Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks - Zoubir Hajar (EDP Sciences, 2021)
Using Unmanned Aerial Systems (UASs) for bridge visual inspection automation necessitates the implementation of Deep Convolutional Neural Networks (DCNNs) to process efficiently the large amount of data collected by the UASs sensors. However, these networks require massive training datasets for the defects recognition and detection tasks. In an effort to expand existing concrete defects datasets, particularly concrete cracks in bridges, this paper proposes a public benchmark annotated image dataset containing over 6900 images of cracked and non cracked concrete bridges and culverts. The presented dataset includes some challenging surface conditions and covers concrete cracks with different sizes and patterns. The authors analyzed the proposed dataset using three state of the art DCNNs in Transfer Learning mode. The three models were used to classify the cracked and non cracked images and the best testing accuracy obtained reached 95.89%. The experimental results showcase the potential use of this dataset to train deep networks for concrete crack recognition in bridges. The dataset is publicly available at https://github.com/MCBDD-ZRE/Concrete-Bridge-Crack-Dataset- for academic purposes.