Concrete Crack Images for Classification

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). http://doi.org/10.1109/ICIP.2016.7533052本数据集包含带裂缝的混凝土图像，数据采集自多栋METU校园建筑。 本数据集面向图像分类任务，划分为裂缝阴性图像与裂缝阳性图像两个类别。 每个类别各包含20000张图像，总计40000张，图像分辨率为227×227像素，采用RGB通道格式。 本数据集基于458张高分辨率原始图像（分辨率为4032×3024像素），采用Zhang等人2016年提出的方法生成。 原始高分辨率图像在表面平整度与光照条件上存在差异，且未针对随机旋转或翻转操作实施数据增强（data augmentation）。 若使用本数据集，请引用以下文献： 1. 2018年：Özgenel, Ç.F.、Gönenç Sorguç, A.，《建筑裂缝检测中预训练卷积神经网络（Convolutional Neural Networks）的性能对比》，发表于2018年国际自动化与机器人建筑会议（ISARC 2018），柏林。 2. Lei Zhang、Fan Yang、Yimin Daniel Zhang及Y. J. Zhu（2016）：《基于深度卷积神经网络（Deep Convolutional Neural Network）的道路裂缝检测》，发表于2016年IEEE国际图像处理会议（ICIP 2016），DOI: 10.1109/ICIP.2016.7533052