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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/13785
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dc.contributor.authorShekhar, Kumar Sheshanken_US
dc.contributor.authorTanti, Harsha Avinashen_US
dc.contributor.authorDatta, Abhirupen_US
dc.date.accessioned2024-06-28T11:38:25Z-
dc.date.available2024-06-28T11:38:25Z-
dc.date.issued2023-
dc.identifier.citationShekhar, K. S., Tanti, H. A., & Datta, A. (2023). Deep Learning Based Real-Time Lunar Terrain Detection for Autonomous Landing Approach. 2023 8th International Conference on Computers and Devices for Communication, CODEC 2023. Scopus. https://doi.org/10.1109/CODEC60112.2023.10466224en_US
dc.identifier.isbn979-8350317176-
dc.identifier.otherEID(2-s2.0-85190065811)-
dc.identifier.urihttps://doi.org/10.1109/CODEC60112.2023.10466224-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/13785-
dc.description.abstractSpace exploration and research have led to many advancements in launch vehicles, landers, and rovers. Conducting in-situ observations requires identifying a safe landing location. To choose a safe landing site, this paper discusses computer vision technology for landers. This study utilizes the YOLOv5n model to identify the moon's terrains. It is observed in this study that an accuracy of 92% can be achieved with near realtime detection using AI-edge devices. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.source2023 8th International Conference on Computers and Devices for Communication, CODEC 2023en_US
dc.subjectAI deviceen_US
dc.subjectComputer Visionen_US
dc.subjectMoon's terrainen_US
dc.subjectSpace vehicle landeren_US
dc.subjectYOLOv5en_US
dc.titleDeep Learning Based Real-Time Lunar Terrain Detection for Autonomous Landing Approachen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Astronomy, Astrophysics and Space Engineering

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