  This paper focuses on autonomously characterizing components such as solar
panels, body panels, antennas, and thrusters of an unknown resident space
object (RSO) using camera feed to aid autonomous on-orbit servicing (OOS) and
active debris removal. Significant research has been conducted in this area
using convolutional neural networks (CNNs). While CNNs are powerful at learning
patterns and performing object detection, they struggle with missed detections
and misclassifications in environments different from the training data, making
them unreliable for safety in high-stakes missions like OOS. Additionally,
failures exhibited by CNNs are often easily rectifiable by humans using
commonsense reasoning and contextual knowledge. Embedding such reasoning in an
object detector could improve detection accuracy. To validate this hypothesis,
this paper presents an end-to-end object detector called SpaceYOLOv2 (SpY),
which leverages the generalizability of CNNs while incorporating contextual
knowledge using traditional computer vision techniques. SpY consists of two
main components: a shape detector and the SpaceYOLO classifier (SYC). The shape
detector uses CNNs to detect primitive shapes of RSOs and SYC associates these
shapes with contextual knowledge, such as color and texture, to classify them
as spacecraft components or "unknown" if the detected shape is uncertain. SpY's
modular architecture allows customizable usage of contextual knowledge to
improve detection performance, or SYC as a secondary fail-safe classifier with
an existing spacecraft component detector. Performance evaluations on
hardware-in-the-loop images of a mock-up spacecraft demonstrate that SpY is
accurate and an ensemble of SpY with YOLOv5 trained for satellite component
detection improved the performance by 23.4% in recall, demonstrating enhanced
safety for vision-based navigation tasks.
