铁氧体磁瓦表面典型缺陷检测方法

为解决人工磁瓦表面缺陷检测质量不稳定的问题,提出了一种自动检测磁瓦表面缺陷的方法.首先利用磁瓦轮廓长度、面积等几何特征及轮廓匹配的相似度作为特征向量,采用支持向量机进行初次分类;然后再利用对凸凹缺陷的分析,得到缺陷数量和面积作为特征向量,采用最小均方误差分类器进行二次分类;最后对上述2步结果做与运算,得出最终判断.实验表明本方法可以达到正确识别率约为91.80%,错误接受率约为0.75%,正确拒绝率约为14.00%. 

Detection Method of Typical Defects in Arc Ferrite Magnet Surface

An automatic detection approach was proposed to solve unstable accuracy problem of bare-eye inspection of surface defects on arc magnets. According to the geometry features such as the length and area of arc magnet contours, a primary classification of defects was implemented by the support vector machine (SVM), using contour matching similarity as the feature vector. Then, the minimum mean square error classifier was used for secondary classification based on the number and area of detects acquired from analysis of convex and concave defects. The final decision was made by performing the AND operation on the two classification results. The experiments show that the proposed method can achieve an overall accuracy rate of about 91.80%, a fault acceptance rate of about 0.75%, and a correct rejection rate of about 14.00%.