# RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles

arXiv: https://arxiv.org/abs/1905.00526 · HTML: https://mlsanity.com/paper/1905.00526/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 30 of 33)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Radar-Camera Pixel Depth Association for Depth Completion](https://mlsanity.com/paper/2106.02778/) (2021)
- [Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection](https://mlsanity.com/paper/2208.12079/) (2022)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving](https://mlsanity.com/paper/2412.10734/) (2024)
- [MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection](https://mlsanity.com/paper/2310.16389/) (2023)
- [RadSegNet: A Reliable Approach to Radar Camera Fusion](https://mlsanity.com/paper/2208.03849/) (2022)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Radar Camera Fusion via Representation Learning in Autonomous Driving](https://mlsanity.com/paper/2103.07825/) (2021)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Scene-aware Learning Network for Radar Object Detection](https://mlsanity.com/paper/2107.01469/) (2021)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [BatMobility: Towards Flying Without Seeing for Autonomous Drones](https://mlsanity.com/paper/2307.11518/) (2023)
- [Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks](https://mlsanity.com/paper/2411.18288/) (2024)
- [Channel Boosting Feature Ensemble for Radar-based Object Detection](https://mlsanity.com/paper/2101.03531/) (2021)
- [DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2302.13577/) (2023)
- [RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving](https://mlsanity.com/paper/2303.05203/) (2023)
- [A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2406.00714/) (2024)
- [Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles](https://mlsanity.com/paper/2506.21885/) (2025)
- [End-to-end system for object detection from sub-sampled radar data](https://mlsanity.com/paper/2203.03905/) (2022)
- [UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5](https://mlsanity.com/paper/2207.00837/) (2022)
- [LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2209.07709/) (2022)
- [RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion](https://mlsanity.com/paper/2305.15883/) (2023)
- [4DRC-OCC: Robust Semantic Occupancy Prediction Through Fusion of 4D Radar and Camera](https://mlsanity.com/paper/2603.07794/) (2026)
- [Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information](https://mlsanity.com/paper/2302.10450/) (2023)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning](https://mlsanity.com/paper/2504.12167/) (2025)
- [InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2405.11293/) (2024)

## Abstract (arXiv)

Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in the image. Nonetheless, region proposal algorithms are known to be the bottleneck in most two-stage object detection networks, increasing the processing time for each image and resulting in slow networks not suitable for real-time applications such as autonomous driving vehicles. In this paper we introduce RRPN, a Radar-based real-time region proposal algorithm for object detection in autonomous driving vehicles. RRPN generates object proposals by mapping Radar detections to the image coordinate system and generating pre-defined anchor boxes for each mapped Radar detection point. These anchor boxes are then transformed and scaled based on the object's distance from the vehicle, to provide more accurate proposals for the detected objects. We evaluate our method on the newly released NuScenes dataset [1] using the Fast R-CNN object detection network [2]. Compared to the Selective Search object proposal algorithm [3], our model operates more than 100x faster while at the same time achieves higher detection precision and recall. Code has been made publicly available at https://github.com/mrnabati/RRPN .