3D Dynamic Target Detection Algorithm Based on Voxel Point Cloud Fusion

Abstract

Current methods used in the field of 3D target detection generally have the problem that target context features are not rich enough to achieve accurate dynamic multi-target detection in feature extraction. A 3D dynamic object detection algorithm based on voxel point cloud fusion is proposed. The algorithm uses a two-stage detection architecture of multiple and fusion of multi-scale feature. In the first stage, point cloud is directly processed to extract key point features and the voxel space is divided to extract multi-scale voxel features, and two features are firstly fused to generate preselection frame. In the second stage, a reference point is set in each voxel and surrounding key points are absorbed for the second feature fusion, and the final feature is input to a detection module to achieve optimization of the preselection box. Additionally, for the problem of inconsistency between classification and location reliability, a mandatory consistency loss function is proposed to further improve accuracy of detection. Compared with other algorithms in Kitti, Waymo and nuScene datasets, the accuracy of 3D dynamic target detection is 92.10%, the results on the real vehicle platform show that the algorithm has strong robustness, portability and generalization ability.