TY - JOUR
T1 - Effects of lidar and radar resolution on DNN-based vehicle detection
AU - Orr, Itai
AU - Damari, Harel
AU - Halachmi, Meir
AU - Raifel, Mark
AU - Twizer, Kfir
AU - Cohen, Moshik
AU - Zalevsky, Zeev
N1 - Publisher Copyright:
© 2021 Optica Publishing Group.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Vehicle detection plays a critical role in autonomous driving, where two central sensing modalities are lidar and radar. Although many deep neural network (DNN)-based methods have been proposed to solve this task, a systematic and methodological examination on the influence of the data on those methods is still missing. In this work, we examine the effects of resolution on the performance of vehicle detection for both lidar and radar sensors. We propose subsampling methods that can improve the performance and efficiency of DNN-based solutions and offer an alternative approach to traditional sensor-design trade-offs.
AB - Vehicle detection plays a critical role in autonomous driving, where two central sensing modalities are lidar and radar. Although many deep neural network (DNN)-based methods have been proposed to solve this task, a systematic and methodological examination on the influence of the data on those methods is still missing. In this work, we examine the effects of resolution on the performance of vehicle detection for both lidar and radar sensors. We propose subsampling methods that can improve the performance and efficiency of DNN-based solutions and offer an alternative approach to traditional sensor-design trade-offs.
UR - http://www.scopus.com/inward/record.url?scp=85116396977&partnerID=8YFLogxK
U2 - 10.1364/josaa.431582
DO - 10.1364/josaa.431582
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C2 - 34612987
AN - SCOPUS:85116396977
SN - 1084-7529
VL - 38
SP - B29-B36
JO - Journal of the Optical Society of America A: Optics and Image Science, and Vision
JF - Journal of the Optical Society of America A: Optics and Image Science, and Vision
IS - 10
ER -