{"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/190503704","title":"Agnostic Lane Detection","arxiv_id":"1905.03704","date":"2019-05-02","proceeding":null,"authors":["Yuenan Hou"],"abstract":"Lane detection is an important yet challenging task in autonomous driving,\nwhich is affected by many factors, e.g., light conditions, occlusions caused by\nother vehicles, irrelevant markings on the road and the inherent long and thin\nproperty of lanes. Conventional methods typically treat lane detection as a\nsemantic segmentation task, which assigns a class label to each pixel of the\nimage. This formulation heavily depends on the assumption that the number of\nlanes is pre-defined and fixed and no lane changing occurs, which does not\nalways hold. To make the lane detection model applicable to an arbitrary number\nof lanes and lane changing scenarios, we adopt an instance segmentation\napproach, which first differentiates lanes and background and then classify\neach lane pixel into each lane instance. Besides, a multi-task learning\nparadigm is utilized to better exploit the structural information and the\nfeature pyramid architecture is used to detect extremely thin lanes. Three\npopular lane detection benchmarks, i.e., TuSimple, CULane and BDD100K, are used\nto validate the effectiveness of our proposed algorithm.","url_abs":"http://arxiv.org/abs/1905.03704v1","url_pdf":"http://arxiv.org/pdf/1905.03704v1.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset":"CULane","model":"ENet-Label","rank_in_archive_order":62,"of":63,"metrics":{"F1 score":"68.8"},"uses_additional_data":false},{"leaderboard":"/sota/lane-detection-on-tusimple","task":"Lane Detection","dataset":"TuSimple","model":"ENet-Label","rank_in_archive_order":21,"of":43,"metrics":{"Accuracy":"96.29%","F1 score":"95.23"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}