{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:53:40Z","timestamp":1769752420688,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011710","name":"Shaanxi Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["2023-YBGY-132"],"award-info":[{"award-number":["2023-YBGY-132"]}],"id":[{"id":"10.13039\/501100011710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system\u2019s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset\u2019s optimization to demonstrate the expanded dataset\u2019s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks.<\/jats:p>","DOI":"10.3390\/s23187967","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T03:02:17Z","timestamp":1695092537000},"page":"7967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Insulator Abnormal Condition Detection from Small Data Samples"],"prefix":"10.3390","volume":"23","author":[{"given":"Qian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Zhixuan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8260-0584","authenticated-orcid":false,"given":"Zhirong","family":"Luan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Rong","family":"Shi","sequence":"additional","affiliation":[{"name":"State Grid Shaanxi Electric Power Company Economic Research Institute, Xi\u2019an 710065, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"ref_1","first-page":"123","article-title":"Detection and Recognition of Insulators in Power Distribution Systems Using Morphological Techniques","volume":"45","author":"Tariq","year":"2017","journal-title":"Int. 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