目的 解决目前纸病分类算法存在的实时性差、难以适应生产线在线检测要求等问题。方法 提出一种基于差影法和支持向量机的在线纸病检测分类方法。首先使用差影法来判断纸张是否含有纸病；对含有纸病的纸张进行打标机打标，同时存储图像，提取纸病区域外接矩形的特征向量；最后使用支持向量机对纸病进行分类。结果 将该方法与已有的BP神经网络以及朴素贝叶斯方法进行对比可知，分类正确率高于目前已有的分类方法，对于4种纸病的分类正确率均在90%以上，而且实时性好，更加适合于在线检测。结论 该方法可以有效地对纸病进行分类，满足生产线实时检测分类的要求。

The work aims to solve the problems of current paper defect classification algorithm, including poor real-time ability and difficulty in adapting to the requirements of on-line detection of the production line. An on-line paper defect classification method based on background subtraction and support vector machine (SVM) was proposed. Firstly, background subtraction method was used to determine whether the paper contained defects. Then, the paper with defects was marked by the marking machine and the images were stored. The eigenvectors of enclosing rectangle in the paper defect area were extracted. Finally, the paper defects were classified by SVM. Based on the comparison of the proposed method and the existing BP neural network as well as the naive Bayesian method, the classification accuracy was higher than that of the existing classification method. The four kinds of paper defects with classification accuracy of over 90% and good real-time ability were more suitable for on-line detection. The proposed method can effectively classify paper defects and meet the requirements of real-time detection and classification of the production line.