Two-branch network architecture has shown its efficiency and effectiveness
for real-time semantic segmentation tasks. However, direct fusion of low-level
details and high-level semantics will lead to a phenomenon that the detailed
features are easily overwhelmed by surrounding contextual information, namely
overshoot in this paper, which limits the improvement of the accuracy of
existed two-branch models. In this paper, we bridge a connection between
Convolutional Neural Network (CNN) and Proportional-Integral-Derivative (PID)
controller and reveal that the two-branch network is nothing but a
Proportional-Integral (PI) controller, which inherently suffers from the
similar overshoot issue. To alleviate this issue, we propose a novel
three-branch network architecture: PIDNet, which possesses three branches to
parse the detailed, context and boundary information (derivative of semantics),
respectively, and employs boundary attention to guide the fusion of detailed
and context branches in final stage. The family of PIDNets achieve the best
trade-off between inference speed and accuracy and their test accuracy
surpasses all the existed models with similar inference speed on Cityscapes,
CamVid and COCO-Stuff datasets. Especially, PIDNet-S achieves 78.6% mIOU with
inference speed of 93.2 FPS on Cityscapes test set and 80.1% mIOU with speed of
153.7 FPS on CamVid test set.

本文提出了一种三支路网络体系结构 PIDNet，能够解决两支路网络体系结构中详细和语境特征混淆的问题，并利用边界关注技术在最终阶段对详细和语境支路进行融合，极大地提高了精度和神经网络处理速度。