Acta Optica Sinica, Volume. 40, Issue 1, 0111020(2020)
Object Detection of Remote Sensing Image Based on Improved Rotation Region Proposal Network
Figures & Tables(17)
Fig. 3. Diagram of multi-scale feature extraction. (a) Faster R-CNN feature extraction method; (b) feature pyramid
Fig. 4. Anchor strategy in our method. (a) Redesigned scale; (b) special ratio; (c) angular parameters
Fig. 5. Calculation process of tilted IoU. (a) Regular intersection; (b) irregular intersection
Fig. 6. Comparison of results of different NMS detections. (a) Traditional NMS with common box; (b) traditional NMS with rotated box; (c) tilted NMS with rotated box
Fig. 8. Comparison of testing results between original RRPN and our method. (a)(c) Original RRPN; (b)(d) improved method
Fig. 9. Detection results of different algorithms for large vehicle. (a) YOLO v2; (b) YOLO v3; (c) Faster R-CNN; (d) RRPN; (e) proposed algorithm
Fig. 10. Detection results of different algorithms for airplane. (a) YOLO v2; (b) YOLO v3; (c) Faster R-CNN; (d) RRPN; (e) proposed algorithm
Fig. 11. Detection results of different algorithms for tennis court. (a) YOLO v2; (b) YOLO v3; (c) Faster R-CNN; (d) RRPN; (e) proposed algorithm
Fig. 12. Detection results of different algorithms for ship. (a) YOLO v2; (b) YOLO v3; (c) Faster R-CNN; (d) RRPN; (e) proposed algorithm
Table 1. Extraction results of different basic networks for typical object
View tableTable 1. Extraction results of different basic networks for typical object
Network AP /% Plane Ship Bridge Harbor Storage-tank VGG16 79.2 42.1 18.5 43.1 44.5 ResNet101+FPN 82.1 44.3 21.6 45.3 47.4
Table 2. Comparison of detection effects of different RoI pooling methods
View tableTable 2. Comparison of detection effects of different RoI pooling methods
Pooling method AP /% Bridge Harbor Storage-tank Plane Ship RoIPooling 21.6 45.3 47.4 82.1 44.3 RoIAlign 23.9 47.0 48.5 83.8 47.4
Table 3. Detection results of different classification networks for 15 types of targets%
View tableTable 3. Detection results of different classification networks for 15 types of targets%
Category AP Original +Conv Bridge 23.9 27.5 Small-vehicle 31.6 32.4 Baseball diamond 67.6 67.3 Basketball court 47.5 46.3 Harbor 47.0 46.9 Ground-track field 40.2 44.6 Soccer ball field 41.2 42.4 Storage-tank 48.5 48.5 Large-vehicle 49.8 51.7 Plane 83.8 84.1 Roundabout 47.6 45.4 Tennis court 89.4 88.8 Helicopter 45.4 42.3 Ship 47.4 47.4 Swimming pool 39.8 38.1 mAP 50.05 50.25
Table 4. Experimental results of proposed method for 15 types of targets%
View tableTable 4. Experimental results of proposed method for 15 types of targets%
Category Precision Recall AP Bridge 59.15 32.92 26.40 Small-vehicle 68.65 44.12 34.10 Baseball diamond 80.40 81.21 78.57 Basketball court 82.01 76.20 73.20 Harbor 77.95 61.41 56.00 Ground-track field 81.29 60.05 55.62 Soccer ball field 78.20 59.95 57.79 Storage-tank 81.40 52.82 51.50 Large-vehicle 62.68 76.20 56.91 Plane 94.10 87.36 86.50 Roundabout 75.52 60.67 56.05 Tennis court 97.04 91.29 91.16 Helicopter 82.05 65.31 61.88 Ship 74.01 55.35 50.10 Swimming pool 71.60 53.45 47.52 Average 77.74 63.89 58.89
Table 5. Experimental results of different methods for 15 types of targets%
View tableTable 5. Experimental results of different methods for 15 types of targets%
Category YOLO v2 YOLO v3 Faster R-CNN RRPN Proposed method Bridge 14.18 10.03 41.82 23.88 26.38 Small-vehicle 13.08 14.79 3.85 34.65 34.15 Baseball diamond 52.79 9.09 72.83 67.61 78.57 Basketball court 42.43 2.27 55.81 47.48 73.21 Harbor 51.99 17.07 59.04 47.30 56.18 Ground-track field 32.57 4.81 84.68 40.19 55.64 Soccer ball field 31.67 0.146 63.60 41.15 57.78 Storage-tank 40.21 24.59 5.31 48.77 51.55 Large-vehicle 22.02 9.09 38.94 49.74 56.91 Plane 80.91 49.44 38.74 83.89 86.52 Roundabout 44.40 21.64 44.44 47.61 56.06 Tennis court 72.52 15.18 89.75 89.40 91.15 Helicopter 21.22 0.02 40.64 45.44 61.91 Ship 46.73 30.31 3.99 47.19 50.15 Swimming pool 34.31 7.54 22.71 39.78 47.55 mAP 39.87 14.40 44.41 50.08 58.91
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Yuan Dai, Benshun Yi, Jinsheng Xiao, Junfeng Lei, Le Tong, Zhiqin Cheng. Object Detection of Remote Sensing Image Based on Improved Rotation Region Proposal Network[J]. Acta Optica Sinica, 2020, 40(1): 0111020
Paper Information
Category: Special Issue on Computational Optical Imaging
Received: Jul. 29, 2019
Accepted: Sep. 19, 2019
Published Online: Jan. 6, 2020
The Author Email: Xiao Jinsheng (xiaojs@whu.edu.cn)
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