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 …

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

This paper compares the performance of common edge detectors and deep convolutional
neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of …

The damage investigation and inspection methods for infrastructures performed in small-
scale (type III) facilities usually involve a visual examination by an inspector using surveying …

Cracking is one of the common manifestations of damage in concrete structures. Crack
detection is currently done using typical computer vision methods. But they are still not …

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 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 …

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 …

Surface cracks on the concrete structures are a key indicator of structural safety and
degradation. To ensure the structural health and reliability of the buildings, frequent structure …

Cracks are often the most intuitive indicators for assessing the condition of in-service
structures. Intelligent detection methods based on regular convolutional neural networks …