SWIFT: Simulated Wildfire Images for Fast Training Dataset

Author:

Fernando Luiz1ORCID,Ghali Rafik1ORCID,Akhloufi Moulay A.1ORCID

Affiliation:

1. Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada

Abstract

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

MDPI AG

Reference58 articles.

1. Jones, M.W., Smith, A., Betts, R., Canadell, J.G., Prentice, I.C., and Quéré, C.L. (2024, March 12). Climate Change Increases the Risk of Wildfires. Available online: https://sciencebrief.org/briefs/wildfires.

2. Driving Forces of Global Wildfires over the Past Millennium and the Forthcoming Century;Pechony;Proc. Natl. Acad. Sci. USA,2010

3. Climate Change Increases the Potential for Extreme Wildfires;Evans;Geophys. Res. Lett.,2019

4. Natural Resources Canada (2024, March 11). National Wildland Fire Situation Report. Available online: https://cwfis.cfs.nrcan.gc.ca/report.

5. European Commission (2024, March 11). 2022 Was the Second-Worst Year for Wildfires. Available online: https://ec.europa.eu/commission/presscorner/detail/en/ip_23_5951.

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