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Evaluation of the Application of YOLO Algorithm in Insulator Identification

In: Liss 2021

Author

Listed:
  • Yaopeng Chu

    (China Power Complete Equipment Company Limited)

Abstract

Insulators have a large amount of use and a large geographical span in the power grid. Conventional inspection methods mainly rely on manual experience, and it is difficult to find weak discharges on the surface of equipment. Solar blind ultraviolet imaging detection has the advantages of high sensitivity, non-contact and intuitive characterization. Based on the characteristics of dual-channel imaging of ultraviolet imagers, this paper proposes a method for identifying insulators of visible light channel images based on artificial intelligence. Based on this, discharge severity assessment of the ultraviolet channel insulator image is realized. A deep learning hardware and software research platform based on the TensorFlow and Darknet frameworks under the Linux environment was established. Based on the pictures collected in the laboratory and the field, a sample database of insulators was established and the sample pictures were marked and the YOLO network training was completed, the effects of training sample number and network depth on recognition accuracy are studied, and a matching scheme is given.

Suggested Citation

  • Yaopeng Chu, 2022. "Evaluation of the Application of YOLO Algorithm in Insulator Identification," Lecture Notes in Operations Research, in: Xianliang Shi & Gábor Bohács & Yixuan Ma & Daqing Gong & Xiaopu Shang (ed.), Liss 2021, pages 273-283, Springer.
    • Handle: RePEc:spr:lnopch:978-981-16-8656-6_26
    DOI: 10.1007/978-981-16-8656-6_26
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