DALNet: A Rail Detection Network Based on Dynamic Anchor Line

TL;DR

DALNet introduces a dynamic anchor line mechanism for rail detection, improving localization accuracy by generating instance-specific anchors, and provides a new diverse urban rail dataset to advance research in this area.

Contribution

The paper proposes DALNet with a novel dynamic anchor line mechanism and introduces the DL-Rail dataset for urban rail detection.

Findings

DALNet achieves state-of-the-art performance on DL-Rail, Tusimple, and LLAMAS datasets.

Dynamic anchor lines improve rail localization accuracy.

DL-Rail dataset enhances urban rail detection research.

Abstract

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.