A4 Conference proceedings

Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks

Open Access publication

Publication Details
Authors: Rudakov Nikolay, 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: German Conference on Pattern Recognition. GCPR 2018.
Journal acronym: LNCS
Volume number: 11269
Start page: 115
End page: 126
Number of pages: 12
ISBN: 978-3-030-12938-5
eISBN: 978-3-030-12939-2
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-fe2019102935477


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.

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