RoadDamageVision: Annotated Dataset of Road Damage Images

Research Hypothesis and Dataset Description

This dataset was developed under the hypothesis that deep learning and computer vision techniques can be effectively used to detect and classify various types of road surface defects from drone-based visual data. By providing annotated images captured in different countries and environments using aerial platforms, the RoadDamageVision dataset supports the development of scalable, low-cost, and automated road monitoring systems.

Data Overview and Collection Process

The dataset consists of annotated images of road surfaces showing visible damage. All images were captured using drones in both China and Spain, enabling aerial perspectives that provide a wide field of view and are suitable for large-scale infrastructure inspection. The imagery includes a variety of road environments, including urban, suburban, and rural settings.

Each image was manually annotated with bounding boxes and class labels for six types of road defects:

D00: Longitudinal cracks

D10: Transverse cracks

D20: Alligator cracks

D40: Potholes

Repair: Previously repaired surfaces

Block Crack: Block-type cracks

The annotations were standardized to facilitate training and evaluation of object detection models.

The dataset includes a total of 7,647 labeled instances of road damage. The most common defect is D40 (potholes), with 3,566 instances, primarily found in the Spanish dataset. In contrast, D10 and D20 appear only in the Chinese imagery. This difference in class distribution provides an opportunity to explore domain adaptation and class imbalance mitigation in model training.