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

Crack detection has vital importance for structural health monitoring and inspection of
buildings. The task is challenging for computer vision methods as cracks have only low-level …

Crack inspection is a crucial but labor-intensive work of maintenance for in-service bridges.
Recently, the development of fully convolutional network (FCN) provides pixel-wise …

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

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 …

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 …

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

This paper proposes a customized convolutional neural network for crack detection in
concrete structures. The proposed method is compared to four existing deep learning …

Crack is one of important damages on real concrete surface. The visual inspection that
depends on inspectors, a primary method to detect cracks, is laborious and time-consuming …

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically
to maintain its reliability and structural health. Visual signs of cracks and depressions …