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Laser & Optoelectronics Progress, Volume. 61, Issue 24, 2437010(2024)

High-Precision and Lightweight Object Detection Model for Drone Aerial Photography Images

Xiangyang Zhao1, Zaifeng Shi1,3、*, Yunfeng Wang1, Xiaowei Niu1, and Tao Luo2
Author Affiliations
  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China
  • 2College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
  • 3Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin 300072, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
    Equations (5)
    References (24)
    Cited By (1)
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    Paper Information
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    Figures & Tables(9)
    Overall structure of LEFE-YOLOv8 model

    Fig. 1. Overall structure of LEFE-YOLOv8 model

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    Internal structure of EFEConv

    Fig. 2. Internal structure of EFEConv

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    Internal structure of DySample upsampling operation

    Fig. 3. Internal structure of DySample upsampling operation

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    Detection results of different models on the HIT-UAV dataset

    Fig. 4. Detection results of different models on the HIT-UAV dataset

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    Detection results of different models on the VisDrone2019 dataset

    Fig. 5. Detection results of different models on the VisDrone2019 dataset

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    Visual heatmap of detection results on the VisDrone2019 dataset

    Fig. 6. Visual heatmap of detection results on the VisDrone2019 dataset

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    • Table 1. Comparative experiments on the HIT-UAV dataset

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      Table 1. Comparative experiments on the HIT-UAV dataset

      MethodmAP50 /%mAP50-95 /%Params /106FLOPs /B
      SSD［5］69.239.726.3131.75
      YOLOv4-tiny67.631.86.069.96

      AF-YOLOv4［24］

      YOLOX

      69.2

      75.9

      40.1

      46.5

      7.95

      6.91

      11.27

      16.31

      YOLOv5s76.847.47.2016.54
      YOLOv8s81.152.111.2328.60
      Ours83.953.38.4123.22

    • Table 2. Comparative experiments on the VisDrone2019 dataset

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      Table 2. Comparative experiments on the VisDrone2019 dataset

      MethodmAP50 /%mAP50-95 /%Params /106FLOPs /B
      SSD［5］27.814.726.0230.07
      YOLOv4-tiny24.213.46.2110.42
      AF-YOLOv4［24］26.615.17.7914.63
      YOLOX36.720.07.0815.89
      YOLOv5s36.519.97.1317.14
      YOLOv8s41.124.410.6128.50
      DBNet65.3439.43>50>100
      Inception40.35>50>100
      LS-DOD42.71>50>100
      Ours42.325.28.7721.98

    • Table 3. Ablation experiments on the VisDrone2019 dataset

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      Table 3. Ablation experiments on the VisDrone2019 dataset

      MethodmAP /%Params /106FLOPs /B
      YOLOv8s41.110.6128.50
      YOLOv8s+LEFE41.88.8521.02
      YOLOv8s+DySample41.610.8728.91
      YOLOv8s+multi-scale head41.411.2529.13
      Ours42.38.7721.98
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    Xiangyang Zhao, Zaifeng Shi, Yunfeng Wang, Xiaowei Niu, Tao Luo. High-Precision and Lightweight Object Detection Model for Drone Aerial Photography Images[J]. Laser & Optoelectronics Progress, 2024, 61(24): 2437010

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    Paper Information

    Category: Digital Image Processing

    Received: Apr. 15, 2024

    Accepted: May. 20, 2024

    Published Online: Dec. 16, 2024

    The Author Email: Zaifeng Shi (shizaifeng@tju.edu.cn)

    DOI:10.3788/LOP241103

    CSTR:32186.14.LOP241103

    Topics

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