Yolov5-Lite-Based Real-Time UAV Detection for High-Security Zones: Ensuring Airspace Protection

In order to solve the problem of low accuracy of traditional UAV detection due to RF failure, electromagnetic interference, complex scenes, etc., this paper proposes a UAV detection method based on transfer learning and YOLOv5-Lite model, aiming to carry out efficient and accurate multi-target detection through computer vision technology. In this paper, a diverse dataset containing flying targets such as drones and birds is constructed, and the pre-trained model is optimized and trained on the self-built dataset enriched by data augmentation technology, so that it can cope with UAV detection tasks in various complex natural scenes. Experimental results show that YOLOv5-Lite has excellent performance in complex environments, especially in multi-target and small-target detection tasks, with high accuracy, low false alarm rate and fast inference ability. Compared with similar models, the proposed method has significant advantages in terms of detection accuracy, inference speed and model parameter quantity, and is especially suitable for scenarios with high real-time and resource-constrained applications.