An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering

Abstract

In India, textile industry has been mainly focused because it increased the economy day by day. But, it has some problem in the field of quality control. At present, it is mainly solved visually through skilled workers. Though, due to the human errors and eye fatigue, the system reliability has been restricted. So, in this research has been focused automatic fabric defect detection scheme. Here, Modular Neural Network (MNN) is proposed for fabric defect detection and classification with low cost and high accurate rate via using image processing schemes in the woven fabrics. At first, the images are collected from the machine and then preprocessed by using Enhanced Directional Switching Median Filter (EDWF) to reduce the impulse and stationary noise. To attain high accurate prediction, the preprocessed image has been segmented by using Alternative Hard C-Means (AHCM) cluster. After clustering, the images are converted to binary image. Then, the first order features has been extracted from the image. The extracted features are given as input to MNN, which classifies the fabric defects. In MNN, the weight factors are calculated by using back propagation algorithm and generate the output. The simulation results show that the proposed MNN attained high accuracy rate of 96.7% when compared to existing Artificial Neural Network (ANN) than Support Vector Machine with Genetic Algorithm (SVM-GA) classification algorithms.