Advanced Approaches in Fire Detection and Prediction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 3667

Special Issue Editors

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
Aix Marseille Univ, Université de Toulon, CNRS, LIS, 83041 Toulon, France
Interests: signal processing; independent component analysis; source separation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, fires have killed hundreds of people and ravaged forests all over the world. In addition to the socioeconomic impact in terms of loss of human lives and first responders, health, infrastructure, and economic activity, fire events also have serious and irreversible ecological impacts when considering soil degradation, water scarcity, and biodiversity loss.

This Special Issue will propose innovative approaches tailored to fire risk detection. Papers are invited that investigate more advanced techniques, models, solutions, and capabilities for preventing, predicting, monitoring fire risk, including advanced technologies, equipment, and decision support systems for first responders. Topics may include computer vision methods for fire and/or smoke detection via visible, IR, and multi-spectral range of ground and/or aerial videos/images. Moreover, papers are welcome that deal with fire propagation forecasting, thanks to models based on precise topography, weather, fuel, and combustion modeling via sensor data.

Dr. Moez Bouchouicha
Prof. Dr. Eric Moreau
Guest Editors

Manuscript Submission Information

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Keywords

  • fire smoke detection
  • computer vision
  • image processing
  • machine learning
  • fire modeling
  • multispectral imaging

Published Papers (1 paper)

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Research

28 pages, 10210 KiB  
Article
Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
by Jorge Francisco Ciprián-Sánchez, Gilberto Ochoa-Ruiz, Lucile Rossi and Frédéric Morandini
Appl. Sci. 2021, 11(15), 7046; https://0-doi-org.brum.beds.ac.uk/10.3390/app11157046 - 30 Jul 2021
Cited by 9 | Viewed by 2579
Abstract
Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order [...] Read more.
Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models. Full article
(This article belongs to the Special Issue Advanced Approaches in Fire Detection and Prediction)
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