Raqabah: A Multi-Severity Car Accident Image Classification Dataset

This dataset was curated to support the development of AI-powered incident detection systems (such as the Raqabah platform). It is specifically structured for Image Classification tasks, aiming to categorize road scenes into different accident severity levels under various environmental conditions. The dataset combines both synthetic data and real-world CCTV footage to maximize model robustness.

Dataset Structure & Classes:
1. Normal: Standard traffic flow and safe driving environments.
2. Minor: Low-impact incidents, minor bumps, or scratches.
3. Moderate: Visible vehicle damage or multi-car scrapes.
4. Severe: High-impact crashes, including sub-scenarios for Flipped (overturned) and Burned (on fire) vehicles.

Environmental Conditions:
Images are captured or generated across multiple atmospheric and lighting variations (Day, Night, Fog, Rain, Dust, and Random backgrounds).