  We suggest the first system that runs real-time semantic segmentation via
deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose
price was \$15) attached to a toy-drone. In particular, since the Raspberry Pi
weighs less than $16$ grams, and its size is half of a credit card, we could
easily attach it to the common commercial DJI Tello toy-drone (<\$100, <90
grams, 98 $\times$ 92.5 $\times$ 41 mm). The result is an autonomous drone (no
laptop nor human in the loop) that can detect and classify objects in real-time
from a video stream of an on-board monocular RGB camera (no GPS or LIDAR
sensors). The companion videos demonstrate how this Tello drone scans the lab
for people (e.g. for the use of firefighters or security forces) and for an
empty parking slot outside the lab.
  Existing deep learning solutions are either much too slow for real-time
computation on such IoT devices, or provide results of impractical quality. Our
main challenge was to design a system that takes the best of all worlds among
numerous combinations of networks, deep learning platforms/frameworks,
compression techniques, and compression ratios. To this end, we provide an
efficient searching algorithm that aims to find the optimal combination which
results in the best tradeoff between the network running time and its
accuracy/performance.
