## 5 Experimental Results and Analysis
### Ablation Experiment and Analysis of Results  
To assess the impact of each enhancement, this paper performed ablation experiments. The outcomes are presented in Table 2.  
Table 2 and Figure 12 show that incorporating the MSCA module into the YOLOv8n backbone network improved precision by 0.3%. This improvement is attributed to the mechanism's optimization of feature extraction. The MSCA module boosts detection performance by dynamically concentrating on multi-scale features and refining feature maps. This process minimizes background noise and irrelevant details, allowing for the effective identification and processing of information-rich visual features. The newly designed EC2f structure significantly improves the extraction of essential information, leading to a precision increase of 1.9%. Meanwhile, the ECA module efficiently captures channel features' global information, adaptively tuning the feature response. This enhancement bolsters the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & \(\Delta_{1}\) & **Recall (\%)** & **mAP (\%)** & **FLOPs (G)** & \(\Delta_{2}\) \\ \hline YOLOv8n & 96.1 & & 93.3 & 96.1 & 8.1 & \\ YOLOv8n + MSCA & 96.4 & **+0.3\%** & 93.5 & 96.4 & 8.2 & \\ YOLOv8n + MSCA + EC2f & 97.9 & **+1.9\%** & 93.8 & 97.3 & 8.2 & \\ YOLOv8n + MSCA + EC2f + ASFF & 98.7 & **+2.7\%** & 95.1 & 98.1 & 8.2 & \\ MEAG-YOLO & **98.4** & **+2.4\%** & **94.2** & **96.5** & **7.6** & \(-\)**7.3\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment results.  
Figure 11: (**a**) YOLOv8n P-R curve; (**b**) MEAG-YOLO P-R curve.  
network's representational capacity without extensive dimensionality reduction. Furthermore, the ASHF module in the Head network greatly strengthens multi-scale information fusion. As a result, the precision rose by 2.7% compared to the original YOLOv8n. This module dynamically manages feature fusion, optimizing the use of spatial information across various resolutions. By integrating features from distinct spatial dimensions, this fusion technique improves scene comprehension and the accuracy of target localization and identification. It is particularly effective in scenarios with considerable target size variations and where precise spatial details are paramount.  
Figure 13 clearly shows the ablation experimental results. Replacing standard convolution with GhostConv led to a 0.9% decrease in precision. The minor precision loss is compensated by a substantial reduction in computational demand, creating a reasonable trade-off. This is particularly valuable in substation monitoring scenarios where high real-time responsiveness is essential. However, MEAG-YOLO achieved a 7.3% reduction in FLOPs across all test datasets, signaling a marked increase in computational efficiency. This improvement stems from the GhostConv module, which produces feature maps using fewer convolutions and creates additional \"ghost\" feature maps via linear transformations. Specifically, GhostConv uses grouped convolutions to replace some standard convolutions, significantly reducing unnecessary computations. It substitutes some operations in standard convolutional layers with lightweight linear operations, cutting computational requirements by about 50%. This reduction decreases hardware performance demands and improves the model's suitability and inference speed in resource-limited environments. This module reduces the number of direct convolutions, cutting computational complexity and resource usage. It efficiently captures the vital features needed for detection tasks, illustrating the module's capability to balance efficiency and performance. The outcomes confirm that the model is both lighter and more computationally efficient. Despite a slight sacrifice in accuracy, the introduction of GhostConv crucially enhances computational efficiency, which is vital for practical deployment.  
Figure 12: Ablation experiment results: (**a**) comparison of the accuracy of each model; (**b**) comparison of the FLOPs.