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.

Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

“…In order to avoid uneven training data and testing data at each grade when randomly sampling images, we randomly chose 80% of the images of each grade of fabric pilling in the database as training samples and the remaining 20% as testing samples. Several researchers, such as Huang and Fu [15] and Lee and Lin [16], also adopted this method to obtain training and testing samples. As for the ratio of training samples to testing samples, researchers can determine this according to the total image database obtained.…”

“…For the machine learning methods, the ANN and the SVM were used to objectively solve the textile grading problem. Meanwhile, Lee and Lin [16] proposed a novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) based on a hybrid of group strategy, and an 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).…”

“…This study compared the results of DPCANN with other methods [2][3][4][15][16][17][18][19][20]. In order to fairly compare our method with other methods, we repeated other methods to achieve the classification of pilling grade.…”

“…Saharkhiz and Abdorazaghi [2] 96.8% Eldessouki et al [3] 87.5% Furferi et al [4] 94.3% Huang and Fu [15] 96.6% Lee and Lin [16] 97.3% Jing et al [19] 95.0% Eldessouki and Hassan [20] 85.8% Fu [17] using k-NN 96.8% Fu [17] using enhanced k-NN [18] 97.1% Proposed method using k-NN 98.4% Proposed method using enhanced k-NN [18] 98.8% Proposed method using neural network 98.6% Proposed method using SVM 99.7%…”

Fractional-Order Fuzzy Control Approach for Photovoltaic/Battery Systems under Unknown Dynamics, Variable Irradiation and Temperature

“…In the most of the above studies, a simple type-1 FLS was used to cope with unknown mathematical models. However, high-order FLSs have more capability in practical nonlinear systems [30][31][32][33][34]. In addition, the tuning process in the most of above mentioned controllers is done as a non-adaptive and off-line approach.…”