Dynamic context-aware high-resolution network for semi-supervised semantic segmentation

Real-time semi-supervised semantic segmentation provides a detailed understanding of dynamic urban situations for Intelligent transportation systems. However, state-of-the-art models following different segmentation tasks fail to incorporate High-Resolution Networks (HRNet) for real-time situations due to the inability to adjust in dynamic environments and high computational complexity. The study aims to examine the efficacy of student-teacher networks in semi-supervised semantic segmentation, mainly focusing on intelligent transportation. We present a Dynamic Context-aware High-Resolution Network (DC-HRNet) to enhance semantic segmentation in a dynamic urban environment incorporating a Student-Teacher knowledge distillation mechanism. We train teacher networks utilizing high-resolution networks to enhance robustness and precision in autonomous driving. Additionally, we employ Multi-path Blocks (MPBs) with HRNet to train our student network to address the challenges of the scarcity of labeled datasets in real-world scenarios. Utilization of MPBs for downsampling helps avoid low-resolution loss and generate pseudo labels by leveraging feature maps. We integrate a Dynamic Context-Aware Segmentation Network (DCSNet) to reduce the computational cost further and increase segmentation accuracy. Integrating DCSNet across various resolutions of HRNet with diverse dilation rates has enhanced contextual data utilization. This study contributes to intelligent transportation systems by improving segmentation accuracy, model generalization, and overall resilience, expanding the research scope in this area. Extensive investigations on the publicly available datasets show that our model can handle high-resolution images in real-time with state-of-the-art performance. © 2025 Elsevier Ltd