A dataset for multi-sensor drone detection
Fredrik Svanstr\"om, Fernando Alonso-Fernandez, Cristofer Englund

TL;DR
This paper introduces a comprehensive multi-sensor dataset for drone detection, including infrared, visible, and audio data, with annotations across different distances and drone types to facilitate research in UAV detection methods.
Contribution
It provides the first publicly available multi-sensor drone detection dataset with annotations across various distances, drone types, and sensor modalities, addressing key gaps in existing research.
Findings
Dataset includes infrared, visible, and audio data.
Annotations cover three distance categories based on industry standards.
Data collected from three Swedish airports in daylight conditions.
Abstract
The use of small and remotely controlled unmanned aerial vehicles (UAVs), or drones, has increased in recent years. This goes in parallel with misuse episodes, with an evident threat to the safety of people or facilities. As a result, the detection of UAV has also emerged as a research topic. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also an issue, despite its success with other targets. Besides, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well, although research in this direction is scarce too. To counteract the mentioned issues and allow fundamental studies with a common public benchmark, we…
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