TY - JOUR T1 - Automatic Defect Detection of Patterned Fabric via Combining the Optimal Gabor Filter and Golden Image Subtraction AU - Junfeng Jing, Shan Chen & Pengfei Li JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 229 EP - 239 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00103 UR - https://global-sci.org/intro/article_detail/jfbi/4702.html KW - Defect Detection KW - Gabor Filter KW - GIS KW - Genetic Algorithm KW - Patterned Fabrics AB - A new algorithm based on optimal Gabor filter and the basic Golden Image Subtraction (GIS) is presented for patterned fabric defect detection. Firstly, the defect-free patterned fabric images are processed to search optimal real Gabor filter parameters using traditional Genetic Algorithm (GA). Then test patterned fabric images are filtered according to the obtained optimal real Gabor filter. Furthermore, the basic GIS are adopted to perform subtractions between golden images from referenced fabric images and test images to get resultant images. Finally, thresholding is obtained by training a large amount of defect-free patterned fabric samples to segment defects from fabric background. Experiment results indicate that the average detection success rate is up to 96.31% with ninety defective patterned images and ninety defect-free patterned images. It demonstrates that the proposed method is more efficient.

Junfeng Jing, Shan Chen and Pengfei Li. (2015). Automatic Defect Detection of Patterned Fabric via Combining the Optimal Gabor Filter and Golden Image Subtraction. Journal of Fiber Bioengineering and Informatics. 8 (2). 229-239. doi:10.3993/jfbim00103