Orr Itai, Damari Harel, Halachmi Meir, Raifel Mark, Twizer Kfir, Cohen Moshik, Zalevsky Zeev
J Opt Soc Am A Opt Image Sci Vis. 2021 Oct 1;38(10):B29-B36. doi: 10.1364/JOSAA.431582.
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.
车辆检测在自动驾驶中起着关键作用，其中两种核心传感方式是激光雷达和雷达。尽管已经提出了许多基于深度神经网络（DNN）的方法来解决这一任务，但对于数据对这些方法的影响仍缺乏系统的方法性研究。在这项工作中，我们研究了分辨率对激光雷达和雷达传感器车辆检测性能的影响。我们提出了下采样方法，该方法可以提高基于DNN的解决方案的性能和效率，并为传统的传感器设计权衡提供了一种替代方法。
