Recently in many industrial fields the exploitation of vision systems for quality control had a considerable increase, which is mainly due to the technological progress experienced by such systems, that, with respect to the past, made their performance more appealing and more reliable while the associated costs are decreased. The advantages of these kind of systems in terms of savings in human resources and improved quality monitoring have become far more evident, by encouraging their adoption in a wide variety of production cycles. The present paper deals with the elaboration and information extraction from images, that represent portions of the surface of flat steel products, and describes an algorithm for defect detection and classification. The overall classification procedure is composed of a preliminary part that is mostly related to image processing and analysis, which aims at pointing out the defect (independently on the class it belongs to), as well as to the extraction of relevant features of the detected defect; the second part exploits a fuzzy inference system in order to analyze the type of defect and solves a classification problem that presently can be addressed only with the support of a human operator. Fuzzy inference systems are suitable to this application because they are able to mimic and reproduce the human reasoning.
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