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Authors: Sangeeth Sarangi and Bappaditya Mandal

Affiliation: Keele University, Newcastle-under-Lyme ST5 5BG, U.K.

Keyword(s): Synchronous Attention, Dual Attention Network, Structural Component Recognition.

Abstract: The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or overlooked by existing methodologies. For the purpose of automating bridge visual inspection efficiently, this research examines and aids vision-based automated bridge component recognition. In this work, we propose a novel deep neural network-based attention model (DNNAM) architecture, which comprises synchronous dual attention modules (SDAM) and residual modules to recognise structural components. These modules help us to extract local discriminative features from structural component images and classify different categories of bridge components. These innovative modules are constructed at the contextual level of information encoding across spatial and channel dimensions. Experimental results and ablation studies on benchmarking bridge components and semantic augmented datasets show that our proposed architecture outperforms current state-of-the-art methodologies for structural component recognition. (More)

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Paper citation in several formats:
Sarangi, S. and Mandal, B. (2023). Deep Neural Network Based Attention Model for Structural Component Recognition. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 317-326. DOI: 10.5220/0011688400003417

@conference{visapp23,
author={Sangeeth Sarangi. and Bappaditya Mandal.},
title={Deep Neural Network Based Attention Model for Structural Component Recognition},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={317-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011688400003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Deep Neural Network Based Attention Model for Structural Component Recognition
SN - 978-989-758-634-7
IS - 2184-4321
AU - Sarangi, S.
AU - Mandal, B.
PY - 2023
SP - 317
EP - 326
DO - 10.5220/0011688400003417
PB - SciTePress

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