基于改进ResNet50的中药材分类识别  

Classification and Recognition of Chinese Medicinal Materials Based on Improved ResNet50

作　　者：葛琪 吴丽丽[1] 康立军[1] GE Qi;WU Lili;KANG Lijun(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《软件工程》2025年第4期16-21,共6页Software Engineering

摘　　要：为了提升中药材图片分类的准确率,提出了一种基于改进ResNet50的中药材分类识别方法。首先,引入了卷积块注意力模块(Convolutional Block Attention Module,CBAM),增强了模型对中药材特定特征的识别能力。其次,对标准的ResNet50中的卷积快捷连接进行了优化,减少了特征图的信息损失。最后,在模型后端集成了金字塔池化模块(Pyramid Pooling Module,PPM),该模块能整合多尺度的上下文信息,显著增强了模型捕获全局特征的能力。实验结果表明,相较于原模型及VGG16,改进后的模型在中药材识别上达到了94.75%的准确率,为中药材分类领域的后续研究工作提供了支持及优化的方向。To improve the accuracy of Chinese medicinal materials image classification,an enhanced ResNet50-based classification method is proposed.Firstly,the Convolutional Block Attention Module(CBAM)is introduced to refine the discriminative features of Chinese medicinal materials.Secondly,the convolutional shortcut connections in standard ResNet50 are structurally optimized to mitigate feature map information loss.Finally,a Pyramid Pooling Module(PPM)is integrated at the backend of the model to aggregate multi-scale contextual information,significantly enhancing global feature representation capabilities.The experimental results show that,compared to the original model and VGG16,the improved model achieves an accuracy of 94.75%in Chinese medicinal materials recognition,providing support and optimization directions for the subsequent research in Chinese medicinal materials classification.

关 键 词：中药材图像分类 ResNet50 CBAM注意力模块 PPM金字塔池化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]