  The escalating risk of collisions and the accumulation of space debris in Low
Earth Orbit (LEO) has reached critical concern due to the ever increasing
number of spacecraft. Addressing this crisis, especially in dealing with
non-cooperative and unidentified space debris, is of paramount importance. This
paper contributes to efforts in enabling autonomous swarms of small chaser
satellites for target geometry determination and safe flight trajectory
planning for proximity operations in LEO. Our research explores on-orbit use of
the You Only Look Once v5 (YOLOv5) object detection model trained to detect
satellite components. While this model has shown promise, its inherent lack of
interpretability hinders human understanding, a critical aspect of validating
algorithms for use in safety-critical missions. To analyze the decision
processes, we introduce Probabilistic Explanations for Entropic Knowledge
extraction (PEEK), a method that utilizes information theoretic analysis of the
latent representations within the hidden layers of the model. Through both
synthetic in hardware-in-the-loop experiments, PEEK illuminates the
decision-making processes of the model, helping identify its strengths,
limitations and biases.
