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

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.

Keywords

• Back propagation neural
• defect classification
• defect inspection
• image moment invariants
• parallel computing