Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network
| Autor: | Didier Stricker, Oliver Wasenmuller, Jason Rambach, Mahdi Chamseddine |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
020301 aerospace & aeronautics
Network architecture Artificial neural network Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud 02 engineering and technology Object detection law.invention Lidar 0203 mechanical engineering law 0202 electrical engineering electronic engineering information engineering 3D radar 020201 artificial intelligence & image processing Computer vision Artificial intelligence Radar business Physics::Atmospheric and Oceanic Physics |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9413247 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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