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.
In view of the difficulty of defects detection of complex metal curve surface in uneven illumination and high speed processing, a new, simple, yet robust algorithm based on statistical feature of local visual field is proposed. This algorithm first performs the ideal image difference by extracting the template from the image itself, and then computes the statistical feature in local visual field to correct the gray-scale fluctuation in each region of image. In this way, the influence of the uneven illumination at low and high frequency is eliminated concurrently, which achieves the equalization of the statistical features of the local visual fields except the position containing the defect, so as to use the global threshold in whole image reasonably; Next, on the search of defects, this paper replaces the pixel level with the local field of vision and compresses the image information with the defects’ scale which is in line with the human eye. This not only reduces the influence of random noise, but also greatly improves the processing speed while preserving defects information, which makes it possible to realize real-time processing ability for image with the large amount of data. To detect complex curved surface on semi-finished metal shell of cell phone, the experimental results demonstrate that the defects detection accuracy of the proposed algorithm can reach 95%, and the detection time for single test area is less than 1ms, which is suitable for accurate and real-time detection on the production line for such surface defect.
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