  Due to the powerful ability to encode image details and semantics, many
lightweight dual-resolution networks have been proposed in recent years.
However, most of them ignore the benefit of boundary information. This paper
introduces a lightweight dual-resolution network, called DRBANet, aiming to
refine semantic segmentation results with the aid of boundary information.
DRBANet adopts dual parallel architecture, including: high resolution branch
(HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a
set of Efficient Inverted Bottleneck Modules (EIBMs), which learn feature
representations with larger receptive fields. LRB is composed of a series of
EIBMs and an Extremely Lightweight Pyramid Pooling Module (ELPPM), where ELPPM
is utilized to capture multi-scale context through hierarchical residual
connections. Finally, a boundary supervision head is designed to capture object
boundaries in HRB. Extensive experiments on Cityscapes and CamVid datasets
demonstrate that our method achieves promising trade-off between segmentation
accuracy and running efficiency.
