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| 引用本文: | 葛 琪,吴丽丽,康立军.基于改进ResNet50的中药材分类识别[J].软件工程,2025,28(4):16-20.【点击复制】 |
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| 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。 |
| 关键词: 中药材图像分类;ResNet50;CBAM注意力模块;PPM金字塔池化 |
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中图分类号: TP391
文献标识码: A
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| Classification and Recognition of Chinese Medicinal Materials Based on Improved ResNet50 |
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GE Qi, WU Lili, KANG Lijun
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(College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China)
geqi1103953519@163.com; wull@gsau.edu.cn; klj@gsau.edu.cn
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| Abstract: To improve the accuracy of Chinese medicinal materials image classification, an enhanced ResNet50based 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. |
| Keywords: image classification of Chinese medicinal materials; ResNet50; Convolutional Block Attention Module(CBAM); Pyramid Pooling Module (PPM) |
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