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

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中图分类号：S781.7 文献标志码：A 文章编号：2095-2945(2025)14-0076-04

Abstract:Thedevelopmentofwoodmodificationtechnologyhasmadewoodclasificationakeyproblem，andthedata processngofnear-infraredspectroscopytechnologischalenging.Inthispper,animprovedResNet18modelbasedonaention mechanismandtransferlearningisproposedfornear-infraredspectralclassficationofwoodBasedonResNet18,combinedwith theattentionmechanism，theimportantbandsarefocused，andtransferlearningisusedtoimprovetheeficiencyand generalizationability.Experimentsshowthatthemodelhasexcelentperformanceintrainingtimeandaccuracytheaention mechanismcanimprove theacuracy，andtransfer learning cansolvetheproblem ofsmallsamples,whichprovidesaneffective method for wood spectral analysis,and can be further optimized and expanded in the future.

Keywords: near-infraredspectroscopy;ResNet18 model;atentionmechanism;transfer learning;woodclassificatior

木材是重要自然资源，在多行业广泛应用，其特性使其用于高附加值产品制造。（剩余5726字）