Abstract
According to the nickel foam surface defect images with the typical characteristics of complex geometry and texture distribution, a nickel foam surface defect detection method based on spatial-frequency multi-scale block local binary pattern is proposed. First, nonsubsampled contourlet is used to carry out foam nickel image multi-scale decomposition, and therefore, low-frequency sub-band images and high-frequency sub-band images are obtained. The multi-scale block local binary pattern is then used to extract the feature histogram vectors of each block region of low- and high-frequency sub-bands, and the histogram feature vectors of the whole image after cascade are formed. The kernel principal component analysis and support vector machine are adopted to reduce the dimension of the feature histogram vectors and used for the defect classification. Experimental results show that the proposed method of feature extraction can extract more detailed texture information, and the average recognition rate reaches to 90%, which meets an enterprise’s needs.
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Acknowledgements
The author would like to thank all of the anonymous reviewers for their valuable comments and thoughtful suggestions, which improved the quality of the presented work. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61703157 and 61403136), the Hunan Province Natural Science Foundation, China (Grant No. 2019JJ50402), the Foundation of Hunan Educational Committee, China (Grant No. 18A360), and PhD research startup foundation of Hunan University of Arts and Science (16BSQD48).
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Cao, Bf., Li, Jq. & Qiao, Ns. Nickel foam surface defect detection based on spatial-frequency multi-scale MB-LBP. Soft Comput 24, 5949–5957 (2020). https://doi.org/10.1007/s00500-019-04513-2
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DOI: https://doi.org/10.1007/s00500-019-04513-2