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Computer Science > Computer Vision and Pattern Recognition

arXiv:2405.16953 (cs)
[Submitted on 27 May 2024]

Title:Evaluation of Resource-Efficient Crater Detectors on Embedded Systems

Authors:Simon Vellas, Bill Psomas, Kalliopi Karadima, Dimitrios Danopoulos, Alexandros Paterakis, George Lentaris, Dimitrios Soudris, Konstantinos Karantzalos
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Abstract: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: this https URL.
Comments: Accepted at 2024 IEEE International Geoscience and Remote Sensing Symposium
Subjects: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
Cite as: arXiv:2405.16953 [cs.CV]
  (or arXiv:2405.16953v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2405.16953

Submission history

From: Bill Psomas [view email]
[v1] Mon, 27 May 2024 08:45:57 UTC (1,355 KB)
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