# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 3 Results
### Visual Analysis  
The evaluation of forest fire target detection outcomes between the enhanced YOLOv8 model and the unmodified YOLOv8 model showed a substantial improvement in the improved model's detection performance. Specifically, the augmented model demonstrated enhanced capabilities in resisting complex background interference and extracting global information related to forest fire targets. Notably, instances of missed detection and false alarms were significantly reduced, particularly showcasing optimal detection performance for small-scale forest fires. Moreover, the model exhibited commendable detection performance under varying lighting conditions, emphasizing its robustness to changes in illumination and further validating the effectiveness of its individual components. In summary, the proposed model proved to be more suitable for the task compared to the unmodified YOLOv8 model.  
To better showcase the feasibility of the model, a selection of images from the test set was utilized for demonstration. The comparative detection results are illustrated in Figure 10, with the left image representing the unmodified model and the right image displaying our detection model. In scenarios where there was interference resembling forest fire targets, YOLOv8 incorrectly identified such targets as forest fire targets, a shortcoming addressed by the improved YOLOv8.  
Examining the detection results in Figure 11, the YOLOv8 model displayed misfit detection of rectangular box positions for forest fires. Particularly when faced with forest fire targets of varying scales, YOLOv8 exhibited multiple instances of missed detection, leading to suboptimal performance. In contrast, the improved YOLOv8 model aligned more accurately with the actual framework of forest fire targets, eliminating instances of missed detection and improving detection performance for forest fire types.  
Figure 10: Model Feasibility Comparison—Enhanced YOLOv8 vs. Original Model in Challenging Fire-Like Object Scenarios.  
Figure 9: Improved YOLOv8 vs. the State-of-the-Art Methods: Bar Charts of FPS and [EMAIL_ADDRESS].  
Promptly identifying initial small-scale forest fires holds paramount importance in forest fire detection. As depicted in Figure 12, YOLOv8 encountered difficulties in discerning small-scale forest fire targets at a distance. In contrast, the improved YOLOv8 showcased precise detection in the image, attaining a confidence level of 0.92.  
As shown in Figure 13, YOLOv8 encountered difficulties in adequately capturing information about forest fire targets in the image. This challenge led to bounding boxes being restricted to localized areas of the forest fire targets, resulting in inadequate positioning. Conversely, the enhanced YOLOv8 effectively extracted vital details regarding forest fire targets, presenting a more comprehensive perceptual scene.  
Figure 11: Comparative Detection Results—Performance of YOLOv8 and Enhanced YOLOv8 in Detecting Forest Fire Targets at Different Scales.  
Figure 12: Comparison of Early Small Target Forest Fire Detection: YOLOv8 vs. Modified YOLOv8.