BSDNet: Semantic Information Distillation-Based for Bilateral-Branch Real-Time Semantic Segmentation on Street Scene Image

Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment. In street scenes, issues such as high image resolution caused by a large viewpoints and differences in object scales lead to a decline in real-time performance and difficulties in multi-scale feature extraction. To address this, we propose a bilateral-branch real-time semantic segmentation method based on semantic information distillation (BSDNet) for street scene images. The BSDNet consists of a Feature Conversion Convolutional Block (FCB), a Semantic Information Distillation Module (SIDM), and a Deep Aggregation Atrous Convolution Pyramid Pooling (DASP). FCB reduces the semantic gap between the backbone and the semantic branch. SIDM extracts high-quality semantic information from the Transformer branch to reduce computational costs. DASP aggregates information lost in atrous convolutions, effectively capturing multi-scale objects. Extensive experiments conducted on Cityscapes, CamVid, and ADE20K, achieving an accuracy of 81.7 Mean Intersection over Union (mIoU) at 70.6 Frames Per Second (FPS) on Cityscapes, demonstrate that our method achieves a better balance between accuracy and inference speed.