• DocumentCode
    508027
  • Title

    Research on Wood Density Detection by X-Ray Based on Neural Network

  • Author

    Qi, Dawei ; Zhang, Peng

  • Author_Institution
    Univ. of Northeast Forestry, Harbin, China
  • Volume
    2
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    429
  • Lastpage
    433
  • Abstract
    X-ray real-time digital imaging technique is applied in getting log image without log destruction. This paper presents the average density value of the specific spot of log is measured quickly and exactly according to log perimeter and log image information using the method of artificial neural network. According to the basic knowledge of X-ray testing technique, the method of getting high quality digital image is provided. This paper presents the improved method of image collection system, and setting method of the best parameters of equipment. This paper creates a BP network prediction model of log average density, analyzes the performance of network with different structures and parameters, and presents the best parameters of BP neural networks which are used for measuring log average density. A new method of log average density fast measuring is provided.
  • Keywords
    X-ray imaging; backpropagation; forestry; image processing; neural nets; real-time systems; wood; BP network prediction model; BP neural networks; X-ray real-time digital imaging technique; X-ray testing technique; artificial neural network; image collection system; log average density; log destruction; log image information; wood density detection; Artificial neural networks; Density measurement; Digital images; Neural networks; Performance analysis; Predictive models; Testing; X-ray detection; X-ray detectors; X-ray imaging; X-ray; and digital image; neural network; wood density;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.499
  • Filename
    5364750