DETECTION OF FABRIC DEFECTS.

Human inspection is the traditional means to promise the quality of fabric. It helps instant correction of small defects, but errors left due to human eye occurs because of fatigue and fine defects are often undetected. Therefore, automated inspection of fabric defect is then a natural way to improve fabric quality and reduce labor costs. The most difficulty faced by industrial inspection problems deals with the textured materials such as textile web, paper, and wood. In order to deal with the problem of defect detection in fabric defect images we have a proposed an efficient algorithm to easily detect the region containing fabric defects. We have converted the input RGB images into other color spaces i.e. Lab and ycbcr which are more close to human perception and gives better results in feature extraction as well as classification stage. For feature detection we have intensity values from these two color spaces in which rough separation of two classes has been evaluated using CSLBP features and DCT algorithms and neural object has been trained and tested to get segment out the defect region from the rest of the fabric image. Experimental results showed better accuracy on various types of defects i.e. hole, broken needle, dye spot, slub defects etc.