Performances of different subset shapes and control points in subset-based digital image correlation and their applications in boundary deformation measurement

R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

MCNN-DIC: a mechanical constraints-based digital image correlation by a neural network approach

Digital image correlation (DIC) is a widely used photomechanical method for measuring surface deformation of materials. Practical engineering applications of DIC often encounter challenges such as discontinuous deformation fields, noise interference, and difficulties in measuring boundary deformations. To address these challenges, a new, to the best of our knowledge, DIC method called MCNN-DIC is proposed in this study by incorporating mechanical constraints using neural network technology. The proposed method applied compatibility equation constraints to the measured deformation field through a semi-supervised learning approach, thus making it more physical. The effectiveness of the proposed MCNN-DIC method was demonstrated through simulated experiments and real deformation fields of nuclear graphite material. The results show that the MCNN-DIC method achieves higher accuracy in measuring non-uniform deformation fields than a traditional mechanical constraints-based DIC and can rapidly measure deformation fields without requiring extensive pre-training of the neural network.

Three-dimensional facial digitization using advanced digital image correlation

Presented in this paper is an effective technique to acquire the three-dimensional (3D) digital images of the human face without the use of active lighting and artificial patterns. The technique is based on binocular stereo imaging and digital image correlation, and it includes two key steps: camera calibration and image matching. The camera calibration involves a pinhole model and a bundle-adjustment approach, and the governing equations of the 3D digitization process are described. For reliable pixel-to-pixel image matching, the skin pores and freckles or lentigines on the human face serve as the required pattern features to facilitate the process. It employs feature-matching-based initial guess, multiple subsets, iterative optimization algorithm, and reliability-guided computation path to achieve fast and accurate image matching. Experiments have been conducted to demonstrate the validity of the proposed technique. The simplicity of the approach and the affordable cost of the implementation show its practicability in scientific and engineering applications.