基于机器视觉的角码孔径测量方法研究  

Research on Corner Connector Aperture Measurement Method based on Machine Vision

在线阅读下载全文

作  者:李百明 LI Baiming(School of Intelligent Manufacturing Engineering,Liming Vocational University,Quanzhou 362000,China)

机构地区:[1]黎明职业大学智能制造工程学院,福建泉州362000

出  处:《吉林化工学院学报》2023年第7期77-82,共6页Journal of Jilin Institute of Chemical Technology

基  金:福建省高职院校智能制造协同创新中心项目资助(2016071)。

摘  要:针对角码孔径测量工作量大、速度慢、受主观因素影响大等问题,提出了一种基于机器视觉的角码孔径测量方法。首先,对采集到的图像进行灰度化、二值化和图像转正操作,以提高图像的处理速度,快速定位角码所在位置和建立准确的物像关系;然后,采用Canny边缘检测算子对角码孔径轮廓进行像素级提取,得到一个复杂多边形轮廓;最后,在复杂轮廓的最小外接圆上建立卡尺工具,实现对角码孔径边缘的亚像素精度提取。实验结果显示,该方法的最大测量误差小于0.03 mm,重复性测量精度近似为0.01 mm,系统的测量时间为126 ms。因此,该方法能够满足角码孔径的测量精度要求且测量数据具有很好的稳定性;能够满足大批量、高强度、实时在线检测需要,具有较好的应用前景。A machine vision-based aperture measurement method was proposed to address the problems of large workload,slow speed,and subjective influence of corner connector aperture measurement.Firstly,the acquired image was greyed out,binarised,and converted to improve the image processing speed,locate the location of the corner connector quickly,and establish an accurate object-image relationship.Then,the Canny edge detection operator was used to extract the corner connector aperture contour at the pixel level to obtain a complex polygon contour.Finally,a caliper tool was built on the smallest outer circle of the complex contour to achieve sub-pixel precision extraction of the corner connector aperture edge.The experimental results show that the maximum measurement error of the method is less than 0.03 mm,the repeatability measurement accuracy is approximately 0.01 mm,and the measurement time of the system is 126 ms.Therefore,the method can meet the measurement accuracy requirements of the corner connector aperture and the measurement data has good stability;it can meet the needs of high-volume,high-intensity,real-time online inspection,and has good application prospects.

关 键 词:边缘检测 CANNY算子 卡尺工具 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象