STEEL STRIP SURFACE DEFECT IDENTIFICATION BASED ON BINARIZED STATISTICAL FEATURES

In the steel hot rolling process, flat products that are shaped by a gradual reduction of the thickness and the increasing of the length may exhibit different surface defects, which should be identified. The solution, widely adopted, and still considered as a challenge is the automatic inspection. It is assumed, allowing an immediate detection with accurate identification of the defect that starts appearing during production. However, for a perfect labeling of the occurring defects, inspection system should be provided with reliable algorithms. In this paper, tools are combined to provide a high-efficiency solution. The suggested method is based on the recent Binarized Statistical Image Feature extractor used, to date, in biometrics. Combined with a relevant reduction-data method and the K nearest neighbors classifier, this solution showed improved recognition rates of the strip surface defects in the hot rolling process, outperforming, the reported results in previous works.