基于色度向量聚类的保险片识别及插接方法
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摘要
针对汽车保险盒的生产所面临的汽车保险片准确识别和快速插接的难题,提出一种基于色度向量聚类的汽车保险片识别及插接方法。该方法使用CCD工业摄像机采集汽车保险片图像信息,通过平均背景法对图像中的背景进行消除后采用污点修复技术进行图像增强,再以不同类别的汽车保险片的色度中心向量为特征提取依据,完成对颜色信息基于色度向量聚类的特征提取,最后通过将提取的特征矢量输入支持向量机,达到对不同类型保险片的识别分类,最终控制SCARA四轴机器人依据分类信息完成精确插接环节。经过大量实验证明,所设计的汽车保险片识别及插接方法对常用的9类汽车保险片识别正确率达到99.7%,在平均插接周期为1 s时的汽车保险盒插接的正确率达95.6%以上。
The production of automobile insurance boxes face the challenge on accurately recognizing and rapidly inserting automobile insurance tablets. Aiming to solve the problem,we propose a method identifying and inserting automobile insurance boxes based on color vector clustering. This method uses the industrial CCD camera to obtain image information of automobile insurance tablets. The average background method is utilized to eliminate the image background,and the stain repair technology is used to enhance the image. The center color vector of different kinds of automobile insurance tablets,obtained from image information,are regarded as the basis of features extraction. Then,the feature vector is input into support vector machine( SVM) so that the classification and identification of different types of insurance can be achieved. Based on the classification results,four-axis robot,SCARA is controlled to complete the accurate insertion. Based on verification experiments,the method of recognizing toward the 9 types of automobile insurance tablets can achieve accuracy rate of 99. 7%. When the average plug cycle is 1 s,the correct rate is above 95. 6%.
The production of automobile insurance boxes face the challenge on accurately recognizing and rapidly inserting automobile insurance tablets. Aiming to solve the problem,we propose a method identifying and inserting automobile insurance boxes based on color vector clustering. This method uses the industrial CCD camera to obtain image information of automobile insurance tablets. The average background method is utilized to eliminate the image background,and the stain repair technology is used to enhance the image. The center color vector of different kinds of automobile insurance tablets,obtained from image information,are regarded as the basis of features extraction. Then,the feature vector is input into support vector machine( SVM) so that the classification and identification of different types of insurance can be achieved. Based on the classification results,four-axis robot,SCARA is controlled to complete the accurate insertion. Based on verification experiments,the method of recognizing toward the 9 types of automobile insurance tablets can achieve accuracy rate of 99. 7%. When the average plug cycle is 1 s,the correct rate is above 95. 6%.
引文
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[17]范涛,朱青,王耀南,等.空瓶检测机器人瓶底缺陷检测方法研究[J].电子测量与仪器学报,2017,31(9):1394-1401.FAN T,ZHU Q,WANG Y N,et al.Research on detection method of bottle bottom defects based on empty bottle detection robot system[J].Journal of Electronic Measurement and Instrument,2017,31(9):1394-1401.
[18]熊安斌,丁其川,赵新刚,等.基于单通道s EMG分解的手部动作识别方法[J].机械工程学报,2016,52(7):6-13.XIONG AN B,DING Q CH,ZHAO J G,et al.Research on detection method of bottle bottom defects based on empty bottle detection robot system[J].Journal of Mechanical Engineering,2016,52(7):6-13.
[19]徐科,王磊,王璟瑜.基于Tetrolet变换的热轧钢板表面缺陷识别方法[J].机械工程学报,2016,52(4):13-19.XU K,WANG L,WANG J Y.Surface defect recognition of hot-rolled steel plates based on tetrolet transform[J].Journal of Mechanical Engineering,2016,52(4):13-19.
[2]任秉银,柴立仁,魏坤,等.正交双目视觉长轴类零件装配端位姿检测方法[J].哈尔滨工业大学学报,2017,49(1):60-65.REN B Y,CHAI L R,LI ZH B,et al.A method for measuring 3D pose of assembly end of long shaft part using orthogonal binocular stereovision[J].Journal of Harbin Institute of Technology,2017,49(1):60-65.
[3]段峰,王耀南,刘焕军.基于机器视觉的智能空瓶检测机器人研究[J].仪器仪表学报,2004,25(5):624-627.DUAN F,WANG Y N,LIU H J.Study on machinevision based empty bottle inspector[J].Chinese Journal of Scientific Instrument,2004,25(5):624-627.
[4]易怀安,刘坚,路恩会.基于图像清晰度评价的磨削表面粗糙度检测方法[J].机械工程学报,2016,52(16):15-21.YI H AN,LIU J,LU EN H.Detection method of grinding surface roughness based on image definition evaluation[J].Journal of Mechanical Engineering,2016,52(16):15-21.
[5]彭忠超,戚媛婧,舒斌,等.基于机器视觉的激光器封装自动对准系统[J].工程设计学报,2017,24(6):687-693.PENG ZH CH,QI Y J,SHU B,et al.Automatic alignment system for laser device packaging based on machine vision[J].Chinese Journal of Engineering Design,2017,24(6):687-693.
[6]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7):178-183.ZHOU ZH,HUANG Y,LI X Y,et al.Automatic detecting and grading method of potatoes based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(7):178-183.
[7]刘政林,周铭,刘欣达,等.基于计算机视觉的塑料瓶颜色识别和分拣系统[J].电子测量技术,2013,36(11):43-48.LIU ZH L,ZHOU M,LIU X D,et al.Computer vision based system of plastic bottle color recognition and sorting[J].Electronic Measurement Technology,2013,36(11):43-48.
[8]傅一平,李志能,袁丁.基于HSI空间的颜色算法提牌照识别的性能[J].计算机工程与设计,2004,25(5):703-707.FU Y P,LI ZH N,YUAN D.Improving license plate recognition system by color algorithms based on HIS space[J].Computer Engineering and Design,2004,25(5):703-707.
[9]王夏黎,周明全,耿国华.一种基于HSV颜色空间的车辆牌照提取方法[J].计算机工程,2004,30(17):133-135.WANG X L,ZHOU M Q,GENG G H.An approach of vehicle plate extract based on HSV color space[J].Computer Engineering,2004,30(17):133-135.
[10]方珍红,谢明元,杨玲.一种新的基于加权颜色特征的图像检索算法[J].激光与红外,2013,43(4):471-474.FANG ZH H,XIE M Y,YANG L.New retrieval algorithm based on weighted color feature[J].Laser&Infrared,2013,43(4):471-474.
[11]陶华伟,赵力,奚吉,等.基于颜色及纹理特征的果蔬种类识别方法[J].农业工程学报,2014,30(16):305-311.TAO H W,ZHAO L,XI J,et al.Fruits and vegetables recognition based on color and texture features[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(16):305-311.
[12]李艳荻,徐熙平,陈江,等.动态特征块匹配的背景更新在运动检测的应用[J].仪器仪表学报,2017,38(2):445-453.LI Y D,XU X P,CHEN J,et al.Background updating based on dynamic feature block matching for the motion detection[J].Chinese Journal of Scientific Instrument,2017,38(2):445-453.
[13]KAARTINEN J,HTNEN J,HYTYNIEMI H,et al.Machine-vision-based control of zinc flotation:A case study[J].Control Engineering Practice,2006,14(12):1455-1466.
[14]SARKAR N,WOLFE R R.Feature extraction techniques for sorting tomatoes by computer vision[J].Transactions of the ASAE,1985,28(3):970-974.
[15]TELEA A.An Image Inpainting Technique Based on the Fast Marching Method[J].Journal of Graphics Tools,2004,9(1):23-34.
[16]白中浩,王鹏辉,李智强.基于Stixel-world及特征融合的双目立体视觉行人检测[J].仪器仪表学报,2017,38(11):2822-2829.BAI ZH H,WANG P H,LI ZH Q.Stereo vision pedestrian detection based on Stixel-world and feature fusion[J].Chinese Journal of Scientific Instrumentation,2017,38(11):2822-2829.
[17]范涛,朱青,王耀南,等.空瓶检测机器人瓶底缺陷检测方法研究[J].电子测量与仪器学报,2017,31(9):1394-1401.FAN T,ZHU Q,WANG Y N,et al.Research on detection method of bottle bottom defects based on empty bottle detection robot system[J].Journal of Electronic Measurement and Instrument,2017,31(9):1394-1401.
[18]熊安斌,丁其川,赵新刚,等.基于单通道s EMG分解的手部动作识别方法[J].机械工程学报,2016,52(7):6-13.XIONG AN B,DING Q CH,ZHAO J G,et al.Research on detection method of bottle bottom defects based on empty bottle detection robot system[J].Journal of Mechanical Engineering,2016,52(7):6-13.
[19]徐科,王磊,王璟瑜.基于Tetrolet变换的热轧钢板表面缺陷识别方法[J].机械工程学报,2016,52(4):13-19.XU K,WANG L,WANG J Y.Surface defect recognition of hot-rolled steel plates based on tetrolet transform[J].Journal of Mechanical Engineering,2016,52(4):13-19.