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Rock Hunting With Martian Machine Vision

2021-04-09 13:44:27
David Noever, Samantha E. Miller Noever
arXiv_CV
           
Abstract

The Mars Perseverance rover applies computer vision for navigation and hazard avoidance. The challenge to do onboard object recognition highlights the need for low-power, customized training, often including low-contrast backgrounds. We investigate deep learning methods for the classification and detection of Martian rocks. We report greater than 97% accuracy for binary classifications (rock vs. rover). We fine-tune a detector to render geo-located bounding boxes while counting rocks. For these models to run on microcontrollers, we shrink and quantize the neural networks' weights and demonstrate a low-power rock hunter with faster frame rates (1 frame per second) but lower accuracy (37%).

Abstract (translated)

URL

https://arxiv.org/abs/2104.04359

PDF

https://arxiv.org/pdf/2104.04359.pdf

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