摘要
目的针对现有复杂背景药材切片自动鉴别准确率较低的问题,实现复杂背景下药材切片图像的准确识别。方法基于整理的藏药材切片图像数据集进行实验,分析切片的RGB、HOG、LBP特征,使用改进的HOG算法进行多特征融合,最后使深度学习网络对任务图像进行识别。结果本文提出的多特征融合结合深度学习方法对32种复杂背景下藏药材切片的3610张图像识别准确率达到91.68%,同时该方法对川贝母、山楂及半夏等20种常见中药饮片的平均鉴别准确率达到98.00%,优于其他现有的复杂背景下的中药饮片识别方法,说明该方法对其他中药材鉴定也同样具有可行性,应用范围较广泛。结论多特征融合能够较好地提取复杂背景下药材切片的区分特征,对于背景复杂且堆积遮挡严重的藏药材切片识别率较高,可有效应用于自然场景下的中药材、藏药材切片与其他中药饮片识别,具有较好的应用前景。
Objective The objective of this study is to improve the accuracy of automatic identification in complex background herbal slice images.The goal is to achieve accurate recognition of herbal slice images in the presence of complex backgrounds.Methods The experiment was conducted on a collected and organized dataset of Tibetan herbal slice images.The RGB,HOG,and LBP features of the slices were analyzed.An improved HOG algorithm was used to fuse multiple features,and a deep learning network was utilized for image recognition.Results The proposed method of multi-feature fusion combined with deep learning achieved an identification accuracy of 91.68%on 3610 Tibetan herbal slice images with complex backgrounds.Furthermore,the average identification accuracy for 20 common traditional Chinese medicine slices,such as Chuan Beimu,Hawthorn,and Pinellia,reached 98.00%.This method outperformed existing methods for identifying herbal slices in complex backgrounds,indicating its feasibility and wide applicability for the identification of other traditional Chinese herbal medicines.Conclusion The fusion of multiple features effectively captures distinguishing characteristics of herbal slices in complex backgrounds.It exhibits high recognition rates for Tibetan herbal slices with complex and heavily occluded backgrounds,and can be successfully applied to the recognition of natural scene-based traditional Chinese herbal medicines and herbal slices.This approach shows promising prospects for practical applications.
作者
周丽媛
高红梅
赵启军
高定国
Zhou Liyuan;Gao Hongmei;Zhao Qijun;Gao Dingguo(School of Information Science and Technology,Tibet University,Lhasa 860000,China;Tibetan Information Technology Innovation Talents Training Demonstration Base,Lhasa 850000,China;College of Computer Science,Sichuan University,Chengdu 610065,China)
出处
《世界科学技术-中医药现代化》
2024年第1期211-217,共7页
Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology
基金
国家自然科学基金委员会地区科学基金项目(62166038):敦煌藏文文献文本识别方法的研究,负责人:高定国
西藏大学研究生“高水平人才培养计划”项目(2020-GSP-S173):基于自然背景下的藏药材植株识别研究,负责人:周丽媛
关键词
特征融合
深度学习
藏药材
图像识别
药材切片
注意力机制
Feature fusion
Deep learning
Tibetan medicinal materials
Image recognition
Medicinal material slices
Attention mechanism
