基于改进ShuffleNet V2的轻量级防风药材道地性智能识别  

Lightweight Intelligent Recognition of Saposhnikovia Divaricata(Turcz.)Schischk Originality Based on Improved ShuffleNet V2

作　　者：赵毓 任艺平 朴欣茹 郑丹阳 李东明[1,3] ZHAO Yu;REN Yiping;PIAO Xinru;ZHENG Danyang;LI Dongming(School of Information Technology,Jilin Agricultural University,Changchun 130118,China;School of Life Science,Jilin Agricultural University,Changchun 130118,China;School of Internet of Things Engineering,Wuxi College,Wuxi 214063,China)

机构地区：[1]吉林农业大学信息技术学院,吉林长春130118 [2]吉林农业大学生命科学学院,吉林长春130118 [3]无锡学院物联网工程学院,江苏无锡214063

出　　处：《智慧农业（中英文）》2023年第2期104-114,共11页Smart Agriculture

基　　金：吉林省科技厅重点研发项目(20210204050YY);吉林省教育厅科研项目(JJKH20210747KJ);吉林省环保厅项目(202107);吉林省生态环境厅科研项目(吉环科字第2021-07号);通辽市科技局重点研发项目(TLCXYD202103)。

摘　　要：[目的/意义]目前,对于防风药材产地和品质的鉴别方法主要是根据其物理或化学特征,其方法需对中药材进行分离提取,存在耗时长,费用高,专业性强,技术难度大等问题,不利于推广应用。随着深度学习的不断发展,其无需人工提取特征、分类精度高等优点被广泛应用在中药材的识别之中。[方法]针对大多数卷积神经网络模型在识别防风药材时计算量大、精度低的问题,本研究提出了一种改进的ShuffieNet V2的轻量级防风道地性识别模型。在不降低网络性能的情况下调整模型架构,减少模型参数量和计算量,用沙漏残差网络(Hourglass Residual Network)代替传统残差网络,同时引入SE(Squeeze-and-Excitation)注意力机制,把具有附加信道注意力的沙漏残差网络嵌入到ShuffleNet V2中,使用SiLU激活函数替换ReLU激活函数,丰富局部特征学习,从而提出轻量化的中药防风道地性识别模型Shuffle-Hourglass SE。为了验证本文所提出模型的有效性,选用VGG16、MobileNet V2、ShuffleNet V2和SqueezeNet V2四种经典网络模型进行对比实验。[结果和讨论]结果表明,本研究提出的模型Shuffle-Hourglass SE获得了最佳性能。在测试集上取得95.32%的准确率、95.28%的召回率,F_(1)分数达到95.27%,测试时间、模型大小为246.34 ms和3.23 M,不仅在传统CNN网络中是最优的,在轻量级网络中也具有较大优势。[结论]本研究所提出的模型在保持较高识别精度的同时占用较少的储存空间,有助于在未来的低性能终端上实现防风道地性的实时诊断。[Objective] Saposhnikovia divaricata(Turcz.) Schischk is a kind of traditional Chinese medicine.Currently,the methods of identifying the origin and quality of Saposhnikovia divaricata(Turcz.) Schischk are mainly based on their physical or chemical characteristics,which is impossible to make an accurate measurement of Groundness identification.With the continuous development of deep learning,its advantages of no manual extraction and high classification accuracy are widely used in different fields,and an attentionembedded ShuffleNet V2-based model was proposed in this study to address the problems of large computation and low accuracy of most convolutional neural network models in the identification of Chinese herbal medicine Saposhnikovia divaricata(Turcz.)Schischk.[Methods] The model architecture was adjusted to reduce the number of model parameters and computation without degrading the network performance,and the traditional residual network was replaced by the Hourglass residual network,while the SE attention mechanism was introduced to embed the hourglass residual network with additional channel attention into ShuffleNet V2.The important features were enhanced and the unimportant features were weakened by controlling the size of the channel ratio to make the extracted features more directional by SE attention.The SiLU activation function was used to replace the ReLU activation function to enhance the generalization ability of the model Enriching local feature learning.Therefore,a lightweight Shuffle-Hourglass SE model was proposed.The samples of Saposhnikovia divaricata(Turcz.) Schischk used in this research were samples from the main production areas,including more than 1000 samples from five production areas in Heilongjiang,Jilin,Hebei,Gansu and Inner Mongolia.A total of 5234 images of Saposhnikovia divaricata(Turcz.) Schischk were obtained by using cell phone photography indoors under white daylight,fully taking into account the geographical distribution differences of different Saposhnikovia divaricata(Tu

关 键 词：防风 道地性识别 ShuffleNet V2 SE注意力机制 沙漏残差网络 中药材 轻量级模型 

分 类 号：R282.5[医药卫生—中药学] TP391.4[医药卫生—中医学]