Cracks in Steel Bridges (CSB) dataset: data underlying the publication: Loss function inversion for improved crack segmentation in steel bridges using a CNN framework

The presented dataset used for the experiments is described in the article "Loss function inversion for improved crack segmentation in steel bridges using a CNN framework" (doi:https://doi.org/10.1016/j.autcon.2024.105896). 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. 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.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. Furthermore, we provide segmentation maps in file "predictions.rar" for entire test images which are given by the method proposed in our research article.For more explanations, please refer to the article: https://doi.org/10.1016/j.autcon.2024.105896

本实验所用数据集已在论文《基于卷积神经网络（Convolutional Neural Network，CNN）框架的损失函数反演用于优化钢结构桥梁裂纹分割》（DOI：https://doi.org/10.1016/j.autcon.2024.105896）中详细阐述。该数据集包含钢结构桥梁图像与像素级疲劳裂纹标注。部分图像展示了存在裂纹或腐蚀现象的桥梁结构，其余图像则为无任何缺陷的完好桥梁结构。本数据集由桥梁基础设施所有者Rijkswatersaat、ProRail以及工程公司Nebest联合提供。图像标注工作采用论文《裂纹图像分割工具》（DOI：https://doi.org/10.1007/978-3-031-35399-4_8）中介绍的半自动标注工具完成，该工具的开源实现地址为https://github.com/akomp22/crack-segmentation-tool。该数据集包含高分辨率图像，存储于"entire images"文件夹内。图像被划分为训练集与测试集：含裂纹的图像分别存放于"crack_train"与"crack_test"文件夹，无裂纹的结构图像则存放于"nocrack_train"与"nocrack_test"文件夹。针对每张图像，对应文件夹下会存储一个同名的.json文件，该文件记录了图像中属于裂纹区域的像素坐标(x,y)。代码示例"read_json_annotation.py"给出了从.json文件生成二值分割图的实现方法。此外，研究人员从完整图像中生成了多组补丁数据集，存储于"patch dataset"文件夹中。不同补丁数据集的差异在于补丁尺寸、补丁总数，以及对应数据集中无裂纹补丁在总补丁数中的占比。本研究论文提出的方法生成的全测试集图像分割结果已打包存储于"predictions.rar"文件中。如需更多详细说明，请参阅该论文：https://doi.org/10.1016/j.autcon.2024.105896

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疲劳裂缝标注，用于计算机视觉裂缝检测研究。数据来源于桥梁所有者和工程公司，采用半自动工具标注，并提供训练集、测试集及补丁数据集，配套JSON标注文件和分割图生成代码。