Outdoor 3D Object Detection Method Based on Multi-Direction Features Fusion

The 3D object detection method is one of the significant technologies in the environmental perception of autonomous driving. However, most existing 3D object detection methods have the problem of inaccurate position recognition and large orientation deviation. To address these issues, a 3D object detection method based on multi-direction features fusion is proposed. First, to perform data encoding for a point cloud scenario, modeling distance and angle and transforming into pseudo image. Second, a multi-direction feature-fusion backbone is proposed for features extraction and fusion. Finally, based on the fused features, a center-based detector regresses and predicts potential objects. Distance-angle modeling can supply the relationship between points and enrich features. The multi-direction feature-fusion backbone enhances the ability of features extraction and improves the accuracy of position and orientation estimation. The experimental results show that the mean Average Precision(mAP)of this method on the KITTI and nuScenes datasets was 64.28% and 50.17%, respectively, which is an improvement of 0.36 and 1.30 percentage points, respectively, compared to those of the suboptimal method. In addition, the best Average Orientation Similarity(AOS)and mean Average Orientation Error(mAOE)were achieved in the orientation prediction accuracy comparison experiments. The generalization experimental results verified that the proposed multi-direction feature-fusion backbone network can improve network detection ability and significantly reduce the number of parameters, thereby improving the application performance of the detection method.