Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm

Lafiosca, Pasquale; Fan, Ip‐Shing; Avdelidis, Nicolas P.

doi:10.3390/s22020433

Cited by 6 publications

(2 citation statements)

References 39 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The signals are obtained scanning flat a white board several times and in different positions with the 3D scanner. In this case a system based on Fourier transform profilometry [19,20] was chosen. An example of its noise distribution over a flat board about 40 cm wide is shown in Fig.…”

Section: Noise Signalsmentioning

confidence: 99%

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Lafiosca¹,

Fan²,

Avdelidis³

2022

SAE Technical Paper Series

Self Cite

View full text Add to dashboard Cite

Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network.The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available. Dents are thus simulated in random positions and shapes, within criteria and definitions of a Boeing 737 structural repair manual. The noise distribution from the scanning apparatus is then added to reflect the complete process of 3D point acquisition on the training. The second proposition is a surface fitting strategy to convert 3D point clouds to 2.5D. This allows higher resolution point clouds to be processed with a small amount of memory compared with state-of-the-art methods involving 3D sampling approaches. Simulations with available ground truth data show that the proposed technique reaches an intersection-over-union of over 80%. Experiments over dent samples prove an effective detection of dents with a speed of over 500 000 points per second.

show abstract

Section: Noise Signalsmentioning

confidence: 99%

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Lafiosca¹,

Fan²,

Avdelidis³

2022

SAE Technical Paper Series

Self Cite

View full text Add to dashboard Cite

show abstract

“…Conversely, the transform-based methods, 12 – 14 requiring only a single fringe for phase calculation, boast high efficiency 15 – 18 The Fourier transform (FT) algorithm, 19 – 21 a typical transform-based technique, transforms the fringe pattern into the frequency domain for phase extraction. Despite its high computation efficiency, the band-pass filtering process in the FT algorithm may compromise accuracy in edge regions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic three-dimensional measurement by spatial-domain phase-shifting method based on fringe projection profilometry

Zhu,

Deng,

Zhang

et al. 2024

Opt. Eng.

View full text Add to dashboard Cite

The Fourier transform (FT) method is one of the foremost single-fringe techniques for high-speed three-dimensional (3D) surface reconstruction within the fringe projection profilometry (FPP). Despite its widespread use, the intrinsic issue of spectrum leakage in the FT method leads to diminished precision in regions of image edge. We introduce a spatial-domain phase-shifting (SDPS) method, employing pixel spatial translation to extract the phase-shifting subfringe sequence, followed by the least-square fitting technique to accurately determine surface phase information. Simulation analyses for continuous and discontinuous structures are conducted to affirm that the SDPS method could mitigate the edge error in comparison to the FT method. Meanwhile, the factors that may influence the performance of the proposed method are also analyzed, including the background, modulation, and spatial translation distance. Experiments of static and dynamic scenarios are carried out to further demonstrate that the SDPS method could achieve improved measurement precision in edge areas when compared with the FT method and has the potential to be applied in dynamic 3D surface reconstruction with high precision and efficiency in FPP.

show abstract

Optimization of YOLOv8 for Defect Detection and Inspection in Aircraft Surface Maintenance using Enhanced Hyper Parameter Tuning

Pasupuleti,

Pattisapu

2024

2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)

View full text Add to dashboard Cite

Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm

Cited by 6 publications

References 39 publications

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Dynamic three-dimensional measurement by spatial-domain phase-shifting method based on fringe projection profilometry

Optimization of YOLOv8 for Defect Detection and Inspection in Aircraft Surface Maintenance using Enhanced Hyper Parameter Tuning

Contact Info

Product

Resources

About