Rapid full-field deformation measurements of tall buildings using UAV videos and deep learning

Full-field deformation of tall buildings is informative and imperative for structural health condition assessments. This study explores the feasibility of using videos recorded by an unmanned aerial vehicle (UAV) to measure the vibration profiles of tall buildings with the aid of deep neural networks. First, a Transformer network with a selfattention mechanism is used, and its outcome is employed for the homography transformation to minimize the drifting problems induced by the UAV. Second, a boundary-aware salient object detection network is adopted to extract the boundary of the buildings in the stabilized videos for further full-field deformation measurements. The accuracy of the proposed method is experimentally validated using a wood frame structure under harmonic excitations. Under a measuring distance of approximately 4.2 m, the proposed method achieves averages of rootmean-square-errors (RMSEs) for the displacement and rotation measurements of 2.13 mm and 1.74 x 10-3 rad, respectively, as compared to that determined by an infrared light tracking system. The study further analyzes the field-test data recorded from the vibration of a 247-m tower. Under a measuring distance of about 55 m, the proposed method is comparable to the vibration measurement at a specific story and estimates the full-field deformation within the region of interest. Overall, the proposed method with rapid UAV deployment is feasible and accurate for monitoring the full-field deformation using the UAV-recorded videos.