Abstract:

[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