LAUSR

Abstract Struck-by accidents have resulted in a significant number of fatal and nonfatal injuries in the construction industry. As a proactive safety measure against struck-by hazards, the authors present an Unmanned Aerial Vehicle (UAV)-assisted visual monitoring method that can automatically measure proximities among construction entities. To attain this end, this research conducts two research thrusts: (i) object localization using a deep neural network, YOLO-V3; and (ii) development of an image rectification method that allows for the measurement of actual distance from a 2D image collected from a UAV. Tests on real-site aerial videos show the promising accuracy of the proposed method; the mean absolute distance errors for estimated proximity were less than 0.9 m and the mean absolute percentage errors were around 4%. The proposed method enables the advanced detection of struck-by hazards around workers, which in turn can make timely intervention possible. This proactive intervention can ultimately promote a safer working environment for construction workers.