Digital image-correlation for 2D displacement measurement based on unsupervised neural network

Abstract

Digital Image Correlation (DIC) is a non-contact measurement technique for deformation with a long-studied challenge to find a balance between calculation efficiency and seed point quantity. Deep learning offers a new solution to improve DIC efficiency, and supervised learning DIC methods require high-quality training data, leading to challenges in ground-truth generation that can be time-consuming. We propose a DIC method for 2D displacement measurement based on unsupervised Convolutional Neural Network (CNN) to address the problem. A speckle image warp model is used to transform the target speckle image to the predicted reference speckle image according to the predicted 2D displacement map. The predicted and original reference speckle images are compared to achieve unsupervised training. Our proposed method eliminates the need for extensive training data annotation. We conducted several experiments to demonstrate its validity and robustness. The MAE and RMSE by unsupervised learning are only 0.0681 pixels and 0.0886 pixels, respectively, demonstrating the potential of our method to achieve accuracy that is comparable to supervised methods.