Vision-Based Natural Frequency Identification Using Laser Speckle Imaging and Parallel Computing

This study focuses on the identification of the natural frequencies of structures through the analysis of the speckle pattern that a laser creates and a camera records. The laser pointer spreads its light over a target area on the structure and creates the speckle pattern. The ambient vibrations affect the pattern and a camera records the changes. The stream of images is fed into a graphics processing unit (GPU). The developed parallel code includes different algorithms: the speckle contrast image (SCI), the speckle flow imaging (SFI), and an innovative application of k-means clustering that uses the geometrical centroid of each cluster as virtual sensors. The tracking of the centroid in time domain through the images creates a vibration signal. The signals from different clusters are processed together to extract the natural frequencies of the structure. This study applies the proposed method to different sample structures both in laboratory and in the field to demonstrate how the obtained signals are reliable and easy to handle. The GPU technology enhances the performance of the entire method and allows the achievement of real-time processing. All these features create an inexpensive, portable, and efficient tool to inspect any structure or its components.