# When Deep Learning Meets Digital Image Correlation

arXiv: https://arxiv.org/abs/2009.03993 · HTML: https://mlsanity.com/paper/2009.03993/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement](https://mlsanity.com/paper/2110.13720/) (2021)
- [Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review](https://mlsanity.com/paper/2312.16174/) (2023)
- [Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network](https://mlsanity.com/paper/2303.05957/) (2023)
- [Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision](https://mlsanity.com/paper/2402.17074/) (2024)
- [Physics-Informed Neural Network Based Digital Image Correlation Method](https://mlsanity.com/paper/2409.00956/) (2024)
- [Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning](https://mlsanity.com/paper/2410.05403/) (2024)
- [Bayes-DIC Net: Estimating Digital Image Correlation Uncertainty with Bayesian Neural Networks](https://mlsanity.com/paper/2512.04323/) (2025)
- [In-situ On-demand Digital Image Correlation: A New Data-rich Characterization Paradigm for Deformation and Damage Development in Solids](https://mlsanity.com/paper/2601.17545/) (2026)
- [Event-based Visual Deformation Measurement](https://mlsanity.com/paper/2602.14376/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular optical flow estimation. Measuring displacement and strain fields can be regarded as a particular case of this problem. However, it seems that CNNs have never been used so far to perform such measurements. This work is aimed at implementing a CNN able to retrieve displacement and strain fields from pairs of reference and deformed images of a flat speckled surface, as Digital Image Correlation (DIC) does. This paper explains how a CNN called StrainNet can be developed to reach this goal, and how specific ground truth datasets are elaborated to train this CNN. The main result is that StrainNet successfully performs such measurements, and that it achieves competing results in terms of metrological performance and computing time. The conclusion is that CNNs like StrainNet offer a viable alternative to DIC, especially for real-time applications.