PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
This addresses the efficiency-accuracy trade-off in real-time semantic segmentation for applications like autonomous driving, though it is an incremental improvement over existing two-branch models.
The paper tackles the problem of overshoot in two-branch networks for real-time semantic segmentation by proposing PIDNet, a three-branch architecture inspired by PID controllers, which achieves state-of-the-art accuracy with high inference speeds, such as 78.6% mIOU at 93.2 FPS on Cityscapes.
Two-branch network architecture has shown its efficiency and effectiveness in real-time semantic segmentation tasks. However, direct fusion of high-resolution details and low-frequency context has the drawback of detailed features being easily overwhelmed by surrounding contextual information. This overshoot phenomenon limits the improvement of the segmentation accuracy of existing two-branch models. In this paper, we make a connection between Convolutional Neural Networks (CNN) and Proportional-Integral-Derivative (PID) controllers and reveal that a two-branch network is equivalent to a Proportional-Integral (PI) controller, which inherently suffers from similar overshoot issues. To alleviate this problem, we propose a novel three-branch network architecture: PIDNet, which contains three branches to parse detailed, context and boundary information, respectively, and employs boundary attention to guide the fusion of detailed and context branches. Our family of PIDNets achieve the best trade-off between inference speed and accuracy and their accuracy surpasses all the existing models with similar inference speed on the Cityscapes and CamVid datasets. Specifically, PIDNet-S achieves 78.6% mIOU with inference speed of 93.2 FPS on Cityscapes and 80.1% mIOU with speed of 153.7 FPS on CamVid.