Year: 2026
Type: report
Abstract: The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), has been conducting the Nuclear Energy Science \& Technology and Human Resource Development Project (hereafter referred to "the Project") from FY2019. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station (1F), 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 FY2023, this report summarizes the research results of the "High-speed 3D modeling for nuclear reactor environment based on feature extraction results from video images" conducted in FY2024. The present study aims to develop a methodology for 3D modeling of workspaces using video footage captured during surveys of the reactor containment vessel and reactor building at 1F. Based on features extracted from the video within a specified timeframe, the method selects a high-information-content 3D reconstruction approach while augmenting the surrounding contextual information. In fiscal year 2024, we developed a methodology to achieve high-precision photogrammetry-based 3D reconstruction within a specified time limit, using a single video sequence obtained from simulation as input. We also devised a 3D modeling algorithm based on structural grouping and concurrently implemented an optimization algorithm to reduce computation time for 3D reconstruction, thereby accelerating the overall modeling process. Furthermore, we quantitatively evaluated the generation accuracy of moving image data using generative models and continued to enhance both hardware construction and software development to further improve computational efficiency.
Language: Japanese
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
Funder: Japan Atomic Energy Agency
OpenAlex ID: W7213474757