 Improving the accuracy of drowning detection based on improved YOLOv5Kaikai Wang, Ruiliang Yang and Libin Yang International Journal of Reliability and Safety, 2025, vol. 19, issue 4, 339-355 Abstract: Drowning stands as a primary cause of unintentional deaths globally. This paper presents an improved YOLOv5 algorithm tailored for drowning detection, aiming to effectively mitigate drowning incidents. The improved YOLOv5 incorporates the Ghost-CBAM-C3 (GCC) module, which comprises Ghost-bottleneck modules and the CBAM module, and the learning rate decay of Cosine Annealing. To gauge the algorithm's efficacy, four self-made data sets were curated utilising a DJI mini3pro drone over both swimming pools and natural water bodies. Experimental findings underscore the heightened performance of the improved YOLOv5 over the original YOLOv5s. This enhancement manifests in a precision boost from 92.8 to 97.1%, and the values for mean average precision (mAP@0.5), weights and the Frames-Per-Second (FPS) are 93.2, 14.1 and 23.70, respectively, affirming its applicability in real-time scenarios. Furthermore, results indicate superior performance of the swimming pool data set compared to those from natural water bodies. Keywords: drowning detection; improved YOLOv5; self-made data sets; CBAM; safety; drone; labelImg software; k -means; SPPF; Ghost module. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijrsaf:v:19:y:2025:i:4:p:339-355 Access Statistics for this article More articles in International Journal of Reliability and Safety from Inderscience Enterprises Ltd |
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