Abstract: The targets to be detected in aerial inspection images are easily affected by complex background and partial occlusion, which makes it difficult for traditional algorithms to detect accurately. Aiming at this problem, a fault detection method of YOLOv3 transmission line based on convolutional block attention model is proposed. Firstly, in the YOLOv3 algorithm framework, the convolutional block attention module is fused to improve the saliency of the fault target area in aerial inspection images. Secondly, the non-maximum suppression method is improved by introducing Gaussian function to reduce the missing rate of the partially occluded targets. Thirdly, the loss function is adopted to improve the detection accuracy of the detection network. Finally, the training samples and test samples are prepared by using the aerial inspection video clips of a power supply bureau in the past three years, and the algorithm proposed in this paper is compared with four classical target detection algorithms. The experimental results show that compared with the four classical algorithms, the proposed algorithm can guarantee higher detection accuracy and better real-time performance. The average detection accuracy of this algorithm can reach to 94.6%, and the image detection speed of 1280×720 is 40 frames per second.