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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 12
3889-3892

Research on defects and textures recognition of solid wood lumbers based on deep belief network

Hu Zhongkang
Liu Ying
Zhou Xiaolin
Zhao Qian
Shen Luxiang
College of Mechanical & Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0438

Abstract

In modern wood processing enterprises, the main quality classification factors of solid wood lumbers are defects and textures. This research proposed a deep learning algorithm based on a mixture of local binary pattern, self-learning deep belief networks and softmax classification to classify solid wood lumbers with defects and textures. Firstly, it extracted the defects and textures features of solid wood lumbers, on which to be based, used the deep belief networks to train and learn the characteristics of the local binarized processing. Afterwards, it adopted the self-learning learning rate algorithm to optimize the convergence speed and reduced the training time. Finally, the algorithm obtained the common defects, straight textures and confused textures by using a softmax classification. Compared with classical algorithms e. g. BP neural network, support vector machine and extreme learning machine, the error rate of solid wood defects and textures recognition obtained by the deep belief networks designed is about 3.59%, and this algorithm works efficiently in recognition the defects and textures of solid wood lumbers.

Foundation Support

国家林业局“948”项目(2014-4-48)
江苏省政策引导类计划(国际科技合作)项目(BZ2016028)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0438
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 12
Section: Technology of Graphic & Image
Pages: 3889-3892
Serial Number: 1001-3695(2019)12-082-3889-04

Publish History

[2019-12-05] Printed Article

Cite This Article

胡忠康, 刘英, 周晓林, 等. 基于深度置信网络的实木板材缺陷及纹理识别研究 [J]. 计算机应用研究, 2019, 36 (12): 3889-3892. (Hu Zhongkang, Liu Ying, Zhou Xiaolin, et al. Research on defects and textures recognition of solid wood lumbers based on deep belief network [J]. Application Research of Computers, 2019, 36 (12): 3889-3892. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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