Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning
Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared im...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2813-477X/2/4/32 |
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| author | Leith Bounenni Mohamed Arbane Clemente Ibarra-Castanedo Yacine Yaddaden Sreedhar Unnikrishnakurup Andrew Ngo Chun Yong Xavier Maldague |
| author_facet | Leith Bounenni Mohamed Arbane Clemente Ibarra-Castanedo Yacine Yaddaden Sreedhar Unnikrishnakurup Andrew Ngo Chun Yong Xavier Maldague |
| author_sort | Leith Bounenni |
| collection | DOAJ |
| description | Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry. |
| format | Article |
| id | doaj-art-b8b1acffbf864232b98765a44795906c |
| institution | Kabale University |
| issn | 2813-477X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | NDT |
| spelling | doaj-art-b8b1acffbf864232b98765a44795906c2025-01-24T13:44:21ZengMDPI AGNDT2813-477X2024-12-012451953110.3390/ndt2040032Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep LearningLeith Bounenni0Mohamed Arbane1Clemente Ibarra-Castanedo2Yacine Yaddaden3Sreedhar Unnikrishnakurup4Andrew Ngo Chun Yong5Xavier Maldague6Department of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec, QC G1V 0A6, CanadaDepartment of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec, QC G1V 0A6, CanadaDepartment of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec, QC G1V 0A6, CanadaUniversity of Quebec at Rimouski, Rimouski, QC G5L-3A1, CanadaInstitute of Materials Research & Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, 08-03 Innovis, Singapore 138634, SingaporeInstitute of Materials Research & Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, 08-03 Innovis, Singapore 138634, SingaporeDepartment of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec, QC G1V 0A6, CanadaDetecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.https://www.mdpi.com/2813-477X/2/4/32deep learninginfrared imagingaeronautical surfacesdefect detection |
| spellingShingle | Leith Bounenni Mohamed Arbane Clemente Ibarra-Castanedo Yacine Yaddaden Sreedhar Unnikrishnakurup Andrew Ngo Chun Yong Xavier Maldague Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning NDT deep learning infrared imaging aeronautical surfaces defect detection |
| title | Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning |
| title_full | Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning |
| title_fullStr | Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning |
| title_full_unstemmed | Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning |
| title_short | Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning |
| title_sort | advanced defect detection on curved aeronautical surfaces through infrared imaging and deep learning |
| topic | deep learning infrared imaging aeronautical surfaces defect detection |
| url | https://www.mdpi.com/2813-477X/2/4/32 |
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