TY  - JOUR
AU  - Zeng, Huan
AU  - Zhang, Jianxun
AU  - Chen, Hongji
AU  - Zhu, Xinwei
PY  - 2025
TI  - BSDNet: Semantic Information Distillation-Based for Bilateral-Branch Real-Time Semantic Segmentation on Street Scene Image
JO  - Computers, Materials & Continua
SN  - 1546-2218
SP  - 3879
EP  - 3896
VL  - 85
IS  - 2
AB  - 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.
UR  - https://doi.org/10.32604/cmc.2025.066803
DO  - 10.32604/cmc.2025.066803