IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2465-d780813.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2465/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2465/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Hongchen Li & Zhong Yang & Jiaming Han & Shangxiang Lai & Qiuyan Zhang & Chi Zhang & Qianhui Fang & Guoxiong Hu, 2020. "TL-Net: A Novel Network for Transmission Line Scenes Classification," Energies, MDPI, vol. 13(15), pages 1-15, July.
    3. 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.
    4. Ju Sik Kim & Kyu Nam Choi & Sung Woo Kang, 2021. "Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    5. Linfeng Wang & Heng Wan & Deqing Huang & Jiayao Liu & Xuliang Tang & Linfeng Gan, 2023. "Sustainable Analysis of Insulator Fault Detection Based on Fine-Grained Visual Optimization," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    6. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    7. Chuanyang Liu & Yiquan Wu & Jingjing Liu & Jiaming Han, 2021. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images," Energies, MDPI, vol. 14(5), pages 1-19, March.
    8. 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.
    9. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2465-:d:780813. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.