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/).
Cite this article
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 -