Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image
Abstract This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision al...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-85061-0 |
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| _version_ | 1841544776609955840 |
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| author | Jianhong Zhao Huamin Yang Yi Sui |
| author_facet | Jianhong Zhao Huamin Yang Yi Sui |
| author_sort | Jianhong Zhao |
| collection | DOAJ |
| description | Abstract This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials. The results demonstrate the method’s potential in automating and improving the reliability of microstructural analysis in materials science. |
| format | Article |
| id | doaj-art-c09639c259e449aca2d2fc76bbdd83dc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c09639c259e449aca2d2fc76bbdd83dc2025-01-12T12:18:30ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-024-85061-0Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM imageJianhong Zhao0Huamin Yang1Yi Sui2School of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologyState Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization, Baotou Research Institute of Rare EarthsAbstract This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials. The results demonstrate the method’s potential in automating and improving the reliability of microstructural analysis in materials science.https://doi.org/10.1038/s41598-024-85061-0 |
| spellingShingle | Jianhong Zhao Huamin Yang Yi Sui Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image Scientific Reports |
| title | Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image |
| title_full | Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image |
| title_fullStr | Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image |
| title_full_unstemmed | Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image |
| title_short | Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image |
| title_sort | computer vision based automatic evaluation method of y2o3 steel coating performance with sem image |
| url | https://doi.org/10.1038/s41598-024-85061-0 |
| work_keys_str_mv | AT jianhongzhao computervisionbasedautomaticevaluationmethodofy2o3steelcoatingperformancewithsemimage AT huaminyang computervisionbasedautomaticevaluationmethodofy2o3steelcoatingperformancewithsemimage AT yisui computervisionbasedautomaticevaluationmethodofy2o3steelcoatingperformancewithsemimage |