CAJ | 학술논문

为了减少自动光学检测系统对用户经验的依赖,提出了一种基于统计分析的PCB组装缺陷特征学习方法.该方法通过对良品和不良品样本图像的统计学习优选出分类能力强的特征,再采用最小风险贝叶斯决策得到特征分类参数.实验结果表明,该算法有效地简化了用户检测程序的编程和调试,提高了AOI的使用效率和准确率.
위료감소자동광학검측계통대용호경험적의뢰,제출료일충기우통계분석적PCB조장결함특정학습방법.해방법통과대량품화불량품양본도상적통계학습우선출분류능력강적특정,재채용최소풍험패협사결책득도특정분류삼수.실험결과표명,해산법유효지간화료용호검측정서적편정화조시,제고료AOI적사용효솔화준학솔.
In order to reduce the experience-dependence of automatic optical inspection (AOI),proposed a Bayesian-based features learning for PCB assembling defects inspection.By statistical learning images of good product sample and defective product sample,selected features with better ability of classification capacity,and based on the risk minimization of Bayesian,worked out the decision parameters for feature classing.Experimental results show that the proposed method effectively simplifies the programming and debugging of user inspection application,and greatly improves the efficiency and accuracy of AOI.