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该数据集收录了带有裂缝的混凝土图像，数据源自于 METU 校园各建筑。数据集被划分为两类，分别为负类和正类裂缝图像，用于图像分类。每个类别包含 20000 张图像，总计 40000 张，图像尺寸为 227 x 227 像素，包含 RGB 通道。数据集由 458 张高分辨率图像（4032x3024 像素）生成，采用 Zhang 等人（2016年）提出的方法。高分辨率图像在表面处理和光照条件下存在差异。未对图像进行随机旋转或翻转等数据增强处理。若使用本数据集，请引用：2018 – Özgenel, Ç.F., Gönenç Sorguç, A. “在建筑裂缝检测中预训练卷积神经网络的性能比较”，ISARC 2018，柏林。Lei Zhang, Fan Yang, Yimin Daniel Zhang, 和 Y. J. Z. Zhang（2016）。基于深度卷积神经网络的路面裂缝检测。在 2016 年 IEEE 国际图像处理会议（ICIP）。http://doi.org/10.1109/ICIP.2016.7533052

搜集汇总

背景与挑战

背景概述

该数据集是一个用于混凝土裂缝分类的图像数据集，包含40000张227x227像素的RGB图像，分为有裂缝和无裂缝两类，每类20000张。数据来源于458张高分辨率图像，在表面处理和光照条件上具有多样性，未应用数据增强，适用于图像分类任务，如裂缝检测。