{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:34:45Z","timestamp":1780515285952,"version":"3.54.1"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2020YFB0905900"],"award-info":[{"award-number":["2020YFB0905900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2023,5,5]]},"abstract":"<jats:p>Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.<\/jats:p>","DOI":"10.1155\/2023\/7192814","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T20:20:11Z","timestamp":1683318011000},"page":"1-9","source":"Crossref","is-referenced-by-count":4,"title":["Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4411-4790","authenticated-orcid":true,"given":"Mengxuan","family":"Li","sequence":"first","affiliation":[{"name":"China Electric Power Research Institute (CEPRI), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingshan","family":"Han","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute (CEPRI), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Yang","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute (CEPRI), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute (CEPRI), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute (CEPRI), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"issue":"5","key":"1","first-page":"7","article-title":"Deep learning-based defect detection and recognition of a power grid inspection image","volume":"49","author":"X. 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Vaswani","year":"2017"},{"issue":"2","key":"8","first-page":"9","article-title":"Research on object detection for UAV images based on CNN and transformer [J]","volume":"44","author":"X. K. Zhu","year":"2022","journal-title":"Journal of Wuhan University of Technology"},{"key":"9","first-page":"1314","article-title":"Searching for MobileNetv3","author":"A. Howard","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision."},{"issue":"3","key":"10","first-page":"8","article-title":"Remote sensing target detection based on improved MobileNetV3 [J]","volume":"40","author":"Y. Y. Wang","year":"2022","journal-title":"Journal of Shaanxi University of Science and Technology"},{"key":"11","first-page":"3","article-title":"CBAM: convolutional block attention module","author":"S. Woo"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2019.2956516"},{"key":"13","first-page":"21","article-title":"SSD: single shot multibox detector","author":"W. Liu"},{"key":"14","article-title":"An Incremental Improvement","author":"J. Redmon","year":"2018"},{"key":"15","article-title":"Yolov4: Optimal Speed and Precision of Object Detection","author":"A. Bochkovskiy","year":"2020"},{"key":"16","first-page":"2980","article-title":"Focal loss for dense object detection","author":"T. Y. Lin"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/7192814.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/7192814.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2023\/7192814.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T20:20:24Z","timestamp":1683318024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2023\/7192814\/"}},"subtitle":[],"editor":[{"given":"Gongping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":16,"alternative-id":["7192814","7192814"],"URL":"https:\/\/doi.org\/10.1155\/2023\/7192814","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"value":"2090-0155","type":"electronic"},{"value":"2090-0147","type":"print"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}