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 hi
The proliferation of UAVs for both legitimate and illicit purposes necessitates advanced detection and classification methods, making multi-sensor fusion a critical area of research.
Improved UAV detection and classification accuracy directly impacts national security, critical infrastructure protection, and public safety by mitigating threats from unauthorized drone activity.
The proposed methodology offers a more robust and accurate way to identify drones, potentially reducing false positives and improving response times compared to single-sensor systems.
- · Defense contractors
- · Security integrators
- · Air traffic control systems
- · Civilian infrastructure operators
- · Malicious drone operators
- · Organizations with inadequate counter-UAV measures
Enhanced ability to autonomously identify and categorize unauthorized UAVs in complex environments.
Development of more sophisticated counter-UAV systems leveraging advanced real-time classification data.
Potential for a drone 'arms race' between autonomous threat classification and stealth/evasion technologies.
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Read at arXiv cs.AI