Yarn defect detection system using image processing.

Abstract

The purpose of this project is to develop an off-line yam defect detection system by a computerized system based on image processing software. After the yam is produced, it is wound onto the bobbin package, different yarn bobbins are sampled and sent to the quality inspection department to test for yam evenness. Yam defect detection is an important index of quality control in textiles since the unevenness of yams increases the end breakage rate during post spinning which will ultimately reduce the productivity. In addition, yam unevenness/defects affect the quality of appearance of textiles. Methods of yam defects detection include; Human Visualisation, Gravimetric method, and Electronic capacitive testers (Uster Tester). The Gravimetric method is rarely adopted in general tests because of large computation, slow and laborious. The Human Visualisation is associated with drawbacks such as; results are subjective in nature, tiredness and boredom. The Electronic Capacitive Tester helps get rid of the influence of man-made factors and fast so, it is applied widely to detect yam defects. However, it has certain limitations such as high cost, testing values are affected by the testing conditions especially the atmosphere state or humidity. The development in computer technology using image processing (MATLAB) introduces a cost effective yarn defect detection system with few components (i.e. computer, USB Web camera, and Blackboard). The described method in this project represents an effective and accurate approach to detection of yam defects. In this work, edge detection and scaling techniques are implemented to examine the structural regularity of yam structures. To improve the efficiency of the technique and overcome the problem of detection errors, parameters of the detection process should be maintained constant Basing on the methods and materials employed in this project, provide a promising stage for the development of an off-line cost effective defect detection system.