BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification  

作　　者：Zihao Wan Hong Yang Jipan Xu Hongbo Mu Dawei Qi 

机构地区：[1]College of Science,Northeast Forestry University,Harbin 150040,People’s Republic of China

出　　处：《Journal of Forestry Research》2024年第4期202-214,共13页林业研究（英文版）

基　　金：This study was supported by the Fundamental Research Funds for the Central Universities(No.2572023DJ02).

摘　　要：Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.

关 键 词：Wood classification Near infrared spectroscopy Bilinear network SE module Anti-noise algorithm 

分 类 号：S781[农业科学—木材科学与技术] TP183[农业科学—林学] TP391.41[自动化与计算机技术—控制理论与控制工程]