基于遗传算法的激光视觉焊缝特征点提取
|
摘要
提出了一种基于遗传算法的平面焊缝特征点提取方法。采用中值滤波、阈值分割法对焊缝图像进行预处理,以减少噪声;利用种子填充法进行图像分割,提取出激光条纹连通域,根据连通域特征抽象出激光条纹骨架提取的数学模型;重点研究基于遗传算法的骨架提取方法,并采用法向直线扫描法沿骨架方向提取中心点坐标;对骨架中心点进行直线拟合,并利用拉依达准则迭代剔除噪声点,获得激光条纹骨架的准确位置和焊缝特征点坐标。经试验验证可知,该方法能够有效消除焊缝图像中多种噪声及激光条纹宽度的干扰,快速准确地检测出焊缝特征点的位置。
A method for feature points extraction of planar weld seams based on genetic algorithm is proposed.In order to reduce the image noises,we use median filtering method and threshold segmentation method to preprocess welding images.The seed filling method is used for the image segmentation,and the mathematical model of laser stripe skeleton extraction is obtained according to the characteristics of the image.The skeleton extraction method of laser stripe based on genetic algorithm is mainly studied,and the coordinate of center point is extracted with linear scanning method.The Pauta criterion is used during the linear fitting of the skeleton to iteratively eliminate the noise data,and the accurate position of the skeleton and feature points are obtained.The experimental results show that the method can effectively eliminate many noises and the interference of laser stripe width in weld image and can extract the weld feature points quickly and accurately.
A method for feature points extraction of planar weld seams based on genetic algorithm is proposed.In order to reduce the image noises,we use median filtering method and threshold segmentation method to preprocess welding images.The seed filling method is used for the image segmentation,and the mathematical model of laser stripe skeleton extraction is obtained according to the characteristics of the image.The skeleton extraction method of laser stripe based on genetic algorithm is mainly studied,and the coordinate of center point is extracted with linear scanning method.The Pauta criterion is used during the linear fitting of the skeleton to iteratively eliminate the noise data,and the accurate position of the skeleton and feature points are obtained.The experimental results show that the method can effectively eliminate many noises and the interference of laser stripe width in weld image and can extract the weld feature points quickly and accurately.
引文
[1]LüX Q,Zhang K,Wu Y X.Development status and prospects automatic seam tracking[J].Mechanical Engineering,2003,39(12):80-85.吕学勤,张轲,吴毅雄.焊缝自动跟踪的发展现状与展望[J].机械工程学报,2003,39(12):80-85.
[2]Sharif L H,Yamane S,Hino Y,et al.Sensing and digital control of weld pool with visual welding robot[C].The 26th Annual Conference of the IEEEIndustrial Electronics Society,2000:1521-1526.
[3]Guo J C,Zhu Z M,Yu Y F,et al.Research and application of visual sensing technology based on laser structured light in welding industry[J].Chinese Journal of Lasers,2017,44(12):1200001.郭吉昌,朱志明,于英飞,等.焊接领域激光结构光视觉传感技术的研究及应用[J].中国激光,2017,44(12):1200001.
[4]Muhammad J,Altun H,Abo-Serie E.Welding seam profiling techniques based on active vision sensing for intelligent robotic welding[J].The International Journal of Advanced Manufacturing Technology,2017,88(1/2/3/4):127-145.
[5]Kiddee P,Fang Z,Tan M.A geometry based feature detection method of V-groove weld seams for thick plate welding robots[C].2nd International Conference on Control and Robotics Engineering,2017:43-48.
[6]Ye Z,Yu H W,Chen H B,et al.An image processing method for seam offset extraction[J].Journal of Shanghai Jiaotong University,2013,47(5):775-778.叶震,余焕伟,陈华斌,等.一种获取焊缝偏差的图像处理方法[J].上海交通大学学报,2013,47(5):775-778.
[7]Zhang Z J,Hao X Y,Liu S L,et al.Line detection based on hough one-dimensional transform[J].Acta Optica Sinica,2016,36(4):0412005.张振杰,郝向阳,刘松林,等.基于Hough一维变换的直线检测算法[J].光学学报,2016,36(4):0412005.
[8]Hong L,Yang X L,Zhong D P.Straight line feature extraction and analysis of weld line based on slope analysis method[J].Transactions of the China Welding Institution,2017,38(8):91-94.洪磊,杨小兰,钟冬平.基于斜率分析法的焊缝条纹直线特征提取分析[J].焊接学报,2017,38(8):91-94.
[9]Li L,Lin B Q,Zou Y B.Study on seam tracking system based on stripe type laser sensor and welding robot[J].Chinese Journal of Lasers,2015,42(5):0502005.李琳,林炳强,邹焱飚.基于条纹式激光传感器的机器人焊缝跟踪系统研究[J].中国激光,2015,42(5):0502005.
[10]Shah H N M,Sulaiman M,Shukor A Z.Autonomous detection and identification of weld seam path shape position[J].The International Journal of Advanced Manufacturing Technology,2017,92(9/10/11/12):3739-3747.
[11]Jiang Y F,Jiang K Y,Lin J Y.Extrication method for sub-pixel center of linear structured light stripe[J].Laser&Optoelectronics Progress,2015,52(7):071502.江永付,江开勇,林俊义.线结构光光条中心亚像素精确提取方法[J].激光与光电子学进展,2015,52(7):071502.
[12]Yang X,Yu X Y,Yang M J,et al.Laser stripe processing in measurement system based on structured-light[J].Computer Engineering,2007,33(16):162-163.杨雪,于晓洋,杨明极,等.结构光截面轮廓测量中激光条纹的处理[J].计算机工程,2007,33(16):162-163.
[13]Zou Y Y,Li P F,Zuo K Z.Field calibration method for three-line structured light vision sensor[J].Infrared and Laser Engineering,2018,47(6):94-99.邹媛媛,李鹏飞,左克铸.三线结构光视觉传感器现场标定方法[J].红外与激光工程,2018,47(6):94-99.
[14]Zou Y B,Zhou W L,Chen X Z.Research of laser vision seam detection and tracking system based on depth hierarchical feature[J].Chinese Journal of Lasers,2017,44(4):0402009.邹焱飚,周卫林,陈向志.基于深度分层特征的激光视觉焊缝检测与跟踪系统研究[J].中国激光,2017,44(4):0402009.
[15]Aviles-Vi1as J F,Rios-Cabrera R,Lopez-Juarez I.On-line learning of welding bead geometry in industrial robots[J].The International Journal of Advanced Manufacturing Technology,2016,83(1/2/3/4):217-231.
[16]Bao S D,Zhang K,Wu Y X.A detailed analysis of welding arc spectrum distribution characteristics to choose light sources of laser sensors[J].Journal of Optoelectronics·Laser,2009,20(4):504-508.包顺东,张轲,吴毅雄.电弧光谱分布特征及激光传感器的光源选择[J].光电子·激光,2009,20(4):504-508.
[17]Qi J Y,Li J Y,Lu Z Y,et al.Application of improved Otsu method in welding image segmentation[J].Transactions of the China Welding Institution,2016,37(10):97-100.齐继阳,李金燕,陆震云,等.改进的Otsu法在焊接图像分割中的应用[J].焊接学报,2016,37(10):97-100.
[18]Liu H R,Zhao C X,Li X,et al.Study on a neural network optimization algorithm based on improved genetic algorithm[J].Chinese Journal of Scientific Instrument,2016,37(7):1573-1580.刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(7):1573-1580.
[19]Yang J,Zhao H Y.The research of floating-point coding improver genetic algorithm in flatness error evaluation[J].Optics and Precision Engineering,2017,25(3):706-711.杨健,赵宏宇.浮点数编码改进遗传算法在平面度误差评定中的研究[J].光学精密工程,2017,25(3):706-711.
[20]Yin A J,Zhao L,Wu H G.Error process based on3σrule used in balancing by correlation theory[J].Journal of Chongqing University(Natural Science Edition),2013,36(10):22-26.尹爱军,赵磊,吴宏钢.相关法动平衡校正中的3σ准则误差处理方法[J].重庆大学学报,2013,36(10):22-26.
[21]Tian L,Liu Z T.Least square piecewise linear fitting[J].Computer Science,2012,39(6A):482-484.田垅,刘宗田.最小二乘法分段直线拟合[J].计算机科学,2012,39(6A):482-484.
[2]Sharif L H,Yamane S,Hino Y,et al.Sensing and digital control of weld pool with visual welding robot[C].The 26th Annual Conference of the IEEEIndustrial Electronics Society,2000:1521-1526.
[3]Guo J C,Zhu Z M,Yu Y F,et al.Research and application of visual sensing technology based on laser structured light in welding industry[J].Chinese Journal of Lasers,2017,44(12):1200001.郭吉昌,朱志明,于英飞,等.焊接领域激光结构光视觉传感技术的研究及应用[J].中国激光,2017,44(12):1200001.
[4]Muhammad J,Altun H,Abo-Serie E.Welding seam profiling techniques based on active vision sensing for intelligent robotic welding[J].The International Journal of Advanced Manufacturing Technology,2017,88(1/2/3/4):127-145.
[5]Kiddee P,Fang Z,Tan M.A geometry based feature detection method of V-groove weld seams for thick plate welding robots[C].2nd International Conference on Control and Robotics Engineering,2017:43-48.
[6]Ye Z,Yu H W,Chen H B,et al.An image processing method for seam offset extraction[J].Journal of Shanghai Jiaotong University,2013,47(5):775-778.叶震,余焕伟,陈华斌,等.一种获取焊缝偏差的图像处理方法[J].上海交通大学学报,2013,47(5):775-778.
[7]Zhang Z J,Hao X Y,Liu S L,et al.Line detection based on hough one-dimensional transform[J].Acta Optica Sinica,2016,36(4):0412005.张振杰,郝向阳,刘松林,等.基于Hough一维变换的直线检测算法[J].光学学报,2016,36(4):0412005.
[8]Hong L,Yang X L,Zhong D P.Straight line feature extraction and analysis of weld line based on slope analysis method[J].Transactions of the China Welding Institution,2017,38(8):91-94.洪磊,杨小兰,钟冬平.基于斜率分析法的焊缝条纹直线特征提取分析[J].焊接学报,2017,38(8):91-94.
[9]Li L,Lin B Q,Zou Y B.Study on seam tracking system based on stripe type laser sensor and welding robot[J].Chinese Journal of Lasers,2015,42(5):0502005.李琳,林炳强,邹焱飚.基于条纹式激光传感器的机器人焊缝跟踪系统研究[J].中国激光,2015,42(5):0502005.
[10]Shah H N M,Sulaiman M,Shukor A Z.Autonomous detection and identification of weld seam path shape position[J].The International Journal of Advanced Manufacturing Technology,2017,92(9/10/11/12):3739-3747.
[11]Jiang Y F,Jiang K Y,Lin J Y.Extrication method for sub-pixel center of linear structured light stripe[J].Laser&Optoelectronics Progress,2015,52(7):071502.江永付,江开勇,林俊义.线结构光光条中心亚像素精确提取方法[J].激光与光电子学进展,2015,52(7):071502.
[12]Yang X,Yu X Y,Yang M J,et al.Laser stripe processing in measurement system based on structured-light[J].Computer Engineering,2007,33(16):162-163.杨雪,于晓洋,杨明极,等.结构光截面轮廓测量中激光条纹的处理[J].计算机工程,2007,33(16):162-163.
[13]Zou Y Y,Li P F,Zuo K Z.Field calibration method for three-line structured light vision sensor[J].Infrared and Laser Engineering,2018,47(6):94-99.邹媛媛,李鹏飞,左克铸.三线结构光视觉传感器现场标定方法[J].红外与激光工程,2018,47(6):94-99.
[14]Zou Y B,Zhou W L,Chen X Z.Research of laser vision seam detection and tracking system based on depth hierarchical feature[J].Chinese Journal of Lasers,2017,44(4):0402009.邹焱飚,周卫林,陈向志.基于深度分层特征的激光视觉焊缝检测与跟踪系统研究[J].中国激光,2017,44(4):0402009.
[15]Aviles-Vi1as J F,Rios-Cabrera R,Lopez-Juarez I.On-line learning of welding bead geometry in industrial robots[J].The International Journal of Advanced Manufacturing Technology,2016,83(1/2/3/4):217-231.
[16]Bao S D,Zhang K,Wu Y X.A detailed analysis of welding arc spectrum distribution characteristics to choose light sources of laser sensors[J].Journal of Optoelectronics·Laser,2009,20(4):504-508.包顺东,张轲,吴毅雄.电弧光谱分布特征及激光传感器的光源选择[J].光电子·激光,2009,20(4):504-508.
[17]Qi J Y,Li J Y,Lu Z Y,et al.Application of improved Otsu method in welding image segmentation[J].Transactions of the China Welding Institution,2016,37(10):97-100.齐继阳,李金燕,陆震云,等.改进的Otsu法在焊接图像分割中的应用[J].焊接学报,2016,37(10):97-100.
[18]Liu H R,Zhao C X,Li X,et al.Study on a neural network optimization algorithm based on improved genetic algorithm[J].Chinese Journal of Scientific Instrument,2016,37(7):1573-1580.刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(7):1573-1580.
[19]Yang J,Zhao H Y.The research of floating-point coding improver genetic algorithm in flatness error evaluation[J].Optics and Precision Engineering,2017,25(3):706-711.杨健,赵宏宇.浮点数编码改进遗传算法在平面度误差评定中的研究[J].光学精密工程,2017,25(3):706-711.
[20]Yin A J,Zhao L,Wu H G.Error process based on3σrule used in balancing by correlation theory[J].Journal of Chongqing University(Natural Science Edition),2013,36(10):22-26.尹爱军,赵磊,吴宏钢.相关法动平衡校正中的3σ准则误差处理方法[J].重庆大学学报,2013,36(10):22-26.
[21]Tian L,Liu Z T.Least square piecewise linear fitting[J].Computer Science,2012,39(6A):482-484.田垅,刘宗田.最小二乘法分段直线拟合[J].计算机科学,2012,39(6A):482-484.