Flare Guard: Real-Time Smoke & Fire Detection with YOLO11

📌 Table of Contents

Introduction
Video Demonstration
Dataset
Training Summary
Example Detections
Installation and Usage

Safeguard Lives with Smart Fire & Smoke Detection

Problem Description

Early fire detection is crucial for minimizing damage and saving lives. Traditional sensors often fail to detect fires quickly, leading to devastating consequences. This project leverages advanced deep learning to enable real-time fire and smoke detection.

Solution

Flare Guard is a cutting-edge real-time fire and smoke detection system using YOLOv11 for rapid identification in video streams. The system provides instant alerts via Telegram/WhatsApp and can operate in diverse environments.

Why Choose Flare Guard?

Real-Time Detection: Processes frames in <15ms on a GPU (~30-60 FPS)
Multi-Platform Alerts: Instant Telegram/WhatsApp notifications with detected images
High Accuracy: 80.6% Precision, 71.7% Recall, 77% mAP@50
Environmental Robustness: Operates in low-light, fog, and crowded areas
Scalable Deployment: Supports live webcam feeds and pre-recorded videos

Video Demonstration

🔥 Fire Detection System in Action

This video demonstrates the system detecting fire immediately upon occurrence.

Alerts are sent instantly to Telegram and WhatsApp, including an attached image.

Thanks to multithreading, the system continues running without interruption.

Dataset

The dataset consists of 9,463 annotated images, available on Roboflow. It includes diverse scenarios to enhance model robustness.

Split	Images	Annotations
Training	9,156	27,468
Validation	872	2,616
Test	435	1,305

# Download dataset via Roboflow
from roboflow import Roboflow
rf = Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("sayed-gamall").project("fire-smoke-detection-yolov11")
dataset = project.version(2).download("yolov11")

Training Summary

The model was trained using YOLOv11 on a dataset of fire and smoke images. Training stopped early due to no improvement over 20 epochs, with the best results observed at Epoch 92.

Final Validation Results

Metric	Value
Precision (P)	0.806
Recall (R)	0.717
mAP@50	0.770
mAP@50-95	0.492

Example Detections

Installation and Usage 🚀

Installation

    git clone https://github.com/sayedgamal99/Real-Time-Smoke-Fire-Detection-YOLO11
    cd Real-Time-Smoke-Fire-Detection-YOLO11

Install the required packages:

pip install ultralytics

Inference

To perform inference with the trained model on test images, run:

yolo detect predict model=models/best_nano_111.pt source=data/house.png conf=0.35 iou=0.1

To perform inference in real-time using a webcam:

yolo detect predict model=models/best_nano_111.pt source=0 conf=0.35 iou=0.1 show=True