###### Abstract
To reduce the annotation cost and improve the feasibility of the method, researchers have proposed regression-based methods [15; 16; 17; 18]. This method uses cheaper point annotations for object counting, avoiding the expensive cost of bounding box annotation, and has become an important direction of object counting research in recent years. However, this type of method still faces two major challenges: interference from similar backgrounds and interference from irrelevant features. The former refers to the influence of noise caused by the background similar to the object, while the latter comes from the fact that when different feature maps are fused, the importance differences in each feature map are not fully considered and all input features are treated equally, thus introducing noise into the fusion process and affecting the counting accuracy. In response to the above challenges, we propose a weighted feature fusion network based on multi-level supervision (MS-WFFNet) for regression counting tasks. The network consists of an EEMA-VGG16 sub-network, a multi-source feature aggregation (MSFA) module, and a density map regression (DMR) module. The EEMA-VGG16 sub-network integrates enhanced efficient multi-scale attention (EEMA) into the truncated VGG16 structure. This approach effectively suppresses similar background interference and enhances the robustness of feature extraction. The MSFA module employs a multi-source information aggregation design to preserve both low-level detailed information and high-level semantic information. It combines the inter-feature weighting approach of BiFPN [19] with the intra-feature weighting mechanism of LSKA [20] to minimize irrelevant feature interference and enhance the expression of key features. The DMR module uses depth-wise convolution to regress the density map while maintaining the feature extraction capabilities of ordinary convolutions, reducing the number of parameters and computing costs and improving network efficiency. In short, the innovations of this article mainly include the following points:  
* We propose EEMA, which uses two multi-head attention mechanisms to learn the relative importance of different positions of feature maps in the H and W directions in a nonlinear way to capture more complex dependencies. In addition, EEMA is cross-injected into the truncated VGG16 structure to enhance the model's ability to focus on the object area. * Combining the design of BiFPN and LSKA, the weights of different feature maps during fusion and the weight of each pixel after fusion are redistributed, reducing the interference of irrelevant features while capturing local structures and global dependencies, thereby constructing a multi-scale feature representation. * A combined loss function consisting of a global MSE loss and a multi-level structure similarity index measure (SSIM) [21] loss is used to implement distributed supervision on the regression results of each path of the network. While improving the overall accuracy of the predicted density map and the fidelity of the local structure, the gradient propagation path is shortened, effectively alleviating the difficulty of parameter updating. * We introduced a dataset DTH on migratory bird counts in East Dongting Lake, which contains 400 high-resolution images and 38,634 precise point annotations. A large number of experiments and ablation studies were conducted on this dataset and multiple other public datasets, verifying the excellent counting performance of MS-WFFNet in different scenarios, as well as the effectiveness and practicality of its components.