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Low Complexity Forest Fire Detection Based on Improved YOLOv8 Network.
- Source :
- Forests (19994907); Sep2024, Vol. 15 Issue 9, p1652, 25p
- Publication Year :
- 2024
-
Abstract
- Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model's computational complexity is significantly reduced, making it suitable for deployment on resource-constrained edge devices. Additionally, Dynamic UpSampling and Coordinate Attention mechanisms enhance the model's ability to capture multi-scale features and focus on relevant regions, improving detection accuracy for small-scale fires. The Distance-Intersection over Union loss function further optimizes the model's training process, leading to more accurate bounding box predictions. Experimental results on a comprehensive dataset demonstrate that our proposed model achieves a 41% reduction in parameters and a 54% reduction in GFLOPs, while maintaining a high mean Average Precision (mAP) of 99.0% at an Intersection over Union (IoU) threshold of 0.5. The proposed model offers a promising solution for real-time forest fire monitoring, enabling a timely detection of, and response to, wildfires. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19994907
- Volume :
- 15
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Forests (19994907)
- Publication Type :
- Academic Journal
- Accession number :
- 180008133
- Full Text :
- https://doi.org/10.3390/f15091652