Traditionally, the quality grades of false twist yarn (FTY) packages are classified by human inspection, but the result may be affected by personal, subjective factors. In this paper, we extract the defect features of FTY packages, such as size, discoloration, formation, and cross-over, with image processing technology and use neural networks to classify the quality grades of FTY packages. From the experimental results, we can obtain a classifying rate of about 90%.
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