Radar Detection

Radar detection research focuses on improving the accuracy and reliability of radar systems for applications like autonomous driving and social distancing monitoring. Current efforts concentrate on enhancing sparse radar point clouds through sensor fusion (combining radar with cameras or LiDAR) and leveraging deep learning techniques, including contrastive learning and adaptations of YOLO and PointCNN architectures, to improve object detection, tracking, and semantic segmentation. These advancements are crucial for enabling robust perception in challenging environments and improving the performance of various applications that rely on accurate and reliable radar data.