# Deep Radar Detector

arXiv: https://arxiv.org/abs/1906.12187 · HTML: https://mlsanity.com/paper/1906.12187/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 22 of 22)

- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users](https://mlsanity.com/paper/2105.00363/) (2021)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](https://mlsanity.com/paper/2205.02111/) (2022)
- [Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data](https://mlsanity.com/paper/2404.06012/) (2024)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks](https://mlsanity.com/paper/2305.15836/) (2023)
- [Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2207.03146/) (2022)
- [Weak-to-Strong 3D Object Detection with X-Ray Distillation](https://mlsanity.com/paper/2404.00679/) (2024)
- [mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction](https://mlsanity.com/paper/2503.02375/) (2025)
- [RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes](https://mlsanity.com/paper/2505.20967/) (2025)
- [Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation](https://mlsanity.com/paper/2207.06379/) (2022)
- [HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds](https://mlsanity.com/paper/2602.11554/) (2026)
- [Exploiting Sparsity in Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2308.07748/) (2023)
- [End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2312.09790/) (2023)
- [Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach](https://mlsanity.com/paper/2502.01940/) (2025)
- [Enhanced Automotive Object Detection via RGB-D Fusion in a DiffusionDet Framework](https://mlsanity.com/paper/2406.03129/) (2024)
- [Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data](https://mlsanity.com/paper/2411.00499/) (2024)
- [Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search](https://mlsanity.com/paper/2506.21772/) (2025)
- [Integrated Detection and Tracking Based on Radar Range-Doppler Feature](https://mlsanity.com/paper/2509.06569/) (2025)
- [The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar](https://mlsanity.com/paper/2509.19644/) (2025)
- [ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity](https://mlsanity.com/paper/2606.09634/) (2026)

## Abstract (arXiv)

While camera and LiDAR processing have been revolutionized since the introduction of deep learning, radar processing still relies on classical tools. In this paper, we introduce a deep learning approach for radar processing, working directly with the radar complex data. To overcome the lack of radar labeled data, we rely in training only on the radar calibration data and introduce new radar augmentation techniques. We evaluate our method on the radar 4D detection task and demonstrate superior performance compared to the classical approaches while keeping real-time performance. Applying deep learning on radar data has several advantages such as eliminating the need for an expensive radar calibration process each time and enabling classification of the detected objects with almost zero-overhead.