{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:38:42Z","timestamp":1775932722249,"version":"3.50.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The construction site is one of the most dangerous industries because the number of occupational injuries and fatalities is significantly higher compared to other industries. Proper use of personal protective equipment (PPE) by workers can reduce the risk of occupational injuries and fatalities. However, for a variety of reasons, workers tend not to wear their PPEs properly. To address these issues, we propose a vision-based framework for monitoring wearing PPE. The developed framework is based on the real-time pixel-level detect model YOLACT, which employs MobileNetV3 as a backbone to lightweight the proposed framework. In addition, the framework uses DeepSORT of object tracking algorithm to interpolate frames not predicted by the model. The post-processing algorithm in our framework classifies the correlation between workers and PPE into four statuses based on the results predicted by YOLACT and the interpolated results from DeepSORT. The results showed that the fine-tuned model achieved 66.4 mean average precision50, and the algorithm successfully determined workers\u2019 PPE-wearing status detection with 91.3% accuracy. This study shows the potential to prevent occupational injuries and reduce social costs by automating monitoring at construction sites in real-time.<\/jats:p>","DOI":"10.1093\/jcde\/qwad019","type":"journal-article","created":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T12:29:18Z","timestamp":1677932958000},"page":"905-917","source":"Crossref","is-referenced-by-count":12,"title":["Deep learning-based framework for monitoring wearing personal protective equipment on construction sites"],"prefix":"10.1093","volume":"10","author":[{"given":"Yeo-Reum","family":"Lee","sequence":"first","affiliation":[{"name":"Department of IT Convergence, Sahmyook University , Seoul, 01795, Republic of Korea"}]},{"given":"Seung-Hwan","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of IT Convergence, Sahmyook University , Seoul, 01795, Republic of Korea"}]},{"given":"Kyung-Su","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Architecture, Sahmyook University , Seoul, 01795, Republic of Korea"}]},{"given":"Han-Cheol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of IT Convergence, Sahmyook University , Seoul, 01795, Republic of Korea"}]},{"given":"Han-Guk","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Architecture, Sahmyook University , Seoul, 01795, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"issue":"7","key":"2023102618005339400_bib1","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1111\/mice.12530","article-title":"A deep-learning-based computer vision solution for construction vehicle detection","volume":"35","author":"Arabi","year":"2020","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"2023102618005339400_bib2","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","article-title":"Fully-convolutional siamese networks for object 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