# A recurrent CNN for online object detection on raw radar frames

arXiv: https://arxiv.org/abs/2212.11172 · HTML: https://mlsanity.com/paper/2212.11172/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 30 of 32)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Flow-Guided Feature Aggregation for Video Object Detection](https://mlsanity.com/paper/1703.10025/) (2017)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Memory Enhanced Global-Local Aggregation for Video Object Detection](https://mlsanity.com/paper/2003.12063/) (2020)
- [SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://mlsanity.com/paper/2101.08833/) (2021)
- [RVOS: End-to-End Recurrent Network for Video Object Segmentation](https://mlsanity.com/paper/1903.05612/) (2019)
- [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)
- [Object Detection in Video with Spatiotemporal Sampling Networks](https://mlsanity.com/paper/1803.05549/) (2018)
- [RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users](https://mlsanity.com/paper/2105.00363/) (2021)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [Video Object Detection with an Aligned Spatial-Temporal Memory](https://mlsanity.com/paper/1712.06317/) (2017)
- [Mobile Video Object Detection with Temporally-Aware Feature Maps](https://mlsanity.com/paper/1711.06368/) (2017)
- [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)
- [Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment](https://mlsanity.com/paper/2105.05207/) (2021)
- [RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar](https://mlsanity.com/paper/2004.03451/) (2020)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [Semantic Segmentation of Video Sequences with Convolutional LSTMs](https://mlsanity.com/paper/1905.01058/) (2019)
- [BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation](https://mlsanity.com/paper/2208.01159/) (2022)
- [Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?](https://mlsanity.com/paper/2006.05485/) (2020)
- [DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections](https://mlsanity.com/paper/2010.09273/) (2020)
- [Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2207.03146/) (2022)

## Built on by (most cited first, 9 of 9)

- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks](https://mlsanity.com/paper/2404.05307/) (2024)
- [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection](https://mlsanity.com/paper/2512.22972/) (2025)
- [AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection](https://mlsanity.com/paper/2504.00559/) (2025)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection](https://mlsanity.com/paper/2402.08427/) (2024)
- [KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving](https://mlsanity.com/paper/2408.02088/) (2024)
- [Graph Query Networks for Object Detection with Automotive Radar](https://mlsanity.com/paper/2511.15271/) (2025)
- [ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity](https://mlsanity.com/paper/2606.09634/) (2026)

## Abstract (arXiv)

Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors suffer from low resolution and huge intra-class variations in the shape of objects. Exploiting the time information (e.g., multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects. Most temporal radar object detectors use 3D convolutions to learn spatial and temporal information. However, these methods are often non-causal and unsuitable for real-time applications. This work presents RECORD, a new recurrent CNN architecture for online radar object detection. We propose an end-to-end trainable architecture mixing convolutions and ConvLSTMs to learn spatio-temporal dependencies between successive frames. Our model is causal and requires only the past information encoded in the memory of the ConvLSTMs to detect objects. Our experiments show such a method's relevance for detecting objects in different radar representations (range-Doppler, range-angle) and outperform state-of-the-art models on the ROD2021 and CARRADA datasets while being less computationally expensive.