Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors

• URL: http://arxiv.org/abs/2403.11875v1
• Date: Mon, 18 Mar 2024 15:27:58 GMT
• Title: Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors
• Authors: Jakub Mandula, Jonas Kühne, Luca Pascarella, Michele Magno,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. This paper presents F-UAV-D (Fast Unmanned Aerial Vehicle Detector), an embedded system that enables fast-moving drone detection.
• Score: 6.03212980984729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. However, uncontrolled access to restricted areas threatens privacy and security. Thus, prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. Although active scanning, mainly based on radars, is one of the most accurate technologies, it can be expensive and less versatile than passive inspections, e.g., object recognition. Dynamic vision sensors (DVS) are bio-inspired event-based vision models that leverage timestamped pixel-level brightness changes in fast-moving scenes that adapt well to low-latency object detection. This paper presents F-UAV-D (Fast Unmanned Aerial Vehicle Detector), an embedded system that enables fast-moving drone detection. In particular, we propose a setup to exploit DVS as an alternative to RGB cameras in a real-time and low-power configuration. Our approach leverages the high-dynamic range (HDR) and background suppression of DVS and, when trained with various fast-moving drones, outperforms RGB input in suboptimal ambient conditions such as low illumination and fast-moving scenes. Our results show that F-UAV-D can (i) detect drones by using less than <15 W on average and (ii) perform real-time inference (i.e., <50 ms) by leveraging the CPU and GPU nodes of our edge computer.

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