Fabric defect detection by Fourier analysis

Many fabric defects are very small and undistinguishable, which makes them very difficult to detect by only monitoring the intensity change, Faultless fabric is a repetitive and regular global texture and Fourier transform ca n be applied to monitor the spatial frequency spectrum of a fabric. When a defect occurs in fabric, its regular structure is changed so that the corre sponding intensity at some specific positions of the frequency spectrum wou ld change, However, the three-dimensional frequency spectrum is very diffic ult to analyze, In this paper, a simulated fabric model Is used to understa nd the relationship between the fabric structure in the image space and in the frequency space. Based on the three-dimensional frequency spectrum, two significant spectral diagrams are defined and used for analyzing the fabri c defect, These two diagrams are called the central spatial frequency spect rums. The defects are broadly classified into four classes: 1) double yarn; 2) missing yarn; 3) webs or broken fabric; and 4) yarn densities variation . After evaluating these four classes of defects using some simulated model s and real samples, seven characteristic parameters for a central spatial f requency spectrum are extracted for defect classification.