西南交通大学学报2013，Vol.48Issue(1)：129-134,140,7.DOI:10.3969/j.issn.0258-2724.2013.01.020
铁氧体磁瓦表面典型缺陷检测方法
Detection Method of Typical Defects in Arc Ferrite Magnet Surface
摘要
Abstract
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% .关键词
磁瓦/凸凹分类/支持向量机/缺陷检测Key words
arc magnet/ convex and concave classification/ support vector machine/ defect detection分类
信息技术与安全科学引用本文复制引用
蒋红海,李雪琴,刘培勇,殷国富..铁氧体磁瓦表面典型缺陷检测方法[J].西南交通大学学报,2013,48(1):129-134,140,7.基金项目
国家科技支撑计划资助项目(2006BAF01A07) （2006BAF01A07）
四川省高新技术产业重大关键技术项目(2010GZ0051) （2010GZ0051）
