Application of Weighted Object Variance Algorithm in Metal Surface Defect Detection

In the process of metal surface defect detection, it is difficult to detect and segment small defects. In order to solve this problem, this paper uses weighted object variance algorithm to detect metal surface defects. Then the feature of defect area is extracted and the defect classification model based on support vector machine is trained. In order to verify the effectiveness of the method, this paper takes the metal surface of bearing cylindrical roller as the specific research object. The experimental results show that the method meets the production requirements. (paper)