# T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals

arXiv: https://arxiv.org/abs/2303.16940 · HTML: https://mlsanity.com/paper/2303.16940/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](https://mlsanity.com/paper/2005.01456/) (2020)
- [RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users](https://mlsanity.com/paper/2105.00363/) (2021)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [2D Car Detection in Radar Data with PointNets](https://mlsanity.com/paper/1904.08414/) (2019)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition](https://mlsanity.com/paper/2011.08981/) (2020)
- [CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals](https://mlsanity.com/paper/2111.03976/) (2021)
- [Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data](https://mlsanity.com/paper/2212.03690/) (2022)

## Built on by (most cited first, 24 of 24)

- [Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion](https://mlsanity.com/paper/2307.16532/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships](https://mlsanity.com/paper/2408.15178/) (2024)
- [Radar Spectra-Language Model for Automotive Scene Parsing](https://mlsanity.com/paper/2406.02158/) (2024)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection](https://mlsanity.com/paper/2512.22972/) (2025)
- [Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models](https://mlsanity.com/paper/2407.07376/) (2024)
- [RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather](https://mlsanity.com/paper/2602.19994/) (2026)
- [Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection](https://mlsanity.com/paper/2402.08427/) (2024)
- [A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data](https://mlsanity.com/paper/2411.13311/) (2024)
- [Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor](https://mlsanity.com/paper/2502.06114/) (2025)
- [Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data](https://mlsanity.com/paper/2306.09839/) (2023)
- [Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data](https://mlsanity.com/paper/2411.00499/) (2024)
- [RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits](https://mlsanity.com/paper/2508.03578/) (2025)
- [mRadNet: A Compact Radar Object Detector with MetaFormer](https://mlsanity.com/paper/2509.16223/) (2025)
- [RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving](https://mlsanity.com/paper/2603.14822/) (2026)
- [AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception](https://mlsanity.com/paper/2603.17979/) (2026)
- [RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation](https://mlsanity.com/paper/2604.04490/) (2026)
- [DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather](https://mlsanity.com/paper/2604.08074/) (2026)
- [Revisiting Radar Perception With Spectral Point Clouds](https://mlsanity.com/paper/2604.08282/) (2026)
- [REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View](https://mlsanity.com/paper/2605.11824/) (2026)
- [DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception](https://mlsanity.com/paper/2607.17351/) (2026)
- [STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar](https://mlsanity.com/paper/2609.24151/) (2026)

## Abstract (arXiv)

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess traits that make it unsuitable for standard emission-based deep learning representations such as point clouds. Radar point clouds tend to be sparse and therefore information extraction is not efficient. To overcome this, more traditional digital signal processing pipelines were adapted to form inputs residing directly in the frequency domain via Fast Fourier Transforms. Commonly, three transformations were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This too has drawbacks, namely the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We explore the possibility of operating on raw radar inputs from analog to digital converters via the utilization of complex transformation layers. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, i.e. relatively low and high numbers of transmitters and receivers, while obtaining on par or better results than the state-of-the-art.