# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 3 Experimental and Analysis
### Performance Comparison Experiment  
In order to verify the performance of the algorithm in this paper, after setting the experimental parameters, the YOLOv8-DCP algorithm and the YOLOv8n algorithm are used to train on the dataset of this paper at the same time, and the change curves of the performance evaluation indexes of the two models are obtained as Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **Parameter Settings** \\ \hline Image size & 640 \(\times\) 640 \(\times\) 3 \\ Epochs & 300 \\ Batch size & 16 \\ Learning rate & 0.01 \\ Optimizer & SGD \\ Device & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model training parameter setting table.  
According to Figure 10, in the initial analysis of overall precision (denoted as Precision), it is observed that during the first 55 training rounds, the YOLOv8n algorithm exhibits superior precision compared to the YOLOv8-DCP algorithm. However, in the subsequent 245 training rounds, the YOLOv8-DCP algorithm demonstrates enhanced precision relative to YOLOv8n. Analyzed from the perspective of [EMAIL_ADDRESS], with the increase of training rounds, especially from 100 rounds, the [EMAIL_ADDRESS] value of YOLOv8-DCP gradually increases and the growth curve is smoother, when the training is completed, the [EMAIL_ADDRESS] of YOLOv8n algorithm is 90.8%, and the [EMAIL_ADDRESS] of YOLOv8-DCP algorithm is 93.9%. Overall, the YOLOv8-DCP algorithm has a higher average detection accuracy and better meets the insulator fault diagnosis needs.  
To further validate the performance of the CW-DRB module proposed in this paper, various improved C2f modules based on YOLOv8 were selected for training on the dataset used in this study. The results are shown in Table 3.  
As shown in Table 3, both C2f-ODConv and C2f-DySnakeConv improve the model's [EMAIL_ADDRESS], while C2f-Faster leads to a decrease in precision. This is because the Faster structure is designed for model lightweighting, which compromises precision to achieve this goal. In contrast, the CW-DRB module proposed in this paper not only enhances  
Figure 10: Overall performance comparison between YOLOv8-DCP and YOLOv8n.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **FPS** \\ \hline YOLOv8 & 93.8 & 86.8 & 90.8 & 157 \\ YOLOv8-C2f-ODConv [26] & 93.9 & 83.7 & 90.9 & 119 \\ YOLOv8-C2f-Faster [27] & 93.4 & 83.9 & 90.3 & 166 \\ YOLOv8-C2f-DySnakeConv [28] & 93.8 & 86.3 & 91.3 & 125 \\ YOLOv8-CW-DRB & 96.1 & 86.7 & 92.1 & 151 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of different C2f improvement modules.  
feature extraction capabilities but also provides a more comprehensive fusion of features from different levels. Compared to other improved C2f modules (C2f-ODConv, C2f-Faster, C2f-DySnakeConv), the CW-DRB module achieves improvements of 2.2%, 2.7%, and 2.3% in precision, 3%, 2.8%, and 0.4% in recall, and 1.2%, 1.8%, and 0.8% in mAP, respectively. These comparative results demonstrate the superior performance of the CW-DRB module and validate the effectiveness of the proposed improvements.