Integrated adaptive particle filtering-based color digital image correlation for large deformation measurement

In large deformation measurement, digital image correlation (DIC1) using functional forms can no longer describe the complex changes in grayscale information and morphology of sub-regions. To address this issue, particle filtering (PF) can be combined with color DIC (PFDIC2) to establish a color distribution model to describe sub-regions, but PFDIC has poor performance. Therefore, this paper proposes an integrated adaptive particle filtering-based color digital image correlation (APF-DIC3). This method first constructs a dynamic color distribution model based on ACFEF4 to describe the sub-region. It then introduces the SSKL5 correlation coefficient to measure the similarity between sub-regions, and establishes TAR6 to adaptively adjust the number of particles, ultimately achieving adaptive matching of sub-regions under complex deformations. Performance evaluation and simulation results show that APF-DIC significantly improves the accuracy, robustness, and computational efficiency of the algorithm. Real experimental results further verify the effectiveness of APF-DIC, demonstrating its excellent illumination invariance.