# SkyEV: RGB-Event UAV detection and tracking dataset and baseline

arXiv: https://arxiv.org/abs/2607.18747 · HTML: https://mlsanity.com/paper/2607.18747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 57 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [Scene Adaptive Sparse Transformer for Event-based Object Detection](https://mlsanity.com/paper/2404.01882/) (2024)
- [Robust Visual Tracking by Segmentation](https://mlsanity.com/paper/2203.11191/) (2022)
- [Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines](https://mlsanity.com/paper/2406.14482/) (2024)
- [Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset](https://mlsanity.com/paper/2412.06647/) (2024)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)
- [CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras](https://mlsanity.com/paper/2401.02826/) (2024)
- [A Deep Learning Approach to Drone Monitoring](https://mlsanity.com/paper/1712.00863/) (2017)
- [Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System](https://mlsanity.com/paper/2306.15767/) (2023)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors](https://mlsanity.com/paper/2403.11875/) (2024)
- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)

## Abstract (arXiv)

Detecting UAVs in air spaces has become increasingly important due to UAVs widespread availability and easy usage. However, due to their small size, they are typically difficult to detect at a sufficient range. For the training of optimized detection algorithms, datasets have been published, covering optical sensing methods ranging from infrared to regular RGB to event-sensor-based. However, these datasets often fail to reflect realistic counter-UAV scenarios, lacking critical factors such as camera ego-motion, extremely small target scales, and diverse lens configurations, and introduce compression artefacts on the frame images. To address this gap, we introduce SkyEV, an open-source dataset featuring highly synchronized uncompressed RGB and event-based data. SkyEV distinguishes itself by capturing complex real-world conditions, including significant camera motion and varied optical setups, which are essential for testing the fundamental trade-off between Field of View and detection range. Furthermore, we provide a unified data loader and establish an experimental baseline using a multi-modal architecture, demonstrating the dataset's efficacy in detecting challenging, small-scale targets.