Guifang Wu, Ke Xu, and Jinwu Xu, Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips, J. Univ. Sci. Technol. Beijing, 14(2007), No. 5, pp. 437-442. https://doi.org/10.1016/S1005-8850(07)60086-3
Cite this article as:
Guifang Wu, Ke Xu, and Jinwu Xu, Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips, J. Univ. Sci. Technol. Beijing, 14(2007), No. 5, pp. 437-442. https://doi.org/10.1016/S1005-8850(07)60086-3
Guifang Wu, Ke Xu, and Jinwu Xu, Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips, J. Univ. Sci. Technol. Beijing, 14(2007), No. 5, pp. 437-442. https://doi.org/10.1016/S1005-8850(07)60086-3
Citation:
Guifang Wu, Ke Xu, and Jinwu Xu, Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips, J. Univ. Sci. Technol. Beijing, 14(2007), No. 5, pp. 437-442. https://doi.org/10.1016/S1005-8850(07)60086-3
Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform (FFF) and sum of valid pixels (SVP), and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.
Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform (FFF) and sum of valid pixels (SVP), and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.
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