Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2212005(2024)

GER-YOLO Fault-Detection Algorithm for Transmission-Line Insulators

Boya Yuan1, Yao Li2, and Qing Ye1、*
Author Affiliations
  • 1School of Computer Science, Yangtze University, Jingzhou 434023, Hubei , China
  • 2School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, Hubei , China
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    Figures & Tables(16)
    Network architecture of YOLOv8s
    Network structure of GER-YOLO
    Ghost module
    DFC long range attention module
    GhostNetV2 bottleneck module
    C2fGhostV2 module
    EMA network
    RFE module
    C2fRFE module
    Example of dataset
    Comparison of loss curves between GER-YOLO and YOLOv8s
    Visual comparison of detection results. (a) Detection effect of self-exploding insulators; (b) detection effect of damaged insulators; (c) detection effect of bird's nest; (d) detection effect of insulators with similar environmental colors
    • Table 1. Comparison of models of different attention mechanisms

      View table

      Table 1. Comparison of models of different attention mechanisms

      ModelPrecision /%Recall /%mAP /%Parameter /106
      YOLOv8s95.386.993.011.13
      YOLOv8s+GAM93.788.792.611.22
      YOLOv8s+ECA95.489.593.711.13
      YOLOv8s+CBAM95.888.793.911.17
      YOLOv8s+EMA95.988.693.911.13
    • Table 2. Results of ablation experiment

      View table

      Table 2. Results of ablation experiment

      ModulePrecision /%Recall /%mAP /%Parameter /106FLOPs /G
      None95.386.993.011.1328.4
      C2fGhostV293.288.093.17.4719.4
      EMA95.988.693.911.1328.7
      C2fRFE94.788.593.610.2827.5
      C2fGhostV2+EMA93.288.393.57.4819.6
      C2fGhostV2+C2fRFE94.387.993.67.7719.3
      EMA+C2fRFE94.289.694.210.2827.7
      C2fGhostV2+EMA+C2fRFE94.589.494.17.7719.6
    • Table 3. Comparative experiment of different algorithms

      View table

      Table 3. Comparative experiment of different algorithms

      ModelPrecision /%Recall /%mAP /%Parameter /106FLOPs /G
      SSD96.757.388.624.3061.3
      YOLOX92.886.890.99.0026.8
      YOLOv5s93.187.991.67.0215.7
      YOLOv7-tiny89.583.589.86.0213.3
      YOLOv8s95.386.993.011.1328.4
      YOLOv5-ODConvNeXt94.685.892.07.0114.9
      GER-YOLO94.589.494.17.7719.6
    • Table 4. Comparison experiment of different dataset partition ratios

      View table

      Table 4. Comparison experiment of different dataset partition ratios

      ModelRatioPrecision /%Recall /%mAP /%
      SSD7∶396.156.388.1
      9∶197.157.888.9
      YOLOX7∶392.286.590.4
      9∶197.157.891.6
      YOLOv5s7∶391.388.291.3
      9∶193.088.392.2
      YOLOv7-tiny7∶388.783.689.1
      9∶188.687.790.7
      YOLOv8s7∶394.087.092.8
      9∶195.988.493.8
      YOLOv5-ODConvNeXt7∶392.086.391.2
      9∶194.787.792.5
      GER-YOLO7∶393.388.293.7
      9∶195.490.394.6
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    Boya Yuan, Yao Li, Qing Ye. GER-YOLO Fault-Detection Algorithm for Transmission-Line Insulators[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2212005

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Jan. 15, 2024

    Accepted: Apr. 11, 2024

    Published Online: Nov. 20, 2024

    The Author Email: Qing Ye (yeqing@yangtzeu.edu.cn)

    DOI:10.3788/LOP240529

    CSTR:32186.14.LOP240529

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