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Volume 43 Issue 3
Apr.  2021
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WU Tianquan, GUO Jing, GOU Xiantai, HUANG Qinqin, ZHOU Weichao. Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel[J]. Infrared Technology , 2021, 43(3): 230-236.
Citation: WU Tianquan, GUO Jing, GOU Xiantai, HUANG Qinqin, ZHOU Weichao. Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel[J]. Infrared Technology , 2021, 43(3): 230-236.
shu

Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel

  • 1.

    State Grid Chaozhou Electric Power Co.Ltd, Chaozhou 521000, China

  • 2.

    College of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China

  • 3.

    Sichuan Scom Intelligent Technology Co. Ltd, Chengdu 610041, China

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  • Received Date: April 15, 2020
  • Revised Date: December 27, 2020
    Graphical Abstract
    • Abstract
  • Slow and inaccurate target detection algorithms used to analyze infrared images are the focus of this study. An infrared image detection method is proposed for substation equipment using an improved Gaussian convolution kernel, which is based on the CenterNet algorithm without an anchor point. In brief, data samples were first collected using on-site substation inspection robot equipment, the algorithm model was trained and verified, and finally, accurate identification and positioning of infrared image substation equipment was achieved. Specifically, based on the infrared image library collected by the substation inspection robot and the infrared thermal imager, methods of deep learning were applied to train and test a model using the dataset, the target detection technology of substation infrared images was studied, and the equipment center was accurately judged through deep learning technology to achieve target classification and regression. The identification and positioning accuracy of the substation target detection were improved by adopting this proposed method, and it provides new ideas for the intelligent detection of infrared images for substation equipment.
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    • References (19)
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