{"url":"/dataset/curvelanes","name":"CurveLanes","full_name":null,"description_markdown":"CurveLanes is a new benchmark lane detection dataset with 150K lanes images for difficult scenarios such as curves and multi-lanes in traffic lane detection. It is collected in real urban and highway scenarios in multiple cities in China. It is the largest lane detection dataset so far and establishes a more challenging benchmark for the community.\r\n\r\nWe separate the whole dataset 150K into three parts: train:100K, val: 20K and testing: 30K. The resolution of most images in this dataset is 2650×1440.\r\n\r\nFor each image, we manually annotate all lanes in image with natural cubic splines. All images are carefully selected so that most of them image contains at least one curve lane. More difficult scenarios such as S-curves, Y-lanes, night and multi-lanes (the number of lane lines is more than 4) can be found in this dataset.","description_withheld":null,"homepage":"https://github.com/SoulmateB/CurveLanes","introduced_date":"2020-07-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/curvelane-nas-unifying-lane-sensitive","title":"CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending","first_author":"Hang Xu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Lane Detection","url":"/task/lane-detection","datasets_with_task":"/datasets/task/lane-detection"}],"languages":[],"variants":["CurveLanes"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lane-detection-on-curvelanes","task":"Lane Detection","dataset_variant":"CurveLanes","rows":19,"metrics":["F1 score","GFLOPs","Precision","Recall","FPS"],"first_row_in_archive_order":{"model":"CondLSTR (ResNet-101)","paper":"/paper/generating-dynamic-kernels-via-transformers","metrics":{"F1 score":"88.47"},"code_links":[{"title":"czyczyyzc/CondLSTR","url":"https://github.com/czyczyyzc/CondLSTR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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":1,"code_links":0,"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":2,"code_links":2,"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":4,"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/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/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":6,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}