SLSDNet: A Real‐Time Stop‐Line Detection Network Integrating the Line Segment Detection Method

Stop‐line detection aids autonomous driving systems in accurately determining vehicle position and driving status. Existing methods typically rely on bounding boxes, failing to capture stop‐line shape. Complex road conditions, such as deteriorated markings or intense lighting, challenge these methods’ detection robustness. To address the above problems, we propose a novel representation approach for stop‐lines that balances high precision with real‐time detection requirements. Inspired by the prevalence of line features in road markings, we propose SLSDNet—a stop‐line segment detection network that fuses image data with line features to prioritise line‐rich regions for detection. Furthermore, we employ a multi‐task learning scheme to extract stop‐line features across multiple dimensions and incorporate a verification mechanism to ensure robust performance. In addition, to address the lack of a stop‐line dataset, we collected images from multiple sources and published our stop‐line dataset at https://github.com/ChengkangLiu/Stop‐Line‐Dataset . Experimental results demonstrate that our method achieves the best F1‐score (97.02) and PR‐AUC (0.9684), outperforming state‐of‐the‐art methods. In terms of efficiency, our method achieves real‐time operation speed at 109 FPS with 4.41 M parameters, capable of running on devices with limited computing resources.