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This research advances UAV detection by implementing feature-level fusion within a deep neural network architecture that integrates thermal, optronic, and radar modalities. By combining high-level features from heterogeneous sensors before classification, the proposed CNN-based approach achieves a superior F1-score of 0.95, demonstrating measurable performance gains over single-sensor or decision-level fusion baselines. The study highlights the efficacy of multi-modal data integration for addressing the limitations inherent in individual sensing systems for autonomous drone identification.
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
arXiv:2410.16089v3 Announce Type: replace Abstract: The cost, flexibility, and efficiency of modern UAVs make them attractive across many applications, but their proliferation has driven a rising number of malicious or accidental incidents, making UAV detection and classification mechanisms essential. Individual sensing modalities each present complementary limitations, and existing detection systems typically rely on a single sensor or fuse modalities only at the decision level, leaving the feature-level fusion of heterogeneous image and radar detectors largely unexplored. We propose a deep neural network that fuses high-level features extracted from the individual object-detection and classification models of thermal, optronic, and radar sensors. A CNN-based architecture combines the three modalities by stacking the thermal and optronic image features along the channel axis prior to fusion with the radar features. Evaluated on a real-world multi-sensor dataset, the proposed three-modality fusion model attains an F1-score of 0.95, compared to 0.93 for the dual-modality (thermal-optronic) configuration and 0.91 for the best-performing single-sensor (thermal) baseline, confirming that fusing complementary sensor features yields measurable gains in UAV classification performance.
Abstract:The cost, flexibility, and efficiency of modern UAVs make them attractive across many applications, but their proliferation has driven a rising number of malicious or accidental incidents, making UAV detection and classification mechanisms essential. Individual sensing modalities each present complementary limitations, and existing detection systems typically rely on a single sensor or fuse modalities only at the decision level, leaving the feature-level fusion of heterogeneous image and radar detectors largely unexplored. We propose a deep neural network that fuses high-level features extracted from the individual object-detection and classification models of thermal, optronic, and radar sensors. A CNN-based architecture combines the three modalities by stacking the thermal and optronic image features along the channel axis prior to fusion with the radar features. Evaluated on a real-world multi-sensor dataset, the proposed three-modality fusion model attains an F1-score of 0.95, compared to 0.93 for the dual-modality (thermal-optronic) configuration and 0.91 for the best-performing single-sensor (thermal) baseline, confirming that fusing complementary sensor features yields measurable gains in UAV classification performance.
8 pages, 6 figures. Accepted and published version. \c{opyright} 2026 IEEE. Published in: 2026 International Symposium on Networks, Computers and Communications (ISNCC), Bristol, UK, 8-10 Sept. 2026. An extended 12-page version is available as v2 of this record
Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2410.16089 [cs.AI]
(or arXiv:2410.16089v3 [cs.AI] for this version)
2026 International Symposium on Networks, Computers and Communications (ISNCC), Bristol, UK, 2026, pp. 1-8 2026 International Symposium on Networks, Computers and Communications (ISNCC), Bristol, UK, 2026, pp. 1-8
https://doi.org/10.1109/ISNCC70543.2026.11693863 DOI(s) linking to related resources
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