Streamlined particle filtering of phase-based magnified videos for quantified operational deflection shapes
Streamlined particle filtering of phase-based magnified videos for quantified operational deflection shapes
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DOI：
10.1016/j.ymssp.2022.109233
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发表时间：
2022
影响因子：
8.4
通讯作者：
Nicholas A. Valente;Aral Sarrafi;Zhu Mao;C. Niezrecki
中科院分区：
文献类型：
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作者：
Nicholas A. Valente;Aral Sarrafi;Zhu Mao;C. Niezrecki
非接触式光学测量通常用于工业和研究领域以获得位移测量。这可以归因于它们相对于传统仪器方法的非侵入性优势。基于相位的运动估计（PME）和放大（PMM）是最近已被用于通过实验模态分析（EMA）和操作模态分析（OMA）从结构中提取定性数据的无目标方法。目前正在通过边缘检测将运动放大的图像序列转换为量化的操作偏转形状（ODS）向量。虽然有效，但这些方法需要人的监督和干预;因此，保证结构的准确特性。在这项研究中，引入了一种新的混合计算机视觉方法来提取量化的ODS向量，从运动放大的图像与最小的人类监督。粒子滤波（PF）点跟踪方法被用来跟踪所需的特征点的运动放大序列的图像。此外，k-means聚类方法作为一种无监督的学习方法来执行分割的颗粒，并将它们分配到特定的特征点的运动放大的图像序列。全变分去噪用于平滑运动放大伪影，这改进了ODS向量提取并提供了鲁棒的离群值去除。结果表明，聚类中心可以用来估计ODS向量，所提出的方法的性能进行了实验室规模的悬臂梁。
Non-contact optical measurements are commonly used in industrial and research domains to obtain displacement measurements. This can be attributed to their noninvasive advantages over traditional instrumentation approaches. Phase-based motion estimation (PME) and magnification (PMM) are targetless methods that have been utilized recently to extract qualitative data from structures via experimental modal analysis (EMA) and operational modal analysis (OMA). Transforming the motion-magnified sequence of images into quantified operating deflection shape (ODS) vectors is currently being conducted via edge detection. Although effective, these methods require human supervision and interference; such that, accurate characteristics of the structure are guaranteed. Within this study, a new hybrid computer vision approach is introduced to extract the quantified ODS vectors from motion-magnified images with minimal human supervision. The particle filter (PF) point tracking method is utilized to track the desired feature points in the motion-magnified sequence of images. Moreover, the k-means clustering method is employed as an unsupervised learning approach to perform the segmentation of the particles and assign them to specific feature points in the motion-magnified sequence of images. Total variation denoising is used to smooth the motion-magnified artifacts, which improves ODS vector extraction and provides a robust outlier removal. The results show that the cluster centers can be applied to estimate the ODS vectors, and the performance of the proposed methodology is evaluated experimentally on a lab-scale cantilever beam.