计算机科学
点云
雷达
人工智能
激光雷达
深度学习
雷达成像
计算机视觉
目标检测
人工神经网络
雷达跟踪器
雷达工程细节
点(几何)
遥感
模式识别(心理学)
地理
电信
几何学
数学
作者
Mahdi Chamseddine,Jason Rambach,Didier Stricker,Oliver Wasenmüller
标识
DOI:10.1109/icpr48806.2021.9413247
摘要
Ghost targets are targets that appear at wrong locations in radar data and are caused by the presence of multiple indirect reflections between the target and the sensor. In this work, we introduce the first point based deep learning approach for ghost target detection in 3D radar point clouds. This is done by extending the PointNet network architecture by modifying its input to include radar point features beyond location and introducing skip connetions. We compare different input modalities and analyze the effects of the changes we introduced. We also propose an approach for automatic labeling of ghost targets 3D radar data using lidar as reference. The algorithm is trained and tested on real data in various driving scenarios and the tests show promising results in classifying real and ghost radar targets.
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