  This paper introduces a lightweight convolutional neural network, called
FDDWNet, for real-time accurate semantic segmentation. In contrast to recent
advances of lightweight networks that prefer to utilize shallow structure,
FDDWNet makes an effort to design more deeper network architecture, while
maintains faster inference speed and higher segmentation accuracy. Our network
uses factorized dilated depth-wise separable convolutions (FDDWC) to learn
feature representations from different scale receptive fields with fewer model
parameters. Additionally, FDDWNet has multiple branches of skipped connections
to gather context cues from intermediate convolution layers. The experiments
show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed
on a single RTX 2080Ti GPU with a 1024x512 input image. The comprehensive
experiments demonstrate that our model achieves state-of-the-art results in
terms of available speed and accuracy trade-off on CityScapes and CamVid
datasets.
