Soft computing applied to the build of textile defects inspection system

The inspection of textile defects is challenging because of the large number of defects categories that are characterised by their imprecision and uncertainty. In this study, novel interval type-2 fuzzy system is proposed for resolving defects recognition problem of textile industries. The proposed system mixes interval type-2 fuzzy reasoning and swarm optimisation algorithm together in order to enhance the defects classification capabilities. Interval type-2 fuzzy logic is powerful in handling high level of indecisions in the human decision making process, including uncertainties in measurements of textile features and data used to calibrate the examination's parameters. Swarm intelligence algorithm is used to optimise parameters of the membership functions to increase the accuracy of fuzzy controller. Besides, the problem of fuzzy linguistic rules learning has been tackled by utilising ant colony meta-heuristic method to reduce the complexity of the inspection system. Excellent recogniser results on real textile samples, using this system, are demonstrated.

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