## 3 Experiment and Analysis
### Ablation Experiment  
To verify the effectiveness of the three improvement points proposed in this paper, YOLOv8 is selected as the baseline model for comparison, and the Break-ID-1632 and cable damage datasets are trained and tested in the same experimental environment. In the training process, a total number of training rounds of 120 epochs is set, and the input image size is uniformly set to 600 \(\times\) 600 pixels. In the experiments, the performance of each improved module is evaluated, including their impact on object detection accuracy, such as mAP, training stability, and convergence speed. Table 2 lists the experimental results of training and testing on the Break-ID-1632 dataset, where \"\(\vee\)\" is used to indicate that the module is added to YOLOv8, and \"\(\times\)\" indicates that it is not added. The test data of each module are compared to analyze their contribution to the model performance. These data include mAP values under different improvement modules, precision, recall for each category, and possible training time and resource consumption. Through these detailed experiments and results analysis, the impact of each improvement module on the overall object detection model's performance can be comprehensively evaluated to verify whether the proposed improvement points can effectively improve the accuracy and efficiency of the model. Figure 7 shows the precision, recall, and [EMAIL_ADDRESS] curve of the YOLOv8 model after adding SENet_v2 and LSK modules in detail. Comparison with the basic YOLOv8 model clearly shows the effectiveness of the three improvements on the performance of the model. These curves and metrics provide an intuitive understanding of the performance of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**YOLOv8** & **LSK** & **SENet_v2** & **Precision\%** & **Recall\%** & **[EMAIL_ADDRESS]\%** & **F1\%** \\ \hline \(\vee\) & \(\times\) & \(\times\) & 80.7 & 82.4 & 89.9 & 81.48 \\ \(\vee\) & \(\vee\) & \(\times\) & 83.5 & 90.8 & 90.5 & 86.98 \\ \(\vee\) & \(\times\) & \(\vee\) & 81.9 & 92.7 & 93.5 & 87.08 \\ \(\vee\) & \(\vee\) & \(\vee\) & 84.6 & 94.6 & 94.1 & 89.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of ablation experiments on the Break-ID-1632 dataset. improved modules under different conditions, thus validating their substantial contribution to the object detection task. Table 3 shows an overview of the training and testing experiments conducted on the cable damage dataset, where \"\(\surd\)\" indicates that a module has been integrated into the YOLOv8 model, and \"\(\times\)\" indicates that the module has not been adopted. The performance of each module is evaluated by comparing its performance on the test set. These evaluation metrics include the average precision under different improvement schemes, the precision and recall of each class, as well as the estimated training time and computing resource consumption. Through this series of detailed experiments and analysis, it is possible to comprehensively evaluate the contribution of each improvement module to the performance of the object detection model to verify whether the improvement measures successfully improve the accuracy and efficiency of the model. Figure 8 shows the precision, recall, and [EMAIL_ADDRESS] curve after introducing the SENet_v2 and LSK modules into the YOLOv8 model in detail. Compared with the original YOLOv8 model, it clearly shows the improvement effect of these two improvements on the performance of the model. These curves and metrics provide an intuitive understanding of the performance of the improved modules under different conditions, thus validating the substantial contribution they bring to the object detection task.