国家自然科学基金(61374022); 浙江省公益性技术应用研究计划(LGG18F030001, GG19F030034)
在导光板生产时, 因生产治具温度过高, 不可避免地会出现黄化缺陷. 为提高黄化缺陷检测精度及效率, 在分析导光板及其黄化缺陷的光学特征基础上, 本文提出了基于机器视觉的导光板黄化缺陷检测方法: 首先, 将图像灰度转换, 用双边滤波器对图像平滑处理, 降低噪声影响; 其次, 对像素点邻域依次差值, 凸显导光板轮廓特征; 进而, 通过自适应的阈值填充算法与设置线段距离阈值, 完成导光板轮廓提取和3个导光板的分割; 最后, 根据导光板坐标生成矩形区域, 构建81维特征向量, 建立并训练SVM模型. 该方法在工业现场采集的导光板图像上进行了大量实验, 实验结果表明, 该算法运行效率高, 鲁棒性强, 在训练样本较少的情况下仍有较高的检测精度.
Yellow defects are inevitable due to the high production temperature of the fixture to prepare light guide plates (LGPs). This study proposes a method for detecting yellowing defects of LGP based on machine vision. Firstly, a bilateral filter is designed after gray-level transformation to reduce noise impact. Secondly, the outline of the LGPs is highlighted by the difference of neighbor pixels. Then, the contour extraction and segmentation of three LGPs are completed by the proposed self-adapting threshold filling algorithm and the line segment distance threshold. Finally, according to LGP coordinates, rectangular regions can be generated, and 81-dimensional eigenvectors and a Support Vector Machine (SVM) model can be built. A large number of experiments were carried out on the basis of the LGP images collected in the industrial field. Experimental results prove that the algorithm has high running efficiency and strong robustness and still presents high detection accuracy in the case of few training samples.