  Semantic segmentation is an important computer vision task, particularly for
scene understanding and navigation of autonomous vehicles and UAVs. Several
variations of deep neural network architectures have been designed to tackle
this task. However, due to their huge computational costs and their high memory
consumption, these models are not meant to be deployed on resource-constrained
systems. To address this limitation, we introduce an end-to-end biologically
inspired semantic segmentation approach by combining Spiking Neural Networks
(SNNs, a low-power alternative to classical neural networks) with event cameras
whose output data can directly feed these neural network inputs. We have
designed EvSegSNN, a biologically plausible encoder-decoder U-shaped
architecture relying on Parametric Leaky Integrate and Fire neurons in an
objective to trade-off resource usage against performance. The experiments
conducted on DDD17 demonstrate that EvSegSNN outperforms the closest
state-of-the-art model in terms of MIoU while reducing the number of parameters
by a factor of $1.6$ and sparing a batch normalization stage.
