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.

通过将脉冲神经网络与事件摄像头相结合，我们设计了一种生物可行的编码器 - 解码器 U 型架构的语义分割方法，旨在在性能与资源使用之间进行权衡。实验证明 EvSegSNN 在 MIoU 方面优于最接近的最新模型，同时参数数量减少了 1.6 倍，省去了批归一化阶段。