{"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/lane-detection-and-classification-using","title":"Lane Detection and Classification using Cascaded CNNs","arxiv_id":"1907.01294","date":"2019-07-02","proceeding":null,"authors":["Fabio Pizzati","Marco Allodi","Alejandro Barrera","Fernando García"],"abstract":"Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.","url_abs":"https://arxiv.org/abs/1907.01294v2","url_pdf":"https://arxiv.org/pdf/1907.01294v2.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":"lane-detection-and-classification-using","repo_url":"https://github.com/fabvio/Cascade-LD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"lane-detection-and-classification-using","repo_url":"https://github.com/fabvio/TuSimple-lane-classes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[{"slug":"tusimple-lane","name":"TuSimple Lane","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-tusimple","task":"Lane Detection","dataset":"TuSimple","model":"End-to-end ERFNet","rank_in_archive_order":36,"of":43,"metrics":{"Accuracy":"95.24%","F1 score":"90.82"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1907.01294","atlas_url":"https://app.syntology.ai/?focus=1907.01294","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}