Computer vision framework for crack detection of civil infrastructure-A review

被引:147
作者
Ai, Dihao [1 ,4 ]
Jiang, Guiyuan J. [2 ]
Lam, Siew-Kei [3 ]
He, Peilan [3 ]
Li, Chengwu [4 ]
机构
[1] Shenzhen Polytech, Sch Construct Engn, Shenzhen 518055, Peoples R China
[2] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266000, Peoples R China
[3] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[4] China Univ Min & Technol, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Crack detection; Civil infrastructure; Computer vision based methods; Data acquisition; Structural health monitoring; CONVOLUTIONAL NEURAL-NETWORKS; 3D ASPHALT SURFACES; CONCRETE CRACK; AUTOMATIC RECOGNITION; PATTERN-RECOGNITION; DAMAGE DETECTION; DEFECT DETECTION; IMAGE-ANALYSIS; EDGE-DETECTION; BRIDGE CRACKS;
D O I
10.1016/j.engappai.2022.105478
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Civil infrastructure (e.g., buildings, roads, underground tunnels) could lose its expected physical and functional conditions after years of operation. Timely and accurate inspection and assessment of such infrastructures are essential to ensure safety and serviceability, e.g., by preventing unsafe working conditions and hazards. Cracks, which are one of the most common distress, can indicate severe structural integrity issues that threaten the safety of the structure and people in the environment. As such, accurate, fast, and automatic detection of cracks on structure surfaces is a major issue for a variety of civil engineering applications. Due to advances in hardware data acquisition systems, significant progress has been made in the automatic detection and quantification of cracks in recent decades. This paper provides a comprehensive review of the research progress and prospects in computer vision frameworks for crack detection of civil infrastructures from multiple materials, including asphalt, concrete, and metal-like materials. The review encompasses major components of typical frameworks, i.e., data acquisition techniques, publicly available datasets, detection algorithms, and evaluation metrics. In particular, we provide a taxonomy of detection algorithms with a detailed discussion of the advantages, limitations, and application scenarios of the methods in each category, as well as the relationships between methods of different categories. We also discuss unsolved issues and key challenges in crack detection that could drive future research directions.
引用
收藏
页数:27
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