Civil Engineering, Faculty of Engineering, Cairo University, Giza Governorate 12613, Egypt.
Construction Engineering Department, The American University in Cairo, Cairo 11865, Egypt.
Sensors (Basel). 2020 Apr 15;20(8):2223. doi: 10.3390/s20082223.
Oscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by terrestrial photogrammetry using non-metric cameras. Tedious camera calibration procedures are eliminated by using a grid target that allows geometric correction to be performed to the frame's region of interest at which oscillations are monitored. Region-based convolutional neural networks (Faster R-CNN) techniques are adopted to minimize the light exposure limitations, commonly constraining applications of terrestrial photogrammetry. The proposed monitoring procedure is tested at outdoor conditions to check its reliability and accuracy and examining the effect of using Faster R-CNN on monitoring results. The proposed artificial intelligence (AI) aided oscillation monitoring allowed sub-millimeter accuracy monitoring with observation rates up to 60 frames per second and gained the benefit of high optical zoom offered by market available bridge cameras to monitor oscillation of targets 100 m apart with high accuracy.
振动监测通常需要复杂的设置，包括各种类型的传感器，并进行大量计算，以实现足够的观测速率和准确性。本研究提出了一种简单、经济有效的方法，利用非量测相机的地面摄影测量进行二维振动监测。通过使用网格目标，消除了繁琐的相机校准过程，该目标允许对监测振动的感兴趣区域的帧进行几何校正。采用基于区域的卷积神经网络（Faster R-CNN）技术，最小化光照限制，这通常限制了地面摄影测量的应用。在户外条件下测试了所提出的监测程序，以检查其可靠性和准确性，并研究了在监测结果中使用 Faster R-CNN 的效果。所提出的人工智能（AI）辅助振动监测允许以亚毫米级的精度进行监测，观测速率高达每秒 60 帧，并受益于市售桥梁相机提供的高光学变焦，可高精度监测相隔 100 米的目标的振动。
