论文标题
用于材料微观结构分析的计算机视觉方法：最先进和未来的观点
Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives
论文作者
论文摘要
找到代表给定材料的微观结构特征的定量描述符是逐个设计范围的持续研究区域。从历史上看，微观结构分析主要依赖于定性描述。但是，为了建立强大而准确的过程结构 - 实验关系，这是设计新的高级高性能材料所必需的，从微观结构分析中提取定量和有意义的统计数据是关键的一步。近年来，计算机视觉（CV）方法，特别是围绕卷积神经网络（CNN）算法的方法，为此目的显示出令人鼓舞的结果。本评论论文重点介绍了已应用于各种多规模微观结构图像分析任务的最新基于CNN的技术，包括分类，对象检测，分割，特征提取和重建。此外，我们确定了这些方法在材料科学研究中的应用方面面临的主要挑战。最后，我们讨论了该领域研究的一些未来研究方向。特别是，我们强调了基于变压器的模型及其能力改善材料微结构分析的能力。
Finding quantitative descriptors representing the microstructural features of a given material is an ongoing research area in the paradigm of Materials-by-Design. Historically, microstructural analysis mostly relies on qualitative descriptions. However, to build a robust and accurate process-structure-properties relationship, which is required for designing new advanced high-performance materials, the extraction of quantitative and meaningful statistical data from the microstructural analysis is a critical step. In recent years, computer vision (CV) methods, especially those which are centered around convolutional neural network (CNN) algorithms have shown promising results for this purpose. This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks, including classification, object detection, segmentation, feature extraction, and reconstruction. Additionally, we identified the main challenges with regard to the application of these methods to materials science research. Finally, we discussed some possible future directions of research in this area. In particular, we emphasized the application of transformer-based models and their capabilities to improve the microstructural analysis of materials.
