Julia Bush
Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering
Bush, Julia; Corradi, Tadeo; Ninić, Jelena; Thermou, Georgia; Bennetts, John
Authors
Tadeo Corradi
Jelena Ninić
Dr GEORGIA THERMOU Georgia.Thermou@nottingham.ac.uk
Assistant Professor in Structural Engineering
John Bennetts
Contributors
Jimmy Abualdenien
Editor
André Borrmann
Editor
Lucian-Constantin Ungureanu
Editor
Timo Hartmann
Editor
Abstract
Ageing infrastructure is a global concern, and current structural health monitoring practices are coming under review. With a view to streamline the visual bridge inspection process, we assess the classification performance of two Deep Neural Networks, VGG16 and MobileNet, on a challenging dataset of over 70,000 unprocessed bridge inspection images of three defect categories: corrosion, crack, and spalling. Grad-CAM "heatmap" visualisations on VGG16 predictions provide a coarse localisation of the defect region and some insight into the functioning of the network. Similar performance is attained on MobileNet, for applications where speed or computational cost is a consideration. We conclude that with further optimisation this approach could have an application in automated defect tagging.
Citation
Bush, J., Corradi, T., Ninić, J., Thermou, G., & Bennetts, J. (2021). Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering. In J. Abualdenien, A. Borrmann, L. Ungureanu, & T. Hartmann (Eds.), EG-ICE 2021 Workshop on Intelligent Computing in Engineering (421-431)
Conference Name | 28th EG-ICE International Workshop on Intelligent Computing in Engineering |
---|---|
Conference Location | Berlin, Germany |
Start Date | Jun 30, 2021 |
End Date | Jul 2, 2021 |
Acceptance Date | May 7, 2021 |
Online Publication Date | Aug 6, 2021 |
Publication Date | Aug 6, 2021 |
Deposit Date | Jun 4, 2021 |
Publicly Available Date | Aug 7, 2021 |
Publisher | Universitätsverlag der TU Berlin |
Pages | 421-431 |
Book Title | EG-ICE 2021 Workshop on Intelligent Computing in Engineering |
ISBN | 9783798332119 |
Public URL | https://nottingham-repository.worktribe.com/output/5625738 |
Files
EG-ICE 2021 Manuscript 52
(681 Kb)
PDF
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