Initial estimation of digital image correlated deformation based on genetic algorithms

Traditional digital image correlation method based on Newton-Raphson iteration is greatly influenced by the initial value of iteration. In order to overcome the problem, a digital image correlation method based on genetic algorithm was proposed. The data point to be measured was chosen as the center and several valuation points in the neighborhood were selected. The coordinates of estimated points before and after deformation were matched by digital image correlation method based on genetic algorithm. Three or more non-collinear pairs of valuation points were randomly selected to be substituted for the affine transformation model. The initial deformation value was estimated based on affine transformation results and it was used as the initial value of Newton-Raphson iteration. Finally, the sub-pixel displacement was calculated by Newton-Raphson iteration method. The results show that the matching time of this method is 37.54% less than that of the traditional method. Compared with the traditional digital image correlation method, it is more reliable in search performance and matching accuracy. This study provides a reference for the effect of iterative initial value optimization on matching speed and accuracy in digital image correlation method.