Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (5): 118-129.doi: 10.11871/jfdc.issn.2096-742X.2021.05.009

• Technology and Applicaton • Previous Articles     Next Articles

Authenticity Identification of Cigarettes Based on Attention Mechanism and High-resolution Network

XIAO Nan1,2(),ZHOU Mingzhu3(),XING Jun3(),LUO Ze1(),LI Xiaohui3,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. China National Tobacco Quality Supervision & Test Center, Zhengzhou, Henan 450001, China
  • Received:2021-03-12 Online:2021-10-20 Published:2021-11-24
  • Contact: LI Xiaohui;;;;


[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.

Key words: cigarette packet, authentication, convolutional neural networks, high-resolution networks, attention mechanism