摘要
为提高立式条烟分拣机的分拣效率,节省分拣时间,采用视觉技术实现了烟草物流配送中心半自动分拣线上运动条烟的在线识别。搭建视觉系统平台进行图像采集;对获取的图像进行预处理,提出了一种"两步法"的轮廓提取算法,并在此基础上提取图像的颜色、纹理、形状等特征;利用获取的图像特征建立图像特征数据库,通过图像最小特征距离准则进行图像识别,自动完成每一个订单内各条烟品牌和数量的核对。结果表明:物流中心按4条配送线日均配送量约5万条计算,可节省时间0.5 h/d。系统运行稳定,可满足6幅/秒的条烟图像处理速度要求,识别正确率在99.99%以上,有效满足了条烟现场识别的需求,保证了条烟分拣的顺利进行。
To improve the sorting efficiency of a vertical cigarette carton sorter, vision technology was adopted to realize the online recognition of moving cigarette cartons on a semi-automatic sorting line in a tobacco distribution center. A vision system platform was configured for image acquisition and preprocessing. A“two-step”algorithm was proposed to extract the image features, including color, texture and shape. An image feature database was established with the obtained image features. Images were recognized via the criterion of minimum feature distance to check the brand and amount of cigarette cartons in each individual order automatically. The results showed that in the case of a distribution center where the total daily capacity was about 50 000 cartons handled by four lines, 0.5 hours could be saved per day. The system is stable, running at a speed of 6 frames per second, recognition correctness is over 99.99%.
出处
《烟草科技》
EI
CAS
CSCD
北大核心
2015年第1期90-95,共6页
Tobacco Science & Technology
基金
国家自然科学基金资助项目"面向大血管疏通的新型仿生游动介入机器人研究"(51075209)
江苏省自然科学基金青年项目"面向冗余双臂工业机器人视觉系统的CGA处理器研究"(BK20140252)
关键词
烟草物流
配送
条烟
分拣机
视觉
图像处理
在线识别
Tobacco logistics
Delivery
Cigarette carton
Sorter
Vision
Image processing
Online recognition
