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Innovations in Deep Learning and Computer Vision for Early Fire and Smoke Detection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 1526

Editors

Dr. Moez Bouchouicha

E-Mail Website
Guest Editor
LIS-CNRS, Université de Toulon, Aix-Marseille University, 83041 Toulon, France
Interests: computer vision; AI; deep learning; medical imaging; fire detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eric Moreau

E-Mail Website
Guest Editor
LIS-CNRS, Université de Toulon, Aix-Marseille University, 83041 Toulon, France
Interests: signal and image processing; independent component analysis; source separation
Dr. Frédéric Bouchara

E-Mail Website
Guest Editor
LIS-CNRS, Université de Toulon, Aix-Marseille University, 83041 Toulon, France
Interests: computer vision; AI; deep learning; maritime surveillance; document image processing

Special Issue Information

Dear Colleagues,

Recent fires, with their scale and unpredictability, have left a trail of devastation, affecting both human communities and our fragile ecosystems. The loss of human lives, the destruction of property, and the ecological impact of these disasters underline the urgent need to rethink our methods of detection and intervention.

Computer vision and deep learning are powerful tools for rapidly detecting the presence of fires and smoke by analyzing real-time images and videos. Their ability to identify early signs in complex environments enhances responsiveness and helps prevent disasters.

In this context, we invite authors to submit their articles on the use of deep learning and computer vision for the early detection of fire and smoke. The objective of this Special Issue is to promote innovative approaches capable of optimizing real-time monitoring and significantly enhancing responsiveness to emerging fires.

Submissions may address, among other topics, the following themes:

  • The development and optimization of deep learning models for image and video analysis;
  • The integration of computer vision systems into intelligent sensor networks;
  • Data fusion strategies for reliable detection under varying conditions;
  • Performance evaluation and field validation of innovative solutions.

We also encourage case studies and feedback based on the implementation of these technologies in real-world situations. Proposals will be evaluated based on scientific quality, the originality of the methods proposed, and their potential impact on fire prevention.

Dr. Moez Bouchouicha
Prof. Dr. Eric Moreau
Dr. Frédéric Bouchara
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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics is an international peer-reviewed open access semimonthly 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

  • smoke and fire detection
  • deep learning
  • computer vision

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Published Papers (2 papers)

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Research

17 pages, 11451 KB  
Article
A Real-World Benchmark for Early Wildfire Detection Using Sequential Data with the PyroNear Dataset
by Mateo Lostanlen, Nicolás Isla, José Guillén, Renzo Zanca, Félix Veith, Cristian Buc and Valentín Barriere
Electronics 2026, 15(12), 2652; https://doi.org/10.3390/electronics15122652 - 15 Jun 2026
Viewed by 113
Abstract
Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts and thus minimize the negative impacts of wildfire spreads. To this end, we present PyroNear2025, a new dataset composed of both images and videos, allowing for the [...] Read more.
Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts and thus minimize the negative impacts of wildfire spreads. To this end, we present PyroNear2025, a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sourced from the following: (i) web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, (ii) videos from our in-house network of cameras, and (iii) a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 640 wildfires; PyroNear2025 surpasses existing datasets in size and diversity. It includes data from France, Spain, Chile, and the United States. Finally, it is composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. We ran cross-dataset experiments using a lightweight state-of-the-art object detection model, similar to the ones used in real-world applications, and found that the proposed dataset is particularly challenging, with an F1 score of around 70%, but it is more stable than existing datasets. Finally, its use in concordance with other public datasets helps to reach higher results overall. Last but not least, the video part of the dataset enables another technical contribution, as it can be used to train a lightweight sequential model, improving global recall while maintaining precision for earlier detections. The output of this work has real-life implications, as it is used to automatically detect wildfires, with our models running on Raspberry Pi in several countries. We will make both our code and data available online. Full article
(This article belongs to the Special Issue Innovations in Deep Learning and Computer Vision for Early Fire and Smoke Detection)
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20 pages, 5205 KB  
Article
Determining the Origin of Multi Socket Fires Using YOLO Image Detection
by Hoon-Gi Lee, Thi-Ngot Pham, Viet-Hoan Nguyen, Ki-Ryong Kwon, Jun-Ho Huh, Jae-Hun Lee and YuanYuan Liu
Electronics 2026, 15(1), 22; https://doi.org/10.3390/electronics15010022 - 22 Dec 2025
Viewed by 888
Abstract
In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire [...] Read more.
In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire root-cause inspections to determine whether these fires are external or internal infrastructure fires. However, obtaining results is a complex process. In addition, the situation has been hampered by the lack of sufficient digital forensics and relevant programs. Apart from electrical devices, multi-sockets are among the main fire instigators. In this study, we aim to verify the feasibility of utilizing YOLO-based deep-learning object detection models for fire-cause inspection systems for multi-sockets. Particularly, we have created a novel image dataset of multi-socket fire causes with 3300 images categorized into the three classes of socket, both burnt-in and burnt-out. This data was used to train various models, including YOLOv4-csp, YOLOv5n, YOLOR-csp, YOLOv6, and YOLOv7-Tiny. In addition, we have proposed an improved YOLOv5n-SE by adding a squeeze-and-excitation network (SE) into the backbone of the conventional YOLOv5 network and deploying it into a two-stage detector framework with a first stage of socket detection and a second stage of burnt-in/burnt-out classification. From the experiment, the performance of these models was evaluated, revealing that our work outperforms other models, with an accuracy of 91.3% mAP@0.5. Also, the improved YOLOv5-SE model was deployed in a web browser application. Full article
(This article belongs to the Special Issue Innovations in Deep Learning and Computer Vision for Early Fire and Smoke Detection)
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