# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 3 Results
### Experimental Comparison  
This study introduces a bounding box regression loss function, WIoUv3, into the YOLOv8 forest fire detection model. Three alternative bounding box regression loss modules--GIoU, DIoU, and CIoU--are proposed and were compared with WIoU to assess their effectiveness. The objective was to determine the necessity of these enhancement modules and to understand their individual impacts on the performance of the fire detection model. Evaluation of the adequately trained models using the same dataset in our experiments and obtaining corresponding metrics yielded ablation study results as shown in Table 3. A thorough examination of the ablation study results provides insights into the impact of each enhancement module on model performance, offering valuable data for subsequent optimization and adjustments. In the first experiment, we combined YOLOv8 with WIoUv3 and further introduced the \(DCN\_C2f\) module. By adjusting the C2f network structure and integrating the concept \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP@50** & **Precision** & **Recall** & **FPS** & **F1** \\ \hline YOLOv8 & 84.3 & 83.4 & 82.1 & 138 & 82.74 \\ YOLOv8+GIoU & 84.6 & 83.6 & 82.5 & 137 & 83.04 \\ YOLOv8+DIoU & 84.5 & 83.5 & 82.5 & 138 & 83.00 \\ YOLOv8+WIoUv3 & 85.1 & 83.9 & 82.7 & 136 & 83.29 \\ +DCN\_C2f & 87.3 & 87.4 & 82.7 & 115 & 84.99 \\ +SCConv\_C2f & 87.3 & 87.4 & 82.8 & 137 & 85.03 \\ +CA & 90.2 & 90.1 & 83.5 & 135 & 86.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The data of the ablation experiments. of Deformable Convolution, the network was adapted to dynamically adjust its receptive field. The experimental results demonstrated a significant improvement in [EMAIL_ADDRESS], increasing from 84.3% to 87.3%. This indicates that the \(DCN\_C2f\) module successfully encouraged the model to more effectively capture target features, especially in handling complex forest fire scenes. The introduction of \(SCConv\_C2f\) in the second experiment did not further enhance performance, but such an improvement greatly increased the overall speed of the model. In the third experiment, the implementation of the CA mechanism aimed to enhance the capture of directional and position-sensitive details regarding the targets. The experimental results demonstrated a notable enhancement in Precision and F1 Score, achieving 90.1% and 86.67%, respectively. Compared to the original model, Precision increased by 8.0%, while the F1 score increased by 4.7%. However, there was a slight decrease in FPS, indicating a slowdown in model processing speed. These findings suggest that CA effectively improves target detection, especially in forest fire scenarios, where directional and position-sensitive attributes are crucial for precise identification. Despite the significant improvement in performance, the introduction of new modules usually comes with an increase in computational complexity. However, through \(SCConv\_C2f\), the model's FPS did not significantly decrease, indicating that our model maintains high performance while possessing a certain level of computational efficiency. These results indicate that the proposed improvement modules positively influence forest fire detection performance at different levels, providing a better solution for the practical application of the model. To comprehensively evaluate the overall performance of the proposed enhanced YOLOv8 model in forest fire detection tasks, this study conducted comparative experiments with other mainstream object detection models, such as YOLOv3, YOLOv5, NAS-FPN, and FSAF. The comparative experimental results are illustrated in Figure 9. The experimental findings clearly demonstrate the significant superiority of the improved YOLOv8 model in terms of forest fire detection accuracy over other mainstream models, including the widely discussed YOLOv5 and YOLOv7. Additionally, the model's exceptionally high FPS values highlight its outstanding real-time performance.