融合热成像、可见光与雷达特征，提升无人机分类准确率
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
- 通过堆叠多传感器提取的高层特征，构建融合模型
- 在真实数据集上实现98.7%分类准确率，优于单一传感器
- 适合安防监控与反无人机系统开发者参考
现代无人机因成本低、灵活性强、速度快、效率高，在当代社会诸多领域备受青睐。然而，此类无人机引发的恶意或意外事件日益增多，因此发展无人机检测与分类机制势在必行。本文提出一种方法，将已处理的多传感器数据（热成像、可见光、雷达）融合至新型深度神经网络中，以提升无人机分类精度。该DNN模型融合了各传感器对应目标检测与分类模型提取的高层特征。特别地，模型采用基于卷积神经网络（CNN）的架构，通过堆叠热成像与可见光传感器的图像特征，实现了高于单一传感器的分类性能。实验在真实场景数据集上验证，整体分类准确率达到98.7%。
原文摘要 · Abstract (English)
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.
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