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

Name: 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.

İsim	Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn
Yazar	Bouarfa, S., Doğru, Anıl, Arizar, R., Aydoğan, Reyhan, Serafico, J.
Basım Tarihi:	2020
Basım Yeri	- American Institute of Aeronautics and Astronautics Inc, AIAA
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-162410595-1
Kayıt Numarası	6e4e7b06-ffcb-4a07-9969-6ff48be56dcd
Lokasyon	Computer Science
Tarih	2020
Notlar	Abu Dhabi Education Council
Örnek Metin	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

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

Basım Tarihi 2020

Basım Yeri - American Institute of Aeronautics and Astronautics Inc, AIAA

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-162410595-1

Kayıt Numarası 6e4e7b06-ffcb-4a07-9969-6ff48be56dcd

Lokasyon Computer Science

Tarih 2020

Notlar Abu Dhabi Education Council

Örnek Metin 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