  Real-time semantic image segmentation on platforms subject to size, weight
and power (SWaP) constraints is a key area of interest for air surveillance and
inspection. In this work, we propose MAVNet: a small, light-weight, deep neural
network for real-time semantic segmentation on micro Aerial Vehicles (MAVs).
MAVNet, inspired by ERFNet, features 400 times fewer parameters and achieves
comparable performance with some reference models in empirical experiments. Our
model achieves a trade-off between speed and accuracy, achieving up to 48 FPS
on an NVIDIA 1080Ti and 9 FPS on the NVIDIA Jetson Xavier when processing high
resolution imagery. Additionally, we provide two novel datasets that represent
challenges in semantic segmentation for real-time MAV tracking and
infrastructure inspection tasks and verify MAVNet on these datasets. Our
algorithm and datasets are made publicly available.
