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.

© 2024 Optica Publishing Group

Experimental verification of full-field accuracy in stereo-DIC based on the ESPI method

Xiangyang Xu, Yinhang Ma, Xinxing Shao, Xiaoyuan He, and Chenggen Quan
Appl. Opt. 61(6) 1539-1544 (2022)

Effect of camera temperature variations on stereo-digital image correlation measurements

Bing Pan, Wentao Shi, and Gilles Lubineau
Appl. Opt. 54(34) 10089-10095 (2015)

Internal displacement measurements based on color fluorescent speckle pattern by multispectral stereo-DIC and refractive index correction

Zhenning Chen, Xinqiao Tang, Yinuo Xu, Xinxing Shao, Wei Sun, and Xiaoyuan He
Appl. Opt. 61(22) 6504-6509 (2022)

MCNN-DIC: a mechanical constraints-based digital image correlation by a neural network approach

Lu Wang, Yawen Deng, Xianzhi Gao, and Guangyan Liu
Appl. Opt. 62(36) 9422-9429 (2023)

DSCNet: lightweight and efficient self-supervised network via depthwise separable cross convolution blocks for speckle image matching

Lin Li, Peng Wang, Lingrui Wang, Changku Sun, and Luhua Fu
Opt. Express 32(6) 10715-10731 (2024)