Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)

Wood Veneer Defect Detection System Based on Machine Vision

Authors
Fan Yang, Yuzeng Wang, Shibing Wang, Yunmei Cheng
Corresponding Author
Fan Yang
Available Online July 2018.
DOI
10.2991/cecs-18.2018.70How to use a DOI?
Keywords
Wood Veneer, Defect Detection System, Machine Vision.
Abstract

In view of the problems of low surface detection efficiency and high error detection rate in domestic wood skin, a corresponding detection system is designed to identify four types of wood skin defects such as dead knot, slipknot, hole and crack on the surface of the wood. Based on the image recognition of industrial cameras, the detection system can collect data in real time and quickly. Then the system can recognize and classify the collected images. The system is of great significance to the rational utilization of wood. The experimental results show that the detection system is safe and reliable, and has high accuracy.

Copyright
© 2018, 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 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
Series
Advances in Computer Science Research
Publication Date
July 2018
ISBN
978-94-6252-571-9
ISSN
2352-538X
DOI
10.2991/cecs-18.2018.70How to use a DOI?
Copyright
© 2018, 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  - Fan Yang
AU  - Yuzeng Wang
AU  - Shibing Wang
AU  - Yunmei Cheng
PY  - 2018/07
DA  - 2018/07
TI  - Wood Veneer Defect Detection System Based on Machine Vision
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 413
EP  - 418
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.70
DO  - 10.2991/cecs-18.2018.70
ID  - Yang2018/07
ER  -