论文标题
在基于地面和空气的收集平台上评估热图像整合到对象检测方法中
Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms
论文作者
论文摘要
通常部署在未螺旋空系统（UAS）上的对象检测模型专注于使用红色绿色蓝色（RGB）图像识别可见光谱中的对象。但是，对将RGB与热长波红外（LWIR）图像融合在一起，以提高对象检测机器学习（ML）模型的性能。目前，LWIR ML模型受到研究的关注较少，尤其是对于基于地面和空气的平台，导致缺乏评估LWIR，RGB和LWIR-RGB FUSE对象检测模型的基线性能指标。因此，这项研究为文献贡献了这种定量指标。结果发现，与RGB或LWIR方法相比，基于地面的混合RGB-LWIR模型表现出较高的性能，获得了98.4％的地图。此外，混合的RGB-LWIR模型也是白天和夜间条件下工作的唯一对象检测模型，提供了出色的操作功能。这项研究还为RGB，LWIR和RGB-LWIR融合图像提供了12,600张图像的新型培训数据集，该图像从地面和空气平台收集，从而实现了进一步的多光谱机器驱动对象检测研究。
Object detection models commonly deployed on uncrewed aerial systems (UAS) focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB) imagery. However, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) images to increase the performance of object detection machine learning (ML) models. Currently LWIR ML models have received less research attention, especially for both ground- and air-based platforms, leading to a lack of baseline performance metrics evaluating LWIR, RGB and LWIR-RGB fused object detection models. Therefore, this research contributes such quantitative metrics to the literature. The results found that the ground-based blended RGB-LWIR model exhibited superior performance compared to the RGB or LWIR approaches, achieving a mAP of 98.4%. Additionally, the blended RGB-LWIR model was also the only object detection model to work in both day and night conditions, providing superior operational capabilities. This research additionally contributes a novel labelled training dataset of 12,600 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from ground-based and air-based platforms, enabling further multispectral machine-driven object detection research.
