A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

DOI Web Site 被引用文献1件
  • Jiaming Han
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Zhong Yang
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Qiuyan Zhang
    Guizhou Power Grid Co., Ltd. Institute of Electric Power Science, Guiyang 550002 China
  • Cong Chen
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Hongchen Li
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Shangxiang Lai
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Guoxiong Hu
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Changliang Xu
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Hao Xu
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Di Wang
    Department of Automation, North China Electric Power University, Baoding 071003, China
  • Rui Chen
    Department of Automation, North China Electric Power University, Baoding 071003, China

書誌事項

公開日
2019-05-16
権利情報
  • https://creativecommons.org/licenses/by/4.0/
DOI
  • 10.3390/app9102009
公開者
MDPI AG

説明

<jats:p>Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.</jats:p>

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