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.
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Acknowledgements
The work in this paper was supported partially by the National Natural Science Foundation of China (No. 61070009, 61100133) and Hubei Provincial Natural Science Funds for Distinguished Young Scholar of China (2010CDA090).
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Hu, H., Li, Y., Liu, M. et al. Classification of defects in steel strip surface based on multiclass support vector machine. Multimed Tools Appl 69, 199–216 (2014). https://doi.org/10.1007/s11042-012-1248-0
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DOI: https://doi.org/10.1007/s11042-012-1248-0