Reliable drone detection is challenging due to limited annotated real-world data, large appearance variability, and the presence of visually similar distractors such as birds. To address these challenges, this paper introduces SimD3, a large-scale high-fidelity synthetic dataset designed for robust drone detection in complex aerial environments. Unlike existing synthetic drone datasets, SimD3 explicitly models drones with heterogeneous payloads, incorporates multiple bird species as realistic distractors, and leverages diverse Unreal Engine 5 environments with controlled weather, lighting, and flight trajectories captured using a 360 six-camera rig. Using SimD3, we conduct an extensive experimental evaluation within the YOLOv5 detection framework, including an attention-enhanced variant termed Yolov5m+C3b, where standard bottleneck-based C3 blocks are replaced with C3b modules. Models are evaluated on synthetic data, combined synthetic and real data, and multiple unseen real-world benchmarks to assess robustness and generalization. Experimental results show that SimD3 provides effective supervision for small-object drone detection and that Yolov5m+C3b consistently outperforms the baseline across in-domain and cross-dataset evaluations. These findings highlight the utility of SimD3 for training and benchmarking robust drone detection models under diverse and challenging conditions.

翻译：可靠的无人机检测因标注真实世界数据有限、外观变化大以及存在鸟类等视觉相似干扰物而具有挑战性。为应对这些挑战，本文提出了SimD3，这是一个专为复杂空中环境中鲁棒无人机检测而设计的大规模高保真合成数据集。与现有合成无人机数据集不同，SimD3明确对携带异构有效载荷的无人机进行建模，引入多种鸟类物种作为真实干扰物，并利用多样化的Unreal Engine 5环境，通过360度六相机阵列捕获受控的天气、光照和飞行轨迹。基于SimD3，我们在YOLOv5检测框架内进行了广泛的实验评估，包括一种名为Yolov5m+C3b的注意力增强变体，其中标准的基于瓶颈结构的C3模块被C3b模块取代。模型在合成数据、合成与真实数据的组合数据以及多个未见过的真实世界基准测试上进行了评估，以检验其鲁棒性和泛化能力。实验结果表明，SimD3为小目标无人机检测提供了有效的监督，并且Yolov5m+C3b在域内和跨数据集评估中均持续优于基线模型。这些发现凸显了SimD3在多样化和挑战性条件下训练与评估鲁棒无人机检测模型的实用性。