Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection
Open accessAbstract
Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Through supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35% respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approaches uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approache combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%)
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Automated inspection systems have been developed for detecting damages on aircraft wings and fuselage, tyres, engines and composite parts.
“In aviation, automated inspection systems have been developed for detecting damages on aircraft wings and fuselage [16-21], tyres [22], engines and composite parts [16,23-26].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [21]
Experiment 3 (page unavailable) reports a model trained to detect dents in aircraft wings, and the literature review describes automated detection of fuselage defects. The copy does not establish that this paper developed systems for inspecting tyres, engines, or composite parts; it mentions engines only as a possible focus for a future part-specific model. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe detection rate is comparable with the performance of neural networks in the reviewed literature, which ranged from 64.4% to 85%.
“The detection rate is comparable with the performance of neural networks in the reviewed literature [21,35,42,48], which ranged from 64.4% to 85%.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [21]
Appendix A, Table 3 reports recall values for this paper’s neural-network dent-detection experiments, including 64.08% and 69.97%, providing an example broadly within the claimed range. It does not establish that detection rates across the reviewed literature ranged from 64.4% to 85%, or explicitly make the claimed comparison. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
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- Authors
- Dogru, A., Bouarfa, S., Arizar, R., & Aydogan, R.
- Corresponding author
- Bouarfa, Soufiane
- ORCID
- Bouarfa, Soufiane
- Venue
- Aerospace
- Publisher
- MDPI AG
- Published
- 2020-11-20
- Volume
- 7
- Issue
- 12
- Pages
- 171-171
- ISSN
- 2226-4310 (ISSN-L)
- OpenAlex
- W3099364866
- Cited by
- 86
- References
- 25
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 17 · 2023: 14 · 2022: 15 · 2021: 11 · 2020: 1
- FWCI
- 5.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Aerospace (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eResearch@Ozyegin (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- aircraft maintenance, automated visual inspection, Mask R-CNN, object detection, data augmentation, class imbalance, paint defects, crack detection, lightning damage, data homogeneity
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