Experimental and simulation investigation of stereo-DIC via a deep learning algorithm based on initial speckle positioning technology

Abstract

For the deep-learning-based stereo-digital image correlation technique, the initial speckle position is crucial as it influences the accuracy of the generated dataset and deformation fields. To ensure measurement accuracy, an optimized extrinsic parameter estimation algorithm is proposed in this study to determine the rotation and translation matrix of the plane in which the speckle is located between the world coordinate system and the left camera coordinate system. First, the accuracy of different extrinsic parameter estimation algorithms was studied by simulations. Subsequently, the dataset of stereo speckle images was generated using the optimized extrinsic parameters. Finally, the improved dual-branch CNN deconvolution architecture was proposed to output displacements and strains simultaneously. Simulation results indicate that DAS-Net exhibits enhanced expressive capabilities, as evidenced by a reduction in displacement errors compared to previous research. The experimental results reveal that the mean absolute percentage error between the stereo-DIC results and the generated dataset is less than 2%, suggesting that the initial speckle positioning technology effectively minimizes the discrepancy between the images in the dataset and those obtained experimentally. Furthermore, the DAS-Net algorithm accurately measures the displacement and strain fields as well as their morphological characteristics.