Ship Object Detection of Remote Sensing Images Based on Adaptive Rotation Region Proposal Network

Zhijing Xu; Ying Ding

doi:10.3788/LOP57.242805

Laser & Optoelectronics Progress, Volume. 57, Issue 24, 242805(2020)

Ship Object Detection of Remote Sensing Images Based on Adaptive Rotation Region Proposal Network

Zhijing Xu and Ying Ding^*

Author Affiliations

College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

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Abstract Get PDF(in Chinese)

Figures & Tables(11)

Fig. 1. Model structure of our network

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Fig. 2. Structure diagram of MFPN. (a) Overall structure diagram; (b) specific connection diagram

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Fig. 3. Structure diagram of AR-RPN

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Fig. 4. Schematic diagram of deformable convolution

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Fig. 5. Schematic diagram of IoU in an oblique frame. (a) Diagram1; (b) diagram2

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Fig. 6. Loss function.(a) Anchor location loss;(b) anchor shape loss;(b) classification loss;(b)regression loss

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Fig. 7. Test results of different methods. (a) RRPN; (b) AR-RPN; (c) MFPN

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Fig. 8. Detection results of our method on different data sets. (a) HRSC2016 (b) DOTA

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Table 1. Result of ablation experiment
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Table 1. Result of ablation experiment
MFPN - √ √ √
AR-RPN - - √ √
ResNet+ResNext - - - √
AP/% 68.13 72.45 87.26 88.64

Table 2. AP of different methods on the DOTA data set
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Table 2. AP of different methods on the DOTA data set
Method AP/% Speed /(tasks·s^-1)
Faster-RCNN^[6] 38.30 10
AO-RCNN^[14] 56.60 6.2
R²PN^[20] 68.13 5.7
CADNet^[21] 76.70 5
RoI Trans^[15] 83.59 5.9
Ours 88.64 6.4

Table 3. AP of different methods on HRSC2016 data set unit: %
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Table 3. AP of different methods on HRSC2016 data set unit: %
Method RC2^[1] RR-CNN^[13] RRD^[22] RoI Trans^[15] Ours
AP 75.7 79.6 84.3 86.2 89.1

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Zhijing Xu, Ying Ding. Ship Object Detection of Remote Sensing Images Based on Adaptive Rotation Region Proposal Network[J]. Laser & Optoelectronics Progress, 2020, 57(24): 242805

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