4 research outputs found
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance
Neural networks have dramatically increased our capacity to learn from large,
high-dimensional datasets across innumerable disciplines. However, their
decisions are not easily interpretable, their computational costs are high, and
building and training them are uncertain processes. To add structure to these
efforts, we derive new mathematical results to efficiently measure the changes
in entropy as fully-connected and convolutional neural networks process data,
and introduce entropy-based loss terms. Experiments in image compression and
image classification on benchmark datasets demonstrate these losses guide
neural networks to learn rich latent data representations in fewer dimensions,
converge in fewer training epochs, and achieve better test metrics.Comment: 13 pages, 4 figure
Comparison of Tracking-By-Detection Algorithms for Real-Time Satellite Component Tracking
With space becoming more and more crowded, there is a growing demand for increasing satellite lifetimes and performing on-orbit servicing (OOS) at a scale that calls for autonomous missions. Many such missions would require chaser satellites to autonomously execute safe and effective flightpath to dock with a non-cooperative target satellite on orbit. Performing this autonomously requires the chaser to be aware of hazards to route around and safe capture points through time, i.e., by first identifying and tracking key components of the target satellite. State-of-the-art object detection algorithms are effective at detecting such objects on a frame-by-frame basis. However, implementing them on a real-time video feed often results in poor performance at tracking objects over time, making errors which could be easily corrected by rejecting non-physical predictions or by exploiting temporal patterns. On the other hand, dedicated object tracking algorithms can be far too computationally expensive for spaceflight computers. Considering this, the paradigm of tracking-by-detection works by incorporating patterns of prior-frame detections and the corresponding physics in tandem with a base object detector. This paper focuses on comparing the performance of object tracking-by-detection algorithms with a YOLOv8 base object detector: namely, BoTSORT and ByteTrack. These algorithms are hardware-in-the-loop tested for autonomous spacecraft component detection for a simulated tumbling target satellite. This will emulate mission conditions, including motion and lighting, with a focus on operating under spaceflight computational and power limitations, providing an experimental comparison of performance. Results demonstrate lightweight tracking-by-detection can improve the reliability of autonomous vision-based navigation
Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations
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