Wood Species Recognition with Small Data: A Deep Learning Approach

Authors

Yongke Sun¹, Qizhao Lin², Xin He², Youjie Zhao², Fei Dai³, Jian Qiu², Yong Cao^{2, *,}

¹Yunnan Provincial Key Laboratory of Wood Adhesives and Glued Products, Southwest Forestry University, Kunming, 650224, Yunnan Province, China

²College of Material Science and Engineering, Southwest Forestry University, Kunming, 650224, Yunnan Province, China

³College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, Yunnan Province, China

^*Corresponding author. Email: cn_caoyong@126.com

DOI

10.2991/ijcis.d.210423.001How to use a DOI?

Keywords

Wood recognition; Transfer learning; Generalization performance; Feature extraction; ResNet50; Linear discriminant analysis; KNN

Abstract

Wood species recognition is an important work in the wood trade and wood commercial activities. Although many recognition methods were presented in recent years, the existing wood species recognition methods mainly use shallow recognition models with low accuracy and are still unsatisfying for many real-world applications. Besides, their generalization ability is not strong. In this paper, a novel deep-learning-based wood species recognition method was proposed, which improved the accuracy and generalization greatly. The method uses 20X amplifying glass to acquire wood images, extracts the image features with ResNet50 neural network, refines the features with linear discriminant analysis (LDA), and recognizes the wood species with a KNN classifier. Our data was small, but we adopted transfer learning to improve our method. About 3000 wood images were used in our wood species recognition experiments and our method was executed in 25 rare wood species and the results showed our method had better generalization performance and accuracy. Compared with traditional deep learning our results were obtained from a small amount of data, which just confirmed the effectiveness of our method.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yongke Sun
AU  - Qizhao Lin
AU  - Xin He
AU  - Youjie Zhao
AU  - Fei Dai
AU  - Jian Qiu
AU  - Yong Cao
PY  - 2021
DA  - 2021/04/30
TI  - Wood Species Recognition with Small Data: A Deep Learning Approach
JO  - International Journal of Computational Intelligence Systems
SP  - 1451
EP  - 1460
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210423.001
DO  - 10.2991/ijcis.d.210423.001
ID  - Sun2021
ER  -