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

By C. Koch, K. Georgieva, Varun Kasireddy, Burcu Akinci, Paul W. Fieguth. Advanced Engineering Informatics, 2015

947 citations recorded by the source corpus.

Visual inspection of civil infrastructure is essential for condition assessment.We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.Accordingly, we review the latest computer vision based defect detection methods.Using computer vision most relevant types of defects can be automatically detected.Automatic defect properties retrieval and assessment has not been achieved yet. 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.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/bb63e3c51b78da88852dac0c67411c8dc930993e

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In Civil and Structural Engineering

Citations in the corpus

Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, methodology): “In recent years, considerable eﬀorts have been dedicated to automating the visual inspection (Jahanshahi, Kelly, Masri, & Sukhatme, 2009; Koch, Georgieva, Kasireddy, Akinci, & Fieguth, 2015; Rose et al., 2014) using computer vision-based approaches.”
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection (2019, background): “, the road pavement [1], [2], the wall of nuclear power plants [3], the ceiling of tunnels [4], etc.”
A review of 3D reconstruction techniques in civil engineering and their applications (2018, background): “[15] reviewed the computer vision based defect detection and condition assessment of civil infrastructure, including reinforced concrete bridges, precast concrete tunnels, underground concrete pipes and asphalt pavements.”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “For further study on identiﬁcation methods for some of these defects, Koch et al. [108] provided an excellent review of computer vision defect detection techniques developed prior to 2015, classi-ﬁed based on the structure to which they are applied.”