Accurate vessel detection and timely information extraction from optical remote sensing imagery are essential for a wide range of maritime surveillance operations, both civilian and defense-related. These operations include vessel tracking, unauthorized fishing, illegal migration monitoring, and search and rescue missions. Although artificial intelligence (AI) is a key component for achieving reliable and accurate detection in satellite imagery, traditional AI-based remote sensing methodologies rely on ground-based image processing. This dependence leads to significant delays between data acquisition and the generation of actionable insights, which may hinder rapid decision-making during critical maritime situations such as sea disasters. To address this challenge, we propose a novel hardware design based on the Microchip PolarFire System-on-Chip for low-power and real-time vessel detection onboard spacecraft. Our design leverages Microchip's CoreVectorBlox, implemented on the programmable logic, to accelerate the inference process of SR-YOLOv5s, an enhanced YOLOv5s-based object detection framework. This detector incorporates a super-resolution backbone that allows the extraction of fine details and features of small targets of interest, thus improving detection accuracy. The results confirm the effectiveness of our approach, showcasing its potential as a solution to enable real-time alerts in the maritime surveillance domain through Earth Observation (EO) image processing at the edge.