# Beyond Clear Skies: Synthetic Seasonal and Weather Variations for Real-World Drone Detection

arXiv: https://arxiv.org/abs/2608.16191 · HTML: https://mlsanity.com/paper/2608.16191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [TAM: Temporal Adaptive Module for Video Recognition](https://mlsanity.com/paper/2005.06803/) (2020)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images](https://mlsanity.com/paper/2405.20469/) (2024)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [A dataset for multi-sensor drone detection](https://mlsanity.com/paper/2111.01888/) (2021)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection](https://mlsanity.com/paper/2411.05633/) (2024)
- [Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts](https://mlsanity.com/paper/2305.16450/) (2023)
- [DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains](https://mlsanity.com/paper/2412.04789/) (2024)
- [YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection](https://mlsanity.com/paper/2406.11641/) (2024)
- [LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges](https://mlsanity.com/paper/2508.03331/) (2025)
- [Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments](https://mlsanity.com/paper/2509.14012/) (2025)
- [Detector-Augmented SAMURAI for Long-Duration Drone Tracking](https://mlsanity.com/paper/2601.04798/) (2026)

## Abstract (arXiv)

Reliable drone detection under real-world deployment conditions requires training data that spans the full operational design domain, including adverse weather and seasonal appearance variation. However, acquiring and annotating such data at scale remains highly resource-intensive, as adverse-weather conditions are inherently difficult to control, reproduce, and sample systematically. Existing datasets therefore typically provide only limited coverage of such conditions. Conversely, synthetic data offers a scalable alternative: environmental variation becomes controllable, while modern game-engine-based pipelines provide realistic rendering and automatic annotations. Leveraging this potential, we introduce SynDroneVision-Weather (SDV-W), an systematic extension of SynDroneVision (SDV) targeting adverse-weather and seasonal domain shifts in urban drone detection. SDV-W comprises 55,187 annotated high-resolution images from three urban environments, rendered across three seasonal configurations and diverse weather conditions, including rain, snow, and fog at multiple severity levels. By preserving SDV's scene and trajectory configuration, SDV-W enables matched clean-adverse comparisons and quantification of condition-specific detector degradation. Across representative YOLO models and real-world datasets, we show that SDV-W improves detector reliability under adverse appearance shifts, reduces missed detections and false alarms, and is most effective as a complement to general-purpose synthetic drone-detection data. SDV-W will be publicly released upon paper acceptance.