A focused review of recent advances in deep learning and computer vision for concrete crack detection and measurement
Year: 2026
Type: article
Abstract: Concrete cracking is one of the most prevalent indicators of structural deterioration, posing significant risks to the safety, serviceability, and longevity of civil infrastructure. Traditional manual visual inspections are labor-intensive, subjective, and prone to human error, making them inadequate for large-scale structural health monitoring (SHM). In recent years, computer vision and deep learning (DL) have emerged as transformative, automated alternatives. This paper presents a focused review of recent advancements in vision-based concrete crack detection and measurement, synthesizing findings from 27 representative and influential studies selected through an expert-driven, non-exhaustive screening process rather than a fully systematic protocol. The review traces the technological evolution from traditional image processing to advanced deep learning architectures, categorizing methodologies into image classification, real-time object detection, and pixel-level semantic segmentation. Furthermore, it highlights novel approaches for translating pixel-based detections into practical physical measurements (millimeters) using laser calibration and RGB-D cameras. The study also addresses critical challenges, such as domain gaps, data imbalance, and environmental noise, while outlining future research directions, including lightweight edge-computing models and multi-modal sensor fusion.
Source: Discover Civil Engineering
Institution: Islamic Azad University, Yazd
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7214426490