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 1197
Special Issue Editors
Interests: computer vision; AI; deep learning; medical imaging; fire detection
Special Issues, Collections and Topics in MDPI journals
Interests: signal and image processing; independent component analysis; source separation
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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