BoostRad: Enhancing Object Detection by Boosting Radar Reflections

Yuval Haitman, Oded Bialer

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

Abstract

Automotive radars have an important role in autonomous driving systems. The main challenge in automotive radar detection is the radar's wide point spread function (PSF) in the angular domain that causes blurriness and clutter in the radar image. Numerous studies suggest employing an 'end-to-end' learning strategy using a Deep Neural Network (DNN) to directly detect objects from radar images. This approach implicitly addresses the PSF's impact on objects of interest. In this paper, we propose an alternative approach, which we term ''Boosting Radar Reflections"(BoostRad). In BoostRad, a first DNN is trained to narrow the PSF for all the reflection points in the scene. The output of the first DNN is a boosted reflection image with higher resolution and reduced clutter, resulting in a sharper and cleaner image. Subsequently, a second DNN is employed to detect objects within the boosted reflection image. We develop a novel method for training the boosting DNN that incorporates domain knowledge of radar's PSF characteristics. BoostRad's performance is evaluated using the RADDet and CARRADA datasets, revealing its superiority over reference methods.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PublisherInstitute of Electrical and Electronics Engineers
Pages1627-1636
Number of pages10
ISBN (Electronic)9798350318920
DOIs
StatePublished - 1 Jan 2024
Event2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States
Duration: 4 Jan 20248 Jan 2024

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Conference

Conference2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period4/01/248/01/24

Keywords

  • Algorithms
  • Algorithms
  • Applications
  • Autonomous Driving
  • Image recognition and understanding
  • Low-level and physics-based vision

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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