{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:22Z","timestamp":1777705222554,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>Aiming at the problems of poor accuracy of insulator defects, bird\u2019s nests and foreign objects detection in transmission lines, and the difficulty of algorithm hardware deployment, this paper proposes an improved YOLOv5s multi-hidden target detection algorithm for transmission lines, firstly, in backbone, the CA attention(Coordinate attention) mechanism is integrated into the C3 module to form the C3CA module, which replaces the C3 module of the sixth and the eighth layers, and enhances the feature fusion capability; secondly, in the neck, the GSConv convolution and VoVGSCSP modules are used to replace the standard convolution and C3 modules to form a BiFPN network, which reduces the floating-point operations of the network; finally, the improved algorithm is deployed into Raspberry Pi and accelerated by OpenVINO to realize the hardware deployment of the algorithm, which is demonstrated by experiments that: the mAP value of the algorithm is comparable to that of YOLOv3, YOLOv5 and YOLOv7 by 4.7%, 1.1%, and 1.2%, respectively. The model size is 14.2MB, and the average time to detect an image in Raspberry Pi is 78.2 milliseconds, which meets the real-time detection requirements.<\/jats:p>","DOI":"10.3233\/jifs-234732","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:31:37Z","timestamp":1700220697000},"page":"923-939","source":"Crossref","is-referenced-by-count":2,"title":["Multi-hidden target detection of transmission line based on improved YOLOv5s and its hardware implementation"],"prefix":"10.1177","volume":"46","author":[{"given":"Xu","family":"Shanyong","sequence":"first","affiliation":[{"name":"School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deng","family":"Jicheng","sequence":"additional","affiliation":[{"name":"School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huang","family":"Yourui","sequence":"additional","affiliation":[{"name":"School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan, China"},{"name":"School of Electrical and Opto Electronic Engineering, West Anhui University, Lu\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-234732_ref1","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1049\/ipr2.12031","article-title":"A new recognition algorithm for high-voltage lines based on improved LSD and convolutional neural networks[J]","volume":"15","author":"Luo","year":"2021","journal-title":"IET Image Processing"},{"key":"10.3233\/JIFS-234732_ref2","doi-asserted-by":"crossref","first-page":"108277","DOI":"10.1016\/j.ijepes.2022.108277","article-title":"Key target and defect detection of high-voltage power transmission lines with deep learning[J]","volume":"142","author":"Liu","year":"2022","journal-title":"International Journal of Electrical Power & Energy Systems"},{"issue":"2","key":"10.3233\/JIFS-234732_ref3","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.3233\/JIFS-189353","article-title":"Component identification and defect detection in transmission lines based on deep learning[J]","volume":"40","author":"Zheng","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-234732_ref4","first-page":"3032","article-title":"Research and Prospect of Optical Fiber Sensing of Transmission Line Operation Status[J]","volume":"48","author":"Ma","year":"2022","journal-title":"High Volt. 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