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In order to realize the automatic identification of typical defects in transmission lines, a typical defect identification method for transmission lines based on You Only Look Once (YOLO)v5 algorithm is proposed, which has the advantages of low missing detection rate and false detection rate. Firstly, a transmission line defect image dataset is constructed for three typical defects: bird’s nest defect, insulator defect and shakeproof hammer defect, and the corresponding image labeling strategy is formulated according to the defect characteristics. Secondly, this paper introduces the network structure of YOLOv5 in detail. For the sake of further improving the neural network’s ability to recognize the visual defects of transmission lines, the attention mechanism and small target detection layer are introduced into YOLOv5 neural network. Finally, this paper design transmission line defect recognition cases based on three kinds of typical defects, and the proposed method is compared with the recognition methods based on Faster Region based Convolutional Neural Network (RCNN) and Single Shot MultiBox Detector (SSD) in terms of the proposed performance indexes (missing detection rate and false detection rate). The results show that the missing detection rate and false detection rate of the YOLOv5-based identification method are much lower than those of the Faster RCNN and SSD-based identification methods, and the improved YOLOv5 network recognition rate is higher, which is significant for reducing the workload of transmission line inspection personnel.
Yuan et al. (Thu,) studied this question.