  Convolutional Neural Networks (CNNs) constitute a class of Deep Learning
models which have been used in the recent past to resolve many problems in
computer vision, in particular optical flow estimation. Measuring displacement
and strain fields can be regarded as a particular case of this problem.
However, it seems that CNNs have never been used so far to perform such
measurements. This work is aimed at implementing a CNN able to retrieve
displacement and strain fields from pairs of reference and deformed images of a
flat speckled surface, as Digital Image Correlation (DIC) does. This paper
explains how a CNN called StrainNet can be developed to reach this goal, and
how specific ground truth datasets are elaborated to train this CNN. The main
result is that StrainNet successfully performs such measurements, and that it
achieves competing results in terms of metrological performance and computing
time. The conclusion is that CNNs like StrainNet offer a viable alternative to
DIC, especially for real-time applications.
