基于改进YOLOv3的川贝母检测识别算法研究

A Methodfor Detectionand Identification of Fritillaria Cirrhosa D.Don Based on Improved YOLOv3

正品川贝母药用价值高,市场上常出现掺伪现象,严重影响川贝母的质量。目前,川贝母的鉴别主要依靠传统性状鉴别、显微鉴别、理化鉴别等,主观性较强,对操作人员的实践经验要求较高,且预处理工作繁琐。针对目前川贝母鉴别方法的缺点,采用深度学习方法实现川贝母的自动检测识别,提出一种改进YOLOv3网络对不同种类的川贝母图像进行训练,嵌入双通路模块和通道注意力机制,分别从特征提取和特征选择上加强模型对川贝母形态特征的表达,提升模型检测精度。实验结果表明,改进的YOLOv3模型能够实现川贝母的快速批量自动化鉴定,平均精确度达到80%,为中草药行业中川贝母品质评价提供一种新的解决方案。

Since the authentic Fritillaria Cirrhosa D.Don resources are scare due to its high valuable medical uses often occurs in the market,which seriously affects the quality of Fritillaria.At present,the identification of Fritillaria mainly relies on traditional trait identification,microscopic identification,physical and chemical identification and etc.,which is subjective and requires high practical experience for operators,and the preprocessing is cumbersome.In view of the shortcomings of the current identification methods of Fritillaria,adopt deep learning method to realize the automatic detection and recognition of Fritillaria,and improve YOLOv3 to train different classes of image data,embeds dual-path modules and channel attention mechanism,respectively strengthen the models'expression of the features of Fritillaria from the feature extraction and feature selection.Experiments results show that this model realize the rapid batch automatic identification of Fritillaria,and the recognition mean average precision can reach 80%,providing a new solution for the quality evaluation of Fritillaria in the Chinese herbal medicine industry.