Fast Image Classification for Grain Size Determination - Jen-Chun Lee; Hsiao-Hung Hsu; Shang-Chi Liu; Chung-Hsien Chen; Huang-Chu Huang (Metals, 2021)
With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.