Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models
This study explores the impact of transfer learning on enhancing deep learning models for detecting defects in aero-engine components. We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and DeiT (data-efficient image transformer). RandomSea...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/1/49 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588104104411136 |
|---|---|
| author | Samira Mohammadi Vahid Rahmanian Sasan Sattarpanah Karganroudi Mehdi Adda |
| author_facet | Samira Mohammadi Vahid Rahmanian Sasan Sattarpanah Karganroudi Mehdi Adda |
| author_sort | Samira Mohammadi |
| collection | DOAJ |
| description | This study explores the impact of transfer learning on enhancing deep learning models for detecting defects in aero-engine components. We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and DeiT (data-efficient image transformer). RandomSearchCV was used for hyperparameter optimization, and we selectively froze some layers during training to help better tailor the models to our dataset. We conclude that the difference in performance across all metrics can be attributed to the adoption of the transformer-based architecture by the DeiT model as it does this well in capturing complex patterns in data. This research demonstrates that transformer models hold promise for improving the accuracy and efficiency of defect detection within the aerospace industry, which will, in turn, contribute to cleaner and more sustainable aviation activities. |
| format | Article |
| id | doaj-art-fafc854ae14b43f8aa7f3e3f6fd02c34 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-fafc854ae14b43f8aa7f3e3f6fd02c342025-01-24T13:39:16ZengMDPI AGMachines2075-17022025-01-011314910.3390/machines13010049Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer ModelsSamira Mohammadi0Vahid Rahmanian1Sasan Sattarpanah Karganroudi2Mehdi Adda3Centre National Intégré du Manufacturier Intelligent, Université du Québec à Trois-Rivières, 575 Boul de l’Université, Drummondville, QC J2C 0R5, CanadaCentre National Intégré du Manufacturier Intelligent, Université du Québec à Trois-Rivières, 575 Boul de l’Université, Drummondville, QC J2C 0R5, CanadaCentre National Intégré du Manufacturier Intelligent, Université du Québec à Trois-Rivières, 575 Boul de l’Université, Drummondville, QC J2C 0R5, CanadaDepartement of Mathematics, Informatics and Engineering, Université du Québec à Rimouski, 1595 Bd Alphonse-Desjardins, Lévis, QC G6V 0A6, CanadaThis study explores the impact of transfer learning on enhancing deep learning models for detecting defects in aero-engine components. We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and DeiT (data-efficient image transformer). RandomSearchCV was used for hyperparameter optimization, and we selectively froze some layers during training to help better tailor the models to our dataset. We conclude that the difference in performance across all metrics can be attributed to the adoption of the transformer-based architecture by the DeiT model as it does this well in capturing complex patterns in data. This research demonstrates that transformer models hold promise for improving the accuracy and efficiency of defect detection within the aerospace industry, which will, in turn, contribute to cleaner and more sustainable aviation activities.https://www.mdpi.com/2075-1702/13/1/49transfer learningdefect detectionaero-engine componentstransformer modelsdeep learning |
| spellingShingle | Samira Mohammadi Vahid Rahmanian Sasan Sattarpanah Karganroudi Mehdi Adda Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models Machines transfer learning defect detection aero-engine components transformer models deep learning |
| title | Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models |
| title_full | Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models |
| title_fullStr | Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models |
| title_full_unstemmed | Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models |
| title_short | Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models |
| title_sort | smart defect detection in aero engines evaluating transfer learning with vgg19 and data efficient image transformer models |
| topic | transfer learning defect detection aero-engine components transformer models deep learning |
| url | https://www.mdpi.com/2075-1702/13/1/49 |
| work_keys_str_mv | AT samiramohammadi smartdefectdetectioninaeroenginesevaluatingtransferlearningwithvgg19anddataefficientimagetransformermodels AT vahidrahmanian smartdefectdetectioninaeroenginesevaluatingtransferlearningwithvgg19anddataefficientimagetransformermodels AT sasansattarpanahkarganroudi smartdefectdetectioninaeroenginesevaluatingtransferlearningwithvgg19anddataefficientimagetransformermodels AT mehdiadda smartdefectdetectioninaeroenginesevaluatingtransferlearningwithvgg19anddataefficientimagetransformermodels |