Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

An analysis of timber sections and deep learning for wood species classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential. We investigated the use of transfer learning from pre-trained deep neural networks for wood species classification and compared their results with a state-of-art pre-designed feature method. The experimental results show that traverse section images using a densely connected network achieved 98.7% of correct classification against 85.9% of standard pre-designed features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://drive.google.com/open?id=1RXateMUqwP8qJb-bA1Q5jXnrepPvRQR4

References

  1. Barmpoutis P, Dimitropoulos K, Barboutis I, Grammalidis N, Lefakis P (2018) Wood species recognition through multidimensional texture analysis. Computers and Electronics in Agriculture 144:241–248. https://doi.org/10.1016/j.compag.2017.12.011. http://www.sciencedirect.com/science/article/pii/S0168169917300959

    Article  Google Scholar 

  2. Bouarara HA (2019) A computer-assisted diagnostic (cad) of screening mammography to detect breast cancer without a surgical biopsy. International Journal of Software Science and Computational Intelligence 11:31–49. https://doi.org/10.4018/IJSSCI.2019100103

    Article  Google Scholar 

  3. da Silva NR, de Ridder M, Baetens JM, den Bulcke JV, Rousseau M, Bruno OM, Beeckman H, Acker JV, Baets BD (2017) Automated classification of wood transverse cross-section micro-imagery from 77 commercial central-african timber species. Annals of Forest Science 74(2)

  4. Franke B, Quenneville P (2011) Numerical modeling of the failure behavior of dowel connections in wood. J Eng Mech 137:186–195. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000217

    Article  Google Scholar 

  5. Hafemann LG, Oliveira LS, Cavalin P (2014) Forest species recognition using deep convolutional neural networks. In: 2014 22Nd international conference on pattern recognition, pp 1103–1107

  6. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 770–778

  7. Hu S, Li K, Bao X (2015) Wood species recognition based on sift keypoint histogram. In: 2015 8Th international congress on image and signal processing (CISP), pp 702–706

  8. Huang G, Liu Z, Maaten VdL, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2261–2269

  9. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size. arXiv:1602.07360

  10. Ibrahim I, Khairuddin ASM, Arof H, Yusof R, Hanafi E (2017) Statistical feature extraction method for wood species recognition system. European Journal of Wood and Wood Products

  11. Ibrahim I, Khairuddin ASM, Talip MSA, Arof H, Yusof R (2017) Tree species recognition system based on macroscopic image analysis. Wood Sci Technol 51:431–444

    Article  Google Scholar 

  12. Jordan R, Feeney F, Nesbitt N, Evertsen J (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36 (1):219–222

    Article  Google Scholar 

  13. Khalid M, Yusof R, Khairuddin ASM (2011) Tropical wood species recognition system based on multi-feature extractors and classifiers. In: 2011 2Nd international conference on instrumentation control and automation, pp 6–11

  14. Kwon O, GuLee H, Lee MR, Jang S, Yang SY, Park SY, Choi IG, Yeo H (2017) Automatic wood species identification of korean softwood based on convolutional neural networks. Journal of the Korean Wood Science and Technology, pp 797–808

  15. Li Y, Shi H, Chen L, Jiang F (2019) Convolutional approach also benefits traditional face pattern recognition algorithm [208!]. International Journal of Software Science and Computational Intelligence 11:1–16. https://doi.org/10.4018/IJSSCI.2019100101

    Article  Google Scholar 

  16. Nisgoski S, Oliveira AA, niz GIBM (2017) Artificial neural network and simca classification in some wood discrimination based on near-infrared spectra. Wood Sci Technol 51:929–942

    Article  Google Scholar 

  17. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1):51–59

    Article  Google Scholar 

  18. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: Elmoataz A, Lezoray O, Nouboud F, Mammass D (eds) Image and signal processing. Springer, Berlin, pp 236–243

  19. Peng Z (2013) Robust wood species recognition using variable color information. Optik - International Journal for Light and Electron Optics 124(17):2833–2836

    Article  Google Scholar 

  20. Rahtu E, Heikkilä J, Ojansivu V, Ahonen T (2012) Local phase quantization for blur-insensitive image analysis. Image Vision Comput 30(8):501–512

    Article  Google Scholar 

  21. Sajjadi M, Javanmardi M, Tasdizen T (2016) Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Proceedings of the 30th international conference on neural information processing systems, pp 1171–1179

  22. Sundaram M, Abitha J, Raj RMM, Ramar K (2015) Wood species classification based on local edge distributions. Optik - International Journal for Light and Electron Optics 126(21):2884–2890

    Article  Google Scholar 

  23. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2818–2826

  24. Tou JY, Tay YH, Lau PY (2009) A comparative study for texture classification techniques on wood species recognition problem. In: 2009 Fifth international conference on natural computation, pp 8–12

  25. Wheeler EA (2011) Inside wood – a web resource for hardwood anatomy. IAWA J 32(2):199–211

    Article  MathSciNet  Google Scholar 

  26. Yadav AR, Dewal ML, Anand RS, Gupta S (2013) Classification of hardwood species using ann classifier. In: 2013 Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), pp 1–5

  27. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14. http://dl.acm.org/citation.cfm?id=2969033.2969197. MIT Press, Cambridge, pp 3320–3328

  28. Zamri MIP, Cordova F, Khairuddin ASM, Mokhtar N, Yusof R (2016) Tree species classification based on image analysis using improved-basic gray level aura matrix. Comput Electron Agric 124:227–233

    Article  Google Scholar 

  29. Zhang Y, Xu J, Cheng H (2019) Adaboost-based conformal prediction with high efficiency. International Journal of High Performance Computing and Networking 13:355. https://doi.org/10.1504/IJHPCN.2019.099260

    Article  Google Scholar 

  30. Zhao P, Cao J (2016) Wood species identification using spectral reflectance feature and optimal illumination radian design. J For Res 27(1):219–224

    Article  Google Scholar 

Download references

Acknowledgements

The Titan Xp graphics card used in this research was donated by the NVIDIA Corporation. This work was supported by the CNPq (Grant 400699/2016-8), CAPES agency, Federal University of Uberlândia and Federal University of Catalão.

Author information

Authors and Affiliations

  1. Faculty of Computing, Federal University of Uberlândia, Uberlândia, Brazil

    André R. de Geus, Flávio O. Silva & Jefferson R. Souza

  2. Faculty of Computing, Federal University of Catalão, Catalão, Brazil

    Sérgio F. da Silva & Marcos A. Batista

  3. Forest Products Laboratory, Brazilian Forest Service, Brasilia, Brazil

    Alexandre B. Gontijo

Authors
  1. André R. de Geus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sérgio F. da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandre B. Gontijo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Flávio O. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcos A. Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jefferson R. Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André R. de Geus.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Geus, A., Silva, S.F.d., Gontijo, A.B. et al. An analysis of timber sections and deep learning for wood species classification. Multimed Tools Appl 79, 34513–34529 (2020). https://doi.org/10.1007/s11042-020-09212-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09212-x

Keywords

Search

Navigation