research-article

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

Authors:

Christian Koch,

Kristina Georgieva,

Varun Kasireddy,

Paul FieguthAuthors Info & Claims

Advanced Engineering Informatics, Volume 29, Issue 2

Pages 196 - 210

https://doi.org/10.1016/j.aei.2015.01.008

Published: 01 April 2015 Publication History

Abstract

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.

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cover image Advanced Engineering Informatics

Advanced Engineering Informatics Volume 29, Issue 2

April 2015

122 pages

ISSN:1474-0346

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Tao XZeng ZZeng JZhao TDai Q(2024)Pavement Crack Detection and Identification Based on Improved YOLOv8International Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.35636318:1(1-20)Online publication date: 16-Oct-2024
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