磁瓦表面缺陷机器视觉检测与识别方法

针对磁瓦生产过程中表面缺陷检测的重要性和人工检测的弊端，研究基于机器视 觉的磁瓦表面缺陷自动检测与识别方法。为解决磁瓦表面缺陷种类多、对比度低、图像中存在 磨痕纹理背景和整体亮度不均匀等难点，定义扫描线梯度，其标准差与扫描线灰度标准差构成 特征向量，提出基于两类支持向量机的图像分割方法来判别和提取缺陷；并提出一种改进的多 类支持向量机方法，对缺陷进行分类识别，解决了多类支持向量机存在不可分区域的问题，提 高了分类器的准确性和有效性。实验结果表明，该方法能准确快速地提检测磁瓦表面各区域的 各类缺陷，检出率可达到96%以上，识别率超过91%。

Machine Vision Inspection of Surface Defect for Arc Magnet

Aiming at the importance of defect inspection and the shortcoming of manual inspection in production process, the automatic machine vision detection and classification method is studied for the surface defect of arc magnet. Firstly, against many kinds of defects with low contrast, textured background and uneven brightness, the scan line gradient is defined to constitute feature vector with the standard deviation of the scan line grayscale. Secondly, the image segmentation method based on two-class support vector machine is presented to identify and extract defects. Finally the improved method on multi-class support vector machine is proposed to classify these extracted defects, which solved the problems of unclassifiable region and improved classification accuracy and effectiveness. The experimental results indicate that all kinds of defects of the different sub-region can be detected rapidly and accurately. The detection rate of defects can reach 96% and the classification rate of defects is higher than 91%.