Lightweight transmission line conductor segmentation algorithm with improved U-Net
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.
deep learningsemantic segmentationcodec networkslight weightingpower line
万方数据
维普
