Share Email Print

Proceedings Paper

Design and performance of the classifier of the projectile body surface defect recognition system
Author(s): Wenfeng Guo; Zhigang Jiao; Degang Liang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In order to solve the identification of projectile surface defect category of which body defect detection system, the classifier of the body defect detection system was designed. The mathematical model of BP neural network and support vector machine (SVM) network classifier were established respectively and realized by using VC + + program and MATLAB, the number of nodes in the middle layer were determined, and the detection performance of the two kinds of classifiers were tested. Test samples were collected from magnetic particle detection images of 3 models which included 20 samples containing cracks and 600 without defects. The results show that the SVM defect classification network classifier has higher recognition rate than the BP neural network, but BP network has stronger stability classification than the SVM.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003314 (29 August 2016); doi: 10.1117/12.2244276
Show Author Affiliations
Wenfeng Guo, Shenyang Ligong Univ. (China)
Zhigang Jiao, Shenyang Ligong Univ. (China)
Northeastern Univ. (China)
Degang Liang, Liaoshen Industrial Group Co., Ltd. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top