A4 Conference proceedings

Fine-Grained Wood Species Identification Using Convolutional Neural Networks

Open Access publication

Publication Details
Authors: Shustrov Dmitrii, Eerola Tuomas, Lensu Lasse, Kälviäinen Heikki, Haario Heikki
Publisher: Springer Verlag (Germany): Series
Publication year: 2019
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: Image Analysis. SCIA 2019. Lecture Notes in Computer Science
Journal acronym: LNCS
Volume number: 11482
Start page: 67
End page: 77
Number of pages: 11
ISBN: 978-3-030-20204-0
eISBN: 978-3-030-20205-7
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019102935417


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.


Last updated on 2020-20-03 at 10:03