Designing image processing tools for testing concrete bridges by a drone based on deep learning

• Published in: Journal of information and telecommunication (Print) Vol. 7; no. 2; pp. 227 - 240
• Main Authors: Ngo, Long, Xuan, Chieu Luong, Luong, Hoang Minh, Thanh, Binh Ngo, Ngoc, Dung Bui
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.04.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Bridge maintenance; bridge surface inspection; Concrete bridges; Concrete crack detection; crack quantification; Cracks; Deep learning; Flaw detection; Image processing; Piers; Teaching methods
• ISSN: 2475-1839; 2475-1847
• DOI: 10.1080/24751839.2023.2186624

Crack detection is one of the crucial aspects of bridge evaluation and maintenance. Several existing image-based methods require capturing the bridge surface and extracting crack features to detect the crack. However, in some positions such as the space under the bridge and piers, it is difficult to capture crack images. This paper aims to apply a method to detect cracks on the bridge surface by using a drone that can capture images in challenging positions. The video recorded from the drone will be automatically identified the cracks by employing the deep learning method. Deep learning is designed for training and testing the dataset with 51.000 images, each image sized 244 × 244. The deep learning method shows the feasibility of detecting the cracks in the transport facility. This is supported by the high accuracy of the experimental results of 95.19%. In addition, the tool can assign an ID containing information to each crack from video so that these cracks can then be mounted on a 3D map of the bridge for research on crack development over time in the task of assessing the health of bridges.