Yoshida, Y. (2023) Automatic object recognition using deep learning for legacy waste treatment. Il nuovo cimento C, 46 (2). pp. 1-8. ISSN 1826-9885
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Abstract
JAEA is addressing the back-end issues with the steadfast promotion of sustainable measures. Research and development plans on the back-end are being rationally pursued by considering priorities based on indicators such as bottleneck issues in waste streams, relevance to WAC settings, and effectiveness of cost reduction. In addition, the future vision (JAEA 2050+) has been formulated to promote cross-disciplinary research and development through a new approach that cannot be reached by conventional R&D methods, and active incorporation of information technologies, such as AI technologies. With regard to back-end issues, we are developing intelligent sensing that combines sensing and information processing technologies to realize automatic sorting technology, and non-destructive evaluation technology using high-energy X-ray CT for legacy wastes. The core technology common to all of these technologies is image evaluation technology using deep learning models, which was confirmed to perform very well in the evaluation of waste object recognition.
| Item Type: | Article |
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| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 13 Dec 2023 03:13 |
| Last Modified: | 13 Dec 2023 03:13 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/22566 |
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