A Feature Pyramid Fusion Detection Algorithm Based on Radar and Camera Sensor
Liangqun Li, Yuanliang Xie
Abstract
Considering the development of object detection based on deep learning framework in recent years, it has brought a new scope for multi-source fusion in the field of autonomous driving. In this paper, we propose a new architecture with a feature pyramid attention module to fuse the projected radar data and camera data. In the proposed algorithm, the detection model of YOLOv3 is employed by us and the feature pyramid module is extended with the input interface of the radar projection image and attention module. Additionally, in order to reduce the interference information from different scales of radar projected block, a new generation mechanism of radar projection images is introduced. Finally, the radar projection image is fused in feature pyramid layers with an attention module. The result shows that the proposed fusion algorithm outperforms better than image-only network for the nuScenes dataset. The code for this research will be made available to the public at: https://github.com/yuanliangxie/nuscenes_data_process.