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AI-Enabled Sensor Fusion of Time-of-Flight Imaging and mmWave for Concealed Metal Detection.

Chaitanya Kaul¹, Kevin J Mitchell², Khaled Kassem²

¹School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces a privacy-preserving sensor fusion system for detecting concealed metal objects on individuals. Combining mmWave radar and depth cameras, it achieves up to 95% accuracy in identifying hidden items.

Keywords:

deep learning information fusion metal detection mmWave mmWave radar sensing multi-modal sensing sensor fusion

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Area of Science:

Computer Vision
Sensor Fusion
Artificial Intelligence

Background:

Effective detection and ranging in dynamic scenes often requires integrating multiple sensing modalities.
Public security and surveillance applications necessitate robust systems that balance efficacy with privacy protection.

Purpose of the Study:

To develop a novel sensor fusion approach for discreetly detecting concealed metal objects on persons while preserving privacy.
To evaluate the efficacy of integrating mmWave radar and depth camera technology for enhanced security screening.

Main Methods:

A novel neural network architecture processing mmWave radar signals with convolutional Long Short-Term Memory (LSTM) blocks and depth signals with convolutional operations.
Deep feature magnification to identify cross-modality dependencies and a specialized decoder for spatial localization guided by radar features.

Main Results:

Successful detection of the presence and precise 3D localization of concealed metal objects.
Achieved detection accuracies of up to 95% with a system robust to multiple individuals.

Conclusions:

The proposed cost-effective and portable sensor fusion system demonstrates significant potential for security applications.
Further development opportunities exist for advancing privacy-preserving concealed object detection technologies.