Evaluation of Resource-Efficient Crater Detectors on Embedded Systems
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 72 references that are in the map. First posted on arXiv 27 May 2024.
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References · 8
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
- 2018Path Aggregation Network for Instance Segmentationcited by 798
- 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
- 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2023Deep Learning based Systems for Crater Detection: A Reviewcited by 5
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