The segmentation of power lines in drone images is one of the challenging tasks in the field of computer vision. Although power lines share the same difficulties with tiny object segmentation, occupying only a very small proportion of pixel areas in the images, the greater challenge is that they also have a very large visual perspective field. Therefore, the results obtained by traditional convolutional neural network-based segmentation methods are still unsatisfactory. To tackle these problems, we propose MiT-Unet(Mixed Transformer Unet), which has an analogous multi-level convolutional neural network structure similar to Unet for encoding and decoding transmission line detailed features. However, when dealing with excessive scales, we employ Efficient Self-Attention based module to enhance and fusion the global features of straight lines. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in transmission line segmentation on the public TTPLA dataset. Moreover, the computational efficiency of the proposed model makes it potentially deployable on mobile platforms.