  Digital image correlation method is a non contact deformation measurement
technique. Despite years of development, it is still difficult to solve the
contradiction between calculation efficiency and seed point quantity.With the
development of deep learning, the DIC algorithm based on deep learning provides
a new solution for the problem of insufficient calculation efficiency in
DIC.All supervised learning DIC methods requires a large set of high quality
training set. However, obtaining such a dataset can be challenging and time
consuming in generating ground truth. To fix the problem,we propose an
unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image
warp model is created to transform the target speckle image to the
corresponding predicted reference speckle image by predicted 2D displacement
map, the predicted reference speckle image is compared with the original
reference speckle image to realize the unsupervised training of the CNN.The
network's parameters are optimized using a composite loss function that
incorporates both the Mean Squared Error and Pearson correlation
coefficient.Our proposed method has a significant advantage of eliminating the
need for extensive training data annotations. We conducted several experiments
to demonstrate the validity and robustness of the proposed method. The
experimental results demonstrate that our method can achieve can achieve
accuracy comparable to previous supervised methods. The PyTorch code will be
available at the following URL: https://github.com/fead1.
