Object Detection For Remote Sensing Image Based on Multiscale Feature Fusion Network

Tingting Tian; Jun Yang

doi:10.3788/LOP202259.1628003

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 16 >Page 1628003 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 16, 1628003 (2022)

Object Detection For Remote Sensing Image Based on Multiscale Feature Fusion Network

Tingting Tian^1、2、3 and Jun Yang^{1、2、3、*}

Author Affiliations

¹Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu , China

²National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, Gansu , China

³Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, Gansu , China

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DOI: 10.3788/LOP202259.1628003 Cite this Article Set citation alerts

Tingting Tian, Jun Yang. Object Detection For Remote Sensing Image Based on Multiscale Feature Fusion Network[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1628003 Copy Citation Text

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Fig. 1. Architecture of proposed object detection model

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Fig. 2. Comparison between convolution and dilated convolution. (a) General convolution; (b) dilated convolution

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Fig. 3. Feature fusion network. (a) FPN; (B) PANet

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Fig. 4. Multi-dimensional attention module

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Fig. 5. Detection results on RSOD dataset. (a) Aircraft; (b) oiltank; (c) overpass; (d) playground

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Fig. 6. Detection results on DOTA dataset. (a) PL; (b) GTF and SBF; (c) BR; (d) BD; (e) LV and SV; (f) RA; (g) HA; (h) ST; (i) SH; (j) BC and TC; (k) SP; (l) HC

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Fig. 7. Examples of miss detection and error detection. (a) Miss detection; (b) error detection

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Dataset	Aircraft	Oiltank	Overpass	Playground	mAP
RSOD	81.02	90.77	100	100	92.95

Table 1. Detection results on RSOD dataset

Dataset	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC	mAP
DOTA	89.68	84.39	52.12	72.71	64.49	67.07	77.45	90.11	83.98	86.01	64.03	63.33	74.45	67.74	62.87	73.39

Table 2. Detection results on DOTA dataset

Type	Aircraft	Oiltank	Overpass	Playground	mAP
FPN	78.86	90.48	99.73	100.00	92.26
FPN++	81.02	90.77	100.00	100.00	92.95

Table 3. Experimental comparison of different FPN on RSOD dataset

Type	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC	mAP
FPN	88.81	84.33	50.98	72.45	62.71	65.35	75.34	89.96	81.16	84.07	53.26	63.33	72.24	66.29	60.11	71.36
FPN++	89.68	84.39	52.12	72.71	64.49	67.07	77.45	90.11	83.98	86.01	55.03	63.33	74.45	67.74	62.87	72.86

Table 4. Experimental comparison of different FPN on DOTA dataset

Algorithm	aircraft	oiltank	overpass	playground	mAP
R-FCN	71.48	90.23	76.84	97.70	84.07
Deformable R-FCN	71.50	90.26	81.48	99.53	85.70
Faster R-CNN	71.90	90.90	100.00	100.00	90.70
RFN	79.10	90.50	100.00	99.70	92.30
MDCF²Det	81.02	90.77	100.00	100.00	92.95

Table 5. Accuracy comparison of different algorithms on RSOD dataset

Algorithm	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC	mAP
YOLO v2	76.90	33.87	22.73	34.88	38.73	32.02	52.37	61.65	48.54	33.91	29.27	36.83	36.44	38.26	11.61	39.20
R-FCN	79.33	44.26	36.58	53.53	39.38	34.15	47.29	45.66	47.74	65.84	37.92	44.23	50.64	50.64	34.90	47.24
CenterFPANet	88.74	71.52	48.95	52.06	48.55	73.37	61.14	90.53	57.63	84.06	66.64	62.71	73.33	57.63	42.76	65.29
FPN	88.70	75.10	52.60	59.20	69.40	78.80	84.50	90.60	81.30	82.60	52.50	62.10	76.60	66.30	60.10	72.00
ICN	90.00	77.70	53.40	73.30	73.50	65.00	78.20	90.80	79.10	84.80	57.20	62.11	73.45	70.22	58.08	72.45
FMSSD	89.11	81.51	48.22	67.94	69.23	73.56	76.87	90.71	82.67	73.33	52.65	67.52	72.37	80.57	60.15	72.43
MDCF²Det	89.68	84.39	52.12	72.71	64.49	67.07	77.45	90.11	83.98	86.01	55.03	63.33	74.45	67.74	62.87	72.86

Table 6. Accuracy comparison of different algorithms on DOTA dataset

Tingting Tian, Jun Yang. Object Detection For Remote Sensing Image Based on Multiscale Feature Fusion Network[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1628003

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