## 4 Experimental Results and Analyses
### Comparison with Other Methods  
#### 4.2.1 Experiments on the DTH Dataset  
We selected six representative object counting algorithms, conducted experiments on the DTH dataset, and compared them with the MS-WFFNet model proposed in this article. The specific results are shown in Table 3. From the perspective of parameter size, MCNN has the smallest number of parameters, only 0.12 M, but its counting accuracy is low, with an MAE and RMSE of 8.1 and 10.4, respectively. In comparison, the parameter size of CSRNet is significantly increased to 15.51 M while reducing MAE and RMSE to 3.1 and 4.0, significantly improving performance. The mainstream algorithms FIDTM and RPNet [47] both reach 3.1 in the MAE index, but the RMSE of FIDTM is 4.2, which is slightly better than RPNet and shows better stability. The parameter size of ASANet is 19.00 M, but its MAE and RMSE are 6.8 and 8.3, respectively, so its performance is relatively low.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline CPU & AMD Ryzen 9 7950X \\ GPU & NVIDIA GTX 4090 \\ Memory & 128 GB \\ Operating System & Windows 11 \\ CUDA & CUDA 11.8 \\ Pytorch & Pytorch 2.0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental running software configuration and hardware configuration.  
In comparison, the proposed MS-WFFNet achieves the best MAE (2.9) and a competitive RMSE (4.1) while maintaining a low number of parameters (14.77 M). This result shows that MS-WFFNet achieves a good balance between counting accuracy and model efficiency. This is mainly due to the design of the MSFA module, which enables it to perform better when dealing with objects of different scales. In addition, although RPNet and FIDTM perform well overall, they are easily disturbed when dealing with occlusions and may misdetect a single object as multiple objects.  
As shown in Figure 7, we selected four representative images from the DTH dataset and showed the prediction results of MS-WFFNet and several classic counting algorithms. The first and second columns are the original image and its GT density map, respectively, and the last five columns are the prediction results of MS-WFFNet, RPNet, FIDTM, CSRNet, and MCNN. Overall, the predicted density map generated by MS-WFFNet and the predicted point map of FIDTM are particularly outstanding in terms of quality and authenticity and can accurately reflect the spatial distribution of migratory birds. From the perspective of local details, due to the small receptive field of the network model of MCNN and the simple feature fusion, the predicted density map generated by it is relatively fuzzy, and the ability to generate object details is insufficient; in contrast, MS-WFFNet performs feature learning by regressing the density map and shows obvious advantages in the recognition of the features of the entire migratory bird. From the prediction results of the first and fourth rows, it can be seen that RPNet and FIDTM are prone to multi-object misdetection when facing complex backgrounds such as branches, while MS-WFFNet shows stronger robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Venue** & **Params** & **MAE** & **RMSE** \\ \hline MCNN [35] & CVPR 2016 & 0.12 M & 8.1 & 10.4 \\ CSRNet [48] & CVPR 2018 & 15.51 M & 3.1 & 4.0 \\ SANet [43] & ECCV 2018 & 1.31 M & 6.7 & 8.9 \\ FIDTM [44] & TMM 2022 & 63.50 M & 3.1 & 4.2 \\ RPNet [47] & Crop 2023 & 19.69 M & 3.1 & 4.5 \\ ASANet [46] & JFJ 2024 & 19.00 M & 6.8 & 8.3 \\ Ours & This Paper & 14.77 M & **2.9** & **4.1** \\ \hline \hline \end{tabular} Note: the best results are highlighted in **bold**, and the second-best results are double-underlined.  
\end{table}
Table 3: Experimental results on the DTH dataset.  
Figure 7: Visualization results of the various methods on the DTH dataset.