MSR-LGM: A Lightweight Graph-Enhanced Module for Robust Lane Detection

Accurate and robust lane detection is critical for autonomous driving systems, especially under complex road conditions and varying visual environments. This paper proposes a novel lane detection framework that integrates a Multi-Scale Residual Lane Graph Module (MSR-LGM) into the Polar R-CNN architecture. MSR-LGM addresses key limitations of conventional graph-based reasoning in lane detection by incorporating three core components: Multi-Scale Node Embedding, Enhanced Edge Encoding, and Feature Preservation via Residual Enhancement. These components collectively enhance lane feature representation, edge connectivity modeling, and learning stability. Experiments conducted on benchmark datasets—CULane—demonstrate that our method achieves state-of-the-art performance, outperforming prior works in accuracy and efficiency while maintaining a lightweight model size. The proposed method shows superior generalization across challenging scenarios such as occlusion, night, shadow, and lane curvature, making it well-suited for real-world autonomous driving applications.