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

Soufiane Bouarfa, Anıl Doğru, Ridwan Arizar, Reyhan Aydoğan, Joselito Serafico

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)
202 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages19
ISBN (Electronic)978-1-62410-595-1
Publication statusPublished - 2020
EventAIAA Scitech 2020 Forum - Orlando, United States
Duration: 6 Jan 202010 Jan 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech 2020 Forum
Country/TerritoryUnited States


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