Artificial neural networks for automated quality control of textile seams
This paper presents an automatic vision based system for quality control of yarn ends ready for splicing, which is aimed to establish a standard quality measure and lower manufacturing cost. New approach for defect detection and classification is presented. In this approach, features describing the shape and surface defects are extracted and defects are classified into different classes. Examples of defects are used to train the classification system using neural network. Experimental results show that a high detection and classification rate can be obtained using this approach.