Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels
In this new era, advances computer engineering in robotic vision to inspect the surface defect automatically is quickly penetrating this area of operation. The advance 3D scanning and image processing systems for quality are trending manufacturing industries. This paper proposes a solution for aut...
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2016
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my.utem.eprints.172772021-09-12T20:30:30Z http://eprints.utem.edu.my/id/eprint/17277/ Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels Muhammad Zuhair Bolqiah, Edris Mohammed Saeed, Jawad Zahriladha, Zakaria T Technology (General) In this new era, advances computer engineering in robotic vision to inspect the surface defect automatically is quickly penetrating this area of operation. The advance 3D scanning and image processing systems for quality are trending manufacturing industries. This paper proposes a solution for automated surface defect detection on automotive body panels in the context of quality control in industrial manufacturing. The 3D image is acquired from the 3D scanner from the surface of the body panel, then it's going through a process that will segment the uneven surface as potential defect area and by using Neural Network to recognize and classify defect area. The result of defect has been classified the system will show the defect occur on the automotive body panels. Institute Of Electrical And Electronics Engineers Inc. (IEEE) 2016 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/17277/1/Surface%20Defect%20Detection%20And%20Neural%20Network%20Recognition%20Of%20Automotive%20Body%20Panels.pdf Muhammad Zuhair Bolqiah, Edris and Mohammed Saeed, Jawad and Zahriladha, Zakaria (2016) Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels. 2015 IEEE International Conference On Control System, Computing And Engineering (ICCSCE). pp. 117-122. ISSN 978-147998252-3 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7482169 10.1109/ICCSCE.2015.7482169 |
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T Technology (General) Muhammad Zuhair Bolqiah, Edris Mohammed Saeed, Jawad Zahriladha, Zakaria Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
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In this new era, advances computer engineering in robotic vision to inspect the surface defect automatically is
quickly penetrating this area of operation. The advance 3D
scanning and image processing systems for quality are trending manufacturing industries. This paper proposes a solution for automated surface defect detection on automotive body panels in the context of quality control in industrial manufacturing. The 3D image is acquired from the 3D scanner from the surface of the body panel, then it's
going through a process that will segment the uneven surface as potential defect area and by using Neural Network to recognize and classify defect area. The result of defect has been classified the system will show the defect occur on the automotive body panels. |
format |
Article |
author |
Muhammad Zuhair Bolqiah, Edris Mohammed Saeed, Jawad Zahriladha, Zakaria |
author_facet |
Muhammad Zuhair Bolqiah, Edris Mohammed Saeed, Jawad Zahriladha, Zakaria |
author_sort |
Muhammad Zuhair Bolqiah, Edris |
title |
Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
title_short |
Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
title_full |
Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
title_fullStr |
Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
title_full_unstemmed |
Surface Defect Detection And Neural Network Recognition Of Automotive Body Panels |
title_sort |
surface defect detection and neural network recognition of automotive body panels |
publisher |
Institute Of Electrical And Electronics Engineers Inc. (IEEE) |
publishDate |
2016 |
url |
http://eprints.utem.edu.my/id/eprint/17277/1/Surface%20Defect%20Detection%20And%20Neural%20Network%20Recognition%20Of%20Automotive%20Body%20Panels.pdf http://eprints.utem.edu.my/id/eprint/17277/ http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7482169 |
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