Classification of defects in steel strip surface based on multiclass support vector machine

Authors:
Huijun Hu;Yuanxiang Li;Maofu Liu;Wenhao Liang
Affiliations:
State Key Lab of Software Engineering Computer School, Wuhan University, Wuhan, China 430072 and College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Chin ...;State Key Lab of Software Engineering Computer School, Wuhan University, Wuhan, China 430072;College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China 430065;Zhejiang Dahua Tecnology Co. Ltd, Hangzhou, China 310053
Venue:
Multimedia Tools and Applications
Year:
2014

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Abstract

In this paper, we use support vector machine to classify the defects in steel strip surface images. After image binarization, three types of image features, including geometric feature, grayscale feature and shape feature, are extracted by combining the defect target image and its corresponding binary image. For the classification model based on support vector machine, we utilize Gauss radial basis as the kernel function, determine model parameters by cross-validation and employ one-versus-one method for multiclass classifier. Experiment results show that support vector machine model outperforms the traditional classification model based on back-propagation neural network in average classification accuracy.