  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 https://github.com/Yzichen/mmLaneDet.
