Automated textile defects recognition system using computer vision and interval type-2 fuzzy logic

In this paper, a modified method for textile defects recognition is proposed. Description of problems in the textile industry is too uncertain, vague, or subjective to be useful. To overcome this uncertainty and achieve automated on-line control, fuzzy expert systems have been used. Interval type-2 fuzzy sets help us to improve the performance result in textile defect recognition. Type-2 fuzzy sets (T2FSs) have been shown to manage uncertainty more effectively than Type-1 fuzzy sets (T1FS). However computing with T2FSs can require undesirably large amount of computations since it involves numerous embedded T2FSs. To reduce the complexity, interval type-2 fuzzy sets (IT2 FSs) have been used, since the secondary memberships are all equal to one. Experimental results for several data sets are given, which showed the effectiveness of the suggested technique for detecting fabric defects and also show the privilege and high accuracy when compared with other methods.