Dynamic radius-based intersection-over-union loss function optimization method for rail line detection

Abstract Rail line detection is a crucial technology for ensuring the safety and efficiency of rail transit systems. Despite significant advancements in computer vision and deep learning, existing methods struggle to accurately detect rail lines in distant and curved sections, particularly under complex environmental conditions. To address these limitations, this study proposes a novel dynamic regression loss function, termed Dynamic Radius-Based intersection-over-union Loss (Dynamic RIoU Loss), designed to optimize detection accuracy in challenging scenarios. The proposed dynamic RIoU (DR) loss function integrates a radius-based intersection-over-union metric with a width-based dynamic weighting mechanism, enabling the model to prioritize distant curved regions and adapt to diverse track geometries. Extensive experiments were conducted on the RailSem19 and DL-Rail datasets, where the DR loss function consistently outperformed existing methods, achieving improvements of 2%–4% in detection accuracy and reductions of 5–8 points in regression errors. This study not only advances rail line detection in complex environments but also provides a foundation for enhancing intelligent rail transit systems, contributing to safer and more reliable rail operations.