{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:24:39Z","timestamp":1758273879372,"version":"3.44.0"},"reference-count":24,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Failure to wear a helmet correctly is a significant cause of injury or death in the construction industry and industrial production. Traditional supervision methods predominantly rely on manual oversight, incurring substantial costs and demonstrating inefficiencies. To address this pressing issue, the present work introduces an advanced intelligent detection technology based on deep learning algorithms, which leverages the YOLOv5 algorithm to train a dataset, enabling real-time assessment of correct helmet usage among personnel while promptly issuing warnings when deviations are detected. Simultaneously, to mitigate challenges related to object leakage and false detections in complex backgrounds, the model\u2019s performance is further enhanced by optimizing Generalized Intersection over Union, Distance Intersection over Union, and Complete Intersection over Union loss functions and improving the Mosaic-9 data enhancement algorithm. Empirical results validate the system\u2019s efficacy, with the optimized YOLOv5 algorithm achieving an impressive precision rate of 93.16% and a robust recall rate of 88.96%. These findings underscore the system\u2019s ability to accurately identify instances of workers\u2019 improper helmet usage. This enhanced YOLOv5-based intelligent detection technology provides a more efficient and accurate method for monitoring helmet compliance within the construction industry and industrial production, effectively addressing the limitations of traditional manual supervision and ensuring precision in complex operational contexts.<\/jats:p>","DOI":"10.1515\/comp-2024-0017","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T08:21:03Z","timestamp":1733473263000},"source":"Crossref","is-referenced-by-count":1,"title":["Detection and tracking of safety helmet wearing based on deep learning"],"prefix":"10.1515","volume":"14","author":[{"given":"Hua","family":"Liang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology , Yancheng , 224051, Jiangsu , P. R. China"}]},{"given":"Liqin","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Jinwei Information Technology Ltd , Yancheng , 224051, Jiangsu , P. R. China"}]},{"given":"Jinhua","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology , Yancheng , 224051, Jiangsu , P. R. China"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology , Yancheng , 224051, Jiangsu , P. R. China"}]},{"given":"Guihua","family":"Hang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology , Yancheng , 224051, Jiangsu , P. R. China"}]}],"member":"374","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"2025090218153498971_j_comp-2024-0017_ref_001","doi-asserted-by":"crossref","unstructured":"H. Song, X. Zhang, J. Song, and J. Zhao, \u201cDetection and tracking of safety helmet based on DeepSort and YOLOv5,\u201d Multimed. Tools Appl., vol. 82, pp. 10781\u201310794, 2023.","DOI":"10.1007\/s11042-022-13305-0"},{"key":"2025090218153498971_j_comp-2024-0017_ref_002","doi-asserted-by":"crossref","unstructured":"A. H. Rubaiyat, T. T. Toma, M. Kalantari-Khandani, S. A. Rahman, L. Chen, Y. Ye, et al., \u201cAutomatic detection of helmet uses for construction safety,\u201d In 2016 IEEE\/WIC\/ACM International Conference on Web Intelligence Workshops (WIW), Omaha, NE, USA, 13\u201316 October 2016, pp. 135\u2013142.","DOI":"10.1109\/WIW.2016.045"},{"key":"2025090218153498971_j_comp-2024-0017_ref_003","doi-asserted-by":"crossref","unstructured":"J. Li, H. Liu, T. Wang, M. Jiang, S. Wang, K. 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