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.
Original language | English (US) |
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Pages (from-to) | 168-182 |
Number of pages | 15 |
Journal | Automation in Construction |
Volume | 99 |
DOIs | |
State | Published - Mar 2019 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction
Keywords
- Computer vision
- Deep neural network
- Proximity monitoring
- Struck-by accident
- Unmanned aerial vehicle