Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision

Abstract

The paper presents an automatic surface defects inspection system for optical Infrared Cut-off (IR-CUT) filter, which is applied in all kinds of color cameras and video devices. The system involves illumination and imaging module, moving module, flipping module and machine vision algorithm. To highlight all the defected regions, the improved dark-field illumination technique is utilized in the imaging module. In order to accurately localize the region of optical IR-CUT filter in the captured image, stationary wavelet transform (SWT) is introduced to template matching algorithm. The introduction of SWT provides a more accurate estimate of the variances in the image and further facilitates the identification of the defected regions. The defects extraction method in this paper avoids the use of complicated learning process from a set of samples. Convexity theory is implemented on the algorithm of defects classification of edge crack. Experimental results on a variety of optical IR-CUT filter samples, including non-defective samples, samples with defects of stain, scratch and edge crack, have shown the efficiency (1.05s per sample) and accuracy (96.44%) of the proposed system. Moreover, defects extraction performances of different filters are compared in this paper. The research and application of the system will greatly liberate the human workforce and inspire ideas to detect the defects of some other small optical elements.