Image-Based Fire Detection in Industrial Environments with YOLOv4

Otto Zell, Joel Pålsson, Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Felix Nilsson

2023

Abstract

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

Paper Citation

in Harvard Style

Zell O., Pålsson J., Hernandez-Diaz K., Alonso-Fernandez F. and Nilsson F. (2023). Image-Based Fire Detection in Industrial Environments with YOLOv4. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 379-386. DOI: 10.5220/0011689400003411

in Bibtex Style

@conference{icpram23,
author={Otto Zell and Joel Pålsson and Kevin Hernandez-Diaz and Fernando Alonso-Fernandez and Felix Nilsson},
title={Image-Based Fire Detection in Industrial Environments with YOLOv4},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={379-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011689400003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Image-Based Fire Detection in Industrial Environments with YOLOv4
SN - 978-989-758-626-2
AU - Zell O.
AU - Pålsson J.
AU - Hernandez-Diaz K.
AU - Alonso-Fernandez F.
AU - Nilsson F.
PY - 2023
SP - 379
EP - 386
DO - 10.5220/0011689400003411