UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images

Abstract

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.

Keywords

• Pavement distress detection
• UAV
• Computer vision
• Deep learning