Abstract: With the normalization of unmanned aerial vehicle（UAV） inspection of transmission lines, the problems of real-time detection of fault images and accuracy of blurred target detection are difficult to meet the actual work requirements. This paper proposes a real-time detection method for transmission line faults, which is based on edge computing and improved YOLOv5 s algorithm. This method is based on YOLOv5 s model, and the model is reconstructed on the basis of Ghost lightweight module to realize the convolution operation process of obtaining data features, which improves the detection speed of the model. The loss function based on KL（Kullback-Leibler） divergence distribution is used as the target box localization loss function to improve the accuracy of blurred image detection. The improved YOLOv5 s algorithm is deployed in the Huawei Atlas 200 DK edge module to detect three types of faults: insulator self-explosion, shock hammer falling-off, and bird’s nest. The mAP can reach 84.75%, and the detection speed is 34 frame/s. The results show that the improved algorithm can improve the detection accuracy of blurred fault target images while ensuring the real-time detection, and meet the inspection requirements of transmission lines equipped with edge devices by UAV.