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Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

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Abstract

In order to improve the efficiency of post-disaster treatment of power distribution network, the application of UAV in disaster reduction and relief has been paid much attention by the power sector. Aiming at the loss assessment needs of overhead transmission lines in distribution network, this paper proposes an innovative solution of pole detection and counting in distribution network based on UAV inspection line video. Combined with the characteristics of YOLO’s rapid detection, the convolution neural network is applied to the image detection of the pole state. In addition, the pole data and corresponding images are obtained at the same time of detecting the inspection line video. Therefore, the power department can quickly count the losses to cope with the disaster. The anchor value is modified before image training by YOLO v3, and sets the corresponding ROI for the UAV inspection line standard. In order to quickly obtain the loss assessment of post-disaster pole lodging, this paper proposes a counting algorithm by using the continuous ordinate change of the bounding box of the same pole in front and rear frame of video, so that the classified counting of pole is accurate and the detection precision is above 0.9. The results obtained in video test show that this method is effective in detecting and counting the state of the pole of overhead transmission line in distribution network.

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  • 04 September 2019

    The article “Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video“, written by Binghuang Chen and Xiren Miao was originally published.

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Funding

Funding was provided by Research Foundation of Fuzhou University (XRC-1623, XRC-17011), Fujian Provincial Department of Science and Technology (CN) (2017J01728), Fujian Science and Technology Department (2017J01470).

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

    Binghuang Chen & Xiren Miao

  2. School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Binghuang Chen

  3. Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fuzhou, China

    Binghuang Chen

Authors
  1. Binghuang Chen
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  2. Xiren Miao
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Correspondence to Binghuang Chen.

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Chen, B., Miao, X. Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video. J. Electr. Eng. Technol. 15, 441–448 (2020). https://doi.org/10.1007/s42835-019-00230-w

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  • DOI: https://doi.org/10.1007/s42835-019-00230-w

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