🚗 CarScanAI

CarScanAI is a Graduation Project designed to detect car damage using AI and provide detailed analysis including damage severity, estimated cost, and repair recommendations.

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📌 Overview

CarScanAI is a smart system that allows users to upload car images and receive an AI-powered analysis of the damage. The system evaluates the severity of the damage and estimates repair costs, while also recommending nearby repair centers.

The project is built using a clean Onion Architecture and integrates a Python AI model with an ASP.NET Core backend.

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🚀 Features

• 🔍 AI Damage Detection

  • Analyze car images using AI model
  • Detect damage severity (Minor / Moderate / Severe)
  • Provide confidence score

• 💰 Cost Estimation

  • Calculate repair cost based on damage severity

• 🧾 User Reports

  • View all previous analyses (reports)
  • Track user cars and history

• 🚗 Car Management

  • Add and manage user cars
  • View number of owned cars

• 🛠 Repair Centers Recommendation

  • Suggest repair centers based on car brand
  • Provide location, contact, and details

• 🌐 Full Stack System

  • Backend: ASP.NET Core Web API
  • AI: Python (Flask + TensorFlow)
  • Frontend Ready: Designed to work with Web & Mobile apps

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🏗 Architecture

The project follows Onion Architecture:

• Domain Layer → Core entities & business rules
• Application Layer (BLL) → Services & business logic
• Infrastructure Layer (DAL) → Database & repositories
• Presentation Layer (API) → Controllers & endpoints

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🤖 AI Integration

• Built using Python & Flask

• Model predicts:

  • Damage type
  • Severity
  • Confidence score

• Connected to .NET backend using HTTP requests

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🛠 Technologies Used

• ASP.NET Core Web API
• Entity Framework Core
• SQL Server
• AutoMapper
• Onion Architecture
• Python
• Flask
• TensorFlow / Keras
• REST APIs

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📷 How It Works

1. User uploads car image

2. Image is stored on server

3. Sent to AI model for prediction

4. AI returns:

   • Severity
   • Confidence

5. System calculates cost

6. Results + repair centers are returned to user

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📊 Example Output

{
  "severity": "moderate",
  "estimatedCost": 7000,
  "confidence": 0.72
}

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📱 Frontend & Mobile

The system is designed to be integrated with:

• Web frontend (React / Angular)
• Mobile apps (Flutter)

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🎯 Project Goal

To build a smart and practical system that helps users:

• Understand car damage easily
• Estimate repair costs
• Find suitable repair centers

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👨‍💻 Author

Ahmed Mahmoud

• GitHub: https://github.com/Ahmedyjnj
• LinkedIn: https://www.linkedin.com/in/ahmed-mahmoud-b4a400225/

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⭐ Notes

This project is part of a Graduation Project and demonstrates real-world integration between AI and backend systems using clean architecture principles.

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