Parallel optimization of tridimensional deformation measurement based on correlation function constraints of a...

This study primarily investigates the low accuracy and redundant time-consuming problem of speckle registration in the full-field deformation measurement of slender and biggish specimens. To solve these problems, a parallel optimization of the tridimensional deformation measurement method is proposed based on what we believe is a novel correlation function constraints of a multi-camera network. First, a neotype correlation function is built based on the joint constraint relationship among the multiple cameras, which is capable of accurately restricting the search for homologous points in image pairs to the epipolar line, instead of the entire image, while significantly narrowing the search space and accelerating the search. The multiple cameras are bundled as a whole, thus reducing the dimension of the Jacobian matrix and the normalized matrix to a certain extent. Subsequently, more speckle images can be calculated in one iteration. Furthermore, the decomposition of the derived correlation function and the scheme of the parallel algorithm are decomposed via the kernel function based on the GPU parallel mechanism of the compute unified device architecture source program, thus increasing the subpixel search speed of speckle matching and ensuring the calculation performance of the stereo deformation measurement method to reach a higher level. Lastly, the experimental results revealed that the proposed strategy could allow the calculation speed-up ratio of speckle sequence and stereo registration to reach 20.390 times and 17.873 times, respectively, while ensuring the out-of-plane displacement average measuring accuracy to be higher than 0.179 mm within the spatial range of [2 m, 2 m, 3 m]. As a result, the proposed approach has crucial applications in rapid and stable tridimensional deformation measurement.