ABSTRACT Cracks are the most recurrent pathological manifestations in concrete, due to the different forms of exposure that the material is subject to. In this scenario, it is important that buildings
Crack detection is essential for structural safety inspection but remains challenging due to noise, illumination variations, and complex backgrounds. In this paper, we propose CrackNet, a segmentation
"B1", "B2", "B3", "B4". Each sheet within the excel worksheet contains the results of the three-point bending test performed on the four beams to test the Hybrid Tendons activation as reported in the
This dataset is the conglomeration of the cataloged crack datasets from the literature, making an extremely diverse crack dataset. There were over 10,995 images which have been merged from CFD (Shi, C
Title: Crack Detection in Concrete and Pavement using Convolutional Neural Networks Summary: This dataset contains 30,000 images of concrete and pavement surfaces, classified into two categories: cra
