Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar

• URL: http://arxiv.org/abs/2205.02111v1
• Date: Tue, 3 May 2022 06:29:03 GMT
• Title: Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
• Authors: Michael Ulrich, Sascha Braun, Daniel K\"ohler, Daniel Niederl\"ohner, Florian Faion, Claudius Gl\"aser and Holger Blume
• Abstract summary: We present novel hybrid architectures that combine grid- and point-based processing to improve radar-based object detection networks. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a following convolutional detection backbone.
• Score: 1.53934570513443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird's-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed information through the discrete grid resolution. This applies in particular to radar object detection, where relatively coarse grid resolutions are commonly used to account for the sparsity of radar point clouds. In contrast, point-based models are not affected by this problem as they continuously process point clouds. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a following convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance and orientation estimates over networks from previous literature.

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