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Authors: Rémi Decelle 1 ; Phuc Ngo 1 ; Isabelle Debled-Rennesson 1 ; Frédéric Mothe 2 and Fleur Longuetaud 2

Affiliations: 1 Université de Lorraine, CNRS, LORIA, UMR 7503, Vandoeuvre-lès-Nancy, F-54506, France ; 2 Université de Lorraine, AgroParisTech, INRAE, SILVA, F-54000 Nancy, France

Keyword(s): Wood Analysis, Mathematical Morphology, Depthwise Separable Convolution, Attention Model.

Abstract: This article focuses on heartwood segmentation from cross-section RGB images (see Fig.1). In this context, we propose a novel attention gate (AG) model for both improving performance and making light convolutional neural networks (CNNs). Our proposed AG is based on mathematical morphology operators. Our light CNN is based on the U-Net architecture and called Light U-net (LU-Net). Experimental results show that AGs consistently improve the prediction performance of LU-Net across different wood cross-section datasets. Our proposed morphological AG achieves better performance than original U-Net with 10 times less parameters.

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Paper citation in several formats:
Decelle, R.; Ngo, P.; Debled-Rennesson, I.; Mothe, F. and Longuetaud, F. (2023). Light U-Net with a New Morphological Attention Gate Model Application to Analyse Wood Sections. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 759-766. DOI: 10.5220/0011626800003411

@conference{icpram23,
author={Rémi Decelle. and Phuc Ngo. and Isabelle Debled{-}Rennesson. and Frédéric Mothe. and Fleur Longuetaud.},
title={Light U-Net with a New Morphological Attention Gate Model Application to Analyse Wood Sections},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={759-766},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011626800003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Light U-Net with a New Morphological Attention Gate Model Application to Analyse Wood Sections
SN - 978-989-758-626-2
IS - 2184-4313
AU - Decelle, R.
AU - Ngo, P.
AU - Debled-Rennesson, I.
AU - Mothe, F.
AU - Longuetaud, F.
PY - 2023
SP - 759
EP - 766
DO - 10.5220/0011626800003411
PB - SciTePress

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