Real-Time Multispectral Semantic Segmentation Network Utilizing Feature Frequency Decomposition and Spatial Division Distillation for Autonomous Driving

In autonomous driving systems, multispectral semantic segmentation integrates thermal images with RGB images to mitigate reliability degradation under challenging illumination conditions. However, most RGB-T methods cannot maintain high accuracy without sacrificing efficiency, thereby hindering efficient scene understanding. This paper presents RADNet, an asymmetric dual-stream network optimized for real-time RGB-T semantic segmentation, which incorporates three important components. The FFD module analyzes thermal features from the perspective of frequency to enhance thermal representations while suppressing interference. Furthermore, the ISD strategy explicitly transfers multimodal knowledge under varying illumination, leveraging the strengths of each modality without adding inference overhead. In addition, the FR module combines standard convolutions and central difference convolutions via structural re-parameterization to enhance detailed information. RADNet shows noticeable efficiency with 198.49 FPS on an RTX 3090 GPU while maintaining 13.21M parameters. Experimental results across three multispectral benchmarks covering diverse scenarios and illumination variations validate its competitive accuracy. Our network strikes a promising balance between segmentation performance and computational efficiency, making it a promising solution for further exploration in autonomous driving systems.