  Modern high-performance semantic segmentation methods employ a heavy backbone
and dilated convolution to extract the relevant feature. Although extracting
features with both contextual and semantic information is critical for the
segmentation tasks, it brings a memory footprint and high computation cost for
real-time applications. This paper presents a new model to achieve a trade-off
between accuracy/speed for real-time road scene semantic segmentation.
Specifically, we proposed a lightweight model named Scale-aware Strip Attention
Guided Feature Pyramid Network (S$^2$-FPN). Our network consists of three main
modules: Attention Pyramid Fusion (APF) module, Scale-aware Strip Attention
Module (SSAM), and Global Feature Upsample (GFU) module. APF adopts an
attention mechanisms to learn discriminative multi-scale features and help
close the semantic gap between different levels. APF uses the scale-aware
attention to encode global context with vertical stripping operation and models
the long-range dependencies, which helps relate pixels with similar semantic
label. In addition, APF employs channel-wise reweighting block (CRB) to
emphasize the channel features. Finally, the decoder of S$^2$-FPN then adopts
GFU, which is used to fuse features from APF and the encoder. Extensive
experiments have been conducted on two challenging semantic segmentation
benchmarks, which demonstrate that our approach achieves better accuracy/speed
trade-off with different model settings. The proposed models have achieved a
results of 76.2\%mIoU/87.3FPS, 77.4\%mIoU/67FPS, and 77.8\%mIoU/30.5FPS on
Cityscapes dataset, and 69.6\%mIoU,71.0\% mIoU, and 74.2\% mIoU on Camvid
dataset. The code for this work will be made available at
\url{https://github.com/mohamedac29/S2-FPN
