Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology
| Autor: | Jianfeng Zhu, Wei Ding, Qing-Guo Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Point light source Computer science Machine vision Image processing 02 engineering and technology dispersed defects Image (mathematics) 020210 optoelectronics & photonics Optical imaging 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision business.industry General Engineering machine vision 021001 nanoscience & nanotechnology imaging analysis resin eyeglass Reflection (mathematics) Automatic detection lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence 0210 nano-technology business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 44661-44670 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2978001 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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