Yan Haohui, Zhang Junfei
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.
School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China.
Data Brief. 2023 Oct 15;51:109692. doi: 10.1016/j.dib.2023.109692. eCollection 2023 Dec.
The UAV-PDD2023 dataset consists of pavement distress images captured by unmanned aerial vehicles (UAVs) in China with more than 11,150 instances under two different weather conditions and across varying levels of construction quality. The roads in the dataset consist of highways, provincial roads, and county roads constructed under different requirements. It contains six typical types of pavement distress instances, including longitudinal cracks, transverse cracks, oblique cracks, alligator cracks, patching, and potholes. The dataset can be used to train deep learning models for automatically detecting and classifying pavement distresses using UAV images. In addition, the dataset can be used as a benchmark to evaluate the performance of different algorithms for solving tasks such as object detection, image classification, etc. The UAV-PDD2023 dataset can be downloaded for free at the URL in this paper.
UAV-PDD2023数据集由中国无人机拍摄的路面病害图像组成，在两种不同天气条件下以及不同施工质量水平下有超过11150个实例。数据集中的道路包括按照不同要求修建的高速公路、省道和县道。它包含六种典型的路面病害实例，包括纵向裂缝、横向裂缝、斜向裂缝、龟裂、修补和坑洼。该数据集可用于训练深度学习模型，以便使用无人机图像自动检测和分类路面病害。此外，该数据集可作为基准，用于评估解决目标检测、图像分类等任务的不同算法的性能。UAV-PDD2023数据集可在本文中的网址免费下载。
