A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch, Christian; Georgieva, Kristina; Kasireddy, Varun; Akinci, Burcu; Fieguth, Paul

Abstract

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research.

Citation

Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), https://doi.org/10.1016/j.aei.2015.01.008

• Journal Article Type: Article
• Acceptance Date: Jan 22, 2015
• Online Publication Date: Feb 21, 2015
• Publication Date: Apr 1, 2015
• Deposit Date: Feb 26, 2016
• Publicly Available Date: Feb 26, 2016
• Journal: Advanced Engineering Informatics
• Print ISSN: 1474-0346
• Electronic ISSN: 1474-0346
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 2
• DOI: https://doi.org/10.1016/j.aei.2015.01.008
• Keywords: Computer Vision, Infrastructure, Condition Assessment, Defect Detection, Infrastructure Monitoring
• Public URL: https://nottingham-repository.worktribe.com/output/745904
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1474034615000208