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2019 | OriginalPaper | Buchkapitel

Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks

verfasst von : Nikolay Rudakov, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, Heikki Haario

Erschienen in: Pattern Recognition

Verlag: 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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Titel: Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks
verfasst von: Nikolay Rudakov
Tuomas Eerola
Lasse Lensu
Heikki Kälviäinen
Heikki Haario
Verlag: Springer International Publishing
Buch: Pattern Recognition
Print ISBN: 978-3-030-12938-5

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

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12939-2_9

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