## 3 Experiment and Analysis
### Comparative Experiment
Figure 11: Visualized bar plots of [EMAIL_ADDRESS] corresponding to Table 4.  
The above results verify the significant improvement of the SL-YOLOv8 in the object detection task, and its superiority in multiple categories and overall detection accuracy is demonstrated. Figures 13 and 14 show the detailed evaluation results of the improved model on the Break-ID-1632 and cable damage datasets, covering the analysis of multiple important indicators, including precision, recall, P-R curve, and harmonic mean. It can be observed from the figure that the SL-YOLOv8 has superior performance on all the indicators, especially achieving extremely high detection accuracy while maintaining high recall. Further comparative analysis shows that compared with the basic model, the SL-YOLOv8 has a significant improvement in the accuracy of prediction results; especially in the target detection task under a complex background, the performance is particularly prominent. This result verifies the effectiveness and practicability of the proposed SL-YOLOv8 and provides strong support for subsequent research and practical application. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Break** & **Thunderbolt** & **[EMAIL_ADDRESS] (\%)** & **Para (M)** & **FLOPs (G)** \\ \hline Faster R-CNN & 0.75 & 0.883 & 82.5 & 50.0 & 200.0 \\ SSD & 0.70 & 0.865 & 80.5 & 25.0 & 100.0 \\ RetinaNet & 0.77 & 0.907 & 84.3 & 55.0 & 210.0 \\ EfficientDet-D1 & 0.75 & 0.825 & 83.2 & 6.6 & 6.2 \\ CenterNet-DLA & 0.73 & 0.745 & 78.7 & 22.5 & 102.2 \\ Deformable DETR & 0.71 & 0.725 & 73.0 & 34.0 & 188.0 \\ YOLOv5 & 0.79 & 0.926 & 86.7 & 15.0 & 30.0 \\ YOLOv6 & 0.81 & 0.934 & 87.8 & 12.0 & 28.0 \\ YOLOv7 & 0.82 & 0.942 & 88.4 & 11.0 & 27.0 \\ YOLOv8s & 0.83 & 0.955 & 89.6 & 10.5 & 26.5 \\ YOLOv8n & 0.84 & 0.979 & 90.2 & 10.2 & 25.5 \\ SL-YOLOv8 & 0.85 & 0.983 & 92.3 & 10.0 & 25.0 \\ \hline \end{tabular}
\end{table} 
Table 5: Results of contrast experiments on the cable damage dataset. Figure 12: Visualized bar plots of [EMAIL_ADDRESS] corresponding to Table 5.  
Figure 14: Visualization of the evaluation parameters for the cable damage dataset. Figure 13: Visualization of the evaluation parameters for the Break-ID-1632 dataset. Based on ablation experimental results and comparative analysis of multiple datasets, the SL-YOLOv8 model shows significant performance improvement in object detection tasks. On the Break-ID-1632 dataset, the [EMAIL_ADDRESS] of SL-YOLOv8 reaches 94.1%, which is 4.2 percentage points higher than that of the baseline YOLOv8 model (_p_ < 0.01, paired _t_-test). The contribution of each improved module is statistically significant (_p_ < 0.05, analysis of variance multiple comparisons). Especially in precision and recall, SL-YOLOv8 reaches 84.6% and 94.6%, respectively, which is significantly better than other comparison models (the effect size index of the _t_-test > 0.8). In the generalization evaluation across datasets, SL-YOLOv8 also performs well on the cable damage dataset, confirming the robustness of the model improvement. It is worth noting that despite the limited sample size (_n_ < 1000), the 95% confidence intervals [92.3%, 95.9%] estimated by the bootstrap method indicate the high reliability of the results. To evaluate the stability and variability of the model performance, we conduct a series of Monte Carlo experiments on the SL-YOLOv8 model, randomly varying the training and testing datasets. On the Break-ID-1632 dataset, after 10 independent experiments, the average [EMAIL_ADDRESS] of SL-YOLOv8 reaches 93.8% \(\pm\) 0.4% (mean \(\pm\) standard deviation). This result is not only significantly higher than the baseline YOLOv8 model (89.9% \(\pm\) 0.6%, \(p\) < 0.001, paired _t_-test) but also has a small standard deviation, indicating good stability of the model performance. In each category, SL-YOLOv8 also performs well. For example, for the \"damage\" class, the average precision is 94.6% \(\pm\) 0.5%, which is significantly better than the other comparison models (_p_ < 0.01, analysis of variance analysis).