Abstract: Rotor defects affect the operation of blower, reduce the working performance and bring safety risks for industrial production. Traditional manual detection is time-consuming and laborious, with low detection and marking accuracy, and it is difficult to accurately classify defects. Therefore, this paper obtains point cloud data based on machine vision, preprocesses it, and compares two defect detection methods respectively based on point cloud registration and workpiece features. The experimental results show that the defect detection based on workpiece features can get more accurate results of defect labeling and classification, and provide a new direction for the research of defect detection methods.

Key words: machine vision, point cloud data, defect detection, point cloud registration, artifact features