摘要
【目的】针对真伪卷烟包装鉴别任务对类别精度要求高、深度残差等网络不能提取出更具判别力特征的问题,本文从保证图像高分辨率表征的角度出发,提出了结合高分辨率网络(High-Resolution Network,HRNet)和注意力机制的方法,以得到更具表现力的特征,从而达到提高真伪卷烟包装鉴别准确度的目的。【方法】以具备并行子网结构的高分辨率网络为骨干网络,通过多分辨率特征融合方法获得鉴别卷烟真伪的高质量特征,并在此网络基础上嵌入了高效通道注意力(Efficient Channel Attention,ECA)模块,有效地增强了通道之间的信息交互。【结果】经过实验验证,本文提出的方法不仅可以学习到更好的特征表示,而且准确率可达到97.21%。【局限】模型着重关注了通道维度的相关性,忽略了特征空间位置信息,还有改进空间。【结论】通过将高分辨率网络和注意力机制相结合,可以有效地提高卷烟真伪鉴别的准确度,并为相关研究提供了一种新的研究思路。
[Objective]Authenticity identification of cigarettes requires high classification accuracy,and classic convolutional neural networks such as deep residual networks cannot extract sufficient discriminative features.Therefore,we propose a method combining high-resolution network and attention mechanism to obtain more expressive features.This method helps us achieve the purpose of improving the accuracy of authenticity cigarette packaging identification.[Methods]We take the high-resolution network,with parallel subnet structure,as the backbone network,and the high-quality features for identifying the authenticity of cigarettes are obtained through the multi-resolution feature fusion method.What’s more,we embedded the efficient channel attention(ECA)module into this network,which effectively enhances information exchange between different channels.[Results]Experimental results show that the method proposed in this paper can not only learn better feature representations but also achieve an accuracy of 97.21%.[Limitations]The model focuses on the correlation of channel dimensions,ignoring the location information of the feature space which may help to improve model performance.[Conclusions]By combining the high-resolution network and the attention mechanism,the accuracy of cigarette authenticity identification can be effectively improved,and a new research idea can be provided for related research.
作者
肖楠
周明珠
邢军
罗泽
李晓辉
XIAO Nan;ZHOU Mingzhu;XING Jun;LUO Ze;LI Xiaohui(Computer Network Information Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;China National Tobacco Quality Supervision&Test Center,Zhengzhou,Henan 450001,China)
出处
《数据与计算发展前沿》
CSCD
2021年第5期118-129,共12页
Frontiers of Data & Computing
基金
中国烟草总公司科技重大专项项目“卷烟产品鉴别大数据构建及应用研究”(110201901026(SJ-05))。
关键词
卷烟包装
真伪鉴别
卷积神经网络
高分辨率网络
注意力机制
cigarette packet
authentication
convolutional neural networks
high-resolution networks
attention mechanism
