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Abnormality detection strategies for surface inspection using robot mounted laser scanners
,
Istvan Biro,
Niels Lohse,
Peter Kinnell
Mechatronics, Volume: 51, Pages: 59 - 74
Swansea University Author:
Sara Sharifzadeh
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© 2018 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.mechatronics.2018.03.001
Abstract
The detection of small surface abnormalities on large complex free-form surfaces represents a significant challenge. Often surfaces abnormalities are less than a millimeter square in area but, must be located on surfaces of multiple meters square. To achieve consistent, cost effective and fast inspe...
| Published in: | Mechatronics |
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| ISSN: | 0957-4158 |
| Published: |
Elsevier BV
2018
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65606 |
| Abstract: |
The detection of small surface abnormalities on large complex free-form surfaces represents a significant challenge. Often surfaces abnormalities are less than a millimeter square in area but, must be located on surfaces of multiple meters square. To achieve consistent, cost effective and fast inspection, robotic or automated inspection systems are highly desirable. The challenge with automated inspection systems is to create a robust and accurate system that is not adversely affected by environmental variation. Robot-mounted laser line scanner systems can be used to acquire surface measurements, in the form of a point cloud1 (PC), from large complex geometries. This paper addresses the challenge of how surface abnormalities can be detected based on PC data by considering two different analysis strategies. First, an unsupervised thresholding strategy is considered, and through an experimental study the factors that affect abnormality detection performance are considered. Second, a robust supervised abnormality detection strategy is proposed. The performance of the proposed robust detection algorithm is evaluated experimentally using a realistic test scenario including a complex surface geometry, inconsistent PC quality and variable PC noise. Test results of the unsupervised analysis strategy shows that besides the abnormality size, the laser projection angle and laser lines spacing play an important role on the performance of the unsupervised detection strategy. In addition, a compromise should be made between the threshold value and the sensitivity and specificity of the results. |
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| Keywords: |
Automatic abnormality detection; Point cloud analysis; Feature extraction; Feature classification; Surface inspection; Sensitivity and specificity |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors acknowledge support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation, and the funding support from EPSRC for this work as part of grant EP/L01498X/1. |
| Start Page: |
59 |
| End Page: |
74 |