{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:41:29Z","timestamp":1778647289603,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Sichuan Electric Power Company Science and Technology Program","award":["521997230014"],"award-info":[{"award-number":["521997230014"]}]},{"name":"State Grid Sichuan Electric Power Company Science and Technology Program","award":["LHJP[2023]07"],"award-info":[{"award-number":["LHJP[2023]07"]}]},{"name":"Southwest Jiaotong University","award":["521997230014"],"award-info":[{"award-number":["521997230014"]}]},{"name":"Southwest Jiaotong University","award":["LHJP[2023]07"],"award-info":[{"award-number":["LHJP[2023]07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Power equipment anomaly detection is essential for ensuring the stable operation of power systems. Existing models have high false and missed detection rates in complex weather and multi-scale equipment scenarios. This paper proposes a YOLO-SRSA-based anomaly detection algorithm. For data enhancement, geometric and color transformations and rain-fog simulations are applied to preprocess the dataset, improving the model\u2019s robustness in outdoor complex weather. In the network structure improvements, first, the ACmix module is introduced to reconstruct the SPPCSPC network, effectively suppressing background noise and irrelevant feature interference to enhance feature extraction capability; second, the BiFormer module is integrated into the efficient aggregation network to strengthen focus on critical features and improve the flexible recognition of multi-scale feature images; finally, the original loss function is replaced with the MPDIoU function, optimizing detection accuracy through a comprehensive bounding box evaluation strategy. The experimental results show significant improvements over the baseline model: mAP@0.5 increases from 89.2% to 93.5%, precision rises from 95.9% to 97.1%, and recall improves from 95% to 97%. Additionally, the enhanced model demonstrates superior anti-interference performance under complex weather conditions compared to other models.<\/jats:p>","DOI":"10.3390\/info16050407","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T08:56:55Z","timestamp":1747299415000},"page":"407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLO-SRSA: An Improved YOLOv7 Network for the Abnormal Detection of Power Equipment"],"prefix":"10.3390","volume":"16","author":[{"given":"Wan","family":"Zou","sequence":"first","affiliation":[{"name":"State Grid Sichuan Electric Power Company, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Company, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Liao","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhai","family":"Fan","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueping","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"ref_1","first-page":"3709","article-title":"Detection technology for external defects of power transmission and transformation equipment based on video imaging and its current application status","volume":"44","author":"Qi","year":"2022","journal-title":"J. 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