Damage detection of structures from motion videos using high-spatial-resolution mode shapes and data fusion

Despite extensive research into damage detection based on mode shapes from vision-based methods in the past decades. There are still some notable insufficiencies, one of which is that vision-based damage detection requires a speckle pattern or mounting the high contrast markers on the surface, and the other is that damage detection accuracy in noisy environments is low, especially when detecting slight damage. In order to address these shortcomings, we proposed a high-precision damage detection strategy by combining an advanced vision-based measurement method with a signal analysis method. In terms of overcoming the requirement for a speckle pattern on the surface, a novel technique called phase-based optical flow is introduced to provide high-precision mode shapes. Then, considering the mode shape curvature is sensitive to damage feature, the Teager energy operator (TEO) together with wavelet transform (WT) to process the mode shape curvature, producing WT-TEO mode shape curvature to search for damage features in noisy environments. Finally, a data fusion algorithm based on Bayesian fusion theory is used to further eliminate the uncertain of noise interference by fusing the damage features across multiscale space. The results of numerical and experiments demonstrate that the proposed strategy has the capability to detect single and multiple damages with high-precision in noisy environments.