A proposed system for cotton yarn defects classification using probabilistic neural network

摘要

Cotton yarn defect such as neps which are highly entangled fibres causes a serious problem in the textile industry. In this study, two types of cotton yarn neps, viz. seed coat and fibrous neps are classified by means of probabilistic neural network (PNN) using the features extracted from the images of neps. A k-fold cross validation technique has been applied to assess the performance of the PNN classifier. The results shows that the neps classification accomplished by means of image recognition through PNN classifier agree eminently well. The robustness, speed of execution, proven accuracy coupled with simplicity in algorithm holds the PNN as a foremost classifier to recognize yarn defects. The five fold cross validation is applied to measure the performance of the proposed method and it achieves nearly 96%–99% accuracy for the test data set.