Wavelet-based Principal Component Analysis Applied to Automated Surface Defect Detection

Automated visual inspection, a crucial manufacturing step, has been replacing the more time-consuming and less accurate human inspection. This research explores automated visual inspection of surface defects in a light-emitting diode (LED) chip. Commonly found on chip surface are water-spot defects which impair the appearance and functionality of LEDs. Automated inspection of water-spot defects is difficult because they have a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the defect may fall across two different background textures, which further increases detection difficulties. The one-level Haar wavelet transform is first used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based principal component analysis (WPCA) approach is applied to integrate the multiple wavelet characteristics. Finally, the principal component analysis of WPCA judges the existence of defects. Experimental results show that the proposed method achieves detection rates of above 93.8%, and false alarm rates of below 3.6%. A valid computer-aided visual defect inspection system is contributed to help meet the quality control needs of LED chip manufacturers. Key-Words: Surface defect detection, Wavelet characteristics, Principal component analysis, Computer vision system.