Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles
AbstractComputer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach. View Full-Text
Share & Cite This Article
Yoon, H.; Hoskere, V.; Park, J.-W.; Spencer, B.F., Jr. Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles. Sensors 2017, 17, 2075.
Yoon H, Hoskere V, Park J-W, Spencer BF, Jr. Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles. Sensors. 2017; 17(9):2075.Chicago/Turabian Style
Yoon, Hyungchul; Hoskere, Vedhus; Park, Jong-Woong; Spencer, Billie F., Jr. 2017. "Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles." Sensors 17, no. 9: 2075.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.