基于多分类支持向量机算法的PCB焊点检测研究
摘要
针对PCB焊点检测分类的应用需求,采用基于机器视觉的"图像特征+分类器"的技术方案,研究总结了焊点图像特征提取方法,并采用多分类支持向量机算法实现焊点分类;在检测算法具体实现上,综合考虑多类别样本空间均衡、类间离散程度以及分类器数量,在算法性能、分类器训练与分类执行效率3方面取得平衡。实验结果表明,该检测方法对焊点类型的正确识别率达到了97.9%,证明了方法的有效性。
引文
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[2]卢盛林,张宪民,邝泳聪.基于神经网络的PCB焊点检测方法[J].华南理工大学学报(自然科学版),2008,36(5):135-139.
[3]XIE H,ZHANG X,KUANG Y,et al.Solder joint inspection method for chip component using improved Adaboost and decision tree[J].IEEE Transaction on Components,Packaging and Manufacturing Technology,2011,1(12):2018-2027.
[4]刘美菊,李凌燕,郭文博.基于机器视觉的焊点检测算法研究[J].电子器件,2017,40(4):1015-1020.
[5]谢宏威,张宪民,邝泳聪,等.印刷电路板焊点的智能检测[J].光学精密工程,2011,19(9):2154-2162.
[6]叶倩,蔡念,李飞洋,等.基于自适应匹配半径视觉背景提取的IC焊点检测算法[J].焊接技术,2017,46(7):12-15.
[7]CAI N,CEN G,WU J,et al.SMT Solder Joint Inspection via a Novel Cascaded Convolutional Neural Network[J].IEEE Transaction on Components,Packaging and Manufacturing Technology,2018,8(4):670-677.
[8]YUN T S,SIM K J,KIM H J.Support vector machine-based inspection of solder joints using circular illumination[J].Electronics Letters,2000,36(11):949-951.
[9]高红霞,蔡锦龙.基于支持向量机的印刷电路板焊点分类算法[C]//中国自动化学会中南六省(区)2010年第28届年会论文集.2010.
[10]WU H,ZHANG X,XIE H,et al.Classification of solder joint using feature selection based on bayes and support vector machine[J].IEEE Transaction on Components,Packaging and Manufacturing Technology,2013,3(3):516-522.
[11]贺海浪,龙绪明,刘明晓,等.(2D)2LDA和支持向量机在PCB焊点检测中的应用[J].现代电子技术,2015,38(2):100-103.
[12]VAPNIK V N.An overview of statistical learning theory[J].IEEE Transactions on Neural Networks,1999,10(5):988-999.
[13]王艳,陈欢欢,沈毅.有向无环图的多类支持向量机分类算法[J].电机与控制学报,2011,15(4):85-89.