{"url":"/dataset/insplad","name":"InsPLAD","full_name":"Inspection Power Line Asset Dataset","description_markdown":"InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them). \r\n\r\nThree image-level computer vision tasks covered by InsPLAD:\r\n\r\n* Object detection, evaluated through the AP metric\r\n\r\n* Defect classification, evaluated through Balanced Accuracy\r\n\r\n* Anomaly detection,  evaluated through the AUROC metric","description_withheld":null,"homepage":"https://github.com/andreluizbvs/InsPLAD","introduced_date":"2023-11-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/insplad-a-dataset-and-benchmark-for-power","title":"InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images","first_author":"André Luiz Buarque Vieira e Silva","url":null},"license":{"name":"CC BY NC 3.0","url":"https://github.com/andreluizbvs/InsPLAD/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["InsPLAD"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-insplad","task":"Anomaly Detection","dataset_variant":"InsPLAD","rows":5,"metrics":["Detection AUROC"],"first_row_in_archive_order":{"model":"AttentDifferNet (SENet-AlexNet)","paper":"/paper/attention-modules-improve-image-level-anomaly","metrics":{"Detection AUROC":"94.34"},"code_links":[{"title":"andreluizbvs/insplad","url":"https://github.com/andreluizbvs/insplad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/attention-modules-improve-image-level-anomaly","title":"Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study","date":"2023-11-05","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-reverse-distillation-for-anomaly","title":"Revisiting Reverse Distillation for Anomaly Detection","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/same-same-but-differnet-semi-supervised","title":"Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows","date":"2020-08-28","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}