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AI-powered e-Challan system using YOLOv8/9, PaddleOCR & Real-ESRGAN to detect vehicles, read number plates (even blurry), and classify violations. Built with FastAPI, PostgreSQL, Celery & React. Auto-calculates fines (double for repeat, 1.25Γ— for new) and generates PDFs.

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🚦 Smart e-Challan System (WIP)

Build Status License Status Made with FastAPI Frontend Database AI/ML OCR

⚑ AI-powered traffic violation detection & digital challan generation system
Bringing Computer Vision + Automation + Smart Enforcement together for the future of safe and smart cities 🌍
🚧 This project is a Work-in-Progress (WIP) and is being actively developed.


✨ Overview

The Smart e-Challan System is an AI-driven solution that detects traffic violations, extracts vehicle number plates using OCR, and generates e-Challans automatically.
It eliminates manual effort, improves transparency, and enables real-time, data-driven enforcement.


πŸ”‘ Features (Planned & In-Progress)

  • 🏍️ Vehicle & Rider Detection β€” YOLOv8 / YOLOv9
  • πŸŽ₯ Multi-Object Tracking β€” ByteTrack
  • πŸ” ANPR (Automatic Number Plate Recognition) β€” EasyOCR / PaddleOCR + GFPGAN / Real-ESRGAN
  • 🚦 Violation Detection β€” Helmetless riders, red-light jumping, overspeeding, more
  • πŸ’³ Penalty Escalation Logic β€”
    • 2Γ— for repeat violations
    • 1.25Γ— for new types
  • πŸ“§ Email Notifications β€” Auto-send challan proof snapshots
  • πŸ—„οΈ Database Integration β€” PostgreSQL (via Supabase)
  • πŸ” Secure APIs β€” FastAPI + JWT Authentication
  • 🌐 Frontend Dashboard β€” Built in Next.js + Tailwind CSS

πŸ—οΈ Architecture

Frontend (Next.js + Tailwind)
         ↓
   FastAPI Backend (Microservices)
         ↓
   β”œβ”€β”€ Detection Service (YOLO + ByteTrack)
   β”œβ”€β”€ ANPR Service (OCR + Image Enhancer)
   β”œβ”€β”€ Violation Service (Rules + Escalation)
   β”œβ”€β”€ Challan Service (Generation + Emailer)
         ↓
   PostgreSQL (Supabase Cloud DB)
🚧 Current Status
βœ… Backend structure built (FastAPI + Supabase)

βœ… Frontend setup (Next.js + Tailwind)

βš™οΈ Detection + OCR modules under testing

πŸ”œ Upcoming:

Live camera streaming

Public dashboard

Docker + Kubernetes deployment

⚑ Tech Stack
Layer	Technologies
Frontend	Next.js, Tailwind CSS
Backend	FastAPI, SQLAlchemy, JWT
Database	Supabase (PostgreSQL)
AI / ML	YOLOv8/YOLOv9, ByteTrack
OCR	EasyOCR, PaddleOCR
Image Enhancement	GFPGAN, Real-ESRGAN
DevOps (Planned)	Docker, Kubernetes

πŸš€ Quick Start

# Clone the repository
git clone https://github.com/your-username/smart-echallan.git
cd smart-echallan

# Backend
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload

# Frontend
cd frontend
npm install
npm run dev
🀝 Contributing
We’re still building this! Contributions are welcome ❀️

Fork the repository

Create a new branch

Submit a PR

πŸ“Œ Disclaimer
⚠️ Prototype β€” Work in Progress.
This is an educational and research project. Not for real-world enforcement use yet.

🌟 Vision
A future where traffic violations are detected instantly, challans are generated digitally, and enforcement becomes transparent, automated, and smart πŸš¦πŸ’‘
That’s the Smart e-Challan vision!

About

AI-powered e-Challan system using YOLOv8/9, PaddleOCR & Real-ESRGAN to detect vehicles, read number plates (even blurry), and classify violations. Built with FastAPI, PostgreSQL, Celery & React. Auto-calculates fines (double for repeat, 1.25Γ— for new) and generates PDFs.

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