Abstract: Aiming at the problems of low efficiency of the manual reinspection and high rate of the misdetection and undetection errors in checking the broken strands or the surface abrasion of the overhead transmission conductors in the UAV inspection images, an intelligent detection for the overhead transmission wire defects based on the deep learning is proposed. Taking the Unet as the base network and combined with the idea of migration learning, the VGG16 is used as the backbone feature extraction network. The weights of the VGG16 trained on the ImageNet dataset are firstly adopted as the pre-training weights to enhance the training effect; Then, the depthwise separable convolution (DS) is taken to replace the ordinary convolution, which effectively reduces the amount of the parameters in the network; Finally, a lightweight efficient channel attention (ECA) module is introduced to achieve a local cross-channel interaction strategy without doing dimensionality reduction, highlighting the important features while overcoming the contradiction between the performance and the complexity. The model is tested for functionality and performance on a self-built transmission conductor defect dataset. Experimental results show that the accuracy of this paper's model reaches 89.81% for the conductor strand break detection, 90.86% for the surface scrape detection, 93.58% for the surface scratch detection, anf 86.12% the MIoU value. The detection speed of a single sheet is about 8 times higher than that of the Unet network, which effectively improves the model detection speed and detection accuracy.