基于近红外光谱与连续投影算法的针叶材表面节子缺陷识别

Knot Defection on Coniferous Wood Surface by Near Infrared Spectroscopy and Successive Projections Algorithm

为了实现木板材依据节子进行自动化分级,利用近红外光谱技术对针叶材表面节子进行检测。比较了光谱预处理和建模方法对节子识别的影响,研究了单一树种板材节子识别模型对其他树种板材节子识别的适应性,建立了混合树种板材的节子识别模型,并利用连续投影算法(SPA)进行了节子特征波长优选。结果显示,一阶导数光谱结合最小二乘支持向量机(LS-SVM)所建单一和混合节子识别模型性能最优。连续投影算法优选了15个特征波长变量,仅占全波长变量的0.87%,所建LS-SVM简化模型对测试集的敏感性、特异性和识别准确率分别为0.990,0.954,97.44%。实验结果表明,近红外光谱技术联合SPA与LS-SVM可以对多种针叶材板材的表面节子进行快速准确检测,连续投影算法是提取板材表面节子缺陷特征的有效方法,能简化模型并提高模型预测精度。

To develop a model for rapid, accurate grading of lumbers based on knots, near infrared spectroscopy was used to detect knots on coniferous wood surface. We explored the effects of spectral preprocess methods and modelling methods on knot detection, and investigated the feasibility of using a model built within one species to discriminate the samples from other species. Successive projections algorithm （SPA） was used to select effective wavelengths. The results showed that least squares-support vector machines （LS-SVM） coupled with first derivative preprocessed spectra achieved the best performance for both single and mixed models. Fifteen effective wavelengths, only 0.87% of the full wavelengths, were selected by SPA to build an LS-SVM model, and the sensitivity, specificity and accuracy in validation set were 0. 990, 0. 954, 97.44%. The results showed that near infrared spectroscopy combined with SPA and LS-SVM can be used to detect surface knots on different coniferous wood varieties. SPA is a powerful tool to select the efficient variables, and it can simplify model and improve model prediction precision.