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Simple Summary: Knowing the species and numbers of birds in nature reserves is essential to achieving the goals of bird conservation. However, it still relies on inefficient and inaccurate manual monitoring methods, such as point counts conducted by researchers and ornithologists in the field. To address this difficulty, this paper explores the feasibility of using computer vision technology for wetland bird monitoring. To this end, we build a dataset of manually labeled wetland birds for species detection and implement taxonomic counts of ten wetland bird species using a deep neural network model with multiple improvements. This tool can improve the accuracy and efficiency of monitoring, providing more precise data for scientists, policymakers, and nature reserve managers to take targeted conservation measures in protecting endangered birds and maintaining ecological balance. The algorithm performance evaluation demonstrates that the artificial intelligence method proposed in this paper is a feasible and efficient method for bird monitoring, opening up a new perspective for bird conservation and serving as a reference for the conservation of other animals. To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation. [ABSTRACT FROM AUTHOR]