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

2026-08-17

AI Summary

This study addresses the critical challenge of data scarcity in UAV detection under adverse weather and seasonal variations by introducing SynDroneVision-Weather, the first systematic synthetic dataset for urban environments. Leveraging a game engine for high-fidelity rendering and automatic annotation, this dataset enables controllable environmental perturbations across diverse meteorological and seasonal conditions, facilitating clean-to-adverse comparative analysis. Experimental results demonstrate that SynDroneVision-Weather serves as an effective complement to general synthetic data, significantly enhancing the robustness of YOLO-series models against complex appearance changes. Specifically, it effectively reduces both missed detections and false alarm rates in real-world scenarios. These findings validate the pivotal role of domain-specific synthetic data in bridging the sim-to-real gap and improving detection performance under challenging environmental conditions.

Abstract

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.

Problem

• Drone Detection
• Adverse Weather
• Seasonal Variation
• Data Scarcity
• Domain Shift

Innovation

• Synthetic Data
• Adverse Weather
• Drone Detection
• Domain Shift
• SDV-W