Unmanned Aerial Vehicle (UAV) imagery has emerged as a critical data source in remote sensing, playing an important role in vehicle detection for intelligent traffic management and urban monitoring. Deep learning–based detectors rely heavily on large-scale, high-quality annotated datasets, however, collecting and labeling real-world UAV data are both costly and time-consuming. Owing to its controllability and scalability, synthetic data has become an effective supplement to address the scarcity of real data. Nevertheless, the significant domain gap between synthetic data and real data often leads to substantial performance degradation during real-world deployment. To address this challenge, this paper proposes a high-fidelity synthetic data generation framework designed to reduce the Sim2Real gap. First, UAV oblique photogrammetry is utilized to reconstruct real-world 3D model, ensuring geometric and textural authenticity; second, diversified rendering strategies that simulate real-world illumination and weather variations are adopted to cover a wide range of environmental conditions; finally, an automated ground-truth generation algorithm based on semantic masks is developed to achieve pixel-level precision and cost-efficient annotation. Based on this framework, we construct a synthetic dataset named UAV-SynthScene. Experimental results show that multiple mainstream detectors trained on UAV-SynthScene achieve competitive performance when evaluated on real data, while significantly enhancing robustness in long-tail distributions and improving generalization on real datasets.