Anton Nikolaev
ABSTRACT
The aim of the study is to increase the reliability of information on the surface quality of galvanized coiled steel in the automated assessment system. The paper presents examples of images of a surface with defects that are visually close but have a different classification. The authors decompose the image into layers and begin to classify the surface defects of materials on the brightness histogram for each layer and image with defects. The research was carried out for the conditions of a large metallurgical enterprise of the Russian Federation. As a result of the study, it was proved that for the bar charts of brightness by image layers, the data used to identify the type of defect are presented.
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