Abstract: Aiming at the problem that targets with complex background and small targets in UAV aerial inspection images are difficult to be accurately detected, we proposed a transmission line insulator fault detection method based on USRNet and improved YOLOv5x algorithm. Firstly, the interference of complex background was reduced by USRNet to reconstruct the test set images with super resolution. Then, K-means++ was used to cluster the marker frames to generate anchor frames matching the size of transmission line fault targets. Meanwhile, a detection head with a larger feature map was introduced at the prediction side to detect faulty small targets by changing the structure of the Neck part. Finally, the overall performance of the model was optimized using the EIOU loss function, and comparison experiments were designed to validate the proposed method. The results show that the mAP value of the proposed method reaches 98.8%, and the fault detection accuracy of transmission lines can be improved to 95.4%, which has better detection performance of complex background targets and small targets.