摘要
针对卷包车间包装工序条烟外观检测中的图像报错率较高,提出了一种基于自学习型数字图像解析算法的条烟检测的改进方案。使用工业相机采集条包外观的良品图像与缺陷图像,通过对图像的二值化,并在检测算法中引入自学习型算法自动区分图像特征点,提高运算效率。改进后检测系统降低了调试难度,提高了现场快速换牌的便捷性,有效提高了卷包车间包装工序条包的检测速度和精度,降低了条包外观检测的报错率。
In response to the high error rate in image reporting during the packaging process of cigarette packaging workshops,an improved scheme for cigarette inspection based on a self-learning digital image analysis algorithm is proposed.Use an industrial camera to capture good and defective images of the appearance of the strip packaging.By binarizing the images and introducing self-learning algorithms into the detection algorithm to automatically distinguish image feature points,the computational efficiency is improved.The improved detection system has reduced the difficulty of debugging,improved the convenience of rapid plate replacement on site,effectively improved the detection speed and accuracy of the packaging process in the rolling and packaging workshop,and reduced the error rate of the appearance inspection of the packaging.
作者
王贺伟
李华
陈飞程
梁佳玉
Wang Hewei;Li Hua;Chen Feicheng;Liang Jiayu(China Tobacco Guangxi Industrial Co.,Ltd.,Liuzhou,Guangxi,China,545006)
出处
《仪器仪表用户》
2024年第7期61-63,共3页
Instrumentation
基金
广西中烟工业有限责任公司资助项目(GXZYZZ2023D001)
关键词
自学习
图像视觉
检测技术
条包外观
self-learning
image vision
inspection technology
cigarette package appearance
