Accurate segmentation of localized corrosion in structural alloys via deep learning
Abstract This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00633-3 |
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| _version_ | 1849402016741195776 |
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| author | Liang Zhao Jenifer Locke Fei Xu Tiankai Yao Xiaolei Guo |
| author_facet | Liang Zhao Jenifer Locke Fei Xu Tiankai Yao Xiaolei Guo |
| author_sort | Liang Zhao |
| collection | DOAJ |
| description | Abstract This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time applications such as automated microscopy. Specifically, a dedicated corrosion segmentation database tailored for this task is constructed. The newly constructed dataset, alongside data from two public databases, are employed to jointly train a deep learning-based model modified with a texture refinement module. Compared to the same model without the texture refinement module, the refined model substantially enhances the efficacy and efficiency of corrosion segmentation. Furthermore, the methodology developed here is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, thereby paving the way for accelerated and automated analysis in corrosion science and beyond. |
| format | Article |
| id | doaj-art-c1968addf8a04a6a84c830c8c955f42a |
| institution | Kabale University |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-c1968addf8a04a6a84c830c8c955f42a2025-08-20T03:37:38ZengNature Portfolionpj Materials Degradation2397-21062025-07-019111010.1038/s41529-025-00633-3Accurate segmentation of localized corrosion in structural alloys via deep learningLiang Zhao0Jenifer Locke1Fei Xu2Tiankai Yao3Xiaolei Guo4Idaho National LaboratoryDepartment of Materials Science and Engineering, The Ohio State UniversityIdaho National LaboratoryIdaho National LaboratoryDepartment of Metallurgical and Materials Engineering, Colorado School of MinesAbstract This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time applications such as automated microscopy. Specifically, a dedicated corrosion segmentation database tailored for this task is constructed. The newly constructed dataset, alongside data from two public databases, are employed to jointly train a deep learning-based model modified with a texture refinement module. Compared to the same model without the texture refinement module, the refined model substantially enhances the efficacy and efficiency of corrosion segmentation. Furthermore, the methodology developed here is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, thereby paving the way for accelerated and automated analysis in corrosion science and beyond.https://doi.org/10.1038/s41529-025-00633-3 |
| spellingShingle | Liang Zhao Jenifer Locke Fei Xu Tiankai Yao Xiaolei Guo Accurate segmentation of localized corrosion in structural alloys via deep learning npj Materials Degradation |
| title | Accurate segmentation of localized corrosion in structural alloys via deep learning |
| title_full | Accurate segmentation of localized corrosion in structural alloys via deep learning |
| title_fullStr | Accurate segmentation of localized corrosion in structural alloys via deep learning |
| title_full_unstemmed | Accurate segmentation of localized corrosion in structural alloys via deep learning |
| title_short | Accurate segmentation of localized corrosion in structural alloys via deep learning |
| title_sort | accurate segmentation of localized corrosion in structural alloys via deep learning |
| url | https://doi.org/10.1038/s41529-025-00633-3 |
| work_keys_str_mv | AT liangzhao accuratesegmentationoflocalizedcorrosioninstructuralalloysviadeeplearning AT jeniferlocke accuratesegmentationoflocalizedcorrosioninstructuralalloysviadeeplearning AT feixu accuratesegmentationoflocalizedcorrosioninstructuralalloysviadeeplearning AT tiankaiyao accuratesegmentationoflocalizedcorrosioninstructuralalloysviadeeplearning AT xiaoleiguo accuratesegmentationoflocalizedcorrosioninstructuralalloysviadeeplearning |