# Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2008.13642 · HTML: https://mlsanity.com/paper/2008.13642/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection](https://mlsanity.com/paper/2206.01772/) (2022)
- [Channel Boosting Feature Ensemble for Radar-based Object Detection](https://mlsanity.com/paper/2101.03531/) (2021)

## Abstract (arXiv)

This paper presents two variations of architecture referred to as RANet and BIRANet. The proposed architecture aims to use radar signal data along with RGB camera images to form a robust detection network that works efficiently, even in variable lighting and weather conditions such as rain, dust, fog, and others. First, radar information is fused in the feature extractor network. Second, radar points are used to generate guided anchors. Third, a method is proposed to improve region proposal network targets. BIRANet yields 72.3/75.3% average AP/AR on the NuScenes dataset, which is better than the performance of our base network Faster-RCNN with Feature pyramid network(FFPN). RANet gives 69.6/71.9% average AP/AR on the same dataset, which is reasonably acceptable performance. Also, both BIRANet and RANet are evaluated to be robust towards the noise.