3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

  • Igor Jovančević
  • Huy-Hieu Pham
  • Jean-José Orteu
  • Rémi Gilblas
  • Jacques Harvent
  • Xavier Maurice
  • Ludovic Brèthes
Article

Abstract

Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capable of detecting and characterizing defects on an airplane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and extract the information about the undesired defects such as dents, protrusions or scratches based on local surface properties. Surface dents and protrusions are identified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Region Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data obtained from an Airbus A320, for different types of defect. The accuracy of the system is evaluated by comparing the measurements of our approach with ground truth measurements obtained by a high-accuracy measuring device. The result shows that our work is robust, effective and promising for industrial applications.

Keywords

Aircraft Defect detection Defect characterization Non destructive evaluation 3D scanner Unorganized point cloud 

List of symbols

\(P_{N} = \{p_{1}, p_{2},...,p_{N}\}\)

A set of N points, \(p_{i}\) is the \(i^{th}\) data point

\(p_{i} = (x_{i}, y_{i}, z_{i})\)

A point in three-dimensional

\(P^K= {p_{1}, p_{2},..., p_{K}}\)

The set of points which are located in the k-neighborhood of a query point \(p_{i}\)

\(\overline{p}\)

The centroid of the data e.g., given a set of points \(P_{N}\), we have: \(\overline{p} = \frac{1}{N}(\sum \limits _{i = 1}^{N}x_{i}, \sum \limits _{i = 1}^{N} y_{i}, \sum \limits _{i = 1}^{N} z_{i})\)

\(n_{i}\)

A surface normal estimated at a point \(p_{i}\)

\(\cdot \)

The dot product

\(\times \)

The cross product

\(\Vert \circ \Vert \)

The Euclidean norm of \( \circ \)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institut Clément Ader (ICA)Université de Toulouse, CNRS, INSA, UPS, Mines Albi, ISAEAlbiFrance
  2. 2.KEONYSBlagnacFrance

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