Journal of South China University of Technology (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (10): 76-87.doi: 10.12141/j.issn.1000-565X.190890

• Mechanical Engineering • Previous Articles     Next Articles

Online Detection System of Bearing Roller's Surface Defects Based on Computational Vision

WEN Shengping1,2 ZHOU Zhengjun3 ZHANG Xiaoyan1,2 CHEN Zhihong1,2   

  1. 1． Guangdong Advanced Polymer Manufacturing Technology and Equipment Key Laboratory，South China University of Technology，Guangzhou 510640，Guangdong，China; 2． Key Laboratory of Polymer Processing Engineering，the Ministry of Education，South China University of Technology，Guangzhou 510640，Guangdong，China; 3． Guangzhou Jingyuan Mechano-Electric Equipment Co． ，Ltd． ，Guangzhou 511480，Guangdong，China
  • Received:2019-12-09 Revised:2020-04-24 Online:2020-10-25 Published:2020-09-14
  • Contact: 文生平(1966-)，男，博士，教授，主要从事智能控制与机器视觉等的研究。 E-mail:shpwen@scut. edu. cn
  • About author:文生平(1966-)，男，博士，教授，主要从事智能控制与机器视觉等的研究。
  • Supported by:
    Supported by the National Natural Science Foundation of China (51973068) and the National Key R＆D Pro-gram of China (2019YFC1908201)

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Abstract: 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．

Key words: bearing, surface defect, online detection, deep learning, convolution neural network