Abstract
This paper proposes a method for automated visual inspection of metal surfaces. Firstly, the modified grey-level co-occurrence matrices of metal images are used to access the information of metal surfaces. Secondly, the difference moment and the entropy of the grey-level co-occurrence matrices are extracted as the features of the metal surfaces. Finally, the features of the inspecting images are then compared with the preset confidence interval to determine whether the inspecting metal is defective or not. Some combinations of relative positions between two positioning pixels and feature descriptors were tested in the experiments to find the best one. The experimental results show that the proposed method can detect the defects effectively and has better correct detection rates than the conventional method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.