基于民国纸币的图元素匹配检索

Graph element detection matching based on Republic of China banknotes

民国纸币种类数量众多,不同纸币类别间的视觉差异小,部分纸币经过流通后发霉、毛边以及破损。针对传统的细粒度图像检索方法对民国纸币识别分类能力差的问题,提出了一种基于多尺度特征融合的民国纸币细粒度检索模型。在使用YOLOv4对纸币图像做图元素检测,减少手动标记数据时间的基础上,利用纸币主景图作为输入特征图,使用EfficientNet-B0作为主干网络进行检索,减少了冗余信息对网络的负担,提升了网络的精度。在模型中,使用PANet融合网络的第2,4,10和15层的特征向量,生成全局特征向量库,提升了纸币匹配检索能力,并使用自适应K均值对特征向量进行聚类,简化了匹配的时间与计算量。实验结果表明,该模型准确率达到了89.6%,相比于使用纸币原图作为输入图像提升了10个百分点,提高了检索精度。改进后的模型分类效果更好,推理时间成本更少,实现了纸币的精细化分类。满足工业实际要求。

In view of the fact that there are numerous types of Republic of China banknotes,which often have slight visual differences between different banknote,combined with the issues of mold,burrs or breakage after circulation,the recognition and classification ability of traditional fine-grained image retrieval methods for Republican banknotes is inadequate.To address these issues,this paper proposed a fine-grained retrieval model of Republican banknotes based on multiscale feature fusion.To reduce the time of manual data labeling,YOLOv4 was employed for graph element detection on banknote images,with the main view of banknotes being adopted as the input feature map.EfficientNet-B0 was utilized as the backbone network for retrieval,thereby reducing the burden of redundant information in the network and enhancing network accuracy.In the model,the feature vectors of layers 2,4,10,and 15 of the PANet fusion network were utilized to generate a global feature vector library,improving the banknote matching retrieval capability.Furthermore,the feature vectors were clustered using adaptive K-means to simplify the matching time and computation.The experimental results demonstrated that the proposed model achieved an accuracy of 89.6%,improving the retrieval accuracy by 10 percentage points compared to using the original image of banknotes as the input image.The improved model exhibited better classification performance,less inference time cost,and fine classification of banknotes.These results could meet the practical requirements of industry.