Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm
The search for dents is a consistent part of the aircraft inspection workload. The engineer is required to find, measure, and report each dent over the aircraft skin. This process is not only hazardous, but also extremely subject to human factors and environmental conditions. This study discusses the feasibility of automated dent scanning via a single-shot triangular stereo Fourier transform algorithm, designed to be compatible with the use of an unmanned aerial vehicle. The original algorithm is modified introducing two main contributions. First, the automatic estimation of the pass-band filter removes the user interaction in the phase filtering process. Secondly, the employment of a virtual reference plane reduces unwrapping errors, leading to improved accuracy independently of the chosen unwrapping algorithm. Static experiments reached a mean absolute error of ∼0.1 mm at a distance of 60 cm, while dynamic experiments showed ∼0.3 mm at a distance of 120 cm. On average, the mean absolute error decreased by ∼34%, proving the validity of the proposed single-shot 3D reconstruction algorithm and suggesting its applicability for future automated dent inspections.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © Pasquale Lafiosca et al.
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Authors:
- Lafiosca, Pasquale
- Fan, Ip-Shing
- Avdelidis, Nicolas P
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Digital/other
- Pagination: 433
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Serial:
- Sensors
- Volume: 22
- Issue Number: 2
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Aircraft; Airworthiness; Algorithms; Fourier transforms; Profilometers; Quality control
- Subject Areas: Aviation; Data and Information Technology; Maintenance and Preservation; Vehicles and Equipment;
Filing Info
- Accession Number: 01833654
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 24 2022 5:24PM