@Article{cmc.2025.060244,
AUTHOR = {Shijie Xiang, Dong Zhou, Dan Tian, Zihao Wang},
TITLE = {Bilateral Dual-Residual Real-Time Semantic Segmentation Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {1},
PAGES = {497--515},
URL = {http://www.techscience.com/cmc/v83n1/60091},
ISSN = {1546-2226},
ABSTRACT = {Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction. Furthermore, the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images, improving the ability to detect both detailed and global semantic features. Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity. In particular, the network achieved a mean Intersection over Union (mIoU) of  on the Cityscapes validation set, surpassing many existing semantic segmentation models. Additionally, in terms of inference speed, the network reached  frames per second when tested on an NVIDIA GeForce RTX 3090 GPU, significantly improving real-time performance.},
DOI = {10.32604/cmc.2025.060244}
}