JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2013, Vol. 43 ›› Issue (2): 23-28.

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Wood defects recognition based on the convolutional neural network

XU Shan-shan, LIU Ying-an*, XU Sheng   

  1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
  • Received:2012-05-20 Online:2013-04-20 Published:2012-05-20

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Abstract:

To improve the efficiency of wood defects identification, a method based on the convolutional neural network was proposed. A convolutional neural network was presented to recognize the wood defect, and the numbers of training samples were determined by an incremental learning method; the corresponding network structure was designed, and the time consumption could be reduced. Experimental results showed that the preprocessing of a complex image was not needed, and the multiclass defects could be recognized with high accuracy, small complexity and good robustness, while the inherent shortcomings of the traditional algorithm were overcame.

Key words: wood defects, convolutional neural networks, image processing, learning methods, incremental

CLC Number: 

  • TP301
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