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基于机器视觉的冲压件表面缺陷在线检测研究

Online Stamping Parts Surface Defects Detection Based on Machine Vision

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摘要

为实现图像处理技术在冲压件表面缺陷在线检测中的应用, 开发了一套冲压件表面缺陷实时在线快速检测系统。使用基于多模板匹配算法获取图像中冲压件的位置, 建立感兴趣区域;提出基于拉普拉斯-高斯(LoG)算子的实时浓淡补正算法实现冲压件表面缺陷的增强;使用大津法和形态学操作实现冲压件表面缺陷位置的提取。系统使用MATLAB实现基于LoG算子的滤波算法;使用LabVIEW实现其余算法, 并在其中调用MATLAB 脚本节点;使用多线程技术实现高效的检测算法。经实验, 系统能够对生产线上每一个冲压件进行快速检测, 并检测出有缺陷的冲压件, 整个过程耗时在100 ms以内, 能够满足在线实时检测需求。

Abstract

In order to apply the image processing technique to the surface defects online-detecting of stamping parts, a real-time fast detection system is designed. Multi-pattern matching algorithm is used to locate the stamping parts in the image, then the region of interest is built. The shading correction algorithm based on the Laplace-Gaussian (LoG) operator is proposed to enhance the defect parts of workpieces. The Otsu algorithm and morphology are used to extract the defect parts. The system adopts MATLAB to realize the filtering algorithm based on LoG operator, the rest of the algorithm are implemented by LabVIEW and the MATLAB script node is called by LabVIEW, the multithreading technology is used to accelerate the computation. According to the experiment results, the proposed system can detect each stamping parts on the production line and detect the defectives. The whole process takes less than 100 ms, which can satisfy the demand of online detection.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP753

DOI:10.3788/lop55.011501

所属栏目:机器视觉

基金项目:中央高校基本科研业务费专项资金(17D110305)

收稿日期:2017-06-08

修改稿日期:2017-07-24

网络出版日期:--

作者单位    点击查看

陈广锋:东华大学机械工程学院, 上海 201620
管观洋:东华大学机械工程学院, 上海 201620
魏鑫:东华大学机械工程学院, 上海 201620

联系人作者:管观洋(18817326014@163.com)

备注:陈广锋(1976-), 男, 博士, 副教授, 主要从事机器视觉等方面的研究。E-mail: chengf@dhu.edu.cn

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引用该论文

Chen Guangfeng,Guan Guanyang,Wei Xin. Online Stamping Parts Surface Defects Detection Based on Machine Vision[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011501

陈广锋,管观洋,魏鑫. 基于机器视觉的冲压件表面缺陷在线检测研究[J]. 激光与光电子学进展, 2018, 55(1): 011501

被引情况

【1】史天意,周龙早,王春明,米高阳,蒋平. 基于机器视觉的铝合金激光清洗实时检测系统. 中国激光, 2019, 46(4): 402007--1

【2】李丹,白国君,金媛媛,童艳. 基于机器视觉的包装袋缺陷检测算法研究与应用. 激光与光电子学进展, 2019, 56(9): 91501--1

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