Radar-Lidar Fusion for Object Detection by Designing Effective
  Convolution Networks
Farzeen Munir Shoaib Azam Tomasz Kucner Ville Kyrki Moongu Jeon
Object detection is a core component of perception systems, providing the ego
vehicle with information about its surroundings to ensure safe route planning.
While cameras and Lidar have significantly advanced perception systems, their
performance can be limited in adverse weather conditions. In contrast,
millimeter-wave technology enables radars to function effectively in such
conditions. However, relying solely on radar for building a perception system
doesn't fully capture the environment due to the data's sparse nature. To
address this, sensor fusion strategies have been introduced. We propose a
dual-branch framework to integrate radar and Lidar data for enhanced object
detection. The primary branch focuses on extracting radar features, while the
auxiliary branch extracts Lidar features. These are then combined using
additive attention. Subsequently, the integrated features are processed through
a novel Parallel Forked Structure (PFS) to manage scale variations. A region
proposal head is then utilized for object detection. We evaluated the
effectiveness of our proposed method on the Radiate dataset using COCO metrics.
The results show that it surpasses state-of-the-art methods by $1.89\%$ and
$2.61\%$ in favorable and adverse weather conditions, respectively. This
underscores the value of radar-Lidar fusion in achieving precise object
detection and localization, especially in challenging weather conditions.