Journal
INTERNATIONAL JOURNAL OF OPTOMECHATRONICS
Volume 12, Issue 1, Pages 1-10Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15599612.2018.1444829
Keywords
Back propagation neural; defect classification; defect inspection; image moment invariants; parallel computing
Funding
- National Natural Science Foundation of China [51605171]
- Research Foundation for Advanced Talents of Huaqiao University [16BS504]
- Open Project of Key Laboratory of Modern Precision Measurement and Laser NDT in Fujian Province [2016XKA001]
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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 1000cycle iterations in BPN, the accurate classification of surface defects for a 350x350 pixels image can be completed in less than 0.1 ms. Moreover, the inspection of a 43mmx229mm sample that yields an 800 megapixel raw data can be completed remarkably fast in less than 3s. Thus, the AOI system is capable of performing fast, reliable, and fully integrated inspection and classification equipment for in-line measurements.
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