arXiv 2010.033202020-10cs.CVcs.LG

Yodar：基于不确定性的传感器融合，用于相机和雷达传感器的车辆检测

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors

University of Wuppertal（伍珀塔尔大学）

Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk

摘要：

在这项工作中，我们提出了一种基于不确定性的方法与相机和雷达数据的传感器融合。两个神经网络的输出，一个处理相机和另一个雷达数据，以不确定性感知的方式组合。为此，我们收集了两个网络的输出和相应的Meta信息。对于每个预测对象，通过梯度提升方法对收集的信息进行后处理，以产生两个网络的联合预测。在我们的实验中，我们将YOLOv3目标检测网络与定制的$1D$雷达分割网络相结合，并在nuScenes数据集上评估我们的方法。特别是，我们专注于夜间场景，其中基于摄像机数据的目标检测网络的能力是潜在的障碍。我们的实验表明，这种不确定性感知融合的方法，这也是非常模块化的性质，显着提高性能相比，单个传感器基线，是专门定制的范围内的深度学习为基础的融合方法。

英文摘要：

In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we gather the outputs and corresponding meta information for both networks. For each predicted object, the gathered information is post-processed by a gradient boosting method to produce a joint prediction of both networks. In our experiments we combine the YOLOv3 object detection network with a customized $1D$ radar segmentation network and evaluate our method on the nuScenes dataset. In particular we focus on night scenes, where the capability of object detection networks based on camera data is potentially handicapped. Our experiments show, that this approach of uncertainty aware fusion, which is also of very modular nature, significantly gains performance compared to single sensor baselines and is in range of specifically tailored deep learning based fusion approaches.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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