Multi-sensor fusion 3D object detection based on channel attention

The existing 3D target detection network based on feature layer fusion of multi-view images and lidar point cloud fusion is mostly fused by directly splicing the multi-sensor features output by the backbone or the BEV features under the unified perspective of the two modalities. The features obtained by this method will be affected by the original data feature modality conversion and multi-sensor feature fusion`s effect. Aiming at this problem, a 3D object detection network based on feature fusion based on channel attention is proposed to improve the feature aggregation ability of BEV feature fusion, thereby improving the representation ability of the fused features. The experimental results on the nuScenes open source dataset show that compared with the baseline network, the overall feature grasp of the object is increased, and the average orientation error and average speed error are reduced by 4.9% and 4.0%, respectively. In the process of automatic driving, It can improve the vehicle's ability to perceive moving obstacles on the road, which has certain practical value.