Aiming at the problem of low detection accuracy and slow detection speed of insulator defects in transmission lines, a defect detection method for transmission line insulators based on multi-scale feature coding and double attention fusion is proposed. First, in order to adapt the detection model to the diversity of characteristic scales of defective insulators, the coding network uses Res2Net50 to extract more fine-grained features, and then embeds the atrous spatial pyramid pooling structure to capture the characteristics of insulators and their defects at multiple scales. Second, in order to reduce the lack of feature information in the decoding network, The different feature layers of the backbone network are connected in series with the efficient channel attention attention module, and they are added to the deconvolution features of the squeeze and excitation attention module to form a double attention fusion. Finally, Experiments show that the mean average precision index of the proposed method reaches about 95.35%, and the frames per second reaches about 65.95, and compared with other algorithms, this method has certain reference value for realizing the accurate detection of insulator defects of unmanned aerial vehicles.