🦅 FALCON

Automatic Crime Alert and Reporting System

Real-Time Weapon Detection using YOLOv8 • Flask • OpenCV • Raspberry Pi • Twilio

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📑 Table of Contents

• Overview
• Problem Statement
• Solution
• Features
• System Architecture
• Technology Stack
• Project Structure
• Screenshots
• Installation
• Configuration
• Running the Project
• Workflow
• Model Performance
• Security
• Limitations
• Future Roadmap
• Contributing
• License
• Author

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📖 Overview

FALCON is an AI-powered surveillance and emergency response platform that performs real-time weapon detection using a custom-trained YOLOv8 model. The system automatically identifies potential threats from live video streams, stores incident data, captures evidence, updates an interactive dashboard, and immediately notifies authorities through Twilio SMS alerts.

Designed as an end-to-end solution, FALCON combines computer vision, backend engineering, edge AI, and web technologies into a complete security platform suitable for educational demonstrations, research, and smart surveillance prototypes.

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❗ Problem Statement

Traditional CCTV systems continuously record video but rely on human operators to identify threats, often delaying emergency response.

FALCON addresses this challenge by automatically detecting weapons in live video streams and triggering immediate alerts while maintaining searchable incident records.

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💡 Solution

The system continuously monitors live video feeds using YOLOv8.

When a weapon is detected:

• Detects the object
• Captures an image
• Records the video clip
• Stores incident details
• Sends SMS alerts
• Updates the dashboard
• Generates detection reports

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✨ Features

🤖 AI Detection

• Real-time weapon detection
• Custom YOLOv8 model
• Live RTSP/IP Camera support
• USB Camera support
• Confidence-based detection

🚨 Alert System

• Twilio SMS alerts
• Detection history
• Alert logs
• Timestamp recording

📊 Dashboard

• Incident analytics
• Active alerts
• Detection statistics
• Weapon gallery
• Interactive reports

👤 Authentication

• User Registration
• Secure Login
• Session Management

📁 Reports

• Detection reports
• Captured Images
• Video Evidence
• Historical records

🖥 Edge AI

• Raspberry Pi compatible
• Lightweight deployment
• Local inference support

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🏗 System Architecture

             Live Camera
                  │
                  ▼
          YOLOv8 Detection
                  │
        ┌─────────┴──────────┐
        ▼                    ▼
 Save Image & Video     Twilio SMS Alert
        │                    │
        ▼                    ▼
    SQLite Database      Emergency Contact
              │
              ▼
      Flask Web Dashboard
              │
              ▼
     Reports & Analytics

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🛠 Technology Stack

Layer	Technology
Programming	Python
Backend	Flask
Frontend	HTML CSS JavaScript
Computer Vision	OpenCV
AI Model	YOLOv8
Database	SQLite
Alerts	Twilio API
Deployment	Raspberry Pi
Version Control	Git & GitHub

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📂 Project Structure

FALCON/
│
├── screenshots/
├── static/
├── templates/
├── train_detect_weapons/
│
├── app.py
├── yolo_detect.py
├── live_rtsp_detector.py
├── train_yolo.py
├── twilio_alert.py
├── database.py
├── alert_db.py
│
├── requirements.txt
├── .gitignore
└── README.md

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Screenshots

Login

Register

Dashboard

Detection Report

Alert

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⚙ Installation

git clone https://github.com/Astik97/FALCON.git

cd FALCON

Create virtual environment

python -m venv venv

Windows

venv\Scripts\activate

Linux

source venv/bin/activate

Install dependencies

pip install -r requirements.txt

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🔐 Environment Variables

Create a .env file.

TWILIO_ACCOUNT_SID=

TWILIO_AUTH_TOKEN=

TWILIO_PHONE=

SECRET_KEY=

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▶ Running the Project

python app.py

Open

http://127.0.0.1:5000

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🔄 Workflow

Start Camera

↓

YOLOv8 Detection

↓

Weapon Detected

↓

Capture Evidence

↓

Store in Database

↓

SMS Alert

↓

Dashboard Update

↓

Generate Report

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📈 Model Performance

Metric	Value
Model	YOLOv8
Dataset	9,633 Images
Epochs	50
Precision	85%
Recall	75%
mAP@0.5	81%

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⚡ Performance Benchmark

Device	FPS	Latency
GPU	15–25	40–60 ms
CPU	5–10	120–200 ms
Raspberry Pi	1–3	400–800 ms

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🔒 Security

• Environment variables for secrets
• .gitignore excludes credentials
• Secure authentication
• Session management
• No API keys committed

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⚠ Limitations

• Reduced accuracy in poor lighting
• False positives possible
• Raspberry Pi has limited inference speed
• Stable network required for RTSP

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🗺 Future Roadmap

• Docker Support
• PostgreSQL Migration
• Multi-camera Monitoring
• Email Alerts
• Push Notifications
• Cloud Deployment
• ONNX/TensorRT Optimization
• Mobile Dashboard
• Face Recognition Module

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🤝 Contributing

Contributions, issues, and feature requests are welcome.

Fork the repository.

Create a feature branch.

Commit your changes.

Open a Pull Request.

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Astik Mohapatra

Backend Developer • AI Systems Developer • Computer Vision Enthusiast

📧 Email: astikm7007@gmail.com

🔗 LinkedIn

https://linkedin.com/in/astik-mohapatra

🐙 GitHub

https://github.com/Astik97

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⭐ Support

If you found this project helpful,

⭐ Star the repository

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Happy Coding 🚀