简川霞,高健.手机玻璃屏表面缺陷视觉检测方法研究[J].包装工程,2018,39(5):16-21.
JIAN Chuan-xia,GAO Jian.Visual Detection Method for Surface Defect of Mobile Phone Screen Glass[J].Packaging Engineering,2018,39(5):16-21. |
| 手机玻璃屏表面缺陷视觉检测方法研究 |
| Visual Detection Method for Surface Defect of Mobile Phone Screen Glass |
| 投稿时间：2017-11-14 修订日期：2018-03-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.05.004 |
| 中文关键词: 手机玻璃屏 缺陷检测 差影法 模板 |
| 英文关键词: mobile phone screen glass defect detection image subtraction template |
| 基金项目:国家自然科学基金（51675106，51605101）；广东省自然科学基金（2015A030312008，2016A030308016）；广东省科技计划（2015B010104008）；广东省数控一代机械产品创新应用示范工程专项资金（2013B011301023)；广东工业大学校青年基金（ZD2017001） |
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| 中文摘要: |
| 目的 针对手机玻璃屏表面缺陷人工检测存在的准确率低、稳定性差等问题，提出一种基于机器视觉技术的手机玻璃屏表面缺陷检测方法。方法 采用统计平均法建立模板图像，以减少外界光照对模板图像的灰度影响。采用基于互信息的配准方法实现模板图像和待测图像的像素对齐，将配准后的待测图像与模板图像进行差分运算，获取残差图像，并采用Niblack方法实现残差图像上的缺陷判断。通过搭建的实验平台获取了300幅手机玻璃屏图像，并采用文中提出的方法、模板匹配法和人工检测法对300幅图像实施缺陷检测。结果 实验结果显示，文中方法的真正率为92%，真负率为96.5%和准确率为95%。与模板匹配法和人工检测法相比，文中方法在真正率、真负率和准确率上分别至少提高了5%，4%和4.3%。结论 文中方法与人工检测方法相比，提高了手机玻璃屏表面缺陷检测的准确率和稳定性。 |
| 英文摘要: |
| The work aims to propose a method of detecting the surface defects of the mobile phone screen glass based on the machine vision technology to solve the problems of low accuracy and poor stability in the manual detection of the surface defects of the mobile phone screen glass. The statistical average method was used to establish the template image to reduce the influence of the external illumination on the gray level of the template image. Firstly, the pixel alignment between the template image and the image to be tested was implemented by the registration method based on mutual information. Secondly, the residual image was obtained by subtracting the template image from the aligned image to be tested. Finally, the Niblack method was used to determine the defects of residual image. 300 images of mobile phone screen glass were obtained through the experimental platform established and their defects were detected by the proposed method, the template matching method and the manual method. The experimental results showed that, the proposed method achieved the true positive rate of 92%, the true negative rate of 96.5% and the accuracy of 95%. Compared with the template matching method and the manual detection method, the proposed method improved at least 5%, 4% and 4.3% in terms of true positive rate, true negative rate and accuracy respectively. The proposed method outperforms the manual detection method in terms of accuracy and stability of surface defect detection of mobile phone screen glass. |
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