{"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/efficient-road-lane-marking-detection-with","title":"Efficient Road Lane Marking Detection with Deep Learning","arxiv_id":"1809.03994","date":"2018-09-11","proceeding":null,"authors":["Ping-Rong Chen","Shao-Yuan Lo","Hsueh-Ming Hang","Sheng-Wei Chan","Jing-Jhih Lin"],"abstract":"Lane mark detection is an important element in the road scene analysis for\nAdvanced Driver Assistant System (ADAS). Limited by the onboard computing\npower, it is still a challenge to reduce system complexity and maintain high\naccuracy at the same time. In this paper, we propose a Lane Marking Detector\n(LMD) using a deep convolutional neural network to extract robust lane marking\nfeatures. To improve its performance with a target of lower complexity, the\ndilated convolution is adopted. A shallower and thinner structure is designed\nto decrease the computational cost. Moreover, we also design post-processing\nalgorithms to construct 3rd-order polynomial models to fit into the curved\nlanes. Our system shows promising results on the captured road scenes.","url_abs":"http://arxiv.org/abs/1809.03994v1","url_pdf":"http://arxiv.org/pdf/1809.03994v1.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":"deep-learning","task_name":"Deep Learning"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"real-time-semantic-segmentation","task_name":"Real-Time Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/real-time-semantic-segmentation-on-camvid","task":"Real-Time Semantic Segmentation","dataset":"CamVid","model":"LMDNet","rank_in_archive_order":26,"of":29,"metrics":{"Frame (fps)":"34.4 (1080)","Time (ms)":"29.1","mIoU":"63.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-camvid","task":"Semantic Segmentation","dataset":"CamVid","model":"LMDNet","rank_in_archive_order":17,"of":21,"metrics":{"Mean IoU":"63.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}