{"url":"/sota/lane-detection-on-culane","task":{"name":"Lane Detection","url":"/task/lane-detection","note":null},"dataset":{"name":"CULane","url":"/dataset/culane"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Lane Detection** is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts. \r\n\r\nLane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [End-to-end Lane Detection\r\n](https://github.com/wvangansbeke/LaneDetection_End2End) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1 score","mF1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 score":"higher","mF1":null}},"counts":{"rows":63,"rows_with_code":50,"rows_with_paper_page":63,"rows_dated":63,"rows_using_additional_data":3},"rows":[{"rank_in_archive_order":1,"model":"DLNet","metrics":{"F1 score":"81.23"},"uses_additional_data":false,"paper_date":"2025-06-09","paper":"/paper/dlnet-direction-aware-feature-integration-for","paper_url":"https://github.com/RDXiaoLu/DLNet.git","paper_title":"DLNet: Direction-Aware Feature Integration for Robust Lane Detection in Complex Environments","code":"https://github.com/RDXiaoLu/DLNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"CLRerNet-DLA34","metrics":{"F1 score":"81.12"},"uses_additional_data":false,"paper_date":"2023-05-15","paper":"/paper/clrernet-improving-confidence-of-lane","paper_url":"https://arxiv.org/abs/2305.08366v1","paper_title":"CLRerNet: Improving Confidence of Lane Detection with LaneIoU","code":"https://github.com/hirotomusiker/clrernet","n_code_links":2,"syntology":null},{"rank_in_archive_order":3,"model":"CLRerNet-Res101","metrics":{"F1 score":"80.91"},"uses_additional_data":false,"paper_date":"2023-05-15","paper":"/paper/clrernet-improving-confidence-of-lane","paper_url":"https://arxiv.org/abs/2305.08366v1","paper_title":"CLRerNet: Improving Confidence of Lane Detection with LaneIoU","code":"https://github.com/hirotomusiker/clrernet","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"CondLSTR(ResNet-101)","metrics":{"F1 score":"80.77"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/generating-dynamic-kernels-via-transformers","paper_url":"http://openaccess.thecvf.com//content/ICCV2023/html/Chen_Generating_Dynamic_Kernels_via_Transformers_for_Lane_Detection_ICCV_2023_paper.html","paper_title":"Generating Dynamic Kernels via Transformers for Lane Detection","code":"https://github.com/czyczyyzc/CondLSTR","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"CLRerNet-Res34","metrics":{"F1 score":"80.76"},"uses_additional_data":false,"paper_date":"2023-05-15","paper":"/paper/clrernet-improving-confidence-of-lane","paper_url":"https://arxiv.org/abs/2305.08366v1","paper_title":"CLRerNet: Improving Confidence of Lane Detection with LaneIoU","code":"https://github.com/hirotomusiker/clrernet","n_code_links":2,"syntology":null},{"rank_in_archive_order":6,"model":"CLRNetV2 (DLA34)","metrics":{"F1 score":"80.68","mF1":"57.27"},"uses_additional_data":false,"paper_date":"2025-03-18","paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","paper_url":"https://ieeexplore.ieee.org/abstract/document/10930685","paper_title":"CLRNetV2: A Faster and Stronger Lane Detector","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"CLRKDNet (DLA-34)","metrics":{"F1 score":"80.68"},"uses_additional_data":false,"paper_date":"2024-05-21","paper":"/paper/clrkdnet-speeding-up-lane-detection-with","paper_url":"https://arxiv.org/abs/2405.12503v1","paper_title":"CLRKDNet: Speeding up Lane Detection with Knowledge Distillation","code":"https://github.com/weiqingq/CLRKDNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"CondLSTR(ResNet-34)","metrics":{"F1 score":"80.55"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/generating-dynamic-kernels-via-transformers","paper_url":"http://openaccess.thecvf.com//content/ICCV2023/html/Chen_Generating_Dynamic_Kernels_via_Transformers_for_Lane_Detection_ICCV_2023_paper.html","paper_title":"Generating Dynamic Kernels via Transformers for Lane Detection","code":"https://github.com/czyczyyzc/CondLSTR","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"CLRNet(DLA-34)","metrics":{"F1 score":"80.47"},"uses_additional_data":false,"paper_date":"2022-03-19","paper":"/paper/clrnet-cross-layer-refinement-network-for","paper_url":"https://arxiv.org/abs/2203.10350v1","paper_title":"CLRNet: Cross Layer Refinement Network for Lane Detection","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"CLRNetV2 (ResNet101)","metrics":{"F1 score":"80.43"},"uses_additional_data":false,"paper_date":"2025-03-18","paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","paper_url":"https://ieeexplore.ieee.org/abstract/document/10930685","paper_title":"CLRNetV2: A Faster and Stronger Lane Detector","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"CondLSTR(ResNet-18)","metrics":{"F1 score":"80.36"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/generating-dynamic-kernels-via-transformers","paper_url":"http://openaccess.thecvf.com//content/ICCV2023/html/Chen_Generating_Dynamic_Kernels_via_Transformers_for_Lane_Detection_ICCV_2023_paper.html","paper_title":"Generating Dynamic Kernels via Transformers for Lane Detection","code":"https://github.com/czyczyyzc/CondLSTR","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"FENetV2","metrics":{"F1 score":"80.19","mF1":"56.17"},"uses_additional_data":false,"paper_date":"2023-12-28","paper":"/paper/fenet-focusing-enhanced-network-for-lane","paper_url":"https://arxiv.org/abs/2312.17163v6","paper_title":"FENet: Focusing Enhanced Network for Lane Detection","code":"https://github.com/hanyangzhong/fenet","n_code_links":1,"syntology":null},{"rank_in_archive_order":13,"model":"FENetV1","metrics":{"F1 score":"80.15","mF1":"56.27"},"uses_additional_data":false,"paper_date":"2023-12-28","paper":"/paper/fenet-focusing-enhanced-network-for-lane","paper_url":"https://arxiv.org/abs/2312.17163v6","paper_title":"FENet: Focusing Enhanced Network for Lane Detection","code":"https://github.com/hanyangzhong/fenet","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"CLRNet(ResNet-101)","metrics":{"F1 score":"80.13"},"uses_additional_data":false,"paper_date":"2022-03-19","paper":"/paper/clrnet-cross-layer-refinement-network-for","paper_url":"https://arxiv.org/abs/2203.10350v1","paper_title":"CLRNet: Cross Layer Refinement Network for Lane Detection","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":15,"model":"CLRmatchNet (Enhancing curved lane, Resnet-101)","metrics":{"F1 score":"80.00"},"uses_additional_data":false,"paper_date":"2023-09-26","paper":"/paper/clrmatchnet-enhancing-curved-lane-detection","paper_url":"https://arxiv.org/abs/2309.15204v2","paper_title":"CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process","code":"https://github.com/sapirkontente/clrmatchnet","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"CLRNetV2 (ResNet34)","metrics":{"F1 score":"79.94"},"uses_additional_data":false,"paper_date":"2025-03-18","paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","paper_url":"https://ieeexplore.ieee.org/abstract/document/10930685","paper_title":"CLRNetV2: A Faster and Stronger Lane Detector","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":17,"model":"CANet-L(ResNet101)","metrics":{"F1 score":"79.86"},"uses_additional_data":false,"paper_date":"2023-04-23","paper":"/paper/canet-curved-guide-line-network-with-adaptive","paper_url":"https://arxiv.org/abs/2304.11546v1","paper_title":"CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":18,"model":"CLRNet(ResNet-34)","metrics":{"F1 score":"79.73"},"uses_additional_data":false,"paper_date":"2022-03-19","paper":"/paper/clrnet-cross-layer-refinement-network-for","paper_url":"https://arxiv.org/abs/2203.10350v1","paper_title":"CLRNet: Cross Layer Refinement Network for Lane Detection","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":19,"model":"CLRNetV2 (ResNet18)","metrics":{"F1 score":"79.68"},"uses_additional_data":false,"paper_date":"2025-03-18","paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","paper_url":"https://ieeexplore.ieee.org/abstract/document/10930685","paper_title":"CLRNetV2: A Faster and Stronger Lane Detector","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":20,"model":"CLRKDNet (ResNet-18)","metrics":{"F1 score":"79.66"},"uses_additional_data":false,"paper_date":"2024-05-21","paper":"/paper/clrkdnet-speeding-up-lane-detection-with","paper_url":"https://arxiv.org/abs/2405.12503v1","paper_title":"CLRKDNet: Speeding up Lane Detection with Knowledge Distillation","code":"https://github.com/weiqingq/CLRKDNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":21,"model":"GANet(ResNet-101)","metrics":{"F1 score":"79.63"},"uses_additional_data":false,"paper_date":"2022-04-15","paper":"/paper/a-keypoint-based-global-association-network","paper_url":"https://arxiv.org/abs/2204.07335v1","paper_title":"A Keypoint-based Global Association Network for Lane Detection","code":"https://github.com/wolfwjs/ganet","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":4,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":22,"model":"CLRNet(ResNet-18)","metrics":{"F1 score":"79.58"},"uses_additional_data":false,"paper_date":"2022-03-19","paper":"/paper/clrnet-cross-layer-refinement-network-for","paper_url":"https://arxiv.org/abs/2203.10350v1","paper_title":"CLRNet: Cross Layer Refinement Network for Lane Detection","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":23,"model":"CondLaneNet-L(ResNet-101)","metrics":{"F1 score":"79.48"},"uses_additional_data":false,"paper_date":"2021-05-11","paper":"/paper/condlanenet-a-top-to-down-lane-detection","paper_url":"https://arxiv.org/abs/2105.05003v3","paper_title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":24,"model":"GANet(ResNet-34)","metrics":{"F1 score":"79.39"},"uses_additional_data":false,"paper_date":"2022-04-15","paper":"/paper/a-keypoint-based-global-association-network","paper_url":"https://arxiv.org/abs/2204.07335v1","paper_title":"A Keypoint-based Global Association Network for Lane Detection","code":"https://github.com/wolfwjs/ganet","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":4,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":25,"model":"CLRNet - CLLD","metrics":{"F1 score":"79.27"},"uses_additional_data":false,"paper_date":"2023-08-16","paper":"/paper/contrastive-learning-for-lane-detection-via","paper_url":"https://arxiv.org/abs/2308.08242v4","paper_title":"Contrastive Learning for Lane Detection via cross-similarity","code":"https://github.com/zkyntu/UnLanedet","n_code_links":2,"syntology":null},{"rank_in_archive_order":26,"model":"CANet-M","metrics":{"F1 score":"79.16"},"uses_additional_data":false,"paper_date":"2023-04-23","paper":"/paper/canet-curved-guide-line-network-with-adaptive","paper_url":"https://arxiv.org/abs/2304.11546v1","paper_title":"CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":27,"model":"FOLOLane(ERFNet)","metrics":{"F1 score":"78.8"},"uses_additional_data":false,"paper_date":"2021-05-28","paper":"/paper/focus-on-local-detecting-lane-marker-from","paper_url":"https://arxiv.org/abs/2105.13680v1","paper_title":"Focus on Local: Detecting Lane Marker from Bottom Up via Key Point","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":28,"model":"GANet(ResNet-18)","metrics":{"F1 score":"78.79"},"uses_additional_data":false,"paper_date":"2022-04-15","paper":"/paper/a-keypoint-based-global-association-network","paper_url":"https://arxiv.org/abs/2204.07335v1","paper_title":"A Keypoint-based Global Association Network for Lane Detection","code":"https://github.com/wolfwjs/ganet","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":4,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":29,"model":"CondLaneNet-M(ResNet-34)","metrics":{"F1 score":"78.74"},"uses_additional_data":false,"paper_date":"2021-05-11","paper":"/paper/condlanenet-a-top-to-down-lane-detection","paper_url":"https://arxiv.org/abs/2105.05003v3","paper_title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":30,"model":"CLRNetV2 (ResNet18-lite)","metrics":{"F1 score":"78.66"},"uses_additional_data":false,"paper_date":"2025-03-18","paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","paper_url":"https://ieeexplore.ieee.org/abstract/document/10930685","paper_title":"CLRNetV2: A Faster and Stronger Lane Detector","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":31,"model":"CANet-S(ResNet18)","metrics":{"F1 score":"78.46"},"uses_additional_data":false,"paper_date":"2023-04-23","paper":"/paper/canet-curved-guide-line-network-with-adaptive","paper_url":"https://arxiv.org/abs/2304.11546v1","paper_title":"CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":32,"model":"CondLaneNet-S(ResNet-18)","metrics":{"F1 score":"78.14"},"uses_additional_data":false,"paper_date":"2021-05-11","paper":"/paper/condlanenet-a-top-to-down-lane-detection","paper_url":"https://arxiv.org/abs/2105.05003v3","paper_title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":33,"model":"AtrousFormer(ResNet-34)","metrics":{"F1 score":"78.08"},"uses_additional_data":false,"paper_date":"2022-03-08","paper":"/paper/lane-detection-with-versatile-atrousformer","paper_url":"https://arxiv.org/abs/2203.04067v1","paper_title":"Lane Detection with Versatile AtrousFormer and Local Semantic Guidance","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"O2SFormer(ResNet50)","metrics":{"F1 score":"78.0"},"uses_additional_data":false,"paper_date":"2023-05-01","paper":"/paper/end-to-end-lane-detection-with-one-to-several","paper_url":"https://arxiv.org/abs/2305.00675v4","paper_title":"End-to-End Lane detection with One-to-Several Transformer","code":"https://github.com/zkyseu/PPlanedet","n_code_links":3,"syntology":null},{"rank_in_archive_order":35,"model":"AtrousFormer(ResNet-18)","metrics":{"F1 score":"77.63"},"uses_additional_data":false,"paper_date":"2022-03-08","paper":"/paper/lane-detection-with-versatile-atrousformer","paper_url":"https://arxiv.org/abs/2203.04067v1","paper_title":"Lane Detection with Versatile AtrousFormer and Local Semantic Guidance","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":36,"model":"LaneAF (DLA-34)","metrics":{"F1 score":"77.41"},"uses_additional_data":true,"paper_date":"2021-03-22","paper":"/paper/laneaf-robust-multi-lane-detection-with","paper_url":"https://arxiv.org/abs/2103.12040v4","paper_title":"LaneAF: Robust Multi-Lane Detection with Affinity Fields","code":"https://github.com/sel118/LaneAF","n_code_links":1,"syntology":null},{"rank_in_archive_order":37,"model":"SGNet (ResNet-34)","metrics":{"F1 score":"77.27"},"uses_additional_data":false,"paper_date":"2021-05-12","paper":"/paper/structure-guided-lane-detection","paper_url":"https://arxiv.org/abs/2105.05403v2","paper_title":"Structure Guided Lane Detection","code":"https://github.com/Jinming-Su/SGNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":38,"model":"Eigenlanes (ResNet-50)","metrics":{"F1 score":"77.2"},"uses_additional_data":false,"paper_date":"2022-03-29","paper":"/paper/eigenlanes-data-driven-lane-descriptors-for","paper_url":"https://arxiv.org/abs/2203.15302v1","paper_title":"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes","code":"https://github.com/dongkwonjin/eigenlanes","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":12,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":39,"model":"LaneATT (ResNet-122)","metrics":{"F1 score":"77.02"},"uses_additional_data":false,"paper_date":"2020-10-22","paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","paper_url":"https://arxiv.org/abs/2010.12035v2","paper_title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","code":"https://github.com/lucastabelini/LaneATT","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":40,"model":"LaneATT (ResNet-34)","metrics":{"F1 score":"76.68"},"uses_additional_data":false,"paper_date":"2020-10-22","paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","paper_url":"https://arxiv.org/abs/2010.12035v2","paper_title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","code":"https://github.com/lucastabelini/LaneATT","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":41,"model":"Eigenlanes (ResNet-18)","metrics":{"F1 score":"76.5"},"uses_additional_data":false,"paper_date":"2022-03-29","paper":"/paper/eigenlanes-data-driven-lane-descriptors-for","paper_url":"https://arxiv.org/abs/2203.15302v1","paper_title":"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes","code":"https://github.com/dongkwonjin/eigenlanes","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":12,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":42,"model":"RESA - CLLD","metrics":{"F1 score":"76.26"},"uses_additional_data":false,"paper_date":"2023-08-16","paper":"/paper/contrastive-learning-for-lane-detection-via","paper_url":"https://arxiv.org/abs/2308.08242v4","paper_title":"Contrastive Learning for Lane Detection via cross-similarity","code":"https://github.com/zkyntu/UnLanedet","n_code_links":2,"syntology":null},{"rank_in_archive_order":43,"model":"LaneAF (ERFNet)","metrics":{"F1 score":"75.63"},"uses_additional_data":false,"paper_date":"2021-03-22","paper":"/paper/laneaf-robust-multi-lane-detection-with","paper_url":"https://arxiv.org/abs/2103.12040v4","paper_title":"LaneAF: Robust Multi-Lane Detection with Affinity Fields","code":"https://github.com/sel118/LaneAF","n_code_links":1,"syntology":null},{"rank_in_archive_order":44,"model":"BézierLaneNet (ResNet-34)","metrics":{"F1 score":"75.57"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/rethinking-efficient-lane-detection-via-curve","paper_url":"https://arxiv.org/abs/2203.02431v2","paper_title":"Rethinking Efficient Lane Detection via Curve Modeling","code":"https://github.com/voldemortX/pytorch-auto-drive","n_code_links":2,"syntology":{"n_ran":0,"n_unverified":12,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":45,"model":"RESA","metrics":{"F1 score":"75.3"},"uses_additional_data":false,"paper_date":"2020-08-31","paper":"/paper/resa-recurrent-feature-shift-aggregator-for","paper_url":"https://arxiv.org/abs/2008.13719v2","paper_title":"RESA: Recurrent Feature-Shift Aggregator for Lane Detection","code":"https://github.com/Turoad/lanedet","n_code_links":4,"syntology":null},{"rank_in_archive_order":46,"model":"LaneATT (ResNet-18)","metrics":{"F1 score":"75.13"},"uses_additional_data":false,"paper_date":"2020-10-22","paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","paper_url":"https://arxiv.org/abs/2010.12035v2","paper_title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","code":"https://github.com/lucastabelini/LaneATT","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":47,"model":"CurveLane-L","metrics":{"F1 score":"74.8"},"uses_additional_data":false,"paper_date":"2020-07-23","paper":"/paper/curvelane-nas-unifying-lane-sensitive","paper_url":"https://arxiv.org/abs/2007.12147v1","paper_title":"CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending","code":"https://github.com/huawei-noah/vega","n_code_links":1,"syntology":null},{"rank_in_archive_order":48,"model":"PINet","metrics":{"F1 score":"74.4"},"uses_additional_data":true,"paper_date":"2020-02-16","paper":"/paper/key-points-estimation-and-point-instance","paper_url":"https://arxiv.org/abs/2002.06604v4","paper_title":"Key Points Estimation and Point Instance Segmentation Approach for Lane Detection","code":"https://github.com/koyeongmin/PINet","n_code_links":10,"syntology":{"n_ran":1,"n_unverified":7,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":49,"model":"LaneAF (ENet)","metrics":{"F1 score":"74.24"},"uses_additional_data":false,"paper_date":"2021-03-22","paper":"/paper/laneaf-robust-multi-lane-detection-with","paper_url":"https://arxiv.org/abs/2103.12040v4","paper_title":"LaneAF: Robust Multi-Lane Detection with Affinity Fields","code":"https://github.com/sel118/LaneAF","n_code_links":1,"syntology":null},{"rank_in_archive_order":50,"model":"ERFNet-H&VESA","metrics":{"F1 score":"74.2"},"uses_additional_data":false,"paper_date":"2021-02-14","paper":"/paper/robust-lane-detection-via-expanded-self","paper_url":"https://arxiv.org/abs/2102.07037v3","paper_title":"Robust Lane Detection via Expanded Self Attention","code":"https://github.com/Hydragon516/ESA-official","n_code_links":1,"syntology":null},{"rank_in_archive_order":51,"model":"SwiftLane","metrics":{"F1 score":"74.03"},"uses_additional_data":false,"paper_date":"2021-10-22","paper":"/paper/swiftlane-towards-fast-and-efficient-lane","paper_url":"https://arxiv.org/abs/2110.11779v1","paper_title":"SwiftLane: Towards Fast and Efficient Lane Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":52,"model":"ERFNet-E2E","metrics":{"F1 score":"74"},"uses_additional_data":false,"paper_date":"2020-05-06","paper":"/paper/end-to-end-lane-marker-detection-via-row-wise","paper_url":"https://arxiv.org/abs/2005.08630v1","paper_title":"End-to-End Lane Marker Detection via Row-wise Classification","code":"https://github.com/Vipermdl/E2E-ERFNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":53,"model":"BézierLaneNet (ResNet-18)","metrics":{"F1 score":"73.67"},"uses_additional_data":false,"paper_date":"2022-03-04","paper":"/paper/rethinking-efficient-lane-detection-via-curve","paper_url":"https://arxiv.org/abs/2203.02431v2","paper_title":"Rethinking Efficient Lane Detection via Curve Modeling","code":"https://github.com/voldemortX/pytorch-auto-drive","n_code_links":2,"syntology":{"n_ran":0,"n_unverified":12,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":54,"model":"CurveLane-M","metrics":{"F1 score":"73.5"},"uses_additional_data":false,"paper_date":"2020-07-23","paper":"/paper/curvelane-nas-unifying-lane-sensitive","paper_url":"https://arxiv.org/abs/2007.12147v1","paper_title":"CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending","code":"https://github.com/huawei-noah/vega","n_code_links":1,"syntology":null},{"rank_in_archive_order":55,"model":"ERFNet-IntRA-KD (ours)","metrics":{"F1 score":"72.4"},"uses_additional_data":false,"paper_date":"2020-04-11","paper":"/paper/inter-region-affinity-distillation-for-road","paper_url":"https://arxiv.org/abs/2004.05304v1","paper_title":"Inter-Region Affinity Distillation for Road Marking Segmentation","code":"https://github.com/cardwing/Codes-for-IntRA-KD","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":56,"model":"ResNet34-UFAST","metrics":{"F1 score":"72.3"},"uses_additional_data":false,"paper_date":"2020-04-24","paper":"/paper/ultra-fast-structure-aware-deep-lane","paper_url":"https://arxiv.org/abs/2004.11757v4","paper_title":"Ultra Fast Structure-aware Deep Lane Detection","code":"https://github.com/cfzd/Ultra-Fast-Lane-Detection","n_code_links":10,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":57,"model":"ResNet-101-E2E","metrics":{"F1 score":"71.9"},"uses_additional_data":false,"paper_date":"2020-05-06","paper":"/paper/end-to-end-lane-marker-detection-via-row-wise","paper_url":"https://arxiv.org/abs/2005.08630v1","paper_title":"End-to-End Lane Marker Detection via Row-wise Classification","code":"https://github.com/Vipermdl/E2E-ERFNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":58,"model":"SCNN","metrics":{"F1 score":"71.6"},"uses_additional_data":true,"paper_date":"2017-12-17","paper":"/paper/spatial-as-deep-spatial-cnn-for-traffic-scene","paper_url":"http://arxiv.org/abs/1712.06080v1","paper_title":"Spatial As Deep: Spatial CNN for Traffic Scene Understanding","code":"https://github.com/cardwing/Codes-for-Lane-Detection","n_code_links":9,"syntology":null},{"rank_in_archive_order":59,"model":"CurveLane-S","metrics":{"F1 score":"71.4"},"uses_additional_data":false,"paper_date":"2020-07-23","paper":"/paper/curvelane-nas-unifying-lane-sensitive","paper_url":"https://arxiv.org/abs/2007.12147v1","paper_title":"CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending","code":"https://github.com/huawei-noah/vega","n_code_links":1,"syntology":null},{"rank_in_archive_order":60,"model":"ENet-SAD","metrics":{"F1 score":"70.8"},"uses_additional_data":false,"paper_date":"2019-08-02","paper":"/paper/learning-lightweight-lane-detection-cnns-by","paper_url":"https://arxiv.org/abs/1908.00821v1","paper_title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","code":"https://github.com/cardwing/Codes-for-Lane-Detection","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":6,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":61,"model":"UNet - CLLD","metrics":{"F1 score":"70.56"},"uses_additional_data":false,"paper_date":"2023-08-16","paper":"/paper/contrastive-learning-for-lane-detection-via","paper_url":"https://arxiv.org/abs/2308.08242v4","paper_title":"Contrastive Learning for Lane Detection via cross-similarity","code":"https://github.com/zkyntu/UnLanedet","n_code_links":2,"syntology":null},{"rank_in_archive_order":62,"model":"ENet-Label","metrics":{"F1 score":"68.8"},"uses_additional_data":false,"paper_date":"2019-05-02","paper":"/paper/190503704","paper_url":"http://arxiv.org/abs/1905.03704v1","paper_title":"Agnostic Lane Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":63,"model":"ResNet18-UFAST","metrics":{"F1 score":"68.4"},"uses_additional_data":false,"paper_date":"2020-04-24","paper":"/paper/ultra-fast-structure-aware-deep-lane","paper_url":"https://arxiv.org/abs/2004.11757v4","paper_title":"Ultra Fast Structure-aware Deep Lane Detection","code":"https://github.com/cfzd/Ultra-Fast-Lane-Detection","n_code_links":10,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":22,"rows_with_any_sample_ran":15,"distinct_papers_with_graph_line":10,"distinct_papers_with_any_sample_ran":7,"samples_over_distinct_papers":{"n_ran":13,"n_unverified":55,"n_samples":68,"n_pointer_only_licence":3,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":28,"n_unverified":110,"n_samples":138,"n_pointer_only_licence":6,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}