摘要
在高速流水线上对小视场物体大批量在线检测过程中,针对滚子、钢珠、卷烟爆珠等球形颗粒因体积过小、发生破裂、传输振动而引起的颗粒粘连,致使其尺寸无法精准检测的问题,采用机器视觉的检测手段,提出基于贝叶斯估计的尺寸视觉检测方法。首先,经图像采集系统获取粘连颗粒图像,对采集图像预处理去除干扰因素。之后,利用边缘检测方法进行轮廓坐标点提取,通过数据滤波选取有效轮廓点数据,代入到该文所提出的贝叶斯尺寸测量数学模型中,从而实现对粘连颗粒尺寸的精确测量。以卷烟爆珠为例进行试验验证,该方法测得的尺寸最小均方根误差为0.0496 mm,测量误差均值最小为0.02263 mm,验证所提方法的可靠性与稳定性,满足实际工业检测精度要求。该方法的提出可为高速流水线上小视场粘连颗粒测量研究提供基础。
In the process of mass on-line detection of objects with small field of view on high-speed pipeline,aiming at the problem that the size of spherical beads such as roller,steel ball and cigarette capsules cannot be accurately detected due to the bead touching caused by too small volume,rupture and transmission vibration,a size detection method based on Bayesian estimation is proposed by using machine vision.Firstly,the image of adhesive particles is acquired by the image acquisition system,and the interference factors are removed by preprocessing the acquired image.Then,the contour coordinate points are extracted by using the edge detection method,and the effective contour points are selected by data filtering,and the data is substituted into the Bayesian size measurement mathematical model proposed in this paper,so as to realize the accurate measurement of the size of cigarette capsules.The results show that the minimum root mean square error is 0.0496 mm and the minimum mean error is 0.02263 mm,which verifies the reliability and stability of the proposed Bayesian mathematical model measurement method and meets the actual industrial detection accuracy requirements.The proposed method provides a basis for the measurement of small field of view particles on high-speed assembly line.
作者
常景景
郑鹏
曹满义
王文秀
张乃勇
CHANG Jingjing;ZHENG Peng;CAO Manyi;WANG Wenxiu;ZHANG Naiyong(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China;Jiaozuo Cigarette Logistics Distribution Center,Jiaozuo 454000,China)
出处
《中国测试》
CAS
北大核心
2023年第4期26-32,共7页
China Measurement & Test
基金
国家重点研发计划项目(2017YFF0206501)。
关键词
粘连颗粒
尺寸测量
机器视觉
贝叶斯估计
touching beads
size measurement
machine vision
Bayesian estimation
