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Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering

Bush, Julia; Corradi, Tadeo; Ninić, Jelena; Thermou, Georgia; Bennetts, John

Deep Neural Networks for visual bridge inspections and defect visualisation in Civil Engineering Thumbnail


Authors

Julia Bush

Tadeo Corradi

Jelena Ninić

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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.

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

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