DEVELOPMENT OF AUTOMATED CERAMIC TILES SURFACE DEFECT DETECTION AND CLASSIFICATION SYSTEM

Abstract

This research presented the development of an automated system for ceramic tiles surface defect detection and classification. The production process of ceramic tiles is very fast through the use of automated system except the inspection process that is manually carried out. The fast rate of production and numerous amounts to be produced make it difficult to manually inspect the tiles defects. Currently many literatures have proposed various automated systems for detecting and classifying defects on ceramic tiles. In this research different defected and non-defected images of ceramic tiles were taking at the firing unit in a Ceramic Company with a Nikon D40 camera. A statistical method called Rotation Invariant Measure of Local Variance (RIMLV) operator was used for detection of the defects while morphological operator was used to fill and smooth detected regions. Then, the detected defects are labelled to extract the corresponding features vectors using Fourier descriptors. To categorize the defect, multi-class support vector machine classifier (SVM) was used. The proposed system recorded an accuracy of 98 percent for classification and 0.094939 seconds for classification using one-against all SVM classifier.