  Deep learning has become the gold standard for image processing over the past
decade. Simultaneously, we have seen growing interest in orbital activities
such as satellite servicing and debris removal that depend on proximity
operations between spacecraft. However, two key challenges currently pose a
major barrier to the use of deep learning for vision-based on-orbit proximity
operations. Firstly, efficient implementation of these techniques relies on an
effective system for model development that streamlines data curation,
training, and evaluation. Secondly, a scarcity of labeled training data (images
of a target spacecraft) hinders creation of robust deep learning models. This
paper presents an open-source deep learning pipeline, developed specifically
for on-orbit visual navigation applications, that addresses these challenges.
The core of our work consists of two custom software tools built on top of a
cloud architecture that interconnects all stages of the model development
process. The first tool leverages Blender, an open-source 3D graphics toolset,
to generate labeled synthetic training data with configurable model poses
(positions and orientations), lighting conditions, backgrounds, and commonly
observed in-space image aberrations. The second tool is a plugin-based
framework for effective dataset curation and model training; it provides common
functionality like metadata generation and remote storage access to all
projects while giving complete independence to project-specific code.
Time-consuming, graphics-intensive processes such as synthetic image generation
and model training run on cloud-based computational resources which scale to
any scope and budget and allow development of even the largest datasets and
models from any machine. The presented system has been used in the Texas
Spacecraft Laboratory with marked benefits in development speed and quality.
