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Poster
MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS
- Citation Author(s):
- Submitted by:
- Gianluca Agresti
- Last updated:
- 8 May 2019 - 9:34am
- Document Type:
- Poster
- Document Year:
- 2019
- Event:
- Presenters:
- Simone MIlani
- Paper Code:
- 4700
- Categories:
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Recent years have assisted a widespreading of Radio-Frequency-based tracking and mapping algorithms for a wide range of applications, ranging from environment surveillance to human-computer interface.
This work presents a material identification system based on a portable 3D imaging radar-based system, the Walabot sensor by Vayyar Technologies; the acquired three-dimensional radiance map of the analyzed object is processed by a Convolutional Neural Network in order to identify which material the object is made of. Experimental results show that processing the three-dimensional radiance volume proves to be more efficient thas processing the raw signals from antennas. Moreover, the proposed solution presents a higher accuracy with respect to some previous state-of-the-art solutions.