A texture enhanced attention model for defect detection in thermal protection materials
Abstract Thermal protection materials are widely used in the aerospace field, where detecting internal defects is crucial for ensuring spacecraft structural integrity and safety in extreme temperature environments. Existing detection models struggle with these materials due to challenges like defect...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89376-4 |
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| author | Jialin Song Zhaoba Wang Kailiang Xue Youxing Chen Guodong Guo Maozhen Li Asoke K. Nandi |
| author_facet | Jialin Song Zhaoba Wang Kailiang Xue Youxing Chen Guodong Guo Maozhen Li Asoke K. Nandi |
| author_sort | Jialin Song |
| collection | DOAJ |
| description | Abstract Thermal protection materials are widely used in the aerospace field, where detecting internal defects is crucial for ensuring spacecraft structural integrity and safety in extreme temperature environments. Existing detection models struggle with these materials due to challenges like defect-background similarity, tiny size, and multi-scale characteristics. Besides, there is a lack of defect datasets in real-world scenarios. To address these issues, we first construct a thermal protection material digital radiographic (DR) image dataset (TPMDR-dataset), which contains 670 images from actual production and 6,269 defect instances annotated under expert guidance. And we propose an innovative texture-enhanced attention defect detection (TADD) model that enables accurate, efficient, and real-time defect detection. To implement the TADD model, we design a texture enhancement module that can enhance the concealed defect textures and features. Then we develop a non-local dual attention module to address the issue of severe feature loss in tiny defects. Moreover, we improve the model’s ability to detect multi-scale defects through a path aggregation network. The evaluation on the TPMDR-dataset and public dataset shows that the TADD model achieves a higher mean Average Precision (mAP) compared to other methods while maintaining 25 frames per second, exceeding the baseline model by 11.05%. |
| format | Article |
| id | doaj-art-3e409b9c07b248c0bd26ecd13f83dafe |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3e409b9c07b248c0bd26ecd13f83dafe2025-08-20T02:48:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111710.1038/s41598-025-89376-4A texture enhanced attention model for defect detection in thermal protection materialsJialin Song0Zhaoba Wang1Kailiang Xue2Youxing Chen3Guodong Guo4Maozhen Li5Asoke K. Nandi6School of Information and Communication Engineering, North University of ChinaSchool of Information and Communication Engineering, North University of ChinaSchool of Information and Communication Engineering, North University of ChinaSchool of Information and Communication Engineering, North University of ChinaSchool of Information and Communication Engineering, North University of ChinaDepartment of Electronic and Electrical Engineering, Brunel University LondonDepartment of Electronic and Electrical Engineering, Brunel University LondonAbstract Thermal protection materials are widely used in the aerospace field, where detecting internal defects is crucial for ensuring spacecraft structural integrity and safety in extreme temperature environments. Existing detection models struggle with these materials due to challenges like defect-background similarity, tiny size, and multi-scale characteristics. Besides, there is a lack of defect datasets in real-world scenarios. To address these issues, we first construct a thermal protection material digital radiographic (DR) image dataset (TPMDR-dataset), which contains 670 images from actual production and 6,269 defect instances annotated under expert guidance. And we propose an innovative texture-enhanced attention defect detection (TADD) model that enables accurate, efficient, and real-time defect detection. To implement the TADD model, we design a texture enhancement module that can enhance the concealed defect textures and features. Then we develop a non-local dual attention module to address the issue of severe feature loss in tiny defects. Moreover, we improve the model’s ability to detect multi-scale defects through a path aggregation network. The evaluation on the TPMDR-dataset and public dataset shows that the TADD model achieves a higher mean Average Precision (mAP) compared to other methods while maintaining 25 frames per second, exceeding the baseline model by 11.05%.https://doi.org/10.1038/s41598-025-89376-4Defect detectionThermal protection materialConcealed object detectionTexture enhancementAttention mechanism |
| spellingShingle | Jialin Song Zhaoba Wang Kailiang Xue Youxing Chen Guodong Guo Maozhen Li Asoke K. Nandi A texture enhanced attention model for defect detection in thermal protection materials Scientific Reports Defect detection Thermal protection material Concealed object detection Texture enhancement Attention mechanism |
| title | A texture enhanced attention model for defect detection in thermal protection materials |
| title_full | A texture enhanced attention model for defect detection in thermal protection materials |
| title_fullStr | A texture enhanced attention model for defect detection in thermal protection materials |
| title_full_unstemmed | A texture enhanced attention model for defect detection in thermal protection materials |
| title_short | A texture enhanced attention model for defect detection in thermal protection materials |
| title_sort | texture enhanced attention model for defect detection in thermal protection materials |
| topic | Defect detection Thermal protection material Concealed object detection Texture enhancement Attention mechanism |
| url | https://doi.org/10.1038/s41598-025-89376-4 |
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