Vehicle Damage Detection System
Building an AI-assisted vehicle inspection system that combines computer vision with structured decision workflows to support scalable insurance assessment processes.
Why This Project?
Vehicle damage assessment is often manual, time-consuming, and dependent on human inspection. These processes can slow down claim handling and introduce inconsistencies across insurance workflows.
This project explores how AI can automate parts of this process and create a more scalable and data-driven approach to vehicle assessment.
Project Vision
Rather than building a standalone image classifier, the objective is to develop an intelligent inspection pipeline that gradually moves from image understanding toward decision support.
The system is designed to evolve over time while remaining modular and easy to extend.
Pipeline Architecture
- Detection → Identify damage types and their locations
- Assessment → Interpret severity using detected information
- Risk Estimation → Map severity to basic cost or risk levels
Engineering Approach
The project follows a modular architecture where each component can evolve independently without affecting the overall workflow.
- Independent detection, assessment, and risk estimation layers
- API-first design for future integrations
- Extensible architecture for additional AI capabilities
Future Roadmap
- Improve multi damage detection performance
- Build structured severity scoring systems
- Enhance risk estimation workflows
- Generate AI-assisted inspection summaries