Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Improved Defect Detection Algorithm of Jean Fabric Based on Optimized Gabor Filter

  • Ma, Shuangbao (Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University) ;
  • Liu, Wen (School of Mechanical Engineering and Automation, Wuhan Textile University) ;
  • You, Changli (School of Mechanical Engineering and Automation, Wuhan Textile University) ;
  • Jia, Shulin (School of Mechanical Engineering and Automation, Wuhan Textile University) ;
  • Wu, Yurong (Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University)
  • Received : 2019.12.17
  • Accepted : 2020.07.27
  • Published : 2020.10.31
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Abstract

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

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References

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