Applications of Artificial Intelligence and Computer Vision on Fire and Smoke Detection

Dear Colleagues,

Global warming, the effects of which are witnessed every day, is here and happening right now. The average temperature of Earth’s land surfaces is consistently increasing. This temperature rise consequently results in a higher risk of (wild)fire outbreaks. Wildfires spread, not only in hot and dry parts of the world, such as countries around the Mediterranean coast, the southwest of the US, and in Australia, but also in relatively humid and rather cold regions, like the arctic.

The early detection of fires is of paramount importance for containing them in a timely manner. Fire detection technology for outdoor spaces and/or large and spacious indoors is shifting continuously, from utilizing limited capacity point detectors to volumetric sensors, such as visible range/IR/hyperspectral cameras, and passive infrared sensors (PIR). Point detectors installed in buildings and rooms require smoke to reach them for fire detection. This means that it takes some time to trigger an alarm. On the other hand, volumetric sensors can react instantaneously. They can issue an alarm once they “see” fire at a distance.

Due to recent advances in artificial intelligence, image and video analysis, and machine learning techniques, as well as progress in enabling technologies, such as semiconductor device design and fabrication, volumetric fire detection methods yield superior results, as compared to other methods and point detectors. These developments pave the way for embedded computer vision systems equipped with lenses and cameras attached to computing platform which process an acquired sequence of images.

The Journal of Imaging Editorial Board launches this call for papers for a Special Issue on “Applications of Artificial Intelligence and Computer Vision on Fire and Smoke Detection”.

Topics of interest include, but are not limited to:

1. Datasets, data platforms, artificial intelligence and computer vision approaches that facilitate our understanding of any aspect of global warming;
2. Computer vision and artificial intelligence methods for smoke/flame detection in images/image sequences;
3. Fire detection in compressed domain;
4. Embedded computer vision for fire detection;
5. Multi-modal/multi-sensor flame/smoke detection;
6. Fire detection using imaging sensors sensitive to parts of the EM spectrum other than the visible range;
7. Fire measurement and spread modeling.

Dr. Turgay Celik
Dr. Behçet Uğur Töreyin
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. Journal of Imaging 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 1800 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.

• artificial intelligence
• machine learning
• computer vision
• fire detection
• flame detection
• smoke detection