Fall Prevention from Scaffolding Using Computer Vision and IoT-Based Monitoring
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 7
Abstract
Fall from height (FFH) is the most significant cause of occupational fatalities in the construction industry, accounting for approximately 54% of all accidents. Such fatalities have decreased considerably due to the use of personal protective equipment (PPE). However, the manual monitoring of compliance to PPE is complex and challenging for site managers. Automation in construction safety presents multiple solutions for monitoring safety at sites. In this study, a smart safety hook (SSH) monitoring method is proposed to eliminate the risk associated with FFH accidents by integrating computer vision [closed-circuit TV (CCTV)-imagery] and Internet-of-Things (IoT)-based [inertial measurement unit (IMU)IMU and altimeter] monitoring technologies. The proposed monitoring approach is validated through five real-time scenarios: (1) attached to the scaffolding and (6 ft), (2) attached to the worker and , (3) unattached and , (4) , and (5) outside of the risk zone. The proposed technique aims to relieve the site manager’s or safety engineer’s workload by smartly and instantaneously alerting of workers’ unsafe behavior (via alarm, LED blinking, and bounding box on live camera feed). Moreover, the IoT-based hardware setup goes to low power to extend the battery life when there is no unsafe behavior. The experimental results demonstrate that the proposed solution exhibits more than 98% accuracy for real-time detection and classification. Furthermore, it can be extended to monitor several workers and their location data in the future.
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Data Availability Statement
Relevant data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A4A4078916) and by the Chung-Ang University Young Scientist Scholarship 2020.
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Journal of Construction Engineering and Management
Volume 148 • Issue 7 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Jul 27, 2021
Accepted: Jan 26, 2022
Published online: Apr 25, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 25, 2022
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