Bird strikes are considered a significant threat to civil aviation flight safety and economic operations. Real-time accurate detection of flying birds in the low altitude region of airports is regarded as a key technology to prevent bird strike accidents. However, the existing algorithms are often limited by insufficient accuracy in detecting minimal flying bird instances. To address this issue, an algorithm named ABRT-DETR(AirBirds RT-DETR) was proposed, which is based on the RT-DETR-R18 framework. Firstly, a new BasicBlock-PCR module was constructed and used instead of the original BasicBlock to enhance the feature extraction capability of flying bird instances, the model complexity and computational burden were significantly reduced while the precision was improved. Second, HS-FPN (high-level screening-feature fusion pyramid) was introduced, the high-level features are used to screen and fuse the low-level feature information, which enhances the expression effect of multi-level features, thus further improving the detection accuracy and reducing the computational burden. Finally, the original GIoU loss function was replaced by EIoU, optimizing the localization accuracy and overall detection performance of flying bird targets. The experimental results show that compared with the original RT-DETR-R18 model, the precision, recall and mAP50 are improved by 3.8%,2.5% and 3.2%, respectively, and the model’s parameters and FLOPs are greatly reduced, which provides an efficient and reliable technological solution for low-altitude bird monitoring at airports.