Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

• URL: http://arxiv.org/abs/2101.06085v1
• Date: Fri, 15 Jan 2021 12:56:18 GMT
• Title: Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
• Authors: Yuanduo Hong, Huihui Pan, Weichao Sun, Senior Member, IEEE, Yisong Jia
• Abstract summary: We propose novel deep dual-resolution networks ( DDRNets) for real-time semantic segmentation of road scenes. Our method achieves new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset.
• Score: 0.23090185577016442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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