{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:04:17Z","timestamp":1781622257160,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanxi Provincial Key Research and Development Project","award":["202102060301020"],"award-info":[{"award-number":["202102060301020"]}]},{"name":"Shanxi Provincial Key Research and Development Project","award":["2022L524"],"award-info":[{"award-number":["2022L524"]}]},{"name":"Shanxi Provincial Higher Education Science and Technology Innovation Project","award":["202102060301020"],"award-info":[{"award-number":["202102060301020"]}]},{"name":"Shanxi Provincial Higher Education Science and Technology Innovation Project","award":["2022L524"],"award-info":[{"award-number":["2022L524"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge\u2013real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model\u2019s complexity and improve the model\u2019s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model\u2019s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network\u2019s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment.<\/jats:p>","DOI":"10.3390\/sym16101378","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:58:32Z","timestamp":1729065512000},"page":"1378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ETLSH-YOLO: An Edge\u2013Real-Time Transmission Line Safety Hazard Detection Method"],"prefix":"10.3390","volume":"16","author":[{"given":"Liangliang","family":"Zhao","sequence":"first","affiliation":[{"name":"Shanxi Energy Internet Research Institute, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Energy Internet Research Institute, Taiyuan 030032, China"},{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Department of Automation, Taiyuan Institute of Technology, Taiyuan 030008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1723-8262","authenticated-orcid":false,"given":"Yinke","family":"Dou","sequence":"additional","affiliation":[{"name":"Shanxi Energy Internet Research Institute, Taiyuan 030032, China"},{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Department of Automation, Taiyuan Institute of Technology, Taiyuan 030008, China"},{"name":"Key Laboratory of Cleaner Intelligent Control on Coal Electricity, Ministry of Education, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyang","family":"Jiao","sequence":"additional","affiliation":[{"name":"Shanxi Energy Internet Research Institute, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanxi Energy Internet Research Institute, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","unstructured":"Wu, D., Zhang, J., Zhou, Q., Zhang, L., and Gong, H. 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