Computer vision-aided fabric inspection system for on-circular knitting machine

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.