Defects Recognition Algorithm Development from Visual UAV Inspections

Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 22; H. 13; S. 4682
Hauptverfasser:	Avdelidis, Nicolas P., Tsourdos, Antonios, Lafiosca, Pasquale, Plaster, Richard, Plaster, Anna, Droznika, Mark
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 21.06.2022 MDPI
Schlagworte:	Accuracy Aircraft aircraft inspection Algorithms Automation Classification CNN Concrete Coronaviruses COVID-19 Datasets deep learning defect classification defect recognition Defects Disease transmission Human error Localization Machine learning Neural networks Sensors UAV
ISSN:	1424-8220, 1424-8220
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Abstract	Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI’s maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents.
AbstractList	Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI's maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents.Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI's maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents. Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI’s maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents.
Author	Plaster, Richard Avdelidis, Nicolas P. Tsourdos, Antonios Lafiosca, Pasquale Droznika, Mark Plaster, Anna
AuthorAffiliation	1 School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield MK43 0AL, UK; a.tsourdos@cranfield.ac.uk (A.T.); pasquale.lafiosca@cranfield.ac.uk (P.L.) 2 ADDIT, 17 Railton Road, Wolseley Business Park, Kempston, Bedford MK42 7PN, UK; richardjamesplaster@gmail.com (R.P.); annaplaster@gmail.com (A.P.) 3 TUI Airline, Area 8, Hangar 61, Percival Way, London Luton Airport, Luton LU2 9PA, UK; mark.droznika@tui.co.uk
AuthorAffiliation_xml	– name: 3 TUI Airline, Area 8, Hangar 61, Percival Way, London Luton Airport, Luton LU2 9PA, UK; mark.droznika@tui.co.uk – name: 1 School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield MK43 0AL, UK; a.tsourdos@cranfield.ac.uk (A.T.); pasquale.lafiosca@cranfield.ac.uk (P.L.) – name: 2 ADDIT, 17 Railton Road, Wolseley Business Park, Kempston, Bedford MK42 7PN, UK; richardjamesplaster@gmail.com (R.P.); annaplaster@gmail.com (A.P.)
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Cites_doi	10.1117/12.259117 10.3390/s16122118 10.1109/MRA.2002.1160067 10.3390/s19081824 10.1109/CVPR.2009.5206848 10.1016/j.patcog.2016.07.001 10.1260/1369-4332.17.3.289 10.3390/s18103452 10.3390/app8091575 10.1155/2018/6357185 10.1117/1.JEI.24.6.061110 10.1111/mice.12334 10.1109/MetroAeroSpace.2017.7999594 10.1007/s12541-010-0075-3 10.36001/phmconf.2019.v11i1.776 10.1007/978-3-030-01424-7_27 10.1016/j.ifacol.2016.09.055 10.3390/robotics5030014 10.3390/app8091678 10.1109/MetroAeroSpace.2018.8453598 10.1111/mice.12375
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SubjectTerms	Accuracy Aircraft aircraft inspection Algorithms Automation Classification CNN Concrete Coronaviruses COVID-19 Datasets deep learning defect classification defect recognition Defects Disease transmission Human error Localization Machine learning Neural networks Sensors UAV
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Title	Defects Recognition Algorithm Development from Visual UAV Inspections
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