The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30–40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms.

The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30-40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms.