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CenterPNets: A Multi-Task Shared Network for Traffic Perception.

Guangqiu Chen¹, Tao Wu¹, Jin Duan¹

¹College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

CenterPNets is a novel multi-task network for autonomous driving, efficiently handling target detection, drivable area segmentation, and lane detection. This solution enhances traffic perception accuracy and inference speed.

Keywords:

multi-task learning semantic segmentation target detection traffic perception

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Area of Science:

Computer Vision
Autonomous Driving Systems
Deep Learning Architectures

Background:

Panoramic traffic perception is crucial for autonomous driving safety and efficiency.
Existing methods often require separate models for distinct perception tasks, limiting resource utilization and overall performance.
High-accuracy shared networks are needed to integrate multiple traffic sensing tasks.

Purpose of the Study:

To introduce CenterPNets, a unified multi-task shared sensing network for autonomous driving.
To enhance the accuracy and efficiency of simultaneous target detection, drivable area segmentation, and lane detection.
To propose key optimizations for improved multi-task learning performance.

Main Methods:

Developed CenterPNets, a multi-task shared sensing network integrating detection, segmentation, and lane detection heads.
Utilized a shared path aggregation network for efficient feature reuse between tasks.
Implemented an anchor-free detection head for faster target localization and a split-head branch for multi-scale feature fusion.
Designed an efficient multi-task joint training loss function for model optimization.

Main Results:

CenterPNets achieved 75.8% average detection accuracy on the Berkeley DeepDrive dataset.
The network demonstrated high performance in segmentation tasks with 92.8% intersection ratio for drivable areas and 32.1% for lane areas.
Optimizations led to improved overall detection performance and inference speed.

Conclusions:

CenterPNets provides a precise and effective solution for integrated multi-task traffic perception.
The proposed network architecture and optimizations significantly advance the capabilities of autonomous driving systems.
This unified approach demonstrates the potential of shared networks for complex real-world traffic sensing challenges.