Automatic discrimination between dusts and digs for large fine optics

In surface defects evaluation of large fine optics, dusts and digs are very difficult to be distinguished from each other. In this paper, a pattern-recognition-based automatic discrimination method between dusts and digs is proposed to solve the problem. Based on the existing surface defects evaluation system(SDES), the process of feature selection and feature extraction is described on the basis of factor analysis. The dusts and the digs are classified according to the principle of Bayes discrimination. With the images of the calibration board, a data set that can be used for training and identifying dusts and digs is prepared. In order to obtain the suitable classifier, several comparison experiments are performed. The classifier has also been employed to the unlabeled data to verify the theory. The result shows that the accuracy is above 95%, which greatly reduces the number of false alarm that would otherwise result in remanufacturing. This method has already been applied to classifying dusts and digs for large fine optics in the inertial confinement fusion (ICF) system.