ISANet: A Real-Time Semantic Segmentation Network Based on Information Supplementary Aggregation Network

In autonomous-driving real-time semantic segmentation, simultaneously maximizing accuracy, minimizing model size, and sustaining high inference speed remains challenging. This tripartite demand poses significant constraints on the design of lightweight neural networks, as conventional frameworks often suffer from a trade-off between computational efficiency and feature representation capability, thereby limiting their practical deployment in resource-constrained autonomous driving systems. We introduce ISANet, an information supplementary aggregation framework that markedly elevates segmentation accuracy without sacrificing frame rate. ISANet integrates three key components: (i) the Spatial-Supplementary Lightweight Bottleneck Unit (SLBU) that splits channels and employs compensatory branches to extract highly expressive features with minimal parameters; (ii) the Missing Spatial Information Recovery Branch (MSIRB) that recovers spatial details lost during feature extraction; and (iii) the Object Boundary Feature Attention Module (OBFAM) that fuses multi-stage features and strengthens inter-layer information interaction. Evaluated on Cityscapes and CamVid, ISANet attains 76.7% and 73.8% mIoU, respectively, while delivering 58 FPS and 90 FPS with only 1.37 million parameters.