The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering the need for the development of UAV detection and classification mechanisms essential. We propose a methodology for developing a system that fuses already processed multi-sensor data into a new Deep Neural Network to increase its classification accuracy towards UAV detection. The DNN model fuses high-level features extracted from individual object detection and classification models associated with thermal, optronic, and radar data. Additionally, emphasis is given to the model's Convolutional Neural Network (CNN) based architecture that combines the features of the three sensor modalities by stacking the extracted image features of the thermal and optronic sensor achieving higher classification accuracy than each sensor alone.

翻译：现代无人机凭借其独特的成本、灵活性、速度和效率优势，已成为当代社会众多应用中的理想选择。然而，这也导致恶意或意外事件的报告数量持续增长，使得开发无人机检测与分类机制变得至关重要。我们提出了一种系统开发方法，通过将已处理的多传感器数据融合至新型深度神经网络中，以提升其对无人机检测的分类精度。该DNN模型融合了从热成像传感器、光电传感器和雷达数据各自关联的目标检测与分类模型中提取的高层特征。此外，研究重点在于模型基于卷积神经网络的架构设计，该架构通过堆叠热成像传感器与光电传感器提取的图像特征，实现了三种传感器模态特征的融合，其分类精度显著优于单一传感器方案。