关键词: 带钢, 表面缺陷, 多分类器集成, 机器视觉, 模式识别, 加权投票算法

Abstract: To solve the problems that a single classifier recognizes the surface defects of steel strips ineffectively and over-depends on training samples, a new method with combination of multiple classifiers was proposed. The LVQ and RBF neural networks, and the support vector machine were used as the basic classifiers. The weighted voting algorithm was applied to integrating these basic classifiers, thus the complementary of the recognition system was realized. The experiments showed that the common surface defects of steel strips such as zigzag edges, welding seams, inclusions and wrinkles can be more effectively recognized by the combined multiple classifiers. The classification accuracy is better than that of a single classifier, with the overall recognition rate above 96%.