一种平板玻璃表面缺陷检测方法

目的 针对平板玻璃缺陷检测中灰尘干扰划痕、亮点检测的问题，提出一种基于全反射?掠入射组合照明的平板玻璃表面缺陷检测方法。方法 通过控制全反射光源和掠入射光源的发光时序，在相应发光时序内采集玻璃图像，根据缺陷在不同照明下的灰度纹理差异计算灰度、几何特征等一系列相对偏差特征，开发BP神经网络算法，实现玻璃表面灰尘和表面缺陷的检测。结果 BP神经网络在测试集上各类别预测的查准率、查全率均在90%以上，整体准确率达到97.2%。结论 全反射–掠入射组合照明成像系统结构简单，降低了玻璃图像中灰尘和内部点缺陷分类难度，有效减少灰尘和内部缺陷的误判。

Method for Detecting Surface Defects of Flat Glass

The work aims to propose a surface defect detection method of flat glass based on total reflection-grazing incidence combined lighting to solve the dust interferes with the detection of scratches and bright spots in defect detection of flat glass. By controlling the time sequences of lighting of total reflection and grazing incidence light sources, the glass images of glass in the corresponding time sequences of lighting were collected. The gray-scale, geometric characteristics and a series of relative deviation characteristics were calculated according to the difference of gray-scale texture of defects under different lighting. The BP neural network was developed to detect the dust and defects on the glass surface. In the end, the accuracy and recall rates of each category prediction of the BP neural network on the test set were all above 90%, and the overall accuracy rate reached 97.2%. From this point of view, the total reflection-grazing combined lighting imaging system has a simple structure, which reduces the difficulty of classification of dust and internal point defects in the glass image, and effectively reduces the misjudgment of dust and internal defects.