A Comprehensive Framework for Evaluating Vision-Based on-Board Rail Track Detection

Abstract

In this work a CNN-based rail track detection algorithm and two novel evaluation metrics are proposed. Rails define the region of interest for object detection and localization algorithms of railbound vehicles, like lane markings do for automotive driver assistance functions. Looking at the analogies in significance and appearance of both, it becomes apparent that rail and lane marking detection could be solved similarly. Hence, this paper firstly introduces rail detection using an adopted version of PINet, a regression net for lane marking detection. The network is completely re-trained using a novel loss function and our own railway dataset. Secondly, a post-processing approach for clustering the detected rails into tracks using geometric constraints is proposed. Finally, two track detection metrics are introduced: The rail position offset metric (RPOM) and the track centerline offset metric (TCOM), which allow precise assessment of rail and track centerline detection results and can be cornerstones to foster future developments in this area.