Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 (Autor:in) / Liu, Meiyin (Autor:in) / Lee, SangHyun (Autor:in) / Kamat, Vineet R. (Autor:in)
Automation in Construction ; 99 ; 168-182
13.12.2018
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Remote proximity monitoring between mobile construction resources using camera-mounted UAVs
| British Library Online Contents | 2019
| British Library Conference Proceedings | 2019