{"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/clrkdnet-speeding-up-lane-detection-with","title":"CLRKDNet: Speeding up Lane Detection with Knowledge Distillation","arxiv_id":"2405.12503","date":"2024-05-21","proceeding":null,"authors":["Weiqing Qi","Guoyang Zhao","Fulong Ma","Linwei Zheng","Ming Liu"],"abstract":"Road lanes are integral components of the visual perception systems in intelligent vehicles, playing a pivotal role in safe navigation. In lane detection tasks, balancing accuracy with real-time performance is essential, yet existing methods often sacrifice one for the other. To address this trade-off, we introduce CLRKDNet, a streamlined model that balances detection accuracy with real-time performance. The state-of-the-art model CLRNet has demonstrated exceptional performance across various datasets, yet its computational overhead is substantial due to its Feature Pyramid Network (FPN) and muti-layer detection head architecture. Our method simplifies both the FPN structure and detection heads, redesigning them to incorporate a novel teacher-student distillation process alongside a newly introduced series of distillation losses. This combination reduces inference time by up to 60% while maintaining detection accuracy comparable to CLRNet. This strategic balance of accuracy and speed makes CLRKDNet a viable solution for real-time lane detection tasks in autonomous driving applications.","url_abs":"https://arxiv.org/abs/2405.12503v1","url_pdf":"https://arxiv.org/pdf/2405.12503v1.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":"clrkdnet-speeding-up-lane-detection-with","repo_url":"https://github.com/weiqingq/CLRKDNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"lane-detection","task_name":"Lane Detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"clrnet","method_name":"CLRNet"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset":"CULane","model":"CLRKDNet (DLA-34)","rank_in_archive_order":7,"of":63,"metrics":{"F1 score":"80.68"},"uses_additional_data":false},{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset":"CULane","model":"CLRKDNet (ResNet-18)","rank_in_archive_order":20,"of":63,"metrics":{"F1 score":"79.66"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}