EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire Smoke Detection

V. C. Ranganayaki, Javid J., Jaideep Reddy M.

2025

Abstract

Edge Fire Smoke is the lightweight CNN model optimized toward real-time fire and smoke detection in video streams and especially toward edge computing. The system responds to the surging demand for effective solutions in fire prevention, industrial safety, urban fire monitoring, and forest fire management. Unlike the traditional solutions of reliance on centralized processing, Edge Fire Smoke exploits its lightweight architecture to function easily on devices such as surveillance cameras and drones, as well as IoT devices, without letting the latency on the fire and smoke pattern detection reduce. Besides, the model has also been trained in large, heterogeneous datasets ensuring robust performance against changing environmental conditions. It comes with adjustable sensitivity levels, so it can be configured to the specific application and operational requirement. Real- time alerting mechanisms are integrated so that users or alarms can be notified right away upon detection. Comprehensive logging capabilities enable recording of detection events for further analysis or audits. A user- friendly interface makes it possible to monitor and configure a system with minimal technical complexity, thereby making the technology available to users without much technical know-how. Edge Fire Smoke is cost- effective, scalable, and dependable proactive fire management. The deployment of this technology in edge environments reduces dependence on cloud infrastructure, thereby lowering costs while improving response times. The new system plays a great role in safeguarding lives, infrastructural facilities, and the environment against any fire risks.

Paper Citation

in Harvard Style

Ranganayaki V., J. J. and M. J. (2025). EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire Smoke Detection. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 130-137. DOI: 10.5220/0013909200004919

in Bibtex Style

@conference{icrdicct`2525,
author={V. Ranganayaki and Javid J. and Jaideep M.},
title={EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire Smoke Detection},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={130-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013909200004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire Smoke Detection
SN - 978-989-758-777-1
AU - Ranganayaki V.
AU - J. J.
AU - M. J.
PY - 2025
SP - 130
EP - 137
DO - 10.5220/0013909200004919
PB - SciTePress