In order to solve the problem that the road image semantic segmentation model has huge parameter number and complex calculation in deep learning, and is not suitable for deployment on mobile terminals for real-time segmentation, a light-weighted symmetric U-shaped encoder-decoder image semantic segmentation network constructed by depthwise separable convolution was introduced, namely MUNet. First, a U-shaped encoder-decoder network was designed; then, the sparse short connection design was added in the convolution blocks; at last, the attention mechanism and Group Normalization (GN) method were introduced to reduce the amount of model parameters and calculation while improving the segmentation accuracy. For the CamVid dataset of road images, after 1 000 rounds of training, the Mean Intersection over Union (MIoU) of the segmentation results of the MUNet was 61.92% when the test image was cropped to a size of 720×720. Experimental results show that compared with the common image semantic segmentation networks such as Pyramid Scene Parsing Network (PSPNet), RefineNet, Global Convolutional Network (GCN) and DeepLabv3+, MUNet has fewer parameters and calculation with better network segmentation performance.