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基于机器视觉的食品内包装缺陷检测装置设计与实现
引用本文:贾真真,张涛,曹兴强,曾建,李晓,姚二民.基于机器视觉的食品内包装缺陷检测装置设计与实现[J].食品与机械,2018,34(7):111-114.
作者姓名:贾真真  张涛  曹兴强  曾建  李晓  姚二民
作者单位:郑州轻工业学院食品与生物工程学院;河南中烟工业有限责任公司南阳卷烟厂;深圳三叶草科技开发有限公司研发部
基金项目:河南省科技攻关计划项目(编号:142102210639);郑州轻工业学院2017年研究生教育创新计划基金项目(编号:2017021)
摘    要:为解决食品包装机生产过程中内包装质量缺陷等问题,基于机器视觉原理设计食品内包装缺陷检测装置。该装置主要由图像采集、图像处理及判断与剔除等系统组成,使用超小型CCD相机对包装材料的内部缺陷进行扫描,通过PLC程序实现对缺陷包装的判别和剔除。以内衬纸为例,利用不同程度缺陷内衬纸及FLUKE热成像仪,对机器视觉装置进行内衬纸缺陷剔除率及机械部件运行情况的测试。结果表明:采用机器视觉的检测装置较好地解决了生产过程中缺陷内衬纸漏检及检测效率低等问题,各类型缺陷内衬纸检测准确率高达95%,研发后缺陷烟包白班减少了13.3包/月、前夜班减少了8.4包/月、后夜班减少了8.0包/月,降低了缺陷烟包量,提高了设备运行的稳定性,所有仪器均满足长期稳定运行的要求,实现了包装质量的闭环式自动检测技术。

关 键 词:机器视觉  食品包装  内衬纸  缺陷检测
收稿时间:2018/2/2 0:00:00

Design and realization of the food inner packaging detection device based on the machine vision
JIAZhenzhen,ZHANGTao,CAOXingqiang,ZENGJian,LIXiao,YAOErmin.Design and realization of the food inner packaging detection device based on the machine vision[J].Food and Machinery,2018,34(7):111-114.
Authors:JIAZhenzhen  ZHANGTao  CAOXingqiang  ZENGJian  LIXiao  YAOErmin
Affiliation:College of Food and Bioengineering, Zhengzhou Univ. of Light Ind., Zhengzhou, Henan 450000, China;Nanyang Cigarette Factor, China Tobacco Henan Industrial Co., Ltd., Nanyang, Henan 473000, China;Shenzhen Clover Technology Development Co., Ltd., Shenzhen, Guangdong 518000, China
Abstract:In order to solve inner packaging caused by food packaging machine faults, a food inner packaging detection device was designed based on the machine vision. The device was mainly composed of image acquisition, image processing system, judging and eliminating system. Depending on ultra-small CCD cameras to detect inner liner, at the same time the defective inner liner was determined and rejected via PLC program. By adopting different defective inner liner and FLUKE thermal imager, the detection rates of the machine vision and the stability of the equipment were tested. The results showed that the detection accuracy of the machine vision based device for defective inner liner reached 95%. After development, the defective cigarette packs on white shift, eve shift and night shift reduced by 13.3, 8.4 and 8.0 packs per month, respectively, and all instruments met the requirements of long-term stable operation.
Keywords:machine vision  food packaging  inner liner  defective detection
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