We focus on the very challenging task of semantic segmentation for autonomous
driving system. It must deliver decent semantic segmentation result for traffic
critical objects real-time. In this paper, we propose a very efficient yet
powerful deep neural network for driving scene semantic segmentation termed as
Driving Segmentation Network (DSNet). DSNet achieves state-of-the-art balance
between accuracy and inference speed through efficient units and architecture
design inspired by ShuffleNet V2 and ENet. More importantly, DSNet highlights
classes most critical with driving decision making through our novel Driving
Importance-weighted Loss. We evaluate DSNet on Cityscapes dataset, our DSNet
achieves 71.8% mean Intersection-over-Union (IoU) on validation set and 69.3%
on test set. Class-wise IoU scores show that Driving Importance-weighted Loss
could improve most driving critical classes by a large margin. Compared with
ENet, DSNet is 18.9% more accurate and 1.1+ times faster which implies great
potential for autonomous driving application.

本文提出了一种强大且高效的深度神经网络驾驶场景语义分割模型 DSNet，通过 ShuffleNet V2 和 ENet 的启发式设计，以及实现的 Driving Importance-weighted Loss 作为考虑驾驶决策的重要因素，等方面提高准确率和实时性。在 Cityscapes 数据集上的实验结果表明 DSNet 比 ENet 的准确率提高了 18.9％，速度提高了 1.1 倍，具有应用于自动驾驶的巨大潜力。