The pavement crack datasets used in paper, crack detection results on each datasets, trained model, and crack annotation tool are stored in Google Drive, One Drive, and Daidu Yunpan …

This repository contains the code for crack detection on concrete surfaces. It is a PyTorch implementation of Deep Learning-Based Crack Damage Detection Using Convolutional Neural …

In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this …

Comprehensive and versatile infrastructural crack types are supported in the dataset, including the pavements, bridges, and buildings cracks. The dataset for crack segmentation contains …

You can detect crack paths and crack tips fully automatically using our crack detection module. This module provides two independent methodologies for crack detection - our line intercept …

Here we tried with around twenty images of both crack and non-crack to test. Without some cases, the cracks become very visible accurately in our output image. So we can say that 80 …

Therefore, to evaluate the robustness of the crack model, I tried to come up with several cases that could happen in practice. These images could be found in the folder ./test_imgs in the …

The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex …

I analyzed confusion matrix results of the CNN models, then determined accuracy, precision, recall, and F1 score metrics. Next, I plotted prediction probabilities for each type of …

(1) This dataset is used for crack detection based on the three types of images: the visible image, infrared image, and fusion image. (2) The dataset also considers different conditions and …