提出一种基于支持向量机分类的油封缺陷图像检测方法，把油封外观中的有无缺陷看作两种不同的类别模式，应用支持向量机对两类不同的样本采样学习，然后进行分类判断。采集油封各部位图像并进行预处理，利用算法切割出各个检测区域图像，根据油封主要部位的各类缺陷特点，选取不同特征参数。应用径向基核函数建立支持向量机识别模型，并通过实验实现核函数参数寻优。实验结果表明,该方法具有检测成本低、可靠性高、泛化能力强、容易在线实施等特点，具有实用推广价值。

A method based on Support Vector Machine (SVM) classification algorithm to detect the defects of oil-seal surface was put forward, in which the defective area and non-defective area were treated as two different textures and were sampled respectively to be learned for classification by SVM. Testing areas were cut out of the preprocessed images of oil-sea different sections, and different feature parameters were selected according to the characteristics of various defects in oil-seal testing area on the base of image analysis. SVM recognition model was established by application of Radial Basis Function (RBF), and the parameters of RBF were optimized through cross validation experiments. The results showed that the proposed approach was characterized by low cost, high reliability, excellent generalization, and easy on-line implementation and so on, and could be applied for defect detection of various products.