## 6 Conclusions  
This paper proposes a multi-level supervised weighted feature fusion network (MS-WFFNet) to automatically count the number of migratory birds in each scene. By cross injecting EEMA into the truncated VGG16 structure, the network's ability to focus on the object is improved, and the noise interference caused by similar objects is effectively reduced. MS-WFFNet captures multi-scale feature representations by densely connecting the feature maps output by five EEMA modules and combines the weighting mechanism to reduce the influence of irrelevant features. In addition, the model adopts a combined loss function consisting of a global MSE loss and a multi-level SSIM loss to perform distributed supervision on the regression results of each path of the network, thereby ensuring the local consistency and spatial correlation between multiple regression results.  
Relying on local surveillance equipment in East Dongting Lake, we constructed a migratory bird counting dataset DTH and conducted relevant experiments. The experimental results show that MS-WFFNet can achieve efficient and accurate migratory bird counting. To further verify its generalization performance, we conducted experiments on three other commonly used object counting datasets. Compared with various mainstream counting methods, MS-WFFNet shows strong competitiveness. In addition, we conducted ablation experiments on public datasets to verify the effectiveness of the proposed components. Finally, we explore the limitations of MS-WFFNet and propose future improvements, including (1) introducing additional loss terms to enhance the constraint capability of the model and (2) replacing the fixed weight values of the loss terms with a set of learnable parameters to improve the adaptability and flexibility of the model.  
Conceptualization, [PERSON] ([PERSON]) and H.Z. ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), H.Z. ([PERSON]) and H.Z. ([PERSON]); formal analysis, [PERSON] ([PERSON]), Q.L. and G.L.; investigation, Y.K.; resources, H.Z. ([PERSON]), Q.L. and G.L.; data curation, Y.K.; writing--original draft, H.Z. ([PERSON]); writing--review and editing, H.Z. ([PERSON]), H.Z. ([PERSON]), Q.L., Y.K., G.L. and H.Z. ([PERSON]); visualization, H.Z. ([PERSON]); supervision, Y.K., Q.L. and G.L.; project administration, H.Z. ([PERSON]); funding acquisition, H.Z. ([PERSON]), Q.L. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Topic of Hunan Provincial Key Laboratory of Remote Sensing Monitoring of Ecological Environment in the Dongting Lake area (No: DTH Key Lab.2024-13).  
Not applicable.  
The data presented in this study can be obtained upon request from the corresponding author. Other datasets can be obtained from their original papers.  
We declare that this paper has no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MS-WFFNet Weighted Feature Fusion Network based on Multi-level Supervision  
EEMA Enhanced Efficient Multi-scale Attention  
VGG16 Visual Geometry Group 16-layer network  
MSFA Multi-source Feature Aggregation  
DMR Density Map Regression  
Faster Region-based Convolutional Neural Networks  
Mask R-CNN Mask Region-based Convolutional Neural Networks  
YOLO You Only Look Once  
PConv Pyramid Convolution  
AFPN Asymptotic Feature Pyramid Network