  The role of unmanned vehicles for searching and localizing the victims in
disaster impacted areas such as earthquake-struck zones is getting more
important. Self-navigation on an earthquake zone has a unique challenge of
detecting irregularly shaped obstacles such as road cracks, debris on the
streets, and water puddles. In this paper, we characterize a number of
state-of-the-art FCN models on mobile embedded platforms for self-navigation at
these sites containing extremely irregular obstacles. We evaluate the models in
terms of accuracy, performance, and energy efficiency. We present a few
optimizations for our designed vision system. Lastly, we discuss the trade-offs
of these models for a couple of mobile platforms that can each perform
self-navigation. To enable vehicles to safely navigate earthquake-struck zones,
we compiled a new annotated image database of various earthquake impacted
regions that is different than traditional road damage databases. We train our
database with a number of state-of-the-art semantic segmentation models in
order to identify obstacles unique to earthquake-struck zones. Based on the
statistics and tradeoffs, an optimal CNN model is selected for the mobile
vehicular platforms, which we apply to both low-power and extremely low-power
configurations of our design. To our best knowledge, this is the first study
that identifies unique challenges and discusses the accuracy, performance, and
energy impact of edge-based self-navigation mobile vehicles for
earthquake-struck zones. Our proposed database and trained models are publicly
available.

Computer Vision and Pattern Recognition;Hardware Architecture