|Place of Conferral||北京|
|Keyword||缺陷分割 缺陷分类 视觉显著性 视觉测量 手机白玻|
|Other Abstract||White glass cover is one of the most significant components of mobile phone. Its quality directly affects the appearance of mobile phone, thus affecting phone’s sales. Traditional defect detection of the white glass cover is mainly dependent on experienced workers. Faced to the change of the market, such as more fierce competition, increased demand and a higher requirement of quality, the manual detection method cannot satisfy the needs of industrial applications. This thesis focuses on the problem of automatic defect detection of white glass cover and a detection system is developed based on micro vision. Several related issues are proposed and investigated, including the segmentation, classification and fusion of the scratch and the crack detection for the edge.The main contributions are as follows:|
Firstly, focusing on the problem of automatic defect detection of white glass cover, a detection system is developed based on micro vision. Two subsystems are included, which respectively are microscopic visual device and precision transmission device. By properly setting the parameters of the light source and camera, high quality images for many types of the defects could be effectively achieved.
Secondly, a method is proposed to segment the scratches in white glass images, in which the low contrast between the scratch and surrounding background is taken into consideration. In the method, the HC algorithm is employed to enhance the contrast, and an iterative threshold algorithm is proposed to segment scratches in the enhanced image. The method could effectively segment the scratch with improved real-time performance. Moreover, this method has certain anti-interference ability.
Thirdly, due to unfixed shape, it is hard to measure the size of scratch directly. In addition, some scratches are intermittent and scattered, which makes the measurement of the scratch even harder. To solve this problem, we firstly extracted the features of scratches and then the SVM is utilized to determine the subclass of scratches. Subsequently, a scratch fusion algorithm is proposed based on region growing to connect intermittent and scattered scratches. Finally, the minimum circumscribed rectangle fitting scratch is obtained to approximate the size of the scratch. Improved accuracy has been achieved when applying the method to the measurement of scratches with irregular shapes.
Finally, for the detection of the collapse defect, a collapses extraction and measurement method is proposed. In the method, we firstly use the 0-1 step algorithm to locate the edge of white glass precisely, and then the width of the edge is obtained. During the process, the RANSAC algorithm is employed to fit the vertical edge, and the titled angle is determined to modify the error of the measured width, which is caused by the skew of the image. Consequently, the Wave Peak Crop algorithm is proposed to detect the collapses on vertical edge. Furthermore, the algorithm is improved by the curvature when detecting the collapses on arc edge. The collapses are determined with the improved algorithm. Experiments demonstrate that, the collapses, even those with small sizes, could be effectively located with satisfactory accuracy and a high real-time capacity.
|袁伦喜. 基于显微视觉的手机白玻表面缺陷检测方法研究[D]. 北京. 中国科学院研究生院,2017.|
|Files in This Item:|
|基于显微视觉的手机白玻表面缺陷检测方法研（3432KB）||学位论文||暂不开放||CC BY-NC-SA||Application Full Text|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.