The deployment of neural networks in space has opened new possibilities for achieving high-precision proximity operations, which are crucial for inspection, maintenance, and debris removal in the context of on-orbit servicing (OOS). A core requirement for these missions is accurate pose estimation, defined as the capability of an active spacecraft to estimate the relative position and orientation of a non-cooperative one. It involves substantial technological challenges in sensor architecture selection and algorithm development. For this purpose, visual navigation employs monocular cameras, favoured for their compact form and low resource demands, which serve as ideal sensors for capturing visual data in space environments.