Construction Safety and Health Monitoring Using Unmanned Aerial Vehicles and Deep Learning

Akinsemoyin, Aliu
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Construction is a high-risk industry characterized by many factors that are potentially hazardous to workers. The continuous monitoring of unsafe behaviors and conditions has been identified as a proactive and active means of eliminating potential safety and health hazards on construction sites. Digital technologies combined with deep learning and computer vision can be applied to create a robust learning environment and enhance the analysis of safety data for generating insights needed to improve safety performance. This study presents the development and validation of a framework that implements the use of Unmanned Aerial Vehicles (UAVs) and deep learning for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework is validated using a pilot case study. Digital images of construction safety activities are collected on active construction sites and an integrated computer vision and deep learning model is used to extract relevant features from the collected data. This study provides valuable findings that can be utilized to improve decision-making in safety management because rapid collection and analysis of safety and health data would enable safety personnel to take faster preventive actions to avoid future accidents.

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Construction, Deep learning, Safety, UAV
Civil and Environmental Engineering