Road Damage Detection Dataset

Key Features:

• Annotation Format: Annotations are provided in YOLO’s txt format, ensuring compatibility and ease of use across different YOLO versions.
• Diverse Scenarios: Images cover a spectrum of road damage types, including cracks, potholes, pavement erosion, and other common infrastructural issues.
• Quality Assurance: Each image is meticulously annotated to facilitate precise model training and validation.
• Scale and Variability: The dataset encompasses a range of environments, lighting conditions, and road types to enhance model robustness.

Additional Content:

• Null Image Handling: Approximately 5-10% of images in the dataset are designated as null, ensuring a balanced representation across categories and scenarios.
• Training Optimization: Optimized for deep learning frameworks, facilitating seamless integration into your road damage detection pipeline.
• Applications: Suitable for applications in autonomous driving, infrastructure monitoring, and smart city initiatives, empowering advanced computer vision solutions.