{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:07:48Z","timestamp":1774948068921,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T00:00:00Z","timestamp":1674259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFE020527"],"award-info":[{"award-number":["2022YFE020527"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0996"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0996"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Chongqing, China","award":["2022YFE020527"],"award-info":[{"award-number":["2022YFE020527"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["CSTB2022NSCQ-MSX0996"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0996"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures\/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.<\/jats:p>","DOI":"10.3390\/s23031256","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Lightweight Helmet Detection Algorithm Using an Improved YOLOv4"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7688-6851","authenticated-orcid":false,"given":"Junhua","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Sihao","family":"Deng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xueda","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yanfei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Y., Yang, L., Thirunavukarasu, A., Evison, C., and Zhao, Y. 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