Advancing multi-class object detection from LEO/VLEO: model evaluation and onboard deployment tailored for a 16U CubeSat

Abstract On-board AI-based image recognition for Very Low Earth Orbit (VLEO) and Low Earth Orbit (LEO) missions is crucial for enabling timely Earth Observation while minimizing data transmission. This paper presents a proof-of-concept study for autonomous object detection, including the creation of a novel EO dataset and deployment on a 16U CubeSat's target hardware. The custom IceBrain-EO dataset contains 25,675 images across 12 classes. In this study, two models were benchmarked, with the fine-tuned YOLOv11m model achieving a mean Average Precision ([email protected]) of 0.843. On the target NVIDIA Jetson Orin Nano edge device, this model demonstrated real-time performance, achieving an inference speed of 11.94 FPS on 640 × 640 images while consuming just 2.6W in its 15W power mode. These results validate the feasibility of autonomous payload operations and demonstrate a viable pathway for significantly reducing data downlink for future EO missions.