双向博弈平行反卷积网络下的饮片高光谱测量鉴别

陈雁; 巢建国; 徐艳; 严慧深

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双向博弈平行反卷积网络下的饮片高光谱测量鉴别

光学图像处理 | 更新时间：2026-05-20

- 双向博弈平行反卷积网络下的饮片高光谱测量鉴别
- Hyperspectral measurement and identification of chinese herbal slices based on bidirectional game parallel deconvolution network
- 光学技术 2026年52卷第3期页码：368-377
- 作者机构：
  
  1.扬州职业技术大学，江苏扬州225000
  2.南京中医药大学药学院，江苏南京210000
- 作者简介：
  
  陈雁（1987— ），女，副教授，从事中药鉴定、深度学习研究。Chenyan2010_1987@163.com
- 基金信息：
  
  江苏省高等学校教育信息化研究项目(2025JSETKT225);江苏高校“青蓝工程”资助
- DOI：
  中图分类号： TP394.1;TH691.9
- 收稿：2025-11-26，
  
  修回：2026-01-03，
  
  纸质出版：2026-05-20
- 稿件说明：
移动端阅览
陈雁,巢建国,徐艳等.双向博弈平行反卷积网络下的饮片高光谱测量鉴别[J].光学技术,2026,52(03):368-377.

CHEN Yan,CAO Jianguo,XU Yan,et al.Hyperspectral measurement and identification of chinese herbal slices based on bidirectional game parallel deconvolution network[J].OPTICAL TECHNIQUE,2026,52(03):368-377.
陈雁,巢建国,徐艳等.双向博弈平行反卷积网络下的饮片高光谱测量鉴别[J].光学技术,2026,52(03):368-377. DOI：

CHEN Yan,CAO Jianguo,XU Yan,et al.Hyperspectral measurement and identification of chinese herbal slices based on bidirectional game parallel deconvolution network[J].OPTICAL TECHNIQUE,2026,52(03):368-377. DOI：

摘要

实现饮片智能鉴别代替传统感官判断，可被推广于日常生活或医药生产等领域。结合高、低卷积层特征提取网络提取丰富的饮片特征，并在生成对抗网络模型中引入全特征连接网络，可进一步提取高光谱曲线、均值、方差、最大和最小局部隐层特征。然后在生成和判别网络中增加条件参数，通过博弈思想提升网络的稳定性和有效性，最后将所得的特征传送到分类装置中完成分类鉴别。所提方法在简单和复杂背景下测试与训练样本占比小于4:7后，饮片鉴别准确率分别约为90.2%和79.6%，并可植入手机完成饮片鉴别。所提方法的各指标均优于IC-Net、Res-TCMNet、ResNet18、GhostNet。饮片图像背景复杂程度与清晰度均会影响饮片鉴别的准确率。

Abstract

Intelligent identification of prepared slices of Chinese materia medica replacing traditional sensory judgment can be popularized in daily life

pharmaceutical production and other fields. A feature extraction network with high and low convolutional layers is combined to extract abundant features of prepared slices

and a full-feature connection network is introduced into the Generative Adversarial Network （GAN） model to further extract the hyperspectral curves

mean values

variances

and the maximum and minimum local hidden layer features. Conditional parameters are then added to the generator and discriminator networks

and the stability and effectiveness of the network are improved based on the game theory. Finally

the extracted features are fed into the classification module to complete the classification and identification. The proposed method is tested under simple and complex background conditions with the ratio of test samples to training samples less than 4:7

achieving an identification accuracy of approximately 90.2% and 79.6% for prepared slices respectively

and it can be embedded in mobile phones for on-site identification. All evaluation indicators of the proposed method outperform those of IC-Net

Res-TCMNet

ResNet18 and GhostNet. Moreover

the complexity and definition of the background of prepared slice images both have a significant impact on the accuracy of prepared slice identification.

关键词

Keywords

references

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