• Laser & Optoelectronics Progress
  • Vol. 60, Issue 12, 1228007 (2023)
Xin Yang, Qiong Wang, Yazhou Yao, and Zhenmin Tang*
Author Affiliations
  • School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
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    DOI: 10.3788/LOP221679 Cite this Article Set citation alerts
    Xin Yang, Qiong Wang, Yazhou Yao, Zhenmin Tang. Improved Aircraft Detection of Optical Remote Sensing Image Based on Faster R-CNN[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228007 Copy Citation Text show less
    Faster R-CNN algorithm framework after basic module introduced
    Fig. 1. Faster R-CNN algorithm framework after basic module introduced
    Structure of ResNet50
    Fig. 2. Structure of ResNet50
    Structure of ResNet50 and FPN
    Fig. 3. Structure of ResNet50 and FPN
    Structure of CBAM
    Fig. 4. Structure of CBAM
    Cutting process of sliding window
    Fig. 5. Cutting process of sliding window
    Post-processing of prediction results
    Fig. 6. Post-processing of prediction results
    Examples of aircraft categories
    Fig. 7. Examples of aircraft categories
    Distribution of aircraft number by class
    Fig. 8. Distribution of aircraft number by class
    Distribution of aircraft size
    Fig. 9. Distribution of aircraft size
    Feature map before adding channel attention
    Fig. 10. Feature map before adding channel attention
    Feature map after adding channel attention
    Fig. 11. Feature map after adding channel attention
    Feature map comparison before and after adding spatial attention.(a) Input; (b) feature map of channel 14 that is not added to CBAM; (c) feature map of channel 14 after CBAM added
    Fig. 12. Feature map comparison before and after adding spatial attention.(a) Input; (b) feature map of channel 14 that is not added to CBAM; (c) feature map of channel 14 after CBAM added
    Comparison before and after use of post-processing
    Fig. 13. Comparison before and after use of post-processing
    Feature extraction networkmF1Size /106FPS
    ResNet5085.982241.916.3
    ResNet41-387.480225.818.2
    Res2Net5078.855941.614.1
    Res2Net41-383.005625.515.7
    ResNext10159.866260.010.6
    ResNext92-377.356844.411.4
    ResNet50+FPN-486.583041.417.1
    Table 1. Performance comparison of feature extraction network before and after lightweight
    MethodmF1Size /106FPS
    Faster R-CNN+ResNet5085.982241.916.3
    Faster R-CNN+ResNet41-387.480225.818.2
    Cascade R-CNN+ResNet5087.174069.210.7
    Cascade R-CNN+ResNet41-388.534653.112.9
    FCOS+ResNet5074.134832.116.9
    FCOS+ResNet41-378.877116.118.5
    Table 2. Performance comparison of different target detection algorithms before and after lightweight
    DatasetMethodmF1Size /106FPS
    RSAICP-planeFaster R-CNN+ResNet5085.982241.916.3
    Faster R-CNN+ResNet41-387.480225.818.2
    SAR-shipFaster R-CNN+ResNet5090.274841.933.1
    Faster R-CNN+ResNet41-391.161725.837.9
    CASIA-aircraftFaster R-CNN+ResNet5096.933042.125.5
    Faster R-CNN+ResNet41-397.324026.027.8
    Table 3. Performance comparison of different target detection tasks before and after lightweight
    Feature extraction networkmF1Size /106FPS
    ResNet4187.480225.818.2
    ResNet41-3_CBAM-1687.767725.800515.3
    ResNet41-3_CBAM-888.780325.80115.3
    ResNet41-3_CBAM-487.974525.80215.3
    Table 4. Performance comparison of CBAM attention mechanism
    Feature extraction networkmF1
    ResNet41-387.4802
    ResNet41-3_ms88.9047
    ResNet41-3_ms*88.9465
    ResNet41-3_CBAM-888.7803
    ResNet41-3_CBAM-8_ms88.7926
    ResNet41-3_CBAM-8_ms*88.9682
    Table 5. Experiment results of midline single frame prediction and multi-scale training
    Xin Yang, Qiong Wang, Yazhou Yao, Zhenmin Tang. Improved Aircraft Detection of Optical Remote Sensing Image Based on Faster R-CNN[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228007
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