## 4 Experimental Design and Result Analysis
### Experimental Results and Analysis
#### 4.3.2 Ablation Experiment  
To assess the beneficial impact of the proposed modules on the YOLOv8n model, six series of ablation experiments were devised. On the YOLOv8n network model, improvements were made to the small target layer, most of the standard convolutions in the backbone network were replaced with RFCAConv, and the C2f_EMA module was introduced. The various improved modules were accumulated sequentially to complete the ablation experiments. The findings of the experimental study are displayed in Table 4. (Where indicates that the module was added and - indicates that the module was not added).  
The empirical findings show that, when compared to YOLOv8n, following the enhancement of the small object detection layer, the model's [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 both increased by 0.7%, while the number of parameters decreased by \(1.0\times 10^{6}\) and the model size also reduced by 1.8 MB. Furthermore, when using RFCAConv to replace the standard convolution in the backbone, the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 improved by 0.5% and 0.8%, respectively, compared to the original model. It is remarkable that following the simultaneous introduction of the small object detection layer and RFCAConv convolution, the model's detection performance was further enhanced, with the improvement in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 reaching 1.2% and 1.9%, respectively. In addition, the model's parameter count and size still decreased. On this basis, the addition of C2f_EMA further strengthened the model's capacity for feature perception, capturing more characteristic information, thereby further enhancing the model's performance. Compared to the original YOLOv8n model, the improved model achieved an increase of 2.2% and 2.6% in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95, respectively, while the parameter count decreased by 33.3% and the model size was reduced by 1.8 MB. The ablation experiments comprehensively proved that the advanced algorithm proposed in this paper not only boosted detection accuracy but also notably decreased the model's parameter count and size, achieving a comprehensive  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Improve the Small** \\ **Target Layer** \\ \end{tabular} & **RFCAConv** & **C2f\_EMA** & \begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **[EMAIL_ADDRESS]:0.95** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Params** \\ **10\({}^{6}\)** \\ \end{tabular} &
\begin{tabular}{c} **Model Size** \\ **(MB)** \\ \end{tabular} \\ \hline - & - & - & 93.5 & 64.3 & 3.0 & 6.2 \\ \(\blacktriangledown\) & - & - & 94.4 & 65.2 & 2.0 & 4.4 \\ - & \(\blacktriangledown\) & - & 94.0 & 65.1 & 3.0 & 6.3 \\ - & - & \(\blacktriangledown\) & 93.6 & 64.5 & 3.0 & 6.2 \\ \(\blacktriangledown\) & \(\blacktriangledown\) & - & 94.7 & 66.2 & 2.0 & 4.4 \\ \(\blacktriangledown\) & \(\blacktriangledown\) & **95.7** & **66.9** & **2.0** & **4.4** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of different improved ablation experiments on YOLOv8n.  
improvement in model performance. This enhancement effect is visually demonstrated in the comparison curve shown in Figure 8.