Detection of wood surface defects based on particle swarm-genetic hybrid algorithm

Zhen Nan Ke, Qi Jie Zhao, Chun Hui Huang, Pu Ai, Jin Gang Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Wood defect detection has an important influence on the automation of wood industry. In view of the complexity of wood defect segmentation, this paper proposes the hybrid algorithm of genetic algorithm and particle swarm optimization algorithm. Firstly, the contrast of the image is enhanced by the linear transformation function. Then, applying genetic algorithm and particle swarm-genetic hybrid algorithm respectively for the image segmentation, and finally morphological processing is performed. The result shows that the particle swarm-genetic hybrid algorithm has a better and more stable effect on detecting wood defects compared with the genetic algorithm.

Original languageEnglish (US)
Title of host publicationICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings
EditorsFa-Long Luo, Xiaoqing Yu, Wanggen Wan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages375-379
Number of pages5
ISBN (Electronic)9781509006533
DOIs
StatePublished - Feb 7 2017
Event5th International Conference on Audio, Language and Image Processing, ICALIP 2016 - Shanghai, China
Duration: Jul 11 2016Jul 12 2016

Publication series

NameICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings

Other

Other5th International Conference on Audio, Language and Image Processing, ICALIP 2016
CountryChina
CityShanghai
Period7/11/167/12/16

Fingerprint

Surface defects
Wood
Genetic algorithms
Defects
Linear transformations
Image segmentation
Particle swarm optimization (PSO)
Automation
Processing
Industry

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Keywords

  • Defect detection
  • Genetic algorithm
  • Particle swarm-genetic hybrid optimization algorithm

Cite this

Ke, Z. N., Zhao, Q. J., Huang, C. H., Ai, P., & Yi, J. G. (2017). Detection of wood surface defects based on particle swarm-genetic hybrid algorithm. In F-L. Luo, X. Yu, & W. Wan (Eds.), ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings (pp. 375-379). [7846635] (ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICALIP.2016.7846635
Ke, Zhen Nan ; Zhao, Qi Jie ; Huang, Chun Hui ; Ai, Pu ; Yi, Jin Gang. / Detection of wood surface defects based on particle swarm-genetic hybrid algorithm. ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings. editor / Fa-Long Luo ; Xiaoqing Yu ; Wanggen Wan. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 375-379 (ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings).
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abstract = "Wood defect detection has an important influence on the automation of wood industry. In view of the complexity of wood defect segmentation, this paper proposes the hybrid algorithm of genetic algorithm and particle swarm optimization algorithm. Firstly, the contrast of the image is enhanced by the linear transformation function. Then, applying genetic algorithm and particle swarm-genetic hybrid algorithm respectively for the image segmentation, and finally morphological processing is performed. The result shows that the particle swarm-genetic hybrid algorithm has a better and more stable effect on detecting wood defects compared with the genetic algorithm.",
keywords = "Defect detection, Genetic algorithm, Particle swarm-genetic hybrid optimization algorithm",
author = "Ke, {Zhen Nan} and Zhao, {Qi Jie} and Huang, {Chun Hui} and Pu Ai and Yi, {Jin Gang}",
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Ke, ZN, Zhao, QJ, Huang, CH, Ai, P & Yi, JG 2017, Detection of wood surface defects based on particle swarm-genetic hybrid algorithm. in F-L Luo, X Yu & W Wan (eds), ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings., 7846635, ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 375-379, 5th International Conference on Audio, Language and Image Processing, ICALIP 2016, Shanghai, China, 7/11/16. https://doi.org/10.1109/ICALIP.2016.7846635

Detection of wood surface defects based on particle swarm-genetic hybrid algorithm. / Ke, Zhen Nan; Zhao, Qi Jie; Huang, Chun Hui; Ai, Pu; Yi, Jin Gang.

ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings. ed. / Fa-Long Luo; Xiaoqing Yu; Wanggen Wan. Institute of Electrical and Electronics Engineers Inc., 2017. p. 375-379 7846635 (ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Detection of wood surface defects based on particle swarm-genetic hybrid algorithm

AU - Ke, Zhen Nan

AU - Zhao, Qi Jie

AU - Huang, Chun Hui

AU - Ai, Pu

AU - Yi, Jin Gang

PY - 2017/2/7

Y1 - 2017/2/7

N2 - Wood defect detection has an important influence on the automation of wood industry. In view of the complexity of wood defect segmentation, this paper proposes the hybrid algorithm of genetic algorithm and particle swarm optimization algorithm. Firstly, the contrast of the image is enhanced by the linear transformation function. Then, applying genetic algorithm and particle swarm-genetic hybrid algorithm respectively for the image segmentation, and finally morphological processing is performed. The result shows that the particle swarm-genetic hybrid algorithm has a better and more stable effect on detecting wood defects compared with the genetic algorithm.

AB - Wood defect detection has an important influence on the automation of wood industry. In view of the complexity of wood defect segmentation, this paper proposes the hybrid algorithm of genetic algorithm and particle swarm optimization algorithm. Firstly, the contrast of the image is enhanced by the linear transformation function. Then, applying genetic algorithm and particle swarm-genetic hybrid algorithm respectively for the image segmentation, and finally morphological processing is performed. The result shows that the particle swarm-genetic hybrid algorithm has a better and more stable effect on detecting wood defects compared with the genetic algorithm.

KW - Defect detection

KW - Genetic algorithm

KW - Particle swarm-genetic hybrid optimization algorithm

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T3 - ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings

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BT - ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings

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Ke ZN, Zhao QJ, Huang CH, Ai P, Yi JG. Detection of wood surface defects based on particle swarm-genetic hybrid algorithm. In Luo F-L, Yu X, Wan W, editors, ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 375-379. 7846635. (ICALIP 2016 - 2016 International Conference on Audio, Language and Image Processing - Proceedings). https://doi.org/10.1109/ICALIP.2016.7846635