## 4 Experimental Results and Analyses
### Ablation Experiment
#### 4.3.3 Ablation Experiments Using MSFA Module and Other Feature Aggregation Modules  
The MSFA module plays a crucial role in the model and is composed of BiFPN and LSKAB. Its primary function is to facilitate the effective fusion of multi-scale information by densely connecting the multi-layer feature maps generated by the EEMA-VGG16 sub-network. By leveraging the two weighting strategies of BiFPN and LSKA, the MSFA module dynamically adjusts the weight proportion of each feature map during the fusion process and simultaneously weights the fused pixel values. This approach significantly minimizes the interference from irrelevant features. To evaluate the actual effect of the MSFA module, we designed three different experimental configurations, in which the EEMA-VGG16 sub-network, the DMR module, and the multi-level supervision strategy are uniformly recorded as \"Others\".  
In the \"Others+FPN\" experimental setting, we use the classic FPN network to simply aggregate multiple feature maps. Subsequently, in the \"Others+BiFPN\" experimental setting, BiFPN is introduced instead of FPN, and the model's ability to capture and integrate key features is further enhanced through more complex feature aggregation methods and weighted fusion mechanisms between features. Finally, in the \"Others+MSFA\" experimental setting, LSKA is introduced based on BiFPN. The large kernel convolution simulates long-range dependencies, improves the perception of global information, and realizes the weighting of pixel values in the feature map, thereby significantly improving the overall counting performance. Table 9 lists the experimental results under three different settings. It can be seen that with the introduction and optimization of the module, the MAE of the model on the ShanghaiTech PartA dataset gradually decreased from 66.1 to 57.3, and the RMSE gradually decreased from 104.5 to 85.2. At the same time, on the self-built dataset DTH, compared with the FPN module, the MSFA module also showed superior performance, with MAE decreasing from 3.8 to 2.9 and RMSE decreasing from 5.0 to 4.1.  
Figure 11: Visualization of heatmaps using EMA and EEMA in the backbone network.  
This result fully demonstrates the generalization ability of the MSFA module on different datasets, as well as its effectiveness in fusing multi-scale information and enhancing the expression ability of network features.  
In addition, to further verify the rationality and superiority of the weighted mechanism design, we show the density map visualization results of three experimental settings in Figure 12, namely sub-images (c), (d), and (e), where (a) and (b) are the input image and the corresponding GT density map, respectively. It can be seen that the sub-image (c) corresponding to the \"Others+FPN\" experimental setting is most seriously affected by irrelevant features, resulting in poor quality and blur; the sub-image (d) corresponding to the \"Others+BiFPN\" experimental setting has improved quality; and the sub-image (e) corresponding to our \"Others+MSFA\" experimental setting has the best quality. In particular, by introducing large kernel convolution, our method can effectively capture ultra-large-scale information and further improve the adaptability to complex scenes.