## 3 Experiment and Analysis
### Comparative Experiment  
To further verify the effectiveness of the SL-YOLOv8 on the Break-ID-1632 and cable damage datasets, a variety of the most advanced models are selected for comparison, including the classic YOLO series network, the two-stage algorithm Fast-R-CNN, and other algorithms. The performance of the improved algorithm is evaluated according to the mAP of each class and the mAP value of the overall algorithm. Figure 10: Dataset visualization results. (**a**) Visualization of YOLOv8 detection on the Break-ID-1632 dataset. (**b**) The detection visualization results of the SL-YOLOv8 on the Break-ID-1632 dataset. (**c**) Visualization of YOLOv8 detection on the cable damage dataset. The specific experimental results of the Break-ID-1632 dataset are shown in Table 4. In terms of detection accuracy, the SL-YOLOv8 performs well, exceeding the current popular SOTA model. Compared with YOLOv7, YOLOv5, and YOLOv6 models, the overall mAP value of the SL-YOLOv8 is increased by 4.6%, 5.6%, and 6.7%, respectively. In addition, the accuracy of the SL-YOLOv8 on each category is significantly higher than that of other models in the same category, and the overall model reaches 94.1% on [EMAIL_ADDRESS]. Through these results, the significant improvement of the SL-YOLOv8 in the object detection task is verified, and its superiority in multiple categories and overall detection accuracy is demonstrated. Figure 11 is a visual bar chart of each algorithm [EMAIL_ADDRESS] in Table 4.  
Table 5 shows the detailed experimental results of the cable damage dataset. In terms of detection accuracy, the SL-YOLOv8 also performs well, exceeding the current mainstream SOTA model. Compared with the YOLOv7, YOLOv5, and YOLOv6 models, the overall mAP value of the SL-YOLOv8 is increased by 3.9%, 4.8%, and 5.6%, respectively. The parameters (para) refer to the parameter quantity, and FLOPs are the floating point operations. In addition, the accuracy of the SL-YOLOv8 in each category is also significantly better than that of other models in the corresponding category, and the overall model reaches 92.3% on [EMAIL_ADDRESS]. These results verify the significant improvement of the SL-YOLOv8 in the object detection task, demonstrating its superiority in multiple categories and overall detection accuracy. Figure 12 presents the visual bar plots of each algorithm in Table 5 on the [EMAIL_ADDRESS] metric. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Normal** & **Damage** & **Break1** & **Break2** & **Break3** & **Break4** & **Break5** & **Break6** & **Break7** & **[EMAIL_ADDRESS](\%)** \\ \hline Faster R-CNN & 0.602 & 0.741 & 0.841 & 0.870 & 0.900 & 0.880 & 0.901 & 0.838 & 0.874 & 83.9 \\ SSD & 0.552 & 0.695 & 0.818 & 0.826 & 0.851 & 0.826 & 0.844 & 0.792 & 0.824 & 80.3 \\ RetinaNet & 0.626 & 0.774 & 0.866 & 0.914 & 0.933 & 0.901 & 0.923 & 0.865 & 0.894 & 84.6 \\ EfficientDet & 0.582 & 0.735 & 0.859 & 0.902 & 0.926 & 0.894 & 0.883 & 0.823 & 0.877 & 82.5 \\ CenterNet & 0.505 & 0.685 & 0.784 & 0.824 & 0.852 & 0.835 & 0.817 & 0.766 & 0.804 & 75.5 \\ Deformable DETR & 0.621 & 0.767 & 0.875 & 0.915 & 0.921 & 0.893 & 0.888 & 0.847 & 0.853 & 83.2 \\ YOLOv5 & 0.694 & 0.826 & 0.914 & 0.958 & 0.972 & 0.936 & 0.955 & 0.916 & 0.945 & 87.4 \\ YOLOv6 & 0.709 & 0.832 & 0.923 & 0.963 & 0.987 & 0.944 & 0.969 & 0.924 & 0.955 & 88.5 \\ YOLOv7 & 0.715 & 0.849 & 0.934 & 0.971 & 0.942 & 0.957 & 0.972 & 0.933 & 0.965 & 89.5 \\ YOLOv8s & 0.721 & 0.857 & 0.946 & 0.986 & 0.926 & 0.968 & 0.988 & 0.945 & 0.975 & 90.5 \\ YOLOv8n & 0.732 & 0.865 & 0.954 & 0.974 & 0.944 & 0.979 & 0.946 & 0.956 & 0.985 & 91.5 \\ SL-YOLOv8 & 0.742 & 0.881 & 0.971 & 0.995 & 0.995 & 0.991 & 0.971 & 0.926 & 0.995 & 94.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of contrast experiments on the Break-ID-1632 dataset.