Application of deep metric learning model to microscope image analysis for the determination of UOC samples in nuclear forensics analysis

木村 祥紀; 松本 哲也*; 山口 知輝

Journal of Radioanalytical and Nuclear Chemistry, 333(7), p.3541 - 3551, 2024/07

https://doi.org/10.1007/s10967-023-09300-w

This study discusses the application of a deep metric learning model based on a convolutional neural network to scanning electron microscope image analysis to determine UOC samples. One of the unique features of this technique is that it can detect a sample that comes from an unknown material not listed in the reference for comparison, in addition to the classification of a sample based on surface characteristics captured in the microscopic images. It was confirmed that the present technique could detect hypothetical unknown samples with 0.8 of Area Under the ROC Curve, and it can effectively provide preliminary observations in nuclear forensics analysis.