Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks

Ana Stroescu; Liam Daniel; Dominic Phippen; Mikhail Cherniakov; Marina Gashinova

doi:10.1109/EuRAD48048.2021.00041

Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks

Ana Stroescu, Liam Daniel, Dominic Phippen, Mikhail Cherniakov, Marina Gashinova

Electronic, Electrical and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.

Original language	English
Title of host publication	2020 17th European Radar Conference (EuRAD)
Publisher	IEEE
Pages	120-123
Number of pages	4
ISBN (Electronic)	9782874870613
ISBN (Print)	9781728170602 (PoD)
DOIs	https://doi.org/10.1109/EuRAD48048.2021.00041
Publication status	Published - 3 Feb 2021
Event	17th European Radar Conference, EuRAD 2020 - Utrecht, Netherlands Duration: 13 Jan 2021 → 15 Jan 2021

Publication series

Name	European Radar Conference (EURAD)

Conference

Conference	17th European Radar Conference, EuRAD 2020
Country/Territory	Netherlands
City	Utrecht
Period	13/01/21 → 15/01/21

Bibliographical note

Funding Information:
The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme.

Publisher Copyright:
© 2021 EuMA.

Keywords

Autonomous Driving
Deep Neural Networks
Object Detection
Radar

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/EuRAD48048.2021.00041

Pervasive low-TeraHz and video sensing for car autonomy and driver assistance (PATH CAD)
Cherniakov, M. (Co-Investigator), Gardner, P. (Co-Investigator) & Gashinova, M. (Principal Investigator)
Engineering & Physical Science Research Council, JAGUAR CARS LIMITED
1/12/15 → 31/03/20
Project: Research

Cite this

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title = "Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks",

abstract = "This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.",

keywords = "Autonomous Driving, Deep Neural Networks, Object Detection, Radar",

author = "Ana Stroescu and Liam Daniel and Dominic Phippen and Mikhail Cherniakov and Marina Gashinova",

note = "Funding Information: The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme. Publisher Copyright: {\textcopyright} 2021 EuMA.; 17th European Radar Conference, EuRAD 2020 ; Conference date: 13-01-2021 Through 15-01-2021",

year = "2021",

month = feb,

day = "3",

doi = "10.1109/EuRAD48048.2021.00041",

language = "English",

isbn = "9781728170602 (PoD)",

series = "European Radar Conference (EURAD)",

publisher = "IEEE",

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Stroescu, A, Daniel, L, Phippen, D, Cherniakov, M & Gashinova, M 2021, Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks. in 2020 17th European Radar Conference (EuRAD)., 9337402, European Radar Conference (EURAD), IEEE, pp. 120-123, 17th European Radar Conference, EuRAD 2020, Utrecht, Netherlands, 13/01/21. https://doi.org/10.1109/EuRAD48048.2021.00041

TY - GEN

T1 - Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks

AU - Stroescu, Ana

AU - Daniel, Liam

AU - Phippen, Dominic

AU - Cherniakov, Mikhail

AU - Gashinova, Marina

N1 - Funding Information: The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme. Publisher Copyright: © 2021 EuMA.

PY - 2021/2/3

Y1 - 2021/2/3

N2 - This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.

AB - This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.

KW - Autonomous Driving

KW - Deep Neural Networks

KW - Object Detection

KW - Radar

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U2 - 10.1109/EuRAD48048.2021.00041

DO - 10.1109/EuRAD48048.2021.00041

M3 - Conference contribution

AN - SCOPUS:85100887898

SN - 9781728170602 (PoD)

T3 - European Radar Conference (EURAD)

SP - 120

EP - 123

BT - 2020 17th European Radar Conference (EuRAD)

PB - IEEE

T2 - 17th European Radar Conference, EuRAD 2020

Y2 - 13 January 2021 through 15 January 2021

ER -

Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Projects

Pervasive low-TeraHz and video sensing for car autonomy and driver assistance (PATH CAD)

Cite this