Detection and classification of glass defects based on machine vision

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.