Abstract: In order to solve the problem of poor real-time and accuracy of single manual patrol mode and traditional video monitoring mode in transmission line patrol inspection, this paper explores a target detection algorithm for transmission line hidden danger based on deep learning framework YOLO-V5, which can provide more accurate warning for all kinds of hidden dangers of transmission line. This study used YOLO-V5 pre-training model on COCO dataset after detailed labeling and image enlargement of 17386 images containing all kinds of potential transmission line hazards. After two adjustments of learning rate, the final convergence model was obtained. After analyzing the test set containing 6933 hidden and non-hidden images, the final prediction results show that the overall accuracy is 79.10%, the comprehensive recall rate is 73.45%, the miss rate is 3.93%, and the false detection rate is 4.97%. The hidden danger identification model proposed in this paper can be used to identify hidden dangers of transmission lines in real-time. It has a high recognition accuracy and a low rate of false positives and false positives, which can effectively improve the efficiency of transmission line inspection.