Design of multifeature fusion network based on PointRCNN

In the domain of autonomous vehicles, 3D object detection technology plays a crucial role in improving driving safety and efficiency. This study aims to enhance the ability of the PointRCNN model to identify and detect small-scale objects. To address the limitations of existing models in detecting small objects, we propose an innovative multi-feature fusion network architecture. Initially, this architecture utilizes voxel feature extraction technology to simultaneously acquire point cloud features and voxel features, including Voxel and Pillar features. Subsequently, by comprehensively integrating these features, a more comprehensive and enriched feature representation is constructed, thereby significantly enhancing the model's understanding of target spatial structures and the accuracy of small-scale object identification. Lastly, the integrated features are utilized in the SegNeXt semantic segmentation module, enabling more precise object detection. Experimental results indicate that our method improves pedestrian detection accuracy by 3.84% compared to the original PointRCNN model, thereby validating the effectiveness of this approach in detecting small objects such as pedestrians. This study not only offers new technical support for enhancing the safety and efficiency of autonomous driving vehicles but also provides fresh perspectives and solutions for the advancement of the 3D point cloud object detection field.