Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks

  • Nikolay Rudakov
  • Tuomas EerolaEmail author
  • Lasse Lensu
  • Heikki Kälviäinen
  • Heikki Haario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)


The quality control of timber products is vital for the sawmill industry pursuing more efficient production processes. This paper considers the automatic detection of mechanical damages in wooden board surfaces occurred during the sawing process. Due to the high variation in the appearance of the mechanical damages and the presence of several other surface defects on the boards, the detection task is challenging. In this paper, an efficient convolutional neural network based framework that can be trained with a limited amount of annotated training data is proposed. The framework includes a patch extraction step to produce multiple training samples from each damaged region in the board images, followed by the patch classification and damage localization steps. In the experiments, multiple network architectures were compared: the VGG-16 architecture achieved the best results with over 92% patch classification accuracy and it enabled accurate localization of the mechanical damages.



The research was carried out in the DigiSaw project (No. 2894/31/2017) funded by Business Finland. The authors would to thank FinScan Oy for providing the data for the experiments.


  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features SURF. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  2. 2.
    Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017)CrossRefGoogle Scholar
  3. 3.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31st International Conference on Machine Learning, ICML, vol. 32, pp. 647–655. PMLR (2014)Google Scholar
  4. 4.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  5. 5.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)CrossRefGoogle Scholar
  6. 6.
    Hashim, U., Hashim, S., Muda, A.: Automated vision inspection of timber surface defect: a review. Jurnal Teknologi 77(20), 127–135 (2015)CrossRefGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR, pp. 770–778. IEEE (2016)Google Scholar
  8. 8.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th Conference on Neural Information Processing Systems, NIPS, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th International Conference on Computer Vision, ICCV, vol. 2, pp. 1150–1157. IEEE (1999)Google Scholar
  11. 11.
    Nuutinen, Y., Väätäinen, K., Asikainen, A., Prinz, R., Heinonen, J.: Operational efficiency and damage to sawlogs by feed rollers of the harvester head. Silva Fennica 44(1), 121–139 (2010)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  14. 14.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR, pp. 779–788. IEEE (2016)Google Scholar
  15. 15.
    Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48(3), 929–940 (2018)CrossRefGoogle Scholar
  16. 16.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th Conference on Neural Information Processing Systems, NIPS, pp. 91–99 (2015)Google Scholar
  17. 17.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015). Scholar
  18. 18.
    Shustrov, D.: Species identification of wooden material using convolutional neural networks. Master’s thesis. Lappeenranta University of Technology, Finland (2018)Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations, ICLR (2014)Google Scholar
  20. 20.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–9. IEEE (2015)Google Scholar
  21. 21.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2818–2826. IEEE (2016)Google Scholar
  22. 22.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)CrossRefGoogle Scholar
  23. 23.
    Tong, H.L., Ng, H., Yap, T.V.T., Ahmad, W.S.H.M.W., Fauzi, M.F.A.: Evaluation of feature extraction and selection techniques for the classification of wood defect images. J. Eng. Appl. Sci. 12(3), 602–608 (2017)Google Scholar

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Authors and Affiliations

  1. 1.School of Engineering Science, Machine Vision and Pattern Recognition LaboratoryLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.School of Engineering Science, Inverse Problems Research GroupLappeenranta University of TechnologyLappeenrantaFinland
  3. 3.FinScan OyEspooFinland

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