PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers (2206.02066v3)

Abstract: 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.

• The paper introduces PIDNet, a three-branch architecture that integrates a derivative component to counter overshoot in semantic segmentation.
• The model achieves 78.6% mIOU at 93.2 FPS on Cityscapes and 80.1% mIOU at 153.7 FPS on CamVid, outperforming similar-speed models.
• By linking CNN design with control theory, PIDNet offers a practical solution for real-time applications in fields like autonomous driving and medical imaging.

PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

The paper presents PIDNet, a novel architecture for semantic segmentation, drawing inspiration from Proportional-Integral-Derivative (PID) controllers. This new architecture aims to address the limitations of traditional two-branch networks by introducing a third branch for enhanced performance in real-time scenarios.

Problem Context and Insights

Traditional two-branch networks, while effective at merging high-resolution details with low-frequency contextual information, suffer from what's termed the "overshoot phenomenon." This is where detailed features get overwhelmed by the surrounding context, reducing segmentation accuracy. The authors draw an insightful comparison between two-branch networks and Proportional-Integral (PI) controllers. They argue that similar to PI controllers, which encounter overshoot issues due to the lack of a derivative component, two-branch networks also face challenges in balancing detail and context.

The PIDNet Architecture

To mitigate this overshoot, the paper introduces PIDNet, which incorporates an additional boundary-focused branch. This design takes inspiration from how PID controllers operate, utilizing:

• Proportional (P) Branch: Focuses on preserving detail.
• Integral (I) Branch: Aggregates low-frequency context information.
• Derivative (D) Branch: Targets high-frequency components, especially boundaries.

By explicitly modeling boundary detection, the network ensures that detailed and contextual information is fused more judiciously, avoiding the dominance of one over the other.

Numerical Results and Implementation

The PIDNet exhibits a commendable balance between inference speed and accuracy. Specifically, PIDNet-S achieves 78.6% mIOU at 93.2 FPS on the Cityscapes dataset, and 80.1% mIOU at 153.7 FPS on the CamVid dataset, surpassing existing models with similar inference speeds. These metrics underscore PIDNet's efficiency and effectiveness, demonstrating a significant advancement in real-time semantic segmentation.

Implications and Future Directions

The introduction of PIDNet represents a substantial contribution to semantic segmentation, particularly for real-time applications such as autonomous driving and medical imaging. Its design offers a robust alternative to conventional networks by systematically addressing the overshoot issue.

From a theoretical standpoint, linking CNN architectures with control theory offers a novel perspective that could stimulate further interdisciplinary research. Practically, the high accuracy-speed trade-off of PIDNet makes it a compelling choice for applications necessitating swift and precise scene parsing.

Future research could explore the adaptability of this architecture to other domains, such as 3D vision or multimodal tasks, further extending the utility and versatility of the PID-inspired approach. Additionally, optimizing the computational load for even greater speed without sacrificing accuracy would be a valuable avenue for exploration.

In summary, PIDNet not only provides an effective solution to a common problem in semantic segmentation but also opens new avenues for integrating control theory concepts within neural network architectures.