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.
作者
文生平
周正军
张啸言
陈志鸿
WEN Shengping;ZHOU Zhengjun;ZHANG Xiaoyan;CHEN Zhihong(Guangdong Advanced Polymer Manufacturing Technology and Equipment Key Laboratory,South China University of Technology,Guangzhou 510640,Guangdong,China;Key Laboratory of Polymer Processing Engineering,the Ministry of Education,South China University of Technology,Guangzhou 510640,Guangdong,China;Guangzhou Jingyuan Mechano-Electric Equipment Co.,Ltd.,Guangzhou 511480,Guangdong,China)
出处
《华南理工大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2020年第10期76-87,共12页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51973068)
国家重点研发计划项目(2019YFC1908201)。
关键词
轴承
表面缺陷
在线检测
深度学习
卷积神经网络
bearing
surface defect
online detection
deep learning
convolution neural network