烟草科技2024，Vol.57Issue(5)：103-112,10.
基于改进ConvNeXt模型的真假卷烟烟丝识别方法
Method for identifying cut tobacco from genuine and fake cigarettes based on improved ConvNeXt model
摘要
Abstract
In order to quickly identify genuine and fake cigarettes,a method for classification and identification of cut tobacco from cigarettes of different brands based on ConvNeXt convolutional neural network model was proposed.The images of cut tobacco from genuine and fake cigarettes of four brands were collected to create a deep learning dataset.Based on the ConvNeXt model,a Convolutional Block Attention Module(CBAM)was introduced to improve the feature extraction capability of the model.A feature pyramid structure was developed to achieve feature fusion of different scales and improve the feature expression ability of the model.GhostNetV2 convolution was introduced into the multi-scale fusion structure to reduce model complexity and computational work.The improved ConvNeXt_CM model and the commonly used image classification models including ResNet50,DensNet121,and EfficientNetV2 were comparatively tested.The results showed that:1)Compared with the original ConvNeXt model,the macro F1 score and average accuracy of the ConvNeXt_CM model reached 95.46％and 87.13％,respectively;its macro precision and macro recall rates elevated by 6.08 and 6.13 percentage points,respectively.The size of the model was 27.31 M,and the time needed for identifying an image averaged 0.024 s.2)The ConvNeXt_CM model advantaged over the ResNet50,DensNet121,and EfficientNetV2 models in image recognition efficiency,and its macro F1 score elevated by 21.94,20.19,and 31.05 percentage points,respectively.The proposed method helps improve the image recognition capabilities of the models and cigarette authentication.关键词
卷烟/烟丝图像/ConvNeXt/真假识别/卷积神经网络Key words
Cigarette/Cut tobacco image/ConvNeXt/Authentication/Convolutional neural network分类
轻工纺织引用本文复制引用
王树才,黄开虎,丁美宙,纪晓楠,陶栩..基于改进ConvNeXt模型的真假卷烟烟丝识别方法[J].烟草科技,2024,57(5):103-112,10.基金项目
烟草行业烟草加工形态研究重点实验室项目"烟草物料二维形态特征与成丝特性研究"(A202019). （A202019）
