Deep Learning for Object Detection in Materials-Science Images: A tutorial

Lan Fu, Hongkai Yu, Xiaoguang Li, Craig P. Przybyla, Song Wang

Abstract: Deep neural networks and deep learning have achieved great success in many signal and image processing applications, especially those with large-scale annotated training data for supervised learning. Although in principle deep-learning methods can be applied to boost the performance of processing materials-science images, i.e., microscopic images that capture important microstructures of various material samples, many priors and requirements in materials science must be considered to maximize performance gains. In this article, we focus on the important problem of detecting objects of interest from microscopic materials-science images and introduce different approaches to incorporate several such priors, including object shape, symmetry, and 3D consistency, into deep learning to tackle this problem. In particular, we explore the use of these three priors to enable network training with fewer data annotations, which is highly desired in materials science. This tutorial-style article will summarize contributions in the literature as well as our current research achievements, and we hope it can provide an initial insight to new researchers who are interested in using deep learning for materials-science image processing.