Deflection measurement is the research focus of health monitoring for bridges during the operation period. This study develops a contactless measurement technique to monitor the bridge deflection, leveraging visual information from a team of unmanned aerial vehicles (UAVs). On the basis of the collinearity of the laser spots projected on the plane by the coplanar laser indicator, we can eliminate the motion of UAV, and calculate the vertical displacement of the position to be measured relative to the bridge pier. In the proposed method, the center of the laser spot is extracted through a method based on deep learning, and an algorithm based on scale-invariant features registration was developed to track the feature points of the bridge in the image sequence. According to the algorithm, we demonstrate the accuracy and feasibility of our approach through simulation and simulated bridge experiments. The result shows that the root mean squared error (RMSE) of measurement through our technique is less than 0.5 mm in the laboratory conditions. In addition, the limits and scalability of the presented method have been explored through a field experiment.

中文翻译：

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通过多无人机系统的桥梁非接触挠度测量

挠度测量是桥梁运营期健康监测的研究热点。本研究开发了一种非接触式测量技术来监测桥梁偏转，利用来自无人驾驶飞行器 (UAV) 团队的视觉信息。根据共面激光指示器投射到平面上的激光光斑的共线性，可以消除无人机的运动，计算出待测位置相对于桥墩的垂直位移。该方法通过基于深度学习的方法提取激光光斑的中心，并开发了一种基于尺度不变特征配准的算法来跟踪图像序列中桥梁的特征点。根据算法，我们通过模拟和模拟桥梁实验证明了我们方法的准确性和可行性。结果表明，在实验室条件下，通过我们的技术测量的均方根误差 (RMSE) 小于 0.5 mm。此外，已通过现场实验探索了所提出方法的局限性和可扩展性。