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

• Developed a dynamic color distribution model. • Proposed the Smoothed Symmetric Kullback-Leibler Correlation Coefficient . • Established a Tanh-Adaptive Resampling Strategy. In large deformation measurement, digital image correlation (DIC 1 1 Digital image correlation (DIC). ) 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 (PFDIC 2 2 Color digital image correlation method integrated with particle filtering. (PFDIC). ) 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-DIC 3 3 Integrated adaptive particle filtering-based color digital image correlation (APF-DIC). ). This method first constructs a dynamic color distribution model based on ACFEF 4 4 Adaptive color feature extraction function (ACFEF). to describe the sub-region. It then introduces the SSKL 5 5 Smoothed symmetric Kullback Leibler (SSKL). correlation coefficient to measure the similarity between sub-regions, and establishes TAR 6 6 Hyperbolic tangent adaptive resampling (tanh-adaptive resampling, TAR). 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.