## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.4 Comparative Experiments  
To verify the superiority of SimAM, Bi-Level Routing Attention, MLCA, Triplet Attention, CPCA, MPCA, SegNext Attention, and SimAM were added to the C2f module at the front end of the detection head for experimental comparison, and the experimental data obtained are shown in Table 9.  
According to the data presented in Table 9, SimAM demonstrates significant advantages in object detection tasks. Compared with other attention modules, SimAM performs best regarding mean Average Precision ([EMAIL_ADDRESS]/%) and Recall Rate (R%) and maintains the lowest levels of Params and FLOPs, thoroughly verifying its efficiency. It is worth noting that although some attention modules perform excellently in Precision (P%), they perform poorly in Recall Rate (R%). This may be due to the excessive complexity of these modules, which leads to a slowdown in model inference speed, thereby affecting the Recall Rate. In contrast, SimAM can achieve the best results in both mean Average Precision \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Baseline** & **SimAM** & **GhoHGBlock LSCDHead** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **Params/M** & **FLOPs/G** & **FPS** \\ \hline
0 & \(\surd\) & & & 88.5 & 64.8 & 3.00 & 8.1 & **188.4** \\
1 & \(\surd\) & \(\surd\) & & 92.8(\(\uparrow\)4.3\%) & 68.0(\(\uparrow\)3.2\%) & 3.00 & 8.1 & 179 \\
2 & \(\surd\) & \(\surd\) & \(\surd\) & **93.1(\(\uparrow\)0.3\%)** & 67.3(\(\downarrow\)0.7\%) & 2.31(\(\downarrow\)23.0\%) & 6.8(\(\downarrow\)16.1\%) & 154.5 \\
3 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 92.4(\(\downarrow\)0.7\%) & **68.2(\(\uparrow\)0.9\%)** & **1.67(\(\downarrow\)27.7\%)** & **5.3(\(\downarrow\)22.1\%)** & 151.3 \\ \hline \hline \end{tabular} Note: The upward (\(\uparrow\))and downward (\(\downarrow\)) arrows indicate the percentage change in performance compared to the previous row.  
\end{table} 
Table 8: Results of ablation experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attention** & **P\%** & **R\%** & **[EMAIL_ADDRESS]/\%** & **Params/M** & **FLOPs/G** \\ \hline Bi-Level Routing & **95.9** & 84.4 & 91.1 & 3.36 & 9.0 \\ Attention & & 91.5 & 81.8 & 89.3 & 3.00 & 8.1 \\ MICA & 93.7 & 81.4 & 91.9 & 3.00 & 8.1 \\ CPCA & 88.4 & 83.6 & 90.2 & 3.19 & 8.7 \\ MPCA & 89.8 & 88.2 & 89.3 & 3.00 & 8.1 \\ SegNext Attention & 93.5 & 86.4 & 92.3 & 3.14 & 8.4 \\ SimAM & 91.6 & **89.6** & **92.8** & **3.00** & **8.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative results of multiple attention mechanisms. ([EMAIL_ADDRESS]/%) and Recall Rate (R%) while maintaining high efficiency. This is primarily attributed to SimAM's comprehensive focus on channel and spatial positional features and the practical application of its energy function. In summary, SimAM outperforms other attention modules in object detection tasks. It performs best in [EMAIL_ADDRESS]/% and Recall Rate (R%) while maintaining lower Params and FLOPs. Thus, SimAM was selected as the attention module for this paper. To verify the superiority of the proposed APCR-YOLOv8 network, this paper listed the performance metrics comparison among Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv6n, YOLOv7-tiny, YOLOv8n, and our proposed model (OURS). Performance metrics include Precision (P%), Recall (R%), Average Precision at IOU = 0.5 ([EMAIL_ADDRESS]), Average Precision from IOU = 0.5 to 0.95 ([EMAIL_ADDRESS]-0.95), number of parameters in millions (Params/M), GFLOPs (billion floating-point operations per second), and frames per second (FPS). The comparison results are shown in Table 10.  
Based on the data analyzed in Table 10, APCR-YOLOv8 demonstrates the best performance in the emergency airdrop station detection task. Compared to the other models, APCR-YOLOv8 achieves the highest scores in both Precision and Recall, which fully demonstrates its accuracy in the target detection task. It is worth noting that APCR-YOLOv8 improves detection accuracy and optimizes model complexity and computational efficiency.