353	9	17	brought	bring	VERB
fcis-6353	9	18	serious	serious	ADJ
fcis-6353	9	19	environmental	environmental	ADJ
fcis-6353	9	20	problems	problem	NOUN
fcis-6353	9	21	.	.	PUNCT
fcis-6353	10	1	changes	change	NOUN
fcis-6353	10	2	in	in	ADP
fcis-6353	10	3	production	production	NOUN
fcis-6353	10	4	and	and	CCONJ
fcis-6353	10	5	lifestyle	lifestyle	NOUN
fcis-6353	10	6	have	have	AUX
fcis-6353	10	7	brought	bring	VERB
fcis-6353	10	8	more	more	ADV
fcis-6353	10	9	serious	serious	ADJ
fcis-6353	10	10	environmental	environmental	ADJ
fcis-6353	10	11	pollution	pollution	NOUN
fcis-6353	10	12	.	.	PUNCT
fcis-6353	11	1	the	the	DET
fcis-6353	11	2	global	global	PROPN
fcis-6353	11	3	environment	environment	PROPN
fcis-6353	11	4	outlook	outlook	VERB
fcis-6353	11	5	6	6	NUM
fcis-6353	12	1	[	[	X
fcis-6353	12	2	1	1	NUM
fcis-6353	12	3	]	]	PUNCT
fcis-6353	12	4	mentions	mention	VERB
fcis-6353	12	5	that	that	SCONJ
fcis-6353	12	6	the	the	DET
fcis-6353	12	7	earth	earth	NOUN
fcis-6353	12	8	on	on	ADP
fcis-6353	12	9	which	which	PRON
fcis-6353	12	10	people	people	NOUN
fcis-6353	12	11	depend	depend	VERB
fcis-6353	12	12	for	for	ADP
fcis-6353	12	13	their	their	PRON
fcis-6353	12	14	survival	survival	NOUN
fcis-6353	12	15	has	have	AUX
fcis-6353	12	16	suffered	suffer	VERB
fcis-6353	12	17	extremely	extremely	ADV
fcis-6353	12	18	serious	serious	ADJ
fcis-6353	12	19	environmental	environmental	ADJ
fcis-6353	12	20	pollution	pollution	NOUN
fcis-6353	12	21	and	and	CCONJ
fcis-6353	12	22	damage	damage	NOUN
fcis-6353	12	23	.	.	PUNCT
fcis-6353	13	1	if	if	SCONJ
fcis-6353	13	2	effective	effective	ADJ
fcis-6353	13	3	and	and	CCONJ
fcis-6353	13	4	more	more	ADV
fcis-6353	13	5	vigorous	vigorous	ADJ
fcis-6353	13	6	actions	action	NOUN
fcis-6353	13	7	are	be	AUX
fcis-6353	13	8	not	not	PART
fcis-6353	13	9	taken	take	VERB
fcis-6353	13	10	to	to	PART
fcis-6353	13	11	protect	protect	VERB
fcis-6353	13	12	the	the	DET
fcis-6353	13	13	ecological	ecological	ADJ
fcis-6353	13	14	environment	environment	NOUN
fcis-6353	13	15	,	,	PUNCT
fcis-6353	13	16	the	the	DET
fcis-6353	13	17	earth	earth	NOUN
fcis-6353	13	18	's	's	PART
fcis-6353	13	19	ecosystem	ecosystem	NOUN
fcis-6353	13	20	and	and	CCONJ
fcis-6353	13	21	the	the	DET
fcis-6353	13	22	sustainable	sustainable	ADJ
fcis-6353	13	23	development	development	NOUN
fcis-6353	13	24	of	of	ADP
fcis-6353	13	25	mankind	mankind	NOUN
fcis-6353	13	26	will	will	AUX
fcis-6353	13	27	inevitably	inevitably	ADV
fcis-6353	13	28	be	be	AUX
fcis-6353	13	29	more	more	ADV
fcis-6353	13	30	severely	severely	ADV
fcis-6353	13	31	threatened	threaten	VERB
fcis-6353	13	32	.	.	PUNCT
fcis-6353	14	1	in	in	ADP
fcis-6353	14	2	the	the	DET
fcis-6353	14	3	face	face	NOUN
fcis-6353	14	4	of	of	ADP
fcis-6353	14	5	the	the	DET
fcis-6353	14	6	destruction	destruction	NOUN
fcis-6353	14	7	of	of	ADP
fcis-6353	14	8	the	the	DET
fcis-6353	14	9	ecological	ecological	ADJ
fcis-6353	14	10	environment	environment	NOUN
fcis-6353	14	11	and	and	CCONJ
fcis-6353	14	12	the	the	DET
fcis-6353	14	13	increasing	increase	VERB
fcis-6353	14	14	production	production	NOUN
fcis-6353	14	15	of	of	ADP
fcis-6353	14	16	garbage	garbage	NOUN
fcis-6353	14	17	,	,	PUNCT
fcis-6353	14	18	how	how	SCONJ
fcis-6353	14	19	to	to	PART
fcis-6353	14	20	effectively	effectively	ADV
fcis-6353	14	21	and	and	CCONJ
fcis-6353	14	22	quickly	quickly	ADV
fcis-6353	14	23	resolve	resolve	VERB
fcis-6353	14	24	garbage	garbage	NOUN
fcis-6353	14	25	classification	classification	NOUN
fcis-6353	14	26	,	,	PUNCT
fcis-6353	14	27	monitoring	monitoring	NOUN
fcis-6353	14	28	,	,	PUNCT
fcis-6353	14	29	and	and	CCONJ
fcis-6353	14	30	recycling	recycling	NOUN
fcis-6353	14	31	,	,	PUNCT
fcis-6353	14	32	and	and	CCONJ
fcis-6353	14	33	improve	improve	VERB
fcis-6353	14	34	the	the	DET
fcis-6353	14	35	ecological	ecological	ADJ
fcis-6353	14	36	environment	environment	NOUN
fcis-6353	14	37	,	,	PUNCT
fcis-6353	14	38	is	be	AUX
fcis-6353	14	39	one	one	NUM
fcis-6353	14	40	of	of	ADP
fcis-6353	14	41	the	the	DET
fcis-6353	14	42	urgent	urgent	ADJ
fcis-6353	14	43	issues	issue	NOUN
fcis-6353	14	44	that	that	PRON
fcis-6353	14	45	need	need	VERB
fcis-6353	14	46	to	to	PART
fcis-6353	14	47	be	be	AUX
fcis-6353	14	48	addressed	address	VERB
fcis-6353	14	49	throughout	throughout	ADP
fcis-6353	14	50	the	the	DET
fcis-6353	14	51	world	world	NOUN
fcis-6353	14	52	and	and	CCONJ
fcis-6353	14	53	all	all	DET
fcis-6353	14	54	mankind	mankind	NOUN
fcis-6353	14	55	.	.	PUNCT
fcis-6353	15	1	from	from	ADP
fcis-6353	15	2	may	may	PROPN
fcis-6353	15	3	1	1	NUM
fcis-6353	15	4	,	,	PUNCT
fcis-6353	15	5	2020	2020	NUM
fcis-6353	15	6	,	,	PUNCT
fcis-6353	15	7	the	the	DET
fcis-6353	15	8	"	"	PUNCT
fcis-6353	15	9	beijing	beijing	PROPN
fcis-6353	15	10	municipal	municipal	ADJ
fcis-6353	15	11	regulations	regulation	NOUN
fcis-6353	15	12	on	on	ADP
fcis-6353	15	13	the	the	DET
fcis-6353	15	14	administration	administration	NOUN
fcis-6353	15	15	of	of	ADP
fcis-6353	15	16	domestic	domestic	ADJ
fcis-6353	15	17	waste	waste	NOUN
fcis-6353	15	18	"	"	PUNCT
fcis-6353	15	19	[	[	X
fcis-6353	15	20	2	2	NUM
fcis-6353	15	21	]	]	PUNCT
fcis-6353	15	22	proposed	propose	VERB
fcis-6353	15	23	the	the	DET
fcis-6353	15	24	concept	concept	NOUN
fcis-6353	15	25	of	of	ADP
fcis-6353	15	26	domestic	domestic	ADJ
fcis-6353	15	27	waste	waste	NOUN
fcis-6353	15	28	classification	classification	NOUN
fcis-6353	15	29	,	,	PUNCT
fcis-6353	15	30	requiring	require	VERB
fcis-6353	15	31	the	the	DET
fcis-6353	15	32	public	public	NOUN
fcis-6353	15	33	to	to	PART
fcis-6353	15	34	establish	establish	VERB
fcis-6353	15	35	a	a	DET
fcis-6353	15	36	sense	sense	NOUN
fcis-6353	15	37	of	of	ADP
fcis-6353	15	38	waste	waste	NOUN
fcis-6353	15	39	classification	classification	NOUN
fcis-6353	15	40	and	and	CCONJ
fcis-6353	15	41	actively	actively	ADV
fcis-6353	15	42	classify	classify	VERB
fcis-6353	15	43	domestic	domestic	ADJ
fcis-6353	15	44	waste	waste	NOUN
fcis-6353	15	45	,	,	PUNCT
fcis-6353	15	46	which	which	PRON
fcis-6353	15	47	is	be	AUX
fcis-6353	15	48	conducive	conducive	ADJ
fcis-6353	15	49	to	to	ADP
fcis-6353	15	50	the	the	DET
fcis-6353	15	51	classified	classified	ADJ
fcis-6353	15	52	treatment	treatment	NOUN
fcis-6353	15	53	and	and	CCONJ
fcis-6353	15	54	recycling	recycling	NOUN
fcis-6353	15	55	of	of	ADP
fcis-6353	15	56	waste	waste	NOUN
fcis-6353	15	57	.	.	PUNCT
fcis-6353	16	1	it	it	PRON
fcis-6353	16	2	can	can	AUX
fcis-6353	16	3	be	be	AUX
fcis-6353	16	4	seen	see	VERB
fcis-6353	16	5	that	that	SCONJ
fcis-6353	16	6	the	the	DET
fcis-6353	16	7	task	task	NOUN
fcis-6353	16	8	of	of	ADP
fcis-6353	16	9	garbage	garbage	NOUN
fcis-6353	16	10	classification	classification	NOUN
fcis-6353	16	11	has	have	AUX
fcis-6353	16	12	become	become	VERB
fcis-6353	16	13	an	an	DET
fcis-6353	16	14	integral	integral	ADJ
fcis-6353	16	15	part	part	NOUN
fcis-6353	16	16	of	of	ADP
fcis-6353	16	17	garbage	garbage	NOUN
fcis-6353	16	18	treatment	treatment	NOUN
fcis-6353	16	19	and	and	CCONJ
fcis-6353	16	20	environmental	environmental	ADJ
fcis-6353	16	21	protection	protection	NOUN
fcis-6353	16	22	.	.	PUNCT
fcis-6353	17	1	due	due	ADP
fcis-6353	17	2	to	to	ADP
fcis-6353	17	3	the	the	DET
fcis-6353	17	4	insufficient	insufficient	ADJ
fcis-6353	17	5	awareness	awareness	NOUN
fcis-6353	17	6	of	of	ADP
fcis-6353	17	7	environmental	environmental	ADJ
fcis-6353	17	8	protection	protection	NOUN
fcis-6353	17	9	among	among	ADP
fcis-6353	17	10	some	some	DET
fcis-6353	17	11	people	people	NOUN
fcis-6353	17	12	,	,	PUNCT
fcis-6353	17	13	some	some	DET
fcis-6353	17	14	garbage	garbage	NOUN
fcis-6353	17	15	has	have	AUX
fcis-6353	17	16	been	be	AUX
fcis-6353	17	17	dumped	dump	VERB
fcis-6353	17	18	into	into	ADP
fcis-6353	17	19	the	the	DET
fcis-6353	17	20	water	water	NOUN
fcis-6353	17	21	.	.	PUNCT
fcis-6353	18	1	failure	failure	NOUN
fcis-6353	18	2	to	to	PART
fcis-6353	18	3	clean	clean	VERB
fcis-6353	18	4	it	it	PRON
fcis-6353	18	5	up	up	ADP
fcis-6353	18	6	in	in	ADP
fcis-6353	18	7	a	a	DET
fcis-6353	18	8	timely	timely	ADJ
fcis-6353	18	9	manner	manner	NOUN
fcis-6353	18	10	will	will	AUX
fcis-6353	18	11	have	have	VERB
fcis-6353	18	12	a	a	DET
fcis-6353	18	13	serious	serious	ADJ
fcis-6353	18	14	impact	impact	NOUN
fcis-6353	18	15	on	on	ADP
fcis-6353	18	16	people	people	NOUN
fcis-6353	18	17	's	's	PART
fcis-6353	18	18	health	health	NOUN
fcis-6353	18	19	and	and	CCONJ
fcis-6353	18	20	production	production	NOUN
fcis-6353	18	21	.	.	PUNCT
fcis-6353	19	1	the	the	DET
fcis-6353	19	2	traditional	traditional	ADJ
fcis-6353	19	3	method	method	NOUN
fcis-6353	19	4	of	of	ADP
fcis-6353	19	5	water	water	NOUN
fcis-6353	19	6	surface	surface	NOUN
fcis-6353	19	7	garbage	garbage	NOUN
fcis-6353	19	8	removal	removal	NOUN
fcis-6353	19	9	is	be	AUX
fcis-6353	19	10	manual	manual	ADJ
fcis-6353	19	11	salvage	salvage	NOUN
fcis-6353	19	12	.	.	PUNCT
fcis-6353	20	1	this	this	DET
fcis-6353	20	2	traditional	traditional	ADJ
fcis-6353	20	3	method	method	NOUN
fcis-6353	20	4	of	of	ADP
fcis-6353	20	5	water	water	NOUN
fcis-6353	20	6	surface	surface	NOUN
fcis-6353	20	7	garbage	garbage	NOUN
fcis-6353	20	8	removal	removal	NOUN
fcis-6353	20	9	not	not	PART
fcis-6353	20	10	only	only	ADV
fcis-6353	20	11	can	can	AUX
fcis-6353	20	12	not	not	PART
fcis-6353	20	13	ensure	ensure	VERB
fcis-6353	20	14	safety	safety	NOUN
fcis-6353	20	15	and	and	CCONJ
fcis-6353	20	16	trash	trash	NOUN
fcis-6353	20	17	removal	removal	NOUN
fcis-6353	20	18	,	,	PUNCT
fcis-6353	20	19	but	but	CCONJ
fcis-6353	20	20	also	also	ADV
fcis-6353	20	21	has	have	VERB
fcis-6353	20	22	high	high	ADJ
fcis-6353	20	23	salvage	salvage	NOUN
fcis-6353	20	24	time	time	NOUN
fcis-6353	20	25	and	and	CCONJ
fcis-6353	20	26	economic	economic	ADJ
fcis-6353	20	27	cost	cost	NOUN
fcis-6353	20	28	,	,	PUNCT
fcis-6353	20	29	which	which	PRON
fcis-6353	20	30	is	be	AUX
fcis-6353	20	31	not	not	PART
fcis-6353	20	32	sufficient	sufficient	ADJ
fcis-6353	20	33	to	to	PART
fcis-6353	20	34	meet	meet	VERB
fcis-6353	20	35	the	the	DET
fcis-6353	20	36	requirements	requirement	NOUN
fcis-6353	20	37	of	of	ADP
fcis-6353	20	38	safety	safety	NOUN
fcis-6353	20	39	and	and	CCONJ
fcis-6353	20	40	timeliness	timeliness	NOUN
fcis-6353	20	41	of	of	ADP
fcis-6353	20	42	water	water	NOUN
fcis-6353	20	43	surface	surface	NOUN
fcis-6353	20	44	garbage	garbage	NOUN
fcis-6353	20	45	removal	removal	NOUN
fcis-6353	20	46	.	.	PUNCT
fcis-6353	21	1	the	the	DET
fcis-6353	21	2	emergence	emergence	NOUN
fcis-6353	21	3	of	of	ADP
fcis-6353	21	4	surface	surface	NOUN
fcis-6353	21	5	garbage	garbage	NOUN
fcis-6353	21	6	salvage	salvage	NOUN
fcis-6353	21	7	robots	robot	NOUN
fcis-6353	21	8	has	have	AUX
fcis-6353	21	9	solved	solve	VERB
fcis-6353	21	10	the	the	DET
fcis-6353	21	11	problems	problem	NOUN
fcis-6353	21	12	of	of	ADP
fcis-6353	21	13	safety	safety	NOUN
fcis-6353	21	14	and	and	CCONJ
fcis-6353	21	15	timeliness	timeliness	NOUN
fcis-6353	21	16	in	in	ADP
fcis-6353	21	17	surface	surface	NOUN
fcis-6353	21	18	garbage	garbage	NOUN
fcis-6353	21	19	salvage	salvage	NOUN
fcis-6353	21	20	the	the	DET
fcis-6353	21	21	water	water	NOUN
fcis-6353	21	22	surface	surface	NOUN
fcis-6353	21	23	garbage	garbage	NOUN
fcis-6353	21	24	detection	detection	NOUN
fcis-6353	21	25	algorithm	algorithm	NOUN
fcis-6353	21	26	is	be	AUX
fcis-6353	21	27	one	one	NUM
fcis-6353	21	28	of	of	ADP
fcis-6353	21	29	the	the	DET
fcis-6353	21	30	core	core	NOUN
fcis-6353	21	31	technologies	technology	NOUN
fcis-6353	21	32	of	of	ADP
fcis-6353	21	33	the	the	DET
fcis-6353	21	34	water	water	NOUN
fcis-6353	21	35	surface	surface	NOUN
fcis-6353	21	36	garbage	garbage	NOUN
fcis-6353	21	37	salvage	salvage	NOUN
fcis-6353	21	38	robot	robot	NOUN
fcis-6353	21	39	[	[	X
fcis-6353	21	40	3	3	NUM
fcis-6353	21	41	]	]	PUNCT
fcis-6353	21	42	,	,	PUNCT
fcis-6353	21	43	and	and	CCONJ
fcis-6353	21	44	the	the	DET
fcis-6353	21	45	research	research	NOUN
fcis-6353	21	46	and	and	CCONJ
fcis-6353	21	47	development	development	NOUN
fcis-6353	21	48	of	of	ADP
fcis-6353	21	49	the	the	DET
fcis-6353	21	50	water	water	NOUN
fcis-6353	21	51	surface	surface	NOUN
fcis-6353	21	52	garbage	garbage	NOUN
fcis-6353	21	53	detection	detection	NOUN
fcis-6353	21	54	algorithm	algorithm	NOUN
fcis-6353	21	55	is	be	AUX
fcis-6353	21	56	closely	closely	ADV
fcis-6353	21	57	related	relate	VERB
fcis-6353	21	58	to	to	ADP
fcis-6353	21	59	the	the	DET
fcis-6353	21	60	water	water	NOUN
fcis-6353	21	61	surface	surface	NOUN
fcis-6353	21	62	garbage	garbage	NOUN
fcis-6353	21	63	salvage	salvage	NOUN
fcis-6353	21	64	robot	robot	NOUN
fcis-6353	21	65	.	.	PUNCT
fcis-6353	22	1	in	in	ADP
fcis-6353	22	2	recent	recent	ADJ
fcis-6353	22	3	years	year	NOUN
fcis-6353	22	4	,	,	PUNCT
fcis-6353	22	5	the	the	DET
fcis-6353	22	6	research	research	NOUN
fcis-6353	22	7	on	on	ADP
fcis-6353	22	8	water	water	NOUN
fcis-6353	22	9	surface	surface	NOUN
fcis-6353	22	10	target	target	NOUN
fcis-6353	22	11	detection	detection	NOUN
fcis-6353	22	12	algorithms	algorithm	NOUN
fcis-6353	22	13	has	have	AUX
fcis-6353	22	14	developed	develop	VERB
fcis-6353	22	15	vigorously	vigorously	ADV
fcis-6353	22	16	,	,	PUNCT
fcis-6353	22	17	and	and	CCONJ
fcis-6353	22	18	a	a	DET
fcis-6353	22	19	large	large	ADJ
fcis-6353	22	20	number	number	NOUN
fcis-6353	22	21	of	of	ADP
fcis-6353	22	22	water	water	NOUN
fcis-6353	22	23	surface	surface	NOUN
fcis-6353	22	24	target	target	NOUN
fcis-6353	22	25	detection	detection	NOUN
fcis-6353	22	26	algorithms	algorithm	NOUN
fcis-6353	22	27	have	have	AUX
fcis-6353	22	28	emerged	emerge	VERB
fcis-6353	22	29	.	.	PUNCT
fcis-6353	23	1	in	in	ADP
fcis-6353	23	2	2005	2005	NUM
fcis-6353	23	3	,	,	PUNCT
fcis-6353	23	4	hou	hou	PROPN
fcis-6353	24	1	[	[	X
fcis-6353	24	2	4	4	X
fcis-6353	24	3	]	]	PUNCT
fcis-6353	24	4	et	et	PROPN
fcis-6353	24	5	al	al	PROPN
fcis-6353	24	6	.	.	PROPN
fcis-6353	24	7	proposed	propose	VERB
fcis-6353	24	8	a	a	DET
fcis-6353	24	9	method	method	NOUN
fcis-6353	24	10	for	for	ADP
fcis-6353	24	11	detecting	detect	VERB
fcis-6353	24	12	significant	significant	ADJ
fcis-6353	24	13	water	water	NOUN
fcis-6353	24	14	surface	surface	NOUN
fcis-6353	24	15	targets	target	NOUN
fcis-6353	24	16	based	base	VERB
fcis-6353	24	17	on	on	ADP
fcis-6353	24	18	change	change	NOUN
fcis-6353	24	19	detection	detection	NOUN
fcis-6353	24	20	background	background	NOUN
fcis-6353	24	21	modeling	modeling	NOUN
fcis-6353	24	22	.	.	PUNCT
fcis-6353	25	1	when	when	SCONJ
fcis-6353	25	2	the	the	DET
fcis-6353	25	3	difference	difference	NOUN
fcis-6353	25	4	in	in	ADP
fcis-6353	25	5	target	target	NOUN
fcis-6353	25	6	background	background	NOUN
fcis-6353	25	7	changes	change	NOUN
fcis-6353	25	8	is	be	AUX
fcis-6353	25	9	small	small	ADJ
fcis-6353	25	10	,	,	PUNCT
fcis-6353	25	11	the	the	DET
fcis-6353	25	12	detection	detection	NOUN
fcis-6353	25	13	accuracy	accuracy	NOUN
fcis-6353	25	14	of	of	ADP
fcis-6353	25	15	the	the	DET
fcis-6353	25	16	algorithm	algorithm	NOUN
fcis-6353	25	17	will	will	AUX
fcis-6353	25	18	decrease	decrease	VERB
fcis-6353	25	19	.	.	PUNCT
fcis-6353	26	1	in	in	ADP
fcis-6353	26	2	2013	2013	NUM
fcis-6353	26	3	,	,	PUNCT
fcis-6353	26	4	huang	huang	PROPN
fcis-6353	26	5	et	et	PROPN
fcis-6353	26	6	al[5	al[5	PROPN
fcis-6353	26	7	]	]	PUNCT
fcis-6353	26	8	.	.	PUNCT
fcis-6353	27	1	proposed	propose	VERB
fcis-6353	27	2	an	an	DET
fcis-6353	27	3	algorithm	algorithm	NOUN
fcis-6353	27	4	for	for	ADP
fcis-6353	27	5	extracting	extract	VERB
fcis-6353	27	6	and	and	CCONJ
fcis-6353	27	7	detecting	detect	VERB
fcis-6353	27	8	water	water	NOUN
fcis-6353	27	9	objects	object	NOUN
fcis-6353	27	10	in	in	ADP
fcis-6353	27	11	natural	natural	ADJ
fcis-6353	27	12	scenes	scene	NOUN
fcis-6353	27	13	based	base	VERB
fcis-6353	27	14	on	on	ADP
fcis-6353	27	15	water	water	NOUN
fcis-6353	27	16	color	color	NOUN
fcis-6353	27	17	and	and	CCONJ
fcis-6353	27	18	texture	texture	NOUN
fcis-6353	27	19	features	feature	NOUN
fcis-6353	27	20	.	.	PUNCT
fcis-6353	28	1	in	in	ADP
fcis-6353	28	2	2016	2016	NUM
fcis-6353	28	3	,	,	PUNCT
fcis-6353	28	4	yang	yang	PROPN
fcis-6353	28	5	et	et	PROPN
fcis-6353	28	6	al	al	PROPN
fcis-6353	28	7	.	.	PUNCT
fcis-6353	29	1	[	[	X
fcis-6353	29	2	6	6	NUM
fcis-6353	29	3	]	]	PUNCT
fcis-6353	29	4	proposed	propose	VERB
fcis-6353	29	5	a	a	DET
fcis-6353	29	6	garbage	garbage	NOUN
fcis-6353	29	7	classification	classification	NOUN
fcis-6353	29	8	system	system	NOUN
fcis-6353	29	9	based	base	VERB
fcis-6353	29	10	on	on	ADP
fcis-6353	29	11	support	support	NOUN
fcis-6353	29	12	vector	vector	NOUN
fcis-6353	29	13	machine	machine	NOUN
fcis-6353	29	14	(	(	PUNCT
fcis-6353	29	15	svm	svm	PROPN
fcis-6353	29	16	)	)	PUNCT
fcis-6353	29	17	,	,	PUNCT
fcis-6353	29	18	which	which	PRON
fcis-6353	29	19	uses	use	VERB
fcis-6353	29	20	a	a	DET
fcis-6353	29	21	sliding	slide	VERB
fcis-6353	29	22	window	window	NOUN
fcis-6353	29	23	of	of	ADP
fcis-6353	29	24	constant	constant	ADJ
fcis-6353	29	25	size	size	NOUN
fcis-6353	29	26	to	to	PART
fcis-6353	29	27	extract	extract	VERB
fcis-6353	29	28	features	feature	NOUN
fcis-6353	29	29	from	from	ADP
fcis-6353	29	30	the	the	DET
fcis-6353	29	31	input	input	NOUN
fcis-6353	29	32	image	image	NOUN
fcis-6353	29	33	,	,	PUNCT
fcis-6353	29	34	and	and	CCONJ
fcis-6353	29	35	then	then	ADV
fcis-6353	29	36	uses	use	VERB
fcis-6353	29	37	a	a	DET
fcis-6353	29	38	svm	svm	ADJ
fcis-6353	29	39	classifier	classifier	NOUN
fcis-6353	29	40	to	to	PART
fcis-6353	29	41	achieve	achieve	VERB
fcis-6353	29	42	garbage	garbage	NOUN
fcis-6353	29	43	classification	classification	NOUN
fcis-6353	29	44	.	.	PUNCT
fcis-6353	30	1	this	this	PRON
fcis-6353	30	2	achieved	achieve	VERB
fcis-6353	30	3	a	a	DET
fcis-6353	30	4	classification	classification	NOUN
fcis-6353	30	5	accuracy	accuracy	NOUN
fcis-6353	30	6	of	of	ADP
fcis-6353	30	7	63	63	NUM
fcis-6353	30	8	%	%	NOUN
fcis-6353	30	9	on	on	ADP
fcis-6353	30	10	a	a	DET
fcis-6353	30	11	self	self	NOUN
fcis-6353	30	12	-	-	PUNCT
fcis-6353	30	13	built	build	VERB
fcis-6353	30	14	dataset	dataset	NOUN
fcis-6353	30	15	,	,	PUNCT
fcis-6353	30	16	but	but	CCONJ
fcis-6353	30	17	it	it	PRON
fcis-6353	30	18	can	can	AUX
fcis-6353	30	19	only	only	ADV
fcis-6353	30	20	achieve	achieve	VERB
fcis-6353	30	21	garbage	garbage	NOUN
fcis-6353	30	22	image	image	NOUN
fcis-6353	30	23	classification	classification	NOUN
fcis-6353	30	24	and	and	CCONJ
fcis-6353	30	25	can	can	AUX
fcis-6353	30	26	not	not	PART
fcis-6353	30	27	detect	detect	VERB
fcis-6353	30	28	the	the	DET
fcis-6353	30	29	specific	specific	ADJ
fcis-6353	30	30	location	location	NOUN
fcis-6353	30	31	of	of	ADP
fcis-6353	30	32	garbage	garbage	NOUN
fcis-6353	30	33	in	in	ADP
fcis-6353	30	34	the	the	DET
fcis-6353	30	35	image	image	NOUN
fcis-6353	30	36	.	.	PUNCT
fcis-6353	31	1	in	in	ADP
fcis-6353	31	2	2021	2021	NUM
fcis-6353	31	3	,	,	PUNCT
fcis-6353	31	4	wang	wang	PROPN
fcis-6353	31	5	et	et	PROPN
fcis-6353	31	6	al	al	PROPN
fcis-6353	31	7	.	.	PUNCT
fcis-6353	32	1	[	[	X
fcis-6353	32	2	7	7	X
fcis-6353	32	3	]	]	PUNCT
fcis-6353	32	4	proposed	propose	VERB
fcis-6353	32	5	a	a	DET
fcis-6353	32	6	garbage	garbage	NOUN
fcis-6353	32	7	detection	detection	NOUN
fcis-6353	32	8	method	method	NOUN
fcis-6353	32	9	based	base	VERB
fcis-6353	32	10	on	on	ADP
fcis-6353	32	11	the	the	DET
fcis-6353	32	12	two	two	NUM
fcis-6353	32	13	-	-	PUNCT
fcis-6353	32	14	stage	stage	NOUN
fcis-6353	32	15	target	target	NOUN
fcis-6353	32	16	detection	detection	NOUN
fcis-6353	32	17	algorithm	algorithm	NOUN
fcis-6353	32	18	faster	fast	ADV
fcis-6353	32	19	-	-	PUNCT
fcis-6353	32	20	rcnn	rcnn	NOUN
fcis-6353	32	21	,	,	PUNCT
fcis-6353	32	22	which	which	PRON
fcis-6353	32	23	can	can	AUX
fcis-6353	32	24	achieve	achieve	VERB
fcis-6353	32	25	83.4	83.4	NUM
fcis-6353	32	26	%	%	NOUN
fcis-6353	32	27	map	map	NOUN
fcis-6353	32	28	(	(	PUNCT
fcis-6353	32	29	average	average	ADJ
fcis-6353	32	30	accuracy	accuracy	NOUN
fcis-6353	32	31	)	)	PUNCT
fcis-6353	32	32	at	at	ADP
fcis-6353	32	33	6.3fps	6.3fps	NUM
fcis-6353	32	34	and	and	CCONJ
fcis-6353	32	35	85.5	85.5	NUM
fcis-6353	32	36	%	%	NOUN
fcis-6353	32	37	map	map	NOUN
fcis-6353	32	38	(	(	PUNCT
fcis-6353	32	39	average	average	ADJ
fcis-6353	32	40	accuracy	accuracy	NOUN
fcis-6353	32	41	)	)	PUNCT
fcis-6353	32	42	at	at	ADP
fcis-6353	32	43	6.8fps	6.8fps	NUM
fcis-6353	32	44	on	on	ADP
fcis-6353	32	45	their	their	PRON
fcis-6353	32	46	own	own	ADJ
fcis-6353	32	47	dry	dry	ADJ
fcis-6353	32	48	and	and	CCONJ
fcis-6353	32	49	wet	wet	ADJ
fcis-6353	32	50	garbage	garbage	NOUN
fcis-6353	32	51	datasets	dataset	NOUN
fcis-6353	32	52	,	,	PUNCT
fcis-6353	32	53	respectively	respectively	ADV
fcis-6353	32	54	.	.	PUNCT
fcis-6353	33	1	although	although	SCONJ
fcis-6353	33	2	the	the	DET
fcis-6353	33	3	accuracy	accuracy	NOUN
fcis-6353	33	4	has	have	AUX
fcis-6353	33	5	met	meet	VERB
fcis-6353	33	6	the	the	DET
fcis-6353	33	7	needs	need	NOUN
fcis-6353	33	8	of	of	ADP
fcis-6353	33	9	intelligent	intelligent	ADJ
fcis-6353	33	10	garbage	garbage	NOUN
fcis-6353	33	11	detection	detection	NOUN
fcis-6353	33	12	,	,	PUNCT
fcis-6353	33	13	timeliness	timeliness	NOUN
fcis-6353	33	14	is	be	AUX
fcis-6353	33	15	still	still	ADV
fcis-6353	33	16	a	a	DET
fcis-6353	33	17	major	major	ADJ
fcis-6353	33	18	pain	pain	NOUN
fcis-6353	33	19	point	point	NOUN
fcis-6353	33	20	.	.	PUNCT
fcis-6353	34	1	in	in	ADP
fcis-6353	34	2	2022	2022	NUM
fcis-6353	34	3	,	,	PUNCT
fcis-6353	34	4	xu	xu	PROPN
fcis-6353	34	5	et	et	PROPN
fcis-6353	34	6	al	al	PROPN
fcis-6353	34	7	.	.	PUNCT
fcis-6353	35	1	[	[	X
fcis-6353	35	2	8	8	NUM
fcis-6353	35	3	]	]	PUNCT
fcis-6353	35	4	proposed	propose	VERB
fcis-6353	35	5	a	a	DET
fcis-6353	35	6	lightweight	lightweight	ADJ
fcis-6353	35	7	garbage	garbage	NOUN
fcis-6353	35	8	target	target	NOUN
fcis-6353	35	9	detection	detection	NOUN
fcis-6353	35	10	algorithm	algorithm	NOUN
fcis-6353	35	11	ghost	ghost	NOUN
fcis-6353	35	12	-	-	PUNCT
fcis-6353	35	13	yolo	yolo	PROPN
fcis-6353	35	14	based	base	VERB
fcis-6353	35	15	on	on	ADP
fcis-6353	35	16	low	low	ADJ
fcis-6353	35	17	-	-	PUNCT
fcis-6353	35	18	power	power	NOUN
fcis-6353	35	19	devices	device	NOUN
fcis-6353	35	20	.	.	PUNCT
fcis-6353	36	1	this	this	DET
fcis-6353	36	2	algorithm	algorithm	NOUN
fcis-6353	36	3	ensures	ensure	VERB
fcis-6353	36	4	lightweight	lightweight	ADJ
fcis-6353	36	5	while	while	SCONJ
fcis-6353	36	6	maintaining	maintain	VERB
fcis-6353	36	7	high	high	ADJ
fcis-6353	36	8	garbage	garbage	NOUN
fcis-6353	36	9	detection	detection	NOUN
fcis-6353	36	10	accuracy	accuracy	NOUN
fcis-6353	36	11	,	,	PUNCT
fcis-6353	36	12	but	but	CCONJ
fcis-6353	36	13	while	while	SCONJ
fcis-6353	36	14	meeting	meet	VERB
fcis-6353	36	15	the	the	DET
fcis-6353	36	16	lightweight	lightweight	ADJ
fcis-6353	36	17	requirements	requirement	NOUN
fcis-6353	36	18	,	,	PUNCT
fcis-6353	36	19	there	there	PRON
fcis-6353	36	20	are	be	VERB
fcis-6353	36	21	certain	certain	ADJ
fcis-6353	36	22	errors	error	NOUN
fcis-6353	36	23	in	in	ADP
fcis-6353	36	24	accuracy	accuracy	NOUN
fcis-6353	36	25	,	,	PUNCT
fcis-6353	36	26	which	which	PRON
fcis-6353	36	27	affects	affect	VERB
fcis-6353	36	28	the	the	DET
fcis-6353	36	29	garbage	garbage	NOUN
fcis-6353	36	30	detection	detection	NOUN
fcis-6353	36	31	effect	effect	NOUN
fcis-6353	36	32	.	.	PUNCT
fcis-6353	37	1	to	to	PART
fcis-6353	37	2	meet	meet	VERB
fcis-6353	37	3	both	both	PRON
fcis-6353	37	4	accuracy	accuracy	NOUN
fcis-6353	37	5	and	and	CCONJ
fcis-6353	37	6	real	real	ADJ
fcis-6353	37	7	-	-	PUNCT
fcis-6353	37	8	time	time	NOUN
fcis-6353	37	9	requirements	requirement	NOUN
fcis-6353	37	10	.	.	PUNCT
fcis-6353	38	1	most	most	ADJ
fcis-6353	38	2	existing	exist	VERB
fcis-6353	38	3	water	water	NOUN
fcis-6353	38	4	surface	surface	NOUN
fcis-6353	38	5	garbage	garbage	NOUN
fcis-6353	38	6	detection	detection	NOUN
fcis-6353	38	7	algorithms	algorithm	VERB
fcis-6353	38	8	155	155	NUM
fcis-6353	38	9	focus	focus	NOUN
fcis-6353	38	10	on	on	ADP
fcis-6353	38	11	the	the	DET
fcis-6353	38	12	accuracy	accuracy	NOUN
fcis-6353	38	13	or	or	CCONJ
fcis-6353	38	14	timeliness	timeliness	NOUN
fcis-6353	38	15	of	of	ADP
fcis-6353	38	16	detection	detection	NOUN
fcis-6353	38	17	.	.	PUNCT
fcis-6353	39	1	however	however	ADV
fcis-6353	39	2	,	,	PUNCT
fcis-6353	39	3	for	for	ADP
fcis-6353	39	4	water	water	NOUN
fcis-6353	39	5	surface	surface	NOUN
fcis-6353	39	6	garbage	garbage	NOUN
fcis-6353	39	7	detection	detection	NOUN
fcis-6353	39	8	tasks	task	NOUN
fcis-6353	39	9	,	,	PUNCT
fcis-6353	39	10	it	it	PRON
fcis-6353	39	11	is	be	AUX
fcis-6353	39	12	necessary	necessary	ADJ
fcis-6353	39	13	to	to	PART
fcis-6353	39	14	achieve	achieve	VERB
fcis-6353	39	15	a	a	DET
fcis-6353	39	16	trade	trade	NOUN
fcis-6353	39	17	-	-	PUNCT
fcis-6353	39	18	off	off	NOUN
fcis-6353	39	19	between	between	ADP
fcis-6353	39	20	accuracy	accuracy	NOUN
fcis-6353	39	21	and	and	CCONJ
fcis-6353	39	22	real	real	ADJ
fcis-6353	39	23	-	-	PUNCT
fcis-6353	39	24	time	time	NOUN
fcis-6353	39	25	performance	performance	NOUN
fcis-6353	39	26	,	,	PUNCT
fcis-6353	39	27	and	and	CCONJ
fcis-6353	39	28	to	to	PART
fcis-6353	39	29	address	address	VERB
fcis-6353	39	30	issues	issue	NOUN
fcis-6353	39	31	such	such	ADJ
fcis-6353	39	32	as	as	ADP
fcis-6353	39	33	low	low	ADJ
fcis-6353	39	34	photo	photo	NOUN
fcis-6353	39	35	clarity	clarity	NOUN
fcis-6353	39	36	and	and	CCONJ
fcis-6353	39	37	reflection	reflection	NOUN
fcis-6353	39	38	caused	cause	VERB
fcis-6353	39	39	by	by	ADP
fcis-6353	39	40	different	different	ADJ
fcis-6353	39	41	shooting	shooting	NOUN
fcis-6353	39	42	angles	angle	NOUN
fcis-6353	39	43	and	and	CCONJ
fcis-6353	39	44	shooting	shoot	VERB
fcis-6353	39	45	environments	environment	NOUN
fcis-6353	39	46	.	.	PUNCT
fcis-6353	40	1	therefore	therefore	ADV
fcis-6353	40	2	,	,	PUNCT
fcis-6353	40	3	the	the	DET
fcis-6353	40	4	algorithm	algorithm	NOUN
fcis-6353	40	5	not	not	PART
fcis-6353	40	6	only	only	ADV
fcis-6353	40	7	requires	require	VERB
fcis-6353	40	8	real	real	ADJ
fcis-6353	40	9	-	-	PUNCT
fcis-6353	40	10	time	time	NOUN
fcis-6353	40	11	detection	detection	NOUN
fcis-6353	40	12	,	,	PUNCT
fcis-6353	40	13	but	but	CCONJ
fcis-6353	40	14	also	also	ADV
fcis-6353	40	15	needs	need	VERB
fcis-6353	40	16	to	to	PART
fcis-6353	40	17	be	be	AUX
fcis-6353	40	18	able	able	ADJ
fcis-6353	40	19	to	to	PART
fcis-6353	40	20	eliminate	eliminate	VERB
fcis-6353	40	21	the	the	DET
fcis-6353	40	22	impact	impact	NOUN
fcis-6353	40	23	of	of	ADP
fcis-6353	40	24	water	water	NOUN
fcis-6353	40	25	surface	surface	NOUN
fcis-6353	40	26	interference	interference	NOUN
fcis-6353	40	27	factors	factor	NOUN
fcis-6353	40	28	to	to	PART
fcis-6353	40	29	improve	improve	VERB
fcis-6353	40	30	detection	detection	NOUN
fcis-6353	40	31	accuracy	accuracy	NOUN
fcis-6353	40	32	.	.	PUNCT
fcis-6353	41	1	in	in	ADP
fcis-6353	41	2	summary	summary	NOUN
fcis-6353	41	3	,	,	PUNCT
fcis-6353	41	4	this	this	DET
fcis-6353	41	5	study	study	NOUN
fcis-6353	41	6	proposes	propose	VERB
fcis-6353	41	7	a	a	DET
fcis-6353	41	8	water	water	NOUN
fcis-6353	41	9	surface	surface	NOUN
fcis-6353	41	10	garbage	garbage	NOUN
fcis-6353	41	11	detection	detection	NOUN
fcis-6353	41	12	algorithm	algorithm	NOUN
fcis-6353	41	13	based	base	VERB
fcis-6353	41	14	on	on	ADP
fcis-6353	41	15	gfl	gfl	PROPN
fcis-6353	41	16	(	(	PUNCT
fcis-6353	41	17	generalized	generalize	VERB
fcis-6353	41	18	focal	focal	ADJ
fcis-6353	41	19	loss	loss	NOUN
fcis-6353	41	20	)	)	PUNCT
fcis-6353	42	1	[	[	X
fcis-6353	42	2	9	9	NUM
fcis-6353	42	3	]	]	PUNCT
fcis-6353	42	4	for	for	ADP
fcis-6353	42	5	complex	complex	ADJ
fcis-6353	42	6	scenes	scene	NOUN
fcis-6353	42	7	to	to	PART
fcis-6353	42	8	meet	meet	VERB
fcis-6353	42	9	the	the	DET
fcis-6353	42	10	real	real	ADJ
fcis-6353	42	11	-	-	PUNCT
fcis-6353	42	12	time	time	NOUN
fcis-6353	42	13	and	and	CCONJ
fcis-6353	42	14	effective	effective	ADJ
fcis-6353	42	15	requirements	requirement	NOUN
fcis-6353	42	16	of	of	ADP
fcis-6353	42	17	water	water	NOUN
fcis-6353	42	18	surface	surface	NOUN
fcis-6353	42	19	garbage	garbage	NOUN
fcis-6353	42	20	detection	detection	NOUN
fcis-6353	42	21	in	in	ADP
fcis-6353	42	22	complex	complex	ADJ
fcis-6353	42	23	scenes	scene	NOUN
fcis-6353	42	24	.	.	PUNCT
fcis-6353	43	1	2	2	X
fcis-6353	43	2	.	.	X
fcis-6353	43	3	methodology	methodology	NOUN
fcis-6353	43	4	2.1	2.1	NUM
fcis-6353	43	5	.	.	PUNCT
fcis-6353	44	1	overall	overall	ADJ
fcis-6353	44	2	construction	construction	NOUN
fcis-6353	44	3	process	process	NOUN
fcis-6353	44	4	of	of	ADP
fcis-6353	44	5	surface	surface	NOUN
fcis-6353	44	6	waste	waste	NOUN
fcis-6353	44	7	detection	detection	NOUN
fcis-6353	44	8	in	in	ADP
fcis-6353	44	9	order	order	NOUN
fcis-6353	44	10	to	to	PART
fcis-6353	44	11	meet	meet	VERB
fcis-6353	44	12	the	the	DET
fcis-6353	44	13	real	real	ADJ
fcis-6353	44	14	-	-	PUNCT
fcis-6353	44	15	time	time	NOUN
fcis-6353	44	16	and	and	CCONJ
fcis-6353	44	17	accuracy	accuracy	NOUN
fcis-6353	44	18	requirements	requirement	NOUN
fcis-6353	44	19	of	of	ADP
fcis-6353	44	20	water	water	NOUN
fcis-6353	44	21	surface	surface	NOUN
fcis-6353	44	22	garbage	garbage	NOUN
fcis-6353	44	23	detection	detection	NOUN
fcis-6353	44	24	tasks	task	NOUN
fcis-6353	44	25	,	,	PUNCT
fcis-6353	44	26	we	we	PRON
fcis-6353	44	27	propose	propose	VERB
fcis-6353	44	28	a	a	DET
fcis-6353	44	29	knowledge	knowledge	NOUN
fcis-6353	44	30	based	base	VERB
fcis-6353	44	31	gfl	gfl	PROPN
fcis-6353	44	32	(	(	PUNCT
fcis-6353	44	33	generalized	generalize	VERB
fcis-6353	44	34	focal	focal	ADJ
fcis-6353	44	35	loss	loss	NOUN
fcis-6353	44	36	)	)	PUNCT
fcis-6353	44	37	model	model	NOUN
fcis-6353	44	38	based	base	VERB
fcis-6353	44	39	gfl	gfl	PROPN
fcis-6353	44	40	_	_	PROPN
fcis-6353	44	41	ham	ham	PROPN
fcis-6353	44	42	water	water	NOUN
fcis-6353	44	43	surface	surface	NOUN
fcis-6353	44	44	garbage	garbage	NOUN
fcis-6353	44	45	detection	detection	NOUN
fcis-6353	44	46	algorithm	algorithm	NOUN
fcis-6353	44	47	.	.	PUNCT
fcis-6353	45	1	first	first	ADV
fcis-6353	45	2	,	,	PUNCT
fcis-6353	45	3	construct	construct	VERB
fcis-6353	45	4	a	a	DET
fcis-6353	45	5	water	water	NOUN
fcis-6353	45	6	surface	surface	NOUN
fcis-6353	45	7	garbage	garbage	NOUN
fcis-6353	45	8	detection	detection	NOUN
fcis-6353	45	9	dataset	dataset	VERB
fcis-6353	45	10	.	.	PUNCT
fcis-6353	46	1	then	then	ADV
fcis-6353	46	2	,	,	PUNCT
fcis-6353	46	3	using	use	VERB
fcis-6353	46	4	rotation	rotation	NOUN
fcis-6353	46	5	,	,	PUNCT
fcis-6353	46	6	changing	change	VERB
fcis-6353	46	7	brightness	brightness	NOUN
fcis-6353	46	8	,	,	PUNCT
fcis-6353	46	9	and	and	CCONJ
fcis-6353	46	10	changing	change	VERB
fcis-6353	46	11	contrast	contrast	NOUN
fcis-6353	46	12	to	to	PART
fcis-6353	46	13	simulate	simulate	VERB
fcis-6353	46	14	the	the	DET
fcis-6353	46	15	possible	possible	ADJ
fcis-6353	46	16	impact	impact	NOUN
fcis-6353	46	17	of	of	ADP
fcis-6353	46	18	the	the	DET
fcis-6353	46	19	construction	construction	NOUN
fcis-6353	46	20	process	process	NOUN
fcis-6353	46	21	of	of	ADP
fcis-6353	46	22	the	the	DET
fcis-6353	46	23	water	water	NOUN
fcis-6353	46	24	surface	surface	NOUN
fcis-6353	46	25	garbage	garbage	NOUN
fcis-6353	46	26	datasets	dataset	NOUN
fcis-6353	46	27	,	,	PUNCT
fcis-6353	46	28	the	the	DET
fcis-6353	46	29	datasets	dataset	NOUN
fcis-6353	46	30	is	be	AUX
fcis-6353	46	31	enhanced	enhance	VERB
fcis-6353	46	32	.	.	PUNCT
fcis-6353	47	1	next	next	ADV
fcis-6353	47	2	,	,	PUNCT
fcis-6353	47	3	use	use	VERB
fcis-6353	47	4	the	the	DET
fcis-6353	47	5	surface	surface	NOUN
fcis-6353	47	6	garbage	garbage	NOUN
fcis-6353	47	7	detection	detection	NOUN
fcis-6353	47	8	datasets	dataset	NOUN
fcis-6353	47	9	to	to	PART
fcis-6353	47	10	train	train	VERB
fcis-6353	47	11	gfl	gfl	NOUN
fcis-6353	47	12	_	_	PRON
fcis-6353	47	13	ham	ham	NOUN
fcis-6353	47	14	network	network	NOUN
fcis-6353	47	15	.	.	PUNCT
fcis-6353	48	1	finally	finally	ADV
fcis-6353	48	2	,	,	PUNCT
fcis-6353	48	3	we	we	PRON
fcis-6353	48	4	tested	test	VERB
fcis-6353	48	5	our	our	PRON
fcis-6353	48	6	model	model	NOUN
fcis-6353	48	7	using	use	VERB
fcis-6353	48	8	real	real	ADJ
fcis-6353	48	9	water	water	NOUN
fcis-6353	48	10	surface	surface	NOUN
fcis-6353	48	11	garbage	garbage	NOUN
fcis-6353	48	12	images	image	NOUN
fcis-6353	48	13	from	from	ADP
fcis-6353	48	14	natural	natural	ADJ
fcis-6353	48	15	scenes	scene	NOUN
fcis-6353	48	16	.	.	PUNCT
fcis-6353	49	1	the	the	DET
fcis-6353	49	2	overall	overall	ADJ
fcis-6353	49	3	construction	construction	NOUN
fcis-6353	49	4	process	process	NOUN
fcis-6353	49	5	of	of	ADP
fcis-6353	49	6	the	the	DET
fcis-6353	49	7	algorithm	algorithm	NOUN
fcis-6353	49	8	is	be	AUX
fcis-6353	49	9	shown	show	VERB
fcis-6353	49	10	in	in	ADP
fcis-6353	49	11	figure	figure	NOUN
fcis-6353	49	12	1	1	NUM
fcis-6353	49	13	.	.	PUNCT
fcis-6353	49	14	figure	figure	NOUN
fcis-6353	49	15	1	1	NUM
fcis-6353	49	16	.	.	PUNCT
fcis-6353	50	1	overall	overall	ADJ
fcis-6353	50	2	algorithm	algorithm	NOUN
fcis-6353	50	3	construction	construction	NOUN
fcis-6353	50	4	process	process	NOUN
fcis-6353	50	5	2.2	2.2	NUM
fcis-6353	50	6	.	.	PUNCT
fcis-6353	51	1	gfl	gfl	NOUN
fcis-6353	51	2	_	_	PRON
fcis-6353	51	3	ham	ham	PROPN
fcis-6353	51	4	detection	detection	NOUN
fcis-6353	51	5	network	network	NOUN
fcis-6353	51	6	structure	structure	NOUN
fcis-6353	51	7	in	in	ADP
fcis-6353	51	8	order	order	NOUN
fcis-6353	51	9	to	to	PART
fcis-6353	51	10	improve	improve	VERB
fcis-6353	51	11	the	the	DET
fcis-6353	51	12	detection	detection	NOUN
fcis-6353	51	13	accuracy	accuracy	NOUN
fcis-6353	51	14	of	of	ADP
fcis-6353	51	15	the	the	DET
fcis-6353	51	16	model	model	NOUN
fcis-6353	51	17	to	to	PART
fcis-6353	51	18	meet	meet	VERB
fcis-6353	51	19	the	the	DET
fcis-6353	51	20	requirements	requirement	NOUN
fcis-6353	51	21	of	of	ADP
fcis-6353	51	22	water	water	NOUN
fcis-6353	51	23	surface	surface	NOUN
fcis-6353	51	24	garbage	garbage	NOUN
fcis-6353	51	25	detection	detection	NOUN
fcis-6353	51	26	application	application	NOUN
fcis-6353	51	27	scenarios	scenario	NOUN
fcis-6353	51	28	,	,	PUNCT
fcis-6353	51	29	we	we	PRON
fcis-6353	51	30	introduced	introduce	VERB
fcis-6353	51	31	the	the	DET
fcis-6353	51	32	ham	ham	NOUN
fcis-6353	51	33	attention	attention	NOUN
fcis-6353	51	34	mechanism	mechanism	NOUN
fcis-6353	51	35	based	base	VERB
fcis-6353	51	36	on	on	ADP
fcis-6353	51	37	the	the	DET
fcis-6353	51	38	gfl	gfl	NOUN
fcis-6353	51	39	(	(	PUNCT
fcis-6353	51	40	generalized	generalize	VERB
fcis-6353	51	41	focal	focal	ADJ
fcis-6353	51	42	loss	loss	NOUN
fcis-6353	51	43	)	)	PUNCT
fcis-6353	51	44	model	model	NOUN
fcis-6353	51	45	to	to	PART
fcis-6353	51	46	enhance	enhance	VERB
fcis-6353	51	47	the	the	DET
fcis-6353	51	48	network	network	NOUN
fcis-6353	51	49	feature	feature	NOUN
fcis-6353	51	50	extraction	extraction	NOUN
fcis-6353	51	51	ability	ability	NOUN
fcis-6353	51	52	and	and	CCONJ
fcis-6353	51	53	improve	improve	VERB
fcis-6353	51	54	network	network	NOUN
fcis-6353	51	55	accuracy	accuracy	NOUN
fcis-6353	51	56	.	.	PUNCT
fcis-6353	52	1	gfl	gfl	NOUN
fcis-6353	52	2	_	_	PUNCT
fcis-6353	52	3	the	the	DET
fcis-6353	52	4	entire	entire	ADJ
fcis-6353	52	5	network	network	NOUN
fcis-6353	52	6	of	of	ADP
fcis-6353	52	7	ham	ham	NOUN
fcis-6353	52	8	is	be	AUX
fcis-6353	52	9	divided	divide	VERB
fcis-6353	52	10	into	into	ADP
fcis-6353	52	11	four	four	NUM
fcis-6353	52	12	parts	part	NOUN
fcis-6353	52	13	:	:	PUNCT
fcis-6353	52	14	the	the	DET
fcis-6353	52	15	backbone	backbone	NOUN
fcis-6353	52	16	,	,	PUNCT
fcis-6353	52	17	the	the	DET
fcis-6353	52	18	attention	attention	NOUN
fcis-6353	52	19	mechanism	mechanism	NOUN
fcis-6353	52	20	of	of	ADP
fcis-6353	52	21	ham	ham	PROPN
fcis-6353	52	22	(	(	PUNCT
fcis-6353	52	23	hybrid	hybrid	ADJ
fcis-6353	52	24	attention	attention	NOUN
fcis-6353	52	25	model	model	NOUN
fcis-6353	52	26	)	)	PUNCT
fcis-6353	52	27	,	,	PUNCT
fcis-6353	52	28	the	the	DET
fcis-6353	52	29	neck	neck	NOUN
fcis-6353	52	30	,	,	PUNCT
fcis-6353	52	31	and	and	CCONJ
fcis-6353	52	32	the	the	DET
fcis-6353	52	33	head	head	NOUN
fcis-6353	52	34	,	,	PUNCT
fcis-6353	52	35	as	as	SCONJ
fcis-6353	52	36	shown	show	VERB
fcis-6353	52	37	in	in	ADP
fcis-6353	52	38	figure	figure	NOUN
fcis-6353	52	39	2	2	NUM
fcis-6353	52	40	.	.	PUNCT
fcis-6353	53	1	the	the	DET
fcis-6353	53	2	backbone	backbone	NOUN
fcis-6353	53	3	network	network	NOUN
fcis-6353	53	4	of	of	ADP
fcis-6353	53	5	the	the	DET
fcis-6353	53	6	model	model	NOUN
fcis-6353	53	7	is	be	AUX
fcis-6353	53	8	a	a	DET
fcis-6353	53	9	resnet50	resnet50	NOUN
fcis-6353	53	10	[	[	X
fcis-6353	53	11	10	10	NUM
fcis-6353	53	12	]	]	PUNCT
fcis-6353	53	13	structure	structure	NOUN
fcis-6353	53	14	;	;	PUNCT
fcis-6353	53	15	the	the	DET
fcis-6353	53	16	neck	neck	NOUN
fcis-6353	53	17	network	network	NOUN
fcis-6353	53	18	is	be	AUX
fcis-6353	53	19	an	an	DET
fcis-6353	53	20	fpn	fpn	NOUN
fcis-6353	53	21	[	[	X
fcis-6353	53	22	11	11	NUM
fcis-6353	53	23	]	]	PUNCT
fcis-6353	53	24	structure	structure	NOUN
fcis-6353	53	25	;	;	PUNCT
fcis-6353	53	26	the	the	DET
fcis-6353	53	27	header	header	NOUN
fcis-6353	53	28	network	network	NOUN
fcis-6353	53	29	is	be	AUX
fcis-6353	53	30	composed	compose	VERB
fcis-6353	53	31	of	of	ADP
fcis-6353	53	32	gfl	gfl	PROPN
fcis-6353	53	33	_	_	PRON
fcis-6353	53	34	head	head	NOUN
fcis-6353	54	1	[	[	X
fcis-6353	54	2	9	9	NUM
fcis-6353	54	3	]	]	PUNCT
fcis-6353	54	4	structure	structure	NOUN
fcis-6353	54	5	composition	composition	NOUN
fcis-6353	54	6	.	.	PUNCT
fcis-6353	55	1	the	the	DET
fcis-6353	55	2	input	input	NOUN
fcis-6353	55	3	image	image	NOUN
fcis-6353	55	4	undergoes	undergo	VERB
fcis-6353	55	5	feature	feature	NOUN
fcis-6353	55	6	extraction	extraction	NOUN
fcis-6353	55	7	and	and	CCONJ
fcis-6353	55	8	fusion	fusion	NOUN
fcis-6353	55	9	operations	operation	NOUN
fcis-6353	55	10	through	through	ADP
fcis-6353	55	11	the	the	DET
fcis-6353	55	12	backbone	backbone	NOUN
fcis-6353	55	13	network	network	NOUN
fcis-6353	55	14	and	and	CCONJ
fcis-6353	55	15	neck	neck	NOUN
fcis-6353	55	16	network	network	NOUN
fcis-6353	55	17	,	,	PUNCT
fcis-6353	55	18	and	and	CCONJ
fcis-6353	55	19	then	then	ADV
fcis-6353	55	20	inputs	input	VERB
fcis-6353	55	21	the	the	DET
fcis-6353	55	22	extracted	extract	VERB
fcis-6353	55	23	features	feature	NOUN
fcis-6353	55	24	into	into	ADP
fcis-6353	55	25	the	the	DET
fcis-6353	55	26	gfl	gfl	PROPN
fcis-6353	55	27	_	_	DET
fcis-6353	55	28	head	head	NOUN
fcis-6353	55	29	obtains	obtain	VERB
fcis-6353	55	30	the	the	DET
fcis-6353	55	31	predicted	predict	VERB
fcis-6353	55	32	results	result	NOUN
fcis-6353	55	33	of	of	ADP
fcis-6353	55	34	the	the	DET
fcis-6353	55	35	model	model	NOUN
fcis-6353	55	36	.	.	PUNCT
fcis-6353	56	1	figure	figure	NOUN
fcis-6353	56	2	2	2	NUM
fcis-6353	56	3	.	.	PUNCT
fcis-6353	57	1	gfl	gfl	PROPN
fcis-6353	57	2	_	_	PRON
fcis-6353	57	3	ham	ham	PROPN
fcis-6353	57	4	network	network	NOUN
fcis-6353	57	5	structure	structure	NOUN
fcis-6353	57	6	diagram	diagram	NOUN
fcis-6353	57	7	the	the	DET
fcis-6353	57	8	gfl	gfl	PROPN
fcis-6353	57	9	model	model	NOUN
fcis-6353	57	10	mainly	mainly	ADV
fcis-6353	57	11	re	re	VERB
fcis-6353	57	12	represents	represent	VERB
fcis-6353	57	13	the	the	DET
fcis-6353	57	14	physical	physical	ADJ
fcis-6353	57	15	objects	object	NOUN
fcis-6353	57	16	at	at	ADP
fcis-6353	57	17	the	the	DET
fcis-6353	57	18	end	end	NOUN
fcis-6353	57	19	of	of	ADP
fcis-6353	57	20	the	the	DET
fcis-6353	57	21	head	head	NOUN
fcis-6353	57	22	.	.	PUNCT
fcis-6353	58	1	in	in	ADP
fcis-6353	58	2	previous	previous	ADJ
fcis-6353	58	3	work	work	NOUN
fcis-6353	58	4	,	,	PUNCT
fcis-6353	58	5	training	training	NOUN
fcis-6353	58	6	and	and	CCONJ
fcis-6353	58	7	reasoning	reasoning	NOUN
fcis-6353	58	8	were	be	AUX
fcis-6353	58	9	inconsistent	inconsistent	ADJ
fcis-6353	58	10	in	in	ADP
fcis-6353	58	11	terms	term	NOUN
fcis-6353	58	12	of	of	ADP
fcis-6353	58	13	quality	quality	NOUN
fcis-6353	58	14	estimation	estimation	NOUN
fcis-6353	58	15	and	and	CCONJ
fcis-6353	58	16	classification	classification	NOUN
fcis-6353	58	17	scores	score	NOUN
fcis-6353	58	18	.	.	PUNCT
fcis-6353	59	1	during	during	ADP
fcis-6353	59	2	the	the	DET
fcis-6353	59	3	training	training	NOUN
fcis-6353	59	4	process	process	NOUN
fcis-6353	59	5	,	,	PUNCT
fcis-6353	59	6	quality	quality	NOUN
fcis-6353	59	7	estimation	estimation	NOUN
fcis-6353	59	8	and	and	CCONJ
fcis-6353	59	9	classification	classification	NOUN
fcis-6353	59	10	scores	score	NOUN
fcis-6353	59	11	are	be	AUX
fcis-6353	59	12	trained	train	VERB
fcis-6353	59	13	separately	separately	ADV
fcis-6353	59	14	.	.	PUNCT
fcis-6353	60	1	in	in	ADP
fcis-6353	60	2	the	the	DET
fcis-6353	60	3	reasoning	reasoning	NOUN
fcis-6353	60	4	process	process	NOUN
fcis-6353	60	5	,	,	PUNCT
fcis-6353	60	6	quality	quality	NOUN
fcis-6353	60	7	estimates	estimate	NOUN
fcis-6353	60	8	and	and	CCONJ
fcis-6353	60	9	classification	classification	NOUN
fcis-6353	60	10	scores	score	NOUN
fcis-6353	60	11	are	be	AUX
fcis-6353	60	12	used	use	VERB
fcis-6353	60	13	together	together	ADV
fcis-6353	60	14	.	.	PUNCT
fcis-6353	61	1	the	the	DET
fcis-6353	61	2	product	product	NOUN
fcis-6353	61	3	of	of	ADP
fcis-6353	61	4	quality	quality	NOUN
fcis-6353	61	5	estimation	estimation	NOUN
fcis-6353	61	6	and	and	CCONJ
fcis-6353	61	7	classification	classification	NOUN
fcis-6353	61	8	score	score	NOUN
fcis-6353	61	9	is	be	AUX
fcis-6353	61	10	used	use	VERB
fcis-6353	61	11	to	to	PART
fcis-6353	61	12	act	act	VERB
fcis-6353	61	13	on	on	ADP
fcis-6353	61	14	the	the	DET
fcis-6353	61	15	nms	nms	NOUN
fcis-6353	61	16	score	score	NOUN
fcis-6353	61	17	of	of	ADP
fcis-6353	61	18	non	non	ADJ
fcis-6353	61	19	-	-	ADJ
fcis-6353	61	20	maximum	maximum	ADJ
fcis-6353	61	21	suppression	suppression	NOUN
fcis-6353	61	22	post	post	NOUN
fcis-6353	61	23	processing	processing	NOUN
fcis-6353	61	24	[	[	X
fcis-6353	61	25	12	12	NUM
fcis-6353	61	26	]	]	PUNCT
fcis-6353	61	27	,	,	PUNCT
fcis-6353	61	28	which	which	PRON
fcis-6353	61	29	is	be	AUX
fcis-6353	61	30	unreasonable	unreasonable	ADJ
fcis-6353	61	31	.	.	PUNCT
fcis-6353	62	1	quality	quality	NOUN
fcis-6353	62	2	estimation	estimation	NOUN
fcis-6353	62	3	only	only	ADV
fcis-6353	62	4	performs	perform	VERB
fcis-6353	62	5	position	position	NOUN
fcis-6353	62	6	monitoring	monitor	VERB
fcis-6353	62	7	training	training	NOUN
fcis-6353	62	8	on	on	ADP
fcis-6353	62	9	positive	positive	ADJ
fcis-6353	62	10	samples	sample	NOUN
fcis-6353	62	11	,	,	PUNCT
fcis-6353	62	12	resulting	result	VERB
fcis-6353	62	13	in	in	ADP
fcis-6353	62	14	a	a	DET
fcis-6353	62	15	gap	gap	NOUN
fcis-6353	62	16	between	between	ADP
fcis-6353	62	17	training	training	NOUN
fcis-6353	62	18	and	and	CCONJ
fcis-6353	62	19	testing	testing	NOUN
fcis-6353	62	20	,	,	PUNCT
fcis-6353	62	21	which	which	PRON
fcis-6353	62	22	reduces	reduce	VERB
fcis-6353	62	23	model	model	NOUN
fcis-6353	62	24	performance	performance	NOUN
fcis-6353	62	25	.	.	PUNCT
fcis-6353	63	1	the	the	DET
fcis-6353	63	2	gfl	gfl	PROPN
fcis-6353	63	3	model	model	NOUN
fcis-6353	63	4	proposes	propose	VERB
fcis-6353	63	5	a	a	DET
fcis-6353	63	6	new	new	ADJ
fcis-6353	63	7	solution	solution	NOUN
fcis-6353	63	8	that	that	PRON
fcis-6353	63	9	combines	combine	VERB
fcis-6353	63	10	classification	classification	NOUN
fcis-6353	63	11	scores	score	NOUN
fcis-6353	63	12	and	and	CCONJ
fcis-6353	63	13	quality	quality	NOUN
fcis-6353	63	14	estimation	estimation	NOUN
fcis-6353	63	15	by	by	ADP
fcis-6353	63	16	introducing	introduce	VERB
fcis-6353	63	17	quality	quality	NOUN
fcis-6353	63	18	focal	focal	ADJ
fcis-6353	63	19	loss	loss	NOUN
fcis-6353	63	20	(	(	PUNCT
fcis-6353	63	21	qfl	qfl	NOUN
fcis-6353	63	22	):	):	PUNCT
fcis-6353	63	23	qfl	qfl	NOUN
fcis-6353	63	24	(	(	PUNCT
fcis-6353	63	25	σ)=	σ)=	NOUN
fcis-6353	63	26	-|y	-|y	PUNCT
fcis-6353	63	27	−	−	PROPN
fcis-6353	64	1	σ|β((1y)log(1	σ|β((1y)log(1	NOUN
fcis-6353	64	2	−	−	PROPN
fcis-6353	64	3	σ)+y	σ)+y	PROPN
fcis-6353	64	4	log(σ	log(σ	PROPN
fcis-6353	64	5	)	)	PUNCT
fcis-6353	64	6	)	)	PUNCT
fcis-6353	65	1	(	(	PUNCT
fcis-6353	65	2	1	1	X
fcis-6353	65	3	)	)	PUNCT
fcis-6353	65	4	(	(	PUNCT
fcis-6353	65	5	where	where	SCONJ
fcis-6353	65	6	y	y	PROPN
fcis-6353	65	7	is	be	AUX
fcis-6353	65	8	a	a	DET
fcis-6353	65	9	quality	quality	NOUN
fcis-6353	65	10	label	label	NOUN
fcis-6353	65	11	of	of	ADP
fcis-6353	65	12	0	0	NUM
fcis-6353	65	13	-	-	SYM
fcis-6353	65	14	1	1	NUM
fcis-6353	65	15	and	and	CCONJ
fcis-6353	65	16	sigma	sigma	PROPN
fcis-6353	65	17	is	be	AUX
fcis-6353	65	18	a	a	DET
fcis-6353	65	19	prediction	prediction	NOUN
fcis-6353	65	20	.	.	PUNCT
fcis-6353	66	1	note	note	VERB
fcis-6353	66	2	that	that	SCONJ
fcis-6353	66	3	the	the	DET
fcis-6353	66	4	global	global	ADJ
fcis-6353	66	5	minimum	minimum	ADJ
fcis-6353	66	6	solution	solution	NOUN
fcis-6353	66	7	for	for	ADP
fcis-6353	66	8	qfl	qfl	NOUN
fcis-6353	66	9	is	be	AUX
fcis-6353	66	10	sigma	sigma	NOUN
fcis-6353	66	11	=	=	NOUN
fcis-6353	66	12	y.	y.	NOUN
fcis-6353	66	13	in	in	ADP
fcis-6353	66	14	this	this	DET
fcis-6353	66	15	way	way	NOUN
fcis-6353	66	16	,	,	PUNCT
fcis-6353	66	17	the	the	DET
fcis-6353	66	18	cross	cross	NOUN
fcis-6353	66	19	entropy	entropy	PROPN
fcis-6353	66	20	part	part	NOUN
fcis-6353	66	21	becomes	become	VERB
fcis-6353	66	22	a	a	DET
fcis-6353	66	23	complete	complete	ADJ
fcis-6353	66	24	cross	cross	NOUN
fcis-6353	66	25	entropy	entropy	NOUN
fcis-6353	66	26	,	,	PUNCT
fcis-6353	66	27	while	while	SCONJ
fcis-6353	66	28	the	the	DET
fcis-6353	66	29	adjustment	adjustment	NOUN
fcis-6353	66	30	factor	factor	NOUN
fcis-6353	66	31	becomes	become	VERB
fcis-6353	66	32	a	a	DET
fcis-6353	66	33	power	power	NOUN
fcis-6353	66	34	function	function	NOUN
fcis-6353	66	35	of	of	ADP
fcis-6353	66	36	the	the	DET
fcis-6353	66	37	absolute	absolute	ADJ
fcis-6353	66	38	value	value	NOUN
fcis-6353	66	39	of	of	ADP
fcis-6353	66	40	the	the	DET
fcis-6353	66	41	distance	distance	NOUN
fcis-6353	66	42	.	.	PUNCT
fcis-6353	67	1	similar	similar	ADJ
fcis-6353	67	2	to	to	ADP
fcis-6353	67	3	focal	focal	ADJ
fcis-6353	67	4	loss	loss	NOUN
fcis-6353	67	5	[	[	X
fcis-6353	67	6	13	13	NUM
fcis-6353	67	7	]	]	PUNCT
fcis-6353	67	8	,	,	PUNCT
fcis-6353	67	9	it	it	PRON
fcis-6353	67	10	is	be	AUX
fcis-6353	67	11	generally	generally	ADV
fcis-6353	67	12	optimal	optimal	ADJ
fcis-6353	67	13	to	to	PART
fcis-6353	67	14	take	take	VERB
fcis-6353	67	15	beta=2	beta=2	PROPN
fcis-6353	67	16	.	.	PUNCT
fcis-6353	67	17	)	)	PUNCT
fcis-6353	68	1	instead	instead	ADV
fcis-6353	68	2	of	of	ADP
fcis-6353	68	3	our	our	PRON
fcis-6353	68	4	usual	usual	ADJ
fcis-6353	68	5	focal	focal	ADJ
fcis-6353	68	6	loss	loss	NOUN
fcis-6353	68	7	:	:	PUNCT
fcis-6353	68	8	fl(p)=	fl(p)=	PROPN
fcis-6353	69	1	-(1	-(1	PROPN
fcis-6353	69	2	−	−	PROPN
fcis-6353	69	3	pt)γ	pt)γ	PROPN
fcis-6353	69	4	log(pt	log(pt	NUM
fcis-6353	69	5	)	)	PUNCT
fcis-6353	69	6	,	,	PUNCT
fcis-6353	69	7	pt	pt	X
fcis-6353	69	8	=	=	SYM
fcis-6353	69	9	{	{	PUNCT
fcis-6353	69	10	p	p	X
fcis-6353	69	11	,	,	PUNCT
fcis-6353	69	12	y	y	PROPN
fcis-6353	69	13	=	=	SYM
fcis-6353	69	14	1	1	NUM
fcis-6353	69	15	1	1	NUM
fcis-6353	69	16	−	−	PROPN
fcis-6353	70	1	p	p	X
fcis-6353	70	2	,	,	PUNCT
fcis-6353	70	3	y	y	PROPN
fcis-6353	70	4	=	=	SYM
fcis-6353	70	5	0	0	PUNCT
fcis-6353	70	6	(	(	PUNCT
fcis-6353	70	7	2	2	NUM
fcis-6353	70	8	)	)	PUNCT
fcis-6353	70	9	to	to	PART
fcis-6353	70	10	solve	solve	VERB
fcis-6353	70	11	the	the	DET
fcis-6353	70	12	problem	problem	NOUN
fcis-6353	70	13	of	of	ADP
fcis-6353	70	14	inconsistency	inconsistency	NOUN
fcis-6353	70	15	in	in	ADP
fcis-6353	70	16	the	the	DET
fcis-6353	70	17	quality	quality	NOUN
fcis-6353	70	18	estimation	estimation	NOUN
fcis-6353	70	19	and	and	CCONJ
fcis-6353	70	20	classification	classification	NOUN
fcis-6353	70	21	score	score	NOUN
fcis-6353	70	22	training	training	NOUN
fcis-6353	70	23	reasoning	reasoning	NOUN
fcis-6353	70	24	process	process	NOUN
fcis-6353	70	25	.	.	PUNCT
fcis-6353	71	1	in	in	ADP
fcis-6353	71	2	complex	complex	ADJ
fcis-6353	71	3	scenes	scene	NOUN
fcis-6353	71	4	,	,	PUNCT
fcis-6353	71	5	targets	target	NOUN
fcis-6353	71	6	can	can	AUX
fcis-6353	71	7	have	have	VERB
fcis-6353	71	8	many	many	ADJ
fcis-6353	71	9	uncertainties	uncertainty	NOUN
fcis-6353	71	10	,	,	PUNCT
fcis-6353	71	11	such	such	ADJ
fcis-6353	71	12	as	as	ADP
fcis-6353	71	13	surfboards	surfboard	NOUN
fcis-6353	71	14	blocked	block	VERB
fcis-6353	71	15	by	by	ADP
fcis-6353	71	16	water	water	NOUN
fcis-6353	71	17	and	and	CCONJ
fcis-6353	71	18	highly	highly	ADV
fcis-6353	71	19	overlapping	overlap	VERB
fcis-6353	71	20	elephants	elephant	NOUN
fcis-6353	71	21	.	.	PUNCT
fcis-6353	72	1	in	in	ADP
fcis-6353	72	2	previous	previous	ADJ
fcis-6353	72	3	work	work	NOUN
fcis-6353	72	4	,	,	PUNCT
fcis-6353	72	5	boundary	boundary	PROPN
fcis-6353	72	6	box	box	PROPN
fcis-6353	72	7	regression	regression	NOUN
fcis-6353	72	8	was	be	AUX
fcis-6353	72	9	modeled	model	VERB
fcis-6353	72	10	as	as	ADP
fcis-6353	72	11	a	a	DET
fcis-6353	72	12	random	random	ADJ
fcis-6353	72	13	distribution	distribution	NOUN
fcis-6353	72	14	(	(	PUNCT
fcis-6353	72	15	dirac	dirac	NOUN
fcis-6353	72	16	or	or	CCONJ
fcis-6353	72	17	gaussian	gaussian	ADJ
fcis-6353	72	18	distribution	distribution	NOUN
fcis-6353	72	19	,	,	PUNCT
fcis-6353	72	20	etc	etc	X
fcis-6353	72	21	.	.	X
fcis-6353	72	22	)	)	PUNCT
fcis-6353	72	23	,	,	PUNCT
fcis-6353	72	24	resulting	result	VERB
fcis-6353	72	25	in	in	ADP
fcis-6353	72	26	inaccurate	inaccurate	ADJ
fcis-6353	72	27	boundary	boundary	ADJ
fcis-6353	72	28	box	box	NOUN
fcis-6353	72	29	regression	regression	NOUN
fcis-6353	72	30	in	in	ADP
fcis-6353	72	31	complex	complex	ADJ
fcis-6353	72	32	scenes	scene	NOUN
fcis-6353	72	33	.	.	PUNCT
fcis-6353	73	1	research	research	NOUN
fcis-6353	73	2	has	have	AUX
fcis-6353	73	3	found	find	VERB
fcis-6353	73	4	that	that	SCONJ
fcis-6353	73	5	too	too	ADV
fcis-6353	73	6	random	random	ADJ
fcis-6353	73	7	distribution	distribution	NOUN
fcis-6353	73	8	is	be	AUX
fcis-6353	73	9	not	not	PART
fcis-6353	73	10	conducive	conducive	ADJ
fcis-6353	73	11	to	to	ADP
fcis-6353	73	12	learning	learn	VERB
fcis-6353	73	13	network	network	NOUN
fcis-6353	73	14	regression	regression	NOUN
fcis-6353	73	15	,	,	PUNCT
fcis-6353	73	16	resulting	result	VERB
fcis-6353	73	17	in	in	ADP
fcis-6353	73	18	a	a	DET
fcis-6353	73	19	decline	decline	NOUN
fcis-6353	73	20	in	in	ADP
fcis-6353	73	21	network	network	NOUN
fcis-6353	73	22	accuracy	accuracy	NOUN
fcis-6353	73	23	.	.	PUNCT
fcis-6353	74	1	instead	instead	ADV
fcis-6353	74	2	of	of	ADP
fcis-6353	74	3	using	use	VERB
fcis-6353	74	4	a	a	DET
fcis-6353	74	5	loss	loss	NOUN
fcis-6353	74	6	function	function	NOUN
fcis-6353	74	7	similar	similar	ADJ
fcis-6353	74	8	to	to	ADP
fcis-6353	74	9	giou	giou	NOUN
fcis-6353	74	10	-	-	PUNCT
fcis-6353	74	11	loss	loss	NOUN
fcis-6353	74	12	[	[	X
fcis-6353	74	13	14	14	NUM
fcis-6353	74	14	]	]	PUNCT
fcis-6353	74	15	,	,	PUNCT
fcis-6353	74	16	the	the	DET
fcis-6353	74	17	gfl	gfl	PROPN
fcis-6353	74	18	model	model	NOUN
fcis-6353	74	19	redesigns	redesign	VERB
fcis-6353	74	20	the	the	DET
fcis-6353	74	21	distribution	distribution	NOUN
fcis-6353	74	22	focal	focal	ADJ
fcis-6353	74	23	loss	loss	NOUN
fcis-6353	74	24	(	(	PUNCT
fcis-6353	74	25	dfl	dfl	PROPN
fcis-6353	74	26	)	)	PUNCT
fcis-6353	74	27	dfl	dfl	PROPN
fcis-6353	74	28	:	:	PUNCT
fcis-6353	74	29	𝐷𝐹𝐿(𝑆𝑖	𝐷𝐹𝐿(𝑆𝑖	X
fcis-6353	74	30	,	,	PUNCT
fcis-6353	74	31	𝑆𝑖+1	𝑆𝑖+1	X
fcis-6353	74	32	)	)	PUNCT
fcis-6353	74	33	=	=	PUNCT
fcis-6353	75	1	|	|	ADV
fcis-6353	75	2	𝛽	𝛽	NOUN
fcis-6353	75	3	(	(	PUNCT
fcis-6353	75	4	(	(	PUNCT
fcis-6353	75	5	1	1	NUM
fcis-6353	75	6	−	−	PROPN
fcis-6353	75	7	𝑦	𝑦	X
fcis-6353	75	8	)	)	PUNCT
fcis-6353	75	9	𝑙𝑜𝑔	𝑙𝑜𝑔	PROPN
fcis-6353	75	10	(	(	PUNCT
fcis-6353	75	11	1	1	NUM
fcis-6353	75	12	−	−	PROPN
fcis-6353	75	13	𝜎	𝜎	X
fcis-6353	75	14	)	)	PUNCT
fcis-6353	75	15	+	+	X
fcis-6353	75	16	𝑦	𝑦	X
fcis-6353	75	17	𝑙𝑜𝑔	𝑙𝑜𝑔	NOUN
fcis-6353	75	18	(	(	PUNCT
fcis-6353	75	19	𝜎	𝜎	NOUN
fcis-6353	75	20	)	)	PUNCT
fcis-6353	75	21	)	)	PUNCT
fcis-6353	75	22	realize	realize	VERB
fcis-6353	75	23	prediction	prediction	NOUN
fcis-6353	75	24	of	of	ADP
fcis-6353	75	25	edge	edge	NOUN
fcis-6353	75	26	distribution	distribution	NOUN
fcis-6353	75	27	,	,	PUNCT
fcis-6353	75	28	predict	predict	VERB
fcis-6353	75	29	sharp	sharp	ADJ
fcis-6353	75	30	distribution	distribution	NOUN
fcis-6353	75	31	in	in	ADP
fcis-6353	75	32	clear	clear	ADJ
fcis-6353	75	33	boundary	boundary	ADJ
fcis-6353	75	34	regions	region	NOUN
fcis-6353	75	35	,	,	PUNCT
fcis-6353	75	36	predict	predict	VERB
fcis-6353	75	37	smooth	smooth	ADJ
fcis-6353	75	38	distribution	distribution	NOUN
fcis-6353	75	39	at	at	ADP
fcis-6353	75	40	fuzzy	fuzzy	ADJ
fcis-6353	75	41	locations	location	NOUN
fcis-6353	75	42	,	,	PUNCT
fcis-6353	75	43	and	and	CCONJ
fcis-6353	75	44	improve	improve	VERB
fcis-6353	75	45	model	model	NOUN
fcis-6353	75	46	accuracy	accuracy	NOUN
fcis-6353	75	47	.	.	PUNCT
fcis-6353	76	1	in	in	ADP
fcis-6353	76	2	order	order	NOUN
fcis-6353	76	3	to	to	PART
fcis-6353	76	4	improve	improve	VERB
fcis-6353	76	5	the	the	DET
fcis-6353	76	6	detection	detection	NOUN
fcis-6353	76	7	ability	ability	NOUN
fcis-6353	76	8	of	of	ADP
fcis-6353	76	9	the	the	DET
fcis-6353	76	10	model	model	NOUN
fcis-6353	76	11	and	and	CCONJ
fcis-6353	76	12	more	more	ADV
fcis-6353	76	13	accurately	accurately	ADV
fcis-6353	76	14	detect	detect	VERB
fcis-6353	76	15	surface	surface	NOUN
fcis-6353	76	16	garbage	garbage	NOUN
fcis-6353	76	17	,	,	PUNCT
fcis-6353	76	18	we	we	PRON
fcis-6353	76	19	introduce	introduce	VERB
fcis-6353	76	20	the	the	DET
fcis-6353	76	21	ham	ham	NOUN
fcis-6353	76	22	attention	attention	NOUN
fcis-6353	76	23	mechanism	mechanism	NOUN
fcis-6353	76	24	into	into	ADP
fcis-6353	76	25	the	the	DET
fcis-6353	76	26	backbone	backbone	NOUN
fcis-6353	76	27	network	network	NOUN
fcis-6353	76	28	of	of	ADP
fcis-6353	76	29	the	the	DET
fcis-6353	76	30	gfl	gfl	PROPN
fcis-6353	76	31	model	model	NOUN
fcis-6353	76	32	to	to	PART
fcis-6353	76	33	improve	improve	VERB
fcis-6353	76	34	the	the	DET
fcis-6353	76	35	network	network	NOUN
fcis-6353	76	36	's	's	PART
fcis-6353	76	37	feature	feature	NOUN
fcis-6353	76	38	extraction	extraction	NOUN
fcis-6353	76	39	ability	ability	NOUN
fcis-6353	76	40	,	,	PUNCT
fcis-6353	76	41	thereby	thereby	ADV
fcis-6353	76	42	improving	improve	VERB
fcis-6353	76	43	network	network	NOUN
fcis-6353	76	44	accuracy	accuracy	NOUN
fcis-6353	76	45	.	.	PUNCT
fcis-6353	77	1	ham	ham	NOUN
fcis-6353	77	2	(	(	PUNCT
fcis-6353	77	3	hybrid	hybrid	ADJ
fcis-6353	77	4	attention	attention	NOUN
fcis-6353	77	5	model	model	NOUN
fcis-6353	77	6	)	)	PUNCT
fcis-6353	77	7	is	be	AUX
fcis-6353	77	8	a	a	DET
fcis-6353	77	9	channel	channel	NOUN
fcis-6353	77	10	spatial	spatial	ADJ
fcis-6353	77	11	attention	attention	NOUN
fcis-6353	77	12	mechanism	mechanism	NOUN
fcis-6353	77	13	,	,	PUNCT
fcis-6353	77	14	which	which	PRON
fcis-6353	77	15	consists	consist	VERB
fcis-6353	77	16	of	of	ADP
fcis-6353	77	17	two	two	NUM
fcis-6353	77	18	parts	part	NOUN
fcis-6353	77	19	:	:	PUNCT
fcis-6353	77	20	the	the	DET
fcis-6353	77	21	first	first	ADJ
fcis-6353	77	22	part	part	NOUN
fcis-6353	77	23	is	be	AUX
fcis-6353	77	24	the	the	DET
fcis-6353	77	25	channel	channel	NOUN
fcis-6353	77	26	156	156	NUM
fcis-6353	77	27	attention	attention	NOUN
fcis-6353	77	28	mechanism	mechanism	NOUN
fcis-6353	77	29	,	,	PUNCT
fcis-6353	77	30	and	and	CCONJ
fcis-6353	77	31	the	the	DET
fcis-6353	77	32	other	other	ADJ
fcis-6353	77	33	part	part	NOUN
fcis-6353	77	34	is	be	AUX
fcis-6353	77	35	the	the	DET
fcis-6353	77	36	spatial	spatial	ADJ
fcis-6353	77	37	attention	attention	NOUN
fcis-6353	77	38	mechanism	mechanism	NOUN
fcis-6353	77	39	.	.	PUNCT
fcis-6353	78	1	as	as	SCONJ
fcis-6353	78	2	shown	show	VERB
fcis-6353	78	3	in	in	ADP
fcis-6353	78	4	figure	figure	NOUN
fcis-6353	78	5	3	3	NUM
fcis-6353	78	6	:	:	PUNCT
fcis-6353	78	7	figure	figure	NOUN
fcis-6353	78	8	3	3	NUM
fcis-6353	78	9	.	.	PUNCT
fcis-6353	79	1	ham	ham	PROPN
fcis-6353	79	2	(	(	PUNCT
fcis-6353	79	3	hybrid	hybrid	ADJ
fcis-6353	79	4	attention	attention	NOUN
fcis-6353	79	5	model	model	NOUN
fcis-6353	79	6	)	)	PUNCT
fcis-6353	79	7	attention	attention	NOUN
fcis-6353	79	8	mechanism	mechanism	NOUN
fcis-6353	79	9	structure	structure	NOUN
fcis-6353	79	10	diagram	diagram	NOUN
fcis-6353	79	11	first	first	ADV
fcis-6353	79	12	,	,	PUNCT
fcis-6353	79	13	the	the	DET
fcis-6353	79	14	input	input	NOUN
fcis-6353	79	15	feature	feature	NOUN
fcis-6353	79	16	is	be	AUX
fcis-6353	79	17	passed	pass	VERB
fcis-6353	79	18	through	through	ADP
fcis-6353	79	19	the	the	DET
fcis-6353	79	20	channel	channel	NOUN
fcis-6353	79	21	attention	attention	NOUN
fcis-6353	79	22	module	module	NOUN
fcis-6353	79	23	(	(	PUNCT
fcis-6353	79	24	shown	show	VERB
fcis-6353	79	25	in	in	ADP
fcis-6353	79	26	the	the	DET
fcis-6353	79	27	following	follow	VERB
fcis-6353	79	28	formula	formula	NOUN
fcis-6353	79	29	)	)	PUNCT
fcis-6353	79	30	to	to	PART
fcis-6353	79	31	obtain	obtain	VERB
fcis-6353	79	32	a	a	DET
fcis-6353	79	33	channel	channel	NOUN
fcis-6353	79	34	weighted	weight	VERB
fcis-6353	79	35	feature	feature	NOUN
fcis-6353	79	36	.	.	PUNCT
fcis-6353	79	37	)	)	PUNCT
fcis-6353	80	1	]	]	PUNCT
fcis-6353	80	2	(	(	PUNCT
fcis-6353	80	3	[	[	PUNCT
fcis-6353	80	4	f	f	X
fcis-6353	80	5	in	in	ADP
fcis-6353	80	6	caaw=	caaw=	PROPN
fcis-6353	80	7	(	(	PUNCT
fcis-6353	80	8	3	3	NUM
fcis-6353	80	9	)	)	PUNCT
fcis-6353	80	10	wff	wff	PROPN
fcis-6353	80	11	*	*	PUNCT
fcis-6353	80	12	inout	inout	PROPN
fcis-6353	80	13	=	=	SYM
fcis-6353	80	14	(	(	PUNCT
fcis-6353	80	15	4	4	NUM
fcis-6353	80	16	)	)	PUNCT
fcis-6353	80	17	where	where	SCONJ
fcis-6353	80	18	represents	represent	VERB
fcis-6353	80	19	the	the	DET
fcis-6353	80	20	channel	channel	NOUN
fcis-6353	80	21	weighting	weight	VERB
fcis-6353	80	22	coefficient	coefficient	NOUN
fcis-6353	80	23	,	,	PUNCT
fcis-6353	80	24	ca	can	AUX
fcis-6353	80	25	represents	represent	VERB
fcis-6353	80	26	the	the	DET
fcis-6353	80	27	channel	channel	NOUN
fcis-6353	80	28	attention	attention	NOUN
fcis-6353	80	29	mechanism	mechanism	NOUN
fcis-6353	80	30	of	of	ADP
fcis-6353	80	31	fast	fast	ADJ
fcis-6353	80	32	onedimensional	onedimensional	ADJ
fcis-6353	80	33	convolution	convolution	NOUN
fcis-6353	80	34	,	,	PUNCT
fcis-6353	80	35	and	and	CCONJ
fcis-6353	80	36	a	a	PRON
fcis-6353	80	37	represents	represent	VERB
fcis-6353	80	38	the	the	DET
fcis-6353	80	39	adaptive	adaptive	ADJ
fcis-6353	80	40	mechanism	mechanism	NOUN
fcis-6353	80	41	,	,	PUNCT
fcis-6353	80	42	represents	represent	VERB
fcis-6353	80	43	input	input	NOUN
fcis-6353	80	44	characteristics	characteristic	NOUN
fcis-6353	80	45	,	,	PUNCT
fcis-6353	80	46	and	and	CCONJ
fcis-6353	80	47	represents	represent	VERB
fcis-6353	80	48	output	output	NOUN
fcis-6353	80	49	characteristics	characteristic	NOUN
fcis-6353	80	50	.	.	PUNCT
fcis-6353	81	1	then	then	ADV
fcis-6353	81	2	,	,	PUNCT
fcis-6353	81	3	the	the	DET
fcis-6353	81	4	obtained	obtain	VERB
fcis-6353	81	5	channel	channel	NOUN
fcis-6353	81	6	weighted	weight	VERB
fcis-6353	81	7	features	feature	NOUN
fcis-6353	81	8	are	be	AUX
fcis-6353	81	9	input	input	VERB
fcis-6353	81	10	into	into	ADP
fcis-6353	81	11	the	the	DET
fcis-6353	81	12	spatial	spatial	ADJ
fcis-6353	81	13	attention	attention	NOUN
fcis-6353	81	14	mechanism	mechanism	NOUN
fcis-6353	81	15	,	,	PUNCT
fcis-6353	81	16	cc	cc	PROPN
fcis-6353	81	17	inim	inim	NOUN
fcis-6353	81	18	=	=	SYM
fcis-6353	81	19	(	(	PUNCT
fcis-6353	81	20	5	5	NUM
fcis-6353	81	21	)	)	PUNCT
fcis-6353	81	22	cff	cff	NOUN
fcis-6353	81	23	iminim	iminim	NOUN
fcis-6353	81	24	*	*	PUNCT
fcis-6353	81	25	=	=	X
fcis-6353	81	26	(	(	PUNCT
fcis-6353	81	27	6	6	NUM
fcis-6353	81	28	)	)	PUNCT
fcis-6353	81	29	)	)	PUNCT
fcis-6353	81	30	1	1	NUM
fcis-6353	81	31	(	(	PUNCT
fcis-6353	81	32	*	*	PUNCT
fcis-6353	81	33	cff	cff	PROPN
fcis-6353	81	34	iminnim	iminnim	PROPN
fcis-6353	81	35	−=	−=	X
fcis-6353	81	36	(	(	PUNCT
fcis-6353	81	37	7	7	NUM
fcis-6353	81	38	)	)	PUNCT
fcis-6353	81	39	fff	fff	PROPN
fcis-6353	81	40	nimimout	nimimout	PROPN
fcis-6353	81	41	sasa	sasa	PROPN
fcis-6353	81	42	*	*	PUNCT
fcis-6353	82	1	*	*	PUNCT
fcis-6353	82	2	+	+	PUNCT
fcis-6353	82	3	=	=	X
fcis-6353	82	4	(	(	PUNCT
fcis-6353	82	5	8)	8)	NUM
fcis-6353	82	6	among	among	ADP
fcis-6353	82	7	them	they	PRON
fcis-6353	82	8	,	,	PUNCT
fcis-6353	82	9	、	、	PROPN
fcis-6353	82	10	represent	represent	VERB
fcis-6353	82	11	important	important	ADJ
fcis-6353	82	12	and	and	CCONJ
fcis-6353	82	13	input	input	NOUN
fcis-6353	82	14	feature	feature	NOUN
fcis-6353	82	15	channels	channel	NOUN
fcis-6353	82	16	,	,	PUNCT
fcis-6353	82	17	and	and	CCONJ
fcis-6353	82	18	,	,	PUNCT
fcis-6353	82	19	、	、	PROPN
fcis-6353	82	20	、	、	NOUN
fcis-6353	82	21	represent	represent	VERB
fcis-6353	82	22	important	important	ADJ
fcis-6353	82	23	,	,	PUNCT
fcis-6353	82	24	non	non	ADJ
fcis-6353	82	25	-	-	ADJ
fcis-6353	82	26	important	important	ADJ
fcis-6353	82	27	,	,	PUNCT
fcis-6353	82	28	and	and	CCONJ
fcis-6353	82	29	input	input	NOUN
fcis-6353	82	30	features	feature	NOUN
fcis-6353	82	31	,	,	PUNCT
fcis-6353	82	32	represent	represent	VERB
fcis-6353	82	33	channel	channel	NOUN
fcis-6353	82	34	importance	importance	NOUN
fcis-6353	82	35	separation	separation	NOUN
fcis-6353	82	36	coefficients	coefficient	NOUN
fcis-6353	82	37	,	,	PUNCT
fcis-6353	82	38	and	and	CCONJ
fcis-6353	82	39	represent	represent	VERB
fcis-6353	82	40	spatial	spatial	ADJ
fcis-6353	82	41	attention	attention	NOUN
fcis-6353	82	42	mechanisms	mechanism	NOUN
fcis-6353	82	43	based	base	VERB
fcis-6353	82	44	on	on	ADP
fcis-6353	82	45	onedimensional	onedimensional	ADJ
fcis-6353	82	46	convolution	convolution	NOUN
fcis-6353	82	47	.	.	PUNCT
fcis-6353	83	1	the	the	DET
fcis-6353	83	2	ham	ham	PROPN
fcis-6353	83	3	attention	attention	NOUN
fcis-6353	83	4	mechanism	mechanism	NOUN
fcis-6353	83	5	alleviates	alleviate	VERB
fcis-6353	83	6	the	the	DET
fcis-6353	83	7	burden	burden	NOUN
fcis-6353	83	8	of	of	ADP
fcis-6353	83	9	the	the	DET
fcis-6353	83	10	channel	channel	NOUN
fcis-6353	83	11	attention	attention	NOUN
fcis-6353	83	12	mechanism	mechanism	NOUN
fcis-6353	83	13	through	through	ADP
fcis-6353	83	14	fast	fast	ADJ
fcis-6353	83	15	one	one	NUM
fcis-6353	83	16	-	-	PUNCT
fcis-6353	83	17	dimensional	dimensional	ADJ
fcis-6353	83	18	convolution	convolution	NOUN
fcis-6353	83	19	,	,	PUNCT
fcis-6353	83	20	and	and	CCONJ
fcis-6353	83	21	introduces	introduce	VERB
fcis-6353	83	22	channel	channel	NOUN
fcis-6353	83	23	separation	separation	NOUN
fcis-6353	83	24	technology	technology	NOUN
fcis-6353	83	25	into	into	ADP
fcis-6353	83	26	the	the	DET
fcis-6353	83	27	spatial	spatial	ADJ
fcis-6353	83	28	attention	attention	NOUN
fcis-6353	83	29	mechanism	mechanism	NOUN
fcis-6353	83	30	to	to	PART
fcis-6353	83	31	adaptively	adaptively	ADV
fcis-6353	83	32	emphasize	emphasize	VERB
fcis-6353	83	33	important	important	ADJ
fcis-6353	83	34	features	feature	NOUN
fcis-6353	83	35	,	,	PUNCT
fcis-6353	83	36	reducing	reduce	VERB
fcis-6353	83	37	the	the	DET
fcis-6353	83	38	amount	amount	NOUN
fcis-6353	83	39	of	of	ADP
fcis-6353	83	40	parameters	parameter	NOUN
fcis-6353	83	41	while	while	SCONJ
fcis-6353	83	42	increasing	increase	VERB
fcis-6353	83	43	the	the	DET
fcis-6353	83	44	representation	representation	NOUN
fcis-6353	83	45	ability	ability	NOUN
fcis-6353	83	46	of	of	ADP
fcis-6353	83	47	the	the	DET
fcis-6353	83	48	model	model	NOUN
fcis-6353	83	49	,	,	PUNCT
fcis-6353	83	50	it	it	PRON
fcis-6353	83	51	solves	solve	VERB
fcis-6353	83	52	the	the	DET
fcis-6353	83	53	problem	problem	NOUN
fcis-6353	83	54	that	that	SCONJ
fcis-6353	83	55	existing	exist	VERB
fcis-6353	83	56	attention	attention	NOUN
fcis-6353	83	57	mechanisms	mechanism	NOUN
fcis-6353	83	58	such	such	ADJ
fcis-6353	83	59	as	as	ADP
fcis-6353	83	60	cbam	cbam	NOUN
fcis-6353	83	61	(	(	PUNCT
fcis-6353	83	62	convolutional	convolutional	ADJ
fcis-6353	83	63	block	block	NOUN
fcis-6353	83	64	attention	attention	NOUN
fcis-6353	83	65	module	module	NOUN
fcis-6353	83	66	)	)	PUNCT
fcis-6353	83	67	[	[	X
fcis-6353	83	68	15	15	NUM
fcis-6353	83	69	]	]	PUNCT
fcis-6353	83	70	attention	attention	NOUN
fcis-6353	83	71	mechanisms	mechanism	NOUN
fcis-6353	83	72	are	be	AUX
fcis-6353	83	73	difficult	difficult	ADJ
fcis-6353	83	74	to	to	PART
fcis-6353	83	75	achieve	achieve	VERB
fcis-6353	83	76	a	a	DET
fcis-6353	83	77	good	good	ADJ
fcis-6353	83	78	balance	balance	NOUN
fcis-6353	83	79	between	between	ADP
fcis-6353	83	80	performance	performance	NOUN
fcis-6353	83	81	and	and	CCONJ
fcis-6353	83	82	model	model	NOUN
fcis-6353	83	83	complexity	complexity	NOUN
fcis-6353	83	84	.	.	PUNCT
fcis-6353	84	1	3	3	X
fcis-6353	84	2	.	.	NOUN
fcis-6353	84	3	results	result	NOUN
fcis-6353	84	4	and	and	CCONJ
fcis-6353	84	5	discussion	discussion	NOUN
fcis-6353	84	6	in	in	ADP
fcis-6353	84	7	this	this	DET
fcis-6353	84	8	section	section	NOUN
fcis-6353	84	9	,	,	PUNCT
fcis-6353	84	10	a	a	DET
fcis-6353	84	11	series	series	NOUN
fcis-6353	84	12	of	of	ADP
fcis-6353	84	13	experiments	experiment	NOUN
fcis-6353	84	14	have	have	AUX
fcis-6353	84	15	been	be	AUX
fcis-6353	84	16	designed	design	VERB
fcis-6353	84	17	to	to	PART
fcis-6353	84	18	evaluate	evaluate	VERB
fcis-6353	84	19	the	the	DET
fcis-6353	84	20	gfl	gfl	NOUN
fcis-6353	84	21	(	(	PUNCT
fcis-6353	84	22	generalized	generalize	VERB
fcis-6353	84	23	focal	focal	ADJ
fcis-6353	84	24	loss	loss	NOUN
fcis-6353	84	25	)	)	PUNCT
fcis-6353	84	26	improvement	improvement	NOUN
fcis-6353	84	27	proposed	propose	VERB
fcis-6353	84	28	in	in	ADP
fcis-6353	84	29	this	this	DET
fcis-6353	84	30	article	article	NOUN
fcis-6353	84	31	_	_	PUNCT
fcis-6353	85	1	the	the	DET
fcis-6353	85	2	performance	performance	NOUN
fcis-6353	85	3	of	of	ADP
fcis-6353	85	4	ham	ham	PROPN
fcis-6353	85	5	model	model	NOUN
fcis-6353	85	6	water	water	PROPN
fcis-6353	85	7	surface	surface	PROPN
fcis-6353	85	8	garbage	garbage	NOUN
fcis-6353	85	9	mark	mark	NOUN
fcis-6353	85	10	detection	detection	NOUN
fcis-6353	85	11	algorithm	algorithm	NOUN
fcis-6353	85	12	.	.	PUNCT
fcis-6353	86	1	3.1	3.1	NUM
fcis-6353	86	2	.	.	PUNCT
fcis-6353	86	3	introduction	introduction	NOUN
fcis-6353	86	4	to	to	ADP
fcis-6353	86	5	dataset	dataset	ADJ
fcis-6353	86	6	preparation	preparation	NOUN
fcis-6353	86	7	and	and	CCONJ
fcis-6353	86	8	experimental	experimental	ADJ
fcis-6353	86	9	platform	platform	NOUN
fcis-6353	86	10	the	the	DET
fcis-6353	86	11	dataset	dataset	NOUN
fcis-6353	86	12	used	use	VERB
fcis-6353	86	13	in	in	ADP
fcis-6353	86	14	this	this	DET
fcis-6353	86	15	experiment	experiment	NOUN
fcis-6353	86	16	is	be	AUX
fcis-6353	86	17	a	a	DET
fcis-6353	86	18	water	water	NOUN
fcis-6353	86	19	surface	surface	NOUN
fcis-6353	86	20	garbage	garbage	NOUN
fcis-6353	86	21	dataset	dataset	VERB
fcis-6353	86	22	in	in	ADP
fcis-6353	86	23	a	a	DET
fcis-6353	86	24	natural	natural	ADJ
fcis-6353	86	25	scene	scene	NOUN
fcis-6353	86	26	.	.	PUNCT
fcis-6353	87	1	this	this	DET
fcis-6353	87	2	dataset	dataset	NOUN
fcis-6353	87	3	contains	contain	VERB
fcis-6353	87	4	a	a	DET
fcis-6353	87	5	total	total	NOUN
fcis-6353	87	6	of	of	ADP
fcis-6353	87	7	2400	2400	NUM
fcis-6353	87	8	images	image	NOUN
fcis-6353	87	9	,	,	PUNCT
fcis-6353	87	10	1200	1200	NUM
fcis-6353	87	11	for	for	ADP
fcis-6353	87	12	training	training	NOUN
fcis-6353	87	13	,	,	PUNCT
fcis-6353	87	14	600	600	NUM
fcis-6353	87	15	for	for	ADP
fcis-6353	87	16	validation	validation	NOUN
fcis-6353	87	17	,	,	PUNCT
fcis-6353	87	18	and	and	CCONJ
fcis-6353	87	19	600	600	NUM
fcis-6353	87	20	for	for	ADP
fcis-6353	87	21	testing	testing	NOUN
fcis-6353	87	22	.	.	PUNCT
fcis-6353	88	1	it	it	PRON
fcis-6353	88	2	includes	include	VERB
fcis-6353	88	3	eight	eight	NUM
fcis-6353	88	4	categories	category	NOUN
fcis-6353	88	5	:	:	PUNCT
fcis-6353	88	6	bottom	bottom	ADJ
fcis-6353	88	7	,	,	PUNCT
fcis-6353	88	8	grass	grass	NOUN
fcis-6353	88	9	,	,	PUNCT
fcis-6353	88	10	branch	branch	NOUN
fcis-6353	88	11	,	,	PUNCT
fcis-6353	88	12	milk	milk	NOUN
fcis-6353	88	13	-	-	PUNCT
fcis-6353	88	14	box	box	NOUN
fcis-6353	88	15	,	,	PUNCT
fcis-6353	88	16	plastic	plastic	NOUN
fcis-6353	88	17	-	-	PUNCT
fcis-6353	88	18	bag	bag	NOUN
fcis-6353	88	19	,	,	PUNCT
fcis-6353	88	20	plastic	plastic	NOUN
fcis-6353	88	21	-	-	PUNCT
fcis-6353	88	22	garbage	garbage	NOUN
fcis-6353	88	23	,	,	PUNCT
fcis-6353	88	24	ball	ball	NOUN
fcis-6353	88	25	,	,	PUNCT
fcis-6353	88	26	and	and	CCONJ
fcis-6353	88	27	leaf	leaf	NOUN
fcis-6353	88	28	.	.	PUNCT
fcis-6353	89	1	3.2	3.2	NUM
fcis-6353	89	2	.	.	PUNCT
fcis-6353	90	1	experimental	experimental	ADJ
fcis-6353	90	2	setup	setup	NOUN
fcis-6353	90	3	all	all	DET
fcis-6353	90	4	water	water	NOUN
fcis-6353	90	5	surface	surface	NOUN
fcis-6353	90	6	garbage	garbage	NOUN
fcis-6353	90	7	data	datum	NOUN
fcis-6353	90	8	sets	set	NOUN
fcis-6353	90	9	are	be	AUX
fcis-6353	90	10	divided	divide	VERB
fcis-6353	90	11	into	into	ADP
fcis-6353	90	12	80	80	NUM
fcis-6353	90	13	%	%	NOUN
fcis-6353	90	14	of	of	ADP
fcis-6353	90	15	the	the	DET
fcis-6353	90	16	data	data	NOUN
fcis-6353	90	17	sets	set	NOUN
fcis-6353	90	18	into	into	ADP
fcis-6353	90	19	training	training	NOUN
fcis-6353	90	20	sets	set	NOUN
fcis-6353	90	21	according	accord	VERB
fcis-6353	90	22	to	to	ADP
fcis-6353	90	23	the	the	DET
fcis-6353	90	24	principle	principle	NOUN
fcis-6353	90	25	of	of	ADP
fcis-6353	90	26	scene	scene	NOUN
fcis-6353	90	27	co	co	NOUN
fcis-6353	90	28	distribution	distribution	NOUN
fcis-6353	90	29	,	,	PUNCT
fcis-6353	90	30	10	10	NUM
fcis-6353	90	31	%	%	NOUN
fcis-6353	90	32	of	of	ADP
fcis-6353	90	33	the	the	DET
fcis-6353	90	34	data	data	NOUN
fcis-6353	90	35	sets	set	NOUN
fcis-6353	90	36	are	be	AUX
fcis-6353	90	37	used	use	VERB
fcis-6353	90	38	as	as	ADP
fcis-6353	90	39	verification	verification	NOUN
fcis-6353	90	40	sets	set	NOUN
fcis-6353	90	41	,	,	PUNCT
fcis-6353	90	42	and	and	CCONJ
fcis-6353	90	43	the	the	DET
fcis-6353	90	44	remaining	remain	VERB
fcis-6353	90	45	10	10	NUM
fcis-6353	90	46	%	%	NOUN
fcis-6353	90	47	are	be	AUX
fcis-6353	90	48	used	use	VERB
fcis-6353	90	49	as	as	ADP
fcis-6353	90	50	test	test	NOUN
fcis-6353	90	51	sets	set	NOUN
fcis-6353	90	52	.	.	PUNCT
fcis-6353	91	1	the	the	DET
fcis-6353	91	2	super	super	ADJ
fcis-6353	91	3	parameters	parameter	NOUN
fcis-6353	91	4	set	set	VERB
fcis-6353	91	5	are	be	AUX
fcis-6353	91	6	as	as	SCONJ
fcis-6353	91	7	follows	follow	VERB
fcis-6353	91	8	:	:	PUNCT
fcis-6353	91	9	the	the	DET
fcis-6353	91	10	total	total	ADJ
fcis-6353	91	11	training	training	NOUN
fcis-6353	91	12	round	round	NOUN
fcis-6353	91	13	is	be	AUX
fcis-6353	91	14	12	12	NUM
fcis-6353	91	15	rounds	round	NOUN
fcis-6353	91	16	,	,	PUNCT
fcis-6353	91	17	using	use	VERB
fcis-6353	91	18	a	a	DET
fcis-6353	91	19	random	random	ADJ
fcis-6353	91	20	gradient	gradient	ADJ
fcis-6353	91	21	descent	descent	NOUN
fcis-6353	91	22	strategy[16	strategy[16	PROPN
fcis-6353	91	23	]	]	PUNCT
fcis-6353	91	24	,	,	PUNCT
fcis-6353	91	25	and	and	CCONJ
fcis-6353	91	26	the	the	DET
fcis-6353	91	27	initial	initial	ADJ
fcis-6353	91	28	learning	learning	NOUN
fcis-6353	91	29	rate	rate	NOUN
fcis-6353	91	30	is	be	AUX
fcis-6353	91	31	set	set	VERB
fcis-6353	91	32	to	to	ADP
fcis-6353	91	33	0	0	NUM
fcis-6353	91	34	001	001	NUM
fcis-6353	91	35	,	,	PUNCT
fcis-6353	91	36	momentum	momentum	NOUN
fcis-6353	91	37	and	and	CCONJ
fcis-6353	91	38	weight	weight	NOUN
fcis-6353	91	39	attenuation	attenuation	NOUN
fcis-6353	91	40	are	be	AUX
fcis-6353	91	41	set	set	VERB
fcis-6353	91	42	to	to	ADP
fcis-6353	91	43	0	0	NUM
fcis-6353	91	44	937	937	NUM
fcis-6353	91	45	and	and	CCONJ
fcis-6353	91	46	0	0	NUM
fcis-6353	91	47	0005	0005	NUM
fcis-6353	91	48	。	。	PUNCT
fcis-6353	91	49	perform	perform	VERB
fcis-6353	91	50	training	training	NOUN
fcis-6353	91	51	using	use	VERB
fcis-6353	91	52	a	a	DET
fcis-6353	91	53	single	single	ADJ
fcis-6353	91	54	gpu	gpu	NOUN
fcis-6353	91	55	with	with	ADP
fcis-6353	91	56	a	a	DET
fcis-6353	91	57	batch	batch	NOUN
fcis-6353	91	58	size	size	NOUN
fcis-6353	91	59	of	of	ADP
fcis-6353	91	60	2	2	NUM
fcis-6353	91	61	.	.	NOUN
fcis-6353	91	62	3.3	3.3	NUM
fcis-6353	91	63	.	.	PUNCT
fcis-6353	92	1	analysis	analysis	NOUN
fcis-6353	92	2	of	of	ADP
fcis-6353	92	3	experimental	experimental	ADJ
fcis-6353	92	4	results	result	NOUN
fcis-6353	92	5	this	this	DET
fcis-6353	92	6	experiment	experiment	NOUN
fcis-6353	92	7	was	be	AUX
fcis-6353	92	8	improved	improve	VERB
fcis-6353	92	9	on	on	ADP
fcis-6353	92	10	gfl	gfl	PROPN
fcis-6353	92	11	(	(	PUNCT
fcis-6353	92	12	generalized	generalize	VERB
fcis-6353	92	13	focal	focal	ADJ
fcis-6353	92	14	loss	loss	NOUN
fcis-6353	92	15	)	)	PUNCT
fcis-6353	92	16	.	.	PUNCT
fcis-6353	93	1	in	in	ADP
fcis-6353	93	2	order	order	NOUN
fcis-6353	93	3	to	to	PART
fcis-6353	93	4	verify	verify	VERB
fcis-6353	93	5	the	the	DET
fcis-6353	93	6	effectiveness	effectiveness	NOUN
fcis-6353	93	7	of	of	ADP
fcis-6353	93	8	the	the	DET
fcis-6353	93	9	ham	ham	NOUN
fcis-6353	93	10	(	(	PUNCT
fcis-6353	93	11	hbrid	hbrid	VERB
fcis-6353	93	12	attention	attention	NOUN
fcis-6353	93	13	model	model	NOUN
fcis-6353	93	14	)	)	PUNCT
fcis-6353	93	15	module	module	NOUN
fcis-6353	93	16	,	,	PUNCT
fcis-6353	93	17	this	this	DET
fcis-6353	93	18	article	article	NOUN
fcis-6353	93	19	designed	design	VERB
fcis-6353	93	20	a	a	DET
fcis-6353	93	21	group	group	NOUN
fcis-6353	93	22	of	of	ADP
fcis-6353	93	23	experiments	experiment	NOUN
fcis-6353	93	24	to	to	PART
fcis-6353	93	25	verify	verify	VERB
fcis-6353	93	26	.	.	PUNCT
fcis-6353	94	1	in	in	ADP
fcis-6353	94	2	order	order	NOUN
fcis-6353	94	3	to	to	PART
fcis-6353	94	4	verify	verify	VERB
fcis-6353	94	5	the	the	DET
fcis-6353	94	6	feasibility	feasibility	NOUN
fcis-6353	94	7	of	of	ADP
fcis-6353	94	8	the	the	DET
fcis-6353	94	9	proposed	propose	VERB
fcis-6353	94	10	method	method	NOUN
fcis-6353	94	11	,	,	PUNCT
fcis-6353	94	12	a	a	DET
fcis-6353	94	13	series	series	NOUN
fcis-6353	94	14	of	of	ADP
fcis-6353	94	15	ablation	ablation	NOUN
fcis-6353	94	16	experiments	experiment	NOUN
fcis-6353	94	17	were	be	AUX
fcis-6353	94	18	conducted	conduct	VERB
fcis-6353	94	19	on	on	ADP
fcis-6353	94	20	our	our	PRON
fcis-6353	94	21	proposed	propose	VERB
fcis-6353	94	22	garbage	garbage	NOUN
fcis-6353	94	23	detection	detection	NOUN
fcis-6353	94	24	dataset	dataset	VERB
fcis-6353	94	25	under	under	ADP
fcis-6353	94	26	natural	natural	ADJ
fcis-6353	94	27	scenarios	scenario	NOUN
fcis-6353	94	28	,	,	PUNCT
fcis-6353	94	29	as	as	SCONJ
fcis-6353	94	30	shown	show	VERB
fcis-6353	94	31	in	in	ADP
fcis-6353	94	32	table	table	NOUN
fcis-6353	94	33	1	1	NUM
fcis-6353	94	34	.	.	PUNCT
fcis-6353	95	1	in	in	ADP
fcis-6353	95	2	table	table	NOUN
fcis-6353	95	3	1	1	NUM
fcis-6353	95	4	,	,	PUNCT
fcis-6353	95	5	"	"	PUNCT
fcis-6353	95	6	√	√	NUM
fcis-6353	95	7	"	"	PUNCT
fcis-6353	95	8	indicates	indicate	VERB
fcis-6353	95	9	adding	add	VERB
fcis-6353	95	10	the	the	DET
fcis-6353	95	11	module	module	NOUN
fcis-6353	95	12	,	,	PUNCT
fcis-6353	95	13	otherwise	otherwise	ADV
fcis-6353	95	14	,	,	PUNCT
fcis-6353	95	15	it	it	PRON
fcis-6353	95	16	is	be	AUX
fcis-6353	95	17	not	not	PART
fcis-6353	95	18	added	add	VERB
fcis-6353	95	19	.	.	PUNCT
fcis-6353	96	1	the	the	DET
fcis-6353	96	2	impact	impact	NOUN
fcis-6353	96	3	of	of	ADP
fcis-6353	96	4	ham	ham	NOUN
fcis-6353	96	5	on	on	ADP
fcis-6353	96	6	the	the	DET
fcis-6353	96	7	experimental	experimental	ADJ
fcis-6353	96	8	results	result	NOUN
fcis-6353	96	9	was	be	AUX
fcis-6353	96	10	considered	consider	VERB
fcis-6353	96	11	in	in	ADP
fcis-6353	96	12	the	the	DET
fcis-6353	96	13	experiment	experiment	NOUN
fcis-6353	96	14	,	,	PUNCT
fcis-6353	96	15	and	and	CCONJ
fcis-6353	96	16	the	the	DET
fcis-6353	96	17	test	test	NOUN
fcis-6353	96	18	set	set	VERB
fcis-6353	96	19	image	image	NOUN
fcis-6353	96	20	size	size	NOUN
fcis-6353	96	21	was	be	AUX
fcis-6353	96	22	512	512	NUM
fcis-6353	96	23	×	×	NOUN
fcis-6353	96	24	512	512	NUM
fcis-6353	96	25	,	,	PUNCT
fcis-6353	96	26	with	with	ADP
fcis-6353	96	27	a	a	DET
fcis-6353	96	28	training	training	NOUN
fcis-6353	96	29	cycle	cycle	NOUN
fcis-6353	96	30	of	of	ADP
fcis-6353	96	31	12	12	NUM
fcis-6353	96	32	epochs	epoch	NOUN
fcis-6353	96	33	.	.	PUNCT
fcis-6353	97	1	in	in	ADP
fcis-6353	97	2	this	this	DET
fcis-6353	97	3	experiment	experiment	NOUN
fcis-6353	97	4	,	,	PUNCT
fcis-6353	97	5	gfl	gfl	NOUN
fcis-6353	97	6	(	(	PUNCT
fcis-6353	97	7	generalized	generalize	VERB
fcis-6353	97	8	focal	focal	ADJ
fcis-6353	97	9	loss	loss	NOUN
fcis-6353	97	10	)	)	PUNCT
fcis-6353	97	11	is	be	AUX
fcis-6353	97	12	used	use	VERB
fcis-6353	97	13	as	as	ADP
fcis-6353	97	14	the	the	DET
fcis-6353	97	15	basic	basic	ADJ
fcis-6353	97	16	model	model	NOUN
fcis-6353	97	17	,	,	PUNCT
fcis-6353	97	18	and	and	CCONJ
fcis-6353	97	19	the	the	DET
fcis-6353	97	20	results	result	NOUN
fcis-6353	97	21	of	of	ADP
fcis-6353	97	22	ablation	ablation	NOUN
fcis-6353	97	23	experiments	experiment	NOUN
fcis-6353	97	24	are	be	AUX
fcis-6353	97	25	shown	show	VERB
fcis-6353	97	26	in	in	ADP
fcis-6353	97	27	table	table	NOUN
fcis-6353	97	28	1	1	NUM
fcis-6353	97	29	.	.	PUNCT
fcis-6353	98	1	table.1	table.1	NOUN
fcis-6353	98	2	ablation	ablation	NOUN
fcis-6353	98	3	experiment	experiment	NOUN
fcis-6353	98	4	the	the	DET
fcis-6353	98	5	results	result	NOUN
fcis-6353	98	6	show	show	VERB
fcis-6353	98	7	that	that	SCONJ
fcis-6353	98	8	compared	compare	VERB
fcis-6353	98	9	to	to	ADP
fcis-6353	98	10	gfl	gfl	PROPN
fcis-6353	98	11	(	(	PUNCT
fcis-6353	98	12	generalized	generalize	VERB
fcis-6353	98	13	focal	focal	ADJ
fcis-6353	98	14	loss	loss	NOUN
fcis-6353	98	15	)	)	PUNCT
fcis-6353	98	16	,	,	PUNCT
fcis-6353	98	17	gfl	gfl	PROPN
fcis-6353	98	18	_	_	PUNCT
fcis-6353	98	19	the	the	DET
fcis-6353	98	20	ham	ham	PROPN
fcis-6353	98	21	model	model	NOUN
fcis-6353	98	22	map	map	NOUN
fcis-6353	98	23	has	have	AUX
fcis-6353	98	24	been	be	AUX
fcis-6353	98	25	improved	improve	VERB
fcis-6353	98	26	by	by	ADP
fcis-6353	98	27	1.61	1.61	NUM
fcis-6353	98	28	%	%	NOUN
fcis-6353	98	29	.	.	PUNCT
fcis-6353	99	1	from	from	ADP
fcis-6353	99	2	the	the	DET
fcis-6353	99	3	experimental	experimental	ADJ
fcis-6353	99	4	results	result	NOUN
fcis-6353	99	5	,	,	PUNCT
fcis-6353	99	6	it	it	PRON
fcis-6353	99	7	can	can	AUX
fcis-6353	99	8	be	be	AUX
fcis-6353	99	9	seen	see	VERB
fcis-6353	99	10	that	that	SCONJ
fcis-6353	99	11	the	the	DET
fcis-6353	99	12	ham	ham	NOUN
fcis-6353	99	13	(	(	PUNCT
fcis-6353	99	14	hbrid	hbrid	VERB
fcis-6353	99	15	attention	attention	NOUN
fcis-6353	99	16	model	model	NOUN
fcis-6353	99	17	)	)	PUNCT
fcis-6353	99	18	module	module	NOUN
fcis-6353	99	19	is	be	AUX
fcis-6353	99	20	effective	effective	ADJ
fcis-6353	99	21	,	,	PUNCT
fcis-6353	99	22	improving	improve	VERB
fcis-6353	99	23	the	the	DET
fcis-6353	99	24	detection	detection	NOUN
fcis-6353	99	25	accuracy	accuracy	NOUN
fcis-6353	99	26	of	of	ADP
fcis-6353	99	27	the	the	DET
fcis-6353	99	28	model	model	NOUN
fcis-6353	99	29	,	,	PUNCT
fcis-6353	99	30	making	make	VERB
fcis-6353	99	31	the	the	DET
fcis-6353	99	32	model	model	NOUN
fcis-6353	99	33	more	more	ADV
fcis-6353	99	34	suitable	suitable	ADJ
fcis-6353	99	35	for	for	ADP
fcis-6353	99	36	target	target	NOUN
fcis-6353	99	37	detection	detection	NOUN
fcis-6353	99	38	in	in	ADP
fcis-6353	99	39	natural	natural	ADJ
fcis-6353	99	40	scenes	scene	NOUN
fcis-6353	99	41	.	.	PUNCT
fcis-6353	100	1	to	to	PART
fcis-6353	100	2	verify	verify	VERB
fcis-6353	100	3	the	the	DET
fcis-6353	100	4	superiority	superiority	NOUN
fcis-6353	100	5	of	of	ADP
fcis-6353	100	6	the	the	DET
fcis-6353	100	7	algorithm	algorithm	NOUN
fcis-6353	100	8	,	,	PUNCT
fcis-6353	100	9	gfl	gfl	PROPN
fcis-6353	100	10	_	_	PRON
fcis-6353	100	11	ham	ham	NOUN
fcis-6353	100	12	is	be	AUX
fcis-6353	100	13	experimentally	experimentally	ADV
fcis-6353	100	14	compared	compare	VERB
fcis-6353	100	15	with	with	ADP
fcis-6353	100	16	common	common	ADJ
fcis-6353	100	17	target	target	NOUN
fcis-6353	100	18	detection	detection	NOUN
fcis-6353	100	19	algorithms	algorithm	NOUN
fcis-6353	100	20	yolov3	yolov3	PUNCT
fcis-6353	101	1	[	[	X
fcis-6353	101	2	17	17	NUM
fcis-6353	101	3	]	]	PUNCT
fcis-6353	101	4	,	,	PUNCT
fcis-6353	101	5	ssd	ssd	NOUN
fcis-6353	101	6	[	[	X
fcis-6353	101	7	18	18	NUM
fcis-6353	101	8	]	]	PUNCT
fcis-6353	101	9	,	,	PUNCT
fcis-6353	101	10	and	and	CCONJ
fcis-6353	101	11	gfl	gfl	NOUN
fcis-6353	101	12	on	on	ADP
fcis-6353	101	13	a	a	DET
fcis-6353	101	14	water	water	NOUN
fcis-6353	101	15	surface	surface	NOUN
fcis-6353	101	16	garbage	garbage	NOUN
fcis-6353	101	17	detection	detection	NOUN
fcis-6353	101	18	dataset	dataset	VERB
fcis-6353	101	19	.	.	PUNCT
fcis-6353	102	1	the	the	DET
fcis-6353	102	2	framework	framework	NOUN
fcis-6353	102	3	used	use	VERB
fcis-6353	102	4	is	be	AUX
fcis-6353	102	5	pythoch	pythoch	NOUN
fcis-6353	102	6	,	,	PUNCT
fcis-6353	102	7	with	with	ADP
fcis-6353	102	8	a	a	DET
fcis-6353	102	9	default	default	NOUN
fcis-6353	102	10	input	input	NOUN
fcis-6353	102	11	image	image	NOUN
fcis-6353	102	12	size	size	NOUN
fcis-6353	102	13	of	of	ADP
fcis-6353	102	14	512x512	512x512	PROPN
fcis-6353	102	15	,	,	PUNCT
fcis-6353	102	16	and	and	CCONJ
fcis-6353	102	17	a	a	DET
fcis-6353	102	18	training	training	NOUN
fcis-6353	102	19	cycle	cycle	NOUN
fcis-6353	102	20	of	of	ADP
fcis-6353	102	21	12	12	NUM
fcis-6353	102	22	epochs	epoch	NOUN
fcis-6353	102	23	.	.	PUNCT
fcis-6353	103	1	table	table	NOUN
fcis-6353	103	2	2	2	NUM
fcis-6353	103	3	shows	show	VERB
fcis-6353	103	4	the	the	DET
fcis-6353	103	5	comparison	comparison	NOUN
fcis-6353	103	6	of	of	ADP
fcis-6353	103	7	our	our	PRON
fcis-6353	103	8	method	method	NOUN
fcis-6353	103	9	with	with	ADP
fcis-6353	103	10	other	other	ADJ
fcis-6353	103	11	algorithms	algorithm	NOUN
fcis-6353	103	12	.	.	PUNCT
fcis-6353	104	1	table	table	NOUN
fcis-6353	104	2	.	.	PUNCT
fcis-6353	105	1	2	2	NUM
fcis-6353	105	2	comparison	comparison	NOUN
fcis-6353	105	3	with	with	ADP
fcis-6353	105	4	different	different	ADJ
fcis-6353	105	5	target	target	NOUN
fcis-6353	105	6	detection	detection	NOUN
fcis-6353	105	7	algorithms	algorithm	NOUN
fcis-6353	105	8	as	as	SCONJ
fcis-6353	105	9	shown	show	VERB
fcis-6353	105	10	in	in	ADP
fcis-6353	105	11	table	table	NOUN
fcis-6353	105	12	2	2	NUM
fcis-6353	105	13	,	,	PUNCT
fcis-6353	105	14	the	the	DET
fcis-6353	105	15	experimental	experimental	ADJ
fcis-6353	105	16	results	result	NOUN
fcis-6353	105	17	show	show	VERB
fcis-6353	105	18	that	that	SCONJ
fcis-6353	105	19	our	our	PRON
fcis-6353	105	20	w	w	PROPN
fcis-6353	105	21	f	f	PROPN
fcis-6353	105	22	in	in	ADP
fcis-6353	105	23	f	f	PROPN
fcis-6353	105	24	out	out	PROPN
fcis-6353	105	25	cim	cim	PROPN
fcis-6353	105	26	cin	cin	PROPN
fcis-6353	105	27	f	f	PROPN
fcis-6353	106	1	i	i	PRON
fcis-6353	106	2	m	m	VERB
fcis-6353	106	3	f	f	PROPN
fcis-6353	106	4	nim	nim	PROPN
fcis-6353	106	5	f	f	PROPN
fcis-6353	106	6	in	in	ADP
fcis-6353	106	7			PROPN
fcis-6353	106	8	sa	sa	PROPN
fcis-6353	106	9	157	157	NUM
fcis-6353	106	10	method	method	NOUN
fcis-6353	106	11	has	have	VERB
fcis-6353	106	12	a	a	DET
fcis-6353	106	13	significant	significant	ADJ
fcis-6353	106	14	advantage	advantage	NOUN
fcis-6353	106	15	in	in	ADP
fcis-6353	106	16	accuracy	accuracy	NOUN
fcis-6353	106	17	compared	compare	VERB
fcis-6353	106	18	to	to	ADP
fcis-6353	106	19	current	current	ADJ
fcis-6353	106	20	mainstream	mainstream	NOUN
fcis-6353	106	21	target	target	NOUN
fcis-6353	106	22	detection	detection	NOUN
fcis-6353	106	23	algorithms	algorithm	NOUN
fcis-6353	106	24	,	,	PUNCT
fcis-6353	106	25	with	with	ADP
fcis-6353	106	26	map	map	NOUN
fcis-6353	106	27	increasing	increase	VERB
fcis-6353	106	28	1.09	1.09	NUM
fcis-6353	106	29	%	%	NOUN
fcis-6353	106	30	,	,	PUNCT
fcis-6353	106	31	1.36	1.36	NUM
fcis-6353	106	32	%	%	NOUN
fcis-6353	106	33	,	,	PUNCT
fcis-6353	106	34	and	and	CCONJ
fcis-6353	106	35	1.61	1.61	NUM
fcis-6353	106	36	%	%	NOUN
fcis-6353	106	37	compared	compare	VERB
fcis-6353	106	38	to	to	ADP
fcis-6353	106	39	yolov3	yolov3	PROPN
fcis-6353	106	40	,	,	PUNCT
fcis-6353	106	41	ssd	ssd	PROPN
fcis-6353	106	42	,	,	PUNCT
fcis-6353	106	43	and	and	CCONJ
fcis-6353	106	44	gfl	gfl	PROPN
fcis-6353	106	45	,	,	PUNCT
fcis-6353	106	46	respectively	respectively	ADV
fcis-6353	106	47	.	.	PUNCT
fcis-6353	107	1	figure	figure	NOUN
fcis-6353	107	2	4	4	NUM
fcis-6353	107	3	shows	show	VERB
fcis-6353	107	4	our	our	PRON
fcis-6353	107	5	model	model	NOUN
fcis-6353	107	6	gfl	gfl	PROPN
fcis-6353	107	7	_	_	PROPN
fcis-6353	107	8	visualization	visualization	NOUN
fcis-6353	107	9	of	of	ADP
fcis-6353	107	10	garbage	garbage	NOUN
fcis-6353	107	11	detection	detection	NOUN
fcis-6353	107	12	in	in	ADP
fcis-6353	107	13	natural	natural	ADJ
fcis-6353	107	14	scenes	scene	NOUN
fcis-6353	107	15	using	use	VERB
fcis-6353	107	16	ham	ham	PROPN
fcis-6353	107	17	.	.	PUNCT
fcis-6353	108	1	figure	figure	NOUN
fcis-6353	108	2	4	4	NUM
fcis-6353	108	3	.	.	PUNCT
fcis-6353	109	1	visualization	visualization	NOUN
fcis-6353	109	2	results	result	NOUN
fcis-6353	109	3	from	from	ADP
fcis-6353	109	4	the	the	DET
fcis-6353	109	5	visualization	visualization	NOUN
fcis-6353	109	6	result	result	NOUN
fcis-6353	109	7	graph	graph	NOUN
fcis-6353	109	8	,	,	PUNCT
fcis-6353	109	9	it	it	PRON
fcis-6353	109	10	can	can	AUX
fcis-6353	109	11	be	be	AUX
fcis-6353	109	12	seen	see	VERB
fcis-6353	109	13	that	that	SCONJ
fcis-6353	109	14	our	our	PRON
fcis-6353	109	15	model	model	NOUN
fcis-6353	109	16	can	can	AUX
fcis-6353	109	17	accurately	accurately	ADV
fcis-6353	109	18	detect	detect	VERB
fcis-6353	109	19	most	most	ADJ
fcis-6353	109	20	objects	object	NOUN
fcis-6353	109	21	.	.	PUNCT
fcis-6353	110	1	however	however	ADV
fcis-6353	110	2	,	,	PUNCT
fcis-6353	110	3	due	due	ADP
fcis-6353	110	4	to	to	ADP
fcis-6353	110	5	the	the	DET
fcis-6353	110	6	small	small	ADJ
fcis-6353	110	7	number	number	NOUN
fcis-6353	110	8	of	of	ADP
fcis-6353	110	9	data	datum	NOUN
fcis-6353	110	10	sets	set	NOUN
fcis-6353	110	11	,	,	PUNCT
fcis-6353	110	12	uneven	uneven	ADJ
fcis-6353	110	13	distribution	distribution	NOUN
fcis-6353	110	14	,	,	PUNCT
fcis-6353	110	15	image	image	NOUN
fcis-6353	110	16	shooting	shooting	NOUN
fcis-6353	110	17	angle	angle	NOUN
fcis-6353	110	18	,	,	PUNCT
fcis-6353	110	19	and	and	CCONJ
fcis-6353	110	20	light	light	ADJ
fcis-6353	110	21	reflection	reflection	NOUN
fcis-6353	110	22	,	,	PUNCT
fcis-6353	110	23	some	some	DET
fcis-6353	110	24	problems	problem	NOUN
fcis-6353	110	25	such	such	ADJ
fcis-6353	110	26	as	as	ADP
fcis-6353	110	27	target	target	NOUN
fcis-6353	110	28	misdetection	misdetection	NOUN
fcis-6353	110	29	and	and	CCONJ
fcis-6353	110	30	overlapping	overlap	VERB
fcis-6353	110	31	detection	detection	NOUN
fcis-6353	110	32	frames	frame	NOUN
fcis-6353	110	33	have	have	AUX
fcis-6353	110	34	resulted	result	VERB
fcis-6353	110	35	.	.	PUNCT
fcis-6353	111	1	4	4	X
fcis-6353	111	2	.	.	X
fcis-6353	111	3	conclusion	conclusion	NOUN
fcis-6353	111	4	in	in	ADP
fcis-6353	111	5	order	order	NOUN
fcis-6353	111	6	to	to	PART
fcis-6353	111	7	improve	improve	VERB
fcis-6353	111	8	the	the	DET
fcis-6353	111	9	accuracy	accuracy	NOUN
fcis-6353	111	10	of	of	ADP
fcis-6353	111	11	water	water	NOUN
fcis-6353	111	12	surface	surface	NOUN
fcis-6353	111	13	garbage	garbage	NOUN
fcis-6353	111	14	detection	detection	NOUN
fcis-6353	111	15	,	,	PUNCT
fcis-6353	111	16	this	this	DET
fcis-6353	111	17	paper	paper	NOUN
fcis-6353	111	18	proposes	propose	VERB
fcis-6353	111	19	a	a	DET
fcis-6353	111	20	garbage	garbage	NOUN
fcis-6353	111	21	detection	detection	NOUN
fcis-6353	111	22	algorithm	algorithm	NOUN
fcis-6353	111	23	based	base	VERB
fcis-6353	111	24	on	on	ADP
fcis-6353	111	25	improved	improved	ADJ
fcis-6353	111	26	gfl	gfl	NOUN
fcis-6353	111	27	(	(	PUNCT
fcis-6353	111	28	generalized	generalize	VERB
fcis-6353	111	29	focal	focal	ADJ
fcis-6353	111	30	loss	loss	NOUN
fcis-6353	111	31	)	)	PUNCT
fcis-6353	111	32	due	due	ADP
fcis-6353	111	33	to	to	ADP
fcis-6353	111	34	the	the	DET
fcis-6353	111	35	small	small	ADJ
fcis-6353	111	36	number	number	NOUN
fcis-6353	111	37	of	of	ADP
fcis-6353	111	38	data	datum	NOUN
fcis-6353	111	39	sets	set	NOUN
fcis-6353	111	40	,	,	PUNCT
fcis-6353	111	41	uneven	uneven	ADJ
fcis-6353	111	42	distribution	distribution	NOUN
fcis-6353	111	43	,	,	PUNCT
fcis-6353	111	44	image	image	NOUN
fcis-6353	111	45	shooting	shooting	NOUN
fcis-6353	111	46	angle	angle	NOUN
fcis-6353	111	47	,	,	PUNCT
fcis-6353	111	48	and	and	CCONJ
fcis-6353	111	49	light	light	ADJ
fcis-6353	111	50	reflection	reflection	NOUN
fcis-6353	111	51	in	in	ADP
fcis-6353	111	52	water	water	NOUN
fcis-6353	111	53	surface	surface	NOUN
fcis-6353	111	54	garbage	garbage	NOUN
fcis-6353	111	55	data	datum	NOUN
fcis-6353	111	56	sets	set	NOUN
fcis-6353	111	57	.	.	PUNCT
fcis-6353	112	1	this	this	DET
fcis-6353	112	2	algorithm	algorithm	NOUN
fcis-6353	112	3	introduces	introduce	VERB
fcis-6353	112	4	the	the	DET
fcis-6353	112	5	ham	ham	NOUN
fcis-6353	112	6	(	(	PUNCT
fcis-6353	112	7	hybrid	hybrid	ADJ
fcis-6353	112	8	attention	attention	NOUN
fcis-6353	112	9	model	model	NOUN
fcis-6353	112	10	)	)	PUNCT
fcis-6353	112	11	attention	attention	NOUN
fcis-6353	112	12	mechanism	mechanism	NOUN
fcis-6353	112	13	into	into	ADP
fcis-6353	112	14	the	the	DET
fcis-6353	112	15	feature	feature	NOUN
fcis-6353	112	16	extraction	extraction	NOUN
fcis-6353	112	17	network	network	NOUN
fcis-6353	112	18	resnet	resnet	VERB
fcis-6353	112	19	[	[	X
fcis-6353	112	20	10	10	NUM
fcis-6353	112	21	]	]	PUNCT
fcis-6353	112	22	,	,	PUNCT
fcis-6353	112	23	greatly	greatly	ADV
fcis-6353	112	24	improving	improve	VERB
fcis-6353	112	25	the	the	DET
fcis-6353	112	26	detection	detection	NOUN
fcis-6353	112	27	accuracy	accuracy	NOUN
fcis-6353	112	28	at	at	ADP
fcis-6353	112	29	the	the	DET
fcis-6353	112	30	expense	expense	NOUN
fcis-6353	112	31	of	of	ADP
fcis-6353	112	32	a	a	DET
fcis-6353	112	33	small	small	ADJ
fcis-6353	112	34	amount	amount	NOUN
fcis-6353	112	35	of	of	ADP
fcis-6353	112	36	detection	detection	NOUN
fcis-6353	112	37	speed	speed	NOUN
fcis-6353	112	38	.	.	PUNCT
fcis-6353	113	1	on	on	ADP
fcis-6353	113	2	the	the	DET
fcis-6353	113	3	surface	surface	NOUN
fcis-6353	113	4	garbage	garbage	NOUN
fcis-6353	113	5	datasets	dataset	NOUN
fcis-6353	113	6	,	,	PUNCT
fcis-6353	113	7	gfl	gfl	PROPN
fcis-6353	113	8	_	_	PUNCT
fcis-6353	113	9	the	the	DET
fcis-6353	113	10	accuracy	accuracy	NOUN
fcis-6353	113	11	and	and	CCONJ
fcis-6353	113	12	speed	speed	NOUN
fcis-6353	113	13	of	of	ADP
fcis-6353	113	14	the	the	DET
fcis-6353	113	15	ham	ham	NOUN
fcis-6353	113	16	algorithm	algorithm	NOUN
fcis-6353	113	17	are	be	AUX
fcis-6353	113	18	better	well	ADJ
fcis-6353	113	19	than	than	ADP
fcis-6353	113	20	other	other	ADJ
fcis-6353	113	21	current	current	ADJ
fcis-6353	113	22	mainstream	mainstream	ADJ
fcis-6353	113	23	single	single	ADJ
fcis-6353	113	24	stage	stage	NOUN
fcis-6353	113	25	algorithms	algorithm	NOUN
fcis-6353	113	26	,	,	PUNCT
fcis-6353	113	27	but	but	CCONJ
fcis-6353	113	28	based	base	VERB
fcis-6353	113	29	on	on	ADP
fcis-6353	113	30	the	the	DET
fcis-6353	113	31	visualization	visualization	NOUN
fcis-6353	113	32	results	result	NOUN
fcis-6353	113	33	,	,	PUNCT
fcis-6353	113	34	there	there	PRON
fcis-6353	113	35	are	be	VERB
fcis-6353	113	36	still	still	ADV
fcis-6353	113	37	some	some	DET
fcis-6353	113	38	problems	problem	NOUN
fcis-6353	113	39	.	.	PUNCT
fcis-6353	114	1	due	due	ADP
fcis-6353	114	2	to	to	ADP
fcis-6353	114	3	insufficient	insufficient	ADJ
fcis-6353	114	4	quantity	quantity	NOUN
fcis-6353	114	5	and	and	CCONJ
fcis-6353	114	6	uneven	uneven	ADJ
fcis-6353	114	7	data	datum	NOUN
fcis-6353	114	8	,	,	PUNCT
fcis-6353	114	9	there	there	PRON
fcis-6353	114	10	are	be	VERB
fcis-6353	114	11	some	some	DET
fcis-6353	114	12	cases	case	NOUN
fcis-6353	114	13	of	of	ADP
fcis-6353	114	14	false	false	ADJ
fcis-6353	114	15	detection	detection	NOUN
fcis-6353	114	16	and	and	CCONJ
fcis-6353	114	17	missed	miss	VERB
fcis-6353	114	18	detection	detection	NOUN
fcis-6353	114	19	of	of	ADP
fcis-6353	114	20	targets	target	NOUN
fcis-6353	114	21	.	.	PUNCT
fcis-6353	115	1	therefore	therefore	ADV
fcis-6353	115	2	,	,	PUNCT
fcis-6353	115	3	in	in	ADP
fcis-6353	115	4	the	the	DET
fcis-6353	115	5	future	future	ADJ
fcis-6353	115	6	work	work	NOUN
fcis-6353	115	7	,	,	PUNCT
fcis-6353	115	8	how	how	SCONJ
fcis-6353	115	9	to	to	PART
fcis-6353	115	10	achieve	achieve	VERB
fcis-6353	115	11	the	the	DET
fcis-6353	115	12	balance	balance	NOUN
fcis-6353	115	13	between	between	ADP
fcis-6353	115	14	accuracy	accuracy	NOUN
fcis-6353	115	15	and	and	CCONJ
fcis-6353	115	16	speed	speed	NOUN
fcis-6353	115	17	of	of	ADP
fcis-6353	115	18	water	water	NOUN
fcis-6353	115	19	surface	surface	NOUN
fcis-6353	115	20	garbage	garbage	NOUN
fcis-6353	115	21	detection	detection	NOUN
fcis-6353	115	22	in	in	ADP
fcis-6353	115	23	natural	natural	ADJ
fcis-6353	115	24	scenes	scene	NOUN
fcis-6353	115	25	remains	remain	VERB
fcis-6353	115	26	the	the	DET
fcis-6353	115	27	focus	focus	NOUN
fcis-6353	115	28	of	of	ADP
fcis-6353	115	29	work	work	NOUN
fcis-6353	115	30	.	.	PUNCT
fcis-6353	116	1	acknowledgments	acknowledgment	NOUN
fcis-6353	116	2	this	this	DET
fcis-6353	116	3	project	project	NOUN
fcis-6353	116	4	is	be	AUX
fcis-6353	116	5	supported	support	VERB
fcis-6353	116	6	by	by	ADP
fcis-6353	116	7	the	the	DET
fcis-6353	116	8	southwest	southwest	PROPN
fcis-6353	116	9	university	university	PROPN
fcis-6353	116	10	for	for	ADP
fcis-6353	116	11	nationalities	nationality	NOUN
fcis-6353	116	12	graduate	graduate	VERB
fcis-6353	116	13	innovative	innovative	ADJ
fcis-6353	116	14	scientific	scientific	ADJ
fcis-6353	116	15	research	research	NOUN
fcis-6353	116	16	project	project	NOUN
fcis-6353	116	17	(	(	PUNCT
fcis-6353	116	18	project	project	NOUN
fcis-6353	116	19	no	no	INTJ
fcis-6353	116	20	.	.	PUNCT
fcis-6353	117	1	yb2022758	yb2022758	NOUN
fcis-6353	117	2	)	)	PUNCT
fcis-6353	117	3	references	reference	NOUN
fcis-6353	117	4	[	[	X
fcis-6353	117	5	1	1	NUM
fcis-6353	117	6	]	]	X
fcis-6353	117	7	duan	duan	PROPN
fcis-6353	117	8	,	,	PUNCT
fcis-6353	117	9	ling	ling	PROPN
fcis-6353	117	10	.	.	PUNCT
fcis-6353	118	1	"	"	PUNCT
fcis-6353	118	2	global	global	ADJ
fcis-6353	118	3	environment	environment	NOUN
fcis-6353	118	4	outlook	outlook	VERB
fcis-6353	118	5	6	6	NUM
fcis-6353	118	6	"	"	PUNCT
fcis-6353	118	7	was	be	AUX
fcis-6353	118	8	released	release	VERB
fcis-6353	118	9	after	after	ADP
fcis-6353	118	10	5	5	NUM
fcis-6353	118	11	years	year	NOUN
fcis-6353	118	12	:	:	PUNCT
fcis-6353	118	13	the	the	DET
fcis-6353	118	14	earth	earth	NOUN
fcis-6353	118	15	has	have	AUX
fcis-6353	118	16	been	be	AUX
fcis-6353	118	17	severely	severely	ADV
fcis-6353	118	18	damaged	damage	VERB
fcis-6353	118	19	[	[	X
fcis-6353	118	20	j	j	X
fcis-6353	118	21	]	]	X
fcis-6353	118	22	.	.	PUNCT
fcis-6353	119	1	the	the	DET
fcis-6353	119	2	world	world	NOUN
fcis-6353	119	3	environment,2020(2):3	environment,2020(2):3	PROPN
fcis-6353	119	4	.	.	PUNCT
fcis-6353	120	1	[	[	X
fcis-6353	120	2	2	2	NUM
fcis-6353	120	3	]	]	X
fcis-6353	120	4	regulations	regulation	NOUN
fcis-6353	120	5	of	of	ADP
fcis-6353	120	6	beijing	beijing	PROPN
fcis-6353	120	7	municipality	municipality	PROPN
fcis-6353	120	8	on	on	ADP
fcis-6353	120	9	the	the	DET
fcis-6353	120	10	administration	administration	NOUN
fcis-6353	120	11	of	of	ADP
fcis-6353	120	12	domestic	domestic	ADJ
fcis-6353	120	13	waste	waste	NOUN
fcis-6353	121	1	[	[	X
fcis-6353	121	2	j	j	X
fcis-6353	121	3	]	]	X
fcis-6353	121	4	.	.	PUNCT
fcis-6353	122	1	communiqu	communiqu	PROPN
fcis-6353	122	2	é	é	PROPN
fcis-6353	122	3	of	of	ADP
fcis-6353	122	4	the	the	DET
fcis-6353	122	5	standing	standing	PROPN
fcis-6353	122	6	committee	committee	PROPN
fcis-6353	122	7	of	of	ADP
fcis-6353	122	8	the	the	DET
fcis-6353	122	9	beijing	beijing	PROPN
fcis-6353	122	10	municipal	municipal	PROPN
fcis-6353	122	11	people	people	PROPN
fcis-6353	122	12	's	's	PART
fcis-6353	122	13	congress	congress	PROPN
fcis-6353	122	14	,	,	PUNCT
fcis-6353	122	15	2020	2020	NUM
fcis-6353	122	16	,	,	PUNCT
fcis-6353	122	17	000(005):p.83	000(005):p.83	PROPN
fcis-6353	122	18	-	-	SYM
fcis-6353	122	19	95	95	NUM
fcis-6353	122	20	.	.	PUNCT
fcis-6353	123	1	[	[	X
fcis-6353	123	2	3	3	X
fcis-6353	123	3	]	]	X
fcis-6353	123	4	meinecke	meinecke	NOUN
fcis-6353	123	5	g	g	NOUN
fcis-6353	123	6	,	,	PUNCT
fcis-6353	123	7	r	r	NOUN
fcis-6353	123	8	atmeyer	atmeyer	NOUN
fcis-6353	123	9	v	v	NOUN
fcis-6353	123	10	,	,	PUNCT
fcis-6353	123	11	renken	renken	PROPN
fcis-6353	123	12	j	j	PROPN
fcis-6353	123	13	,	,	PUNCT
fcis-6353	123	14	hybrid	hybrid	NOUN
fcis-6353	123	15	-	-	PUNCT
fcis-6353	123	16	rovdevelopment	rovdevelopment	NOUN
fcis-6353	123	17	of	of	ADP
fcis-6353	123	18	a	a	DET
fcis-6353	123	19	new	new	ADJ
fcis-6353	123	20	underwater	underwater	ADJ
fcis-6353	123	21	vehicle	vehicle	NOUN
fcis-6353	123	22	for	for	ADP
fcis-6353	123	23	high	high	ADJ
fcis-6353	123	24	-	-	PUNCT
fcis-6353	123	25	risk	risk	NOUN
fcis-6353	123	26	areas	area	NOUN
fcis-6353	123	27	[	[	X
fcis-6353	123	28	c]/	c]/	NOUN
fcis-6353	123	29	/oceans11	/oceans11	PUNCT
fcis-6353	123	30	-	-	PUNCT
fcis-6353	123	31	mts	mts	NOUN
fcis-6353	123	32	/ieee	/ieee	NOUN
fcis-6353	123	33	kona	kona	PROPN
fcis-6353	123	34	,	,	PUNCT
fcis-6353	123	35	2011	2011	NUM
fcis-6353	123	36	:	:	PUNCT
fcis-6353	123	37	1	1	NUM
fcis-6353	123	38	-	-	SYM
fcis-6353	123	39	6	6	NUM
fcis-6353	123	40	.	.	PUNCT
fcis-6353	124	1	[	[	X
fcis-6353	124	2	4	4	X
fcis-6353	124	3	]	]	X
fcis-6353	124	4	hou	hou	PROPN
fcis-6353	124	5	z	z	PROPN
fcis-6353	124	6	q	q	PROPN
fcis-6353	124	7	,	,	PUNCT
fcis-6353	124	8	han	han	PROPN
fcis-6353	124	9	c	c	PROPN
fcis-6353	124	10	z	z	PROPN
fcis-6353	124	11	,	,	PUNCT
fcis-6353	124	12	a	a	DET
fcis-6353	124	13	background	background	NOUN
fcis-6353	124	14	reconstruction	reconstruction	NOUN
fcis-6353	124	15	algorithm	algorithm	NOUN
fcis-6353	124	16	based	base	VERB
fcis-6353	124	17	on	on	ADP
fcis-6353	124	18	pixel	pixel	PROPN
fcis-6353	124	19	intensity	intensity	NOUN
fcis-6353	124	20	classification	classification	NOUN
fcis-6353	125	1	[	[	X
fcis-6353	125	2	j	j	X
fcis-6353	125	3	]	]	X
fcis-6353	125	4	,	,	PUNCT
fcis-6353	125	5	journal	journal	NOUN
fcis-6353	125	6	of	of	ADP
fcis-6353	125	7	software	software	NOUN
fcis-6353	125	8	,	,	PUNCT
fcis-6353	125	9	2005	2005	NUM
fcis-6353	125	10	,	,	PUNCT
fcis-6353	125	11	16	16	NUM
fcis-6353	125	12	(	(	PUNCT
fcis-6353	125	13	9	9	NUM
fcis-6353	125	14	)	)	PUNCT
fcis-6353	125	15	:	:	PUNCT
fcis-6353	125	16	1568	1568	NUM
fcis-6353	125	17	-	-	SYM
fcis-6353	125	18	1576	1576	NUM
fcis-6353	125	19	,	,	PUNCT
fcis-6353	125	20	[	[	X
fcis-6353	125	21	5	5	NUM
fcis-6353	125	22	]	]	X
fcis-6353	125	23	huang	huang	PROPN
fcis-6353	125	24	,	,	PUNCT
fcis-6353	125	25	tao	tao	PROPN
fcis-6353	125	26	,	,	PUNCT
fcis-6353	125	27	research	research	NOUN
fcis-6353	125	28	on	on	ADP
fcis-6353	125	29	water	water	NOUN
fcis-6353	125	30	object	object	NOUN
fcis-6353	125	31	extraction	extraction	NOUN
fcis-6353	125	32	algorithms	algorithm	NOUN
fcis-6353	125	33	in	in	ADP
fcis-6353	125	34	natural	natural	ADJ
fcis-6353	125	35	scenes[j	scenes[j	NOUN
fcis-6353	125	36	]	]	PUNCT
fcis-6353	125	37	.	.	PUNCT
fcis-6353	126	1	communication	communication	PROPN
fcis-6353	126	2	technology,2013(3	technology,2013(3	PROPN
fcis-6353	126	3	)	)	PUNCT
fcis-6353	126	4	:	:	PUNCT
fcis-6353	126	5	80	80	NUM
fcis-6353	126	6	-	-	SYM
fcis-6353	126	7	82	82	NUM
fcis-6353	126	8	.	.	PUNCT
fcis-6353	127	1	[	[	X
fcis-6353	127	2	6	6	NUM
fcis-6353	127	3	]	]	X
fcis-6353	127	4	yang	yang	PROPN
fcis-6353	127	5	m	m	PROPN
fcis-6353	127	6	,	,	PUNCT
fcis-6353	127	7	thung	thung	PROPN
fcis-6353	127	8	g.	g.	PROPN
fcis-6353	127	9	classification	classification	NOUN
fcis-6353	127	10	of	of	ADP
fcis-6353	127	11	trash	trash	NOUN
fcis-6353	127	12	for	for	ADP
fcis-6353	127	13	recyclability	recyclability	NOUN
fcis-6353	127	14	status[r].stanford	status[r].stanford	PROPN
fcis-6353	127	15	cs229	cs229	PROPN
fcis-6353	127	16	project	project	PROPN
fcis-6353	127	17	report,2016:832	report,2016:832	NOUN
fcis-6353	127	18	-	-	SYM
fcis-6353	127	19	841	841	NUM
fcis-6353	127	20	.	.	PUNCT
fcis-6353	128	1	[	[	X
fcis-6353	128	2	7	7	NUM
fcis-6353	128	3	]	]	X
fcis-6353	128	4	wang	wang	PROPN
fcis-6353	128	5	,	,	PUNCT
fcis-6353	128	6	xie	xie	PROPN
fcis-6353	128	7	,	,	PUNCT
fcis-6353	128	8	yang	yang	PROPN
fcis-6353	128	9	,	,	PUNCT
fcis-6353	128	10	et	et	PROPN
fcis-6353	128	11	al	al	PROPN
fcis-6353	128	12	.	.	PUNCT
fcis-6353	128	13	a	a	DET
fcis-6353	128	14	garbage	garbage	NOUN
fcis-6353	128	15	classification	classification	NOUN
fcis-6353	128	16	and	and	CCONJ
fcis-6353	128	17	detection	detection	NOUN
fcis-6353	128	18	method	method	NOUN
fcis-6353	128	19	based	base	VERB
fcis-6353	128	20	on	on	ADP
fcis-6353	128	21	deep	deep	ADJ
fcis-6353	128	22	learning	learning	NOUN
fcis-6353	128	23	[	[	X
fcis-6353	128	24	j	j	X
fcis-6353	128	25	]	]	X
fcis-6353	128	26	.modern	.modern	VERB
fcis-6353	128	27	electronic	electronic	ADJ
fcis-6353	128	28	technology	technology	NOUN
fcis-6353	128	29	,	,	PUNCT
fcis-6353	128	30	2021	2021	NUM
fcis-6353	128	31	,	,	PUNCT
fcis-6353	128	32	44(21):4	44(21):4	NOUN
fcis-6353	128	33	.	.	PUNCT
fcis-6353	129	1	[	[	X
fcis-6353	129	2	8	8	NUM
fcis-6353	129	3	]	]	SYM
fcis-6353	129	4	xu	xu	PROPN
fcis-6353	129	5	,	,	PUNCT
fcis-6353	129	6	xiong	xiong	PROPN
fcis-6353	129	7	.	.	PUNCT
fcis-6353	130	1	an	an	DET
fcis-6353	130	2	improved	improve	VERB
fcis-6353	130	3	lightweight	lightweight	ADJ
fcis-6353	130	4	garbage	garbage	NOUN
fcis-6353	130	5	target	target	NOUN
fcis-6353	130	6	detection	detection	NOUN
fcis-6353	130	7	algorithm	algorithm	NOUN
fcis-6353	131	1	[	[	X
fcis-6353	131	2	j	j	X
fcis-6353	131	3	]	]	X
fcis-6353	131	4	.	.	PUNCT
fcis-6353	132	1	computer	computer	NOUN
fcis-6353	132	2	technology	technology	NOUN
fcis-6353	132	3	and	and	CCONJ
fcis-6353	132	4	development	development	NOUN
fcis-6353	132	5	,	,	PUNCT
fcis-6353	132	6	2022(002):032	2022(002):032	NUM
fcis-6353	132	7	.	.	PUNCT
fcis-6353	133	1	[	[	X
fcis-6353	133	2	9	9	NUM
fcis-6353	133	3	]	]	SYM
fcis-6353	133	4	li	li	PROPN
fcis-6353	133	5	,	,	PUNCT
fcis-6353	133	6	x.	x.	PROPN
fcis-6353	133	7	,	,	PUNCT
fcis-6353	133	8	wang	wang	PROPN
fcis-6353	133	9	,	,	PUNCT
fcis-6353	133	10	w.	w.	PROPN
fcis-6353	133	11	,	,	PUNCT
fcis-6353	133	12	wu	wu	PROPN
fcis-6353	133	13	,	,	PUNCT
fcis-6353	133	14	l.	l.	PROPN
fcis-6353	133	15	,	,	PUNCT
fcis-6353	133	16	chen	chen	PROPN
fcis-6353	133	17	,	,	PUNCT
fcis-6353	133	18	s.	s.	PROPN
fcis-6353	133	19	,	,	PUNCT
fcis-6353	133	20	hu	hu	PROPN
fcis-6353	133	21	,	,	PUNCT
fcis-6353	133	22	x.	x.	PROPN
fcis-6353	133	23	,	,	PUNCT
fcis-6353	133	24	li	li	PROPN
fcis-6353	133	25	,	,	PUNCT
fcis-6353	133	26	j.	j.	PROPN
fcis-6353	133	27	,	,	PUNCT
fcis-6353	133	28	tang	tang	PROPN
fcis-6353	133	29	,	,	PUNCT
fcis-6353	133	30	j.	j.	PROPN
fcis-6353	133	31	,	,	PUNCT
fcis-6353	133	32	&	&	CCONJ
fcis-6353	133	33	yang	yang	PROPN
fcis-6353	133	34	,	,	PUNCT
fcis-6353	133	35	j.	j.	PROPN
fcis-6353	133	36	(	(	PUNCT
fcis-6353	133	37	2020	2020	NUM
fcis-6353	133	38	)	)	PUNCT
fcis-6353	133	39	.	.	PUNCT
fcis-6353	134	1	generalized	generalize	VERB
fcis-6353	134	2	focal	focal	ADJ
fcis-6353	134	3	loss	loss	NOUN
fcis-6353	134	4	:	:	PUNCT
fcis-6353	134	5	learning	learn	VERB
fcis-6353	134	6	qualified	qualified	ADJ
fcis-6353	134	7	and	and	CCONJ
fcis-6353	134	8	distributed	distribute	VERB
fcis-6353	134	9	bounding	bounding	NOUN
fcis-6353	134	10	boxes	box	NOUN
fcis-6353	134	11	for	for	ADP
fcis-6353	134	12	dense	dense	ADJ
fcis-6353	134	13	object	object	NOUN
fcis-6353	134	14	detection	detection	NOUN
fcis-6353	134	15	.	.	PUNCT
fcis-6353	135	1	arxiv	arxiv	PROPN
fcis-6353	135	2	,	,	PUNCT
fcis-6353	135	3	abs/2006.04388	abs/2006.04388	PRON
fcis-6353	135	4	.	.	PUNCT
fcis-6353	136	1	[	[	X
fcis-6353	136	2	10	10	NUM
fcis-6353	136	3	]	]	X
fcis-6353	136	4	he	he	PRON
fcis-6353	136	5	k	k	PROPN
fcis-6353	136	6	,	,	PUNCT
fcis-6353	136	7	zhang	zhang	PROPN
fcis-6353	136	8	x	x	X
fcis-6353	136	9	,	,	PUNCT
fcis-6353	136	10	ren	ren	PROPN
fcis-6353	136	11	s	s	PART
fcis-6353	136	12	,	,	PUNCT
fcis-6353	136	13	et	et	PROPN
fcis-6353	136	14	al	al	PROPN
fcis-6353	136	15	.	.	PUNCT
fcis-6353	137	1	deep	deep	ADJ
fcis-6353	137	2	residual	residual	ADJ
fcis-6353	137	3	learning	learning	NOUN
fcis-6353	137	4	for	for	ADP
fcis-6353	137	5	image	image	NOUN
fcis-6353	137	6	recognition[j	recognition[j	NOUN
fcis-6353	137	7	]	]	PUNCT
fcis-6353	137	8	.	.	PUNCT
fcis-6353	138	1	ieee	ieee	PROPN
fcis-6353	138	2	,	,	PUNCT
fcis-6353	138	3	2016	2016	NUM
fcis-6353	138	4	.	.	PUNCT
fcis-6353	139	1	[	[	X
fcis-6353	139	2	11	11	NUM
fcis-6353	139	3	]	]	X
fcis-6353	139	4	lin	lin	PROPN
fcis-6353	139	5	t	t	PROPN
fcis-6353	139	6	y	y	PROPN
fcis-6353	139	7	,	,	PUNCT
fcis-6353	139	8	dollarp	dollarp	ADJ
fcis-6353	139	9	,	,	PUNCT
fcis-6353	139	10	gir	gir	ADJ
fcis-6353	139	11	shickr	shickr	NOUN
fcis-6353	139	12	,	,	PUNCT
fcis-6353	139	13	et	et	PROPN
fcis-6353	139	14	al	al	PROPN
fcis-6353	139	15	,	,	PUNCT
fcis-6353	139	16	feature	feature	VERB
fcis-6353	139	17	pyramid	pyramid	NOUN
fcis-6353	139	18	networks	network	NOUN
fcis-6353	139	19	for	for	ADP
fcis-6353	139	20	object	object	NOUN
fcis-6353	139	21	detection[c]/	detection[c]/	NOUN
fcis-6353	139	22	/2017	/2017	PUNCT
fcis-6353	139	23	ieee	ieee	NOUN
fcis-6353	139	24	conference	conference	NOUN
fcis-6353	139	25	on	on	ADP
fcis-6353	139	26	computer	computer	NOUN
fcis-6353	139	27	vision	vision	NOUN
fcis-6353	139	28	and	and	CCONJ
fcis-6353	139	29	pattern	pattern	NOUN
fcis-6353	139	30	recognition	recognition	NOUN
fcis-6353	139	31	(	(	PUNCT
fcis-6353	139	32	cvpr	cvpr	NOUN
fcis-6353	139	33	)	)	PUNCT
fcis-6353	139	34	,	,	PUNCT
fcis-6353	139	35	piscataway	piscataway	NOUN
fcis-6353	139	36	:	:	PUNCT
fcis-6353	139	37	ieee	ieee	NOUN
fcis-6353	139	38	,	,	PUNCT
fcis-6353	139	39	2017	2017	NUM
fcis-6353	139	40	:	:	PUNCT
fcis-6353	139	41	2117	2117	NUM
fcis-6353	139	42	-	-	SYM
fcis-6353	139	43	2125	2125	NUM
fcis-6353	139	44	,	,	PUNCT
fcis-6353	139	45	[	[	X
fcis-6353	139	46	12	12	NUM
fcis-6353	139	47	]	]	PUNCT
fcis-6353	139	48	neubeck	neubeck	NOUN
fcis-6353	139	49	a	a	PRON
fcis-6353	139	50	,	,	PUNCT
fcis-6353	139	51	gool	gool	PROPN
fcis-6353	139	52	ljv	ljv	PROPN
fcis-6353	139	53	.	.	PUNCT
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fcis-6353	140	2	non	non	ADJ
fcis-6353	140	3	-	-	ADJ
fcis-6353	140	4	maximum	maximum	ADJ
fcis-6353	140	5	suppression[c	suppression[c	PROPN
fcis-6353	140	6	]	]	PUNCT
fcis-6353	140	7	//	//	X
fcis-6353	140	8	18th	18th	ADJ
fcis-6353	140	9	international	international	ADJ
fcis-6353	140	10	conference	conference	NOUN
fcis-6353	140	11	on	on	ADP
fcis-6353	140	12	pattern	pattern	NOUN
fcis-6353	140	13	recognition	recognition	NOUN
fcis-6353	140	14	(	(	PUNCT
fcis-6353	140	15	icpr	icpr	PROPN
fcis-6353	140	16	2006	2006	NUM
fcis-6353	140	17	)	)	PUNCT
fcis-6353	140	18	,	,	PUNCT
fcis-6353	140	19	20	20	NUM
fcis-6353	140	20	-	-	SYM
fcis-6353	140	21	24	24	NUM
fcis-6353	140	22	august	august	PROPN
fcis-6353	140	23	2006	2006	NUM
fcis-6353	140	24	,	,	PUNCT
fcis-6353	140	25	hong	hong	PROPN
fcis-6353	140	26	kong	kong	PROPN
fcis-6353	140	27	,	,	PUNCT
fcis-6353	140	28	china	china	PROPN
fcis-6353	140	29	.	.	PUNCT
fcis-6353	141	1	ieee	ieee	PROPN
fcis-6353	141	2	computer	computer	NOUN
fcis-6353	141	3	society	society	NOUN
fcis-6353	141	4	,	,	PUNCT
fcis-6353	141	5	2006	2006	NUM
fcis-6353	141	6	.	.	PUNCT
fcis-6353	142	1	[	[	X
fcis-6353	142	2	13	13	NUM
fcis-6353	142	3	]	]	X
fcis-6353	142	4	lin	lin	PROPN
fcis-6353	142	5	t	t	PROPN
fcis-6353	142	6	y	y	PROPN
fcis-6353	142	7	,	,	PUNCT
fcis-6353	142	8	goyal	goyal	PROPN
fcis-6353	142	9	p	p	NOUN
fcis-6353	142	10	,	,	PUNCT
fcis-6353	142	11	girshick	girshick	ADJ
fcis-6353	142	12	r	r	NOUN
fcis-6353	142	13	,	,	PUNCT
fcis-6353	142	14	et	et	PROPN
fcis-6353	142	15	al	al	PROPN
fcis-6353	142	16	.	.	PROPN
fcis-6353	142	17	focal	focal	ADJ
fcis-6353	142	18	loss	loss	NOUN
fcis-6353	142	19	for	for	ADP
fcis-6353	142	20	dense	dense	ADJ
fcis-6353	142	21	object	object	NOUN
fcis-6353	142	22	detection[j	detection[j	PROPN
fcis-6353	142	23	]	]	PUNCT
fcis-6353	142	24	.	.	PUNCT
fcis-6353	143	1	ieee	ieee	NOUN
fcis-6353	143	2	transactions	transaction	NOUN
fcis-6353	143	3	on	on	ADP
fcis-6353	143	4	pattern	pattern	NOUN
fcis-6353	143	5	analysis	analysis	NOUN
fcis-6353	143	6	&	&	CCONJ
fcis-6353	143	7	machine	machine	NOUN
fcis-6353	143	8	intelligence	intelligence	NOUN
fcis-6353	143	9	,	,	PUNCT
fcis-6353	143	10	2017	2017	NUM
fcis-6353	143	11	,	,	PUNCT
fcis-6353	143	12	pp(99):2999	pp(99):2999	NOUN
fcis-6353	143	13	-	-	PUNCT
fcis-6353	143	14	3007	3007	NUM
fcis-6353	143	15	.	.	PUNCT
fcis-6353	144	1	[	[	X
fcis-6353	144	2	14	14	NUM
fcis-6353	144	3	]	]	PUNCT
fcis-6353	144	4	rezatofighi	rezatofighi	PROPN
fcis-6353	144	5	,	,	PUNCT
fcis-6353	144	6	hamid	hamid	PROPN
fcis-6353	144	7	,	,	PUNCT
fcis-6353	144	8	et	et	PROPN
fcis-6353	144	9	al	al	PROPN
fcis-6353	144	10	.	.	PUNCT
fcis-6353	145	1	"	"	PUNCT
fcis-6353	145	2	generalized	generalized	ADJ
fcis-6353	145	3	intersection	intersection	NOUN
fcis-6353	145	4	over	over	ADP
fcis-6353	145	5	union	union	NOUN
fcis-6353	145	6	:	:	PUNCT
fcis-6353	145	7	a	a	DET
fcis-6353	145	8	metric	metric	ADJ
fcis-6353	145	9	and	and	CCONJ
fcis-6353	145	10	a	a	DET
fcis-6353	145	11	loss	loss	NOUN
fcis-6353	145	12	for	for	ADP
fcis-6353	145	13	bounding	bound	VERB
fcis-6353	145	14	box	box	NOUN
fcis-6353	145	15	regression	regression	NOUN
fcis-6353	145	16	.	.	PUNCT
fcis-6353	145	17	"	"	PUNCT
fcis-6353	146	1	proceedings	proceeding	NOUN
fcis-6353	146	2	of	of	ADP
fcis-6353	146	3	the	the	DET
fcis-6353	146	4	ieee	ieee	NOUN
fcis-6353	146	5	/	/	SYM
fcis-6353	146	6	cvf	cvf	NOUN
fcis-6353	146	7	conference	conference	NOUN
fcis-6353	146	8	on	on	ADP
fcis-6353	146	9	computer	computer	NOUN
fcis-6353	146	10	vision	vision	NOUN
fcis-6353	146	11	and	and	CCONJ
fcis-6353	146	12	pattern	pattern	NOUN
fcis-6353	146	13	recognition	recognition	NOUN
fcis-6353	146	14	.	.	PUNCT
fcis-6353	147	1	2019	2019	NUM
fcis-6353	147	2	.	.	PUNCT
fcis-6353	148	1	[	[	X
fcis-6353	148	2	15	15	NUM
fcis-6353	148	3	]	]	X
fcis-6353	148	4	woo	woo	NOUN
fcis-6353	148	5	,	,	PUNCT
fcis-6353	148	6	sanghyun	sanghyun	NOUN
fcis-6353	148	7	et	et	PROPN
fcis-6353	148	8	al	al	PROPN
fcis-6353	148	9	.	.	PUNCT
fcis-6353	149	1	“cbam	“cbam	NOUN
fcis-6353	149	2	:	:	PUNCT
fcis-6353	149	3	convolutional	convolutional	ADJ
fcis-6353	149	4	block	block	NOUN
fcis-6353	149	5	attention	attention	NOUN
fcis-6353	149	6	module	module	NOUN
fcis-6353	149	7	.	.	PUNCT
fcis-6353	149	8	”european	”european	ADJ
fcis-6353	149	9	conference	conference	PROPN
fcis-6353	149	10	on	on	ADP
fcis-6353	149	11	computer	computer	NOUN
fcis-6353	149	12	vision	vision	NOUN
fcis-6353	149	13	(	(	PUNCT
fcis-6353	149	14	2018	2018	NUM
fcis-6353	149	15	)	)	PUNCT
fcis-6353	149	16	.	.	PUNCT
fcis-6353	150	1	[	[	X
fcis-6353	150	2	16	16	NUM
fcis-6353	150	3	]	]	SYM
fcis-6353	150	4	nty	nty	NOUN
fcis-6353	150	5	,	,	PUNCT
fcis-6353	150	6	dollarp	dollarp	NOUN
fcis-6353	150	7	,	,	PUNCT
fcis-6353	150	8	girshickr	girshickr	PROPN
fcis-6353	150	9	,	,	PUNCT
fcis-6353	150	10	et	et	PROPN
fcis-6353	150	11	al	al	PROPN
fcis-6353	150	12	,	,	PUNCT
fcis-6353	150	13	feature	feature	VERB
fcis-6353	150	14	pyramid	pyramid	NOUN
fcis-6353	150	15	networks	network	NOUN
fcis-6353	150	16	for	for	ADP
fcis-6353	150	17	object	object	NOUN
fcis-6353	150	18	detection	detection	NOUN
fcis-6353	150	19	[	[	X
fcis-6353	150	20	c]/	c]/	NOUN
fcis-6353	150	21	/2017	/2017	PUNCT
fcis-6353	150	22	ieee	ieee	NOUN
fcis-6353	150	23	conference	conference	NOUN
fcis-6353	150	24	on	on	ADP
fcis-6353	150	25	computer	computer	NOUN
fcis-6353	150	26	vision	vision	NOUN
fcis-6353	150	27	and	and	CCONJ
fcis-6353	150	28	pattern	pattern	NOUN
fcis-6353	150	29	recognition	recognition	NOUN
fcis-6353	150	30	(	(	PUNCT
fcis-6353	150	31	cvpr	cvpr	NOUN
fcis-6353	150	32	)	)	PUNCT
fcis-6353	150	33	,	,	PUNCT
fcis-6353	150	34	piscataway	piscataway	PROPN
fcis-6353	150	35	:	:	PUNCT
fcis-6353	150	36	ieee	ieee	NOUN
fcis-6353	150	37	,	,	PUNCT
fcis-6353	150	38	2017	2017	NUM
fcis-6353	150	39	:	:	PUNCT
fcis-6353	150	40	2117	2117	NUM
fcis-6353	150	41	-	-	SYM
fcis-6353	150	42	2125	2125	NUM
fcis-6353	150	43	.	.	PUNCT
fcis-6353	151	1	[	[	X
fcis-6353	151	2	17	17	NUM
fcis-6353	151	3	]	]	X
fcis-6353	151	4	redmon	redmon	PROPN
fcis-6353	151	5	j	j	PROPN
fcis-6353	151	6	,	,	PUNCT
fcis-6353	151	7	farhadi	farhadi	PROPN
fcis-6353	151	8	a	a	PRON
fcis-6353	151	9	.	.	PUNCT
fcis-6353	152	1	yolov3	yolov3	PROPN
fcis-6353	152	2	:	:	PUNCT
fcis-6353	153	1	an	an	DET
fcis-6353	153	2	incremental	incremental	ADJ
fcis-6353	153	3	improvement	improvement	NOUN
fcis-6353	153	4	[	[	X
fcis-6353	153	5	j	j	X
fcis-6353	153	6	]	]	X
fcis-6353	153	7	.	.	PUNCT
fcis-6353	154	1	arxiv	arxiv	PROPN
fcis-6353	154	2	e	e	PROPN
fcis-6353	154	3	-	-	NOUN
fcis-6353	154	4	prints	print	NOUN
fcis-6353	154	5	,	,	PUNCT
fcis-6353	154	6	2018	2018	NUM
fcis-6353	154	7	.	.	PUNCT
fcis-6353	155	1	[	[	X
fcis-6353	155	2	18	18	NUM
fcis-6353	155	3	]	]	X
fcis-6353	155	4	berg	berg	PROPN
fcis-6353	155	5	ac	ac	PROPN
fcis-6353	155	6	,	,	PUNCT
fcis-6353	155	7	fu	fu	PROPN
fcis-6353	155	8	cy	cy	PROPN
fcis-6353	155	9	,	,	PUNCT
fcis-6353	155	10	szegedy	szegedy	VERB
fcis-6353	155	11	c	c	NOUN
fcis-6353	155	12	,	,	PUNCT
fcis-6353	155	13	et	et	PROPN
fcis-6353	155	14	al	al	PROPN
fcis-6353	155	15	.	.	PROPN
fcis-6353	155	16	ssd	ssd	PROPN
fcis-6353	155	17	:	:	PUNCT
fcis-6353	155	18	single	single	ADJ
fcis-6353	155	19	shot	shoot	VERB
fcis-6353	155	20	multi	multi	PROPN
fcis-6353	155	21	box	box	PROPN
fcis-6353	155	22	detector	detector	NOUN
fcis-6353	155	23	:	:	PUNCT
fcis-6353	155	24	,	,	PUNCT
fcis-6353	155	25	10.1007/978	10.1007/978	NUM
fcis-6353	155	26	-	-	SYM
fcis-6353	155	27	3	3	NUM
fcis-6353	155	28	-	-	NUM
fcis-6353	155	29	319	319	NUM
fcis-6353	155	30	-	-	PUNCT
fcis-6353	155	31	46448	46448	NUM
fcis-6353	155	32	-	-	SYM
fcis-6353	155	33	0_2[p	0_2[p	PROPN
fcis-6353	155	34	]	]	PUNCT
fcis-6353	155	35	.	.	PUNCT
fcis-6353	155	36	2015	2015	NUM
fcis-6353	155	37	.	.	PUNCT