文献类型：期刊文献

英文题名：Wood Microscopic Image Identification Method Based on Convolution Neural Network

作者：Zhao, Ziyu[1] Yang, Xiaoxia[1] Ge, Zhedong[1] Guo, Hui[2] Zhou, Yucheng[1,2]

第一作者：Zhao, Ziyu

通信作者：Ge, ZD[1]

机构：[1]Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Peoples R China;[2]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China

年份：2021

卷号：16

期号：3

起止页码：4986-4999

外文期刊名：BIORESOURCES

收录：;EI(收录号：20223712730236);Scopus(收录号：2-s2.0-85133552994);WOS:【SCI-EXPANDED(收录号:WOS:000688342600014)】；

基金：The authors are grateful for the support of the youth fund of Shandong Natural Science Foundation, Grant No. ZR2020QC174, the Doctoral Foundation of Shandong Jianzhu University, Grant No. XNBS1622, and the Taishan Scholar Advantage Characteristic Discipline Talent Team Project of Shandong Province of China, Grant No. 2015162.

语种：英文

外文关键词：Wood identification; Microstructure; Convolution neural network

摘要：To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodlDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodlDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodlDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodlDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodlDNet model was analyzed to verify the feasibility and effectiveness of the PWoodlDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification.