Insulator defect detection in power inspection image using focal loss based on YOLO v4
Xin Yu Hu, Yunqiang Zhou · 2021
With the continuous expansion of the power industry, insulators, as one of the important power transmission equipment, have been exposed for a long time, causing wear, string loss and other problems, which threaten the safe operation of the power system. In this paper,yolov4 target detection algorithm based on deep learning is used. Based on CSPDarknet-53 and adding SPP Net, the receptive field is greatly increased. PANet is introduced to strengthen the feature extraction network，and focal loss is used as a function to calculate the loss.Then the models trained using RetinaNet and yolov4 are compared.The experimental data show that the defect detection accuracy can reach 96.2% and the recall rate can reach 97.7%.