A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Worker Safety and Health Activity Monitoring in Construction Using Unmanned Aerial Vehicles and Deep Learning
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 and health data for generating insights needed to improve safety and health performance. This study provides a framework that implements the use of Unmanned Aerial Vehicles (UAVs) and deep learning (DL) for worker safety and health activity monitoring to improve safety performance on construction sites. The findings of this study provide useful information with which UAVs and DL can be effectively deployed for the reduction and prevention of injuries, illnesses, and accidents on construction sites. It is expected that the application of UAVs and DL for worker safety and health activity monitoring can 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.
Worker Safety and Health Activity Monitoring in Construction Using Unmanned Aerial Vehicles and Deep Learning
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 and health data for generating insights needed to improve safety and health performance. This study provides a framework that implements the use of Unmanned Aerial Vehicles (UAVs) and deep learning (DL) for worker safety and health activity monitoring to improve safety performance on construction sites. The findings of this study provide useful information with which UAVs and DL can be effectively deployed for the reduction and prevention of injuries, illnesses, and accidents on construction sites. It is expected that the application of UAVs and DL for worker safety and health activity monitoring can 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.
Worker Safety and Health Activity Monitoring in Construction Using Unmanned Aerial Vehicles and Deep Learning
Awolusi, Ibukun (author) / Akinsemoyin, Aliu (author) / Chakraborty, Debaditya (author) / Al-Bayati, Ahmed (author)
Construction Research Congress 2022 ; 2022 ; Arlington, Virginia
Construction Research Congress 2022 ; 463-473
2022-03-07
Conference paper
Electronic Resource
English
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