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Related Experiment Video

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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

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation.

Tongrui Zhang¹, Yunsheng Fan¹

¹College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This study introduces a novel radar semantic segmentation network using high-dimensional radar heat maps for automatic target recognition. This approach improves efficiency and accuracy over traditional point cloud methods.

Keywords:

3D U-Net data cube radar semantic segmentation transformer

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Area of Science:

Engineering
Computer Science
Signal Processing

Background:

Current radar target recognition often uses point cloud data, which has limited information due to computational constraints.
High-dimensional radar data offers richer information but processing it efficiently remains a challenge.

Purpose of the Study:

To propose a semantic segmentation network for processing high-dimensional radar data for automatic target recognition.
To enhance the efficiency and accuracy of radar data utilization compared to existing methods.

Main Methods:

Developed a semantic segmentation network utilizing high-dimensional radar heat maps instead of point cloud data.
Introduced a dimension collapse module based on a vision transformer for effective feature extraction in high-dimensional data.

Main Results:

The proposed network demonstrates superior performance and requires fewer parameters than existing segmentation networks.
Validation on a real radar dataset confirms the effectiveness of the radar heat map and vision transformer approach.

Conclusions:

The novel radar semantic segmentation network effectively processes high-dimensional data for improved automatic target recognition.
The dimension collapse module is a versatile component for networks dealing with high-dimensional data transformations.