Digital optics and machine learning algorithms for aircraft maintenance

Digital optics and machine learning algorithms for aircraft maintenance

Salvatore Merola, Michele Guida, Francesco Marulo

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Abstract. The objective of this study is to present a novel approach for airplane inspection to identify damages on the fuselage. Machine learning algorithms in the aeronautic industry can be used as an instrument for automating the process of inspection and detection, decreasing human error, and increasing productivity and security for operators. An overview of the problems, methods, and recent developments in the field of deep learning algorithms used for general damage detection on aircraft components is provided in this extended abstract. Data were collected using a high-quality acquisition system and the dataset was populated by collecting defect images from 2 typologies of aircraft: a commercial partial full-scale airplane fuselage section in primer paint and a general aviation fuselage white painted, both situated in the laboratory of heavy structures of University of Naples Federico II. The Convolutional Neural Networks (CNNs) and machine learning models were trained on large datasets of annotated images, enabling them to learn complex features associated with different types of damage. Data augmentation techniques are adopted to add diversity to the training data. Transfer learning techniques, which leverage pre-trained models on large-scale image datasets, have also proved to be effective in achieving accurate and robust detection results.

Image Processing, Aircraft Maintenance, Deep Learning, Convolutional Neural Networks, Computer Vision

Published online 6/1/2024, 4 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Salvatore Merola, Michele Guida, Francesco Marulo, Digital optics and machine learning algorithms for aircraft maintenance, Materials Research Proceedings, Vol. 42, pp 18-21, 2024


The article was published as article 5 of the book Aerospace Science and Engineering

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] Airbus. ref 51-11-00 Description – DAMAGE CLASSIFICATION. Tech. rep. Airbus Group, 2003.
[2] Airbus. ref 51-11-13 – Description – DAMAGE/DEFECT REPORTING. Tech. rep. Airbus Group, 2003.
[3] Airbus. ref 51-11-11 Description – Allowable Damage. Tech. rep. Airbus Group, 2003.
[4] Y. LeCun, Y. Bengio, and G. Hinton. “Deep learning”. In: nature 521.7553 (2015), pp. 436–444.
[5] Zhang, Lei, et al. “Road crack detection using deep convolutional neural network.” 2016 IEEE international conference on image processing (ICIP). IEEE, 2016.
[6] W. R. L. da Silva and D. Schwerz de Lucena. “Concrete cracks detection based on deep learning image classification”. In: Proceedings. Vol. 2. 8. MDPI. 2018, p. 489.
[7] M. Rice, L. Li, G.Ying, M. Wan, E.T. Lim, G. Feng, J. Ng, M. N. T. Jin-Li, and V.S. Babu. Automating the Visual Inspection of Aircraft. Proceedings of the Singapore Aerospace Technology and Engineering Conference (SATEC), Singapore. Vol. 7. 2018.
[8] D. Han, Q. Liu, and W. Fan. “A new image classification method using CNN transfer learning and web data augmentation”. In: Expert Systems with Applications 95 (2018), pp. 43–56.