Spacecraft Recognition Leveraging Knowledge of Space Environment: Simulator, Dataset, Competition Design and Analysis

Abstract

SPARK represents the first edition of the SPAcecraft Recognition leveraging Knowledge of space environment competition organized by the Interdisciplinary Centre for Security, Reliability and Trust (SnT) in conjunction with the 2021 IEEE International Conference in Image Processing (ICIP 2021). By providing a unique synthetic dataset composed of 150k annotated multi-modal images, SPARK aims at encouraging researchers to develop innovative solutions for space target recognition and detection. This paper introduces the proposed dataset and provides a global analysis of the results obtained for the 17 submissions.

Localization and classification of space objects using EfficientDet detector for space situational awareness

“…In addition, various research has been conducted utilizing vision-based sensors, and to perform unsupervised near-earth missions in space with refractory targets, and durable and fast onboard posture stabilization techniques that are necessary on the spacecraft [11][12][13] . As a result, various image-based studies suggest the use of motion sensors such as Light Detection and Ranging (LiDAR) to achieve this goal [14][15][16] . To utilize neural network models successfully, a lot of data are needed.…”

“…To reduce the possibility of collisions occurring in space, the process of target recognition ought to be carried out automatically 30 . The most important component in SSA for analyzing visual data and developing data-driven AI solutions is the vision-based sensor [13][14][15]31 , which can take the form of a camera. On one hand, past research papers have presented a variety of technologies to detect and manage active and inactive satellites, while on the other hand, several strategies have been suggested to eliminate debris from space.…”

“…On one hand, past research papers have presented a variety of technologies to detect and manage active and inactive satellites, while on the other hand, several strategies have been suggested to eliminate debris from space. In addition, LiDAR sensors have been utilized for the removal of debris, the recognition of targets, and the estimation of poses [13][14][15][16] . It was discovered that there are ways for estimating the pose of a 3D spacecraft by comparing the wireframe of the target with a 2D image.…”

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y www.nature.com/scientificreports/ teams have begun to examine satellite data collection. The SPARK dataset offers a realistic representation of the earth and of the objects in and around it 14,17,18 .Various object recognition algorithms have been published over the last decade, and it is fascinating to explore the suitability of these techniques to space data, as well as to find ways for improving their efficiency in the space domain. Unsupervised object detection is performed by convolutional neural networks (CNNs), which eliminate the need for features to be generated and obtained individually 19 .

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“…y www.nature.com/scientificreports/ teams have begun to examine satellite data collection. The SPARK dataset offers a realistic representation of the earth and of the objects in and around it 14,17,18 .…”