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

<p>The images in the dataset ( VOC format) were captured by a UAV at an altitude of 30 meters. The collected images were annotated in PASCAL VOC format. A total of 11,158&nbsp;instances in 2,440&nbsp;images are incorporated in the dataset.</p> <ul> <li>The UAV-PDD2023 dataset, captured by unmanned aerial vehicles (UAVs), provides a benchmark for road damage detection. It is highly useful for municipal authorities and road agencies to conduct low-cost road condition monitoring.&nbsp;</li> <li>Six types of road damages are labeled in the dataset: Longitudinal cracks (LC), Transverse cracks (TC), Alligator cracks (AC), Oblique cracks (OC), Repair (RP), and Potholes (PH).&nbsp;</li> <li>Researchers can use this dataset as a benchmark to evaluate the performance of different algorithms in addressing similar problems, such as image classification and object detection.&nbsp;</li> </ul> <p>&nbsp;</p>