Speeding up Semantic Segmentation for Autonomous Driving

Michael Treml, José Arjona-Medina, Thomas Unterthiner, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, Andreas Mayr, Martin Heusel, Markus Hofmarcher, Michael Widrich, Bernhard Nessler, Sepp Hochreiter,

Abstract:

Deep learning has considerably improved semantic image segmentation. However,
its high accuracy is traded against larger computational costs which makes it unsuit-
able for embedded devices in self-driving cars. We propose a novel deep network
architecture for image segmentation that keeps the high accuracy while being
efficient enough for embedded devices. The architecture consists of ELU activation
functions, a SqueezeNet-like encoder, followed by parallel dilated convolutions,
and a decoder with SharpMask-like refinement modules. On the Cityscapes dataset,
the new network achieves higher segmentation accuracy than other networks that
are tailored to embedded devices. Simultaneously the frame-rate is still sufficiently
high for the deployment in autonomous vehicles.

Conflicts: bioinf.jku.at, audi.de