摘要
针对电子元件缺陷传统人工检测方法存在劳动量大、检测效率和自动化程度低、成本高等问题,提出一种基于Halcon的视觉检测系统。针对研究对象的特殊性,提出两次采集、两次判断的多特征自动检测方法,并构建验证试验平台;利用CCD相机实时采集元件图像,再对图像进行中值滤波等预处理,降低图形噪声;采用阈值分割、Blob分析的方法对图像缺陷特征进行形态学特征识别和提取,得到判断结果。实验结果表明:该检测方式能快速、准确、高效地提取电子元件缺陷特征;单幅图平均图像处理时间为131 ms,检测平均准确率为95%;另一方面,自动控制系统稳定性强,精度高,单个元件检测周期平均时间为4.7 s,相教于人工检测效率提高了38%,满足工业要求。
In view of the problems in traditional manual inspection methods for electronic component defects,such as large amount of labor,low efficiency and automation,and high cost,a vision inspection system based on Halcon was proposed.According to the particularity of the research object,a multi-feature automatic detection method of twice acquisition and twice judgment was pro⁃posed,and a verification test platform was built;the CCD camera was used to acquire the component image in real time,and then the image was preprocessed with median filter to reduce the image noise.Finally,the methods of threshold segmentation and Blob analysis were used to recognize the morphological features of the image defect features and the judgment result was gotten.The experimental re⁃sults show that the detection method can be used to extract the defect features of electronic components quickly,accurately and effi⁃ciently.The average image processing time of a single image is 131 ms,and the average detection accuracy is 95%;on the other hand,the automatic control system has strong stability and high accuracy,the average detection cycle time of a single component is 4.7 s,and the detection efficiency is increased by 38%compared with manual detection,which meets the industrial requirements.
作者
王猛
何庆中
王佳
赵献丹
雷涛
WANG Meng;HE Qingzhong;WANG Jia;ZHAO Xiandan;LEI Tao(School of Mechanical Engineering,Sichuan University of Science and Engineering,Yibin Sichuan 644000,China)
出处
《机床与液压》
北大核心
2021年第4期89-93,共5页
Machine Tool & Hydraulics
基金
人工智能四川省重点实验室开放基金项目(2017RYY01)
人工智能四川省重点实验室项目(2018RZY01)
自贡市科技局项目(2018YYJC17)。