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

本研究实验所用数据集已在论文《基于卷积神经网络（Convolutional Neural Network, CNN）框架的钢桥裂缝分割优化：损失函数反演方法》（doi:https://doi.org/10.1016/j.autcon.2024.105896）中详细说明。
该数据集包含钢桥结构图像及逐像素疲劳裂缝标注信息。部分图像拍摄了带有裂缝或锈蚀的桥体结构，其余图像则记录了无任何缺陷的桥体结构。
本数据集的图像由桥梁基础设施运营方Rijkswatersaat、ProRail以及工程公司Nebest提供。
图像标注工作采用论文《裂缝图像分割工具》（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，该工具的实现代码可通过https://github.com/akomp22/crack-segmentation-tool获取。
本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：带有裂缝的图像分别存放于"crack_train"与"crack_test"文件夹，无裂缝的图像则分别存放于"nocrack_train"与"nocrack_test"文件夹。
每张图像对应一个同名的JSON文件，存储于同一文件夹下。该JSON文件记录了图像中属于裂缝区域的像素坐标(x,y)。"read_json_annotation.py"文件中提供了从JSON文件生成二值分割掩码的示例代码。
研究团队还基于完整图像生成了多组补丁数据集，存储于"patch dataset"文件夹中。
各组补丁数据集的差异包括补丁尺寸、补丁数量，以及特定数据集中不含裂缝的补丁占比。
此外，本研究还提供了基于本文提出方法生成的全尺寸测试图像分割掩码，压缩包文件为"predictions.rar"。
如需更多细节，请参阅该论文：https://doi.org/10.1016/j.autcon.2024.105896