Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method

Abstract

A measurement method of bridge vibration by unmanned aerial vehicles (UAVs) combined with convolutional neural networks (CNNs) and Kanade–Lucas–Tomasi (KLT) optical-flow method is proposed. In this method, the stationary reference points in the structural background are required, a UAV is used to shoot the structure video, and the KLT optical-flow method is used to track the target points on the structure and the background reference points in the video to obtain the coordinates of these points on each frame. Then, the characteristic relationship between the reference points and the target points can be learned by a CNN according to the coordinates of the reference points and the target points, so as to correct the displacement time–history curves of target points containing the false displacement caused by the UAV’s egomotion. Finally, operational modal analysis (OMA) is used to extract the natural frequency of the structure from the displacement signal. In addition, the reliability of UAV measurement combined with CNN is proved by comparing the measurement results of the fixed camera and those of UAV combined with CNN, and the reliability of the KLT optical-flow method is proved by comparing the tracking results of the digital image correlation (DIC) and KLT optical-flow method in the experiment of this paper.