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Artificial neural networks (ANN), with capabilities of fault tolerance and learning, can be used to detect fabric defects. Because the back propagation algorithm, has higher learning accuracy and successful applications, we have used it in this study to identify missing ends, missing picks, oily fabric, and broken fabric, all often found as defects in fabrics. The correct selection of characteristic parameters for the input layer in an ANN plays a great role in the recognition rate. The spatial periodicity of a fabric image can be transferred into spatial frequency by fast Fourier transform owing to the fabric's periodicity. Once a defect occurs in the fabric, its periodicity is changed so that the corresponding intensities at the specific positions of the spectrum obviously change. These intensities can act as characteristic parameters and can be substituted in the ANN for learning. Altogether, nine parameters derived from the spectrum have been selected by the ordinary method, which provides the characteristic parameters without any extra modification, and by the statistical method, which modifies the characteristic parameters with variations between the defective and normal fabrics. Of the two plain fabrics used (with densities of 70 X 60 and 65 X 45), for each fabric, the results show that the total classification rates each above 96%. The total classification rate is 88% with the statistical method while the ordinary method is 24% if only one fabric is selected and the learned mode is applied for a new, unlearned fabric. The statistical method can be used for fabric defect recognition, and any inconvenience caused by various specifications of warp and weft densities can be minimized.
Tsai et al. (Mon,) studied this question.