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Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn

Title	Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn
Author	Bouarfa, S., Doğru, Anıl, Arizar, R., Aydoğan, Reyhan, Serafico, J.
Publication Date:	2020
Publication Place	- American Institute of Aeronautics and Astronautics Inc, AIAA
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-162410595-1
Record ID	6e4e7b06-ffcb-4a07-9969-6ff48be56dcd
Library Location	Computer Science
Date	2020
Notes	Abu Dhabi Education Council
Sample Text	Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this paper is to demonstrate the potential of this method in supporting aircraft engineers to automatically detect aircraft dents. The novelty of the work lies in applying a recently developed neural network architecture know by Mask R-CNN, which enables the detection of objects in an image while simultaneously generating a segmentation mask for each instance. Despite the small dataset size used for training, the results are promising and demonstrate the potential of deep learning to automate aircraft maintenance inspection. The model can be trained to identify additional types of damage such as lightning strike entry and exit points, paint damage, cracks and holes, missing markings, and can therefore be a useful decision-support system for aircraft engineers.
DOI	10.2514/6.2020-0389
Cilt	1

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Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn

Author Bouarfa, S., Doğru, Anıl, Arizar, R., Aydoğan, Reyhan, Serafico, J.

Publication Date 2020

Publication Place - American Institute of Aeronautics and Astronautics Inc, AIAA

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-162410595-1

Record ID 6e4e7b06-ffcb-4a07-9969-6ff48be56dcd

Library Location Computer Science

Date 2020

Notes Abu Dhabi Education Council

Sample Text Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this paper is to demonstrate the potential of this method in supporting aircraft engineers to automatically detect aircraft dents. The novelty of the work lies in applying a recently developed neural network architecture know by Mask R-CNN, which enables the detection of objects in an image while simultaneously generating a segmentation mask for each instance. Despite the small dataset size used for training, the results are promising and demonstrate the potential of deep learning to automate aircraft maintenance inspection. The model can be trained to identify additional types of damage such as lightning strike entry and exit points, paint damage, cracks and holes, missing markings, and can therefore be a useful decision-support system for aircraft engineers.

DOI 10.2514/6.2020-0389

Cilt 1