The ability to perform pixel-wise semantic segmentation in real-time is of
paramount importance in mobile applications. Recent deep neural networks aimed
at this task have the disadvantage of requiring a large number of floating
point operations and have long run-times that hinder their usability. In this
paper, we propose a novel deep neural network architecture named ENet
(efficient neural network), created specifically for tasks requiring low
latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less
FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy
to existing models. We have tested it on CamVid, Cityscapes and SUN datasets
and report on comparisons with existing state-of-the-art methods, and the
trade-offs between accuracy and processing time of a network. We present
performance measurements of the proposed architecture on embedded systems and
suggest possible software improvements that could make ENet even faster.

本篇论文提出一种名为 ENet 的新型深度神经网络架构，其最大特点是快速、参数少，适用于对实时性要求较高的像素级语义分割任务，且在 CamVid、Cityscapes 和 SUN 等数据集上测试表明效果相对优于同类型已有网络。