Abstract
Quality assessment is an important step in production processes of metal parts. This step is required in order to check whether surface quality meets the requirements. Progress in the field of computing technologies and computer vision gives the possibility of visual surface quality control with industrial cameras and image processing methods. Authors of different papers proposed various texture feature algorithms which are suitable for different fields of images processing. In this research 27 texture features were calculated for surface images taken in different lighting conditions. Correlation coefficients between these 2D texture features and 11 roughness 3D parameters were calculated. A strong correlation between 2D features and 3D parameters occurred for images captured under ring light conditions.
Highlights
A surfaces quality control is an important step during the production process of metal parts
Quality assessment is an important step in production processes of metal parts
Progress in the field of computing technologies and computer vision gives the possibility of visual surface quality control with industrial cameras and image processing methods
Summary
A surfaces quality control is an important step during the production process of metal parts. Haralick’s features were calculated for these decompositions This method showed a reliable classification of surfaces based on their finish quality. It is possible to use these features for in-focus surface images as the descriptors of surface roughness. The different surface quality will influence the surface visual appearance on the image These surfaces will have different contrast and intensity variations, which can be described with the help of the focus texture features. In this paper the focus is on the quality control of metal surfaces using various texture features. For this goal a correlation between different texture features and roughness parameters was calculated.
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