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Related Experiment Video

Updated: May 9, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Indoor fire and smoke detection based on optimized YOLOv5.

Md Shafak Shahriar Sozol¹, M Rubaiyat Hossain Mondal¹, Achmad Husni Thamrin²

¹Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

|April 29, 2025

Summary

A new hyperparameter-optimized YOLOv5 model significantly improves indoor fire and smoke detection accuracy. This advanced system offers real-time monitoring and reliable hazard notification, outperforming existing methods.

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Area of Science:

Computer Vision
Artificial Intelligence
Fire Safety Engineering

Background:

Traditional fire detection systems suffer from false alarms and slow responses.
Current deep learning object detectors lack accuracy and real-time tracking for dynamic indoor fire scenarios.

Purpose of the Study:

To develop an accurate and real-time fire and smoke detection model for indoor environments.
To address limitations of existing detection methods using advanced deep learning techniques.

Main Methods:

Developed a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model using a genetic algorithm.
Created a novel dataset of 5,000 indoor fire and smoke images.
Integrated YOLOv5 with DeepSORT for real-time object tracking and used Grad-CAM for model interpretability.

Main Results:

The HPO-YOLOv5 model achieved a mean average precision (mAP@0.5) of 92.1%, surpassing state-of-the-art models.
Demonstrated a 2.4% improvement over the baseline YOLOv5 model.
The system provides reliable real-time monitoring and accurate fire hazard notification.

Conclusions:

The HPO-YOLOv5 model offers a dependable and effective solution for indoor fire hazard detection.
This research lays the groundwork for future advancements in intelligent fire safety systems.
The study highlights the importance of optimized deep learning models and interpretable AI in critical safety applications.