With the development of autonomous driving technology, object detection in long tail scenarios has become one of the key challenges. Insufficient samples of rare or extreme cases (long tail data) in real-world scenarios limit the model's generalization ability. Therefore, this study proposes a solution based on virtual real transfer learning, combining YOLOv5 algorithm and Cycle Generative Adversarial Networks (CycleGAN) algorithm to achieve cross domain object detection optimization from virtual to real scenes Firstly, YOLOv5 is pretrained on a virtual dataset to learn universal features through its efficient detection capability; Subsequently, CycleGAN was introduced for unsupervised domain adaptation, transforming the virtual image style into a realistic real scene style while preserving the integrity of annotation information, thereby reducing the domain difference between virtual and real data. Experiments have shown that this method significantly improves model performance in object detection tasks in long tail scenes, with Mean Average Precision (mAP) improved compared to the baseline model, especially in rare categories such as special vehicles and extreme weather targets. This study provides a scalable technical path for solving the long tail problem in autonomous driving, and verifies the effectiveness of virtual real transfer learning in cross domain perception tasks.