In order to realize accurate and rapid positioning and state recognition of an insulator aerial image of high-voltage transmission lines

an insulator string diagnosis method based on improved YOLOv3 algorithm is proposed. Based on the self-established aerial insulator substring database

the Focal Loss function and the Balanced Cross Entropy function are used to improve the loss function of the YOLOv3 algorithm to sovle the problems of complexity imbalance and class imbalance of the data set samples. The convolutional layer filters trained by the original network on the COCO data set are visually analyzed. The frozen layers are selected and the network is trained by the multi-stage transfer learning strategy. The results of training and testing in Python environment show that the improved loss function can optimize the loss value of network training and improve the accuracy of the algorithm; multi-stage transfer learning can effectively deal with the easy fitting problem of the small data set while improving the accuracy of the algorithm. The proposed method can realize the positioning and state recognition of the insulator string end-to-end

and the diagnostic accuracy reaches 0.918. The results fully verify that the accuracy and real-time of the algorithm are better.