Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

A K Basu,Jayanta K Chandra,P K Banerjee,S K Bhattacharyya,Asit K Datta

Computing Communication & Networking Technologies（2012）

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摘要

Human visual system can identify larger defects taking place on the woven fabric. But it is very difficult to classify and identify the small fabric defects by a human inspector. In the textile industries the defect detection by a human inspector affects the production tremendously. Thus this paper gives a solution of this problem by developing an automatic fabric defect detection system, based on the computer vision. The sub image based PCA method is applied for the extraction of the feature from the training and test fabric images and the multi-class SVM classifier is used for carrying out the classification task. The method is tested on the standard TILDA database of fabric defect and a success rate of 96.36% is achieved.

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关键词

computer vision,eigenvalues and eigenfunctions,fabrics,feature extraction,image classification,object detection,principal component analysis,production engineering computing,support vector machines,textile industry,woven composites,tilda database,automatic fabric defect detection system,fabric image,human visual system,multiclass svm classifier,subimage based pca method,subimage based eigen fabrics method,woven fabric defect classification,woven fabric defect identification,fabric defect detection and classification,multi-class svm,sub image based pca,woven fabric,visualization,weaving

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