Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Guifang Wu, Ke Xu, Jinwu Xu

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    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://dx.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://dx.doi.org/10.1016/S1005-8850(07)60086-3
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    Materials

    Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

    • National Engineering Research Center for Advanced Rolling Technology, University of Science and Technology Beijing, Beijing 100083, China

    • Electronic Information Engineering College, Henan University of Science and Technology, Luoyang 471003, China

    基金项目: 

    This work was financially supported by the National High Technology Research and Development Program of China (No.2003AA331080 and 2001AA339030) and the Talent Science Research Foundation of Henan University of Science & Technology (No.09001121).

      通信作者:

      Guifang Wu E-mail: easyfancy@126.com

    中文摘要

    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.

     

    Materials

    Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

    Author Affilications

      • National Engineering Research Center for Advanced Rolling Technology, University of Science and Technology Beijing, Beijing 100083, China

      • Electronic Information Engineering College, Henan University of Science and Technology, Luoyang 471003, China

    • Funds: 

      This work was financially supported by the National High Technology Research and Development Program of China (No.2003AA331080 and 2001AA339030) and the Talent Science Research Foundation of Henan University of Science & Technology (No.09001121).

    • Received: 23 September 2006;
    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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