2020/08/19 by Seokwoo Jung, Jung, Seokwoo, Sungha Choi +5 · 1 citation
Computer Science · Engineering · Environmental Science · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.2008.08311
Preprint - work in progress
openalex publication_date 2020/08/19 · arxiv created 2020/08/27 · arxiv updated 2020/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A number of lane detection methods depend on a proposal-free instance segmentation because of its adaptability to flexible object shape, occlusion, and real-time application. This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize. A translation invariance of convolution, which is one of the supposed strengths, causes challenges in optimizing pixel embedding. In this work, we propose a lane detection method based on proposal-free instance segmentation, directly optimizing spatial embedding of pixels using image coordinate. Our proposed method allows the post-processing step for center localization and optimizes clustering in an end-to-end manner. The proposed method enables real-time lane detection through the simplicity of post-processing and the adoption of a lightweight backbone. Our proposed method demonstrates competitive performance on public lane detection datasets.