  Semantic segmentation is a critical technology for autonomous vehicles to
understand surrounding scenes. For practical autonomous vehicles, it is
undesirable to spend a considerable amount of inference time to achieve
high-accuracy segmentation results. Using light-weight architectures
(encoder-decoder or two-pathway) or reasoning on low-resolution images, recent
methods realize very fast scene parsing which even run at more than 100 FPS on
single 1080Ti GPU. However, there are still evident gaps in performance between
these real-time methods and models based on dilation backbones. To tackle this
problem, we propose novel deep dual-resolution networks (DDRNets) for real-time
semantic segmentation of road scenes. Besides, we design a new contextual
information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) to
enlarge effective receptive fields and fuse multi-scale context. Our method
achieves new state-of-the-art trade-off between accuracy and speed on both
Cityscapes and CamVid dataset. Specially, on single 2080Ti GPU, DDRNet-23-slim
yields 77.4% mIoU at 109 FPS on Cityscapes test set and 74.4% mIoU at 230 FPS
on CamVid test set. Without utilizing attention mechanism, pre-training on
larger semantic segmentation dataset or inference acceleration, DDRNet-39
attains 80.4% test mIoU at 23 FPS on Cityscapes. With widely used test
augmentation, our method is still superior to most state-of-the-art models,
requiring much less computation. Codes and trained models will be made publicly
available.
