# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 5 Experimental Results and Analysis
### Performance Comparison of Different Detection Networks  
To validate the effectiveness and generalizability of the proposed approach and to provide a comprehensive assessment of the model's performance, we compared it with five other target detection algorithms: YOLOX [25], YOLOv7 [26], YOLOv8 [27], RetinaNet [28], and Faster RCNN. All the algorithms underwent training and testing using identical experimental settings. The evaluation metrics for the network models are presented in Table 5.  
From the results in Table 5, it can be inferred that both the two-stage algorithm Faster RCNN and the one-stage algorithm RetinaNet no longer possess any advantages in terms of detection accuracy and speed when compared to the YOLO series. Among the YOLO series algorithms, YOLOv7 achieved a high mAP of 92.3% by addressing the issue of sample imbalance with a deeper network structure. The proposed AC-YOLO algorithm showed a 2.9% increase in mAP compared to YOLOv8. YOLOX performed well in terms of both detection accuracy and speed. However, the YOLOv5 algori  
Figure 15: Convergence speed of different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**AP/\%**} \\ \cline{2-10}  & **Insulators** & \begin{tabular}{c} **Breakage** \\ **Defect** \\ \end{tabular} & \
\begin{tabular}{c} **Flashover** \\ **Defect** \\ \end{tabular} &
\begin{tabular}{c} **P/\%** \\ **Defect** \\ \end{tabular} & **R/\%** & **F1/\%** & **[EMAIL_ADDRESS]/\%** & **Parameters/M** & **FPS/(f/s)** \\ \hline YOLOv5 & 99.7 & 83.4 & 81.8 & 92.5 & 81.2 & 86.3 & 88.3 & 6.63 & 42.24 \\ YOLOX & 99.7 & 86.3 & 82.4 & 91.8 & 85.8 & 88.3 & 89.5 & 8.94 & 39.20 \\ YOLOv7 & 99.6 & 90.1 & 87.2 & 94.0 & 89.4 & 91.7 & 92.3 & 37.21 & 28.75 \\ YOLOv8 & 99.5 & 89.4 & 82.6 & 92.6 & 86.7 & 89.6 & 90.5 & 11.20 & 40.38 \\ RetinaNet & 99.5 & 81.1 & 44.5 & 95.4 & 65.1 & 74.7 & 73.7 & 36.37 & 24.30 \\ Faster RCNN & 99.7 & 79.6 & 50.8 & 53.9 & 87.9 & 65.3 & 76.7 & 137.08 & 16.31 \\ AC-YOLO & 99.8 & 95.0 & 85.2 & 93.8 & 89.2 & 91.3 & 93.4 & 8.38 & 40.58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison experiments of classical target detection networks.  
types of algorithms, achieving superior detection accuracy and a simultaneous detection speed of 42.24 FPS. In this study, the improved AC-YOLO network had a mAP of 93.4%, an accuracy of 93.8%, and a recall of 89.2%, which is further subdivided into an insulator breakage defect detection accuracy of 95.0% and an insulator flashover defect detection accuracy of 85.2%. Compared with the original YOLOv5 network, the improved AC-YOLO network had a higher mAP in terms of overall detection accuracy. The accuracy and the recall were improved by 5.1%, 1.3%, and 8.0%, respectively. In contrast, the detection accuracy of small target defects, such as insulator breakage and insulator flashover, was improved by 11.4% and 3.4%, respectively. The improved AC-YOLO network achieved a significant improvement in the modeling accuracy, as well as insulator defect detection. At the same time, the AC-YOLO network model size increased by 1.75M, and the detection speed decreased by 1.66 FPS compared to the original YOLOv5 because the increase in model complexity will inevitably bring about a reduction in the detection speed and an increase in model volume. Therefore, the detection speed and model volume, compared to the original network model, are slightly inferior, but a small amount of time and a small amount of increase in the model volume was noted. However, a small increase in time and a small increase in model volume will not substantially impact the model's lightweight and real-time aspects. At the same time, it is very cost effective to increase the detection accuracy at the cost of a small increase in detection speed and a small increase in model volume. Therefore, the method proposed in this paper strikes a good balance between detection speed and accuracy.