Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations
This addresses the need for interpretability in safety-critical missions for autonomous satellite swarms in space debris management, but it is incremental as it builds on existing YOLOv5 models.
The paper tackled the problem of interpreting YOLOv5 for detecting satellite components in space debris by introducing PEEK, an entropy-based method that analyzes latent representations, resulting in insights into the model's decision-making processes and biases through hardware-in-the-loop experiments.
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.