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林业研究(英文版) >

2024 , Vol. 35 >Issue 1: 4

DOI: https://doi.org/10.1007/s11676-023-01652-z

  • Zihao Wan 1 ,
  • Hong Yang 1 ,
  • Jipan Xu 1 ,
  • Hongbo Mu 1, d ,
  • Dawei Qi 1, e
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收稿日期: 2022-11-26

  录用日期: 2023-05-23

  网络出版日期: 2024-10-16

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

  • Zihao Wan 1 ,
  • Hong Yang 1 ,
  • Jipan Xu 1 ,
  • Hongbo Mu 1, d ,
  • Dawei Qi 1, e
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  • 1 College of Science, Northeast Forestry University, 150040, Harbin, People’s Republic of China
d
e

Received date: 2022-11-26

  Accepted date: 2023-05-23

  Online published: 2024-10-16

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© The Author(s) 2023
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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本文引用格式

Zihao Wan , Hong Yang , Jipan Xu , Hongbo Mu , Dawei Qi . [J]. 林业研究(英文版), 2024 , 35(1) : 4 . DOI: 10.1007/s11676-023-01652-z

Abstract

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.

Key words： Wood classification; Near infrared spectroscopy; Bilinear network; SE module; Anti-noise algorithm

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参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
1
Acquarelli J, van Laarhoven T, Gerretzen J, Tran TN, BuydensMarchiori LME. Convolutional neural networks for vibrational spectroscopic data analysis. Anal Chim Acta, 2017, 954: 22-31,

DOI

2
Asadi K, Littman M L (2017) In: An alternative softmax operator for reinforcement learning. In: Proc. 28th int’l conf. mach. Learn. Bellevuepp, WA, 243–252

3
Chen YY, Wang ZB. Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks. Chemometr Intell Lab Syst, 2018, 181: 1-10,

DOI

4
Gao SH, Han Q, Li D, Chen MM, Peng P. Representative batch normalization with feature calibration. Virtual, 2021, 1: 8669-8679

5
Gao MY, Wang F, Song P, Liu JY, Qi DW. BLNN: multiscale feature fusion-based bilinear fine-grained convolutional neural network for image classification of wood knot defects. J Sens, 2021, 2021: 1-18

6
Graham B, Engelhardt B, Van den Oord A (2014) Fractional max-pooling. arXiv preprint arXiv:1412.6071

7
He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. CVPR, 2016 Las Vegas Nevada 770-778

8
Hu C, Qu JJ, Xu CP, Zhu AJ. Garment image recognition based on adaptive pooling neural network. J Comput Appl, 2018, 38(8): 2211

9
Hu J, Shen L, Sun G. Squeeze-and-excitation networks, 2018 Salt Lake City Utah CVPR 7132-7141

10
Huang PG, Fan Z, Li XP, Guan C, Zhang YF, Wu ZK. Review of computer-based wood feature extraction and identification. World for Res, 2020, 33(01): 44-48

11
Huang G, Liu Z, Van Der Maaten L, Weinberger Kilian Q (2017) Densely Connected Convolutional Networks. CVPR 2017. Honolulu Hawaii. 4700–4708

12
Jia WS, Zhang HZ, Ma J, Liang G, Wang JH, Liu X. Study on the predication modeling of COD for water based on UV-VIS spectroscopy and CNN algorithm of deep learning. Spectrosc Spectr Anal, 2020, 40(9): 2981

13
Jiao LC, Zhang F, Liu F, Yang SY, Li LL, Feng ZX, Qu R. A survey of deep learning-based object detection. IEEE Access, 2019, 7: 128837-128868,

DOI

14
Kauppinen JK. Fourier Self-Deconvolution in Spectroscopy. Spectrom Tech, 1983, 1983: 199-232,

DOI

15
Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Commun ACM, 2012, 60(6): 84-90,

DOI

16
Kuesel AC, Stoyanova R, Aiken NR, Li CW, Szwergold BS, Shaller C, Brown TR. Quantitation of resonances in biological 31P NMR spectra via principal component analysis: potential and limitations. NMR Biomed, 1996, 9(3): 93-104,

DOI

17
Laakmann F, Petersen P. Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs. Adv Comput Math Dio, 2021,

DOI

18
Lecun Y, Bottou L, Bengio Y. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324,

DOI

19
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444,

DOI

20
Ma HY, Li XH, Qiang L, Xie XB, Chong J. Zhao CX (2020) Research on identification technology of explosive vibration based on EEMD energy entropy and multiclassification SVM. Shock Vib, 2020, 2: 1-10

21
Macior A, Zaborniak I, Chmielarz P, Smenda J, Wolski K, Ciszkowicz E, Lecka-Szlachta K. A new protocol for ash wood modification: synthesis of hydrophobic and antibacterial brushes from the wood surface. Molecules, 2022, 27(3): 890,

DOI

22
Nisgoski S, DeOliveira A, DeMuñiz G. Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra. Wood Sci Technol, 2017, 51(4): 929-942,

DOI

23
Pachuta SJ. Enhancing and automating TOF-SIMS data interpretation using principal component analysis. Appl Surf Sci, 2004, 231(6): 217-223,

DOI

24
Pan X, Qiu J, Yang Z. Identification of softwood species using convolutional neural networks and raw near-infrared spectroscopy. Wood Mater Sci Eng, 2022, 1: 1-11

25
Pradhan T, Kumar P, Pal S. CLAVER: an integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation. Inf Sci, 2021, 559: 212-235,

DOI

26
Qin YH, Ding XQ, Gong HL. High dimensional feature selection in near infrared spectroscopy classification. Infrared Laser Eng, 2013, 42(5): 1355-1359

27
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536,

DOI

28
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, https://arxiv.org/abs/1409.1556.

29
Soares SFC, Medeiros EP, Pasquini C. Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging. Anal Methods, 2016, 8(48): 8498-8505,

DOI

30
Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Rabinovich A. Going deeper with convolutions, 2015 Boston Massachusetts CVPR 1-9

31
Tang YS, Chen ZG. Soil pH prediction based on convolution neural network and near infrared spectroscopy. Spectrosc Spectr Anal, 2021, 41(3): 892

32
Wang XS, Sun YD, Huang AM. Research on infrared spectrum for timber species identification. For Eng, 2015, 31(6): 65-70

33
Wang XS, Sun YD, Huang MG, Huang AM. Back propagation artificial neural network combined with near infrared spectroscopy for timber recognition. J Northeast for Univ, 2015, 43(12): 82-85

34
Wang QQ, Gao QX, Gao XB, Nie FP. l(2, p)-Norm based PCA for image recognition. IEEE Trans Image Process, 2017, 27(3): 1336-1346,

DOI

35
Wang WQ, Zhang J, Wang FL. Attention bilinear pooling for fine-grained classification. Symmetry, 2019, 11(8): 1033,

DOI

36
Xia JJ, Huang Y, Li QQ, Xiong YM, Min SG. Convolutional neural network with near-infrared spectroscopy for plastic discrimination. ECL, 2021, 19(5): 3547-3555

37
Yang SY, Kwon O, Park Y, Chung H, Kim H, Park SY, Choi IG, Yeo H. Application of neural networks for classifying softwood species using near infrared spectroscopy. J near Infrared Spectrosc, 2020, 28(5–6): 298-307,

DOI

38
Zhang W, Li CH, Peng GL, Chen YH, Zhang ZJ. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process, 2017, 100: 439-453,

DOI

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