@article{Zeng2025, 
author = {Huan Zeng and Jianxun Zhang and Hongji Chen and Xinwei Zhu},
title = {BSDNet: Semantic Information Distillation-Based for Bilateral-Branch Real-Time Semantic Segmentation on Street Scene Image},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {2},
pages = {3879-3896},
keywords = {Street scene understanding, real-time semantic segmentation, knowledge distillation, multi-scale feature extraction},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.066803},
doi = {10.32604/cmc.2025.066803},
abstract = {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.}
}