Abstract:In order to improve the performance and practical application of road scene semantic segmentation, A lightweight multi feature fusion network combining traditional image processing and deep learning technology is proposed for road scene semantic segmentation. Firstly, algorithms such as color space transformation, image equalization and edge detection are used to enhance image feature information. Secondly, the depth separable convolution is used as the basic unit to build a high-efficiency feature extraction structure, which is used for information fusion and extraction of the image after feature enhancement, and the preliminary segmentation is realized combined with the skip layer upsampling operation; Finally, the edge detection branch is introduced to refine the boundary information of the segmented image to ensure the high-precision segmentation of the network. Experimental results show that the proposed network effectively balances the network segmentation accuracy and computational efficiency. At the same time, in the actual application of substation road scene, the network can also achieve efficient semantic segmentation and provide effective road information for inspection robots.