基于机器视觉的制袋机裁切定位系统设计
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摘要
目的针对圆角包装袋传统裁切工艺中存在的毛刺和废料问题,设计一种基于机器视觉的高精度制袋机裁切定位系统。方法利用LED背光源、低畸变镜头、CMOS黑白相机和工控机搭建视觉系统硬件平台,在Visual Studio开发环境中,基于OpenCV函数库设计视觉软件,用基于最小二乘法检测图像边缘,进而标定相机,提取包装袋圆角轮廓为特征信息。系统以特征信息为匹配模板对圆角位置进行精确定位,得到亚像素级角点位置,最终控制伺服系统执行误差补偿裁切。结果该定位算法可实现16 ms内精确定位圆角坐标,绝对误差小于±0.02像素点。结论基于机器视觉的定位系统具有运算速度快、定位精度高等特点,有效改进了传统制袋工艺,能够满足生产需求。
The work aims to design a location system for cutting of high-precision bag making machine based on machine vision, aiming at the problems of burrs and wastes in the traditional cutting process of fillet packaging bags. LED backlight, low distortion lens, CMOS black-and-white camera and industrial personal computer were used to build the hardware platform of the vision system. In the Visual Studio development environment, the vision software was programmed based on OpenCV function library. The image edge was detected based on the least square method for the camera calibration. The package fillet contour was extracted as the feature information. The system took the feature information as the matching template to accurately locate the fillet position, obtain the sub-pixel level point, and finally control the servo system to perform error compensation and bag cutting. The location algorithm could achieve precise location of fillet in 16 ms, and the absolute error of location was less than ±0.02 pixels. Featured by fast operation speed and high precision location, etc., the location system based on machine vision effectively improves the traditional bag making process, and can meet the production requirements.
The work aims to design a location system for cutting of high-precision bag making machine based on machine vision, aiming at the problems of burrs and wastes in the traditional cutting process of fillet packaging bags. LED backlight, low distortion lens, CMOS black-and-white camera and industrial personal computer were used to build the hardware platform of the vision system. In the Visual Studio development environment, the vision software was programmed based on OpenCV function library. The image edge was detected based on the least square method for the camera calibration. The package fillet contour was extracted as the feature information. The system took the feature information as the matching template to accurately locate the fillet position, obtain the sub-pixel level point, and finally control the servo system to perform error compensation and bag cutting. The location algorithm could achieve precise location of fillet in 16 ms, and the absolute error of location was less than ±0.02 pixels. Featured by fast operation speed and high precision location, etc., the location system based on machine vision effectively improves the traditional bag making process, and can meet the production requirements.
引文
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[2]王文静,辛洪兵,刘振宝.在包装流水线中边缘检测算法的比较[J].包装工程,2006, 27(3):94-96.WANG Wen-jing, XIN Hong-bing, LIU Zhen-bao.Comparison of Edge Detection Algorithms for Pack-aging Line[J]. Packaging Engineering,2006,27(3):94-96.
[3]王保升,左健民.基于数字图像处理的卷筒包装材料自动纠偏[J].包装工程,2007, 28(3):16-18.WANG Bao-sheng, ZUO Jian-min. Automatic Rectification of Rewinding Packaging Material Based on Digital Image Processing[J]. Packaging Engineering,2007,28(3):16-18.
[4]曹飞,熊和金.基于DSP的纱线张力检测系统[J].电脑编程技巧与维护,2008(10):71-14.CAO Fei, XIONG He-jin. The Tension Measurement System of Yarn Based on DSP[J]. Computer Programming Skills&Maintenance, 2008(10):71-14.
[5]彭珍瑞,董海棠,殷红,等.新型纸纱复合制袋机控制系统的研制[J].传感器与微系统,2011,30(6):90-92.PENG Zhen-rui, DONG Hai-tang, YIN Hong, et al.Research and Fabrication of Control System of New Paper and Yarn Compounding Bag-making Machine[J].Transducer and Microsystem Technologies, 2011,30(6):90-92.
[6]彭泽光,彭杭,辛少荣,等.全自动多功能多制式制袋机的创新与研究[J].中国包装工业,2012(2):58-59.PENG Ze-guang, PENG Hang, XIN Shao-rong, et al.Innovation and Research of Automatic Multi-functional Multi-standard Bag Making Machine[J].China Packaging Industry, 2012(2):58-59.
[7] ZHANG Z Y. A Flexible New Technique for Camera Calibration[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
[8] ZHANG Z. Camera Calibration with One-dimensional Objects[J]. Pattern Analysis and Machine Intelligence,IEEE Transactions on, 2004, 26(7):892-899.
[9]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述[J].自动化学报,2000, 26(1):43-55.QIU Mao-lin, MA Song-de, LI Yi. Overview of Camera Calibration for computer Vision[J]. Acta Automatica Sinica, 2000, 26(1):43-55.
[10] FAN Fang, WANG Zhen-wei, LIU Shuang-yin. 3D Reconstruction of Weak Texture Surface Based on Binocular Vision[C]//Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology. Shenzhen:Atlantis Press, 2016:125-128.
[11]孙少红,袁华,张彤.常用的亚像素边缘检测方法的对比研究[J].大众科技,2014, 16(4):27-29.SUN Shao-hong, YUAN Hua, ZHANG Tong. Comparative Study on the Methods of Common Sub-pixel Edge Detection[J]. Popular Science&Technology,2014, 16(4):27-29.
[12] IRAKAWA K H, PARKS T W, Image Denoising Using Total Least Squares[J]. IEEE Trans on Image Processing, 2006, 15(9):2730-2742.
[13]王怡萱,阚江明,张俊梅,等.基于VC++的植物种类模式识别系统研究[J].湖南农业科学,2011(23):131-135.WANG Yi-xuan, KAN Jiang-ming, ZHANG Jun-mei,et al. Pattern Recognition System for Plant Species Based on VC++[J]. Hunan Agricultural Sciences,2011(23):131-135.
[14]章毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003.ZHANG Yu-jin. Visual Information Retrieval Based on Content[M]. Beijing:Science Press, 2003.
[15]宁丽,林意.基于几何形状的点集聚类[J].计算机工程与设计,2008, 29(10):2613-2615.NING Li, LIN Yi. Point-set Clustering Based on Geometry-figure[J]. Computer Engineering and Design,2008, 29(10):2613-2615.