Quantitative defects detection has always been the one of the difficulties in optical element surface quality evaluation. In order to solve this problem, the optical element surface defects detection based on dark-field imaging system, which has been researched by our group team for nearly twenty years, has been summarized. The plane and sphere optical element surface defects detection details are introduced. Specifically, it involves plane optical element surface leveling, sphere optical element spherical center alignment, low magnification image acquisition, low magnification image stitching, feature extraction, high magnification defects detection and report output based on the form of specific standard (Such as America Military Standard MIL-PRF-13830B or China National Standard GB/T 1185-2006). Besides, a China National Standard about digitized quantitative measurement of the defect, which is proposed by our group (now is in the stage of request for public advice), is also introduced.
An electromagnetic simulation model of microscopic scattering dark-field imaging was built based on the finite difference time domain (FDTD) method. The scattered light distribution of different defect’s size was obtained. Results show the span of distribution curve and the distribution peak are relative to the defect’s width and depth respectively. In the width range of 0.5 μm to 1 μm, there is a linear relationship between the distribution span and the defect’s width. Its goodness of linear fit reaches 0.9. Within the depth range of 0.1μm, the distribution peak linearly changes with the depth. But with the depth becomes deeper, the linear relationship between distribution peak and defect’s depth disappears. The results in this paper can provide instructive reference for the defect’s size inversion.
Surface defects inspection is a critical part in manufacturing of mobile phone cover glass. Considering the defects in the optical elements surface caused by the imperfection of manufacturing technique, the classification and the position information should be carried out for the necessary repairing process. The traditional manual inspection method is always labor-consuming and inefficient. Surface defects digital evaluation system based on machine vision draws much attention in the recent years. This paper proposed algorithms and applications for the detection task with higher efficiency and reliability comparing to the manual inspection. The detection method is based on machine vision and machine learning techniques. The images of optical elements surface are captured by line-scanning cameras, with the imaging systems of dark-field, bright-field and transmission-field. Only one image system is not enough to detect all kind of defects like scratch, bubble, crack of glass and edge chipping etc. The position information and category of defects are obtained based on image processing technique. The defective area was calculated by image filtering algorithm, the feature selection techniques based on segmentation methods are explored and the feature vector can be extracted before the next step of classification with Support Vector Machine (SVM) technique. Verified by the experiments, the results reveal this method has good performance and is very suitable for recognition and classification of glass defects.
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