## 5 Discussion  
Object counting is a key research direction in computer vision, with methods broadly categorized into image processing, detection, and regression approaches. Image processing-based methods leverage image enhancement, classification, and morphological operations to differentiate object regions from the background, enabling accurate counting. Detection-based methods rely on object detection models trained to identify and generate bounding boxes for objects, while regression-based methods utilize simpler point annotations. Among regression approaches, density map-based methods are particularly effective in complex scenarios. For objects with regular shapes and well-defined boundaries, such as plant leaves and fruits, image processing-based methods can greatly enhance counting accuracy. However,  
Figure 12: Visualization of results using different feature aggregation modules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Setting**} & \multirow{2}{*}{**Params (M)**} & \multicolumn{2}{c}{**ShanghaiTech PartA**} & \multicolumn{2}{c}{**DTH**} \\ \cline{3-5}  & & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline Others + FPN & 15.19 & 66.1 & 104.5 & 3.8 & 5.0 \\ Others + BiFPN & 14.62 & 60.0 & 92.9 & 3.5 & 4.8 \\ Others + MSFA (Ours) & 14.77 & **57.3** & **85.2** & **2.9** & **4.1** \\ \hline \hline \end{tabular} Note: the best results are highlighted in **bold.**  
\end{table} 
Table 9: Experimental results of applying different feature aggregation modules on the ShanghaiTech PartA dataset and the DTH dataset. for objects with significant morphological variation and indistinct boundaries, like animals, such methods face challenges in thresholding and contour detection, often complicating the counting process. Detection-based methods, conversely, are less constrained by object morphology but heavily depend on the performance of the detection model. Training such models requires extensive manual annotation, and the process is both time-intensive and labor-intensive. Given this, we proposed the MS-WFFNet model. As a method based on density map regression, this method can not only handle objects of different forms but also does not require complex bounding box annotation, thereby reducing the cost of data preparation. On the DTH dataset, MS-WFFNet outperforms the mainstream RPNet in counting accuracy: MAE is reduced by 6.4% and MSE is reduced by 8.9%. On the ShanghaiTech PartA dataset, compared with the latest RAQNet, MS-WFFNet's MAE is reduced by 2.9% and MSE is reduced by 15.8%. In addition, MS-WFFNet also performs well on the URCAL and PUCPR+ datasets. The performance improvement of MS-WFFNet is mainly attributed to three innovative designs. First, by introducing EEMA, after the one-dimensional global pooling of input features in the H and W directions, the multi-head attention mechanism is used to learn the relative importance of different positions in the same direction in a nonlinear way, thereby effectively capturing complex dependencies. Cross-injecting EEMA into the truncated VGG16 structure significantly enhances the model's ability to focus on the object area. Secondly, the fusion process of multi-source feature maps is optimized by combining the weighting strategies of BiFPN and LSKA. While redistributing the fusion weights, the local structure and global dependency are fully captured, and a more accurate multi-scale feature representation is constructed. Finally, a combined loss function consisting of a global MSE loss and a multi-level SSIM loss is designed to impose distributed supervision on the regression results of each path of the network, which not only improves the overall accuracy of the density map but also improves the fidelity of local details while showing excellent performance in the migratory bird counting task. In addition, we conducted ablation experiments on these three innovations on public datasets to further verify their effectiveness.