AI 中文总结
本研究针对热成像无人机检测难题，提出合成数据优先的训练策略，结合合成场景生成与真实数据微调，证实数据集对齐对性能影响强于模型规模，为合成与真实红外数据的结合应用奠定基础。
AI 中文摘要
地面对空（G2A）无人机在中波和长波红外（MWIR/LWIR）图像中的检测极具挑战性，原因在于纹理信息减少、传感器噪声、热对比度弱以及标注数据稀缺。本研究提出一种以合成数据为先的训练策略，将合成场景生成与真实数据微调相结合。研究表明，合成数据为学习初始目标表示提供了有效基础，而真实领域内的热图像对于可靠部署仍至关重要；即便少量真实红外数据也能大幅缩小领域差距。实验显示，数据集对齐对性能的影响强于模型规模。最后，对数据集的分析表明，特征空间中的语义对齐是模型性能的最强预测指标，而熵和动态范围等辐射特性也有助于提升检测鲁棒性。本研究为结合合成与真实红外数据开展有效的G2A无人机检测奠定了基础。
英文摘要
Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the dataset suggests that semantic alignment in feature space is the strongest predictor of model performance, while radiometric properties such as entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.
CommentsTo be presented at SPIE: Sensors + Imaging, Artificial Intelligence for Security and Defence Applications IV
