A segmentation network for enhancing autonomous driving scene understanding using skip connection and adaptive weighting

Abstract

With the rapid development of autonomous driving technology, accurate and efficient scene understanding has become particularly important. Semantic segmentation technology for autonomous driving aims to accurately identify and segment elements such as roads, sidewalks, and vegetation to provide the necessary perceptual information. However, current semantic segmentation algorithms still face some challenges, mainly inaccurate segmentation of road edge contours, misclassification of a part of the whole object into other categories, and difficulty in segmenting objects with fewer pixels. Therefore, this paper proposes a Segmentation Network based on Swin-UNet and Skip Connection (SUSC-SNet). It includes skip connection module (SCM), multi-branch fusion module (MFM), and dual branch fusion module (DBFM). SCM uses a dense skip connection method to achieve aggregated semantic extension and highly flexible encoder features in the decoder. MFM and DBFM control the degree of fusion of each branch through weights, increasing flexibility and adaptability. We conducted a fair experimental comparison between SUSC-SNet and several advanced segmentation networks on two publicly available autonomous driving datasets. SUSC-SNet increased mean intersection over union by 0.67% and 0.9%, respectively, and it increased mean class accuracy by 0.95% and 0.67%, respectively. A series of experiments demonstrated the efficiency, robustness, and applicability of SUSC-SNet.