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1 2011

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Automated Vision System for Recognising Lycra Spandex Defects

Su T. L., Lu C. F.

Fibres & Textiles in Eastern Europe

2011

Vol. 19, no. 1 (84)

43-46

Fabric defect detection and classifcation plays a very important role in the automatic detection process for fabrics. This paper refers to the seven commonly seen defects of lycra spandex: laddering, end-out, hole, oil spot, dye stain, snag, and crease mark. First of all, the gray level co-occurrence matrix was used to collect the features of the fabric image texture, and then the back-propagation neural network was used to establish faw classifcations of the fabric. In addition, by using the Taguchi method combined with BPNN, the BPNN drawback was improved upon, which requires overly time consuming trial-and-error to fnd the learning parameters, and could therefore converge even faster with an even smaller convergence error and better recognition rate. The experimental results proved that the fnal root-mean-square error convergence of the Taguchi-based BPNN was 0.000104, and that the recognition rate can reach 97.14%.