{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-lightweight-lane-detection-by","title":"Towards Lightweight Lane Detection by Optimizing Spatial Embedding","arxiv_id":"2008.08311","date":"2020-08-19","proceeding":"arXiv.org 2020 8","authors":["Seokwoo Jung","Sungha Choi","Mohammad Azam Khan","Jaegul Choo"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2008.08311v2","url_pdf":"https://arxiv.org/pdf/2008.08311v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-lightweight-lane-detection-by","repo_url":"https://github.com/JungSeokWoo/Lightweight-LaneDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-tusimple","task":"Lane Detection","dataset":"TuSimple","model":"HarD-SP","rank_in_archive_order":13,"of":43,"metrics":{"Accuracy":"96.58%","F1 score":"96.38"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2008.08311","atlas_url":"https://app.syntology.ai/?focus=2008.08311","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}