Digital image correlation (DIC) has become an industry standard to retrieve accurate displacement and strain measurement in tensile testing and other material characterization. Though traditional DIC offers a high precision estimation of deformation for general tensile testing cases, the prediction becomes unstable at large deformation or when the speckle patterns start to tear. In addition, traditional DIC requires a long computation time and often produces a low spatial resolution output affected by filtering and speckle pattern quality. To address these challenges, we propose a new deep learning-based DIC approach – Deep DIC, in which two convolutional neural networks, DisplacementNet and StrainNet, are designed to work together for end-to-end prediction of displacements and strains. DisplacementNet predicts the displacement field and adaptively tracks a region of interest. StrainNet predicts the strain field directly from the image input without relying on the displacement prediction, which significantly improves the strain prediction accuracy. A new dataset generation method is developed to synthesize a realistic and comprehensive dataset, including the generation of speckle patterns and the deformation of the speckle image with synthetic displacement fields. Though trained on synthetic datasets only, Deep DIC gives highly consistent and comparable predictions of displacement and strain with those obtained from commercial DIC software for real experiments, while it outperforms commercial software with very robust strain prediction even at large and localized deformation and varied pattern qualities. In addition, Deep DIC is capable of real-time prediction of deformation with a calculation time down to milliseconds.

数字图像相关 (DIC) 已成为在拉伸测试和其他材料表征中检索准确位移和应变测量的行业标准。虽然传统的 DIC 为一般拉伸测试案例提供了高精度的变形估计，但在大变形或散斑图案开始撕裂时，预测变得不稳定。此外，传统的DIC需要很长的计算时间，并且经常产生低空间分辨率的输出，受滤波和散斑图案质量的影响。为了应对这些挑战，我们提出了一种新的基于深度学习的 DIC 方法——Deep DIC，其中两个卷积神经网络DisplacementNet 和 StrainNet, 旨在协同工作以进行位移和应变的端到端预测。DisplacementNet预测位移场并自适应地跟踪感兴趣的区域。StrainNet直接从图像输入预测应变场，不依赖位移预测，显着提高了应变预测精度。开发了一种新的数据集生成方法来合成一个真实而全面的数据集，包括散斑图案的生成和散斑图像的变形与合成位移场。尽管仅在合成数据集上进行了训练，但Deep DIC与从商业 DIC 软件中获得的用于实际实验的位移和应变预测高度一致和可比，同时它的性能优于商业软件，即使在大的局部变形和不同的图案质量下也具有非常强大的应变预测。此外，Deep DIC能够实时预测变形，计算时间低至毫秒。