🔥 Fire & Smoke Detection System

A modular detection system designed for real-time fire and smoke detection with a scalable architecture that can be extended to other detection tasks such as PPE detection, human detection, and more.

📋 Table of Contents

• Overview
• Features
• Tech Stack
• System Architecture
• Installation
• Usage
• API Documentation
• Database Schema
• Notification System
• Video Sources
• Environment Configuration
• License

🔍 Overview

This project is a comprehensive solution for a fire/smoke detection system that integrates with pretrained AI models to provide real-time detection alerts. The system is designed with modularity in mind, allowing for easy extension to other detection tasks in the future.

The dashboard provides a user-friendly interface for monitoring multiple camera feeds, configuring detection parameters, managing camera regions and sub-regions, and viewing detection logs and analytics.

✨ Features

🖥️ Dashboard

• Real-time monitoring of multiple camera feeds
• Intuitive navigation between different system modules
• Responsive design for desktop and mobile devices

📹 Camera Management

• Add, edit, and delete camera configurations
• Configure RTSP URLs for video streams
• Organize cameras by regions and sub-regions
• Toggle camera status (Active/Inactive)
• Assign access levels to control camera visibility

🗺️ Area Management

• Create and manage regions and sub-regions
• Hierarchical organization of physical spaces
• Associate cameras with specific areas for better organization
• Simplified navigation through camera feeds by location

🚨 Detection Alerts

• Real-time fire and smoke detection alerts
• Configurable alert thresholds
• Visual and optional audio notifications

📊 Analytics & Reporting

• Historical detection data visualization
• Exportable reports in multiple formats
• Customizable date range filtering

👥 User Management

• Role-based access control
• User activity logging
• Secure authentication system

⚙️ System Configuration

• Adjustable detection sensitivity
• Notification preferences
• System performance settings

🛠️ Tech Stack

Frontend

• React: Core framework for building the user interface
• React Router: For navigation and routing
• React Bootstrap: UI component library
• React Icons: For iconography

Backend

• Flask: Python web framework for the API
• MySQL Connector: For database interactions
• JWT: For secure authentication
• OpenCV: For video feed processing and frame extraction
• yt-dlp: For YouTube video stream extraction

Database

• MySQL: For storing user data, camera configurations, regions, and system settings

AI/ML

• Ultralytics YOLO: For object detection and fire/smoke identification
• OpenCV: For image processing and computer vision tasks
• Custom pretrained models: For fire and smoke detection

🏗️ System Architecture

The system follows a modular architecture with the following components:

1. Frontend Module: React-based dashboard for user interaction
2. Backend Module: Flask API with Blueprint-based organization
3. Detection Module: Handles integration with AI models
4. Database Module: Manages data storage and retrieval
5. Authentication Module: Handles user authentication and authorization
6. Notification Module: Manages alerts and notifications to administrators

This modular approach allows for:

• Independent development and testing of components
• Easy replacement or upgrade of individual modules
• Scalability to handle additional detection tasks or features

📥 Installation

Prerequisites

• Node.js (v14 or higher)
• npm (v6 or higher)
• Python (v3.8 or higher)
• MySQL (v8.0 or higher)

Backend Setup

1. Clone the repository:

git clone https://github.com/your-organization/fire-smoke-detection.git
cd fire-smoke-detection

2. Set up Python virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r backend/requirements.txt

3. Configure environment variables:

# Edit backend/.env file with your database credentials
MYSQL_HOST=localhost
MYSQL_USER=your_mysql_username
MYSQL_PASSWORD=your_mysql_password
MYSQL_DATABASE=asadel_db
JWT_SECRET_KEY=your_secret_key
EMAIL_APP_PASSWORD=your_email_app_password

4. Initialize the database:

python backend/init_db.py

5. Start the backend server:

python backend/app.py

Frontend Setup

1. Install dependencies:

npm install

2. Start the development server:

npm start

🚀 Usage

Accessing the Dashboard

Navigate to http://localhost:3000 in your web browser to access the dashboard.

Adding Cameras

1. Go to the Camera Management section
2. Click "Add Camera"
3. Enter the camera details including RTSP URL
4. Select a region and sub-region from the dropdown menus
5. Set the access level and description
6. Save the configuration

Managing Areas

1. Go to the Area Management section
2. Use the interface to add new regions
3. For each region, add relevant sub-regions
4. Existing regions and sub-regions can be edited or deleted

Configuring Detection Settings

1. Go to the Settings section
2. Adjust detection sensitivity
3. Configure notification preferences
4. Save changes

Viewing Detection Logs

1. Go to the Reports & Analytics section
2. Select the desired date range
3. View detection events and export reports as needed

📚 API Documentation

The system exposes the following API endpoints:

Authentication

• POST /api/login: User login

Users

• GET /api/users: List all users (paginated)
• GET /api/users/:id: Get user details
• POST /api/users: Create a new user
• PUT /api/users/:id: Update user details
• DELETE /api/users/:id: Delete a user

Cameras

• GET /api/cameras: List all cameras (paginated)
• GET /api/cameras/:id: Get camera details
• POST /api/cameras: Add a new camera
• PUT /api/cameras/:id: Update camera details
• PUT /api/cameras/:id/status: Update camera status
• DELETE /api/cameras/:id: Delete a camera

Regions & Sub-regions

• GET /api/regions: Get all regions with their sub-regions
• GET /api/regions/:id: Get details of a specific region
• GET /api/regions-list: Get a simplified list of regions
• POST /api/regions: Create a new region
• PUT /api/regions/:id: Update a region
• DELETE /api/regions/:id: Delete a region
• POST /api/sub-regions: Add a sub-region to a region
• PUT /api/sub-regions/:id: Update a sub-region
• DELETE /api/sub-regions/:id: Delete a sub-region

💾 Database Schema

The database consists of the following main tables:

Users

• id: Primary key
• username: User's login name
• password_hash: Hashed password
• role: User role (admin, operator, viewer)
• created_at: Account creation timestamp
• email: Email address for notifications

Cameras

• id: Primary key
• name: Camera name
• rtsp_url: RTSP URL for the video stream
• region: Foreign key to regions table
• sub_region: Foreign key to sub_regions table
• description: Camera description
• access_level: Access level required to view this camera
• status: Camera status (Active, Inactive)
• created_at: Creation timestamp

Regions

• id: Primary key
• name: Region name
• description: Optional description
• created_at: Creation timestamp

Sub_Regions

• id: Primary key
• name: Sub-region name
• region_id: Foreign key to regions table
• description: Optional description
• created_at: Creation timestamp

Detection_Logs

• id: Primary key
• camera_id: Foreign key to cameras table
• region_id: Foreign key to regions table
• sub_region_id: Foreign key to sub_regions table
• detection_type: Type of detection (fire, smoke)
• confidence: Detection confidence level
• timestamp: When the detection occurred
• image_path: Path to saved screenshot of detection

🔄 Extending the System

The system is designed to be easily extended to other detection tasks:

• Add new detection models to the frontend
• Create new database tables and API endpoints for new features
• Implement new UI components for additional functionality

📧 Notification System

The system includes a robust email notification system to alert administrators when fire or smoke is detected:

Email Notifications

• Automatic email alerts to all admin users when fire/smoke is detected
• Detailed information including camera name, region, sub-region, and timestamp
• Intelligent deduplication to prevent alert fatigue
• Configurable thresholds for notification triggering

Setup Requirements

• Gmail account for sending alert emails
• App password for secure authentication (2FA required)
• Admin user emails stored in database

Configuration

Email notification system is configured through the .env file with the following parameters:

EMAIL_APP_PASSWORD=your_app_password

The system uses a dedicated Gmail account ([email protected]) to send notifications.

📹 Video Sources

The system supports multiple video sources for detection:

Supported Video Inputs

• RTSP Streams: Connect to IP cameras or security DVR/NVR systems
• YouTube Links: Process live streams or uploaded videos from YouTube
• Local Files: Analyze video files stored on the server
• CCTV Cameras: Connect to existing CCTV infrastructure

YouTube Integration

YouTube links can be directly entered as camera sources. The system will:

1. Extract the video stream using yt-dlp
2. Process frames through the detection model
3. Display the results on the dashboard

URL Format Examples

• RTSP: rtsp://username:[email protected]:554/stream1
• YouTube: https://www.youtube.com/watch?v=VIDEO_ID
• Local File: /path/to/local/video.mp4

⚙️ Environment Configuration

The application uses environment variables for configuration. Create a .env file in the project root:

# Database configuration
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=your_password
DB_NAME=asadel_db

# JWT secret key for authentication
JWT_SECRET_KEY=your-secret-key

# Email credentials
EMAIL_APP_PASSWORD=your_app_password

# Server configuration
PORT=5000
DEBUG=True

📄 License

This project is licensed under the MIT License