Deep-Learning-based Dent Detection of Aircraft Surfaces using Synthetic Data
Aditi MHATRE, Salvatore MEROLA, Ann-Kathrin KOSCHLIK, Rebecca RODECK, Gerko WENDE
Abstract. Detecting and localizing dents on aircraft surfaces is crucial for maintaining their structural integrity. However, this task can be challenging for humans as dents are not very prominent to the naked eye and require the assistance of light reflections to reveal the damages across the surface. Latest state-of-the-art technologies such as lasers or cameras digitize this step, however the workload is shifted to identifying the dents in the virtual image. The integration of deep-learning methodologies can help automate dent detection. This study compares two object detection architectures: You Look Only Once (YOLOv11) and RealTime DETection TRansformer (RT-DETR) for dent detection. A high-quality dent dataset is prepared, consisting of real and synthetic images of common long- and mid-range aircraft fuselages, to train and test the models. The results indicate that YOLOv11 marginally outperforms RT-DETR in detecting dents with a mean accuracy precision (mAP50) score of 0.66 against the mAP50 value of 0.57 for RT-DETR.
Keywords
Aircraft Surface Inspection, Dent Detection, Deep Learning, Aircraft Maintenance, Visual Check, Generative AI
Published online 7/20/2026, 5 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Aditi MHATRE, Salvatore MEROLA, Ann-Kathrin KOSCHLIK, Rebecca RODECK, Gerko WENDE, Deep-Learning-based Dent Detection of Aircraft Surfaces using Synthetic Data, Materials Research Proceedings, Vol. 69, pp 37-41, 2026
DOI: https://doi.org/10.21741/9781644904251-7
The article was published as article 7 of the book CEAS – AIDAA Conference 2025
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.
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