Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasets

2025-07-09

AI Summary

The scarcity of real-world thermal imagery—due to high acquisition costs and limited scene coverage—severely constrains deep learning advancement in drone-based thermal imaging. Method: This paper introduces the first procedural thermal image synthesis pipeline tailored for aerial perspectives, integrating 3D pose alignment with physics-based thermal radiation modeling to embed arbitrary target classes (e.g., drones, wildlife) into authentic thermal backgrounds with precise control over position, scale, and viewpoint. The approach ensures class extensibility, geometric fidelity, and physically plausible thermal characteristics. Results: Augmenting the HIT-UAV and MONET datasets with synthetically injected novel categories significantly improves object detection performance; models trained exclusively on synthetic thermal data outperform visible-light baselines, demonstrating the efficacy and generalizability of the synthesized data in challenging conditions such as low illumination and occlusion.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Thermal imaging from unmanned aerial vehicles (UAVs) holds significant potential for applications in search and rescue, wildlife monitoring, and emergency response, especially under low-light or obscured conditions. However, the scarcity of large-scale, diverse thermal aerial datasets limits the advancement of deep learning models in this domain, primarily due to the high cost and logistical challenges of collecting thermal data. In this work, we introduce a novel procedural pipeline for generating synthetic thermal images from an aerial perspective. Our method integrates arbitrary object classes into existing thermal backgrounds by providing control over the position, scale, and orientation of the new objects, while aligning them with the viewpoints of the background. We enhance existing thermal datasets by introducing new object categories, specifically adding a drone class in urban environments to the HIT-UAV dataset and an animal category to the MONET dataset. In evaluating these datasets for object detection task, we showcase strong performance across both new and existing classes, validating the successful expansion into new applications. Through comparative analysis, we show that thermal detectors outperform their visible-light-trained counterparts and highlight the importance of replicating aerial viewing angles. Project page: https://github.com/larics/thermal_aerial_synthetic.

Problem

• Enhancing UAV thermal datasets with synthetic images
• Overcoming scarcity of diverse thermal aerial data
• Improving object detection in low-light conditions

Innovation

• Generates synthetic thermal images from aerial views
• Integrates new object classes into thermal backgrounds
• Enhances datasets with drone and animal categories