###### Abstract  
East Dongting Lake is an important habitat for migratory birds. Accurately counting the number of migratory birds is crucial to assessing the health of the wetland ecological environment. Traditional manual observation and low-precision methods make it difficult to meet this demand. To this end, this paper proposes a weighted feature fusion network based on multi-level supervision (MS-WFFNet) to count migratory birds. MS-WFFNet consists of three parts: an EEMA-VGG16 sub-network, a multi-source feature aggregation (MSFA) module, and a density map regression (DMR) module. Among them, the EEMA-VGG16 sub-network cross-injects enhanced efficient multi-scale attention (EEMA) into the truncated VGG16 structure. It uses multi-head attention to nonlinearly learn the relative importance of different positions in the same direction. With only a few parameters added, EEMA effectively suppresses the noise interference caused by a cluttered background. The MSFA module integrates a weighted mechanism to fully preserve low-level detail information and high-level semantic information. It achieves this by aggregating multi-source features and enhancing the expression of key features. The DMR module applies density map regression to the output of each path in the MSFA module. It ensures local consistency and spatial correlation among multiple regression results by using distributed supervision. In addition, this paper presents the migratory bird counting dataset DTH, collected using local monitoring equipment in East Dongting Lake. It is combined with other object counting datasets for extensive experiments, showcasing the proposed method's excellent performance and generalization capability. m  
migratory birds may indicate that protection measures are effective. Conversely, a decline in numbers necessitates an in-depth investigation into the causes. It also requires adjustments and optimizations to the protection strategy to ensure the continued improvement of the East Dongting Lake ecosystem and the effective conservation of migratory bird resources. Traditional methods for counting migratory birds (such as manual observation) have problems such as strong subjectivity and high cost and are easily affected by complex environmental factors. With the rapid development of computer vision in fields such as image recognition and object detection, the problem of automated counting has gradually attracted widespread attention. It has become a hot topic in object counting research. At present, object counting methods mainly include image processing-based methods, detection-based methods, and regression-based methods. Image processing-based methods [6; 7; 8; 9] extract and count the number of objects from images through techniques such as foreground-background segmentation and adhesion object separation. These methods perform well in counting regular objects with clear boundaries (such as the leaves and fruits of plants). However, for objects with large morphological differences and fuzzy boundaries (such as animals), this method relies on thresholding and contour detection, which makes parameter selection difficult and requires a lot of post-processing, thereby increasing the complexity of counting. Detection-based methods [10; 11; 12; 13] rely on object detection algorithms to identify and locate objects in images and then count them based on the detection results. This method is not limited by object morphology and can provide information about the size and location of the object, but its accuracy depends on the performance of the detection model. Training an accurate and robust detection model requires a large number of bounding box annotations, which is time-consuming and labor-intensive, and the quality of annotations directly affects the final detection effect. In addition, some studies have pointed out that object detection is more complex than object counting, so detection should not be used as a prerequisite for counting [14].