In recent years, China’s power system has followed the pace of science and technology and achieved unprecedented development. Therefore, in order to ensure the stable operation of the transmission system, the detection and identification method of foreign matter adhesion fault of transmission line has become a research hotspot of relevant personnel in the power industry. In order to effectively identify and detect the foreign object attachment fault of transmission line, so as to improve the efficiency of power inspection, combined with the image characteristics of foreign object attachment fault of transmission line, this paper effectively improves the common SSD algorithm (Single Shot MultiBox Detector), replaces the VGG16 feature extraction network with ResNet50, and aims at the shortcomings of the original model in small target detection, the feature fusion module is designed and applied, and the data of 1241 foreign object attachment fault images of transmission line are expanded and made into a data set containing more than 5000 images, so as to train the target detection network model. Finally, the mean accuracy mAP (mean Average Precision) of the training data set is about 97%, which meets the requirements of fault detection accuracy.