Unsupervised CNN-Based DIC for 2D Displacement Measurement

TL;DR

This paper introduces an unsupervised CNN-based digital image correlation method for 2D displacement measurement, eliminating the need for large annotated datasets while maintaining accuracy comparable to supervised approaches.

Contribution

The paper proposes a novel unsupervised deep learning approach for DIC that does not require extensive labeled training data, addressing a key limitation of previous methods.

Findings

Achieves accuracy comparable to supervised DIC methods

Eliminates the need for large annotated training datasets

Demonstrates robustness across various experiments

Abstract

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.