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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Cascade contour-enhanced panoptic segmentation for robotic vision perception.

Yue Xu^1,2,3, Runze Liu^1,2, Dongchen Zhu^1,3

¹Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.

Frontiers in Neurorobotics

|November 5, 2024

Summary

This summary is machine-generated.

This study introduces CCPSNet, a novel approach to panoptic segmentation that enhances robot scene understanding. The network excels in challenging conditions by integrating contour information for improved object and texture discrimination.

Keywords:

cascade feature enhancement panoptic contour detection panoptic segmentation robot vision structure perception visual pathway

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Panoptic segmentation is vital for robot environmental comprehension.
Existing methods struggle with weak textures and small objects.

Purpose of the Study:

To enhance robot scene understanding using a novel cascaded contour-enhanced panoptic segmentation network (CCPSNet).
To improve instance discriminability by incorporating structural knowledge inspired by biological vision.

Main Methods:

Designed a cascade contour detection stream with a channel regulation structural perception module and coarse-to-fine strategy.
Developed a contour-guided multi-scale feature enhancement stream using structural-aware feature modulation and inverse aggregation.
Integrated contour information with multi-scale context features.

Main Results:

Achieved improved accuracy on Cityscapes (61.2 PQ) and COCO (43.5 PQ) datasets.
Demonstrated robustness in simulated challenging real-world scenarios like dirty cameras and rain.

Conclusions:

CCPSNet significantly enhances robot perception capabilities in complex environments.
Future work may explore unsupervised training to reduce computational costs.