Abstract: Aiming at the problem that it is difficult to accurately detect  the small defects targets of insulator self-detonation in transmission line UAV inspection images, this paper proposes an insulator self-detonation defect detection algorithm based on Faster R-CNN and the improved YOLO v3 cascaded dual model. Firstly, the insulator string defect dataset is constructed using UAV inspection images, and the training image samples are pre-processed by flipping to increase the number of samples and improve the generalization ability of the model and avoid over fitting; then the Faster R-CNN is used to detect the insulator strings in the images, and then the detected insulator string images are fed into the improved YOLO v3 network for locating the self-exploding defects. The improved YOLO v3 network is based on YOLO v3 by borrowing the idea of FPN, adding feature extraction layer and performing feature fusion to make full use of deep and shallow features; meanwhile, the CIoU Loss function is used as the loss function to solve the boundary frame aspect ratio scale information. The experimental results show that the detection accuracy of the proposed algorithm reaches 91.2% on the constructed insulator defect dataset, which is more than 3.31 percentage points higher than that of single-model detection algorithms such as Faster R-CNN or YOLO v3, and can effectively realize the detection of insulator self-detonation defects in UAV inspection, which provides methodological support for intelligent inspection fault diagnosis of transmission lines.

Key words: insulator self-detonation, target detection, Faster R-CNN, YOLO v3, CIoU Loss