{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T17:40:39Z","timestamp":1789666839901,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972324"],"award-info":[{"award-number":["61972324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Detecting insulators on a power transmission line is of great importance for the safe operation of power systems. Aiming at the problem of the missed detection and misjudgment of the original feature extraction network VGG16 of a faster region-convolutional neural network (R-CNN) in the face of insulators of different sizes, in order to improve the accuracy of insulators\u2019 detection on power transmission lines, an improved faster R-CNN algorithm is proposed. The improved algorithm replaces the original backbone feature extraction network VGG16 in faster R-CNN with the Resnet50 network with deeper layers and a more complex structure, adding an efficient channel attention module based on the channel attention mechanism. Experimental results show that the feature extraction performance has been effectively improved through the improvement of the backbone feature extraction network. The network model is trained on a training set consisting of 6174 insulator pictures, and is tested on a testing set consisting of 686 pictures. Compared with the traditional faster R-CNN, the mean average precision of the improved faster R-CNN increases to 89.37%, with an improvement of 1.63%.<\/jats:p>","DOI":"10.3390\/a15030083","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T08:17:03Z","timestamp":1646122623000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Detection of Insulators on Power Transmission Line Based on an Improved Faster Region-Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Haijian","family":"Hu","sequence":"first","affiliation":[{"name":"State Grid Sichuan Electric Power Company, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yicen","family":"Liu","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6072-3652","authenticated-orcid":false,"given":"Haina","family":"Rong","sequence":"additional","affiliation":[{"name":"Chengdu Zhonglian Huarui Artificial Intelligence Technology Co., Ltd., Chengdu 610041, China"},{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"ref_1","first-page":"2817","article-title":"Detection of insulators on power transmission line","volume":"4","author":"Birlasekaran","year":"2000","journal-title":"Power Eng. 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