Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Object detection is a core component of perception systems, providing the ego vehicle with information about its surroundings to ensure safe route planning. While cameras and Lidar have significantly advanced perception systems, their performance can be limited in adverse weather conditions. In contrast, millimeter-wave technology enables radars to function effectively in such conditions. However, relying solely on radar for building a perception system doesn't fully capture the environment due to the data's sparse nature. To address this, sensor fusion strategies have been introduced. We propose a dual-branch framework to integrate radar and Lidar data for enhanced object detection. The primary branch focuses on extracting radar features, while the auxiliary branch extracts Lidar features. These are then combined using additive attention. Subsequently, the integrated features are processed through a novel Parallel Forked Structure (PFS) to manage scale variations. A region proposal head is then utilized for object detection. We evaluated the effectiveness of our proposed method on the Radiate dataset using COCO metrics. The results show that it surpasses state-of-the-art methods by 1.89% and 2.61% in favorable and adverse weather conditions, respectively. This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.

Original languageEnglish
Title of host publication2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PublisherIEEE
Pages3575-3582
Number of pages8
ISBN (Electronic)979-8-3503-9946-2
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventIEEE International Conference on Intelligent Transportation Systems - Bilbao, Spain
Duration: 24 Sept 202328 Sept 2023
Conference number: 26

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

ConferenceIEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC
Country/TerritorySpain
CityBilbao
Period24/09/202328/09/2023

Keywords

  • attention
  • radar object detection
  • sensor fusion

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