Semantic segmentation plays a key role in applications such as autonomous
driving and medical image. Although existing real-time semantic segmentation
models achieve a commendable balance between accuracy and speed, their
multi-path blocks still affect overall speed. To address this issue, this study
proposes a Reparameterizable Dual-Resolution Network (RDRNet) dedicated to
real-time semantic segmentation. Specifically, RDRNet employs a two-branch
architecture, utilizing multi-path blocks during training and reparameterizing
them into single-path blocks during inference, thereby enhancing both accuracy
and inference speed simultaneously. Furthermore, we propose the
Reparameterizable Pyramid Pooling Module (RPPM) to enhance the feature
representation of the pyramid pooling module without increasing its inference
time. Experimental results on the Cityscapes, CamVid, and Pascal VOC 2012
datasets demonstrate that RDRNet outperforms existing state-of-the-art models
in terms of both performance and speed. The code is available at
this https URL

该研究提出了一种用于实时语义分割的重参数化双分辨率网络（RDRNet），通过训练时使用多路径块（multi-path blocks），并在推理时将其重参数化为单路径块（single-path blocks），从而同时提高准确性和推理速度；此外还提出了可重参数化金字塔汇聚模块（RPPM），以增强特征表达能力。实验结果表明，RDRNet 在 Cityscapes、CamVid 和 Pascal VOC 2012 数据集上的性能和速度均优于现有最先进模型。