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

doi:10.12133/j.smartag.SA202304003

Abstract

Abstract:

[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 attention-embedded 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 (Turcz.) Schischk. The data set of Saposhnikovia divaricata (Turcz.) Schischk images was expanded to 10,120 by using random flip, random crop, brightness and contrast enhancement processes. In order to verify the effectiveness of the model proposed, four classical network models, VGG16, MobileNet V2, ShuffleNet V2 and SqueezeNet V2, were selected for comparison experiments, ECA ( Efficient Channel Attention ) attention mechanism, CBAM ( Convolutional Block Attention Module ) attention mechanism and CA attention mechanism were chosen to compare with SE. All attention mechanisms were introduced into the same position in the ShuffleNet V2 model, and ReLU, H-swish and ELU activation functions were selected for contrast experiments under the condition in which other parameters unchanged. In order to explore the performance improvement of ShuffleNet V2 model by using the attention mechanism of SE module, Hourglass residual block and activation function, Shuffle-Hourglass SE model ablation experiment was carried out. Finally, loss, accuracy, precision, recall and F₁ score in test set and training set were used as evaluation indexes of model performances. [Results and Discussions] The results showed that the Shuffle-Hourglass SE model proposed achieved the best performances. An accuracy of 95.32%, recall of 95.28%, and F₁ score of 95.27% were obtained in the test set, which was 2.09%, 2.1 %, and 2.19 % higher than the ShuffleNet V2 model, respectively. The test duration and model size were 246.34 ms and 3.23 M, respectively, which were not only optimal among Traditional CNN such as VGG and Desnet,but had great advantages among lightweight networks such as MobileNet V2、SqueezeNet V2 and ShufffleNet V2. Compared with the classical convolutional network VGG, 7.41% of the accuracy was improved, 71.89% of the test duration was reduced, and 96.76% of the model size was reduced by the Shuffle-Hourglass SE model proposed in this study. Although the test duration of ShuffleNet V2 and MobileNet V2 were similar, the accuracy and speed of the Shuffle-Hourglass SE model improved, which proved its better performance. Compared with MobileNet V2, the test duration was reduced by 69.31 ms, the model size was reduced by 1.98 M, and the accuracy was increased by 10.5 %. In terms of classification accuracy, the improved network maintains higher recognition accuracy and better classification performance. [Conclusions] The model proposed in this research is able to identify the Saposhnikovia divaricata (Turcz.) Schischk originality well while maintaining high identification accuracy and consuming less storage space, which is helpful for realizing real-time identification of Saposhnikovia divaricata (Turcz.) Schischk originality in the future low performance terminals.

Key words: Saposhnikovia divaricata (Turcz.) Schischk, originality recognition, ShuffleNet V2, SE attention mechanism, hourglass residual network, traditional Chinese medicine, lightweight model

CLC Number:

R282.5

ZHAO Yu, REN Yiping, PIAO Xinru, ZHENG Danyang, LI Dongming. Lightweight Intelligent Recognition of Saposhnikovia Divaricata (Turcz.) Schischk Originality Based on Improved ShuffleNet V2[J]. Smart Agriculture, 2023, 5(2): 104-114.

Figures/Tables 15

Fig. 1

Table 1

Internal parameters of Shuffle-Hourglass SE model

结构层	输出尺寸	卷积核大小	步长	重复数/次	输出通道
输入	224× $224$	——	——	——	——
卷积1	112×112	3 $×$ 3	2	1	24
深度可分卷积2	56×56	3 $×$ 3	2	1	24
阶段2	28×28 28×28	——	2 1	1 1	244
阶段3	14×14 14×14	——	2 1	1 1	488
阶段4	7×7 7×7	——	2 1	1 1	976
卷积5	7×7	——	1	1	2048
平均池化	1×1	7×7	——	——	——
全连接	——	——	——	——	吉林/内蒙古/甘肃/黑龙江/河北

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 2

Fig. 8

Fig. 9

Table 3

Table 4

Table 5

Table 6

Ablation results of Shuffle-Hourglass SE model

模型	因素				准确率/%	参数量
模型	注意力机制模块	沙漏残差块	激活函数	架构调整	准确率/%	参数量
ShuffleNet V2	$×$	$×$	$×$	$×$	93.23	4.52
	√	$×$	$×$	$×$	94.53	4.52
	$×$	√	$×$	$×$	94.12	5.56
	$×$	$×$	√	$×$	94.44	4.52
	√	√	$×$	$×$	94.92	5.56
	√	√	√	$×$	95.03	5.56
Shuffle-Hourglass SE	√	√	√	√	95.32	3.23

Table 6

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原产地	测试集/幅	训练集/幅	图像数量/幅
甘肃	404	1617	2021
河北	398	1589	1987
黑龙江	411	1642	2053
吉林	470	1879	2349
内蒙古	342	1368	1710

模型	准确率/%	测试时长/ms	召回率/%	F₁分数/%	模型大小/M
MobileNet V2	84.82	315.65	84.91	84.46	5.21
ShuffleNet V2	93.23	324.54	93.18	93.08	4.52
VGG	87.91	876.54	88.02	87.82	99.72
Shuffle-Hourglass SE	95.32	246.34	95.28	95.27	3.23
SqueezeNet V2	62.71	161.98	62.88	62.43	6.03

注意力机制	准确率/%	召回率/%	F₁分数/%	参数量/M
ECA	93.72	93.88	93.45	4.52
SE	95.32	95.44	95.31	4.39
CA	94.64	94.75	94.49	4.98
CBAM	94.13	94.22	93.93	4.52

激活函数	准确率/%	F₁分数/%
ReLU	93.23	93.11
H-Swish	94.08	93.88
ELU	92.98	92.96
SiLU	95.32	95.04

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