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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 longwave infrared (LWIR) images to increase the performance of object detectionmachine learning (ML) models. Currently LWIR ML models have received lessresearch attention, especially for both ground- and air-based platforms,leading to a lack of baseline performance metrics evaluating LWIR, RGB andLWIR-RGB fused object detection models. Therefore, this research contributessuch quantitative metrics to the literature. The results found that theground-based blended RGB-LWIR model exhibited superior performance compared tothe RGB or LWIR approaches, achieving a mAP of 98.4%. Additionally, the blendedRGB-LWIR model was also the only object detection model to work in both day andnight conditions, providing superior operational capabilities. This researchadditionally contributes a novel labelled training dataset of 12,600 images forRGB, LWIR, and RGB-LWIR fused imagery, collected from ground-based andair-based platforms, enabling further multispectral machine-driven objectdetection research.

Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms