Abstract：Insulators are important components of transmission lines, and their failure will increase the risk of outages of transmission lines. Therefore, it is very important to distinguish the defects of insulators in the early stage. A method for identifying self-explosion defects of glass insulators based on the YOLOv4 model is proposed in this paper. Firstly, a large number of detailed images of field insulators are obtained through unmanned aerial vehicle acquisition and data enhancement. Secondly, the training strategy of transfer learning is adopted to train the YOLOv4 network and improve the input image of the network to achieve the accuracy of identification. Finally, the improvement of network performance is verified by experiments. The experimental results show that the proposed method can accurately and effectively realize the identification of insulator defects.
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2021, Vol. 22 
