Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters

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Learning-based Video Motion Magnification (2018, background): “The ability to discern small motions enables important applications such as understanding a building’s structural health [3] and measuring a person’s vital sign [1].”
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks (2017, background): “A number of vision-based methods for detecting damages, primarily using image processing techniques (IPTs), have been proposed to redeem the complexities (Cha et al., 2017; Chen et al., 2015).”
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network (2018, background): “Meanwhile, the amount of required training images is much smaller than CrackNet CNN proposed in Zhang et al. (2017) and traditional CNNs in Cha et al. (2017a).”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “[217] used a phasebased approach together with the unscented Kalman filters for system identification using noisy displacement measurements.”
A review of computer vision–based structural health monitoring at local and global levels (2020, background): “Cha et al.(203) filtered the displacement measurement obtained from phasebased optical flow of a structure and extracted the stiffness and damping coefficient values to detect damages on boundaries of cantilever beams in laboratory.”