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Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning

Author

Listed:
  • Zhaoyun Zhang

    (College of electronic engineering and intelligence, Dongguan University of Technology, Dongguan 523808, China)

  • Shihong Huang

    (College of electronic engineering and intelligence, Dongguan University of Technology, Dongguan 523808, China)

  • Yanxin Li

    (Shenzhen Branch, State Grid Electric Power Research Institute, Shenzhen 518057, China)

  • Hui Li

    (Shenzhen Branch, State Grid Electric Power Research Institute, Shenzhen 518057, China)

  • Houtang Hao

    (Shenzhen Branch, State Grid Electric Power Research Institute, Shenzhen 518057, China)

Abstract

Insulators are an important part of transmission lines; failure may threaten the operation of these transmission lines. For insulator defect detection, an optical image detection method based on deep learning and morphological detection is proposed. First of all, the Faster RCNN is used to locate the insulator and extract its target image from the detection image. In the second place, a segmentation method of insulator image is proposed to remove the background of the target image. In order to simplify insulator defect detection, an insulator shape transformation method is proposed to unify all types of insulator detection. Finally, a mathematical model is established in the binary image to describe the defect of the insulator. Experiments show that our proposed Faster RCNN can accurately detect the insulators in the image. Its AP is as high as 0.9175, and its Recall rate is as high as 0.98, which is higher than the common insulator recognition algorithm. The accuracy of the proposed defect detection method is 0.98, which can accurately locate the defect position of the insulator. In order to prove the efficiency of the proposed method, we compared several common detection methods.

Suggested Citation

  • Zhaoyun Zhang & Shihong Huang & Yanxin Li & Hui Li & Houtang Hao, 2022. "Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning," Energies, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2465-:d:780813
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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.
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