Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM

DOI

10.2991/cmfe-15.2015.148How to use a DOI?

Keywords

aluminum surface defects; BP neural network; SVM; classification; combine

Abstract

Based on a combination of BP neural network and SVM, aluminum plate surface defects classification was discussed. In order to detect the defects, the target image is binaried by adaptive threshold method. After binarizing the target image, extract the characteristic value of six kinds of aluminum plate surface defect images and formed twenty-four dimensional feature vector. The principle and algorithm of BP neural network and support vector machine are introduced, given a way about combination of BP neural network and SVM, and about the important parameters optimization was carried out. The results verify the efficiency, accuracy and robustness of the algorithm about the BP neural network and support vector machine (SVM) classification of combining.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Di Liu
AU  - Qinghua Li
PY  - 2015/07
DA  - 2015/07
TI  - Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM
BT  - Proceedings of the International Conference on Chemical, Material and Food Engineering
PB  - Atlantis Press
SP  - 629
EP  - 632
SN  - 2352-5401
UR  - https://doi.org/10.2991/cmfe-15.2015.148
DO  - 10.2991/cmfe-15.2015.148
ID  - Liu2015/07
ER  -