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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Acknowledgements
The research was carried out in the DigiSaw project (No. 2894/31/2017) funded by Business Finland. The authors would to thank FinScan Oy for providing the data for the experiments.
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Rudakov, N., Eerola, T., Lensu, L., Kälviäinen, H., Haario, H. (2019). Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_9
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