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Volume 43 Issue 1
Jan.  2021
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FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. Infrared Technology , 2021, 43(1): 51-55.
Citation: FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. Infrared Technology , 2021, 43(1): 51-55.
shu

Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

  • 1.

    NARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing 211106, China

  • 2.

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

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  • Received Date: March 07, 2020
  • Revised Date: November 21, 2020
    Graphical Abstract
    • Abstract
  • Because of the effects of the background environment and data volume, the accuracy and efficiency of abnormal defects in traditional infrared images of insulators are generally low. In this study, a deep-learning anomaly diagnosis method is proposed. Based on the improved faster region-based convolutional neural network (R-CNN) method, a detection network is built to test different types of insulators. Results show that compared with the back propagation neural network and faster R-CNN methods, the proposed method can diagnose abnormal defects of insulators efficiently with a mean average precision of 90.2%. In addition, the diagnostic accuracy of single type Ⅰ and type Ⅴ insulators is higher than that of double type Ⅰ insulators. The results can provide a reference for insulator defect identification in transmission lines.
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    • References (19)
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