This paper describes a computer vision-based fabric inspection system implemented on a circular knitting machine to inspect the fabric under construction. The study consisted of two parts. In the first part, detection of defects in knitted fabric was performed and the performance of three different spectral methods, namely the discrete Fourier transform, the wavelet and the Gabor transforms were evaluated off-line. In the second part, knitted fabric defect-detection and classification was implemented on-line. The captured images were subjected to a defect-detection algorithm, which was based on the concepts of the Gabor wavelet transform, and a neural network (as a classifier). An operator encountering defects also evaluated the performance of the system. The fabric images were broadly classified into seven main categories as well as seven combined defects. The results of the designed system were compared with those of human vision.Circular knitting is one of the easiest and fastest ways (20 million stitches per minute) to produce cloth and textile pieces such as garments, socks, and gloves. The fabric roll is removed from the large diameter circular knitting machine, and then sent to an inspection frame. If inspection could be done on the machine, the need for 100% manual inspection would be eliminated [7].The existing defect-detection techniques can be classified into three different categories: statistical, spectral, and model-based. The defect-detection approach by Zhang and Bresee [21] is based on first-order statistics such as mean and standard deviation. The fabric image is divided into sub-blocks with the use of information obtained by auto-correlation. The use of a gray-level cooccurrence matrix of the image is based on second-order statistics [2]. However, these statistical techniques are not useful for the detection of those textured defects whose statistical features, namely the first-and secondorder moments, are significantly close to that of defectfree textured regions [13]. A high level of quality assurance requires identification of such defects, and therefore techniques based on spectral features have been investigated in the literature.Textured materials, such as woven and knitted fabrics, possess strong periodicity due to the repetition of the basic weaving pattern. Therefore spectral techniques using a discrete Fourier transform [4,19], optical Fourier transform [17], and windowed Fourier transform [3] have been used to detect woven fabric defects. Escofet et al. [8] have used the angular correlation of the Fourier spectra to evaluate fabric web resistance to abrasion. and Chan and Pang [4] have used a Fourier transform to detect fabric defects. Ravandi and Toriumi [16] have also used Fourier transform analysis to measure fabric appearance. Escofet et al. [9] have used a bank of multi-scale and multi-orientation Gabor filters for the detection of local fabric defects. Kumar and Pang [12] have demonstrated another approach to fabric defect detection using real Gabor functions. Jasper et ...

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This paper describes fabric inspection system aided by computer vision to detect and classify defects in circular knitted fabrics using different common texture-recognition methods, including co-occurrence matrices, the discrete Fourier transform, wavelets, Gabor, and clustering. The images of the fabrics were broadly classified into six classes: cracks, holes, vertical stripes, horizontal stripes, soil freckles, and defect-free. One hundred and twenty images (256 gray level and 100 dpi) containing 20 images of defect-free fabrics (rib 1x1) as well as 100 images corresponding to five different categories were used. In general, one-half of the images in each category were employed for training and the remaining images were used for testing. The application of the clustering method applied in this work was found to be highly promising at identifying defects in knitted fabrics. With an overall success rate of 91.6%, the clustering method has a higher efficiency value than all of the other methods. In the case of the wavelet and Gabor methods, the results are acceptable. However, the overall success rates of the co-occurrence matrix and Fourier transform methods in recognizing defects in knitted fabrics are not acceptable.

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