根据红外成像特性及金属表面缺陷区域灰度分布变化缓慢的特点，提出了一种基于小波纹理特性统计分析的铜带表面缺陷视觉检测方法。利用CCD视觉传感器获取受检金属表面的红外影像资料，引入一阶Haar小波分解红外图像，抽取4个小波特性，然后分别使用Hotelling T2和X2多变量统计法融合4个小波特性。最后根据统计值判别图像是否存在缺陷，并使用支持向量机对缺陷进行分类。比较分析了两种小波域多变量统计方法检测缺陷的性能。实验结果表明，Hotelling T2统计法在缺陷检测和识别方面的性能较好，对微小缺陷可达到92.8%的检测率和95.42%的识别率。

According to the characteristics of infrared imaging and the gradual change of intensity levels of metal surface defects, a vision inspection method for surface defects of metal based on statistically analyzing wavelet texture has been proposed. Firstly, the CCD sensors are used to obtain infrared video-data for surface of copper strips, and then the first-order Haar wavelet is used to decompose infrared image. Secondly, two multivariate statistical methods, including Hotelling T2 control chart and Chi square test, are used to fuse the four wavelet characteristics. Finally, the statistical values are used to distinguish the existence of defects and classify the defects using support vector machine. The capabilities of two kinds of wavelet-domain-based multivariate statistical approaches in inspecting defects have been researched deeply. The experimental results demonstrate that the Hotelling T2 method gets the better performance, which achieves a 92.8% probability of detecting the existence of micro defects and a 95.42% probability of classifying the defects.