{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T17:39:19Z","timestamp":1726249159808},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Program of Henan Province","award":["232102221032","232102221011"]},{"name":"Natural Science Foundation of China","award":["62293510","62103076","62003312"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model\u2019s understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-_target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects.<\/jats:p>","DOI":"10.3390\/info15040206","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T10:04:58Z","timestamp":1712570698000},"page":"206","source":"Crossref","is-referenced-by-count":2,"title":["Insulator Defect Detection Based on YOLOv8s-SwinT"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhendong","family":"He","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Wenbin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Yanjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Sinohydro Bureau 3 Co., Ltd., Xi\u2019an 710024, China"}]},{"given":"Anping","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Taishan","family":"Lou","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/TII.2021.3073685","article-title":"Designing an automatic detector device to diagnose insulator state on overhead distribution lines","volume":"18","author":"Palangar","year":"2021","journal-title":"IEEE Trans. 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