科技创新与应用2025，Vol.15Issue(14)：76-79,4.DOI:10.19981/j.CN23-1581/G3.2025.14.017
基于注意力机制与迁移学习的改进ResNet18模型在木材近红外光谱分类中的应用
摘要
Abstract
The development of wood modification technology has made wood classification a key problem,and the data processing of near-infrared spectroscopy technology is challenging.In this paper,an improved ResNet18 model based on attention mechanism and transfer learning is proposed for near-infrared spectral classification of wood.Based on ResNet18,combined with the attention mechanism,the important bands are focused,and transfer learning is used to improve the efficiency and generalization ability.Experiments show that the model has excellent performance in training time and accuracy,the attention mechanism can improve the accuracy,and transfer learning can solve the problem of small samples,which provides an effective method for wood spectral analysis,and can be further optimized and expanded in the future.关键词
近红外光谱/ResNet18模型/注意力机制/迁移学习/木材分类Key words
near-infrared spectroscopy/ResNet18 model/attention mechanism/transfer learning/wood classification分类
农业科技引用本文复制引用
邓迪,李昊,牟洪波..基于注意力机制与迁移学习的改进ResNet18模型在木材近红外光谱分类中的应用[J].科技创新与应用,2025,15(14):76-79,4.基金项目
中央高校基本科研业务费专项资金项目(2572023DJ02) （2572023DJ02）
东北林业大学大学生创新训练计划项目(202410225438) （202410225438）
