Concrete Crack Images for Classification

Published: 15 Jan 2018 | Version 1 | DOI: 10.17632/5y9wdsg2zt.1

Description of this data

The dataset contains concrete images having cracks. The data is collected from various METU Campus Buildings.
The dataset is divided into two as negative and positive crack images for image classification.
Each class has 20000images with a total of 40000 images with 227 x 227 pixels with RGB channels.
The dataset is generated from 458 high-resolution images (4032x3024 pixel) with the method proposed by Zhang et al (2016).
High-resolution images have variance in terms of surface finish and illumination conditions.
No data augmentation in terms of random rotation or flipping is applied.

Lei Zhang , Fan Yang , Yimin Daniel Zhang, and Y. J. Z., Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road Crack Detection Using Deep Convolutional Neural Network. In 2016 IEEE International Conference on Image Processing (ICIP).

Experiment data files

This data is associated with the following publication:

Road crack detection using deep convolutional neural network

Published in: IEEE Xplore Digital Library

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Previous versions

  • Version 1


    Published: 2018-01-15

    DOI: 10.17632/5y9wdsg2zt.1

    Cite this dataset

    Özgenel, Çağlar Fırat (2018), “Concrete Crack Images for Classification”, Mendeley Data, v1

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Orta Dogu Teknik Universitesi


Concrete (Composite Building Material), Crack, Concrete Cracking


CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

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You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.