  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%).
