Bush, Julia

Corradi, Tadeo

Ninic, Jelena

Thermou, Georgia

English

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

University of Birmingham Research Portal

Deep Neural Networks for Visual Bridge Inspections and Defect Visualisation in Civil Engineering

http://pure-oai.bham.ac.uk/ws/files/192029474/EG_ICE_2021.pdf

Deep Neural Networks for Visual Bridge Inspections and Defect Visualisation in Civil Engineering

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University of Birmingham Research Portal

University of Birmingham Research Portal