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: this https URL

本研究提出了一个包括铁路数据库和轨道行排列方法的高效铁路检测算法，通过基于行排列的选择方法，使用轻量卷积神经网络和锚定分类器实现，经实验表明，这种方式比传统方法高效且准确。