Training with synthetic data for drone detection in thermal imagery

2026-08-18

AI Summary

本文探讨了通过合成数据与少量真实红外数据结合的方法来解决热成像中无人机检测难题，强调了数据集对齐的重要性。

Abstract

Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the dataset suggests that semantic alignment in feature space is the strongest predictor of model performance, while radiometric properties such as entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.

Problem

• drone detection
• thermal imagery
• synthetic data
• domain gaps

Innovation

• synthetic-first training
• domain gaps
• semantic alignment
• radiometric properties