Annotated Image Dataset of Masonry Building Cracks for Training Machine Learning Models for Crack Segmentation

This dataset is intended for training a Python-based machine learning model for masonry crack segmentation. It comprises 100 original images of masonry cracks collected during structural inspections of low‑rise residential properties in the UK, with corresponding pixel-level crack masks. All images were taken using standard mobile phone or compact camera devices under typical site conditions. The dataset is representative of real-life inspection environments, incorporating various masonry types, crack presentations, lighting conditions and background details. House numbers, street names and other potential sources of personally identifiable information have been manually pixelated for compliance with UK GDPR. EXIF metadata has been removed from the image files. Each image is paired with a binary segmentation mask that identifies the pixel-level crack region. The masks were generated through manual annotation using a digital image-editing tool. Crack pixels are labelled as the positive class (white, value = 255), with all other regions assigned as background (black, value = 0). Annotation was undertaken manually to ensure high accuracy delineation of crack boundaries. These masks form the ground truth for training of CNN-based segmentation models. The dataset uses a one‑to‑one naming convention to link each image file with the corresponding mask file. The filenames match, with the mask file appending the suffix _mask. For example, image: masonry_crack_001.jpg is paired with masonry_crack_001_mask.png. This pattern is consistent for all 100 image–mask pairs.本数据集旨在训练用于砌体裂缝分割的Python机器学习模型。数据集包含100张原始砌体裂缝图像，采集自英国低层住宅建筑的结构巡检现场，同时附带对应的像素级裂缝掩码。 所有图像均使用标准手机或紧凑型相机，在典型的现场作业环境下拍摄。该数据集能够真实反映实际巡检场景，涵盖多种砌体类型、裂缝表现形式、光照条件与背景细节。为符合英国《通用数据保护条例（GDPR）》要求，已通过手动像素化处理移除图像中的门牌号、街道名等潜在个人可识别信息，同时已删除图像文件的EXIF元数据。 每张图像均配有一张二进制分割掩码，用于标记像素级的裂缝区域。掩码通过数字图像编辑工具手动标注生成：裂缝像素被标记为正类（白色，像素值为255），其余区域均作为背景（黑色，像素值为0）。标注全程手动完成，以确保裂缝边界的高精度勾勒。此类掩码可作为基于卷积神经网络（Convolutional Neural Network, CNN）的分割模型训练的真值标签。 本数据集采用一一对应命名规则，将每张图像文件与对应的掩码文件关联。二者文件名完全一致，仅掩码文件附加后缀`_mask`。例如，图像文件`masonry_crack_001.jpg`对应的掩码文件为`masonry_crack_001_mask.png`，该命名规则在全部100组图像-掩码配对中保持统一。