User-Independent, Accurate and Pixel-Wise DIC Measurements with a Task-Optimized Neural Network

Background: Being an image-based optical technique for full-field deformation measurements, the ultimate purpose of digital image correlation (DIC) is to realize accurate, precise and pixel-wise displacement/strain measurements in a full-automatic manner without users’ inputs. Objective: In this work, we propose a task-optimized neural network, called RAFT-DIC, to achieve user-independent, accurate and pixel-wise displacement field measurements. Methods: RAFT-DIC is based on the state-of-the-art optical flow architecture: Recurrent All-Pairs Field Transforms (RAFT). We make two targeted improvements that fundamentally enhanced its measurement accuracy and generalization performance. Firstly, we remove all the down-sampling operations in the encode module to improve the perception of spatial information, and reduce the number of pyramid levels of the correlation layer to increase the small displacement accuracy. By building the correlation layer to compute the similarity of pixel pairs, and iteratively updating the displacement field through a recurrent unit, RAFT-DIC introduces the prior information of DIC measurement to guide the displacement estimation with high accuracy. Secondly, we develop a novel dataset generation method to synthesize customized speckle patterns and diverse displacement fields, which facilitate the construction of a robust and adaptable dataset to improve the network generalization. Results: Both simulated and real experimental results demonstrate that the accuracy of the proposed method is approximately an order of magnitude higher than pervious deep learning-based DIC (DL-DIC). Conclusions: The proposed RAFT-DIC shows higher accuracy as well as stronger practicality and cross-dataset generalization performance over existing DL-DIC methods, and is expected to be a new standard architecture for DL-DIC.