摘要
传统的电容器外观缺陷检测采用人工检测,效率低、出错率高、成本高。为了克服人工检测的缺点,提高电容器生产的自动化程度,设计基于机器视觉的电容器外观缺陷检测系统。首先采集图像、预处理,匹配定位到电容区域;然后采用阈值分割检测溢胶、环氧面气孔气泡缺陷,采用模板匹配检测字符、外壳破损缺陷;最后通过Blob分析,提取缺陷特征,设定阈值参数,以满足不同标准的检测要求。根据样机实验结果显示,检测系统大大提高了检测效率和精度。
The traditional capacitor appearance defect detection adopts manual detection,which has low efficiency,high error rate and high cost.In order to overcome the shortcomings of manual detection and improve the automation of capacitor production,a machine vision based capacitor defect detection system is designed.Firstly,the image is collected,pre-processed,and matched to the capacitor area.Then the threshold segmentation is used to detect the defects of the overflow and epoxy surface pores.The template matching is used to detect the characters and the shell damage defects.Finally,to meet the testing requirements of different standards,the Blob analysis is used to extract the defect features and set the threshold parameters.According to the experimental results of the prototype,the detection system greatly improves the detection efficiency and accuracy.
作者
俞洋
陈佐政
陈祝洋
沈威君
Yu Yang;Chen Zuozheng;Chen Zhuyang;Shen Weijun(Jiangsu University of Technology,Changzhou 213001,China)
出处
《电子技术应用》
2019年第9期97-100,105,共5页
Application of Electronic Technique
基金
国家自然科学基金资助项目(61601208)
江苏省自然科学基金资助项目(BK20160294)
关键词
机器视觉
电容外观
缺陷检测
阈值分割
模板匹配
BLOB分析
machine vision
capacitance appearance
defect detection
threshold segmentation
template matching
Blob analysis
