Cracks in Steel Bridges (CSB) dataset: data underlying the publication: Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges

The presented dataset used for the experiments is described in the article "Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges" (doi:https://doi.org/10.48550/arXiv.2403.17725). The dataset consists of images of steel bridge structures and pixel-wise fatigue crack annotations. Some of the images contain bridge structures with cracks or corrosion, while others capture structures without any defect. <br>The images are provided by bridge infrastructure owners "Rijkswatersaat" and "ProRail" and by "Nebest" engineering company. The annotation of images was made using a semi-automatic annotation tool described in the article "Segmentation Tool for Images of Cracks" (doi:https://doi.org/10.1007/978-3-031-35399-4_8) and which implementation is available at https://github.com/akomp22/crack-segmentation-tool.<br>The dataset consists of high-resolution images and is stored in the folder "entire images". The images are divided into test and train sets. Images that capture cracks are stored in the folder "crack_train" and "crack_test". Images capturing structure without a crack are stored in folders "nocrack_train" and "nocrack_test". For each image, a .json file is stored in the same folder and under the same name as the corresponding image. The .json file stores the position (x,y) of pixels on the image, which lie in a crack region. An example of a code to generate a binary segmentation map from the .json files is given in the "read_json_annotation.py" file.Additional patch datasets were generated from the entire images. The patch datasets are stored in the “patch dataset” folder. The multiple patch datasets differ by the patch size, number of patches, and fraction of patches that do not contain cracks among all patches of the particular dataset.<br>For more explanations, please refer to the article: https://doi.org/10.48550/arXiv.2403.17725

本实验所用数据集已在论文"Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges"（DOI：https://doi.org/10.48550/arXiv.2403.17725）中进行详细说明。该数据集包含钢桥结构图像及逐像素疲劳裂纹标注信息。部分图像呈现带有裂纹或锈蚀的桥体结构，其余图像则为无任何缺陷的桥体结构。
本数据集由桥梁基础设施运营商"Rijkswatersaat"、"ProRail"及工程公司"Nebest"提供。
图像标注采用半自动化标注工具完成，该工具已在论文"Segmentation Tool for Images of Cracks"（DOI：https://doi.org/10.1007/978-3-031-35399-4_8）中进行介绍，其代码实现可在https://github.com/akomp22/crack-segmentation-tool获取。
该数据集包含高分辨率图像，存储于"entire images"文件夹中。图像被划分为训练集与测试集：带有裂纹的图像分别存储于"crack_train"与"crack_test"文件夹，无裂纹的桥体结构图像则分别存储于"nocrack_train"与"nocrack_test"文件夹。
针对每张图像，同级文件夹下均存在一个与图像同名的.json标注文件，该文件存储了图像中属于裂纹区域的像素坐标(x,y)。"read_json_annotation.py"文件中提供了从.json文件生成二值分割图的示例代码。
研究人员还从完整图像中生成了额外的切块数据集，该类数据集存储于"patch dataset"文件夹中。不同切块数据集的区别在于切块尺寸、切块数量，以及特定数据集中无裂纹切块占总切块的比例。
如需更多详细说明，请参阅论文：https://doi.org/10.48550/arXiv.2403.17725

搜集汇总

背景与挑战

背景概述

该数据集包含钢桥结构的高分辨率图像及像素级裂缝标注，用于深度学习中的裂缝分割研究。图像分为有裂缝和无裂缝两类，并提供了补丁数据集以适应不同训练需求。