🚦 Traffic Management System with AI Detection & Blockchain Auditing

A full-stack Traffic Management System built with Flask, React, MySQL, Web3, and AI. The system manages vehicles, violations, and payments while incorporating an AI-powered helmet violation detector, interactive 3D IoT simulations, and an immutable blockchain audit trail to ensure data integrity.

⭐ Key Features

🔐 Authentication

• Login & registration
• Password hashing (bcrypt)
• JWT-based authorization
• Admin/user role support

🚗 Vehicle Management

• Add, view, search, and delete vehicles
• Auto-registration of unknown vehicles detected through AI or IoT
• Admin-only delete access

⚠️ Violation Management

• Add violations manually
• View violations per vehicle
• Check violation details
• Update fine status after payment
• Live Geographic Map: Real-time plotting of incidents across Rajasthan using Leaflet and OpenStreetMap.

🤖 AI Violation Detection

Uses YOLOv8 + EasyOCR to automatically detect:

✔ Helmet Status

• Detects With Helmet / Without Helmet
• Creates bounding boxes + labels on the image

✔ License Plate OCR

• Detects license plate
• Extracts plate number using OCR
• Auto-registers vehicle if not found
• Auto-inserts violation into MySQL
• Saves annotated evidence image

API: POST /autodetect

⛓️ Immutable Blockchain Audit Trail

Ensures database integrity by running alongside the traditional MySQL database:

• Generates a unique SHA-256 cryptographic hash of every violation payload.
• Automatically anchors the hash to a local Ethereum network via a Solidity smart contract (ViolationAuditV2).
• Verification Engine: Allows admins to cross-reference MySQL records against the immutable blockchain ledger to instantly detect database tampering.

📡 3D IoT Radar Gun Simulation

An interactive WebGL digital twin for testing IoT ingestion:

• 3D Environment: Built with React Three Fiber, featuring a controllable vehicle and dynamic lighting.
• Automated Triggers: Automatically fires secure API requests when the simulated vehicle exceeds 90 km/h.
• Validates API key, auto-registers unknown vehicles, and logs the "Speeding" violation.

API: POST /iot/report-speeding

📊 Dashboard Stats

Provides summary stats:

• Total vehicles
• Total violations
• Total fines paid/unpaid
• Most common violation

👤 User Profile Stats

• Username
• Role
• Violations reported
• Vehicles registered

🧠 Machine Learning Models

• YOLOv8 helmet detection → Weights/best.pt
• YOLO license plate detection → license_plate_detector.pt
• OCR → EasyOCR
• OpenCV for image processing

🗄 Database & Smart Contracts

MySQL Tables:

• loginuser
• Vehicle
• Violations
• Fines

Ethereum Smart Contracts:

• ViolationAuditV2.sol (Maintains state lifecycle and evidence hashes on the blockchain)

📸 Screenshots

🔐 Login Page

📊 Dashboard

🚗 Vehicles Page

⚠️ Violations Page

🤖 Auto-Detection

👤 My Profile

🛠️ Tech Stack

• Backend: Python, Flask, MySQL, JWT, bcrypt
• AI & Computer Vision: PyTorch, YOLOv8, EasyOCR, OpenCV, NumPy
• Blockchain: Solidity, Ganache, Web3.py
• Frontend: React.js, Axios, React Router, React Three Fiber (3D WebGL), React-Leaflet (Maps)

▶️ How to Run the Project

1. Install Python dependencies:

pip install -r requirements.txt

2. Create MySQL database TrafficDB and import TrafficDB.sql.
3. Start your local Ganache Ethereum workspace on port 8545.
4. Run backend:

python app.py

4. Run frontend:

cd traffic-violation-frontend
npm install
npm start

📦 Folder Structure

Traffic-Management-System-With-Ai/
├── app.py
├── iot_radar_gun.py
├── TrafficDB.sql
├── Weights/
├── evidence_uploads/
├── blockchain/
├── audit/
├── traffic-violation-frontend/
└── screenshots/

📄 License

Academic & educational use.

🙌 Acknowledgments

YOLOv8, EasyOCR, OpenCV, Flask, React, and Web3 communities.