Fire Detection System using YOLOv5 and IoT Integration for Real-Time Alerts in Safety Applications

Authors

  • Ridza Azri Ramlee Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Muhd Ikhwan Rozaidi Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Mardzulliana Zulkifli Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Mohamad Hairi Osman Faculty of Engineering Technology (FTK), Universiti Tun Hussein Onn, Pagoh, Johor, 84600, Malaysia
  • Aminuddin Ahmad Kayani PPK Technology Sdn. Bhd.Wisma PPK, Lot 2354, Jalan Sungai Putat, 75350 Ayer Keroh, Melaka, Malaysia
  • Ahmad Shukri Fazil Rahman Fakulti Teknologi Kejuruteraan Elektrik (FTKE), Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Muhammad Ilhamdi Rusydi Fakultas Teknik, Universitas Andalas, Kampus Limau Manih, Padang, 25163, Indonesia

DOI:

https://doi.org/10.37934/arfmts.128.1.224235

Keywords:

Fire detection, object detection, alert system, YOLOv5, deep learning

Abstract

The rising threat of heat-related and fire incidents underscores the urgent need for advanced thermal and fire detection systems to ensure timely and accurate responses. This report presents a smart fire detection project utilizing the YOLOv5 deep learning model. The project aims to design an early fire detection system with real-time capabilities. The proposed system implements a convolutional neural network (CNN) and the YOLOv5 real-time object identification system, enhancing fire detection through anchor box optimization. In the incident of a fire, the system sends an alert to the user’s Telegram app bot via the Internet of Things (IoT), assisting in taking necessary precautions. The project demonstrates notable efficiency in fire detection and alerting capabilities, with system evaluation metrics showing an F1 score of 95%, mAP@50 of 97%, accuracy 96.3%, and a recall rate of 89%. These results underscore the system's reliability and precision. The project contributes significantly to Sustainable Development Goals (SDG), goal 9 for industry, innovation, and infrastructure, and 11 for sustainable cities and communities highlighting its potential to enhance fire safety measures in various settings.

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Author Biographies

Ridza Azri Ramlee, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

ridza@utem.edu.my

Muhd Ikhwan Rozaidi, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

muhammad.ikhwan001@gmail.com

Mardzulliana Zulkifli, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

p022210007@student.utem.edu.my

Mohamad Hairi Osman, Faculty of Engineering Technology (FTK), Universiti Tun Hussein Onn, Pagoh, Johor, 84600, Malaysia

mhairi@uthm.edu.my

Aminuddin Ahmad Kayani, PPK Technology Sdn. Bhd.Wisma PPK, Lot 2354, Jalan Sungai Putat, 75350 Ayer Keroh, Melaka, Malaysia

aminkayani@gmail.com

Ahmad Shukri Fazil Rahman, Fakulti Teknologi Kejuruteraan Elektrik (FTKE), Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia

ahmadshukri@unimap.edu.my

Muhammad Ilhamdi Rusydi, Fakultas Teknik, Universitas Andalas, Kampus Limau Manih, Padang, 25163, Indonesia

rusydi@eng.unand.ac.id

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Published

2025-03-10

How to Cite

Ramlee, . R. A. ., Rozaidi, M. I., Zulkifli, M. ., Osman, M. H. ., Ahmad Kayani, A. ., Fazil Rahman, A. S. ., & Rusydi, M. I. . (2025). Fire Detection System using YOLOv5 and IoT Integration for Real-Time Alerts in Safety Applications. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 128(1), 224–235. https://doi.org/10.37934/arfmts.128.1.224235

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