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Fire
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Computer Vision and Artificial Intelligence in Fire and Flame Detection
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Computer Vision and Artificial Intelligence in Fire and Flame Detection

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 20 July 2025 | Viewed by 2047

Special Issue Editors

Prof. Dr. Hongyong Yuan

E-Mail Website
Guest Editor
School of Safety Science, Tsinghua University, Beijing, China
Interests: urban fire safety; gas safety monitoring; predictive analytics; early warning systems; emergency management technologies
Dr. Xiaole Zhang

E-Mail Website
Guest Editor
School of Safety Science, Tsinghua University, Beijing, China
Interests: fire safety; atmospheric dispersion of CBRN pollutants; risk assessment; data assimilation technologies

Special Issue Information

Dear Colleagues,

The evolution of fire detection methodologies has reached a pivotal juncture with the integration of advanced computational technologies. Traditional systems, while essential, face challenges such as delayed response times and a high incidence of false alarms.

The recent advancements in computer vision and artificial intelligence (AI) have profoundly transformed fire detection paradigms. Innovative methods such as computer vision-based detection, laser-based systems, and AI-integrated gas sensors have markedly enhanced the performance and reliability of fire detection.

This Special Issue is dedicated to showcasing research that leverages sophisticated algorithms for the real-time analysis of visual and thermal data, distinguishing genuine fire signatures from non-threatening sources with exceptional precision.

The topics of interest for this Special Issue encompass, but are not limited to, the synergy between multimodal sensing technologies and AI, the development of adaptive algorithms that can interpret sensor data across diverse environmental conditions, and theoretical frameworks that underpin the mechanics of AI-driven detection systems. We also invite submissions that evaluate the scalability and effectiveness of these technologies in practical scenarios, providing insights into their integration and impact on fire safety protocols. 

Through this Special Issue, our objective is to explore cutting-edge developments and also push the boundaries of what is technically feasible in fire safety management. Contributions may include original research, in-depth case studies, or comprehensive reviews that collectively aspire to redefine the technological landscape of fire detection and fire safety.

Prof. Dr. Hongyong Yuan
Dr. Xiaole Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fire detection
  • flame recognition
  • computer vision
  • artificial intelligence for fire safety
  • thermal imaging
  • laser detection technology
  • gas sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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23 pages, 6015 KiB  
Article
FIRE-YOLOv8s: A Lightweight and Efficient Algorithm for Tunnel Fire Detection
by Lingyu Bu, Wenfeng Li, Hongmin Zhang, Hong Wang, Qianqian Tian and Yunteng Zhou
Fire 2025, 8(4), 125; https://doi.org/10.3390/fire8040125 - 24 Mar 2025
Viewed by 354
Abstract
To address the challenges of high algorithmic complexity and low accuracy in current fire detection algorithms for highway tunnel scenarios, this paper proposes a lightweight tunnel fire detection algorithm, FIRE-YOLOv8s. First, a novel feature extraction module, P-C2f, is designed using partial convolution (PConv). [...] Read more.
To address the challenges of high algorithmic complexity and low accuracy in current fire detection algorithms for highway tunnel scenarios, this paper proposes a lightweight tunnel fire detection algorithm, FIRE-YOLOv8s. First, a novel feature extraction module, P-C2f, is designed using partial convolution (PConv). By dynamically determining the convolution kernel’s range of action, the module significantly reduces the model’s computational load and parameter count. Additionally, the ADown module is introduced for downsampling, employing a lightweight and branching design to minimize computational requirements while preserving essential feature information. Secondly, the neck feature fusion network is redesigned using a lightweight CNN-based cross-scale fusion module (CCFF). This module leverages lightweight convolution operations to achieve efficient cross-scale feature fusion, further reducing model complexity and enhancing the fusion efficiency of multi-scale features. Finally, the dynamic head detection head is introduced, incorporating multiple self-attention mechanisms to better capture key information in complex scenes. This improvement enhances the model’s accuracy and robustness in detecting fire targets under challenging conditions. Experimental results on the self-constructed tunnel fire dataset demonstrate that, compared to the baseline model YOLOv8s, FIRE-YOLOv8s reduces the computational load by 47.2%, decreases the number of parameters by 52.2%, and reduces the model size to 50% of the original, while achieving a 4.8% improvement in accuracy and a 1.7% increase in mAP@0.5. Furthermore, deployment experiments on a tunnel emergency firefighting robot platform validate the algorithm’s practical applicability, confirming its effectiveness in real-world scenarios. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Fire and Flame Detection)
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20 pages, 11469 KiB  
Article
Evaluating Segmentation-Based Deep Learning Models for Real-Time Electric Vehicle Fire Detection
by Heejun Kwon, Sugi Choi, Wonmyung Woo and Haiyoung Jung
Fire 2025, 8(2), 66; https://doi.org/10.3390/fire8020066 - 6 Feb 2025
Viewed by 1172
Abstract
The rapid expansion of the electric vehicle (EV) market has raised significant safety concerns, particularly regarding fires caused by the thermal runaway of lithium-ion batteries. To address this issue, this study investigates the real-time fire detection performance of segmentation-based object detection models for [...] Read more.
The rapid expansion of the electric vehicle (EV) market has raised significant safety concerns, particularly regarding fires caused by the thermal runaway of lithium-ion batteries. To address this issue, this study investigates the real-time fire detection performance of segmentation-based object detection models for EVs. The evaluated models include YOLOv5-Seg, YOLOv8-Seg, YOLOv11-Seg, Mask R-CNN, and Cascade Mask R-CNN. Performance is analyzed using metrics such as precision, recall, F1-score, mAP50, and FPS. The experimental results reveal that the YOLO-based models outperform Mask R-CNN and Cascade Mask R-CNN across all evaluation metrics. In particular, YOLOv11-Seg demonstrates superior accuracy in delineating fire and smoke boundaries, achieving minimal false positives and high reliability under diverse fire scenarios. Additionally, its real-time processing speed of 136.99 FPS validates its capability for rapid detection and response, even in complex fire environments. Conversely, Mask R-CNN and Cascade Mask R-CNN exhibit suboptimal performance in terms of precision, recall, and FPS, limiting their applicability to real-time fire detection systems. This study establishes YOLO-based segmentation models, particularly the advanced YOLOv11-Seg, as highly effective EV fire detection and response systems. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Fire and Flame Detection)
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