Real‑Time‑Weapon‑Detection

Detect Threats Instantly, Secure with Confidence

Built with Flask, Python, NumPy, and Twilio, this application uses YOLOv8 to detect weapons live from camera feeds, logging each detection event and sending instant email/SMS alerts. :contentReference[oaicite:1]{index=1}

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Overview

Real‑Time‑Weapon‑Detection is a powerful security tool intended to identify weapons instantly through live camera input. Key features include:

• 🛡 Real‑Time Detection: Uses YOLOv8 to analyze live video streams with high accuracy.
• 📊 Detection Logging: Records events with metadata (timestamps, image snapshots, etc.) for review.
• 📢 Alerting System: Sends notifications via email and SMS, powered by Twilio integration.
• 🌐 Web Dashboard: Provides a responsive interface for viewing live feeds, detection history, and system configuration.
• ⚙ Seamless Setup: Uses a well-defined requirements.txt to ensure environment consistency.
• 🔍 Historical Data Review: Enables users to browse past detection events using the web dashboard UI.

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Getting Started

Prerequisites

• Python (3.8+ recommended)
• Pip package manager
• A webcam or IP camera for streaming input
• Twilio account with API credentials for alerts

Installation

git clone https://github.com/kushalpatel0265/Real-Time-Weapon-Detection.git
cd Real-Time-Weapon-Detection
pip install -r requirements.txt

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Configuration

1. YOLOv8 Model Place your pretrained YOLOv8 .pt model file in the designated folder (e.g. weights/).

2. Environment Variables / Config File Configure the following settings:

   • Twilio account SID, auth token, sender phone number
   • Email SMTP details (server, port, sender credentials, recipient list)
   • Camera stream URL or local webcam index

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Usage

To launch the application:

python app.py

Replace app.py with the actual entrypoint file name configured in your repository.

Once running, access the web dashboard (typically at http://localhost:5000) to view live camera stream, recent detections, and system settings.

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Features & Workflow

1. Live video input is fed into the YOLOv8 model for inference.
2. When a weapon is detected, a log entry is created in the database (e.g. SQLite or JSON).
3. A notification is immediately dispatched via Twilio (SMS) and/or email.
4. All past detections are accessible through a sleek web interface for analysis and verification.

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Dependencies & Technologies

• Flask — lightweight web server and dashboard framework
• NumPy — for numeric operations and image preprocessing
• Python — main programming language
• Twilio SDK — for SMS/email alerting
• YOLOv8 — object detection base model for weapon detection

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Contribution & Feedback

Contributions are welcome! If you’d like to:

• Improve detection accuracy
• Add support for different camera systems
• Enhance notification logic ...please open a GitHub issue or submit a pull request.

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License

Distributed under the MIT License. See the LICENSE file for full details.

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Acknowledgments

• Inspired by weapon detection projects utilizing YOLOv8 architecture and real-time alerting pipelines ([researchgate.net][1], [github.com][2], [arxiv.org][3], [scribd.com][4])
• Many thanks to the open-source communities behind Flask, Twilio, NumPy, and YOLOv8!

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Contact

For questions or business inquiries, please email kushalpatel0265@gmail.com or open an issue on GitHub.

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