Rail detection, essential for railroad anomaly detection, aims to identify the railroad region in video frames. Although various studies on rail detection exist, neither an open benchmark nor a high-speed network is available in the community, making algorithm comparison and development difficult. Inspired by the growth of lane detection, we propose a rail database and a row-based rail detection method. In detail, we make several contributions: (i) We present a real-world railway dataset, Rail-DB, with 7432 pairs of images and annotations. The images are collected from different situations in lighting, road structures, and views. The rails are labeled with polylines, and the images are categorized into nine scenes. The Rail-DB is expected to facilitate the improvement of rail detection algorithms. (ii) We present an efficient row-based rail detection method, Rail-Net, containing a lightweight convolutional backbone and an anchor classifier. Specifically, we formulate the process of rail detection as a row-based selecting problem. This strategy reduces the computational cost compared to alternative segmentation methods. (iii) We evaluate the Rail-Net on Rail-DB with extensive experiments, including cross-scene settings and network backbones ranging from ResNet to Vision Transformers. Our method achieves promising performance in terms of both speed and accuracy. Notably, a lightweight version could achieve 92.77% accuracy and 312 frames per second. The Rail-Net outperforms the traditional method by 50.65% and the segmentation one by 5.86%. The database and code are available at: https://github.com/Sampson-Lee/Rail-Detection.

翻译：铁轨检测是铁路异常检测的关键任务，旨在识别视频帧中的铁路区域。尽管已有若干关于铁轨检测的研究，但学术界既没有公开的基准数据集，也缺乏高速检测网络，导致算法比较与开发面临困难。受车道检测发展的启发，我们提出一个铁轨数据库及一种基于行结构的铁轨检测方法。具体而言，我们做出以下贡献：（i）提出真实世界铁路数据集Rail-DB，包含7432对图像与标注，图像采集自不同光照、道路结构及视角场景，铁轨以折线标注，图像按九类场景分类。Rail-DB有望推动铁轨检测算法的改进。（ii）提出高效的行结构铁轨检测方法Rail-Net，包含轻量化卷积骨干网络与锚点分类器。具体地，我们将铁轨检测过程建模为基于行结构的选择问题，该策略相较于替代性分割方法降低了计算成本。（iii）在Rail-DB上对Rail-Net进行跨场景设置及从ResNet到Vision Transformers不同骨干网络的全面实验评估。我们的方法在速度与精度方面均取得良好表现。值得注意的是，轻量版可实现92.77%的准确率与312帧/秒的处理速度。Rail-Net相较于传统方法性能提升50.65%，较分割方法提升5.86%。数据库与代码见：https://github.com/Sampson-Lee/Rail-Detection。