# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 3 Experimental and Analysis
### Ablation Experiment  
In order to verify the effectiveness of each improvement module proposed in this paper on the performance, a set of ablation experiments are designed for analysis, where \"\(\surd\)\" indicates that the module is used and \"\(\times\)\" indicates that it is not used. YOLOv8n is the baseline model, YOLOv8-C denotes the addition of CARAFE up-sampling operator, YOLOv8-DC adds the CW-DRB module on top of YOLOv8-C, and YOLOv8-DCP adds the small-target detection layer on top of YOLOv8-DC, that is, the algorithmic model of this paper. The results of the ablation experiments are shown in Table 4.  
Analysis of the results in Table 4 shows that the [EMAIL_ADDRESS] value of YOLOv8-C improves by 0.4% compared to the baseline model verifying the effectiveness of CARAFE in replacing the sampling layer on the features and strengthening the model's ability to reconstruct the feature map. The [EMAIL_ADDRESS] value of YOLOv8-DC is improved by 2.4%, which indicates that the introduction of the CW-DRB module can improve the insulator fault diagnosis accuracy and verifies that the module can enhance the model's ability to extract feature information and fuse contextual semantic information. The [EMAIL_ADDRESS] value of YOLOv8-DCP is improved by 3.1% of the [EMAIL_ADDRESS] value, and after the introduction of the small target detection layer, the flashover fault detection accuracy is improved by 1% compared to the baseline model, which verifies the effectiveness of the module. In summary, the ablation experiment results verify the effectiveness of each improved module of YOLOv8-DCP, as well as the effectiveness of the algorithm in improving insulator fault diagnosis accuracy. In order to further verify the effectiveness of the improved module on performance enhancement, other current mainstream algorithm models are used to verify the performance comparison with the YOLOv8-DCP algorithm, and the verification results are shown in Table 5, while the other performance parameters of each model are compared with the curve diagram in Figure 11. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CARAFE**} & \multirow{2}{*}{**CW-DRB**} & \multirow{2}{*}{**P2**} & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} & \multicolumn{4}{c}{**AP\%**} & \multirow{2}{*}{**P/\%**} & \multirow{2}{*}{**FPS**} \\ \cline{5-5} \cline{8-10}  & & & & & & **ins.** & & & **def.** & **bro.** & **fla.** & \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & 90.8 & 92.8 & 84.2 & 96.8 & 89.3 & 93.8 & 157 \\ YOLOv8-C & \(\surd\) & \(\times\) & \(\times\) & 91.2 & 93.2 & 84.6 & 97.1 & 90.1 & 95.2 & 149 \\ YOLOv8-DC & \(\surd\) & \(\surd\) & \(\times\) & 93.2 & 93.6 & 91.1 & 98.2 & 89.9 & 95.4 & 141 \\ YOLOv8-DCP & \(\surd\) & \(\surd\) & \(\surd\) & 93.9 & 94.1 & 92.7 & 98.7 & 90.3 & 97.7 & 126 \\ \hline \hline \end{tabular} Note: ins. stands for insulator; def. stands for defect; bro. stands for broke; fla. stands for flash.  
\end{table} 
Table 4: Results of YOLOv8 DCP ablation experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** & **FPS** \\ \hline YOLOv5n & 96.2 & 84.3 & 90.9 & 147 \\ YOLOv7-tiny & 93.4 & 84.3 & 89.6 & 164 \\ YOLOv8n & 93.8 & 86.8 & 90.8 & 157 \\ YOLOv8-DCP & 97.7 & 86.9 & 93.9 & 126 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training results of different algorithm models. By analyzing Table 5 and Figure 11, YOLOv8-DCP shows improvements over YOLOv5n, YOLOv7-tiny, and YOLOv8n in terms of precision, recall, and [EMAIL_ADDRESS]. Specifically, YOLOv8-DCP improves precision by 1.5%, 4.3%, and 4.8%, respectively, indicating a stronger ability to accurately identify positive samples. In terms of recall, YOLOv8-DCP improves by 2.6%, 2.6%, and 0.1%, suggesting a lower false negative rate and reduced likelihood of incorrectly classifying positive samples as negative. For [EMAIL_ADDRESS], YOLOv8-DCP achieves improvements of 3.0%, 4.3%, and 3.1%, demonstrating superior overall performance.