Abstract: In order to improve the efficiency of transmission line inspection and solve the problem of low accuracy of overhead hanging objects and bird nests detection, a transmission line hidden danger detection algorithm integrating SE attention model into YOLOv5 network is proposed to obtain channel-level global features, improve the sensitivity of the model to channel features, and increase the accuracy of overhead overhang and bird's nest detection. Extensive experiments were conducted on a set of transmission line hazard images, and the results show that the YOLOv5 network with the attention model has an average accuracy of 84.2% for overhead overhangs and 87.4% for bird nests, and the mAP value detected by the proposed method is 2% higher than that directly using the YOLOv5 algorithm.