FDICS: A vision-based system for the identification and classification of fabric defects

In today's global market, the key to a manufacturing enterprise's success lies in being competitive. To achieve this, the enterprise must make use of state-of-the-art techniques such as computer-integrated manufacturing (CIM), just-in-time (JIT), and total quality management (TQM). Computer vision is a relatively new technology that combines computers and video cameras to acquire, analyze, and interpret images in a way that parallels human vision. The objective of the research reported in this paper was to develop an automated defect-inspection and classification system using the principles of machine vision, image-processing, and pattern recognition. In this paper, the design, development, and use of a Fabric Defect Identification and Classification System (FDICS), a vision-based system for the identification and classification of fabric defects, is discussed. FDICS is made up of an image-acquisition module, a feature-extraction module, and a classification module. The image-acquisition module obtains the digitized image of the fabric sample by using a video camera and stores it as an image file. The feature-extraction module extracts the tonal and texture features from the image. The classification module classifies an unknown fabric sample into one of five fabric classes based on a Mahalanobis classifier. FDICS has been shown to provide a higher percentage of correct classification than a similar system reported in the literature for defects considered by both systems. The relative accuracy of using only either the tonal or the texture features was studied. The latter set of features gave a higher percentage of correct classification than the former; however, the percentage was highest when both sets of features were used together.