【摘要】 为提高带钢表面缺陷图像的分类准确率,文章研究了带钢表面缺陷图像多尺度局部二值模式(Local Binary Pattern, LBP)特征。通过提取多种类型的多尺度LBP特征以及不同尺度的LBP联合特征,并与灰度共生矩特征进行对比;利用支持向量机(Support Vector Machine,SVM)进行分类实验。实验结果表明,对于带钢表面缺陷图像的LBP特征,(16,2)尺度LBP特征的分类准确率高于(8,1)尺度LBP特征;两种尺度联合特征分类准确率高于单一尺度特征;各类LBP特征与灰度共生矩特征中,LBP直方图傅里叶变换特征具有更高的分类准确率。更多还原

【Abstract】 In order to improve the classification accuracy of strip surface defect images, multi-scale local binary pattern(LBP) features of strip surface defect images were studied. By extracting multiple types of multi-scale LBP features and LBP joint features at different scales and compare it with the feature of gray level co-occurrence matrix;Use support vector machine(SVM) for classification experiments. The experimental results show that for LBP features of strip surface defect images, the classification accuracy rate of(16,2) scale LBP features is higher than(8,1) scale LBP features; the classification accuracy rate of combined features of two scales is higher than that of single scale features; Among the various types of LBP features and gay level co-occurrence matrix features, the LBP histogram Fourier transform feature has higher classification accuracy.更多还原