  Transmitting Earth observation image data from satellites to ground stations
incurs significant costs in terms of power and bandwidth. For maritime ship
detection, on-board data processing can identify ships and reduce the amount of
data sent to the ground. However, most images captured on board contain only
bodies of water or land, with the Airbus Ship Detection dataset showing only
22.1\% of images containing ships. We designed a low-power, two-stage system to
optimize performance instead of relying on a single complex model. The first
stage is a lightweight binary classifier that acts as a gating mechanism to
detect the presence of ships. This stage runs on Brainchip's Akida 1.0, which
leverages activation sparsity to minimize dynamic power consumption. The second
stage employs a YOLOv5 object detection model to identify the location and size
of ships. This approach achieves a mean Average Precision (mAP) of 76.9\%,
which increases to 79.3\% when evaluated solely on images containing ships, by
reducing false positives. Additionally, we calculated that evaluating the full
validation set on a NVIDIA Jetson Nano device requires 111.4 kJ of energy. Our
two-stage system reduces this energy consumption to 27.3 kJ, which is less than
a fourth, demonstrating the efficiency of a heterogeneous computing system.
