## 3 Experiment and Analysis
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**YOLOv8** & **LSK** & **SENet_v2** & **Precision\%** & **Recall\%** & **[EMAIL_ADDRESS]\%** & **F1\%** \\ \hline \(\surd\) & \(\times\) & \(\times\) & 91.1 & 88.4 & 91.1 & 81.55 \\ \(\surd\) & \(\surd\) & \(\times\) & 91.6 & 89.3 & 91.8 & 86.76 \\ \(\surd\) & \(\times\) & \(\surd\) & 92.7 & 89.2 & 91.5 & 86.73 \\ \(\surd\) & \(\surd\) & \(\surd\) & 93.8 & 90.7 & 92.3 & 89.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments on the cable damage dataset. Figure 8: Visual comparison of the cable damage dataset. Figure 7: Visual comparison of the Break-ID-1632 dataset. Experimental verification shows that the method proposed in this paper significantly improves the detection accuracy of small targets in complex backgrounds. For the Break-ID-1632 dataset, when SENet_v2 is introduced into YOLOv8n alone, compared with the original algorithm, the precision is improved by 1.2%, and the recall is improved by 10.3%. That is an increase of 3.6 percent [EMAIL_ADDRESS]. When the LSK module is introduced alone, the precision accuracy is improved by 2.8%, the recall accuracy is improved by 8.4%, and the [EMAIL_ADDRESS] accuracy is improved by 0.6%. After introducing the SENet_v2 and LSK module at the same time, the precision is 3.9% higher than the original algorithm, the recall is 12.2% higher than the original algorithm, and the [EMAIL_ADDRESS] is 4.2% higher than the original algorithm. The detection accuracy of various small targets is improved. For the cable damage dataset, introducing SENet_v2 alone into YOLOv8n improves the accuracy by 1.6%, the recall accuracy by 0.8%, and the [EMAIL_ADDRESS] accuracy by 0.4%. Introducing the LSK module alone improves precision by 0.5%, recall by 0.9%, and [EMAIL_ADDRESS] by 0.7% compared to the original algorithm. After introducing the SENet_v2 and LSK module at the same time, the precision is increased by 2.7%, the recall is increased by 2.3%, and the [EMAIL_ADDRESS] is increased by 1.2%. These research results show that the introduced SENet_v2 and LSK modules effectively enhance the model's ability to extract fine-grained features from remote sensing images, thereby improving the overall detection accuracy of the model. The SENet_v2 module can capture the important feature information in the image more accurately through its channel attention mechanism. The experimental results show that the mAP of the model is increased by 3.6% after adding SENet_v2. This indicates that SENet_v2 is essential in enhancing the feature representation ability and detection performance. The LSK module effectively expands the receptive field of the model and enhances the recognition ability of complex patterns in images through its large convolution kernel structure. The experimental data show that the introduction of the LSK module improves the average detection accuracy of the model by 0.6%. This improvement significantly optimizes the model's performance when dealing with high-resolution remote sensing images. These improvements do not significantly increase the number of parameters and storage requirements of the model, and maintain the lightweight characteristics of the model. The introduction of the SENet_v2 and LSK module significantly improves the feature extraction ability and detection accuracy of the model, which verifies the effectiveness of these improvements in remote sensing image analysis. The comparison curves of the experimental results between the original algorithm and the improved algorithm on the Break-ID-1632 and cable damage datasets are shown in Figure 9. As the training epoch increases, [EMAIL_ADDRESS] of the SL-YOLOv8 algorithm gradually increases relative to the YOLOv8 algorithm. Figure 10 shows the comparison of the visual results of YOLOv8 and the SL-YOLOv8 in this paper on the Break-ID-1632 dataset and cable damage dataset. The results show that the proposed model significantly reduces the false detection and missed detection, and has higher recognition accuracy.