Semantic segmentation is a key technology for autonomous vehicles to
understand the surrounding scenes. The appealing performances of contemporary
models usually come at the expense of heavy computations and lengthy inference
time, which is intolerable for self-driving. Using light-weight architectures
(encoder-decoder or two-pathway) or reasoning on low-resolution images, recent
methods realize very fast scene parsing, even running at more than 100 FPS on a
single 1080Ti GPU. However, there is still a significant gap in performance
between these real-time methods and the models based on dilation backbones. To
tackle this problem, we proposed a family of efficient backbones specially
designed for real-time semantic segmentation. The proposed deep dual-resolution
networks (DDRNets) are composed of two deep branches between which multiple
bilateral fusions are performed. Additionally, we design a new contextual
information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) to
enlarge effective receptive fields and fuse multi-scale context based on
low-resolution feature maps. Our method achieves a new state-of-the-art
trade-off between accuracy and speed on both Cityscapes and CamVid dataset. In
particular, on a single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 102 FPS
on Cityscapes test set and 74.7% mIoU at 230 FPS on CamVid test set. With
widely used test augmentation, our method is superior to most state-of-the-art
models and requires much less computation. Codes and trained models are
available online.

提出了一组高效的卷积神经网络骨干架构，通过双分辨率融合和深度上下文聚合金字塔池化模块，实现了在 Cityscapes 和 CamVid 数据集上的优于现有模型的精度和速度的实时语义分割。