UAV Dataset for Automated Road Surface Degradation Detection in Real-World Conditions
Description
The RoadAnomaly-YOLO Dataset is a curated collection of 11,024 high-resolution RGB images (640×640 px) designed for road surface anomaly detection using deep learning. The images were captured using UAVs (drones) and ground-based cameras across diverse urban and rural environments, under varying lighting and weather conditions. Each image is annotated using YOLO-format bounding boxes, enabling immediate integration with YOLOv5/YOLOv8/Ultralytics training pipelines etc. Anomalies may appear once or multiple times per image. Dataset Statistics ⦁ Total images: 11,024 ⦁ Split: ⦁ Train: 8,306 images ⦁ Validation: 2,012 images ⦁ Test: 706 images ⦁ Image resolution: 640 × 640 px (uniform) ⦁ Image type: RGB (JPEG/PNG) ⦁ Annotation format: YOLO TXT (class_id x_center y_center width height) Classes Included The dataset covers eight road anomaly categories, widely used in pavement inspection research: ⦁ Alligator Cracking ⦁ Longitudinal Crack ⦁ Transverse Crack ⦁ Rutting ⦁ Pothole ⦁ Stripping ⦁ Raveling ⦁ Bleeding These anomalies were manually annotated using bounding boxes. Purpose and Applications The dataset is intended to support: ⦁ UAV-based road condition monitoring ⦁ Intelligent transportation systems ⦁ Automated maintenance planning ⦁ Object detection model training (YOLO, Faster R-CNN, RetinaNet, etc.) ⦁ Benchmarking and academic research It enables both training and real-time inference for detecting pavement distresses in real-world environments.
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Institutions
- Universite Cadi Ayyad