Sabouri Mohammadreza, Sepidbar Alireza
Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
Data Brief. 2023 Oct 5;51:109642. doi: 10.1016/j.dib.2023.109642. eCollection 2023 Dec.
The SUT-Crack dataset (Sharif University of Technology Crack Dataset) presents a collection of high-quality images depicting asphalt pavement cracks specifically designed to facilitate crack detection using various deep learning methods, including classification, object detection, segmentation, etc. During the dataset creation process, careful consideration was given to encompass all possible crack detection challenges, such as the presence of oil stains and shadows on the pavement surface along with varying lighting conditions. The dataset comprises 130 images designed specifically for segmentation and object detection tasks. Each image is accompanied by precise ground truth annotations. This dataset is well-suited for various crack detection methods, offering accurate annotations that enhance its reliability and usefulness across diverse applications. Moreover, the images were taken from a fixed height of 672 mm above the pavement surface, enabling straightforward calibration to derive real-world crack lengths from pixel measurements. A notable feature of the SUT-Crack dataset is the inclusion of geotags, affixing each image with precise latitude and longitude coordinates. This geotagging capability allows for the visualization of the images on a map and imparting valuable geographical context to the dataset. Additionally, by dividing the original images into 200×200 pixel images, over 25,000 images were produced and then categorized into "with crack" and "without crack" classes which can be used for classification purposes. SUT-Crack is available at https://doi.org/10.17632/gsbmknrhkv.6.
SUT - 裂缝数据集（谢里夫理工大学裂缝数据集）展示了一组高质量图像，这些图像描绘了沥青路面裂缝，专门用于通过各种深度学习方法（包括分类、目标检测、分割等）来促进裂缝检测。在数据集创建过程中，充分考虑了涵盖所有可能的裂缝检测挑战，例如路面表面存在油渍和阴影以及不同的光照条件。该数据集包含130张专门为分割和目标检测任务设计的图像。每张图像都配有精确的地面真值注释。这个数据集非常适合各种裂缝检测方法，提供准确的注释，增强了其在各种应用中的可靠性和实用性。此外，图像是从路面表面上方672毫米的固定高度拍摄的，这使得能够直接进行校准，从像素测量中得出实际的裂缝长度。SUT - 裂缝数据集的一个显著特点是包含地理标签，为每张图像附上精确的纬度和经度坐标。这种地理标记功能允许在地图上可视化图像，并为数据集赋予有价值的地理背景。此外，通过将原始图像划分为200×200像素的图像，生成了超过25,000张图像，然后将其分类为“有裂缝”和“无裂缝”类别，可用于分类目的。SUT - 裂缝数据集可在https://doi.org/10.17632/gsbmknrhkv.6获取。
