Design and performance of the classifier of the projectile body surface defect recognition system

Wenfeng Guo; Zhigang Jiao; Degang Liang

doi:10.1117/12.2244276

29 August 2016 Design and performance of the classifier of the projectile body surface defect recognition system

Wenfeng Guo, Zhigang Jiao, Degang Liang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003314 (2016) https://doi.org/10.1117/12.2244276
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Wenfeng Guo, Zhigang Jiao, and Degang Liang "Design and performance of the classifier of the projectile body surface defect recognition system", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003314 (29 August 2016); https://doi.org/10.1117/12.2244276

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