当深度学习遇到数字图像相关时
When Deep Learning Meets Digital Image Correlation
摘要：
卷积神经网络（CNN）构成了一类深度学习模型，近年来已被用于解决计算机视觉中的许多问题，特别是光流估计。位移场和应变场的测量可以看作是这一问题的一个特例。然而，到目前为止，CNN似乎从未被用于执行此类测量。这项工作的目的是实现一个CNN能够检索位移和应变场从对参考和变形图像的平面斑点表面，数字图像相关（DIC）。本文解释了如何开发一个名为StrainNet的CNN来实现这一目标，以及如何详细说明特定的地面实况数据集来训练这个CNN。主要结果是，StrainNet成功地执行了这样的测量，并且在可重构性能和计算时间方面取得了竞争性的结果。结论是像StrainNet这样的CNN提供了DIC的可行替代方案，特别是对于实时应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
