Rail detection is one of the key factors for intelligent train. In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line. Aiming to solve the problem that the predefined anchor line is image agnostic, we design a novel dynamic anchor line mechanism. It utilizes a dynamic anchor line generator to dynamically generate an appropriate anchor line for each rail instance based on the position and shape of the rails in the input image. These dynamically generated anchor lines can be considered as better position references to accurately localize the rails than the predefined anchor lines. In addition, we present a challenging urban rail detection dataset DL-Rail with high-quality annotations and scenario diversity. DL-Rail contains 7000 pairs of images and annotations along with scene tags, and it is expected to encourage the development of rail detection. We extensively compare DALNet with many competitive lane methods. The results show that our DALNet achieves state-of-the-art performance on our DL-Rail rail detection dataset and the popular Tusimple and LLAMAS lane detection benchmarks. The code will be released at \url{https://github.com/Yzichen/mmLaneDet}.

翻译：铁轨检测是智能列车的关键技术之一。本文受基于锚线的车道检测方法启发，提出了一种基于动态锚线的铁轨检测网络DALNet。针对预定义锚线与图像内容无关的问题，我们设计了一种新颖的动态锚线机制。该机制利用动态锚线生成器，根据输入图像中铁轨的位置和形状，为每个铁轨实例动态生成合适的锚线。这些动态生成的锚线相较于预定义锚线，可作为更精确的定位参考以实现铁轨的准确检测。此外，我们提出了具有高质量标注和场景多样性的城市铁轨检测数据集DL-Rail。该数据集包含7000对图像与标注及场景标签，有望推动铁轨检测领域发展。我们将DALNet与多种先进的车道检测方法进行广泛比较。结果表明，我们的DALNet在DL-Rail铁轨检测数据集以及流行的Tusimple和LLAMAS车道检测基准上均达到了最先进的性能。代码将在\url{https://github.com/Yzichen/mmLaneDet}发布。