The UAV employs U-Net model for identification of forest fires

Xuange Qi

doi:10.1117/12.3055831

9 January 2025 The UAV employs U-Net model for identification of forest fires

Xuange Qi

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860U (2025) https://doi.org/10.1117/12.3055831
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

This paper explores a method for monitoring and identifying forest fires using drone remote sensing technology combined with the U-Net model and its improved attention mechanism. Traditional forest fire prevention measures are costly and have limited coverage, whereas drone technology offers an efficient and flexible solution for early detection of forest fires. In this study, the IEEE flame dataset was used to train and validate the improved U-Net model through high-resolution, multi-angle drone image data. Experimental results indicate that the U-Net model, augmented with channel attention and spatial attention mechanisms, performs excellently in complex backgrounds and high-resolution images, significantly enhancing the accuracy of fire area recognition and segmentation. The findings demonstrate that this method has notable advantages in identifying fire locations and accurately assessing fire conditions, providing robust technical support for the early warning and prevention of forest fires.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuange Qi "The UAV employs U-Net model for identification of forest fires", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860U (9 January 2025); https://doi.org/10.1117/12.3055831

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KEYWORDS

Forest fires

Image segmentation

Convolution

Education and training

Performance modeling

Unmanned aerial vehicles

Feature selection

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