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Volume 44 Issue 7
Jul.  2022
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GE Huangxu, ZHENG Lei, JIANG Hong, GUO Yifan, ZHOU Dongguo. PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework[J]. Infrared Technology , 2022, 44(7): 709-715.
Citation: GE Huangxu, ZHENG Lei, JIANG Hong, GUO Yifan, ZHOU Dongguo. PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework[J]. Infrared Technology , 2022, 44(7): 709-715.
shu

PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework

  • 1.

    State Grid Zhejiang Electric Power Company Jiaxing Power Supply Company, Jiaxing 314599, China

  • 2.

    Wuhan NARI Limited Liability Company of State Grid Electric Power Research Institute, Wuhan 430074, China

  • 3.

    NARI Technology Co. Ltd., Nanjing 211106, China

  • 4.

    Wuhan University, The College of Power and Mechanical Engineering, Wuhan 430072, China

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  • Received Date: January 01, 2019
  • Revised Date: May 05, 2019
    Graphical Abstract
    • Abstract
  • This paper presents a pulse-coupled neural network (PCNN) method for infrared fault region extraction based on maximum similarity thresholding to detect the fault region from the infrared image of a transmission line. In this method, the synchronous pulse characteristics of the PCNN model are used to cluster pixels via inner iteration, and the model is simplified by incorporating the maximum similarity thresholding method, enabling the PCNN model to simplify the thresholding setting. Meanwhile, the minimum clustering variance is introduced to set the linking coefficient. Thus, the PCNN model can efficiently segment an infrared image and obtain the effective thermal fault region in the image. The experimental results show that the proposed method exhibits good performance in region extraction and may be suitable for increasing the efficiency of automatic fault detection along transmission lines.
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    • References (17)
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