VehicleVision AI - Real-Time Vehicle Detection & Traffic Analytics

VehicleVision AI is a premium, futuristic traffic analytics dashboard powered by YOLOv5 deep learning, Vercel serverless web hosting, and Supabase (PostgreSQL + Auth + Row Level Security).

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🌟 Key Features & Architecture

• High-Performance Architecture:
  • Frontend: Deployed globally on Vercel (React 19 + Tailwind CSS v4 Play CDN).
  • Authentication & Database: Powered by Supabase (PostgreSQL + JWT Sessions + Row Level Security).
  • AI Inference Engine: FastAPI Python service executing YOLOv5 real-time vehicle classification & video tracking.
• Supabase User Authentication: Registration and Login with encrypted passwords (never stored in plain text).
• Row Level Security (RLS): Each user's detection history logs are strictly isolated in PostgreSQL so users cannot view or modify other users' data.
• Real-Time Vehicle Detection: Processes vehicle class predictions (Cars, Motorcycles, Trucks, Buses) using YOLOv5.
• Live Stream Studio: Video stream viewer with play/pause controls, dynamic FPS counter, and stream presets.
• Traffic Density Calculator: Adaptive density status indicator (LOW, MEDIUM, HIGH) with congestion advisory metrics.
• Interactive Snapshots & SVG Analytics: Scrollable snapshot gallery with 1-click Lightbox modal inspect, distribution donuts, hourly trend line graphs, and volume bar charts.
• CSV Export & Log Storage: Clean, searchable detection database table with Export to CSV.

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🛠️ Technology Stack

• Frontend: Vercel, React 19 (ES Modules), Tailwind CSS v4, FontAwesome, SVG Charts
• Database & Auth: Supabase (PostgreSQL, Auth, RLS Policies)
• AI Engine: FastAPI (Python), PyTorch, OpenCV Headless, YOLOv5 (Ultralytics)

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⚡ Supabase Setup Instructions

1. Log in to Supabase Console and create a new project.
2. Go to SQL Editor in the left sidebar.
3. Open supabase_schema.sql from this repository, paste the entire SQL script, and click Run.
   • This creates the detection_logs table, profiles table, indexes, user creation trigger, and Row Level Security (RLS) policies.
4. Go to Project Settings -> API to retrieve your:
   • SUPABASE_URL (e.g., https://xyz.supabase.co)
   • SUPABASE_ANON_KEY (Public safe API key)

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🚀 Vercel Deployment Instructions

1. Push this repository to GitHub.
2. Log in to Vercel Dashboard and click Add New... -> Project.
3. Import your GitHub repository (Vehicle-detection).
4. Under Environment Variables, add:
   • SUPABASE_URL: Your Supabase Project URL
   • SUPABASE_ANON_KEY: Your Supabase Anon Key
   • DETECTION_BACKEND_URL: Your FastAPI detection service URL (e.g., https://vehicle-detection-backend.onrender.com)
5. Click Deploy. Vercel will automatically read vercel.json and deploy your application.

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💻 Local Testing & Setup

Step 1: Clone & Configure Environment

git clone https://github.com/Hemanth-08-RA/Vehicle-detection.git
cd Vehicle-detection
cp .env.example .env

Step 2: Set Up Virtual Environment & Dependencies

python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install -r requirements.txt

Step 3: Run Local Detection Engine

python main.py

The FastAPI backend will start on http://localhost:8000.

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📂 Project Architecture

├── main.py              # FastAPI backend engine (YOLOv5 & video streams)
├── requirements.txt     # Python dependency list
├── vercel.json          # Vercel deployment configuration & API rewrites
├── supabase_schema.sql  # Supabase PostgreSQL schema & RLS policies
├── .env.example         # Environment variables template
├── Procfile             # Render/PaaS backend execution process
├── render.yaml          # Render backend blueprint
├── runtime.txt          # Python runtime version
├── .gitignore           # Excludes local caches and weights
├── LICENSE              # Open-source MIT License
├── README.md            # Detailed documentation
├── image/               # Sample test images
├── video/               # Sample test traffic streams
└── static/              # Frontend Directory
    ├── index.html       # HTML entrypoint (Tailwind CSS v4 & Supabase SDK)
    └── app.js           # React app, Supabase Auth/DB, charts, UI

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.