摘要
应用机器视觉技术对孔类零件进行尺寸测量,采用边加工边测量的方式进行在线检测,可有效缩短零件的生产周期,提高生产效率。通过分析零件的工艺特征,选用CMOS相机与高精度远心镜头,对有倒角的特征采用环形光源照明,对未倒角的特征采用同轴光源照明,提高了硬件平台的柔性化。针对图像采集与传输过程中的椒盐噪声与高斯噪声,对采集后的灰度图像先采用中值滤波去除椒盐噪声,然后采用最小误差法选定阈值,将灰度图二值化,完成图像分割,再使用Canny算子进行边缘检测,同时去除高斯噪声。最后,使用标定板进行相机标定来获取标定系数,调用图像处理软件Open eVision进行尺寸测量,将系统检测数据与检具测量数据进行比较分析,实验结果表明本检测系统的精度可达0.02 mm。
The dimension detection of hole parts is carried out by machine vision technology, which is the on-line detection that is implemented simultaneously with processing and measurement. Therefore, it can shorten the production cycle and improve the production efficiency. The CMOS camera and telecentric lens with high precision are selected by analyzing the process features of parts. For features of parts with chamfering, ring light is used for illumination, while coaxial light is used for illumination in the case of features without chamfering. Using those hardwares can increase flexibility of platform. For the pepper and salt noise and Gaussian noise in the process of image acquisition and transmission, median filtering is firstly used to remove the pepper and salt noise in the collected grayscale image, then the minimum error method is used to select the threshold to complete image segmentation, and then the Canny operator is used to detect edges and remove the Gaussian noise. Finally, the camera is calibrated with the calibration target to obtain the calibration coefficient and the pre-processed images are measured by Open eVision that is an image processing tool. Furthermore, the system test data is compared and analyzed with the gage measurement data. The experimental results show that the accuracy of the system can reach 0.02 mm.
作者
谢俊
李玉萍
左飞飞
王子贤
戎建
Xie Jun;Li Yuping;Zuo Feifei;Wang Zixian;Rong Jian(School of Mechanical Engineering,Jiangsu University,Zhenjiang 212013,China;Zhenjiang Daquan Metal Surface Treatment Co.,Ltd.,Zhenjiang 212211,China)
出处
《电子测量技术》
北大核心
2021年第2期93-98,共6页
Electronic Measurement Technology
基金
国家自然科学基金(51675239)项目资助。
关键词
机器视觉
孔类零件
图像预处理
尺寸检测
machine vision
hole parts
image preprocessing
size detection
作者简介
谢俊,工学博士,副教授,硕士研究生导师,主要研究方向为机器视觉、并联机构。E-mail:xiejun@ujs.edu.cn;通信作者:李玉萍,硕士研究生,主要研究方向为机器视觉。E-mail:2810773854@qq.com。
