改进均值漂移算法的焊缝特征点识别分析
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摘要
对于线结构光视觉传感的焊缝跟踪系统,快速、精准地识别和提取焊缝特征点是关键.根据结构光条纹线在焊缝处的变形导致的条纹不连续现象,对不锈钢平板对接焊缝和搭接焊缝进行了跟踪试验,提出以改进的均值漂移算法提取焊缝特征点的算法.与传统算法不同,所提算法免去了提取条纹中心线与拟合条纹线过程,直接通过漂移识别焊缝特征点;通过限制漂移算法的搜索方向,防止搜索"回漂"现象;引入漂移加速因子,提高算法执行效率.试验结果表明,利用改进均值漂移算法能够有效地识别焊缝特征点,显著地提高焊缝跟踪的准确度和实时性能.
A fast and accurate weld seam feature point recognition is the key of the weld seam tracking system based on structured light sensing.For the streak discontinuity caused by the deformation of structured light stripe line at the weld,tracking tests were carried on butt and lap welds of stainnless steel board,and an improved mean-shift algorithm was proposed to extract the feature point of a weld seam.Unlike the traditional algorithm,the improved algorithm eliminated the process of extracting fringe center line and fitting fringe lineand the feature points of welding seam are identified by drift.In order to prevent from back-shifting,the search direction of the algorithm was limited.To improve the running efficiency,a shifting accelerating factor was introduced.The test shows that the improved mean-shift algorithm can effectively recognize the feature point of a weld seam and saliently improve the accuracy and real-time performance.
A fast and accurate weld seam feature point recognition is the key of the weld seam tracking system based on structured light sensing.For the streak discontinuity caused by the deformation of structured light stripe line at the weld,tracking tests were carried on butt and lap welds of stainnless steel board,and an improved mean-shift algorithm was proposed to extract the feature point of a weld seam.Unlike the traditional algorithm,the improved algorithm eliminated the process of extracting fringe center line and fitting fringe lineand the feature points of welding seam are identified by drift.In order to prevent from back-shifting,the search direction of the algorithm was limited.To improve the running efficiency,a shifting accelerating factor was introduced.The test shows that the improved mean-shift algorithm can effectively recognize the feature point of a weld seam and saliently improve the accuracy and real-time performance.
引文
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[10] YE G L,GUO J W,SUN Z Z.Weld bead recognition using laser vision with model-based classification [J].Robotics and Computer-Integrated Manufacturing,2018,52:9- 16.
[11] CHU H H,WANG Z Y.A vision-based system for post-welding quality measurement and defect detection [J].International Journal of Advanced Manufacturing Technology,2016,86(9/10/11/12):3007- 3014.
[12] PRASARN K,FANG Z J,TAN M.An automated weld seam tracking system for thick plate using cross mark structured light [J].International Journal of Advanced Manufacturing Technology,2016,87(9/10/11/12):1- 15.
[13] 丁艳蓉,白瑞林,倪健.基于曲率与灰度复合的角点亚像素定位方法 [J].激光与光电子学进展,2018,55(3):031501.DING Yanrong,BAI Ruilin,NI Jian.Sub-pixel corner location method based on curvature and gray [J].Laser & Optoelectronics Progress,2018,55(3):031501.
[14] 王文超,高向东,丁晓东,等.无坡口对接焊缝特征角点检测方法 [J].焊接学报,2018,39(9):65- 68.WANG Wenchao,GAO Xiangdong,DING Xiaodong,et al.Detection of non-groove butt joint featured based on corner principle [J].Transactions of the China Welding Institution,2018,39(9):65- 68.
[15] ZOU Y,CHEN T.Laser vision seam tracking system based on image processing and continuous convolution operator tracker [J].Optics & Lasers in Engineering,2018,105:141- 149.
[16] JEONG J,YOON T S,JIN B P.Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking [J].Expert Systems with Applications,2017,79(15):194- 206.
[17] 高向东,吴嘉杰.微间隙焊缝磁光成像检测及跟踪方法 [J].机械工程学报,2015,51(4):71- 76.GAO Xiangdong,WU Jiajie.Approach of detecting and tracking micro weld joint based on magneto optical imaging [J].Journal of Mechanical Engineering,2015,51(4):71- 76.
[2] GAO X D,LIU Y Y,LAN C Z,et al.Laser-induced infrared characteristic analysis for evaluating joint deviation during austenitic stainless steel laser welding [J].International Journal of Advanced Manufacturing Technology,2017,88(5/6/7/8):1877- 1888.
[3] AMRUTA R,DEEPAK B B V L,BISWAL B B.Advances in weld seam tracking techniques for robotic welding:a review [J].Robotics and Computer-Integrated Manufacturing,2019,56:12- 37.
[4] 郭波,石永华.基于视觉传感的CO2焊焊接偏差识别 [J].华南理工大学学报(自然科学版),2016,44(7):1- 8.GUO Bo,SHI Yong-hua.Vision-based welding deviation detection for CO2 arc welding [J].Journal of South China University of Technology (Natural Science Edition),2016,44(7):1- 8.
[5] 贾传宝,杜永鹏,武传松,等.厚板窄间隙磁控电弧TIG焊接自动控制系统设计 [J].华南理工大学学报(自然科学版),2017,45(9):40- 46.JIA Chuan-bao,DU Yong-peng,WU Chuan-song.Design of an automatic control system for magnetic controlled na-rrow-gap TIG arc welding of thick plates [J].Journal of South China University of Technology (Natural Science Edition),2017,45(9):40- 46.
[6] ZHANG Y X,YOU D Y,GAO X D,et al.Automatic gap tracking during high power laser welding based on particle filtering method and BP neural network [J].2018,96:685- 696.
[7] ZOU Yanbiao,CHEN Xiangzhi,GONG Guoji,et al.A seam tracking system based on a laser vision sensor [J].Measurement,2018,127:489- 500.
[8] LI X H,LI X D,MOHAMMAD O K,et al.Robust welding seam tracking and recognition [J].IEEE Sensors Journal,2017,17(17):5609- 5617.
[9] DINHAM M,FANG G.Autonomous weld seam identification and localization using eye-in-hand stereo vision for robotic arc welding [J].Robotics and Computer-Integrated Manufacturing,2013,29(5):288- 301.
[10] YE G L,GUO J W,SUN Z Z.Weld bead recognition using laser vision with model-based classification [J].Robotics and Computer-Integrated Manufacturing,2018,52:9- 16.
[11] CHU H H,WANG Z Y.A vision-based system for post-welding quality measurement and defect detection [J].International Journal of Advanced Manufacturing Technology,2016,86(9/10/11/12):3007- 3014.
[12] PRASARN K,FANG Z J,TAN M.An automated weld seam tracking system for thick plate using cross mark structured light [J].International Journal of Advanced Manufacturing Technology,2016,87(9/10/11/12):1- 15.
[13] 丁艳蓉,白瑞林,倪健.基于曲率与灰度复合的角点亚像素定位方法 [J].激光与光电子学进展,2018,55(3):031501.DING Yanrong,BAI Ruilin,NI Jian.Sub-pixel corner location method based on curvature and gray [J].Laser & Optoelectronics Progress,2018,55(3):031501.
[14] 王文超,高向东,丁晓东,等.无坡口对接焊缝特征角点检测方法 [J].焊接学报,2018,39(9):65- 68.WANG Wenchao,GAO Xiangdong,DING Xiaodong,et al.Detection of non-groove butt joint featured based on corner principle [J].Transactions of the China Welding Institution,2018,39(9):65- 68.
[15] ZOU Y,CHEN T.Laser vision seam tracking system based on image processing and continuous convolution operator tracker [J].Optics & Lasers in Engineering,2018,105:141- 149.
[16] JEONG J,YOON T S,JIN B P.Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking [J].Expert Systems with Applications,2017,79(15):194- 206.
[17] 高向东,吴嘉杰.微间隙焊缝磁光成像检测及跟踪方法 [J].机械工程学报,2015,51(4):71- 76.GAO Xiangdong,WU Jiajie.Approach of detecting and tracking micro weld joint based on magneto optical imaging [J].Journal of Mechanical Engineering,2015,51(4):71- 76.