Many current works directly adopt multi-rate depth-wise dilated convolutions to capture multi-scale contextual information simultaneously from one input feature map, thus improving the feature extraction efficiency for real-time semantic segmentation. However, this design may lead to difficult access to multi-scale contextual information because of the unreasonable structure and hyperparameters. To lower the difficulty of drawing multi-scale contextual information, we propose a highly efficient multi-scale feature extraction method, which decomposes the original single-step method into two steps, Region Residualization-Semantic Residualization. In this method, the multi-rate depth-wise dilated convolutions take a simpler role in feature extraction: performing simple semantic-based morphological filtering with one desired receptive field in the second step based on each concise feature map of region form provided by the first step, to improve their efficiency. Moreover, the dilation rates and the capacity of dilated convolutions for each network stage are elaborated to fully utilize all the feature maps of region form that can be achieved.Accordingly, we design a novel Dilation-wise Residual (DWR) module and a Simple Inverted Residual (SIR) module for the high and low level network, respectively, and form a powerful DWR Segmentation (DWRSeg) network. Extensive experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a state-of-the-art trade-off between accuracy and inference speed, in addition to being lighter weight. Without pretraining or resorting to any training trick, we achieve an mIoU of 72.7% on the Cityscapes test set at a speed of 319.5 FPS on one NVIDIA GeForce GTX 1080 Ti card, which exceeds the latest methods of a speed of 69.5 FPS and 0.8% mIoU. The code and trained models are publicly available.

翻译：当前许多工作直接采用多速率深度可分离膨胀卷积，从单个输入特征图中同时捕获多尺度上下文信息，从而提升实时语义分割的特征提取效率。然而，由于不合理的结构和超参数设计，这种方法可能导致多尺度上下文信息难以获取。为降低多尺度上下文信息的提取难度，我们提出一种高效的多尺度特征提取方法，将原始单步方法分解为区域残差化-语义残差化两个步骤。在此方法中，多速率深度可分离膨胀卷积承担了更简单的特征提取角色：基于第一步提供的各区域形式简洁特征图，在第二步中采用单一期望感受野执行简单的语义形态滤波，从而提升其效率。此外，我们精心设计了各网络阶段的膨胀率与膨胀卷积容量，以充分利用所有可获得的区域形式特征图。据此，我们分别针对网络高层和低层设计了新型膨胀残差（DWR）模块与简单倒残差（SIR）模块，构建了强大的DWR分割网络（DWRSeg）。在Cityscapes和CamVid数据集上的大量实验表明，该方法在精度与推理速度之间实现了最先进的平衡，同时模型更轻量化。无需预训练或任何训练技巧，我们在单张NVIDIA GeForce GTX 1080 Ti显卡上以319.5 FPS的速度在Cityscapes测试集上达到72.7%的mIoU，超越了近期方法69.5 FPS的速度和0.8%的mIoU指标。代码与预训练模型已公开。