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An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

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  • Jingjing Liu

    (College of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, China)

  • Chuanyang Liu

    (College of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, China
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Yiquan Wu

    (College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Huajie Xu

    (College of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, China)

  • Zuo Sun

    (College of Mechanical and Electrical Engineering, Chizhou University, Chizhou 247000, China)

Abstract

Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “InSF-detection” is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the “InSF-detection”. The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.

Suggested Citation

  • Jingjing Liu & Chuanyang Liu & Yiquan Wu & Huajie Xu & Zuo Sun, 2021. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images," Energies, MDPI, vol. 14(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4365-:d:597469
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    References listed on IDEAS

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    1. Zhenbing Zhao & Zhen Zhen & Lei Zhang & Yincheng Qi & Yinghui Kong & Ke Zhang, 2019. "Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN," Energies, MDPI, vol. 12(7), pages 1-15, March.
    2. Haiyan Cheng & Yongjie Zhai & Rui Chen & Di Wang & Ze Dong & Yutao Wang, 2019. "Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features," Energies, MDPI, vol. 12(3), pages 1-14, February.
    3. 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.
    4. Jiaming Han & Zhong Yang & Hao Xu & Guoxiong Hu & Chi Zhang & Hongchen Li & Shangxiang Lai & Huarong Zeng, 2020. "Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images," Energies, MDPI, vol. 13(3), pages 1-20, February.
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    Cited by:

    1. Juping Gu & Junjie Hu & Ling Jiang & Zixu Wang & Xinsong Zhang & Yiming Xu & Jianhong Zhu & Lurui Fang, 2023. "Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s," Energies, MDPI, vol. 16(6), pages 1-18, March.

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