解锁热航空成像：无人机数据集的合成增强
Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasets
AI总结：
本文提出一种程序化流程，从航空视角生成合成热图像，通过控制新物体的位置、比例和方向并集成到现有热背景中，增强HIT-UAV和MONET数据集，实验表明热检测器优于可见光训练的对应物，验证了向新应用的成功扩展。
摘要：
来自无人机（UAV）的热成像在搜索救援、野生动物监测和应急响应等应用中具有巨大潜力，尤其是在低光或遮挡条件下。然而，大规模、多样化的热航空数据集的稀缺限制了深度学习模型在该领域的发展，这主要是由于收集热数据的高成本和后勤挑战。在这项工作中，我们引入了一种新颖的程序化流程，用于从航空视角生成合成热图像。我们的方法通过控制新物体的位置、比例和方向，并将其与背景的视角对齐，将任意物体类别集成到现有的热背景中。我们通过引入新的物体类别来增强现有的热数据集，具体是在HIT-UAV数据集的城市场景中添加无人机类别，并在MONET数据集中添加动物类别。在评估这些数据集的目标检测任务时，我们在新类别和现有类别上都展示了强大的性能，验证了向新应用的成功扩展。通过比较分析，我们表明热检测器优于其可见光训练的对应物，并强调了复制航空视角的重要性。项目页面：https://github.com/larics/thermal_aerial_synthetic。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
