一种基于雷达视觉融合的车辆目标检测方法
By combining adaptive radar point cloud enhancement, multi-pooling point cloud feature encoding, and a shareable multi-semantic space attention module, the problem of insufficient detection accuracy for long-range and small-scale targets under sparse radar point clouds is solved, achieving higher detection accuracy and robustness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAN UNIV OF SCI & TECH
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing radar-camera fusion methods struggle to effectively detect distant and small-scale targets when dealing with sparse radar point clouds in complex traffic scenarios, resulting in insufficient detection accuracy.
An adaptive radar point cloud enhancement module (ARHGM) is used to generate hybrid point clouds. Virtual points are generated through Gaussian and uniform distributions. Combined with a multi-pooling point cloud feature encoding module (MultiPool) and a shareable multi-semantic space attention module (SMSA), the point cloud density and feature representation capabilities are improved.
It improves the target detection accuracy under sparse point cloud conditions, reduces missed detections, enhances the detection capability for distant and small-scale targets, and improves the accuracy and confidence of detection boxes.
Smart Images
Figure CN122176691B_ABST