Abstract:

In order to solve the problem of bullet surface defect automatic classification, we propose an automatic recognition method and a classification model of bullet surface defects based on the support vector machine (SVM). Firstly we extract the characteristic parameters from the three aspects of geometry, gray scale, texture according to the characteristics of the bullet surface defect image. Then we establish the classification model of bullet surface defects based on the SVM and the characteristic parameters are optimized. We also analyzed the influence of the penalty coefficients and kernel function parameters on the performance of the classifier. Experimental results show that the proposal based on the SVM outperforms the BP neural network classifier in terms of bullet surface defect classification under small samples.

Key words: support vector machine, bullet surface defect, characteristic parameters, recognition and classification