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Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13644))

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Abstract

Wildfires are an important natural risk which causes enormous damage to the environment. Many researchers are working to improve firefighting using AI. Various vision-based fire detection methods have been proposed to detect fire. However, these techniques are still limited when it comes to identifying the precise fire’s shape as well as small fire areas. For such, we propose deep wildland fire detection and segmentation models based on deep Convolutional Neural Networks (CNNs) and vision Transformers. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfires on aerial images. Vision Transformers (TransUNet, MedT, and TransFire) are adopted in segmenting fire pixels and in detecting the precise shape of the fire areas using aerial and ground images. The achieved results are promising and show the potential of using deep CNNs and vision Transformers for forest fire detection and segmentation.

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Acknowledgment

This research was enabled in part by support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) funding reference number RGPIN-2018-06233 and by the support of WestGrid (www.westgrid.ca/) and Compute Canada (www.computecanada.ca).

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Authors and Affiliations

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

    Rafik Ghali & Moulay A. Akhloufi

Authors
  1. Rafik Ghali
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  2. Moulay A. Akhloufi
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Correspondence to Moulay A. Akhloufi .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

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Ghali, R., Akhloufi, M.A. (2023). Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_19

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  • DOI: https://doi.org/10.1007/978-3-031-37742-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37741-9

  • Online ISBN: 978-3-031-37742-6

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