一种利用雷达、音频与视频数据实现无人机检测及空中目标识别的多模态Transformer方法
A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data
AI总结：
针对单模态无人机检测与空中目标识别方案的局限性，提出融合雷达、音视频、红外数据的多模态Transformer模型，实现了高精度识别与实时推理性能。
摘要：
无人机（UAV）检测与空中目标识别是现代安防与监控领域的核心任务，亟需可突破单模态方案局限性的鲁棒系统。本研究针对上述挑战，设计并严格评估了一款新型多模态Transformer模型，该模型可融合雷达、可见光视频（RGB）、红外（IR）视频、音频四类异构数据流。该架构依托Transformer的自注意力机制有效融合各模态的差异化特征，学习全面、互补且具备高区分度的分类表征。该模型在独立测试集上表现优异，取得的宏平均指标分别为：准确率0.9812、召回率0.9873、精确率0.9787、F1值0.9826、特异度0.9954，尤其在区分无人机与其他空中目标时展现出极高的精确率与召回率。此外，计算分析验证了模型的高效性：其浮点运算量为1.09 GFLOPs，参数量为122万，推理速度达41.11 FPS，适配实时应用场景。本研究推动了空中目标分类领域的显著进展，验证了基于Transformer架构的多模态数据融合可实现当前最优性能，为复杂空域的无人机检测与监控提供了高精度、高鲁棒性的解决方案。
英文摘要：
Unmanned aerial vehicle (UAV) detection and aerial object recognition are critical for modern surveillance and security, prompting a need for robust systems that overcome limitations of single-modality approaches. This research addresses these challenges by designing and rigorously evaluating a novel multimodal Transformer model that integrates diverse data streams: radar, visual band video (RGB), infrared (IR) video, and audio. The architecture effectively fuses distinct features from each modality, leveraging the Transformer's self-attention mechanisms to learn comprehensive, complementary, and highly discriminative representations for classification. The model demonstrated exceptional performance on an independent test set, achieving macro-averaged metrics of 0.9812 accuracy, 0.9873 recall, 0.9787 precision, 0.9826 F1-score, and 0.9954 specificity. Notably, it exhibited particularly high precision and recall in distinguishing drones from other aerial objects. Furthermore, computational analysis confirmed its efficiency, with 1.09 GFLOPs, 1.22 million parameters, and an inference speed of 41.11 FPS, highlighting its suitability for real-time applications. This study presents a significant advancement in aerial object classification, validating the efficacy of multimodal data fusion via a Transformer architecture for achieving state-of-the-art performance, thereby offering a highly accurate and resilient solution for UAV detection and monitoring in complex airspace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
