{"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/ultra-fast-structure-aware-deep-lane","title":"Ultra Fast Structure-aware Deep Lane Detection","arxiv_id":"2004.11757","date":"2020-04-24","proceeding":"ECCV 2020 8","authors":["Zequn Qin","Huanyu Wang","Xi Li"],"abstract":"Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed. Inspired by human perception, the recognition of lanes under severe occlusion and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at extremely fast speed and challenging scenarios. Specifically, we treat the process of lane detection as a row-based selecting problem using global features. With the help of row-based selecting, our formulation could significantly reduce the computational cost. Using a large receptive field on global features, we could also handle the challenging scenarios. Moreover, based on the formulation, we also propose a structural loss to explicitly model the structure of lanes. Extensive experiments on two lane detection benchmark datasets show that our method could achieve the state-of-the-art performance in terms of both speed and accuracy. A light-weight version could even achieve 300+ frames per second with the same resolution, which is at least 4x faster than previous state-of-the-art methods. Our code will be made publicly available.","url_abs":"https://arxiv.org/abs/2004.11757v4","url_pdf":"https://arxiv.org/pdf/2004.11757v4.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":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/cfzd/Ultra-Fast-Lane-Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/Huangdebo/YOLOv4-MultiTask","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/markus-k/ultrafast-lane-detection-tf2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/xiya888/lane_detect_convert","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/Turoad/lanedet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/ibaiGorordo/TfLite-Ultra-Fast-Lane-Detection-Inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/ibaiGorordo/onnx-Ultra-Fast-Lane-Detection-Inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/zkyntu/UnLanedet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/zkyseu/FlowLane","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ultra-fast-structure-aware-deep-lane","repo_url":"https://github.com/zkyseu/PPlanedet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"lane-detection","task_name":"Lane Detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset":"CULane","model":"ResNet34-UFAST","rank_in_archive_order":56,"of":63,"metrics":{"F1 score":"72.3"},"uses_additional_data":false},{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset":"CULane","model":"ResNet18-UFAST","rank_in_archive_order":63,"of":63,"metrics":{"F1 score":"68.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2004.11757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.11757"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Turoad/lanedet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zkyseu/PPlanedet","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ibaiGorordo/TfLite-Ultra-Fast-Lane-Detection-Inference","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ibaiGorordo/onnx-Ultra-Fast-Lane-Detection-Inference","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Huangdebo/YOLOv4-MultiTask","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xiya888/lane_detect_convert","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zkyseu/FlowLane","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zkyntu/UnLanedet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/markus-k/ultrafast-lane-detection-tf2","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cfzd/Ultra-Fast-Lane-Detection","reach":{"status":"unanswered"}}],"summary":{"ran_honours":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"ec338718ed4c4fe1","entry":"get_yolo_layers","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"ec338718ed4c4fe1"}},{"code_sha256_prefix":"388f89a26fdf9028","entry":"inference","repo":"Huangdebo/YOLOv4-MultiTask","repo_kind":"listed","path":"models.py","file_url":"https://github.com/Huangdebo/YOLOv4-MultiTask/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"388f89a26fdf9028"}},{"code_sha256_prefix":"5959383c642fb36b","entry":"make_divisible","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"5959383c642fb36b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}