Deep learning radar object detection and classification for urban automotive scenarios

Rodrigo Pérez, Falk Schubert, Ralph Rasshofer, Erwin Biebl

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

28 Zitate (Scopus)


This paper presents a single shot detection and classification system in urban automotive scenarios with a 77 GHz frequency modulated continuous wave radar sensor. The detection and classification of road users is based on the real-time object detection system YOLO (You Only Look Once) applied to the pre-processed radar range-Doppler-angle power spectrum. To train and test the proposed system, data is gathered with a test vehicle parked on urban roads. A mean average precision of 70.64% is achieved on a separate test data set.

Titel2019 Kleinheubach Conference, KHB 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9783948571009
PublikationsstatusVeröffentlicht - Sept. 2019
Veranstaltung2019 Kleinheubach Conference, KHB 2019 - Miltenberg, Deutschland
Dauer: 23 Sept. 201925 Sept. 2019


Name2019 Kleinheubach Conference, KHB 2019


Konferenz2019 Kleinheubach Conference, KHB 2019


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