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、速度与效率优势，在当代社会的诸多应用领域中成为极具吸引力的选择。然而，这也导致恶意或意外事件报告数量持续增长，使得开发无人机检测与分类机制的需求日益迫切。本文提出一种系统开发方法，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，以提升无人机检测的分类准确率。该深度神经网络模型融合了从热成像、光电与雷达数据相关联的独立目标检测与分类模型中提取的高层特征。此外，研究重点阐述了模型基于卷积神经网络（CNN）的架构设计：通过堆叠热成像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，从而获得了高于单一传感器的分类准确率。