Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
复制标题
DOI：
10.1109/sensors47087.2021.9639455
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Y. Bai;R. M. Abduallah;H. Sezen;A. Yilmaz
中科院分区：
文献类型：
--
作者：
Y. Bai;R. M. Abduallah;H. Sezen;A. Yilmaz
本文提出了一种管道自动跟踪和测量的位移和振动的结构样品在实验室的实验。最新的Mask区域卷积神经网络（Mask R-CNN）可以从固定摄像头记录的视频中定位目标并监控它们的运动。为了提高精度并去除噪声，包括尺度不变特征变换（SIFT）和用于信号处理的各种滤波器等技术。通过3根小比例钢筋混凝土梁的试验和振动台试验，验证了本文方法的有效性。实验结果表明，本文提出的深度学习方法可以实现在实验室实验中自动精确测量被测构件运动的目标。
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments. The latest Mask Regional Convolutional Neural Network (Mask R-CNN) can locate the targets and monitor their movements from videos recorded by a stationary camera. To improve precision and remove the noise, techniques such as Scale-invariant Feature Transform (SIFT) and various filters for signal processing are included. Experiments on three small-scale reinforced concrete beams and a shaking table test are utilized to verify the proposed method. Results show that the proposed deep learning method can achieve the goal to automatically and precisely measure the motion of tested structural members during laboratory experiments.