RU-net for automatic characterization of TRISO fuel cross sections

Abstract

During irradiation, phenomena such as kernel swelling and buffer densification impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to characterize the morphological changes induced by irradiation. However, each fuel compact generally contains thousands of TRISO particles. Manually collecting data to obtain quantitative characterizations of these phenomena is cumbersome and subjective. To address the challenges, we developed a convolutional neural network (CNN), namely RU-Net, to accelerate the characterization of TRISO fuel cross sections. We built a large dataset of irradiated TRISO particles, comprising 2171 microscopic images of cross-sectioned particles and their corresponding annotations. The proposed RU-Net has a two-encoder design that extracts and fuses image context at different scales and accurately segments TRISO layers of varying sizes. Extensive experiments have been conducted on the proposed large dataset to evaluate the performance of the RU-Net and other state-of-the-art CNNs. The results demonstrated that the proposed RU-Net achieved the best overall performance on the test set. Using the results of RU-Net segmentation, we can expedite analysis of TRISO particle cross sections, significantly reducing manual labor and improving the objectivity of the results.