Segmentation of Workpiece Rust Based on Cross Maximum

Abstract

Rust is an oxide produced by the reaction of metal and alloy surface with oxygen. It is a defect that greatly affects the quality of the workpiece. In order to solve the problem of rust segmentation more conveniently, a threshold segmentation algorithm based on cross extremum method is proposed, which determines the threshold of rust segmentation adaptively combining the global and local features of the image. Firstly, as the threshold is hard to determine in the small and medium area rust segmentation of a workpiece, the super red method and saturation method are combined to pre-process the image. Secondly, because the gray value of the rust spot area is the maximum value of the image neighborhood, the cross-extremum method is used to extract the characteristic points of the rust spot. Finally, the threshold of rust segmentation is determined according to the proposed feature points. Experiments show that the horizontal and vertical extremum method can complement each other to extract the feature points of rust as fully as possible. And the algorithm can extract some feature points of rust better for different workpieces in varying backgrounds. The detection accuracy of the algorithm reaches 96.3%, which has important signification of application.