{"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/vpgnet-vanishing-point-guided-network-for","title":"VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition","arxiv_id":"1710.06288","date":"2017-10-17","proceeding":"ICCV 2017 10","authors":["Seokju Lee","Junsik Kim","Jae Shin Yoon","Seunghak Shin","Oleksandr Bailo","Namil Kim","Tae-Hee Lee","Hyun Seok Hong","Seung-Hoon Han","In So Kweon"],"abstract":"In this paper, we propose a unified end-to-end trainable multi-task network\nthat jointly handles lane and road marking detection and recognition that is\nguided by a vanishing point under adverse weather conditions. We tackle rainy\nand low illumination conditions, which have not been extensively studied until\nnow due to clear challenges. For example, images taken under rainy days are\nsubject to low illumination, while wet roads cause light reflection and distort\nthe appearance of lane and road markings. At night, color distortion occurs\nunder limited illumination. As a result, no benchmark dataset exists and only a\nfew developed algorithms work under poor weather conditions. To address this\nshortcoming, we build up a lane and road marking benchmark which consists of\nabout 20,000 images with 17 lane and road marking classes under four different\nscenarios: no rain, rain, heavy rain, and night. We train and evaluate several\nversions of the proposed multi-task network and validate the importance of each\ntask. The resulting approach, VPGNet, can detect and classify lanes and road\nmarkings, and predict a vanishing point with a single forward pass.\nExperimental results show that our approach achieves high accuracy and\nrobustness under various conditions in real-time (20 fps). The benchmark and\nthe VPGNet model will be publicly available.","url_abs":"http://arxiv.org/abs/1710.06288v1","url_pdf":"http://arxiv.org/pdf/1710.06288v1.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":"vpgnet-vanishing-point-guided-network-for","repo_url":"https://github.com/SeokjuLee/VPGNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"vpgnet-vanishing-point-guided-network-for","repo_url":"https://github.com/Ceachi/Project-Self-Driving-Car-Advanced-Lane-Lines","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"vpgnet-vanishing-point-guided-network-for","repo_url":"https://github.com/cciprianmihai/Self_Driving_Car_NanoDegree_P2_AdvancedLaneLines","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"lane-detection","task_name":"Lane Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-caltech-lanes-cordova","task":"Lane Detection","dataset":"Caltech Lanes Cordova","model":"VPGNet","rank_in_archive_order":1,"of":2,"metrics":{"F1":"0.884"},"uses_additional_data":false},{"leaderboard":"/sota/lane-detection-on-caltech-lanes-washington","task":"Lane Detection","dataset":"Caltech Lanes Washington","model":"VPGNet","rank_in_archive_order":1,"of":2,"metrics":{"F1":"0.869"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.06288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.06288"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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