作　　者： (文生平）; ;

机构地区： 华南理工大学

出　　处： 《华南理工大学学报（自然科学版）》 2020年第10期76-87,共12页

摘　　要： 通过分析轴承滚子中最常见的几种表面缺陷类型,设计了针对性的缺陷检测算法,将传统计算机视觉方法与深度学习相结合,并采用改进的RetinaNet模型,实现了轴承滚子的表面缺陷检测。实验结果表明:文中方法的准确率达95%以上;相较于传统的缺陷检测方法,文中方法在准确率、召回率与F1-score上均有一定提升。 A reasonable detection algorithm for surface defects in bearing rollers was designed based on the analysis of some most common surface defect types.It combines the traditional computer vision method with deep learning and adopts the improved RetinaNet model to realize the surface defect detection of bearing rollers.The experimental results show that the accuracy of this method is more than 95%.As compared with the traditional defect detection method,the proposed detection algorithm can make improvement in accuracy,recall rate and F1-score.

关 键 词： 轴承 表面缺陷 在线检测 深度学习 卷积神经网络

领　　域： [机械工程—机械制造及自动化] [自动化与计算机技术—计算机应用技术] [自动化与计算机技术—计算机科学与技术]