# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion
### Ablation Studies  
Ablation experiments are conducted on the TRANCOS, CARPK and PUCPR+ datasets to assess the efficacy of the proposed modules. Tables 3-5 present the ablation experiment results on the TRANCOS, CARPK, and PUCPR+ datasets, respectively. The tables show \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**TRANCOS**} & \multicolumn{2}{c}{**CARPK**} & \multicolumn{2}{c}{**PUCPR+**} & \multicolumn{2}{c}{**UCSD**} \\ \cline{2-9}  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline FamNet & 37.41 & 40.05 & 35.11 & 45.51 & 74.46 & 87.03 & - & - \\ VLCounter & - & - & 6.46 & 8.68 & 48.94 & 69.08 & - & - \\ CSRNet & 5.54 & 7.33 & **4.89** & **6.51** & 4.54 & 5.77 & 2.88 & 3.72 \\ SAFECount & 5.17 & 6.97 & 7.22 & 9.97 & 5.97 & 8.22 & **2.01** & **2.47** \\ \hline SCSAEN (ours) & **4.78** & **6.33** & 6.18 & 6.50 & **4.12** & **5.60** & 2.02 & 2.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results on four different datasets. that after introducing the SSA module into the Baseline model, the MAE improved by 0.28, 0.66, and 0.71, while the RMSE improved by 0.54, 1.75, and 0.79 on the respective datasets. This indicates that the SSA module effectively mitigates the challenges of feature extraction caused by the diversity of object angles and enhances the model's ability to capture object features. Regarding the CAE module, comparing the data from the third row and the first row in both tables, it is evident that both MAE and RMSE decreased, demonstrating that the CAE module's enhancement of critical features in the channel dimension positively contributes to object feature extraction. Furthermore, the combined effect of the two modules is reflected in the fourth row of Tables 3-5. The experimental results show that integrating the two modules yields better performance compared to adding either module individually. This finding highlights that feature information in the spatial and channel dimensions is complementary in object-counting tasks, enabling the model to comprehensively understand image content and improve its ability to count objects accurately. Furthermore, to verify the necessity of further separating the H and W directions in the similarity feature extraction of the spatial similarity-aware module, an ablation study was conducted on the TRANCOS and UCSD datasets. The experimental results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SSA** & **CAE** & **MAE** & **RMSE** \\ \hline Baseline & - & - & 7.22 & 9.97 \\ Baseline-SSA & \(\checkmark\) & - & 6.56 & 7.43 \\ Baseline-CAE & - & \(\checkmark\) & 6.89 & 7.43 \\ \hline SCSAEN & \(\checkmark\) & \(\checkmark\) & **6.18** & **6.50** \\ (ours) & \(\checkmark\) & \(\checkmark\) & **6.18** & **6.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments on the CARPK dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SSA** & **CAE** & **MAE** & **RMSE** \\ \hline Baseline & - & - & 5.17 & 6.97 \\ Baseline-SSA & \(\checkmark\) & - & 4.89 & 6.43 \\ Baseline-CAE & - & \(\checkmark\) & 5.01 & 6.77 \\ \hline SCSAEN & \(\checkmark\) & \(\checkmark\) & **4.78** & **6.33** \\ (ours) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments on the TRANCOS dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SSA** & **CAE** & **MAE** & **RMSE** \\ \hline Baseline & - & - & 5.97 & 8.22 \\ Baseline-SSA & \(\checkmark\) & - & 5.26 & 7.43 \\ Baseline-CAE & - & \(\checkmark\) & 5.51 & 7.43 \\ \hline SCSAEN & \(\checkmark\) & \(\checkmark\) & **4.12** & **5.60** \\ (ours) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ablation experiments on the PUCPR+ dataset. The results in Table 6 clearly demonstrate that the down-up-right-left sequential convolution order achieves the best performance in terms of MAE and RMSE evaluation metrics.