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Wildfires Classification: A Comparative Study

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Image Analysis and Processing - ICIAP 2023 Workshops (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14365))

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Abstract

This paper proposes a comparative study of the performance of deep models for classifying wildfires. First, the paper examines publicly available image datasets for training models to classify fire and smoke. Then, it proposes the Wildfires dataset for the problem of classifying images according to the represented event: fire, smoke and no alarm. The dataset includes images describing real scenarios where images are acquired from a fixed control station far from the place where fire or smoke arises. The paper focuses on convolutional neural networks, residual neural networks, and transformers, and compares model performance on the Wildfires dataset and on a publicly available dataset, FireNet. Based on the experiments conducted in this work, the best accuracy values are achieved by the ResNet-50 network and the Swin-T v2 transformer. On the Wildfires dataset, the latter shows fewer smoke missing and false alarms, and correctly classifies all fire images. Additionally, this paper describes deploying the trained models on an embedded system to develop a fully working prototype for installation in a control station. The experiments show that transformers are not yet suitable for real-time performance when used on embedded systems.

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Notes

  1. 1.

    The dataset is made available upon request via email to Gabriele Ajello (gabriele.ajello@italtel.com).

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Authors and Affiliations

  1. University of Palermo, 90128, Palermo, Italy

    Giorgio Cruciata, Liliana Lo Presti & Marco La Cascia

  2. Italtel S.p.A., 90044, Carini (PA), Italy

    Gabriele Ajello, Paolo Cicero & Giacomo Corvisieri

Authors
  1. Giorgio Cruciata
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  2. Liliana Lo Presti
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  3. Gabriele Ajello
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  4. Paolo Cicero
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  5. Giacomo Corvisieri
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  6. Marco La Cascia
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Corresponding author

Correspondence to Giorgio Cruciata .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Cruciata, G., Lo Presti, L., Ajello, G., Cicero, P., Corvisieri, G., Cascia, M.L. (2024). Wildfires Classification: A Comparative Study. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-51023-6_6

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