In this paper, we propose an algorithm based on YOLOv4 to solve the problem that manual inspection and traditional video monitoring methods cannot identify the external hidden dangers of transmission lines in time. In this algorithm, cluster analysis is performed with the improved K-means algorithm on the size of the targets in the image sample set to select the anchor frames that conform to the characteristics of detection targets. After that, the CSPDarknet-53 residual network is used to extract the deep-seated network feature data of the images, and the feature map is processed by the SPP algorithm to increase the receptive field and extract higher-level semantic features. Finally, in combination with the monitoring pictures of transmission lines, the test results show that the proposed algorithm can detect external hidden dangers timely and accurately.