| Abstract Scope |
Computer vision (CV) focuses on the extraction and analysis of meaningful information from digital images. While CV is being widely being used in fields, such as robotics, aerospace, transportation and medicine, its applications in Materials Science and Engineering (MSE) have been limited and sporadic, despite numerous potential benefits. CV techniques, such as image classification, semantic segmentation, and object detection, can be used for developing new approaches to solve a variety of MSE problems. These include microstructural characterization, phase identification, phase transformations, defect detection and classification, material twinning, crystal structure identification, plasticity, and fracture analysis. This study presents the results of an exploratory study aimed at investigating different CV techniques that have been used/have potential future use. This study maps different CV techniques to different application areas within MSE, demonstrates their use, identifies the challenges in implementing CV and provided a future research roadmap. |