{"url":"/dataset/culane","name":"CULane","full_name":null,"description_markdown":"**CULane** is a large scale challenging dataset for academic research on traffic lane detection. It is collected by cameras mounted on six different vehicles driven by different drivers in Beijing. More than 55 hours of videos were collected and 133,235 frames were extracted. The dataset is divided into 88880 images for training set, 9675 for validation set, and 34680 for test set. The test set is divided into normal and 8 challenging categories.\r\n\r\nSource: [https://xingangpan.github.io/projects/CULane.html](https://xingangpan.github.io/projects/CULane.html)\r\nImage Source: [https://xingangpan.github.io/projects/CULane.html](https://xingangpan.github.io/projects/CULane.html)","description_withheld":null,"homepage":"https://xingangpan.github.io/projects/CULane.html","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/spatial-as-deep-spatial-cnn-for-traffic-scene","title":"Spatial As Deep: Spatial CNN for Traffic Scene Understanding","first_author":"Xingang Pan","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://xingangpan.github.io/projects/CULane.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Lane Detection","url":"/task/lane-detection","datasets_with_task":"/datasets/task/lane-detection"}],"languages":[],"variants":["CULane"],"data_loaders":[],"num_papers_in_archive":85,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lane-detection-on-culane","task":"Lane Detection","dataset_variant":"CULane","rows":63,"metrics":["F1 score","mF1"],"first_row_in_archive_order":{"model":"DLNet","paper":"/paper/dlnet-direction-aware-feature-integration-for","metrics":{"F1 score":"81.23"},"code_links":[{"title":"RDXiaoLu/DLNet","url":"https://github.com/RDXiaoLu/DLNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dlnet-direction-aware-feature-integration-for","title":"DLNet: Direction-Aware Feature Integration for Robust Lane Detection in Complex Environments","date":"2025-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","title":"CLRNetV2: A Faster and Stronger Lane Detector","date":"2025-03-18","rows_on_this_dataset":5,"code_links":0,"syntology":null},{"paper":"/paper/clrkdnet-speeding-up-lane-detection-with","title":"CLRKDNet: Speeding up Lane Detection with Knowledge Distillation","date":"2024-05-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/fenet-focusing-enhanced-network-for-lane","title":"FENet: Focusing Enhanced Network for Lane Detection","date":"2023-12-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/clrmatchnet-enhancing-curved-lane-detection","title":"CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process","date":"2023-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/contrastive-learning-for-lane-detection-via","title":"Contrastive Learning for Lane Detection via cross-similarity","date":"2023-08-16","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/clrernet-improving-confidence-of-lane","title":"CLRerNet: Improving Confidence of Lane Detection with LaneIoU","date":"2023-05-15","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/end-to-end-lane-detection-with-one-to-several","title":"End-to-End Lane detection with One-to-Several Transformer","date":"2023-05-01","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/canet-curved-guide-line-network-with-adaptive","title":"CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection","date":"2023-04-23","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/generating-dynamic-kernels-via-transformers","title":"Generating Dynamic Kernels via Transformers for Lane Detection","date":"2023-01-01","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/a-keypoint-based-global-association-network","title":"A Keypoint-based Global Association Network for Lane Detection","date":"2022-04-15","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/eigenlanes-data-driven-lane-descriptors-for","title":"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes","date":"2022-03-29","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/clrnet-cross-layer-refinement-network-for","title":"CLRNet: Cross Layer Refinement Network for Lane Detection","date":"2022-03-19","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lane-detection-with-versatile-atrousformer","title":"Lane Detection with Versatile AtrousFormer and Local Semantic Guidance","date":"2022-03-08","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/rethinking-efficient-lane-detection-via-curve","title":"Rethinking Efficient Lane Detection via Curve Modeling","date":"2022-03-04","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/swiftlane-towards-fast-and-efficient-lane","title":"SwiftLane: Towards Fast and Efficient Lane Detection","date":"2021-10-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/focus-on-local-detecting-lane-marker-from","title":"Focus on Local: Detecting Lane Marker from Bottom Up via Key Point","date":"2021-05-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/structure-guided-lane-detection","title":"Structure Guided Lane Detection","date":"2021-05-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/condlanenet-a-top-to-down-lane-detection","title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","date":"2021-05-11","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/laneaf-robust-multi-lane-detection-with","title":"LaneAF: Robust Multi-Lane Detection with Affinity Fields","date":"2021-03-22","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/robust-lane-detection-via-expanded-self","title":"Robust Lane Detection via Expanded Self Attention","date":"2021-02-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","date":"2020-10-22","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/resa-recurrent-feature-shift-aggregator-for","title":"RESA: Recurrent Feature-Shift Aggregator for Lane Detection","date":"2020-08-31","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/curvelane-nas-unifying-lane-sensitive","title":"CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending","date":"2020-07-23","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/end-to-end-lane-marker-detection-via-row-wise","title":"End-to-End Lane Marker Detection via Row-wise Classification","date":"2020-05-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/ultra-fast-structure-aware-deep-lane","title":"Ultra Fast Structure-aware Deep Lane Detection","date":"2020-04-24","rows_on_this_dataset":2,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inter-region-affinity-distillation-for-road","title":"Inter-Region Affinity Distillation for Road Marking Segmentation","date":"2020-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/key-points-estimation-and-point-instance","title":"Key Points Estimation and Point Instance Segmentation Approach for Lane Detection","date":"2020-02-16","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-lightweight-lane-detection-cnns-by","title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","date":"2019-08-02","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/190503704","title":"Agnostic Lane Detection","date":"2019-05-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/spatial-as-deep-spatial-cnn-for-traffic-scene","title":"Spatial As Deep: Spatial CNN for Traffic Scene Understanding","date":"2017-12-17","rows_on_this_dataset":1,"code_links":9,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":10,"samples_harvested":68,"samples_ran":13,"samples_unverified":55,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":3,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}