Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …

This paper reviews recent developments in deep learning-based crack segmentation
methods and investigates their performance under the impact from different image types …

Recently, crack segmentation studies have been investigated using deep convolutional
neural networks. However, significant deficiencies remain in the preparation of ground truth …

Previous research has shown the high accuracy of convolutional neural networks (CNNs) in
asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation …

Due to the damage of sensors or transmission equipment, abnormal monitoring data
inevitably exists in the measured raw data, and it significantly impacts the condition …

Routine visual inspection of concrete structures is essential to maintain safe conditions.
Therefore, studies of concrete crack segmentation using deep learning methods have been …

Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …

Crack images collected from civil infrastructures through unmanned aerial vehicles suffer
from motion blur and insufficient resolution, which reduces the accuracy of microcrack …

Large-volume hydraulic concrete structures, such as concrete dams, often suffer from
damage due to the influence of alternating loads and material aging during the service …

Bridge inspection and monitoring are usually used to evaluate the status and integrity of
bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods …