Road Damage Dataset: Potholes, Cracks and Manholes

This dataset provides real-world road surface images collected in urban and rural areas around Rome and Sacrofano (Italy) using two low-cost devices: a GoPro HERO7 mounted on a moving vehicle and a Samsung Galaxy A14 smartphone for stationary captures. All images are 640x360.It aims to support realistic, device-independent road damage detection. Each image is annotated in YOLO format with three damage classes: Class 0 – Pothole Class 1 – Crack Class 2 – Manhole Unlike most existing datasets that omit or misclassify manholes, this dataset explicitly includes them to improve model robustness and reduce false positives in real-world scenarios.The collection ensures diversity in lighting, perspective, and pavement texture, enabling reliable training and cross-device generalization for AI-based road inspection systems.