Automatic detection and classification of the ceramic tiles' surface defects

Defect detection and classification of ceramic tile surface defects occurred in firing units are usually performed by human observations in most factories. In this paper, an automatic image processing system with high accuracy and time efficient approaches is presented. To this end, first, for defect detection, Rotation Invariant Measure of Local Variance (RIMLV) operator from statistical methods is employed for defect edges detection, and cooperatively a Close morphological operator from structural methods is used to fill and smooth detected regions. Then, all the detected defects of one ceramic tile are labeled, and the corresponding geometric features are extracted. Finally, a multi-class support vector machine classifier with winner-takes-all strategy based on statistical pattern recognition theories is employed to identify the defect type. The production process of Ceramic Tiles (CTs) includes various stages such as forming body of CTs, glazing and decorating, and firing.The main CTs defects occur in firing unit because of the many effective variables especially the temperature of kiln. For instance, these defects are combination of glaze and decorative pattern because of the exceeded air pressure in kiln, or are dimensional faults because of the improper velocity rate of roller conveyers in kiln. A sample of these defects and their reasons has been attached.It is very clear that the main specification of a CT is its surface quality; and in firing unit, the glaze is baked on CTs body to create the glassed surface. Hence, the unsuitable temperature circumstance along the roller kilns effects on surfaces of CTs and causes the surface defects.

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