基于支持向量机的钢板表面缺陷检测
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摘要
针对钢板缺陷的传统检测方法存在速度慢、工作量大的问题。采用机器视觉的方法,通过采集钢板表面图像信息,由计算机算法处理得到缺陷的特征样本,使用支持向量机提升分类的速度和准确度。试验结果表明,径向基核函数支持向量机方法对钢板表面各种缺陷的准确识别率达到90%及以上,为钢板表面缺陷检测技术提供了很好的支持。
Aiming at the problem of slow speed and large workload of the traditional detection method for steel plate defects,the method of machine vision was adopted to acquire the image information of the surface of the steel plate,and the feature samples of the defects were processed by a computer algorithm.The speed and accuracy of the classification are improved by using a support vector machine.The experimental results show that the recognition accuracy of various defects on the surface of the steel plate reaches 90% and above with the radial basis kernel function support vector machine method,which provides a good support for surface defect detection technology of steel plate.
Aiming at the problem of slow speed and large workload of the traditional detection method for steel plate defects,the method of machine vision was adopted to acquire the image information of the surface of the steel plate,and the feature samples of the defects were processed by a computer algorithm.The speed and accuracy of the classification are improved by using a support vector machine.The experimental results show that the recognition accuracy of various defects on the surface of the steel plate reaches 90% and above with the radial basis kernel function support vector machine method,which provides a good support for surface defect detection technology of steel plate.
引文
[1]胡亮,段发阶,丁克勤,等.带钢表面缺陷计算机视觉在线检测系统的设计[J].无损检测,2003,25(6):287-290.
[2]韩芳芳.表面缺陷视觉在线检测关键技术研究[D].天津:天津大学精密仪器与光电子工程学院,2011.
[3]侯景忠,夏克文,杨帆.轧制设备生产带钢缺陷图像检测仿真研究[J].计算机仿真,2017,34(2):401-405.
[4]王森,朱凯.基于模式识别的带钢表面缺陷等级判定的研究[J].现代制造工程,2016(11):119-123.
[5]管声启,王燕妮,师红宇.基于图像预处理的神经网络带钢缺陷检测[J].钢铁研究,2013,41(1):22-26.
[6]化春键,周海英.改进组合分类器的冷轧带钢表面缺陷识别研究[J].机械科学与技术,2017,36(11):1785-1790.
[7]VAPNIK V N.The nature of statistical learning theory[M].Berlin:Springer Verlag,1995.
[8]汤勃,孔建益,王兴东,等.基于图像处理的钢板表面缺陷支持向量机识别[J].中国机械工程,2011,22(12):1402-1405.
[9]LI C S,CHEN J S,HAN W L,et al.Thermal scratch on surface of SUS430stainless steel strip in cold rolling process[J].Journal of Iron and Steel Research,2014,21(3):282-286.
[10]LIU Y J,KONG J Y,WANG X D,et al.Research on image acquisition of automatic surface vision inspection systems for steel sheet[C]//International Conference on Advanced Computer Theory and Engineering.2010:V6-189-V6-192.
[2]韩芳芳.表面缺陷视觉在线检测关键技术研究[D].天津:天津大学精密仪器与光电子工程学院,2011.
[3]侯景忠,夏克文,杨帆.轧制设备生产带钢缺陷图像检测仿真研究[J].计算机仿真,2017,34(2):401-405.
[4]王森,朱凯.基于模式识别的带钢表面缺陷等级判定的研究[J].现代制造工程,2016(11):119-123.
[5]管声启,王燕妮,师红宇.基于图像预处理的神经网络带钢缺陷检测[J].钢铁研究,2013,41(1):22-26.
[6]化春键,周海英.改进组合分类器的冷轧带钢表面缺陷识别研究[J].机械科学与技术,2017,36(11):1785-1790.
[7]VAPNIK V N.The nature of statistical learning theory[M].Berlin:Springer Verlag,1995.
[8]汤勃,孔建益,王兴东,等.基于图像处理的钢板表面缺陷支持向量机识别[J].中国机械工程,2011,22(12):1402-1405.
[9]LI C S,CHEN J S,HAN W L,et al.Thermal scratch on surface of SUS430stainless steel strip in cold rolling process[J].Journal of Iron and Steel Research,2014,21(3):282-286.
[10]LIU Y J,KONG J Y,WANG X D,et al.Research on image acquisition of automatic surface vision inspection systems for steel sheet[C]//International Conference on Advanced Computer Theory and Engineering.2010:V6-189-V6-192.