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AVI of Surface Flaws on Manufactures II
Abstract
Automatic visual inspection takes a relevant place in defect detection of industrial production. In this field a fundamental role is played by methods for the detection of superficial anomalies on manufactures. In particular, several systems have been proposed in order to reduce the burden of human operators, avoiding the drawbacks due to the subjectivity of judgement criteria (Kwak, Ventura & Tofang-Sazi 2000, Patil, Biradar & Jadhav 2005). Proposed solutions are required to be able to handle and process a large amount of data. For this reason, neural networks-based methods have been suggested for their ability to deal with a wide spread of data (Kumar 2003, Chang, Lin & Jeng 2005, Garcia 2005, Graham, Maas, Donaldson & Carr 2004, Acciani, Brunetti & Fornarelli 2006). Moreover, in many cases these methods must satisfy time constrains of industrial processes, because the inclusion of the diagnosis inside the production process is needed. To this purpose, architectures, based on Cellular Neural Networks (CNNs), revealed successful in the field of real time defect detection, due to the fact that these networks guarantee a hardware implementation and massive parallelism (Bertucco, Fargione, Nunnari & Risitano 2000), (Occhipinti, Spoto, Branciforte & Doddo 2001), (Perfetti & Terzoli 2000). On the basis of these considerations, a method to identify superficial damages and anomalies in manufactures has been given in (Fornarelli & Giaquinto 2007). This method is aimed at the implementation by means of an architecture entirely formed by Cellular Neural Networks, whose synthesis is illustrated in the present work. The suggested solution reveals effective for the detection of defects, as shown by two test cases carried out on an injection pump and a sample textile.
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