Integrated Computer Vision and Type-2 Fuzzy CMAC Model for Classifying Pilling of Knitted Fabric

Abstract

Human visual inspection for classifying the pilling of knitted fabric not only consumes human resources but also causes occupational hazard because of long-term observation using human eyes. This reduces the efficiency of the entire operation. To overcome this, an integrated computer vision and type-2 fuzzy cerebellar model articulation controller (T2FCMAC) was devised for classifying the pilling of knitted fabric. First, the fast Fourier transform was used for image preprocessing to strengthen the characteristics of the pilling in the fabric image. The background and the pilling of knitted fabric were then segmented through binary and morphological operations. Characteristics of the pilling on the fabric were extracted by using image topography. A novel T2FCMAC based on the hybrid of group strategy and artificial bee colony (HGSABC) was proposed to evaluate the pilling grade of knitted fabric. The proposed T2FCMAC classifier embedded a type-2 fuzzy system within a traditional cerebellar model articulation controller (CMAC). The proposed HGSABC learning algorithm was used for adjusting the parameters of T2FCMAC classifiers and preventing the fall into a local optimum. A group search strategy was used to obtain balanced search capabilities and improve the performance of the artificial bee colony algorithm. The experimental results of the fixed and different illuminations indicated that the proposed method exhibited a superior average accuracy (97.3% and 94.6%, respectively) to other methods.