Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6737
• Main Authors: Kumar, Sourav, Poyyamozhi, Mukilan, Murugesan, Balasubramanian, Rajamanickam, Narayanamoorthi, Alroobaea, Roobaea, Nureldeen, Waleed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2024; MDPI
• Subjects: Accidents; Algorithms; automatic detection; Automation; Building; Compliance; Construction accidents & safety; Construction industry; Construction workers; Data mining; Data processing; Drone aircraft; Drones; Efficiency; image recognition; object detection; Occupational health and safety; Occupational safety; Personal protective equipment; Privacy; Quality control; Quality management; Safety equipment; Safety management; Safety standards; Strikes; Surveillance; tensor flow; Trends; Unmanned Aerial Vehicle; Unmanned aerial vehicles; unsafe site conditions; Weather; Workers' compensation; object detection; automatic detection; unsafe site conditions; tensor flow; image recognition; Unmanned Aerial Vehicle
• DOI: 10.3390/s24206737
• ISSN: 1424-8220

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’s 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.