Authors: Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen, Jianfei Yang, Fen Liu, Jianping Li, Han Wang, Lihua Xie

Published on: February 06, 2024

Impact Score: 8.27

Arxiv code: Arxiv:2402.03706

Summary

• What is new: Introduction of MMAUD, a comprehensive Multi-Modal Anti-UAV Dataset that combines diverse sensory inputs for drone detection, making it unique compared to existing research.
• Why this is important: The growing challenge posed by small unmanned aerial vehicles (UAVs) potentially carrying harmful payloads or causing damage.
• What the research proposes: MMAUD dataset provides diverse sensory inputs including stereo vision, Lidars, Radars, and audio for better drone detection, classification, and trajectory estimation.
• Results: MMAUD enhances the fidelity of UAV threat detection and enables the development of more accurate and efficient solutions by closely simulating real-world scenarios.

Technical Details

Technological frameworks used: Multi-Modal Anti-UAV Dataset (MMAUD) with diverse sensory inputs.

Models used: UAV detection, type classification, trajectory estimation models.

Data used: Stereo vision, various Lidars, Radars, audio arrays, Leica-generated ground truth data.

Potential Impact

Security and surveillance, drone manufacturing, autonomous vehicle sectors, and companies developing UAV detection and mitigation technologies.

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