Insulator Anomaly Detection Method Based on Few-Shot Learning

IEEE Access

Yipin Wang¹  Hongwei Wang²  Zhaoyang Wang²  Dong Li²  Xiao Yu²  Qiang Gao²  Junjie Liu² 

[1] State Grid Tianjin Electric Power Company, Tianjin, China;Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, China;

关键词: Convolutional neural network;    few-shot learning;    object detection;    insulator anomaly detection;   

DOI  :  10.1109/ACCESS.2021.3071305

来源: DOAJ

【 摘 要 】

Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.

【 授权许可】

Unknown