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

Bush, Julia; Corradi, Tadeo; Nini?, Jelena; Thermou, Georgia; Bennetts, John

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., Nini?, 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
• 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