Automated visual inspection of an airplane exterior
Open accessAbstract
This paper deals with the inspection of an airplane using a Pan-Tilt-Zoom camera mounted on a mobile robot moving around the airplane. We present image processing methods for detection and inspection of four different types of items on the airplane exterior. Our detection approach is focused on the regular shapes such as rounded corner rectangles and ellipses, while inspection relies on clues such as uniformity of isolated image regions, convexity of segmented shapes and periodicity of the image intensity signal. The initial results are promising and demonstrate the feasibility of the envisioned robotic system.
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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 [20]
The Introduction describes a project to automate airplane-exterior inspection, and §2.5 describes a method for detecting large defects in engine fan blades. The cited paper does not establish automated damage detection for tyres or composite parts, and its mention of a fuselage crack concerns human inspection rather than the system evaluated here. (LLM, conf=0.85)
Experimental — accuracy not yet measuredEngine parts such as shafts, fan blades, compressor blades and turbine blades are safety-critical to flight operations.
“Engine parts such as shafts [31,32], fan blades [20,33], compressor blades [9,34,35] and turbine blades [9,26,36-43] are also of particular interest as all are safety-critical to flight operations.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [20]
Section 2.5, “Fan blades,” describes inspecting engine fan blades for large defects, including a missing blade. The Introduction places the inspection project in a preflight context, but the paper does not explicitly say that fan blades are safety-critical to flight operations. The other engine parts in the sentence are attached to different citations. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Cites
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Flagged first, then by citations
Research works printed but not linked: 2
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Cited by
Sorted by citations
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
“Some researchers only reported the TP rate [35,42] or the FP and FN rates [20], while others reported the error rate [48], detection rate [21,43] or accuracy [43], and still, others made it dependent of the defect size and used a probability of detection curve [33].”
BackgroundMethodAust, J., & Pons, D. J. (2022) · Applied Sciences · doi:10.3390/app12042250 · Cited by 58 · see citing works
Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot
Jovančević, I., Larnier, S., Orteu, J., & Sentenac, T. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.6.061110 · Cited by 57 · see citing works
Self-citationA mask R-CNN based method for inspecting cable brackets in aircraft
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A context-driven approach to image-based crack detection
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Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review
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Soldering Defect Detection with kNN and SVM for Quality Enhancement in Industrial
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Person Following from a Nonholonomic Mobile Robot with Ultimately Bounded Tracking Error
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Novel methods for inspection of damage on airframes
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A Novel Detection Approach for Quasi-Convex Quadrilateral Aircraft Covers
Yue, W., Liu, Z., Wang, K., Hu, Z., & Wang, H. (2026) · IEEE Access · doi:10.1109/access.2026.3669209
Uncertainty-Guided Hybrid CNN-Transformer Architecture for Aircraft Surface Defect Detection
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- Authors
- Jovančević, I., Orteu, J.-J., Sentenac, T., & Gilblas, R.
- Affiliations
- Institut National des Sciences Appliquées de Toulouse (FR)
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2015-04-30
- Volume
- 9534
- Pages
- 95340Y
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2621429467
- Cited by
- 13
- References
- 14
- Citations by year
- 2026: 2 · 2024: 2 · 2022: 1 · 2021: 2 · 2020: 2 · 2018: 1 · 2016: 2 · 2015: 1
- FWCI
- 2.95
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- Centre National de la Recherche Scientifique
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- automated visual inspection, pan-tilt-zoom camera, mobile robots, object detection, shape detection, ellipse, image segmentation, convexity, inspection robot
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