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Insulator Breakage Detection Based on Improved YOLOv5

Author

Listed:
  • Gujing Han

    (School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
    These authors contributed equally to this work.)

  • Min He

    (School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
    These authors contributed equally to this work.)

  • Mengze Gao

    (School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China)

  • Jinyun Yu

    (School of Electrical and Automation, Wuhan University, Wuhan 430072, China
    These authors contributed equally to this work.)

  • Kaipei Liu

    (School of Electrical and Automation, Wuhan University, Wuhan 430072, China
    These authors contributed equally to this work.)

  • Liang Qin

    (School of Electrical and Automation, Wuhan University, Wuhan 430072, China
    These authors contributed equally to this work.)

Abstract

Aerial images have complex backgrounds, small targets, and overlapping targets, resulting in low accuracy of intelligent detection of overhead line insulators. This paper proposes an improved algorithm for insulator breakage detection based on YOLOv5: The ECA-Net (Efficient Channel Attention Network) attention mechanism is integrated into its backbone feature extraction layer, and the effective distinction between background and target is achieved by increasing the weight of important channels. A bidirectional feature pyramid network is added to the feature fusion layer, and large-scale images with more original information are combined to effectively retain small target features. Incorporating a flexible detection frame selection algorithm Soft-NMS (Soft Non-Maximum Suppression) into the prediction layer to re-screen the target frame, thereby reducing the probability of mistaken deletion of overlapping targets. The effectiveness of the improved YOLOv5 algorithm is verified in the actual aerial image dataset, and the results show that the mean Average Precision (mAP) of the improved algorithm is 95.02% and the detection speed FPS (Frames Per Second) can reach 49.4 frames/s, which meets the real-time and accuracy requirements of engineering applications.

Suggested Citation

  • Gujing Han & Min He & Mengze Gao & Jinyun Yu & Kaipei Liu & Liang Qin, 2022. "Insulator Breakage Detection Based on Improved YOLOv5," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6066-:d:817214
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    References listed on IDEAS

    as
    1. Zahid Ali Siddiqui & Unsang Park, 2020. "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique," Energies, MDPI, vol. 13(13), pages 1-24, June.
    2. Yongjie Zhai & Haiyan Cheng & Rui Chen & Qiang Yang & Xiaoxia Li, 2018. "Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images," Energies, MDPI, vol. 11(2), pages 1-12, February.
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    Cited by:

    1. Jian Zhang & Tian Xiao & Minhang Li & Yucai Zhou, 2023. "Deep-Learning-Based Detection of Transmission Line Insulators," Energies, MDPI, vol. 16(14), pages 1-17, July.

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