{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:42:26Z","timestamp":1772642546060,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61962031"],"award-info":[{"award-number":["61962031"]}]},{"name":"National Natural Science Foundation of China","award":["51667011"],"award-info":[{"award-number":["51667011"]}]},{"name":"National Natural Science Foundation of China","award":["2018FB095"],"award-info":[{"award-number":["2018FB095"]}]},{"name":"Applied Basic Research Project of Yunnan province","award":["61962031"],"award-info":[{"award-number":["61962031"]}]},{"name":"Applied Basic Research Project of Yunnan province","award":["51667011"],"award-info":[{"award-number":["51667011"]}]},{"name":"Applied Basic Research Project of Yunnan province","award":["2018FB095"],"award-info":[{"award-number":["2018FB095"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the pre-part of tasks such as fault detection and line inspection, insulator detection is a crucial task. However, considering the complex environment of high-voltage transmission lines, the traditional insulator detection accuracy is unsatisfactory, and manual inspection is dangerous and inefficient. To improve this situation, this paper proposes an insulator detection model Siamese ID-YOLO based on a deep neural network. The model achieves the best balance between speed and accuracy compared with traditional detection methods. In order to achieve the purpose of image enhancement, this paper adopts the canny-based edge detection operator to highlight the edges of insulators to obtain more semantic information. In this paper, based on the Darknet53 network and Siamese network, the insulator original image and the edge image are jointly input into the model. Siamese IN-YOLO model achieves more fine-grained extraction of insulators through weight sharing between Siamese networks, thereby improving the detection accuracy of insulators. This paper uses statistical clustering analysis on the area and aspect ratio of the insulator data set, then pre-set and adjusts the hyperparameters of the model anchor box to make it more suitable for the insulator detection task. In addition, this paper makes an insulator dataset named InsuDaSet based on UAV(Unmanned Aerial Vehicle) shoot insulator images for model training. The experiments show that the insulator detection can reach 92.72% detection accuracy and 84FPS detection speed, which can fully meet the online insulator detection requirements.<\/jats:p>","DOI":"10.3390\/rs14205153","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Detection of Glass Insulators Using Deep Neural Networks Based on Optical Imaging"],"prefix":"10.3390","volume":"14","author":[{"given":"Jinyu","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China"}]},{"given":"Yingna","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Computer Technology Application Key Lab of the Yunnan Province, Kunming 650500, China"}]},{"given":"Wenxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Computer Technology Application Key Lab of the Yunnan Province, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5242","DOI":"10.1109\/TII.2021.3123107","article-title":"Arbitrary-Oriented Detection of Insulators in Thermal Imagery via Rotation Region Network","volume":"18","author":"Zheng","year":"2022","journal-title":"IEEE Trans. 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