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盖板玻璃以其优异的力学和光学性能成为手机等电子产品显示模组中的必备零部件。随着近几年电子显示屏的普及和智能设备的发展，盖板玻璃的需求量也在逐年攀升。然而现阶段，依靠效率低下的人工进行质量管控早已无法满足日益增加的市场需求。因此，针对盖板玻璃表面缺陷自动检测设备的研制具有重要的实际应用价值。

基于以上背景，本文以解决盖板玻璃表面印刷缺陷检测中的共性问题和突破关键技术为目标，搭建了一套完整的在线检测系统，对其中存在的瓶颈问题进行了深入探究。提出了一种基于小样本集的数据生成方法以解决数据获取的困难。此外，针对高分辨率盖板图像实现了快速预处理算法，并针对不同缺陷设计实现了相应的检测方法。本文完成的主要工作包括以下几个方面：

（1）针对盖板玻璃表面的常见印刷缺陷设计了一套紧凑、快速的在线检测系统。该系统由照明成像子系统、传动子系统、图像采集子系统和图像处理子系统构成，可采集得到满足缺陷检测精度的高分辨率图像。该系统使用面阵光源从背面照射待检测盖板，并通过传动系统匀速传经过成像系统。最后，采集系统将盖板图像传回处理系统中进行检测。

（2）针对红外线照射孔这类半透明印刷区域，设计了基于深度学习的图像分割算法进行缺陷的提取，并提出一种基于对抗训练的模型性能提升方法，提高了原始模型的鲁棒性和抗噪声性。

（3）针对工业视觉检测中数据难以获得以及标注困难等问题，提出了一种基于无标注小样本集的数据生成方法。该方法使用生成模型对传统图像合成算法输出的结果进行真实化渲染，通过对抗训练使生成模型可以学习到无标注数据的特征，达到以假乱真的效果，使得深度学习方法的数据标注困难在特定的视觉检测领域中得到了解决。

（4） 研究并实现了多种缺陷检测方法。针对高分辨率盖板图像处理慢的问题，使用基于积分图优化加速的自适应二值化算法，实现了图像的快速预处理；针对于盖板刮伤缺陷，设计了一种基于统计密度信息的多维特征，进行缺陷的识别与分类；针对盖板上的图案和字符等区域，设计了一种基于分支定界的快速图像匹配方法进行缺陷提取。

基于以上主要工作，本文实现了盖板玻璃表面印刷缺陷在线检测系统中的关键技术。在保证微米级检测精度的前提下，可在3秒内完成典型缺陷的检测与处理，满足了工业生产的效率。

Due to excellent mechanical and optical properties, cover glasses have become essential components in display modules for mobile phones and other electronic accessories. With the popularity of electronic displays and the development of smart devices, the demand for cover glasses has also increased year by year. However, the inspection of cover glasses can no longer meet the increasing market demand constrained by the inefficiency of artificial. Therefore, the develop of automatic defect detection equipment for surface defects of cover glasses is meaningful in practical applications.

Based on the above background, this paper aims to solve the common problems in the surface defect detection of cover glasses. This paper has conducted in-depth research on the bottleneck problems and established an on-line defect detection system. To deal with the difficulty of data acquisition, a data generation method based on a small sample set is proposed. For high-resolution image processing, a fast preprocessing algorithm is designed, and various detection methods are designed for corresponding defects. The main works accomplished in this paper are listed below:

（1）A compact and fast on-line inspection system is designed for common defects on the surface of printing cover glasses. The system which can acquire high-resolution images consists of a lighting imaging subsystem, a transmission subsystem, an image acquisition subsystem and an image processing subsystem. The cover glasses illuminated by the system pass through the imaging subsystem driven by transmission subsystem. Finally, the images captured by acquisition subsystem are transfered to the processing subsystem for detection.

（2）For the semi-transparent printing area such as the infrared radiation hole, an image segmentation algorithm based on deep learning is designed. This paper also proposes a performance improvement method based on adversarial training, which improves the robustness and noise immunities of the original model.

（3）Aiming at the difficulties of data acquisition and labeling the in the industrial vision inspection area, a data generation method based on a small unlabeled sets is proposed. The method uses a generative model to realistically render the results of the image synthesis algorithm, and the adversarial training method enables the generated model to learn the characteristics of the unlabeled data. The generative algorithm solves the problem of data acquisition to some extent in the field of visual inspection.

（4）This paper designs variety defect extraction methods. To improve the real-time performance in time-consuming tasks of high-resolution image processing, an adaptive binarization algorithm based on integral image is employed. Besides, a multidimensional feature based on statistical density information is designed to identify and classify weak defects.

In summary, this paper realizes key technologies in the on-line inspection system for printing defects. On the premise of guaranteeing the accuracy of micro-level detection, the detection and processing of typical defects can be completed within 3 seconds, which meets the efficiency of industrial production.