Wu Fan, Yang Yongying, Jiang Jiabin, Zhang Pengfei, Li Yanwei, Xiao Xiang, Feng Guohua, Bai Jian, Wang Kaiwei, Xu Qiao, Jiang Hongzhen, Gao Bo
Appl Opt. 2019 Feb 1;58(4):1073-1083. doi: 10.1364/AO.58.001073.
In the automatic detection for surface defects of optical components, the digs and dust particles exhibit similar features: point-like shape and variable intensity reflectivity. On this condition, these two types with entirely different damages are easily confused so that misjudgments will be induced. To solve this problem, a polarization-characteristics-based classification method of digs and dust particles (PCCDD) is proposed based on the polarimetric imaging technique and dark-field imaging technique. First, a dark-field imaging system equipped with a polarization state generator (PSG) and a polarization state analyzer (PSA) is employed to measure and establish normalized Mueller matrices' datasets of digs and dust particles. And by a nonlinear global search combined with a separability evaluation method, the optimal number of acquisitions and corresponding polarization measurement states of the PSG and the PSA are obtained, as well as the parameters of classification function. Then, multiple polarization images are acquired under the optimal states to extract a multidimensional feature description that relates only to the polarization characteristics of the defect; this subsequently acts as the input vector of the classifier to finally achieve the classification. This method takes full advantage of both the difference in polarization properties between digs and dust particles and the characteristic that the polarization properties of digs are relatively invariant while those of dust particles have a large variability. The classification process involves only simple matrix operations. Compared to the traditional discrimination method based on intensity images, the features obtained by this method have a higher separability. Experiments show that the classification accuracy reaches over 90%. This method can be further applied to the recognition and discrimination of other defects in the field of surface defects' detection.
在光学元件表面缺陷的自动检测中，划痕和灰尘颗粒呈现出相似的特征：点状形状和可变强度反射率。在此条件下，这两种具有完全不同损伤类型的缺陷很容易混淆，从而导致误判。为了解决这个问题，基于偏振成像技术和暗场成像技术，提出了一种基于偏振特性的划痕和灰尘颗粒分类方法（PCCDD）。首先，采用配备偏振态发生器（PSG）和偏振态分析仪（PSA）的暗场成像系统来测量并建立划痕和灰尘颗粒的归一化穆勒矩阵数据集。然后通过非线性全局搜索结合可分性评估方法，获得PSG和PSA的最佳采集次数及相应的偏振测量状态，以及分类函数的参数。接着，在最佳状态下采集多幅偏振图像，提取仅与缺陷偏振特性相关的多维特征描述，随后将其作为分类器的输入向量，最终实现分类。该方法充分利用了划痕和灰尘颗粒偏振特性的差异，以及划痕偏振特性相对不变而灰尘颗粒偏振特性变化较大的特点。分类过程仅涉及简单的矩阵运算。与基于强度图像的传统判别方法相比，该方法获得的特征具有更高的可分性。实验表明，分类准确率达到90%以上。该方法可进一步应用于表面缺陷检测领域中其他缺陷的识别和判别。
