Abstract
The acceleration of global warming and intensifying global climate anomalies have led to a rise in the frequency of wildfires. However, most existing research on wildfire fields focuses primarily on wildfire identification and prediction, with limited attention given to
the intelligent interpretation of detailed information, such as fire front within fire region. To address this gap, advance the analysis of fire front in UAV-captured visible images, and facilitate future calculations of fire behavior parameters, a new method is proposed for the intelligent segmentation and fire front interpretation of wildfire regions. This proposed method comprises three key steps: deep learning-based fire segmentation, boundary tracking of wildfire regions, and fire front interpretation. Specifically, the YOLOv7-tiny model is enhanced with a Convolutional Block Attention Module (CBAM), which integrates channel and spatial attention mechanisms to improve the model's focus on wildfire regions and boost the segmentation precision. Experimental results show that the proposed method improved detection and segmentation precision by 3.8% and 3.6%, respectively, compared to existing approaches, and achieved an
average segmentation frame rate of 64.72 Hz, which is well above the 30 Hz threshold required for real-time fire segmentation. Furthermore, the method’s effectiveness in boundary tracking and fire front interpreting was validated using an outdoor grassland
fire fusion experiment’s real fire image data. Additional tests were conducted in southern New South Wales, Australia, using data that confirmed the robustness of the method in accurately interpreting the fire front. The findings of this research have potential applications in dynamic data-driven forest fire spread modeling and fire digital twinning areas. The code and dataset are publicly available at
https://github.com/makemoneyokk/fire-segmentation-interpretation.git.
the intelligent interpretation of detailed information, such as fire front within fire region. To address this gap, advance the analysis of fire front in UAV-captured visible images, and facilitate future calculations of fire behavior parameters, a new method is proposed for the intelligent segmentation and fire front interpretation of wildfire regions. This proposed method comprises three key steps: deep learning-based fire segmentation, boundary tracking of wildfire regions, and fire front interpretation. Specifically, the YOLOv7-tiny model is enhanced with a Convolutional Block Attention Module (CBAM), which integrates channel and spatial attention mechanisms to improve the model's focus on wildfire regions and boost the segmentation precision. Experimental results show that the proposed method improved detection and segmentation precision by 3.8% and 3.6%, respectively, compared to existing approaches, and achieved an
average segmentation frame rate of 64.72 Hz, which is well above the 30 Hz threshold required for real-time fire segmentation. Furthermore, the method’s effectiveness in boundary tracking and fire front interpreting was validated using an outdoor grassland
fire fusion experiment’s real fire image data. Additional tests were conducted in southern New South Wales, Australia, using data that confirmed the robustness of the method in accurately interpreting the fire front. The findings of this research have potential applications in dynamic data-driven forest fire spread modeling and fire digital twinning areas. The code and dataset are publicly available at
https://github.com/makemoneyokk/fire-segmentation-interpretation.git.
Original language | English |
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Pages (from-to) | 473-489 |
Number of pages | 17 |
Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
Volume | 220 |
Early online date | 9 Jan 2025 |
DOIs | |
Publication status | Published online - 9 Jan 2025 |
Bibliographical note
Publisher Copyright:© 2024 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Keywords
- Attention mechanism
- Convolutional neural network
- Deep learning
- Fire front interpretation
- Unmanned aerial vehicle
- Wildfire segmentation