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Robust surface abnormality detection for a robotic inspection system

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conference contribution
posted on 2016-11-25, 10:08 authored by Sara Sharifzadeh, Istvan Biro, Niels Lohse, Peter KinnellPeter Kinnell
The detection of surface abnormalities on large complex parts represents a significant automation challenge. This is particularly true when surfaces are large (multiple square metres) but abnormalities are small (less than one mm square), and the surfaces of interest are not simple flat planes. One possible solution is to use a robot-mounted laser line scanner, which can acquire fast surface measurements from large complex geometries. The problem with this approach is that the collected data may vary in quality, and this makes it difficult to achieve accurate and reliable inspection. In this paper a strategy for abnormality detection on highly curved Aluminum surfaces, using surface data obtained by a robot-mounted laser scanner, is presented. Using the laser scanner, data is collected from surfaces containing abnormalities, in the form of surface dents or bumps, of approximately one millimeter in diameter. To examine the effect of scan conditions on abnormality detection, two different curved test surfaces are used, and in addition the lateral spacing of laser scans was also varied. These variables were considered because they influence the distribution of points, in the point cloud (PC), that represent an abnormality. The proposed analysis consists of three main steps. First, a pre-processing step consisting of a fine smoothing procedure followed by a global noise analysis is carried out. Second, an abnormality classifier is trained based on a set of predefined surface abnormalities. Third, the trained classifier is used on suspicious areas of the surface in a general unsupervised thresholding step. This step saves computational time as it avoids analyzing every surface data point. Experimental results show that, the proposed technique can successfully find all present abnormalities for both training and test sets with minor false positives and no false negatives.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

7th IFAC Symposium on Mechatronic Systems MECHATRONICS 2016 IFAC Proceedings Volumes (IFAC-PapersOnline)






301 - 308


SHARIFZADEH, S. ...et al., 2016. Robust surface abnormality detection for a robotic inspection system. IFAC-PapersOnLine, 49(21), pp. 301-308.


© IFAC. Published by Elsevier


  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date


Publication date



This paper was presented at the 7th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2016), Loughborough University, UK, 5-8th Sept. This paper was accepted for publication in the journal IFAC-PapersOnLine and the definitive published version is available at http://dx.doi.org/10.1016/j.ifacol.2016.10.572




  • en


Loughborough University, UK