Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?
This work addresses the critical need for improved image enhancement in space debris monitoring, but it is incremental as it builds on existing deep learning architectures without major breakthroughs.
The paper tackled the problem of detecting and tracking space debris by developing a hybrid UNet-ResNet34 deep learning model to enhance images captured with monocular cameras in space, addressing degradations like blurring and noise, and found it capable of correcting these issues based on visual comparisons with existing methods.
The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined, registered spacecraft, and rogue/inactive space ``objects'', is critical to asset protection. The primary objective of this work is to investigate the validity of Deep Neural Network (DNN) solutions to overcome the limitations and image artefacts most prevalent when captured with monocular cameras in the visible light spectrum. In this work, a hybrid UNet-ResNet34 Deep Learning (DL) architecture pre-trained on the ImageNet dataset, is developed. Image degradations addressed include blurring, exposure issues, poor contrast, and noise. The shortage of space-generated data suitable for supervised DL is also addressed. A visual comparison between the URes34P model developed in this work and the existing state of the art in deep learning image enhancement methods, relevant to images captured in space, is presented. Based upon visual inspection, it is determined that our UNet model is capable of correcting for space-related image degradations and merits further investigation to reduce its computational complexity.