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A platform for research: civil engineering, architecture and urbanism
Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
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.
Highlights A computer vision method for UAV-assisted remote proximity monitoring is presented. A CNN-based localization, YOLO-V3, is applied for robust object localization. An image rectification method is developed for efficient distance measurement. A test on a real-site video illustrates the promising accuracy: around 4% of MAPE. The method can provide the advanced detection of struck-by hazards around workers.
Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
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.
Highlights A computer vision method for UAV-assisted remote proximity monitoring is presented. A CNN-based localization, YOLO-V3, is applied for robust object localization. An image rectification method is developed for efficient distance measurement. A test on a real-site video illustrates the promising accuracy: around 4% of MAPE. The method can provide the advanced detection of struck-by hazards around workers.
Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
Kim, Daeho (author) / Liu, Meiyin (author) / Lee, SangHyun (author) / Kamat, Vineet R. (author)
Automation in Construction ; 99 ; 168-182
2018-12-13
15 pages
Article (Journal)
Electronic Resource
English
Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
| British Library Online Contents | 2019
| British Library Conference Proceedings | 2019