Active Fire Detection in Forests with 5 Deep Learning Techniques

  • Marcelo Kuchar Matte UFMS
  • José Marcato Junior UFMS
  • Newton Loebens UCDB
  • Hemerson Pistori UCDB

Resumo


Identifying active fires in natural and difficult-to-access environments is a real and current problem, especially in recent years. One of the simplest ways to monitor regions of difficult access is the use of satellite images in their various patterns, such as multi-spectral and hyper-spectral images. In this way, we used 5 CNNs (Faster R-CNN, ATSS, RetinaNet, VfNet, and SABL) to detect active fires in satellite images of the Pantanal region in May 2020. In preliminary results, it was possible to reach values greater than 50% of mAP50 in the detections performed. Studies are still ongoing in the search for higher precisions.

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Publicado
13/11/2023
MATTE, Marcelo Kuchar; MARCATO JUNIOR, José; LOEBENS, Newton; PISTORI, Hemerson. Active Fire Detection in Forests with 5 Deep Learning Techniques. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1-5. DOI: https://doi.org/10.5753/wvc.2023.27523.