基于PSO优选特征的实木板材缺陷的压缩感知分选方法

针对实木板材表面缺陷的复杂性与随机性,提出了一种快速、准确的识别方法。首先,对实木板材表面图像进行3级双树复小波分解,提取低频子带、高频子带、原图像的均值、标准差和熵,共40维特征向量;然后,运用粒子群算法(PSO)优选出20个关键特征;最后,采用压缩感知理论将优选后的特征向量作为样本矩阵列,构建训练样本数据字典,通过最小残差完成缺陷识别。对4类柞木样本进行了仿真实验,活结、死结、虫眼、裂纹的分类正确率分别为93.3%、86.7%、100%和93.3%,结果表明:双树复小波良好的方向性能够表达实木板材表面复杂的信息;基于粒子群算法的特征选择能够提高分类效率;压缩感知分类器与传统分类器相比,具有结构简单、分类精度高的特点。 

The method of wood defect recognition based on PSO feature selection and compressed sensing

Aimed at the complexity and randomness of the wood board defects, we propose a novel and efficient method in this paper. Firstly, three-level dual-tree complex wavelet decomposition was used to extract 40 features, including average value, standard deviation and entropy from low-frequency, high-frequency sub-bands and the original image. Then, the particle swarm optimization ( PSO) algorithm was applied and 20 key features obtained. Finally, a data dictionary of training samples was constructed based on compressed sensing, and classification of defects was completed by the minimal reconstruction error. Four types of Xylosma racemosum wood samples, i. e. , live knot, dead knot, pinhole and crack, were used for the experiment. The recognition rates of the four types were 93.3%, 86.7%, 100% and 93.3%, respectively. Experimental results showed that the good directionality of dual-tree complex wavelets can reflect the complex information of wood board, the PSO can improve the efficiency of classification, and the compressed sensing has the advantages of simple structure and high classification accuracy.