Effects of lidar and radar resolution on DNN-based vehicle detection

Itai Orr, Harel Damari, Meir Halachmi, Mark Raifel, Kfir Twizer, Moshik Cohen, Zeev Zalevsky

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים


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.

שפה מקוריתאנגלית
עמודים (מ-עד)B29-B36
כתב עתJournal of the Optical Society of America A: Optics and Image Science, and Vision
מספר גיליון10
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 1 אוק׳ 2021

ASJC Scopus subject areas

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