DCR-KD: Dynamic Class Relation Knowledge Distillation for Semantic Segmentation With the Frontal-Viewing Camera of Limited Field of View in an Internet of Things Environment

Abstract

In autonomous driving with Internet of Things (IoT) devices, real-time road perception and semantic segmentation are essential for intelligent transportation systems. However, deploying models on IoT devices is challenging due to their limited computational power, memory, and energy availability. Additionally, limited field of view (FoV) occurs frequently in real-world scenarios, such as constrained camera angles or occlusion by large objects, leading to significant degradations in segmentation performance. To address these challenges, we propose Dynamic Class Relation Knowledge Distillation (DCR-KD), a framework that generates lightweight models by transferring knowledge from high-performance teacher models. Central to our method is the Limited FoV Edge Module (LFEM), which extracts edge-aware features of the teacher to refine the learning of the student. LFEM is designed to capture edge-based features in regions with limited FoV, effectively representing critical object boundaries. A channel attention mechanism further enhances semantic features, allowing the student to focus on key information in constrained visual contexts. A dynamic class relation map captures global semantic relationships among classes, enriching the scene understanding of the student. The final student model is independent of the teacher during inference, enabling efficient deployment in resource-constrained environments. Extensive evaluations demonstrate the effectiveness of DCR-KD, including segmentation performance and feature visualizations. Our method bridges the performance gap between resource-intensive teacher models and efficient student models, providing a practical solution for IoT-based real-time road perception, particularly under limited FoV conditions. © 2014 IEEE.