Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain
Abstract Foam ceramics are widely used in industrial applications due to their unique properties, including high porosity, lightweight, and high-temperature resistance. However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods ofte...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87305-z |
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| _version_ | 1832585793257865216 |
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| author | Yi Yin Jianwei Pan Fang Wang Peihang Li Zhen Cai Xin Xu |
| author_facet | Yi Yin Jianwei Pan Fang Wang Peihang Li Zhen Cai Xin Xu |
| author_sort | Yi Yin |
| collection | DOAJ |
| description | Abstract Foam ceramics are widely used in industrial applications due to their unique properties, including high porosity, lightweight, and high-temperature resistance. However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods often fall short in capturing both global feature dependencies and detailed representations. To address this, a novel artificial intelligence recognition model, FD-Conv, is proposed, which combines the global information processing capabilities of Transformers with the local feature extraction strengths of convolutional neural networks. Additionally, a frequency domain block detail enhancement mechanism is introduced to improve recognition accuracy. Experimental results demonstrate that the FD-Conv model enhances recognition accuracy by at least 7.6% compared to state-of-the-art methods. Furthermore, the model effectively identifies foam ceramics with varying compositions and formulations and quantifies their microstructural phase characteristics. This research aims to advance the application of foam ceramic microstructure image analysis by improving recognition accuracy, particularly in multi-source microscopic image feature learning and pattern recognition. |
| format | Article |
| id | doaj-art-99b47428d04c4b7db34fedd8d11418b5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-99b47428d04c4b7db34fedd8d11418b52025-01-26T12:28:42ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-87305-zResearch on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domainYi Yin0Jianwei Pan1Fang Wang2Peihang Li3Zhen Cai4Xin Xu5School of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologyWuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyComputer Science, Durham UniversityThe State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologyAbstract Foam ceramics are widely used in industrial applications due to their unique properties, including high porosity, lightweight, and high-temperature resistance. However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods often fall short in capturing both global feature dependencies and detailed representations. To address this, a novel artificial intelligence recognition model, FD-Conv, is proposed, which combines the global information processing capabilities of Transformers with the local feature extraction strengths of convolutional neural networks. Additionally, a frequency domain block detail enhancement mechanism is introduced to improve recognition accuracy. Experimental results demonstrate that the FD-Conv model enhances recognition accuracy by at least 7.6% compared to state-of-the-art methods. Furthermore, the model effectively identifies foam ceramics with varying compositions and formulations and quantifies their microstructural phase characteristics. This research aims to advance the application of foam ceramic microstructure image analysis by improving recognition accuracy, particularly in multi-source microscopic image feature learning and pattern recognition.https://doi.org/10.1038/s41598-025-87305-zFoam ceramicsMulti-source Microstructure imagesFD-ConvFrequency domain block |
| spellingShingle | Yi Yin Jianwei Pan Fang Wang Peihang Li Zhen Cai Xin Xu Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain Scientific Reports Foam ceramics Multi-source Microstructure images FD-Conv Frequency domain block |
| title | Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| title_full | Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| title_fullStr | Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| title_full_unstemmed | Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| title_short | Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| title_sort | research on multi source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain |
| topic | Foam ceramics Multi-source Microstructure images FD-Conv Frequency domain block |
| url | https://doi.org/10.1038/s41598-025-87305-z |
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