Bayes-DIC Net: Estimating Digital Image Correlation Uncertainty with Bayesian Neural Networks

TL;DR

This paper presents Bayes-DIC Net, a Bayesian neural network for digital image correlation that leverages a novel dataset generation method based on non-uniform B-spline surfaces to improve displacement uncertainty estimation.

Contribution

The paper introduces a new dataset generation technique and a Bayesian neural network architecture for enhanced DIC uncertainty quantification and performance.

Findings

Generated realistic displacement datasets using B-spline surfaces.

Bayes-DIC Net provides both predictions and confidence levels.

Improved accuracy and reliability in displacement field estimation.

Abstract

This paper introduces a novel method for generating high-quality Digital Image Correlation (DIC) dataset based on non-uniform B-spline surfaces. By randomly generating control point coordinates, we construct displacement fields that encompass a variety of realistic displacement scenarios, which are subsequently used to generate speckle pattern datasets. This approach enables the generation of a large-scale dataset that capture real-world displacement field situations, thereby enhancing the training and generalization capabilities of deep learning-based DIC algorithms. Additionally, we propose a novel network architecture, termed Bayes-DIC Net, which extracts information at multiple levels during the down-sampling phase and facilitates the aggregation of information across various levels through a single skip connection during the up-sampling phase. Bayes-DIC Net incorporates a series of lightweight convolutional blocks designed to expand the receptive field and capture rich contextual information while minimizing computational costs. Furthermore, by integrating appropriate dropout modules into Bayes-DIC Net and activating them during the network inference stage, Bayes-DIC Net is transformed into a Bayesian neural network. This transformation allows the network to provide not only predictive results but also confidence levels in these predictions when processing real unlabeled datasets. This feature significantly enhances the practicality and reliability of our network in real-world displacement field prediction tasks. Through these innovations, this paper offers new perspectives and methods for dataset generation and algorithm performance enhancement in the field of DIC.