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This paper presents a novel approach for detection and classification of steel sheet defects. A Defects database with enough samples and good imaging conditions introduced. A set of new features proposed to extract the appropriate textural characteristics from defects images. This is followed by the selection of important features using SFFS algorithm. Modifications to SFFS feature selection method were presented to achieve the real-time needs of research. The proposed scheme decrease computational complexity in cost of little decreasing of classification accuracy.

A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION