To improve the inspection efficiency of transmission lines and ensure the segmentation accuracy and speed of transmission lines, this paper proposes GU-Net, a lightweight network based on improved U-Net. Firstly, based on the U-Net network, the lightweight trunk extraction network Ghost-Net is introduced in the encoder part; then a bilinear interpolation method to complete the up-sampling and use the depth-separable convolution to replace part of the ordinary convolution; finally, introduce multiple loss functions in the training process to solve the imbalance between the transmission line and the background pixel occupancy, and train the model with a migration learning strategy. Tested on the E-Wire transmission line dataset, the MIoU and F1-score of the GU-Net network are 80.04% and 87.77%, respectively, which are 4.26% and 2.96% better than Wire-Detection, an existing semantic segmentation network for lightweight transmission lines, with almost no loss in the segmentation speed, and the number of references is about 20% of it. The experimental results show that the algorithm proposed in this paper can achieve fast, efficient and lightweight segmentation of transmission lines in complex images.