Computer-aided visual inspection of surface defects in ceramic capacitor chips

This paper explores the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. The Hotelling T2 statistic of multivariate statistical analysis is applied to integrate the multiple wavelet characteristics. Then, the wavelet-based multivariate statistical approach judges the existence of ripple defects and identifies their locations. Finally, the defect detection performance of the proposed approach is compared with that of the Otsu method. Experimental results show that the proposed approach excels in its 95% probability of accurately detecting the existence of ripple defects and 92% probability of correctly locating their regions.