aiqbench analysis
This research advances AI-driven security by proposing a Deep Neural Network architecture that fuses thermal, optronic, and radar feature maps for superior UAV detection accuracy. The study demonstrates how combining high-level features from multiple sensor modalities within a CNN framework outperforms single-sensor approaches in classifying malicious or accidental drone incidents.
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
arXiv:2410.16089v2 Announce Type: replace Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2410.16089 [cs.AI]
(or arXiv:2410.16089v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2410.16089 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words
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