In the two-dimensional deformation measurement of speckle images, the initial value estimation of the digital image correlation method exerts great influence on the computational efficiency and accuracy of algorithms. The calculation accuracy and speed of sub-pixel displacement iterative search algorithms in digital image correlation methods depend on whether the initial value estimation provided by the integral pixel displacement calculation is reasonable or not, and its convergence radius is generally in the range of several pixels. Therefore, the initial value estimation provided in the integer pixel displacement search phase should be as close to the real value as possible to ensure that the iterative algorithm can converge quickly and accurately, otherwise, it may converge slowly or even fail in the iterative process. The traditional initial value estimation methods including the human-computer interaction method, Fourier transform method, and feature matching method, have some problems such as slow calculation speed and low calculation accuracy in the face of large deformation measurement and unclear speckle image features. Recently, the optical flow estimation network models in deep learning feature fast calculation speed, high calculation accuracy, and strong generalization in predicting motion displacement. We introduce the optical flow estimation network model in deep learning into the digital image correlation method and employ the displacement field predicted by the optical flow network as the initial value of the sub-pixel iterative algorithm. Finally, the inverse compositional Gauss-Newton method is adopted to calculate the displacement field of speckle deformation images. We hope that the strategy of introducing deep learning into the digital image correlation method can provide a new idea for speckle deformation measurement.

First of all, we compare the calculation accuracy of several optical flow network models of FlowNet2, PWC-Net, RAFT, GMA, SeparableFlow, GMFlow, and FlowFormer, which have excellent performance on MPI Sintel test datasets on speckle images. Considering the calculation time, model size, and calculation accuracy, the GMA network model is chosen to provide initial value estimation for sub-pixel iterative algorithms. Then, a feature sampling module is added to the model for solving the problem that the GMA network needs to occupy a lot of GPU resources in high-resolution speckle images, which can effectively reduce the occupation of GPU memory by adjusting the sampling step size. Additionally, the speckle images are utilized to generate many randomly deformed speckle datasets to retrain the model to enhance the generalization of the model in speckle deformation measurement. Finally, the GMA network is combined with the ICGN algorithm, and the performance of the algorithm is evaluated by simulated speckle deformation experiments and real wood block compression experiments.

After optimizing the sampling module, the computing resources needed in the model prediction continue to decrease with the increasing step size, and the sampling step can be reasonably selected by combining the hardware resources of the computer and calculation accuracy. After retraining in the speckle deformation dataset, the average endpoint error of the model in speckle images decreases by 14.76%. In large deformation measurement, the calculation accuracy of the proposed GMA-ICGN algorithm can still be kept at 0.01 pixel. Compared with the Fourier transform method and feature matching method in the initial value estimation algorithms, the computing speed of the GMA network has obvious advantages. In the wood block compression experiments, the GMA-ICGN algorithm successfully measures the displacement field and strain field of woodblock compression deformation.

The integral pixel displacement search algorithm in the digital image correlation method usually takes a long time. We propose a digital image correlation method based on GMA optical flow network. The reliable displacement initial value of speckle deformation images is obtained by the GMA network and then brought into ICGN iterative algorithm to accurately solve the displacement field, which can greatly improve the computational efficiency of the digital image phase method. At the same time, the GMA optical flow network is optimized and retrained, and the average endpoint error is reduced by 14.76%, which improves the prediction accuracy of the GMA network in the speckle images and reduces the GPU memory consumption by adjusting the step size of the network model. Through simulated speckle deformation experiments, a comparison between the calculation accuracy and efficiency of the proposed GMA-ICGN algorithm and the popular SIFT-ICGN algorithm, FFT-ICGN algorithm, Ncorr software, and DICe software proves that the proposed algorithm has higher computational efficiency. In addition, the proposed algorithm has similar accuracy to the SIFT-ICGN algorithm in large deformation scenes and can calculate the speckle deformation displacement field quickly and accurately. Furthermore, the proposed algorithm is applied to woodblock compression experiments, and the displacement field and strain field of woodblock compression deformation are measured successfully.