To solve the problem of poor and missed detection of small objects in complex autonomous driving scenarios,a high-performance network architecture that fused point cloud and voxel information was proposed.The object detection performance of the PV-RCNN network was improved through the preprocessing module,the spatial semantic feature concatenate module,and the coordinate attention mechanism module,and a network architecture PSC-RCNN was constructed.Validated on the KITTI,experimental results show that the recognition accuracy of PSC-RCNN for small objects with complex shapes like bicycle is 82.99％,67.03％,and 59.88％under three categories of detection difficulty(easy,medium,and difficult)respectively,the recognition accuracy is improved by 4.39％,3.32％,and 2.23％respectively.Compared with the existing 3D point cloud object detection network,the recognition accuracy is improved by 0.51％,2.93％,and 2.23％,respectively.
维普
万方数据