  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.
