Research On 3D Occluded Object Detection Method Based On Point Cloud Completion

The 3D object detection based on point clouds is a critical component in autonomous driving perception systems.However,occlusion and signal loss in lidar can result in incomplete point cloud shapes,leading to compromised detection accuracy.To address this issue,this study leverages existing point cloud completion algorithms and develops an occlusion object completion network based on the KITTI dataset.We integrate this network into the 3D object detection network and propose enhancements to further improve detection accuracy.The study focuses on three aspects: the occlusion object completion network,integration with the 3D object detection network,and enhancements to improve detection accuracy.The research content of this study focuses on three aspects:(1)In response to the limitations of existing completion algorithms,such as limited categories and high computational time,this study proposes an occlusion object completion network based on the residual prediction.Unlike previous networks,the subnet predicts the residual between voxel features and voxel centers instead of directly predicting the coordinates of points.Additionally,our approach does not require additional complementary datasets for network training;instead,we extract the object point cloud from the KITTI dataset and perform matching fusion algorithm to obtain complementary labels.Experimental results demonstrate that the approach can effectively recover missing parts of occluded object shapes without excessively increasing the inference time of the network.(2)Aiming at the occlusion problem in 3D object detection,this paper proposes a 3D occluded object detection network based on point cloud completion.In this paper,Voxel RCNN is used as the benchmark model,and the occlusion object completion module is built to integrate the occlusion object completion network proposed above into the benchmark model to restore the complete shape of the occlusion objects.The network proposed in this paper is evaluated on the KITTI dataset,and the average accuracy of the moderate difficulty of the Car is increased by 0.84%.Moreover,evaluations are performed on objects with varying occlusion levels.Results reveal that the proposed algorithm improves the detection accuracy by 0.46% and 0.18% for Car with occlusion levels of 1 and 2,respectively.These results demonstrate the effectiveness of the proposed algorithm in enhancing the detection accuracy of occluded objects.(3)In order to address the issue of changing foreground-background ratio in the completed voxels,this study proposes optimizations to the aforementioned network.Firstly,dynamic voxelization is adopted to replace the voxelization method in the baseline model,and an attention module is introduced in the backbone network to enhance the network’s focus on the target objects.Secondly,anchor boxes are abandoned,and a center-based candidate box generation strategy is employed.Furthermore,the outputs of the completion subnetwork are further utilized in the refinement module.Experimental evaluations are conducted on the KITTI dataset,and the results demonstrate improvements in the average precision for the simple and medium difficulty levels of the vehicle category by 0.11% and 0.99% respectively,with average precision values of 89.52,85.51,and 78.87 for the three difficulty levels.For the three occlusion levels in the vehicle category,the average precision is improved by 0.19%,0.85%,and 0.23% respectively,with average precision values of 87.50,84.37,and 78.73.In bad weather such as rain,snow and fog,the problem of lidar occlusion will become more serious.The algorithm proposed in this paper can complete the shape of the occluded target,increase the robustness of the network,and improve the stability of the autonomous driving perception systems.