The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/154f01fc-9c6a-4da5-9c64-505fd195b6bf

Gallon, Riccardo (2025): Data underlying the publication: Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/154f01fc-9c6a-4da5-9c64-505fd195b6bf.v1

Dataset

• Artificial Intelligence and Image Processing
• Aerospace Engineering
• Engineering
• Information and Computing Sciences

AI, Artificial Intelligence, Camera Sensor, failure detection, isolation and recovery, FDIR, Vision-based Navigation

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Riccardo Gallon

The present dataset is designed to be used in training and testing of AI algorithms for the detection of failures in space camera sensors. It contains simulated images generated from the camera sensor of a sample vehicle for Small Solar System Bodies exploration. The images depict various simulated faults as described in the related publication, and are accompanied by labelling masks, which can be used for both classification and segmentation tasks.

The dataset is intended to support the development of AI applications that can be implemented onboard to improve the reliability of vision-based navigation systems. We encourage the research community to utilise this dataset and enhance it through further advancements.

• 2025-01-21 first online, published, posted

4TU.ResearchData

image/mat

• Astrone KI (grant code 50 RA 2130A) German Federal Ministry for Economic Affairs and Energy (BMWi)

TU Delft, Faculty of Aerospace Engineering, Department of Space Engineering
Airbus Defence and Space GmbH