RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather

2026-02-23

AI Summary

This work addresses the limitations of optical sensors in adverse weather and the information loss inherent in existing radar-based detection methods, which often rely on sparse point clouds or 2D projections. To overcome these challenges, the authors propose a novel 3D polar-coordinate projection tailored for 4D radar tensors and introduce RADE-Net, a lightweight network that integrates spatial and channel attention mechanisms to directly predict object centers and regress oriented 3D bounding boxes in the polar domain. The method uniquely preserves Doppler and elevation information while reducing data volume by 91.9%, substantially improving computational efficiency. Evaluated on the K-Radar dataset, the approach achieves a 16.7% mAP gain over the baseline and outperforms the current state-of-the-art radar-only method by 6.5%, notably surpassing certain LiDAR-based solutions under rain, snow, and fog conditions.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Automotive perception systems are obligated to meet high requirements. While optical sensors such as Camera and Lidar struggle in adverse weather conditions, Radar provides a more robust perception performance, effectively penetrating fog, rain, and snow. Since full Radar tensors have large data sizes and very few datasets provide them, most Radar-based approaches work with sparse point clouds or 2D projections, which can result in information loss. Additionally, deep learning methods show potential to extract richer and more dense features from low level Radar data and therefore significantly increase the perception performance. Therefore, we propose a 3D projection method for fast-Fourier-transformed 4D Range-Azimuth-Doppler-Elevation (RADE) tensors. Our method preserves rich Doppler and Elevation features while reducing the required data size for a single frame by 91.9% compared to a full tensor, thus achieving higher training and inference speed as well as lower model complexity. We introduce RADE-Net, a lightweight model tailored to 3D projections of the RADE tensor. The backbone enables exploitation of low-level and high-level cues of Radar tensors with spatial and channel-attention. The decoupled detection heads predict object center-points directly in the Range-Azimuth domain and regress rotated 3D bounding boxes from rich feature maps in the cartesian scene. We evaluate the model on scenes with multiple different road users and under various weather conditions on the large-scale K-Radar dataset and achieve a 16.7% improvement compared to their baseline, as well as 6.5% improvement over current Radar-only models. Additionally, we outperform several Lidar approaches in scenarios with adverse weather conditions. The code is available under https://github.com/chr-is-tof/RADE-Net.

Problem

• Radar-only object detection
• adverse weather
• RADE tensor
• automotive perception
• 3D object detection

Innovation

• RADE-Net
• 3D projection
• radar-only object detection
• attention mechanism
• adverse weather perception