SGPLane: Efficient lane detection via sampled grid points for autonomous driving

Abstract

Lane detection is one of the critical tasks for autonomous driving. Earlier works revolved around semantic segmentation and object detection with a special program for lanes. However, most methods still suffer from unstable post-processing algorithms which leads to a gap between camera input and downstream applications. In this paper, we propose a novel detection presentation form for lanes and design a simple network without any complicated post-process. Specifically, we use sampled gird points to express lane lines and construct a network for the special lane format, which is called SGPLane. Therefore, the network learns a regression branch and a confidence branch to realize end-to-end lane detection by setting the threshold confidence value. Our model is validated on the typical dataset and real-world driving scenes. Experiments on lane detection benchmarks show that our method outperforms previous methods with accuracy score of 96.84% on Tusimple dataset with high FPS and 76.85% on our real-world dataset.