An Improved Defect Detection Algorithm of Jean Fabric Based on Optimized Gabor Filter
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초록
Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on theoptimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on themaximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter bankswith 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the onedimensionalimage 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, suchas normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has betterdetection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.
Abstract
Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on theoptimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on themaximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter bankswith 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the onedimensionalimage 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, suchas normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has betterdetection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.
참고문헌 (11)
- K. Yang, "Fabric defects detection based on the optimal Gabor filters", Journal of Zhangzhou Teachers College(Natural Science Edition), 2016(1), pp.39~45 (2016)
- T. Xue, "Learning-based Gabor filter in multi-style fabric defect detection", Journal of Xi'an Polytechnic University, 31(6), pp.775~780 (2017)
- M. Yu, "Warp knit fabric defect detection method based on optimal Gabor filter", Journal of Textile Research, 37(11), pp.48~54 (2016)
- W. Li, "New progress of fabric defect detection based on computer vision and image processing", Journal of Textile Research, 35(3), pp.158~164 (2016)
- X. Min, "Fabric defect detection based on gray level co-occurrence matrix using image block processing", Information Technology, 2018(5), pp.90~93 (2015)
- Z. Li, "Fabric defect detection using monogenic wavelet analysis", Journal of Textile Research, 37(9), pp.59~64 (2016)
- G. Hu, "Markov random field image segmentation method combined with edge constraints", Computer Knowledge and Technology, 2018(20), pp.174~178 (2018)
- Z. Cao, "Study on the defect detection of Yarn-dyed fabric with deep learning based on Raspberry Pi", Cotton Textile Technology, 47(1), pp.11~15 (2019)
- W. Ouyang, "Fabric defect detection using activation layer embedded convolutional neural network", IEEE Access, 7, pp.70130~70140 (2019)
- M. Wang, "Fabric defect detection based on gray-level gradient cooccurrence matrix and SVDD", Silk Monthly, 55(12), pp.50~56 (2018)
- J. L. Raheja, "Fabric defect detection based on GLCM and Gabor filter : a comparison", Optik, 124(23), pp.6469~6474 (2013)