Assessment of Speckle-Pattern Quality using Deep-Learning-Based CNN

Background: In digital image correlation (DIC), high-quality speckle patterns and quality assessments have attracted significant attention because successfully performing these assessments could be a key challenge. Previous studies have pointed out that a speckle pattern with a more plentiful gradient generally leads to higher local uniqueness, smaller bias errors and reduced random errors. Most of existing metrics in the literature were based on such qualitative prior knowledge of the speckle-pattern features in DIC measurements. Objective: In this study, a new method using convolution neural networks (CNNs) is proposed to assess speckle patterns without any pre-existing qualitative knowledge while providing better performance than other existing speckle pattern metrics. Methods: Speckle-pattern images of various sizes were fabricated as inputs for training data, and three displacement fields were numerically employed to obtain the average errors of the DIC measurements as outputs. The data sets for training and testing were designed to investigate the learning capability of the CNNs for challenging tasks. The CNNs architecture is explained along with the detailed composition of its layers that were employed in this study. After training, CNNs can learn the underlying relationship between speckle pattern images and target errors, without the need for any pre-existing qualitative or quantitative knowledge about the speckle patterns. Results: The CNN error prediction after training shows a higher correlation coefficient with the target than existing measures, namely, mean intensity gradient (MIG), mean subset fluctuation (MSF), Shannon entropy (SE), and standard deviation of gray intensities within each speckle (SDGIS). The accuracy of the existing measures generally decreased as the image size decreased. However, the correlation coefficient resulting from the CNN prediction not only provides a higher accuracy in all sizes of the images, but the coefficient also was not significantly affected by the image size. Conclusions: This is probably the first attempt to investigate the effectiveness of using a deep learning-based CNN in the quality assessment of speck patterns. The convolution layer in the CNN has local receptive fields and shared weights to extract local features, and these local features are combined by the subsequent layers to detect their global correlations. These features of using CNN are closely related to the local and global assessment of the quality of speckle patterns. Our study builds on the advantages of CNNs, which can be used as an alternative metric to assess speckle-pattern quality.