  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.
