Automated metal surface inspection through machine vision

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 greylevel 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.