Distinguishing Feature Selection for Fabric Defect Classification Using Neural Network

Over the years significant research has been performed for machine vision based fabric inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated fabric inspection systems mainly involve two challenging problems: one is defect detection and another is classification, which remains elusive despite considerable research effort in automated fabric inspection. The research reported to date to solve the defect classification problem appears to be insufficient, particularly in selecting appropriate set of features. Scene analysis and feature selection play a very important role in the classification process. Insufficient scene analysis results in an inappropriate set of features. Selection of an inappropriate feature set increases complexities of subsequent steps and makes the classification task harder. Considering this observation, we present a possibly appropriate feature set in order to address the problem of fabric defect classification using neural network (NN). We justify the features from the point of view of distinguishing quality and feature extraction difficulty. We performed some experiments in order to show the utility of proposed features and compare performances with recently reported relevant works. More than 98% classification accuracy has been found, which appears to be very promising.