Defect Detection in Fabrics with a Joint Transform Correlation Technique: Theoretical Basis and Simulation

Abstract

The theoretical basis of a technique for real time defect detection in fabrics is presented using a joint transform correlator. This correlation technique is an extension of Fourier transform analysis and is extremely useful for real time pattern recognition. The regular periodic nature of a woven fabric makes it possible to use the Fourier transform technique to detect defects. However, classifying various defect types is difficult from the Fourier analytical and experimental results. A solution to this problem is to use a joint Fourier transform of a reference pattern and the test pattern, and the joint power spectrum is further processed. Cross- and auto-correlation peaks, generated after the execution of the second Fourier transform on the filtered joint power spectrum, indicate the existence of a particular defect type. Because of the parallel processing ability of the optical system, implementing the joint transform correlation technique in an optical domain is advanta geous. A fractional power fringe adjusted filter is used for efficient detection of defects. The mathematical formulation of the technique is supported by the simulated results for identifying some defects such as the existence of thick yarns, knots, and missing yarns.