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Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks

Authors : Nikolay Rudakov, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, Heikki Haario

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

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.

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Literature
1.
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features SURF. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef
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)CrossRef
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)
4.
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRef
5.
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)CrossRef
6.
Hashim, U., Hashim, S., Muda, A.: Automated vision inspection of timber surface defect: a review. Jurnal Teknologi 77(20), 127–135 (2015)CrossRef
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)
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)
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)
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)
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)CrossRef
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)CrossRef
13.
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRef
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)
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)CrossRef
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)
17.
18.
Shustrov, D.: Species identification of wooden material using convolutional neural networks. Master’s thesis. Lappeenranta University of Technology, Finland (2018)
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)
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)
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)
22.
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)CrossRef
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)
Metadata
Title
Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks
Authors
Nikolay Rudakov
Tuomas Eerola
Lasse Lensu
Heikki Kälviäinen
Heikki Haario
Copyright Year
2019
Book
Pattern Recognition

Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-12939-2_9

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