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

Abstract

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.