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https://dspace.iiti.ac.in/handle/123456789/13785
| Title: | Deep Learning Based Real-Time Lunar Terrain Detection for Autonomous Landing Approach |
| Authors: | Shekhar, Kumar Sheshank Tanti, Harsha Avinash Datta, Abhirup |
| Keywords: | AI device;Computer Vision;Moon's terrain;Space vehicle lander;YOLOv5 |
| Issue Date: | 2023 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Shekhar, 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/CODEC60112.2023.10466224 |
| Abstract: | Space 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. |
| URI: | https://doi.org/10.1109/CODEC60112.2023.10466224 https://dspace.iiti.ac.in/handle/123456789/13785 |
| ISBN: | 979-8350317176 |
| Type of Material: | Conference Paper |
| Appears in Collections: | Department of Astronomy, Astrophysics and Space Engineering |
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