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Laser & Optoelectronics Progress, Volume. 57, Issue 20, 201505(2020)

Parallel FPN Algorithm Based on Cascade R-CNN for Object Detection from UAV Aerial Images

Yingjie Liu, Fengbao Yang*, and Peng Hu
Author Affiliations
  • School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi 030051, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (21)
    Cited By (6)
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    Figures & Tables(13)
    Object area versus number of objects

    Fig. 1. Object area versus number of objects

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    Number of objects per image versus percentage of number of images

    Fig. 2. Number of objects per image versus percentage of number of images

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    Example of actual aerial images

    Fig. 3. Example of actual aerial images

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    FPN frame

    Fig. 4. FPN frame

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    Parallel FPN frame

    Fig. 5. Parallel FPN frame

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    Structure of Cascade R-CNN

    Fig. 6. Structure of Cascade R-CNN

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    Change of positive proposals in every stage. (a) IoU threshold is 0.5; (b) IoU threshold is 0.6; (c) IoU threshold is 0.7

    Fig. 7. Change of positive proposals in every stage. (a) IoU threshold is 0.5; (b) IoU threshold is 0.6; (c) IoU threshold is 0.7

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    Overall framework of proposed model

    Fig. 8. Overall framework of proposed model

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    Visual detection results of aerial images

    Fig. 9. Visual detection results of aerial images

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    • Table 1. Comparison of classical algorithms %

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      Table 1. Comparison of classical algorithms %

      AlgorithmBackboneAPAP0.5AP0.75APSAPMAPL
      Retina-Net[19]ResNet-1017.113.27.02.912.417.6
      Faster R-CNN4.610.13.72.77.17.8
      R-FCN[20]7.916.66.54.512.418.1
      FPN w nearest17.037.313.611.425.627.7
      FPN w bilinear17.237.414.011.525.928.0
      Proposed algorithm17.537.514.511.526.228.4
      FPN w nearestResNet-101-v1d20.140.917.714.029.132.5
      FPN w bilinear20.341.018.114.029.332.9
      Proposed algorithm20.641.118.714.129.633.3

    • Table 2. Impact of number of cascading stages on parameters %

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      Table 2. Impact of number of cascading stages on parameters %

      NetworkAPAP0.5AP0.75
      Without Cascade R-CNN20.641.118.7
      With CascadeR-CNNStage 1-225.646.625.4
      Stage 1-326.146.425.7
      Stage 1-425.746.225.3

    • Table 3. Comparison of anchor size of proposed algorithm

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      Table 3. Comparison of anchor size of proposed algorithm

      Anchor schemeAP /%APS /%APM /%APL /%
      A25.017.435.736.8
      B26.118.337.038.3
      C25.818.036.436.4

    • Table 4. Detection result of multi-scale training

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      Table 4. Detection result of multi-scale training

      DatasetMulti-scale trainingAP /%AP0.5 /%AP0.75 /%
      MS COCO×/√26.1/26.746.4/48.125.7/26.1
      VisDrone×/√27.34/27.9852.31/52.5424.84/25.62
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    Yingjie Liu, Fengbao Yang, Peng Hu. Parallel FPN Algorithm Based on Cascade R-CNN for Object Detection from UAV Aerial Images[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201505

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    Paper Information

    Category: Machine Vision

    Received: Dec. 10, 2019

    Accepted: Feb. 25, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Fengbao Yang (yfengb@163.com)

    DOI:10.3788/LOP57.201505

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology