TY - GEN
T1 - Deep learning radar object detection and classification for urban automotive scenarios
AU - Pérez, Rodrigo
AU - Schubert, Falk
AU - Rasshofer, Ralph
AU - Biebl, Erwin
N1 - Publisher Copyright:
© 2019 URSI Landesausschuss in der Bundesrepublik Deutschland e.V.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075154677&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85075154677
T3 - 2019 Kleinheubach Conference, KHB 2019
BT - 2019 Kleinheubach Conference, KHB 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Kleinheubach Conference, KHB 2019
Y2 - 23 September 2019 through 25 September 2019
ER -