Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods

Research on the Detection Algorithm of Workpiece Surface Defects Based on Machine Vision

Authors
Yuntao Zhang, Xiaorong Chen, Yin Yi
Corresponding Author
Yuntao Zhang
Available Online November 2015.
DOI
10.2991/tmcm-15.2015.11How to use a DOI?
Keywords
region growing; remove the shadow; defect detection; threshold segmentation
Abstract

It is important to evaluate the workpiece whether the surface is defective or not. Because of the shadow area due to light angle by taking pictures, false edges are obtained. It will lead to that the real edge will be mistaken for surface scratches. Based on region growing, this paper introduces a new algorithm which is proposed to remove the shadow region, After the Gauss filter, the real defect of the component surface is obtained by the threshold segmentation. The experiments show that this method is suitable for a variety of scenarios of component surface defect detection. And it has the characteristics of high efficiency, stability and high precision.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods
Series
Advances in Computer Science Research
Publication Date
November 2015
ISBN
978-94-6252-132-2
ISSN
2352-538X
DOI
10.2991/tmcm-15.2015.11How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yuntao Zhang
AU  - Xiaorong Chen
AU  - Yin Yi
PY  - 2015/11
DA  - 2015/11
TI  - Research on the Detection Algorithm of Workpiece Surface Defects Based on Machine Vision
BT  - Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods
PB  - Atlantis Press
SP  - 40
EP  - 43
SN  - 2352-538X
UR  - https://doi.org/10.2991/tmcm-15.2015.11
DO  - 10.2991/tmcm-15.2015.11
ID  - Zhang2015/11
ER  -