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Finding quantitative descriptors representing the microstructural features ofa given material is an ongoing research area in the paradigm ofMaterials-by-Design. Historically, microstructural analysis mostly relies onqualitative descriptions. However, to build a robust and accurateprocess-structure-properties relationship, which is required for designing newadvanced high-performance materials, the extraction of quantitative andmeaningful statistical data from the microstructural analysis is a criticalstep. In recent years, computer vision (CV) methods, especially those which arecentered around convolutional neural network (CNN) algorithms have shownpromising results for this purpose. This review paper focuses on thestate-of-the-art CNN-based techniques that have been applied to variousmulti-scale microstructural image analysis tasks, including classification,object detection, segmentation, feature extraction, and reconstruction.Additionally, we identified the main challenges with regard to the applicationof these methods to materials science research. Finally, we discussed somepossible future directions of research in this area. In particular, weemphasized the application of transformer-based models and their capabilitiesto improve the microstructural analysis of materials.

Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives