Concrete Crack Images for Classification

Published: 23 Jul 2019 | Version 2 | DOI: 10.17632/5y9wdsg2zt.2

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.

If you use this dataset please cite:
2018 – Özgenel, Ç.F., Gönenç Sorguç, A. “Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings”, ISARC 2018, Berlin.

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

Latest version

  • Version 2


    Published: 2019-07-23

    DOI: 10.17632/5y9wdsg2zt.2

    Cite this dataset

    Özgenel, Çağlar Fırat (2019), “Concrete Crack Images for Classification”, Mendeley Data, v2


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Orta Dogu Teknik Universitesi


Concrete (Composite Building Material), Crack, Concrete Cracking


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

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