# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 4 Experimental Results and Analysis
### Ablation Study  
This section presents ablation studies on the DC2f module, AMFE module, and OCIoU loss to assess their effectiveness. The experimental results are presented in Table 3, where '\(\times\)' indicates that the corresponding module has not been added, while '\(\check{\vee}\)' indicates the opposite meaning. In other words, the first row in Table 3 indicates the performance of the baseline model, i.e., YOLOv8n. The positions of the DC2f module, AMFE module, and the OCIoU loss in the DAO-YOLO model can be seen in Figure 2.  
Experimental results demonstrate that the independent integration of the DC2f module yields respective improvements of 1.4% and 1.2% in the [EMAIL_ADDRESS](%) and [EMAIL_ADDRESS]-0.95(%) metrics. This demonstrates that the DC2f module not only increases object detection accuracy but also improves the model's real-time performance. When the AMFE module is used independently, it improves [EMAIL_ADDRESS](%) by 1.6% and [EMAIL_ADDRESS]-0.95(%) by 1.5%, while keeping the parameter count, GFLOPS, and FPS nearly unchanged, demonstrating its effectiveness. The introduction of the OCIoU loss alone improves [EMAIL_ADDRESS](%) by 0.9% and [EMAIL_ADDRESS]-0.95(%) by 2.2%, while keeping the number of parameters, GFLOPS, and FPS unchanged, demonstrating its effectiveness. Combining DC2f, AMFE, and the OCIoU loss yields additional improvements in both [EMAIL_ADDRESS](%) and [EMAIL_ADDRESS]-0.95(%), while maintaining nearly identical parameters, GFLOPS, and FPS. In combinations involving  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**DC2f** & **AMFE** & **OCIoU** & **[EMAIL_ADDRESS](\%)** & **[EMAIL_ADDRESS]-0.95(\%)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & **Parameters/M** & **GFLOPS** & **FPS** \\ \hline \(\times\) & \(\times\) & \(\times\) & 85.0 & 59.5 & 0.104 & 0.120 & 3.0 & 8.1 & 86.2 \\ \(\check{\check{\prime}}\) & \(\times\) & \(\times\) & 86.4 & 60.7 & 0.093 & 0.115 & 2.9 & 7.9 & 87.1 \\ \(\times\) & \(\check{\check{\prime}}\) & \(\times\) & 86.6 & 61.0 & 0.089 & 0.112 & 3.0 & 8.1 & 85.7 \\ \(\times\) & \(\times\) & \(\check{\check{\prime}}\) & 85.9 & 61.7 & 0.098 & 0.109 & 3.0 & 8.1 & 86.2 \\ \(\check{\check{\prime}}\) & \(\check{\check{\prime}}\) & \(\times\) & 87.6 & 62.6 & 0.075 & 0.106 & 2.9 & 7.9 & 86.6 \\ \(\check{\check{\prime}}\) & \(\times\) & \(\check{\check{\prime}}\) & 87.1 & 63.2 & 0.083 & 0.104 & 2.9 & 7.9 & 87.2 \\ \(\times\) & \(\check{\check{\prime}}\) & \(\check{\check{\prime}}\) & 87.4 & 63.5 & 0.079 & 0.103 & 3.0 & 8.1 & 85.8 \\ \(\check{\check{\prime}}\) & \(\check{\check{\prime}}\) & \(\check{\check{\prime}}\) & 88.2 & 63.9 & 0.070 & 0.101 & 2.9 & 7.9 & 86.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of our DAO-YOLO model.  
DC2f, all three metrics show modest improvements, demonstrating the effectiveness of integrating these three components. Among them, the experiment results are optimal when all three are combined (the model in this paper). The experimental results demonstrate that the integrated model combining all three components delivers the optimal performance. Compared to the baseline YOLOv8, our approach achieves 3.2% and 4.4% improvements in [EMAIL_ADDRESS](%) and [EMAIL_ADDRESS]-0.95(%), respectively. Meanwhile, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 are reduced by 0.34 and 0.019 correspondingly. The number of parameters and computational GFLOPS decrease to 2.9 M and 7.9 G, while the FPS increases to 86.7.