用新型视觉变压器提升雷达目标检测精度与速度
TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection
- 引入保留式视觉变换器，利用空间先验增强雷达数据特征提取
- 在KITTI和Argoverse数据集上2D/3D检测精度超越现有方法
- 适合自动驾驶与智能机器人在恶劣环境下的感知系统研发
尽管自动驾驶和智能机器人在环境感知方面取得显著进展，摄像头和激光雷达在低光及恶劣天气下仍表现不可靠，限制了实际应用。雷达作为低成本且可靠的传感器，可有效弥补上述缺陷。然而，由于雷达数据存在分辨率低、噪声大、缺乏视觉信息等固有弱点，基于雷达的目标检测仍研究不足。本文提出TransRAD，一种新颖的3D雷达目标检测模型，通过引入保留式视觉变换器（RMT）来更高效地从信息密集的雷达距离-方位-多普勒（RAD）数据中学习特征。该方法利用RMT提供的保留式曼哈顿自注意力机制（MaSA），显式引入空间先验，实现对雷达目标空间显著性的精准对齐，从而在距离-方位-多普勒维度上实现高精度3D检测。此外，我们提出位置感知非极大值抑制（Location-Aware NMS），有效缓解深度雷达检测中常见的重复边界框问题。实验表明，TransRAD在KITTI和Argoverse数据集上的2D和3D检测任务中均优于现有最先进方法，具备更高精度、更快推理速度和更低计算复杂度。代码已开源。
原文摘要 · Abstract (English)
Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which limits their effectiveness. Radar serves as a reliable and low-cost sensor that can effectively complement these limitations. However, radar-based object detection has been underexplored due to the inherent weaknesses of radar data, such as low resolution, high noise, and lack of visual information. In this paper, we present TransRAD, a novel 3D radar object detection model designed to address these challenges by leveraging the Retentive Vision Transformer (RMT) to more effectively learn features from information-dense radar Range-Azimuth-Doppler (RAD) data. Our approach leverages the Retentive Manhattan Self-Attention (MaSA) mechanism provided by RMT to incorporate explicit spatial priors, thereby enabling more accurate alignment with the spatial saliency characteristics of radar targets in RAD data and achieving precise 3D radar detection across Range-Azimuth-Doppler dimensions. Furthermore, we propose Location-Aware NMS to effectively mitigate the common issue of duplicate bounding boxes in deep radar object detection. The experimental results demonstrate that TransRAD outperforms state-of-the-art methods in both 2D and 3D radar detection tasks, achieving higher accuracy, faster inference speed, and reduced computational complexity. Code is available at https://github.com/radar-lab/TransRAD
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