Deep learning-based bubble detection with Swin Transformer

Uesawa, Shinichiro; Yoshida, Hiroyuki

Journal of Nuclear Science and Technology, 61(11), p.1438 - 1452, 2024/11

https://doi.org/10.1080/00223131.2024.2348023

We developed a deep learning-based bubble detector with a Shifted window Transformer (Swin Transformer) to detect and segment individual bubbles among overlapping bubbles. To verify the performance of the detector, we calculated its average precision (AP) with different number of training images. The mask AP increased with the increase in the number of training images when there were less than 50 images but remained constant when there were more than 50 images. It was observed that the AP for the Swin Transformer and ResNet were almost the same when there were more than 50 images; however, when few training images were used, the AP of the Swin Transformer were higher than that of the ResNet. Furthermore, with regard to the increase in void fraction, the AP of the Swin Transformer showed a decrease similar to that in the case of the ResNet; however, for few training images, the AP of the Swin Transformer was higher than that of the ResNet in all void fractions. Moreover, we confirmed the detector trained with synthetic bubble images was able to segment overlapping bubbles and deformed bubbles in a bubbly flow experiment. Thus, we verified that the new bubble detector with Swin Transformer provided higher AP than the detector with ResNet for fewer training images.

Research and development of the sample-return technique for fuel debris using the unmanned underwater vehicle (Contract research); FY2021 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; National Institute of Maritime, Port and Aviation Technology*

JAEA-Review 2022-070, 70 Pages, 2023/03

JAEA-Review-2022-070.pdf:5.27MB

The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), had been conducting the Nuclear Energy Science & Technology and Human Resource Development Project (hereafter referred to "the Project") in FY2021. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station, Tokyo Electric Power Company Holdings, Inc. (TEPCO). For this purpose, intelligence was collected from all over the world, and basic research and human resource development were promoted by closely integrating/collaborating knowledge and experiences in various fields beyond the barrier of conventional organizations and research fields. The sponsor of the Project was moved from the Ministry of Education, Culture, Sports, Science and Technology to JAEA since the newly adopted proposals in FY2018. On this occasion, JAEA constructed a new research system where JAEA-academia collaboration is reinforced and medium-to-long term research/development and human resource development contributing to the decommissioning are stably and consecutively implemented. Among the adopted proposals in FY2020, this report summarizes the research results of the "Research and development of the sample-return technique for fuel debris using the unmanned underwater vehicle" conducted in FY2021. The present study aims to develop a fuel debris sampling device that comprises a neutron detector with radiation resistance and enhanced neutron detection efficiency, an end-effector with powerful cutting and collection capabilities, and a manipulator under the Japan-UK joint research team. We will also develop a fuel debris sampling system that can be mounted on an unmanned vehicle. In addition, we will develop a positioning system to identify the system position, and a technique to project the counting information of optical cameras, sonar, and neutron detectors to be developed ...