## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation
#### 4.3.4 Visual Comparison of Recognition Effects  
To verify the model's robustness in detecting devices across complex background conditions, this study conducts a comparative analysis with the YOLOv8n model. The assessment leverages challenging and representative images from the infrared test dataset, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **C2f-fast** & **GSconv** & **VoVGSCSP** & **EMA** & **P/\%** & **R/\%** & **mAP@50/\%** & **GFLOPs/G** \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 92.3 & 91.4 & 94.8 & 12.4 \\ YOLOv8n-C & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 95.2 & 94.0 & 96.8 & 11.3 \\ YOLOv8n-CG & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 96.3 & 95.7 & 97.4 & 10.7 \\ YOLOv8n-CGV & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 96.8 & 96.0 & 98.1 & 10.1 \\ YOLOv8\_Adv & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.3 & 96.6 & 98.7 & 10.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments of the YOLOv8_Adv model.  
From the figure, it is clear that the YOLOv8_Adv model outperforms the YOLOv8n model in terms of performance. The detection results show that the bounding boxes are  
Figure 13: Visualized detection results (**a**) YOLOv8n; (**b**) YOLOv8_Adv.  
drawn around the identified equipment, with each box labeled to indicate the predicted device category (e.g., \"breaker\", \"isolator\", etc.) and confidence score, which reflects the model's confidence in the prediction. These bounding boxes are key to understanding the model's performance in terms of accuracy and object localization.  
According to the data provided in the figure, the YOLOv8n model achieved an average detection accuracy of 87.9% on a randomly selected set of 16 infrared images. In contrast, the YOLOv8_Adv model achieved a detection accuracy of 93.05% on the same images, improving by 5.86% compared to YOLOv8n. This significant improvement highlights the effectiveness of YOLOv8_Adv in handling complex scenes and dynamic backgrounds. The enhanced model generated more accurate bounding boxes, which aligned more closely with the objects, thus reducing errors related to misidentifying non-target objects and failing to identify actual objects.  
Moreover, further analysis shows that YOLOv8_Adv also performs excellently in multi-object detection tasks, with accuracy improving from 86.67% to 92.17%. The detection results in the figure displays multiple bounding boxes for each image, with each box corresponding to a different object in the scene. This indicates that the improved model has an enhanced stability and capability in identifying multiple objects within a single image, especially in complex or crowded scenes.  
These practical detection data clearly indicate that the YOLOv8_Adv model maintains a high level of detection accuracy while achieving network lightweighting, effectively providing a flexible solution tailored to the application requirements of mobile devices. In conclusion, these findings underscore the superior performance of the YOLOv8_Adv model in practical applications and emphasize the importance of its further adoption and integration in infrared image detection technologies.