{"url":"/dataset/cctv-pipe","name":"CCTV-Pipe","full_name":null,"description_markdown":"Our CCTV-Pipe dataset consists of 16 defect categories including structural and functional defects in the pipe. It contains 575 videos with 87 hours, which are collected from real-world urban pipe systems. Different from traditional temporal action localization, our goal in this realistic scenario is to find preferable temporal locations of defects from a untrimmed CCTV video, instead of exact temporal boundaries.","description_withheld":null,"homepage":"https://videopipe.github.io/cctvpipe/index.html","introduced_date":"2022-10-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/videopipe-2022-challenge-real-world-video","title":"VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection","first_author":"Yi Liu","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Temporal Defect Localization","url":"/task/temporal-defect-localization","datasets_with_task":"/datasets/task/temporal-defect-localization"}],"languages":[],"variants":["CCTV-Pipe"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}