针对QR(Quick Response)码在光照变化、污染、破损等情况下识别率低的问题,提出一种多块局部二值模式(MB-LBP)结合改进灰狼优化算法(GWO)优化支持向量机(SVM)的QR码识别算法。首先采用提升小波变换分离出图像的高低频分量,将二级低频和水平高频分量分成互不重叠的子块,分别提取每个子块的MB-LBP特征并融合;然后运用主成分分析(PCA)对样本集进行特征降维;最后采用SVM算法对QR码数据建立分类模型。为进一步提高分类精度,在标准GWO基础上引入基于对数函数的非线性收敛因子提升其寻优性能,并使用改进GWO优化SVM模型。实验根据不同高低频结合方式、SVM优化算法对识别性能进行了测试,结果表明本文方法在识别速度和分类精度方面都有明显提升,具有良好的稳健性。

To address the problem of low recognition rate of QR (Quick Response) codes under changes in illumination, pollution, and damage, a QR-code recognition algorithm based on multiblock local binary patterns (MB-LBP) combined with an improved grey wolf optimization (GWO) algorithm for optimizing a support vector machine (SVM) is proposed. Firstly, the lifting wavelet transform is used to separate the high- and low-frequency components of the image, while the second-level low-frequency and horizontal high-frequency components are divided into nonoverlapping sub-blocks. The MB-LBP features of each sub-block are separately extracted and fused. Then, principal component analysis is applied to reducing the dimension of the sample set. Finally, the classification model of the QR-code data is established using the SVM algorithm. To further improve the classification accuracy, the nonlinear convergence factor based on a logarithmic function is introduced to improve the optimization performance based on the standard GWO; the improved GWO is used to optimize the SVM model. The recognition performance is tested according to different combination modes of high and low frequencies and the SVM optimization algorithm. The experimental results show that the proposed algorithm significantly improves the recognition rate and classification accuracy, and it is highly robust.