Semantic segmentation of road scene based on multi-scale feature extraction and deep supervision

Abstract

Aiming at the problems of inaccurate segmentation edges, poor adaptability to multi-scale road targets, prone to false segmentation and missing segmentation when segmenting road targets with various and changeable occlusions in the traditional U-Net model, a semantic segmentation model of road scene based on multi-scale feature extraction and deep supervision module is proposed. Firstly, the dual attention module is embedded in the U-Net encoder, which can make the model have the ability to capture the context information of channel dimension and spatial dimension in the global range, and enhance the road features; Secondly, before upsampling, the feature map containing high-level semantic information is input into ASPP module to obtain road features of different scales; Finally, the deep supervision module is introduced into the upsampling part to learn the feature representation at different levels and retain more road detail features. Experiments are carried out on CamVid dataset and Cityscapes dataset. The results show that our Network can effectively segment road targets with different scales, and the segmented road contour is more complete and clear, which improves the accuracy of semantic segmentation while ensuring a certain segmentation speed.