This is like putting a smart pair of binoculars on a satellite. The binoculars can zoom and sharpen blurry ocean images, and a built‑in AI spotter (trained like a security guard) automatically finds and labels ships and boats in real time, without needing to send all the raw pictures back to Earth.
Traditional satellite maritime surveillance either misses small or distant vessels due to low image resolution or requires heavy images to be sent to the ground for processing, which is slow, bandwidth‑intensive, and costly. This system performs super‑resolution and object detection directly onboard spacecraft using hardware‑accelerated AI, enabling real‑time, wide‑area vessel detection under tight power, compute, and bandwidth constraints.
Tight integration of a specific super-resolution pipeline with an optimized YOLO-based detector on FPGAs for spaceborne maritime imagery, plus any proprietary training data and hardware-specific optimizations, creates a defensible combination of domain-tuned models and deployment know-how.
Open Source (Llama/Mistral)
Unknown
High (Custom Models/Infra)
Onboard FPGA resource limits (LUTs/BRAM/DSP), model size vs. timing closure, and downlink bandwidth for imagery and detections.
Early Adopters
Unlike generic computer-vision models run in the cloud, this approach combines super-resolution preprocessing with a YOLO-based detector that is specifically optimized to run in real time on FPGA hardware onboard spacecraft, targeting maritime surveillance where bandwidth and compute are severely constrained.