{"url":"/task/insulator-defect-detection","name":"Insulator Defect Detection","slug":"insulator-defect-detection","description_markdown":"In the Insulator Defect Detection task, the primary objective is to automatically detect defects in electrical insulators using computer vision and machine learning techniques. Insulators are critical components in power transmission systems, responsible for supporting and isolating high-voltage power lines to prevent leakage and short circuits. Over time, insulators may develop defects such as cracks, contamination, and breakage due to exposure to harsh environmental conditions like wind, rain, dirt, and high temperatures. If these defects are not detected and addressed promptly, they can lead to power system failures or even severe accidents. Therefore, automating the detection of insulator defects enhances the safety and reliability of power systems while reducing the workload and cost associated with manual inspections. This task typically involves analyzing image or video data to accurately identify and locate various types of defects on insulators.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":5,"papers_with_code":3,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[{"url":"/task/2d-tiny-object-detection","name":"2D Tiny Object Detection"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":3,"of":3,"tagged_in_all":5,"items":[{"url":"/paper/a-lightweight-insulator-defect-detection","title":"A Lightweight Insulator Defect Detection Model Based on Drone Images","date":"2024-08-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/idd-yolov5-a-lightweight-insulator-defect","title":"IDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection Algorithm","date":"2024-08-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/liteyolo-id-a-lightweight-object-detection","title":"LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection","date":"2024-06-24","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}