Opto-Electronic Engineering, Volume. 51, Issue 5, 240051(2024)

Improved YOLOv7 algorithm for target detection in complex environments from UAV perspective

Runmei Zhang1,2,3,4, Yufei Xiao1, Zhennan Jia1, Zhong Chen1,2, Zihua Chen1,2, Bin Yuan1,2,3,4, Weiwei Cao4, and Weiwei Song3、*
Author Affiliations
  • 1School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China
  • 2Key Laboratory of Intelligent Manufacturing of Construction Machinery, Hefei, Anhui 230601, China
  • 3Anhui Simulation Design and Modern Manufacturing Engineering Technology Research Center, Huangshan, Anhui 242700, China
  • 4Key Laboratory of Civil Aviation Flight Technology and Flight Safety, Guanghan, Sichuan 618300, China
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    Figures & Tables(11)
    SimAM attention module
    SSG-YOLOv7 overall structure
    GhostConv structure
    Comparison of (a) NMS and (b) soft NMS detection effect sample chart
    Data augmentation comparison of two kinds of datasets
    Visual comparison of YOLOv7 and SSG-YOLOv7 detection effect
    • Table 1. Anchor frame size generated by K-means++

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      Table 1. Anchor frame size generated by K-means++

      特征图尺寸感受野锚框
      20x20Big[33,49],[63,73]
      40x40Medium[14,35],[27,23]
      80x80Small[20,8,8,15,14]
      160x160Tiny[2,5,4,11]
    • Table 2. Comparison of SPPCSPC and SG-SPPCSPC

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      Table 2. Comparison of SPPCSPC and SG-SPPCSPC

      模块类型Parameters/MGFLOPS
      SPPCSPC模块12.816.2
      SG-SPPCSPC模块3.694.9
    • Table 3. Results of ablation experiments

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      Table 3. Results of ablation experiments

      ModelK-means++SimAMSG-SPPCSPCSoft NMSVis_mAP@0.5/%RSOD_mAP@0.5/%Parameters/MFPSGFLOPs
      A40.8995.6037.682106.5
      B44.1596.9137.682106.5
      C46.4097.2237.687107.2
      D48.6197.9128.59395.9
      E51.34(+10.45)98.27(+2.67)28.59395.9
    • Table 4. Comparison of mAP(%) before and after data enhancement

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      Table 4. Comparison of mAP(%) before and after data enhancement

      Model原始VisDrone增强后VisDrone原始RSOD增强后RSOD
      YOLOv736.7640.8992.0195.60
      SSG-YOLOv742.6351.3493.8298.27
    • Table 5. Comparison of experimental results

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      Table 5. Comparison of experimental results

      MethodVisdrone_mAP@0.5 /%Visdrone_mAP@0.5:0.95 /%RSOD_mAP@0.5 /%RSOD_mAP@0.5:0.95 /%FPSParameters/M
      Faster R-CNN[6]20.08.9185.654.143137.10
      SSD[23]10.25.187.452.624926.29
      YOLOv5s27.415.694.059.51267.28
      YOLOv5m32.018.895.266.49821.38
      YOLOv5l36.521.595.168.37547.10
      YOLOv7[13]40.824.095.669.68237.62
      YOLOv8s43.125.094.163.016011.17
      YOLOv8m39.622.894.168.712225.90
      YOLOv8l43.725.196.068.99843.69
      本文算法51.329.298.370.09328.49
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    Runmei Zhang, Yufei Xiao, Zhennan Jia, Zhong Chen, Zihua Chen, Bin Yuan, Weiwei Cao, Weiwei Song. Improved YOLOv7 algorithm for target detection in complex environments from UAV perspective[J]. Opto-Electronic Engineering, 2024, 51(5): 240051

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    Paper Information

    Category: Article

    Received: Mar. 6, 2024

    Accepted: Apr. 24, 2024

    Published Online: Jul. 31, 2024

    The Author Email: Weiwei Song (宋娓娓)

    DOI:10.12086/oee.2024.240051

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