Journal of the Korean Wood Science and Technology

  • 제49권5호
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  • Pages.491-503
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  • 2021
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  • 1017-0715(pISSN)
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  • 2233-7180(eISSN)
한국목재공학회 (The Korean Society of Wood Science & Technology)
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Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

  • FATHURAHMAN, Taufik (School of Computing, Telkom University) ;
  • GUNAWAN, P.H. (School of Computing, Telkom University) ;
  • PRAKASA, Esa (Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences) ;
  • SUGIYAMA, Junji (Division of Forestry and Biomaterials Science Faculty / Graduate School of Agriculture, Kyoto University Kitashirakawa-Oiwakecho)
  • 투고 : 2020.08.25
  • 심사 : 2021.08.06
  • 발행 : 2021.09.25
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초록

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

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과제정보

The author would like to thank the Indonesian Institute of Sciences (Lembaga Ilmu Pengetahuan Indonesia) and the Research Institute of Sustainable Humanosphere (RISH), Kyoto University, Japan for the assistance and provision of datasets supporting this wood classification research.

참고문헌

  1. Chollet, F. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251-1258.
  2. Fukushima, K. 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36: 193-202. https://doi.org/10.1007/BF00344251
  3. Geron, A. 2019. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media.
  4. Hadiwidjaja, M.L., Gunawan, P.H., Prakasa, E., Rianto, Y., Sugiarto, B., Wardoyo, R., Damaryati, R., Sugiyarto, K., Dewi, L.M., Astutiputri, V.F. 2019. Developing wood identification system by local binary pattern and hough transform method. Journal of Physics: Conference Series 1192(1): 012053. https://doi.org/10.1088/1742-6596/1192/1/012053
  5. He, K., Zhang, X., Ren, S., Sun, J. 2016. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.
  6. Howard, A.G., Zhu, M.C., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv Preprint ArXiv: 1704.04861.
  7. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. 2017. Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708.
  8. Hussain, M., Bird, J.J., Faria, D.R. 2018. A Study on Cnn Transfer Learning for Image Classification. In: Lotfi A., Bouchachia H., Gegov A., Langensiepen C., McGinnity M. (eds) Advances in Computational Intelligence Systems, pp. 191-202.
  9. Hwang, S.-W., Tazuru, S., Sugiyama, J. 2020. Wood identification of historical architecture in Korea by Synchrotron X-ray microtomography-based three-dimensional microstructural imaging. Journal of the Korean Wood Science and Technology 48(3): 283-290. https://doi.org/10.5658/WOOD.2020.48.3.283
  10. Jeon, W.S., Kim, Y.K., Lee, J.A., Kim, A.R., Darsan, B., Chung, W.Y., Kim, N.H. 2018. Anatomical characteristics of three Korean bamboo species. Journal of the Korean Wood Science and Technology 46(1): 29-37. https://doi.org/10.5658/WOOD.2018.46.1.29
  11. Jeon, W.S., Lee, H.M., Park, J.H. 2020. Comparison of anatomical characteristics for wood damaged by oak wilt and sound wood from quercus mongolica. Journal of the Korean Wood Science and Technology 48(6): 807-819. https://doi.org/10.5658/wood.2020.48.6.807
  12. Kobayashi, K., Kegasa, T., Hwang, S.W., Sugiyama, J. 2019. Anatomical features of Fagaceae wood statistically extracted by computer vision approaches: Some relationships with evolution. PloS One 14(8): e0220762. https://doi.org/10.1371/journal.pone.0220762
  13. Kwon, O., Lee, H.G., Lee, M.R., Jang, S., Yang, S.Y., Park, S.Y., Yeo, H. 2017. Automatic wood species identification of Korean softwood based on convolutional neural networks. Journal of the Korean Wood Science and Technology 45(6): 797-808. https://doi.org/10.5658/WOOD.2017.45.6.797
  14. Kwon, O., Lee, H.G., Yang, S.Y., Kim, H., Park, S.Y., Choi, I.G., Yeo, H. 2019. Performance enhancement of automatic wood classification of Korean softwood by ensembles of convolutional neural networks. Journal of the Korean Wood Science and Technology 47(3): 265-276. https://doi.org/10.5658/WOOD.2019.47.3.265
  15. Levi, G., Hassner, T. 2015. Age and Gender Classification Using Convolutional Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34-42.
  16. Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. 2016. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 55(2): 645-657. https://doi.org/10.1109/TGRS.2016.2612821
  17. Marmanis, D., Datcu, M., Esch, T., Stilla, U. 2015. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters 13(1): 105-109. https://doi.org/10.1109/LGRS.2015.2499239
  18. Prislan, P., Gricar, J., Cufar, K. 2014. Wood sample preparation for microscopic analysis. University of Ljubljana, Department of Wood Science and Technology.
  19. Salma, S., Gunawan, P., Prakasa, E., Sugiarto, B., Wardoyo, R., Rianto, Y., Dewi, L.M. 2018. Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern. In: 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 23-27.
  20. Savero, A.M., Wahyudi, I., Rahayu, I.S., Yunianti, A. D., Ishiguri, F. 2020. Investigating the anatomical and physical-mechanical properties of the 8-year-old superior teakwood planted in muna island, Indonesia. Journal of the Korean Wood Science and Technology 48(5): 618-630. https://doi.org/10.5658/WOOD.2020.48.5.618
  21. Schoch, W., Heller, I., Schweingruber, F.H., Kienast, F. 2004. Wood Anatomy of Central European Species. Swiss Federal Institute for Forest.
  22. Seth, W. 2019. Deep Learning from Scratch. O'Reilly Media.
  23. Sewak, M., Karim, M.R., Pujari, P. 2018. Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python. Packt Publishing Ltd.
  24. Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv: 1409.1556.
  25. Sugiarto, B., Prakasa, E., Wardoyo, R., Damayanti, R., Dewi, L.M., Pardede, H.F., Rianto, Y. 2017. Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 337-341.
  26. Yang, S.Y., Lee, H.G., Park, Y., Chung, H., Kim, H., Park, S.Y., Yeo, H. 2019. Wood species classification utilizing ensembles of convolutional neural networks established by near-infrared spectra and images acquired from Korean softwood lumber. Journal of the Korean Wood Science and Technology 47(4): 385-392. https://doi.org/10.5658/wood.2019.47.4.385
  27. Yu, S., Jia, S., Xu, C. 2017. Convolutional neural networks for hyperspectral image classification. Neurocomputing 219: 88-98. https://doi.org/10.1016/j.neucom.2016.09.010