基于AGAST角点域特征的条烟快速识别算法  

Rapid recognition algorithm for cigarette cartons based on feature of AGAST corner domain

作　　者：涂勇涛[1] 张莹[1,2] 邵豪[1] 王飞[1] 张东波[1,2] 

机构地区：[1]湘潭大学信息工程学院 [2]机器人视觉感知与控制技术国家工程实验室,长沙市岳麓区麓山南路麓山门410082

出　　处：《烟草科技》2017年第5期79-86,共8页Tobacco Science & Technology

基　　金：湖南省教育厅重点项目"复杂背景与干扰下线状和点状目标鲁棒检测原理;方法与应用研究"(14A137)

摘　　要：为提高烟草物流中心自动化分拣效率,基于视觉技术提出了一种与高速自动化条烟分拣线相匹配的快速条烟识别算法。将穹形光源和同轴光源相结合设计了一种新型打光方式,使用高速彩色相机获取条烟图像信息,提出一种基于AGAST(Adaptive and Generic Accelerated Segment Test)角点域的特征描述方法。根据提取的特征建立条烟图像数据库,并使用极端学习机(Extreme Learning Machine,ELM)进行训练与识别,实现条烟的快速识别。将本文算法与SIFT和SVM算法的识别效果进行对比,结果表明:本文算法的识别率和实时性均为最优,识别率达到100%,识别耗时在3种算法中最少,能够满足自动化分拣线10帧/秒的要求。该算法为有效提高条烟异常情况检测的精度提供了参考。In order to promote the efficiency of automatic sorting in a tobacco logistics center, a rapid recognition algorithm based on vision technology was proposed to match the high-speed automatic cigarette carton sorting line. A new lighting means combining a dome light source with a coaxial light source was designed, a high-speed color camera was used to capture the image information of cigarette cartons, and a feature description method based on AGAST （Adaptive and Generic Accelerated Segment Test） corner domain was proposed. A database of cigarette carton images was established on the basis of the extracted features, and extreme learning machine （ELM） was adopted for training and cigarette carton recognition. Comparing with SIFT and SVM algorithms, the proposed algorithm boasted a recognition rate of 100% and shorter recognition time, it met the requirements of 10 frames/s required by the automatic sorting line and also provided a reference for effectively promoting the inspection accuracy in the case of abnormal cigarette cartons.

关 键 词：条烟识别 AGAST 特征描述 ELM SIFT SVM 

分 类 号：TP315[自动化与计算机技术—计算机软件与理论]