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Abnormality detection strategies for surface inspection using robot mounted laser scanners

Sara Sharifzadeh Orcid Logo, Istvan Biro, Niels Lohse, Peter Kinnell

Mechatronics, Volume: 51, Pages: 59 - 74

Swansea University Author: Sara Sharifzadeh Orcid Logo

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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...

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Published in: Mechatronics
ISSN: 0957-4158
Published: Elsevier BV 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa65606
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spelling v2 65606 2024-02-09 Abnormality detection strategies for surface inspection using robot mounted laser scanners a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-02-09 SCS 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. Journal Article Mechatronics 51 59 74 Elsevier BV 0957-4158 Automatic abnormality detection; Point cloud analysis; Feature extraction; Feature classification; Surface inspection; Sensitivity and specificity 1 5 2018 2018-05-01 10.1016/j.mechatronics.2018.03.001 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee 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. 2024-04-04T12:52:17.1846293 2024-02-09T01:16:32.3292087 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sharifzadeh 0000-0003-4621-2917 1 Istvan Biro 2 Niels Lohse 3 Peter Kinnell 4 65606__29915__f20dc9822b6641fd8e04208084b2e21c.pdf 65606.VOR.pdf 2024-04-04T12:50:20.2080632 Output 2480570 application/pdf Version of Record true © 2018 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Abnormality detection strategies for surface inspection using robot mounted laser scanners
spellingShingle Abnormality detection strategies for surface inspection using robot mounted laser scanners
Sara Sharifzadeh
title_short Abnormality detection strategies for surface inspection using robot mounted laser scanners
title_full Abnormality detection strategies for surface inspection using robot mounted laser scanners
title_fullStr Abnormality detection strategies for surface inspection using robot mounted laser scanners
title_full_unstemmed Abnormality detection strategies for surface inspection using robot mounted laser scanners
title_sort Abnormality detection strategies for surface inspection using robot mounted laser scanners
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sharifzadeh
Istvan Biro
Niels Lohse
Peter Kinnell
format Journal article
container_title Mechatronics
container_volume 51
container_start_page 59
publishDate 2018
institution Swansea University
issn 0957-4158
doi_str_mv 10.1016/j.mechatronics.2018.03.001
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description 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.
published_date 2018-05-01T12:52:13Z
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score 11.013731

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