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Fine-Grained Wood Species Identification Using Convolutional Neural Networks

  • Dmitrii Shustrov
  • Tuomas EerolaEmail author
  • Lasse Lensu
  • Heikki Kälviäinen
  • Heikki Haario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11482)

Abstract

This paper considers the wood species identification from images of boards. The identification using only visual features of the surface is a challenging task even for an expert. The task becomes especially difficult when the wood species are from the same family. We propose a CNN based framework for the fine-grained classification of wood species. The framework includes a patch extraction procedure where board images are divided into image patches. Each patch is separately classified using the CNN resulting in multiple classification results per board. Finally, the patch classification results for a single board are combined. We evaluate various CNN architectures using the challenging data, consisting of species from the Pinaceae family. In addition, we propose three alternative decision rules for combining the patch classification results. By selecting a suitable amount of image patches, the proposed framework was able to achieve over 99% identification accuracy and real-time performance.

Keywords

Wood species identification Convolutional neural networks Fine-grained classification Visual inspection Machine vision application 

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Engineering Science, Department of Computational and Process Engineering, Machine Vision and Pattern Recognition LaboratoryLappeenranta-Lahti University of Technology LUTLappeenrantaFinland
  2. 2.School of Engineering Science, Department of Computational and Process Engineering, Inverse Problems Research GroupLappeenranta-Lahti University of Technology LUTLappeenrantaFinland

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