卷烟终端陈列识别方法研究

Study on the Recognition Method of Cigarette Terminal Display

为解决零售终端货架图片的陈列信息提取劳动强度大、效率低、易出错的问题,针对手持采集设备提出一种陈列识别方法。首先通过目标检测算法定位出图像中的烟盒,然后由轻量化的旋转对齐网络对单个烟盒图像做相似变换,再使用度量学习提取特征向量后在数据库中检索,最后通过设置双阈值和投票机制识别烟盒细类或排除非烟盒。该系统提供一套完整解决方案,通过改进标签平滑策略和设计地理位置信息与纹理特征相融合网络来提升识别算法精度,在自建的351类烟盒数据集上Top1准确率达到93.7%,其中改进方案比基准提高1.5%,在未知烟盒与非烟盒的区分上准确率提高4.5%;应用该方法可以显著提高市场信息采集人员工作效率并为业务分析提供有效帮助。

In order to reduce labor intensity,improve efficiency,and avoid mistakes,a display recognition method is proposed for handheld acquisition equipment.Firstly,the system locates the cigarette case in the image through the object detection algorithm.Secondly,a lightweight rotating alignment network is used to perform similar transformations on a single cigarette pack image.Thirdly,the system uses metric learning to extract feature vectors and retrieve them in the database,and finally identifies the sub-categories of cigarette cases or exclude non-cigarette cases by setting a dual threshold and voting mechanism.The system provides a complete solution that improves the accuracy of the recognition algorithm through improved label smoothing strategies and designing a network structure that integrates geographic location information and texture features.In the self-built cigarette case datasets with 351 classes,the accuracy rate of Top1 reaches 93.7%,in which the improvement plan is 1.5%higher than the benchmark and the accuracy of distinguishing between unknown cigarette cases and non-cigarette cases increases by 4.5%.The application of this system can significantly improve the work efficiency of market information collectors and provide more effective help for business analysis.