Lessons from a Space Lab: An Image Acquisition Perspective
The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models train...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9944614 |
| _version_ | 1797663768664080384 |
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| author | Leo Pauly Michele Lynn Jamrozik Miguel Ortiz del Castillo Olivia Borgue Inder Pal Singh Mohatashem Reyaz Makhdoomi Olga-Orsalia Christidi-Loumpasefski Vincent Gaudillière Carol Martinez Arunkumar Rathinam Andreas Hein Miguel Olivares-Mendez Djamila Aouada |
| author_facet | Leo Pauly Michele Lynn Jamrozik Miguel Ortiz del Castillo Olivia Borgue Inder Pal Singh Mohatashem Reyaz Makhdoomi Olga-Orsalia Christidi-Loumpasefski Vincent Gaudillière Carol Martinez Arunkumar Rathinam Andreas Hein Miguel Olivares-Mendez Djamila Aouada |
| author_sort | Leo Pauly |
| collection | DOAJ |
| description | The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project. |
| first_indexed | 2024-03-11T19:19:30Z |
| format | Article |
| id | doaj.art-cca49da2b34248238bfe8f66e6a0c9c8 |
| institution | Directory Open Access Journal |
| issn | 1687-5974 |
| language | English |
| last_indexed | 2024-03-11T19:19:30Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj.art-cca49da2b34248238bfe8f66e6a0c9c82023-10-08T00:00:02ZengHindawi LimitedInternational Journal of Aerospace Engineering1687-59742023-01-01202310.1155/2023/9944614Lessons from a Space Lab: An Image Acquisition PerspectiveLeo Pauly0Michele Lynn Jamrozik1Miguel Ortiz del Castillo2Olivia Borgue3Inder Pal Singh4Mohatashem Reyaz Makhdoomi5Olga-Orsalia Christidi-Loumpasefski6Vincent Gaudillière7Carol Martinez8Arunkumar Rathinam9Andreas Hein10Miguel Olivares-Mendez11Djamila Aouada12Interdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityInterdisciplinary Centre for SecurityThe use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.http://dx.doi.org/10.1155/2023/9944614 |
| spellingShingle | Leo Pauly Michele Lynn Jamrozik Miguel Ortiz del Castillo Olivia Borgue Inder Pal Singh Mohatashem Reyaz Makhdoomi Olga-Orsalia Christidi-Loumpasefski Vincent Gaudillière Carol Martinez Arunkumar Rathinam Andreas Hein Miguel Olivares-Mendez Djamila Aouada Lessons from a Space Lab: An Image Acquisition Perspective International Journal of Aerospace Engineering |
| title | Lessons from a Space Lab: An Image Acquisition Perspective |
| title_full | Lessons from a Space Lab: An Image Acquisition Perspective |
| title_fullStr | Lessons from a Space Lab: An Image Acquisition Perspective |
| title_full_unstemmed | Lessons from a Space Lab: An Image Acquisition Perspective |
| title_short | Lessons from a Space Lab: An Image Acquisition Perspective |
| title_sort | lessons from a space lab an image acquisition perspective |
| url | http://dx.doi.org/10.1155/2023/9944614 |
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