2022 Fiscal Year Annual Research Report
Development of AI models for seed and insect classification
Publicly Offered Research
| Project Area | Excavating earthenware: Technology development-type research for construction of 22nd century archeological study and social implementation |
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| Project/Area Number |
21H05355
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| Research Institution | Kumamoto University |
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Principal Investigator |
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| Project Period (FY) |
2021-09-10 – 2023-03-31
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| Keywords | Classification potsherds / deep learning / machine-leaarning / ensemble / computer-vision |
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| Outline of Annual Research Achievements |
Deep Learning models have achieved high accuracy in the experimental dataset, demonstrating their capability to learn complex patterns and make accurate predictions. However, when applied to the Jomon dataset, the accuracy was lower than the experimental dataset. Nevertheless, experiments revealed that the models were relying on the morphology of the object to make predictions, which indicates that the results are reliable.
Despite the challenges faced, we remain remain optimistic about the potential of deep learning models to aid the classification of potsherds. Our experiments have shown that the models can rely on object morphology to make accurate predictions, indicating their effectiveness in supporting archaeologists in their studies.
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| Research Progress Status |
令和4年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和4年度が最終年度であるため、記入しない。
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