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

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...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 20; s. 6737
Hlavní autoři: Kumar, Sourav, Poyyamozhi, Mukilan, Murugesan, Balasubramanian, Rajamanickam, Narayanamoorthi, Alroobaea, Roobaea, Nureldeen, Waleed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.10.2024
MDPI
Témata:
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
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24206737