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High-resolution optical inspection system for fast detection and classification of surface defects.

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Title: High-resolution optical inspection system for fast detection and classification of surface defects.
Authors: Ye, Ruifang1, Chang, Ming1,2 ming@cycu.edu.tw, Pan, Chia-Sheng2, Chiang, Cheng An2, Gabayno, Jacque Lynn3
Source: International Journal of Optomechatronics. Dec2018, Vol. 12 Issue 1, p1-10. 10p.
Subjects: Optical quality control, Surface defects, Parallel computers, Image processing, Iterative methods (Mathematics)
Abstract: A high-resolution automated optical inspection (AOI) system based on parallel computing is developed to achieve fast inspection and classification of surface defects. To perform fast inspection, the AOI apparatus is connected to a central computer which executes image processing instructions in a graphical processing unit. Defect classification is simultaneously implemented with Hu's moment invariants and back propagation neural (BPN) approach. Experiments on touch panel glass show that using 100 training samples and 1000 cycle iterations in BPN, the accurate classification of surface defects for a 350 × 350 pixels image can be completed in less than 0.1 ms. Moreover, the inspection of a 43 mm × 229 mm sample that yields an 800 megapixel raw data can be completed remarkably fast in less than 3 s. Thus, the AOI system is capable of performing fast, reliable, and fully integrated inspection and classification equipment for in-line measurements. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Optomechatronics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Optical+quality+control%22">Optical quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+defects%22">Surface defects</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computers%22">Parallel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink>
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  Data: A high-resolution automated optical inspection (AOI) system based on parallel computing is developed to achieve fast inspection and classification of surface defects. To perform fast inspection, the AOI apparatus is connected to a central computer which executes image processing instructions in a graphical processing unit. Defect classification is simultaneously implemented with Hu's moment invariants and back propagation neural (BPN) approach. Experiments on touch panel glass show that using 100 training samples and 1000 cycle iterations in BPN, the accurate classification of surface defects for a 350 × 350 pixels image can be completed in less than 0.1 ms. Moreover, the inspection of a 43 mm × 229 mm sample that yields an 800 megapixel raw data can be completed remarkably fast in less than 3 s. Thus, the AOI system is capable of performing fast, reliable, and fully integrated inspection and classification equipment for in-line measurements. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Optomechatronics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/15599612.2018.1444829
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Optical quality control
        Type: general
      – SubjectFull: Surface defects
        Type: general
      – SubjectFull: Parallel computers
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Iterative methods (Mathematics)
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            NameFull: Chang, Ming
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            NameFull: Pan, Chia-Sheng
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            NameFull: Chiang, Cheng An
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            NameFull: Gabayno, Jacque Lynn
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            – D: 01
              M: 12
              Text: Dec2018
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              Y: 2018
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