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Volume 45 Issue 11
Nov.  2023
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HU Taishan, LIU Hao, LIU Gang, MEI Qi, MA Yutang, LIAO Minchuan. Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3[J]. Infrared Technology , 2023, 45(11): 1256-1261.
Citation: HU Taishan, LIU Hao, LIU Gang, MEI Qi, MA Yutang, LIAO Minchuan. Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3[J]. Infrared Technology , 2023, 45(11): 1256-1261.
shu

Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3

  • 1.

    State Key Laboratory of HVDC, Electric Power Research Institute, CSG, Guangzhou 510663, China

  • 2.

    Electric Power Research Institute of Yunnan Power Grid Co., Ltd, Kunming 650000, China

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  • Received Date: June 06, 2021
  • Revised Date: August 19, 2021
    Graphical Abstract
    • Abstract
  • Aiming at the problems of low recognition accuracy and slow detection speed of existing metal oxide arrester (MOA) infrared image fault detection methods, a MOA infrared image fault detection method based on improved YOLOv3 is proposed. Firstly, darknet19 network is used to replace the original darknet53 network of YOLOv3. During feature learning, the target frames in MOA images are analyzed by K-means clustering algorithm according to different MOA length width ratios in samples. The anchor frames in the center of samples are re clustered to get the appropriate number and size of anchor frames. Finally, the improved YOLOv3 model is used to complete the MOA infrared image fault detection. The experimental results show that the recognition accuracy of the improved model reaches 96.3%, and the recognition speed is 6.75ms.
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    • References (20)
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