Birds pose a serious threat to the safe operation of aircraft. Among commonly used bird detection methods, manual detection exhibits lower accuracy while radar detection incurs higher costs. In order to solve the above mentioned problems, for small and medium-sized airports, an airport bird detection method based on an improved YOLOv8 model was proposed. Employing video surveillance equipment was used to detect birds allows for efficient detection accuracy and speed at a lower cost. Firstly, a feature fusion C3 (FFC3) module was designed to achieve multi-scale feature fusion at a finer level, followed by the selection of an appropriate channel number for the model to balance detection speed and accuracy. Secondly, a CBAM-spatial pyramid pooling fast (CSPPF) module was devised to enhance mAP@50 and detection speed by introducing a convolutional block attention module (CBAM) to SPPF module. Finally, two shortcomings in the original AirBirds dataset were identified and rectified, and the data augmentation technology was utilized. The results indicate that the improved YOLOv8 achieves a mAP@50 of 0.820, marking an improvement of 0.015 compared to the original YOLOv8, with a processing speed of 32 frames per second. The enhanced YOLOv8 meets the requirements of real-time and accurate bird detection at airports, offering a novel approach for bird detection in complex airport environments.