Artificial Intelligence (AI) applications on Earth Observation (EO) satellite data, such as those for vessel detection, are gaining attention for their potential to meet strict bandwidth and latency requirements. While traditional on-ground computing pipelines often rely on heavy post-processing, implementing these techniques onboard satellites is challenging due to limited computing resources. To support the development of efficient onboard data processing strategies, this study compares the performance of object detection on raw data from Sentinel-2 and VENμS missions. The study demonstrates that the proposed two-stage approach with a focus on efficiency is capable of identifying vessels in raw data with minimal pre-processing. Specifically, our method achieved a remarkable Average Precision (AP) of 0.841 on the VENμS dataset.