Machine Learning‐Aided Spatial Adaptation for Improved Digital Image Correlation Analysis of Complex Geometries
Year: 2026
Type: article
Abstract: ABSTRACT Digital image correlation (DIC) is a widely used experimental technique for measuring full‐field deformation, but its application to complex scenarios involving large deformations, discontinuities, or intricate geometries is often hampered by the need for manual region of interest (ROI) definition. This limitation is particularly acute for incremental tracking strategies, where frequent ROI updates create a significant bottleneck and require substantial user intervention. To overcome this challenge, we present a machine learning‐aided workflow that automates and accelerates the analysis pipeline. Our approach leverages the Segment Anything Model 2 (SAM 2) for rapid initial mask generation, followed by novel spatial and temporal smoothing filters to denoise and refine the ROI sequence. These high‐quality masks are then seamlessly integrated into our SpatioTemporally Adaptive Quadtree‐mesh DIC (STAQ‐DIC) method, which performs automated adaptive meshing and subset splitting near complex boundaries. Through multiple challenging case studies, we demonstrate that our method reduces processing time by one to two orders of magnitude compared to manual methods and shows superior scalability over other automated techniques. The workflow enables robust and accurate full‐field measurements in scenarios that were previously intractable or required extensive manual effort. To facilitate broad adoption and benefit the experimental mechanics community, we have integrated the entire pipeline into a user‐friendly, open‐source code package with graphical user interfaces (GUIs).
Source: Strain
DOI: 10.1111/str.70022
Authors: Jeffrey Leu, Zixiang Tong, Andrew Doty, Solon Tsimpoukis, Bolei Deng, Jin Yang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,220
Awards: Collaborative Research: Integrated Experiments and Modeling for Spatial, Finite, and Fast Rheometry of Graded Hydrogels using Inertial Cavitation, CAREER: Understanding Fracture Propagation in Soft Viscoelastic Materials from Slow to Ultra-high Strain Rates and at Different Temperatures, Automated Model Calibration of Soft Material Fracture from Full-Field Deformation Measurements by Resolving Crack-Tip Inaccuracies
OpenAlex ID: W7123713130