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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.16953 |
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| _version_ | 1866917676363808768 |
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| author | Vellas, Simon Psomas, Bill Karadima, Kalliopi Danopoulos, Dimitrios Paterakis, Alexandros Lentaris, George Soudris, Dimitrios Karantzalos, Konstantinos |
| author_facet | Vellas, Simon Psomas, Bill Karadima, Kalliopi Danopoulos, Dimitrios Paterakis, Alexandros Lentaris, George Soudris, Dimitrios Karantzalos, Konstantinos |
| contents | Real-time analysis of Martian craters is crucial for mission-critical operations, including safe landings and geological exploration. This work leverages the latest breakthroughs for on-the-edge crater detection aboard spacecraft. We rigorously benchmark several YOLO networks using a Mars craters dataset, analyzing their performance on embedded systems with a focus on optimization for low-power devices. We optimize this process for a new wave of cost-effective, commercial-off-the-shelf-based smaller satellites. Implementations on diverse platforms, including Google Coral Edge TPU, AMD Versal SoC VCK190, Nvidia Jetson Nano and Jetson AGX Orin, undergo a detailed trade-off analysis. Our findings identify optimal network-device pairings, enhancing the feasibility of crater detection on resource-constrained hardware and setting a new precedent for efficient and resilient extraterrestrial imaging. Code at: https://github.com/billpsomas/mars_crater_detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluation of Resource-Efficient Crater Detectors on Embedded Systems Vellas, Simon Psomas, Bill Karadima, Kalliopi Danopoulos, Dimitrios Paterakis, Alexandros Lentaris, George Soudris, Dimitrios Karantzalos, Konstantinos Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Performance Real-time analysis of Martian craters is crucial for mission-critical operations, including safe landings and geological exploration. This work leverages the latest breakthroughs for on-the-edge crater detection aboard spacecraft. We rigorously benchmark several YOLO networks using a Mars craters dataset, analyzing their performance on embedded systems with a focus on optimization for low-power devices. We optimize this process for a new wave of cost-effective, commercial-off-the-shelf-based smaller satellites. Implementations on diverse platforms, including Google Coral Edge TPU, AMD Versal SoC VCK190, Nvidia Jetson Nano and Jetson AGX Orin, undergo a detailed trade-off analysis. Our findings identify optimal network-device pairings, enhancing the feasibility of crater detection on resource-constrained hardware and setting a new precedent for efficient and resilient extraterrestrial imaging. Code at: https://github.com/billpsomas/mars_crater_detection. |
| title | Evaluation of Resource-Efficient Crater Detectors on Embedded Systems |
| topic | Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2405.16953 |