The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/6162a9b6-2a20-4600-8207-e9dcd53a264a

Kompanets, Andrii; Leonetti, Davide; Duits, Remco; Snijder, Bert (2024): Cracks in Steel Bridges (CSB) dataset: data underlying the publication: Loss function inversion for improved crack segmentation in steel bridges using a CNN framework. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/6162a9b6-2a20-4600-8207-e9dcd53a264a.v3

Dataset

x choose version: version 2 - 2024-09-30 version 1 - 2024-04-16

• Artificial Intelligence and Image Processing
• Design Practice and Management
• Other Built Environment and Design
• Built Environment and Design
• Information and Computing Sciences

computer vision, crack detection, crack segmentation, fatigue crack, image segmentation, steel bridge inspection

CC BY-NC-ND 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Andrii Kompanets , Davide Leonetti , Remco Duits , Bert Snijder

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

• 2024-04-16 first online

• 2024-12-05 published, posted

4TU.ResearchData

images/jpeg annotations/json application/python

Loss function inversion for improved crack segmentation in steel bridges using a CNN framework

• https://doi.org/10.1007/978-3-031-35399-4_8
• https://doi.org/10.48550/arXiv.2403.17725
• https://doi.org/10.1016/j.autcon.2024.105896

TU Eindhoven, Department of the Built Environment