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

被引：11

作者：

Stroescu, Ana ^{[1
]}

Daniel, Liam ^{[1
]}

Phippen, Dominic ^{[1
]}

Cherniakov, Mikhail ^{[1
]}

Gashinova, Marina ^{[1
]}

机构：

[1] Univ Birmingham, Microwave Integrated Syst Lab, Birmingham B15 2TT, W Midlands, England

来源：

EURAD 2020 THE 17TH EUROPEAN RADAR CONFERENCE | 2021年

基金：

英国工程与自然科学研究理事会;

关键词：

Object Detection; Radar; Autonomous Driving; Deep Neural Networks;

D O I：

10.1109/EuRAD48048.2021.00041

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：120 / 123

页数：4