基于Adaboost算法的环抛机盘面钝化程度分类

Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm

针对环抛机盘面钝化程度难以识别的问题,该文提出了一种基于灰度共生矩阵(gray-level co-occurrence matrix,GLCM)和Adaboost分类器的分类方法。首先获取盘面状态图像,利用GLCM算法对环抛机盘面纹理图像进行特征提取,将GLCM的4个二阶统计量输入至Adaboost分类器进行训练,得到可以识别盘面未钝化和已钝化图像的图像分类器。经过试验数据分析,确定了GLCM用于环抛机盘面钝化程度分类的最优参数为点对间距离d=11、灰度级数k=16,其分类正确率可达98.3%,较LBP算法分类正确率升高9.5%,较PNN算法分类正确率升高2.08%。

This paper describes a classification method based on a gray-level co-occurrence matrix(GLCM) and the Adaboost algorithm for monitoring passivation of a continuously polishing machine disk. The texture image features of a continuously polishing machine disk are derived from a GLCM operator of a disk photograph. Four second-order statistics of the GLCM are input into a Adaboost classifier for training so that the classifier can then identify if the disk has been passivated. Tests show that the classification accuracy is best when the GLCM point-to-point distance is 11 and the GLCM gray level is 16. The image classification accuracy is 98.3%, which is 9.5% higher than that of the LBP algorithm and 2.08% higher than that of the PNN algorithm.