Automatic detection system for printed fabric defects

Abstract

Printed fabrics are high value-added artifacts with rich colors and various patterns. Flawed products occur owing to uncertainties during the manufacturing process. Such defects waste not only raw materials and machine operating time, but also large amounts of labor to inspect, sift and sort. Hence, if the detection process for printed fabric defects could be automated, the product quality of printed fabrics could be increased, and industry efficiency could also be improved by reducing the requirement for manpower. So this study aims to develop such a defect detecting system to investigate printed fabrics with repeated patterns, locate flaw sites by the minimum repeated zone of repeated patterns, and finally find out the most common flaw type. The novelty of this technique is the introduction of an image processing technology known as the RGB accumulative average method (RGBAAM) to test and locate flawed zones, then use fuzzy logic to discern the flaw types. The RGBAAM has the merits of compactness and high execution speed, and it is an efficient algorithm for pattern recognition. The subject fabrics are printed fabrics with repeated patterns, and to interpret this kind of image, pure numeric calculations are faster than the widely used genetic algorithm. Experimental results show that this system can analyze and recognize 96.8% of defect types in printed fabrics, and therefore brings substantial benefits to control the product quality and improve current flaw detecting process in the printed fabric industry.