摘要
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%.
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. performing the AND operation on the two classification results. The method can achieve an overall accuracy rate of about 91.80% , The final decision was made by experiments show that the proposed a fault acceptance rate of about 0.75% , and a correct rejection rate of about 14.00%.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2013年第1期129-134,140,共7页
Journal of Southwest Jiaotong University
基金
国家科技支撑计划资助项目(2006BAF01A07)
四川省高新技术产业重大关键技术项目(2010GZ0051)
关键词
磁瓦
凸凹分类
支持向量机
缺陷检测
arc magnet
convex and concave classification
support vector machine
defect detection