SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.

SUT-Crack数据集囊括了高质量的沥青路面裂缝图像，该数据集专为深度学习技术中的裂缝检测而精心策划，包括分类、目标检测和分割等。在数据集的构建过程中，对裂缝检测所面临的诸多挑战进行了细致的考量，例如油渍、阴影以及不同的光照条件等。图像均从距离路面表面672毫米的固定高度进行采集，以便于进行现实世界中的裂缝长度测量校准。尤为引人注目的是，该数据集还包含了地理标签，为每张图像提供了精确的纬度和经度坐标。

搜集汇总

背景与挑战

背景概述

SUT-Crack是一个专门用于深度学习裂缝检测的高质量沥青路面裂缝图像数据集，支持分类、目标检测和分割任务。数据集考虑了油渍、阴影和光照变化等实际挑战，图像在固定高度拍摄以校准实际裂缝长度测量，并包含地理标签信息。