Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Wei Ding, Qingguo Wang, Jianfeng Zhu

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


This paper is concerned with detection of dispersed defects in the resin eyeglass. At present, manual detection is always used in industry, which inevitably causes impairing eyes and a high rate of false-negative detection. Statistics show that its average accuracy and the average detection time are about 85% and 10s, respectively. We for the first time propose an automatic approach to detection of dispersed defects in resin eyeglass, based on the machine vision technology. It is observed that the refractivity of the normal and the defective regions of an eyeglass are different, and thus the reflection image is also collected in our system in addition to the normal transmission image. Such an optical imaging system is modelled and its analysis shows the gray-scale gradient difference between the normal and defective regions in the acquired image is dramatically enhanced under the illumination of a point light source with our designed system. An image processing algorithm is then developed to reveal the above difference and detect dispersed defects in resin eyeglasses. Our simulation study verifies the proposed approach. Further, its experimental evaluation was carried out and the result was consistent with the simulation one, showing that its detection accuracy and the average detection time were 97.50% and 0.636s, respectively, which meet the requirements for online detection of dispersed defects.

Original languageEnglish
Article number9022969
Pages (from-to)44661-44670
Number of pages10
JournalIEEE Access
Publication statusPublished - 2020


  • Automatic detection
  • dispersed defects
  • imaging analysis
  • machine vision
  • resin eyeglass

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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