The Journal of Grey System ›› 2025, Vol. 37 ›› Issue (1): 118-132.

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  • 出版日期:2025-01-18 发布日期:2025-01-18

Research on Image Recognition of Wood Defects Using TGARG Based on Edge Detection and Characteristic Combination 

  1. 1. College of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, P.R. China;  2. College of Science, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, P.R. China
  • Online:2025-01-18 Published:2025-01-18
  • Supported by:
    The authors are grateful to anonymous for their helpful and constructive comments on this paper. The relevant works done are supported by the Interdisciplinary Research Fund of Inner Mongolia Agricultural University (No. BR231502), Key R&D and Achievement Transformation Plan Project in Inner Mongolia Autonomous Region (2022YFDZ0031), the National Natural Science Foundation of China (No. 32160332), Inner Mongolia Agricultural University High-level Talents Scientific Research Project (No. NDYB2019-35). 

Abstract: Wood defects affect the use value and commodity value of wood, so the research on effective recognition of wood defects has important practical significance. In this study, three types of wood defect images (live knots, cracks, and dead knots) were used as research objects. To investigate the impact of edge detection and characteristics combination on recognition rate, the recognition method based on threedimensional grey absolute relational grade (TGARG) is constructed and the recognition rates of different types of wood defects were compared under different edge detection and characteristics combinations. The results show that, based on TGARG, the recognition rate of live knots is the highest (0.76) under edge detection by Canny operator as well as characteristics combination of energy and homogeneity. The recognition rate of cracks is the highest (1.00) under edge detection by Roberts or Sobel operator as well as characteristics combination of contrast and correlation. The recognition rate of dead knots is the highest (0.90) under without edge detection as well as characteristics combination of correlation, energy and homogeneity. The research method and conclusion proposed in this study on the selection of wood defect recognition from a new perspective will contribute to the development of wood defect recognition.  

Key words:  , Grey relational analysis, Wood defect, Image recognition, Edge detection, GLCM

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