Abstract
The development of an automotive surface recognition system is an important and yet unsolved task. In the current study we are considering a novel approach to surface classification based on the analysis of the image obtained using the Low Terahertz radar. The proposed experimental technique in combination with a deep convolutional neural network provides high surface classification accuracy.
| Original language | English |
|---|---|
| Title of host publication | 2020 21st International Radar Symposium, IRS 2020 |
| Publisher | IEEE Computer Society Press |
| Pages | 24-27 |
| Number of pages | 4 |
| ISBN (Electronic) | 9788394942151 |
| DOIs | |
| Publication status | Published - 5 Oct 2020 |
| Event | 21st International Radar Symposium, IRS 2020 - Warsaw, Poland Duration: 5 Oct 2020 → 7 Oct 2020 |
Publication series
| Name | Proceedings International Radar Symposium |
|---|---|
| Volume | 2020-October |
| ISSN (Print) | 2155-5753 |
Conference
| Conference | 21st International Radar Symposium, IRS 2020 |
|---|---|
| Country/Territory | Poland |
| City | Warsaw |
| Period | 5/10/20 → 7/10/20 |
Bibliographical note
Funding Information:This work was part of UK EPSRC/JLR project EP/N012372/1.
Publisher Copyright:
© 2020 Warsaw University of Technology.
Keywords
- Deep neural network
- Electromagnetic scattering
- Low terahertz
- Radar imaging
- Surface roughness
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing
- Electrical and Electronic Engineering
- Astronomy and Astrophysics
- Instrumentation