This paper presents a target detection algorithm based on PointRCNN. This method is aimed at vehicle targets. Aiming at the problem that the original PointRCNN is poor in vehicle detection at a distance, the method is optimized and the average accuracy of target detection is improved. In the first stage, the lidar point cloud is processed by pseudo-image structure and dimensionality reduction to 2D, and then processed by Point-Focus structure and restored to 3D point cloud. Then it will be sent into the backbone of PointNet++ for feature extraction, classification and regression. In the second stage, 3D frame is optimized and selected, and Point-CSPNet structure is introduced to further improve network learning ability and robustness. In this paper, the Focus and CSPNet structures of YOLO series algorithms are used for reference. The effective information in the original point cloud is fully extracted and the feature, gradient changes in the network operation are effectively integrated to improve the detection accuracy of the network. The average accuracy of the improved algorithm is improved from 81.10% to 81.74% in 3D scenes of KITTI dataset; and it is improved from 86.87% to 88.20% in BEV scenes of KITTI dataset, and the detection effect of vehicle targets in the far distance of visual effect has also been optimized to a certain extent, which has certain positive significance for further optimization and improvement of unmanned driving technology.