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Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D p...
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... These photos can be captured by spacecraft cameras or received from other sources. Annotate the photos using bounding box coordinates or pixel-level labels to show the location of each com-ponent such as developed in [68]. The next step is quantum data encoding. ...
Quantum circuits are the fundamental computing model of quantum computing. It consists of a sequence
of quantum gates that act on a set of qubits to perform a specific computation. For the implementation of
quantum circuits, programmable nanophotonic chips provide a promising foundation with a large number of
qubits. The current study explores the possible potential of quantum circuits implemented on programmable
nanophotonic chips for space technology. In the recent findings, it has been demonstrated that quantum
circuits have several advantages over classical circuits, such as exponential speedups, multiple parallel computations,
and compact size. Apart from this, nanophotonic chips also offer a number of advantages over
traditional chips. They provide high-speed data transfer as light travels faster than electrons. Photons require
less energy to transmit data than electrons, so nanophotonic chips consume less power than conventional
chips. The bandwidth of nanophotonic chips is greater than that of traditional chips, so they can transfer
more data simultaneously. They can be easily scaled to smaller sizes with higher densities and are more
robust to extreme temperatures and radiation than classical chips. The focus of the current study is on how
quantum circuits could revolutionize space technology by providing faster and more efficient computations
for a variety of space-related applications. All the in-depth analysis is carried out while taking currently
available state-of-the-art technologies into consideration.
The utilization of deep learning methods for the detection of space targets and components has received significant attention due to the continuous development of space missions. Effective detection and recognition of space target and components utilizing space-based electro-optical sensors is crucial for the intelligent perception and fine control of autonomous spacecraft. From an engineering application perspective, this article systematically reviews the current research status of space target detection and segmentation algorithms. This paper first summarizes the principle and characteristics of electro-optical sensors in space tasks and their application scenarios, and describes the common synthetic methods utilized for space target datasets. A summary of recent research on space target detection and component segmentation is given. Based on the research summary, we discuss several major issues for space target detection and segmentation. Research suggestions and future development directions are finally proposed.
