Crack detection is important for the inspection and evaluation during the maintenance of
concrete structures. However, conventional image‐based methods need extract crack …

Crack detection is a critical task in monitoring and inspection of civil engineering structures.
Image classification and bounding box approaches have been proposed in existing vision …

Early and timely detection of surface damages is important for maintaining the functionality,
reliability, and safety of concrete bridges. Recent advancement in convolution neural …

Current methods of practice for inspection of civil infrastructure typically involve visual
assessments conducted manually by trained inspectors. For post-earthquake structural …

Immediate assessment of structural integrity of important civil infrastructures, like bridges,
hospitals, or dams, is of utmost importance after natural disasters. Currently, inspection is …

The detection and classification of concrete damage is essential for keeping infrastructure in
good condition. Many deep learning-based methods have been applied, but these methods …

This paper presents a vision-based crack detection approach for concrete bridge decks
using an integrated one-dimensional convolutional neural network (1D-CNN) and long short …

The field of computer vision based civil infrastructure defect detection is constantly evolving
with steady advances being made in sensing technologies, hardware, and image …

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 …

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect,
monitor, and assess infrastructure conditions including detection of cracks. Traditional vision …