Deep learning‐based structural damage detection methods overcome the limitation of
inferior adaptability caused by extensively varying real‐world situations (eg, lighting and …

Computer vision‐based techniques were developed to overcome the limitations of visual
inspection by trained human resources and to detect structural damage in images remotely …

A number of image processing techniques (IPTs) have been implemented for detecting civil
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …

To achieve effective inspection of reinforced concrete structures, a smart solution to obtain
quantitative crack information automatically from inspection is essential. Therefore, a novel …

With the growing number of aging infrastructure across the world, there is a high demand for
a more effective inspection method to assess its conditions. Routine assessment of structural …

Many bridge structures, one of the most critical components in transportation infrastructure
systems, exhibit signs of deteriorations and are approaching or beyond the initial design …

The detection and measurement of crack at pixel level is a challenge to existing methods. To
overcome this challenge, this paper proposes a convolutional encoder-decoder network …

This paper introduces the idea of using deep fully convolutional neural networks for pixel-
level defect detection in concrete infrastructure systems. Although coarse patch-level deep …

Cracks are a potential threat to the safety and endurance of civil infrastructures, and
therefore, careful and regular structural crack inspection is needed during their long-term …

Crack detection on concrete bridges is a critical task to ensure bridge safety. However, many
cracks on concrete bridges show low contrast and blurry edges in practice, which brings …