## 3 Experiments and Analysis
### Ablation Experiment  
The training set serves to optimize the model's parameters. During the training process, the difference between the input data and the corresponding labels before and after the model training is quantified by computing the loss function. The aim is to perform parameter optimization by minimizing this loss function. The loss curve of the training set provides insights into the model's convergence speed and robustness. As depicted in Figure 11(a), a comparison is made between the training set's loss functions before and after the implementation of the improved algorithm proposed in this paper. The validation set is used for performance evaluation and parameter tuning at the end of the epoch to prevent overfitting. Here, the parameter tuning is mainly through hyperparameters, by choosing the best hyperparameter values to improve the performance such as generalization ability. The loss function example of its validation set is shown in Figure 11(b). The YOLOv8n algorithm switches to the WIoU loss function, which converges faster during the training process and the reduction in the total loss is more obvious.  
In order to assess the impact of the improvements made in the modules in this paper on the overall model performance, mAP was selected as the evaluation index of the model detection effect. A total of nine control groups were set up. The Precision-Recall Curve (P-R Curve) graphs before and after the improvement of YOLOv8n are shown in Figure 13; it can be seen that the mAP values of each classification target have been improved.  
Figure 14 below show the comparison curves of Recall, mAP, and Precision before and after the improvement of YOLOv8n. From these figures, it can be observed that all the models have converged, and the performance of the improved indicators has been enhanced.  
A comprehensive analysis of the model's boosting effect was carried out and the results obtained from its training are shown in Table 1. As can be seen from Table 1, firstly, Experiment I achieves an mAP of 94.8% with the original YOLOv8n model. Experiment II embeds SCConv in the C2f structure, which improves the mAP by 1.6% while reducing the computational effort. Experiment III replaces the detection head with SLCD, which reduces the computation by 25.6% while ensuring the mAP. Next, experiment IV introduces the Triplet structure, which improves the mAP by 1.6%, and experiment V uses WIoU instead of the original loss function, which improves the mAP by 2.8%. Experiment IXis the improved method proposed in this paper, and it can be seen that the detection is effectively improved: the recall rate reaches 96.5%, the mAP is able to reach 98.6%, and the precision P reaches 99.8%, which are improvements of 4.4%, 3.8%, and 3.1%, respectively, compared with the original model.  
Figure 12: Comparison of loss function curves before and after algorithm improvement.  
Figure 13: The comparison of P-R Curve before and after model improvement.  
Figure 14: Comparison of various metrics before and after algorithm improvement.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Experiment** & **SCConv** & **Triplet** & **SLD** & **WIoU** & **Recall** & **Precision** & **mAP** & **FLOPs/G** \\ \hline I & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.921 & 0.967 & 0.948 & 8.2 \\ II & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 0.936 & 0.983 & 0.964 & 8.1 \\ III & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 0.941 & 0.986 & 0.964 & 8.2 \\ IV & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 0.944 & 0.981 & 0.959 & 6.1 \\ V & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & 0.951 & 0.991 & 0.976 & 8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative results of ablation experiments.