Automatic flaw detection in textiles using a Neyman-Pearson detector

A system for the automated visual inspection of textiles is discussed. The system consists of two main components, (1) the extraction of the texture features utilising the Karhunen-Loeve (KL) transform which provides optimal compression of the image data into a feature vector and (2) the detection of the flaw patterns using a Neyman-Pearson detector, which maximises the rate of detection for a specified false alarm rate. The performance of the system was evaluated on various fabrics and different types of textile flaws. The results indicate that the system can detect flaws which vary drastically in physical dimension and nature with a very low false alarm rate. Experimental results in the paper demonstrate the performance of the detector on some typical textile flaws.