A Novel Anti-UAV Detection Method for Airport Safety Based on Style Transfer Learning and Deep Learning

Unmanned aerial vehicle (UAV) intrusions cause flight delays and disrupt airport operations, so accurate monitoring is essential for safety. To address the scarcity and mismatch of real-world training data in small-target detection, an anti-UAV approach is proposed that integrates style transfer learning (STL) with deep learning. An airport monitoring platform is established to acquire a real UAV dataset, and a Cycle-Consistent Generative Adversarial Network (CycleGAN) is employed to synthesize multi-scene images that simulate diverse airport backgrounds, thereby enriching the training distribution. Using these simulated scenes, a controlled comparison of YOLOv5/YOLOv6/YOLOv7/YOLOv8 is conducted, in which YOLOv5 achieves the best predictive performance with AP values of 93.95%, 98.09%, and 97.07% across three scenarios. On public UAV datasets, the STL-enhanced model (YOLOv5_STL) is further compared with other small-object detectors and consistently exhibits superior performance, indicating strong cross-scene generalization. Overall, the proposed method provides an economical, real-time solution for airport UAV intrusion prevention while maintaining high accuracy and robustness.