RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding (2310.17659v1)
Abstract: Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted with invalid components such as noise, interference, and clutter, it is necessary to employ a preprocessing algorithm before the 3D object detection with neural networks. In this paper, we propose RTNH+ that is an enhanced version of RTNH, a 4D Radar object detection network, by two novel algorithms. The first algorithm is the combined constant false alarm rate (CFAR)-based two-level preprocessing (CCTP) algorithm that generates two filtered measurements of different characteristics using the same 4D Radar measurements, which can enrich the information of the input to the 4D Radar object detection network. The second is the vertical encoding (VE) algorithm that effectively encodes vertical features of the road objects from the CCTP outputs. We provide details of the RTNH+, and demonstrate that RTNH+ achieves significant performance improvement of 10.14\% in ${{AP}{3D}{IoU=0.3}}$ and 16.12\% in ${{AP}{3D}{IoU=0.5}}$ over RTNH.
- nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621–11631, 2020.
- Analysis of cfar processors in nonhomogeneous background. IEEE Transactions on Aerospace and Electronic Systems, 24(4):427–445, 1988.
- Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354–3361, 2012.
- Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12697–12705, 2019.
- Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017.
- Sparse convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 806–814, 2015.
- Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
- One million scenes for autonomous driving: ONCE dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021.
- Automotive radar dataset for deep learning based 3d object detection. In 2019 16th European Radar Conference (EuRAD), pages 129–132, 2019.
- K-lane: Lidar lane dataset and benchmark for urban roads and highways. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 4449–4458, 2022.
- K-radar: 4d radar object detection for autonomous driving in various weather conditions. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
- Enhanced k-radar: Optimal density reduction to improve detection performance and accessibility of 4d radar tensor-based object detection. arXiv, 2023.
- Multi-class road user detection with 3+1d radar in the view-of-delft dataset. IEEE Robotics and Automation Letters, 7(2):4961–4968, 2022.
- Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
- Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10529–10538, 2020.
- R. Srinivasan. Robust radar detection using ensemble cfar processing. Radar, Sonar and Navigation, IEE Proceedings -, 147:291 – 297, 01 2001.
- Mimo radar for advanced driver-assistance systems and autonomous driving: Advantages and challenges. IEEE Signal Processing Magazine, 37:98–117, 2020.
- Attention is all you need. Advances in neural information processing systems, 30, 2017.
- Rpfa-net: A 4d radar pillar feature attention network for 3d object detection. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 3061–3066. IEEE, 2021.
- Second: Sparsely embedded convolutional detection. Sensors, 18(10), 2018.
- Tj4dradset: A 4d radar dataset for autonomous driving. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 493–498. IEEE, 2022.
- Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4490–4499, 2018.