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

Abstract

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.