An artificial neural network for digital image correlation dynamic subset selection based on speckle pattern quality metrics.

Precise and accurate digital image correlation computed displacement data requires sufficient noise suppression and spatial resolution, which improve and diminish, respectively, with increased subset size. Furthermore, spatially varying speckle pattern quality and displacement field complexity ideally necessitate a location‐specific optimal subset size to obtain a favourable compromise between noise suppression and spatial resolution. Although dynamic subset selection (DSS) methods have been proposed based on speckle pattern quality metrics (SPQMs), they do not ensure such a favourable compromise. This work investigates using an artificial neural network (ANN) for DSS. An ANN is trained to predict the displacement error standard deviation of a subset from multiple SPQMs and the standard deviation of image noise, such that the smallest subset offering sufficient noise suppression, dictated by a displacement error standard deviation threshold, is appointed. Validation, both within and outside the domain of the training images, shows that the smallest subset providing sufficient noise suppression offers a favourable compromise for up to moderate displacement gradients. Additionally, the proposed method is shown to perform with greater consistency and reliability relative to existing SPQM‐based DSS methods. The novel proposition lies in utilising an ANN as an error prediction tool, based on multiple SPQMs, and hence, is an attractive alternative for DSS. [ABSTRACT FROM AUTHOR]