FireMan-UAV-RGBT: A Novel UAV-Based RGB-Thermal Video Dataset for the Detection of Wildfires in the Finnish Forests
Kularatne, S. D.M.W.; Casado, Constantino Álvarez; Rajala, Janne; Hänninen, Tuomo; López, Miguel Bordallo; Nguyen, Le (2024-10-16)
IEEE
16.10.2024
S. D. M. W. Kularatne, C. Á. Casado, J. Rajala, T. Hänninen, M. B. López and L. Nguyen, "FireMan-UAV-RGBT: A Novel UAV-Based RGB-Thermal Video Dataset for the Detection of Wildfires in the Finnish Forests," 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024, pp. 1-8, doi: 10.1109/ETFA61755.2024.10710657
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202411076641
https://urn.fi/URN:NBN:fi:oulu-202411076641
Tiivistelmä
Abstract
Wildfire detection in the densely forested and remote regions of Finland presents substantial challenges. This paper introduces a new publicly available dataset, FireMan-UAV-RGBT, comprising UAV-captured RGB and thermal video data to advance wildfire detection methodologies. The dataset includes high-resolution images of boreal forests that have been carefully annotated both manually and using a semi-automatic method that leverages thermal information for improved RGB image segmentation. The utility of the dataset is assessed by applying established deep learning models (ResN et50 and YOLOv8), and comparing their performance in unimodal and multimodal detection approaches. The performance is evaluated using both intra-set validation on the novel dataset and inter-set evaluation through cross-validation with the Flame-1 and Flame-2 datasets, demonstrating the usability of our dataset in wildfire detection scenarios. The FireMan-UAV-RGBT dataset represents a step forward in wildfire management, offering a resource that may contribute to cost-effective and environmentally sensitive solutions in remote sensing and emergency response strategies.
Wildfire detection in the densely forested and remote regions of Finland presents substantial challenges. This paper introduces a new publicly available dataset, FireMan-UAV-RGBT, comprising UAV-captured RGB and thermal video data to advance wildfire detection methodologies. The dataset includes high-resolution images of boreal forests that have been carefully annotated both manually and using a semi-automatic method that leverages thermal information for improved RGB image segmentation. The utility of the dataset is assessed by applying established deep learning models (ResN et50 and YOLOv8), and comparing their performance in unimodal and multimodal detection approaches. The performance is evaluated using both intra-set validation on the novel dataset and inter-set evaluation through cross-validation with the Flame-1 and Flame-2 datasets, demonstrating the usability of our dataset in wildfire detection scenarios. The FireMan-UAV-RGBT dataset represents a step forward in wildfire management, offering a resource that may contribute to cost-effective and environmentally sensitive solutions in remote sensing and emergency response strategies.
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