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Deep Neural Network Based Attention Model for Structural Component Recognition

Sarangi, S; Mandal, B

Deep Neural Network Based Attention Model for Structural Component Recognition Thumbnail


S Sarangi


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.

Conference Name 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP
Conference Location Lisbon, Portugal
Start Date Feb 19, 2023
End Date Feb 21, 2023
Acceptance Date Jan 1, 2023
Publication Date Jan 1, 2023
Series Title 18th International Conference on Computer Vision Theory and Applications
Series ISSN 2184-4321
ISBN 978-989-758-634-7
Keywords Synchronous Attention, Dual Attention Network, Structural Component Recognition
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