{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:01:23Z","timestamp":1790884883454,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T00:00:00Z","timestamp":1729382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University, Taif, Saudi Arabia","doi-asserted-by":"publisher","award":["TU-DSPP-2024-17"],"award-info":[{"award-number":["TU-DSPP-2024-17"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system\u2019s high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry.<\/jats:p>","DOI":"10.3390\/s24206737","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"6737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles"],"prefix":"10.3390","volume":"24","author":[{"given":"Sourav","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukilan","family":"Poyyamozhi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3596-1955","authenticated-orcid":false,"given":"Balasubramanian","family":"Murugesan","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-3275","authenticated-orcid":false,"given":"Narayanamoorthi","family":"Rajamanickam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1585-2962","authenticated-orcid":false,"given":"Roobaea","family":"Alroobaea","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waleed","family":"Nureldeen","sequence":"additional","affiliation":[{"name":"General Subject Department, University of Business and Technology, Jeddah 23435, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.ssci.2017.06.008","article-title":"Applicability of unmanned aerial system (UAS) for safety inspection on construction sites","volume":"98","author":"Costa","year":"2017","journal-title":"Saf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.jsr.2024.04.006","article-title":"A critical review of the feasibility of emerging technologies for improving safety behavior on construction sites","volume":"89","author":"Newaz","year":"2024","journal-title":"J. Saf. 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