Historical_Building_Crack_2019

- An annotated benchmark image dataset for training and validation of crack detection systems based on Machine learning (ML) and, Deep Learning (DL) for a historical building. - Real images of historical building cracks were taken at an ancient building suffering from cracking problem (the Mosque (Masjed) of Amir al-Maridani, located in Sekat Al Werdani, El-Darb El-Ahmar, in Cairo Governorate). It was built during the era of the Mamluk Sultanate of Cairo, Egypt in 1339- 40 CE. It is distinguished by its octagonal minaret and its large dome and considered as one of the most distinctively decorated historical buildings. - Raw RGB digital images (.jpg) were captured using Canon camera (Canon EOS REBEL T3i) with 5184 × 3456 resolution over two years (2018 and 2019). - The dataset contains most of the challenges facing historical buildings crack defect detection in real-world environments, such as dust, illumination, separators, crack-like, blurring, deep texture, wood patterns, etc. - All images are divided into sub-images 256 X 256 to enlarge the dataset. - The final Crack Dataset consisted of 3886 images [ 757 for crack and 3139 for non-crack] To enlarge dataset size for training deep learning models, data augmentation process can be applied to increase training dataset size via generating new samples that are similar to the training samples. The following steps can be used: 1- Flipping image (vertically, horizontally and, vertically + horizontally), 2- Rotating image by 90 and -90 individually, 3- Flipping rotated images vertically, 4- Combining the output images of (1, 2 and 3) with the original images to create new dataset, 5- Adding noise to images of the new dataset such as Gaussian and salt and pepper noise, 6- Combining the output images of steps (4 and 5) to create the final augmented dataset.

- 本数据集为带标注的基准图像数据集，用于面向历史建筑场景的机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）裂缝检测系统的训练与验证。
- 历史建筑裂缝的实拍图像采集自一处存在开裂问题的古建筑——埃及开罗省Sekat Al Werdani、El-Darb El-Ahmar区域的阿米尔·马尔达尼清真寺（Mosque (Masjed) of Amir al-Maridani）。该清真寺建于公元1339-1340年的埃及马穆鲁克苏丹王朝时期，以八角宣礼塔与大型穹顶为标志性特征，属于装饰风格最为独特的历史建筑之一。
- 原始RGB数字图像（.jpg格式）由佳能EOS REBEL T3i相机拍摄，分辨率为5184×3456，采集周期覆盖2018至2019年两年。
- 本数据集涵盖了真实场景下历史建筑裂缝缺陷检测面临的绝大多数挑战，包括灰尘干扰、光照变化、分隔结构、类裂缝纹理、图像模糊、深层肌理、木质纹理等。
- 所有原始图像均被裁剪为256×256的子图像以扩充数据集规模。
- 最终裂缝数据集共包含3886张图像，其中裂缝样本757张，非裂缝样本3139张。
为进一步扩充深度学习模型训练所需的数据集规模，可通过数据增强技术生成与训练样本相似的新样本以扩充训练集，具体步骤如下：
1. 图像翻转操作（包含垂直翻转、水平翻转以及垂直+水平联合翻转）；
2. 分别将图像旋转90°与-90°；
3. 对经步骤2旋转后的图像执行垂直翻转；
4. 将步骤1、2、3生成的图像与原始图像合并，得到新的数据集；
5. 为新数据集的图像添加噪声，例如高斯噪声与椒盐噪声；
6. 将步骤4与步骤5生成的图像合并，得到最终的增强数据集。

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于历史建筑裂缝检测的标注图像数据集，包含3886张高分辨率图像（757张裂缝和3139张非裂缝），采集自埃及开罗的Amir al-Maridani清真寺，旨在支持机器学习和深度学习模型的训练与验证。数据集涵盖了真实环境中的挑战如灰尘、光照和纹理干扰，并通过图像分割和数据增强技术优化了模型训练效果。