Fabric Defect Detection Technology Research

In fabric industry, quality control and detection is essential and fabric defect detection is the most important issue in it. Currently, the mainly fabric factories achieve the task by human vision and can not get a good performance because of the work force and environment.Also, the result will be affected by the workers'subjective emotion. The work is badly harmful to the workers'eyes.This paper introduces the composition of the fabric defect detection system and the character of the typical defect classes, and presents three different defect detection algorithms:Gabor transform is an important time-frequency analysis method and has a good ability to texture analysis. A set of similar Gabor filters with different orientations and scales can simulate the human vision system.The paper exploits a multichannel Gabor filters detection, quantitatively analysizes each channel's contribution to the defect detection and improves the multichannel data fusion scheme. Finally, an Auto-thresholding iterative algorithm is used to achieve the fabric defect detecion. Experimental results suggest that the algorithm can choose the channels which fit the human vision nicely.Local Binary Pattern and its improved form are powerful texture description operators, which have attracted more and more attentions from researchers. The paper employs the Uniform LBP to describe the differences between the normal fabric and the defect region, presents a block based defect detection algorithm and analysizes the advantages and disadvantages.This paper also proposes a joint texture spatial structure and contrast algorithm. The feature vectors are extracted from the defect-free fabric. The SOM is trained with these vectors and cluster these datas.Then, a density distribution function is estimated to components of each map unit.An unknown feature vector extracted from a pixel is assigned to a best match uint according to some standard function.The distance between the feature vector and the best match uint determines whether the pixel belongs to defect region.