DrBimmer/comprehensive-car-damage

Car Front and Rear Damage Detection Dataset

Dataset Summary

This dataset is designed for training and evaluating machine learning models for car damage detection, specifically focusing on front and rear vehicle damages.

It includes high-quality labeled images categorized into six distinct classes:

• R_Normal: Rear view of undamaged cars
• R_Crushed: Rear view of cars with crushed damage
• R_Breakage: Rear view of cars with visible breakage
• F_Normal: Front view of undamaged cars
• F_Crushed: Front view of cars with crushed damage
• F_Breakage: Front view of cars with visible breakage

Use Cases

With this dataset, researchers and developers can build AI-powered solutions for:

• Automated vehicle inspection systems
• Insurance claim assessment tools
• Road safety and damage analytics
• Training vision models for automotive applications

The clear classification structure enables models to effectively distinguish between normal, crushed, and broken conditions in front and rear views.

Dataset Structure

Each image is stored in a directory named after its class label. The dataset is balanced across the six categories and includes metadata for each image if needed (e.g., angle, lighting conditions).

Example Labels

Label	Description
R_Normal	Rear view of undamaged car
R_Crushed	Rear view, visibly crushed
R_Breakage	Rear view, broken parts visible
F_Normal	Front view of undamaged car
F_Crushed	Front view, visibly crushed
F_Breakage	Front view, broken parts visible